WO2022242200A1 - 一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置 - Google Patents

一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置 Download PDF

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WO2022242200A1
WO2022242200A1 PCT/CN2022/070683 CN2022070683W WO2022242200A1 WO 2022242200 A1 WO2022242200 A1 WO 2022242200A1 CN 2022070683 W CN2022070683 W CN 2022070683W WO 2022242200 A1 WO2022242200 A1 WO 2022242200A1
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permeability
well
logging
layer
production
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PCT/CN2022/070683
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French (fr)
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杨思玉
王拥军
孙圆辉
刘辉
曹鹏
杜政学
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中国石油天然气股份有限公司
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Priority to EP22803538.2A priority Critical patent/EP4357585A1/en
Publication of WO2022242200A1 publication Critical patent/WO2022242200A1/zh

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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells

Definitions

  • the invention relates to a method and a device for identifying high-permeability bands in marine carbonate bioclastic limestone reservoirs, and belongs to the technical field of reservoir heterogeneity evaluation in the field of oil and gas field exploration and development.
  • High-permeability strips are also called high-permeability strips, and the English name is called HPS (High Permeability Streak).
  • HPS High Permeability Streak
  • O.M.Adeoye SPE-177824
  • it is the heterogeneity of reservoirs caused by relatively high permeability reservoirs. It is also a manifestation of the nature of the water flooding front and the reason why some wells lose control due to the increase of water cut. Therefore, in the development of marine carbonate reservoirs with strong heterogeneity, understanding the origin, type and distribution of high-permeability zones is crucial for deploying high-yield development wells, designing injection-production well patterns, and improving injection water displacement. It is very important to improve the efficiency, prolong the oil production period with no water or low water cut, and increase the drilling rate.
  • the research content related to hypertonic strips is rich, and the differences of the present invention include:
  • the hyperpermeable zone proposed by the present invention includes both “thief layer” and “ultra-hyperpermeable layer”, and also includes high-energy bioclastic limestone sedimentary hyperpermeable zone different from typical oolitic limestone
  • "thief layers” and “ultra-high permeability layers” are controlled by stratigraphic sequences, and mostly develop in the third-order sequence interface or near the local exposed surface
  • Abnormally high-energy sedimentary belts with significantly different deposits have strong distribution regularity, are easy to identify and manage, and have little impact on water flooding development management, while a large number of high-energy sedimentary high-permeability belts developed in bioclastic limestone are controlled by sedimentation. The law is poor, the identification is difficult, and it has a great impact on the water injection effect and development management.
  • the second is that the research content is different.
  • the published literature has relatively in-depth research on the geological origin, identification, characterization and application of the thief layer and ultra-high permeability layer (such as SPE-64989, IPTC-16632, SEG-2008-3184, WPC -30164 and Chinese patent CN106121641A, etc.), wherein the identification of thief beds or ultra-high permeability layers mainly focuses on geological, well logging, seismic and dynamic single-disciplinary identification, and the identification results all have a certain coincidence rate but are limited by the influence of the method; in Among the literatures related to hyperpermeable strips (HPS), some of them focus on the establishment of geological models based on field outcrops, coring, logging and geological analysis, revealing and characterizing the spatial distribution of hyperpermeable strips (such as IPTC-13385), the research content of some literatures is mainly focused on analyzing the dynamic characteristics of hyperpermeable zones, using tracers to track the direction of zones, and
  • SPE is the abbreviation of Society of Petroleum Engineers
  • IPTC is the abbreviation of International Petroleum Technology Conference
  • WPC is the abbreviation of World Petroleum Council
  • IPTC-16632 Diah Agustina, etc. Drilling with Casing Technique Successfully Overcome Massive Thief Zone. 2013.03;
  • SEG-2008-3184 Alexandre W.Araman, etc. Thief Zone Identification Through Seismic Monitoring of a CO2 Flood, Weyburn Field, Saskatchewan.2008;
  • IPTC-13385 Agus Sudarsana, etc. High Permeability Streaks Characterizations in Middle East Carbonate. 2009.12;
  • SPE-176110 Hu Dandan, etc. Integrated management and Application of Horizontal Well Water Flooding Technology in a Large-scale Complicated Carbonate Oilfield Containing High permeability Streaks. 2015.08.
  • an object of the present invention is to provide a method for identifying high-permeability bands in marine carbonate bioclastic limestone reservoirs.
  • Another object of the present invention is to provide a device for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs.
  • the present invention also provides a computer device.
  • the present invention also provides a computer-readable storage medium.
  • the invention solves the difficult problems of water breakthrough sequence and injection-production management of the injection-production well group inside the strongly heterogeneous bioclastic limestone reservoir, and provides technical support for the efficient development and balanced production of the reservoir and the improvement of the ultimate recovery rate.
  • the present invention provides a method for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs, wherein the method includes:
  • the production logging data analyze the interlayer interference of multi-layer co-production, and comprehensively use the single-layer production in the production logging data, the single-layer dynamic permeability of the single-well flow unit and the single-layer specific fluid production index to establish a high-permeability strip dynamic standard and extract the single well hypertonic band;
  • a high-permeability zone logging identification mode is established, and the high-permeability zone is identified well by well within the entire reservoir range according to the high-permeability zone logging identification mode, so as to realize the full-reservoir logging interpretation.
  • the single well flow unit refers to a reservoir formed along a certain path under certain production conditions (production pressure difference) and has similar seepage characteristics, which is characterized by a certain liquid production in the vertical direction of a single well. quantity.
  • the well test data is PBU data
  • the production logging data is PLT test data
  • SPI specific fluid production index
  • unit is bbl/(d psi m)
  • q o is daily fluid production
  • unit is bbl/d
  • ⁇ P bottom hole pressure difference (production pressure difference)
  • the unit is psi
  • h is the thickness of the fluid-producing layer, and the unit is m.
  • the specific liquid production index also known as the rice production liquid index
  • SPI is an important parameter reflecting the production capacity of a flow unit, and is defined as the daily liquid production per unit thickness and unit production pressure difference.
  • the single-layer dynamic permeability (K_PBU) of the single-well flow unit is determined according to the PBU data.
  • kh is the formation coefficient of the single well interpreted by PBU data, in mD m; q is the PBU test production, in m 3 /ks; ⁇ is fluid viscosity, in mPa s; m is the slope of the Horner curve used in the interpretation of PBU data, dimensionless; h is the thickness of the flow unit (usually the thickness of the effective reservoir with seepage capacity in the perforation section of the well test test), the unit is m; k is the flow unit of a single well The monolayer dynamic permeability of , in mD;
  • the single-layer dynamic permeability (K_PBU) of the single well flow unit is determined according to the PBU data and PLT test data.
  • k l is the single-layer dynamic permeability of the flow unit, in mD; (kh) l is the single-layer formation coefficient of each flow unit, in mD m; h l is the flow rate in the PLT test data The actual thickness of the unit liquid-producing section, in m; l is the number of actual liquid-producing sections in the PLT test data, which is a positive integer.
  • the object tested by the present invention is an oil layer, so the fluid viscosity ⁇ in formula (2) is generally the viscosity of oil under underground conditions.
  • Determining the thickness of the flow unit on the well logging curve is to determine the thickness h of the flow unit by using the single-layer PBU test, specifically: according to the thickness h1 of the perforation section, after deducting the interlayer h2 from the well logging curve, and then correcting the well deviation to determine the thickness of the flow unit h.
  • the formation coefficient kh of this layer can be obtained through the interpretation of the PBU data obtained by the PBU test, and combined with the thickness of the flow unit (usually the relatively good The effective reservoir thickness) h of the layer can determine the dynamic permeability of the layer; wherein, the acquisition of kh depends on the Horner curve used in the interpretation of PBU data, and Fig. 4 is the Horner curve of well X well pressure recovery test in an oilfield in the Middle East of the embodiment of the present invention, In Fig. 4, t p is the production time of the oil well before the pressure recovery well test, in ks; dt is the pressure recovery time of the oil well, in ks;
  • the kh obtained from PBU data interpretation is the comprehensive formation coefficient of all flow units in the well. Only by combining the PLT test data and dividing kh into each flow unit in batches can the dynamic permeability of different flow units be determined ; Therefore, having well test data and production logging data at the same time is a necessary condition for interpretation of reservoir parameters for combined production wells.
  • the well test data described in the present invention is to determine the formation pressure, formation temperature, flow pressure and single well production of the oil reservoir.
  • Special test data for oil and water wells obtained from the ability to study reservoir parameters, detect oil, gas, water layers and their connectivity, and monitor reservoir performance.
  • PBU also known as pressure recovery well test
  • PBU data is a kind of unstable well test, and the data obtained from pressure recovery well test become PBU data.
  • the production logging data is data such as fluid density, water holdup, temperature, pressure, and production of the production layer measured in the casing, which can directly determine the difference in production capacity of different layers under the same production conditions.
  • PLT is a logging tool produced by Schlumberger, which is mainly used to measure the actual fluid output of different perforated intervals, identify and distinguish oil production, water production and gas production, and quantify the different perforated intervals. Liquid supply capacity under certain working system conditions. Production logging data obtained through PLT is called PLT test data.
  • determining the thickness of the flow unit includes:
  • analyzing the interlayer interference situation of multi-layer commingled production according to the production logging data includes: analyzing the possible high-yield layers according to the production logging data (such as PLT test data) Productivity suppression and disturbance of relatively poor reservoirs.
  • interlayer interference is defined as the phenomenon that interlayer fluid flow interferes with each other due to differences in permeability, fluid properties and pressure between small layers in oil and gas production layers, which is usually the best production capacity
  • the flow unit interferes with the relatively poor flow unit, and it is often judged by unstable well testing and dynamic logging.
  • the dynamic permeability of layer B can be calibrated to represent The single-layer dynamic permeability of layer characteristics, the dynamic permeability data of layer A are regarded as unreliable data, and will not enter the database used in the next operation.
  • extracting the single-well hypertonic band includes: layer-by-layer extracting the hypertonic band in the single well according to the dynamic identification standard, specifically including:
  • the dynamic permeability and specific fluid production index data of the single well flow unit determined in sections are used to draw the dynamic permeability and specific fluid production index rectangular curves respectively, and
  • the dynamic permeability of the single layer and the specific production index of the single layer in the dynamic standard of the hypertonic strip are set as the baseline of the rectangular curve and filled with the right side of the rectangular curve, when the dynamic permeability of the single layer and the specific production index of the single layer
  • the single layer is determined to be a hyperpermeable strip.
  • extracting hyperpermeable bands layer by layer in the single well according to the dynamic identification standard can provide samples for establishing an identification model extended to the whole oil reservoir.
  • the sequence stratigraphy research includes: based on the sequence stratigraphy theory that the sea level change controls the formation and development of the sequence, according to the lithofacies cycle and the seismic sequence structure, a hierarchical sub-marine stratigraphy division scheme is established, through Single well logging curve and 3D seismic are extended to the whole reservoir.
  • the lithofacies research includes: according to the grain structure of carbonate rocks, according to the Dunham scheme to divide the lithofacies types; Standard; use of well logs to identify lithofacies in non-cored wells and generalize to the entire reservoir.
  • the study of sedimentary facies includes: determining the sedimentary environment of the reservoir according to the regional geological background, and establishing a sedimentary model; in the core well, using cores and thin sections to analyze sedimentary characteristics, divide sedimentary microfacies, scale logging and establish sedimentary facies.
  • Microfacies logging identification mode in non-cored wells, sedimentary microfacies are identified by using features such as well logging curve shape, combination, and longitudinal rhythmic changes, so as to be extended to the entire reservoir.
  • the petrophysical phase research includes: according to the provisions of the Petroleum and Natural Gas Industry Standard of the People's Republic of China "Evaluation Method for Oil and Gas Reservoirs" (SY/T 6285-1997)", the carbonate reservoirs according to the rock core
  • the porosity is divided into four levels: high porosity ( ⁇ 20%), medium porosity (12%-20%), low porosity (4%-12%) and ultra-low porosity ( ⁇ 4%), according to the core permeability It is divided into four levels: hypertonic ( ⁇ 100mD), medium osmotic (10-100mD), hypotonic (1-10mD) and ultra-low osmotic ( ⁇ 1mD);
  • the porosity and permeability grading standards may not be completely consistent with the national standards due to differences in reservoir and reservoir formation causes in different oil fields, in the present invention, the main control factors of reservoirs (such as the scatter plot of porosity and permeability constrained by sedimentary microfacies) to determine the classification standard of porosity and permeability.
  • the genetic types of hyperpermeable reservoirs are divided, and the hyperpermeable bands are determined to be Distribution rules in lithofacies, sedimentary facies and sequence boundaries, including:
  • the development of the high-permeability reservoir or the hyperpermeability band is analyzed by using the method of scatter crossplot or frequency histogram Types of lithofacies and sedimentary facies; combined with sedimentary microfacies and sequence stratigraphy division results, analyze the position of high-permeability bands in sedimentary facies and stratigraphic sequences, and provide regular constraints for identifying high-permeability bands using well logging data .
  • the high-permeability reservoir or the high-permeability strip data of the single well divided by the petrophysical phase of the coring well is used to analyze the high-permeability reservoir or the high-permeability
  • the seepage capacity and dynamic permeability in the range of tens to hundreds of meters have the same trend, but the specific values are quite different.
  • the first route it is preferable to use the first route to determine the distribution law of the hyperpermeable strips in the lithofacies, sedimentary facies and sequence boundaries, wherein the hyperpermeable reservoirs come from core analysis data, which is consistent with the trend of dynamic data .
  • the genetic types of hyperpermeable reservoirs are divided, and the hyperpermeable bands are determined to be Distribution rules in lithofacies, sedimentary facies and sequence boundaries, including:
  • the establishment of a high-permeability strip logging identification mode includes:
  • the test section here is the test section of the PLT test
  • the dynamic permeability of the single layer and the ratio of the single layer corresponding to the dynamic standard of the hyperpermeable strip Using the liquid production index as the boundary, the logging response characteristics of hyperpermeable and non-hyperpermeable zones were analyzed, and a qualitative identification mode of logging facies in hyperpermeable bands and a quantitative identification mode of conventional logging in sedimentary hyperpermeable bands were established.
  • the single-layer dynamic permeability corresponding to the dynamic standard of the hypertonic zone is a
  • the dynamic permeability ⁇ a corresponds to the hypertonic zone
  • the dynamic permeability ⁇ a corresponds to the non-hypertonic zone.
  • the establishment of a qualitative identification pattern of a hyperpermeable strip logging facies includes:
  • the high-permeability strips mostly develop in high-energy depositional environments, and most of them belong to pores with pure lithology, coarse grain size, high porosity and high permeability Type hyperpermeable belt; conventional logging curves show low gamma ray, high porosity, high resistivity; FMI imaging image light color, regular or irregular black patches, showing the development of intergranular pores or large dissolution pores ;NMR logging T2 spectrum shows that the T2 value is double-peaked or multi-peaked, and the T2 time and energy spectrum peaks both show high values;
  • the high-permeability zone is thin, and the conventional logging features are not obvious, but the productivity is high, and the "thief layer” feature is often displayed;
  • FMI imaging shows that sinusoidal fractures or irregular micro-fractures are developed. See caves or large dissolution pores; nuclear magnetic logging T2 spectrum shows bimodal or multi-peak characteristics, T2 time and energy spectrum peaks are medium or low, and hyperpermeable strips with caves or large dissolution pores have large T2 values.
  • establishing a sedimentary hyperpermeable strip conventional logging quantitative identification mode includes:
  • the test section In the production logging test section (if the PLT test is carried out, the test section here is the test section of the PLT test), according to the principle of seepage dominant channel, the obvious non-permeable interbeds and low-permeability reservoirs (high Ga Ma, high density, low resistivity and low Am layer in the oil layer), obtain the curve values of the well logging curve in segments according to the sampling interval of the conventional well logging curve, and then make double logging curve classification scatter points according to the dynamic permeability distribution interval Combining with geological law, analyze the distribution law and identification limit (including the upper limit or lower limit) of the log curve values of the high-permeability zone, and establish the quantitative identification mode of the conventional logging of the sedimentary high-permeability zone based on this.
  • the obvious non-permeable interbeds and low-permeability reservoirs high Ga Ma, high density, low resistivity and low Am layer in the oil layer
  • the scatter-point crossplot of the double logging curve classification includes the scatter-point crossplot of any two curves in all the well-logging curves, such as: porosity curve value-resistivity curve value scatter-point crossplot and natural gamma ray curve value - Scatter plot of pore structure index curve values.
  • the logging curve includes: natural gamma ray curve (GR), porosity curve ( RHOB), resistivity curve (Rt) and pore structure index curve (Am).
  • the curve values of the well logging curve include natural gamma ray curve values, porosity curve values, resistivity curve values and pore structure index curve values.
  • the establishment of a sedimentary high-permeability strip conventional logging quantitative identification mode specifically includes the following steps:
  • said curve value comprises natural gamma ray curve value, porosity curve value, resistivity curve value and pore structure index curve value;
  • the dual logging curve classification scatter intersection diagram includes a porosity curve value-resistivity curve value scatter intersection diagram and a natural gamma ray curve value-pore structure index curve value scatter intersection diagram.
  • the present invention determines the dynamic permeability distribution interval according to the following specific principles:
  • 0mD represents no fluid production during the test; other dynamic permeability distribution intervals are based on the maximum value and the normal distribution interval, according to the logarithmic partition principle, considering the possible lower limit of the hypertonic band to determine the distribution interval, as a specific example of the present invention
  • a permeability greater than 100mD may be a hypertonic band
  • a permeability greater than 500mD may be related to hypertonic or thief layer. Therefore, in a specific embodiment of the present invention, the determined dynamic permeability distribution intervals are: 0mD, 0-1mD, 1-10mD, 10-50mD, 50-100mD, 100-200mD, 200-500mD, 500-1000mD and >1000mD.
  • the pore structure index curve used to reflect the storage and seepage characteristics of the reservoir is calculated according to the following formula (7): Curve value:
  • R w is the resistivity of formation water, in ohm m
  • R t is the resistivity of undisturbed formation, and the resistivity corresponding to the deep detection resistivity curve is shown on the logging curve, in ohm m
  • POR reservoir porosity, unit is dimensionless decimal
  • Sw is formation water saturation, unit is dimensionless decimal
  • n saturation index, unit is dimensionless decimal.
  • the hyper-permeability band is identified well by well in the whole reservoir range, so as to realize the logging interpretation of the whole reservoir, including : Quantitative logging identification of high-energy sedimentary high-permeability belts based on the conventional logging quantitative identification model of sedimentary-type hyper-permeable belts, qualitative identification of logging facies based on the qualitative identification model of high-permeability belts, and the constraints and constraints of geological laws mutual verification;
  • the quantitative identification of high-energy sedimentary high-permeability belts is carried out according to the conventional logging quantitative identification mode of sedimentary-type high-permeability belts, including: the four curves of natural gamma ray curve, porosity curve, resistivity curve and pore structure index curve.
  • GR represents lithology and depositional environment, and its low value represents pure lithology, coarse particles, and relatively high depositional energy
  • RHOB represents the total porosity of rock, and its low value represents high porosity and good physical properties
  • Rt represents oil-bearing and permeability, in a pure oil layer, a high value represents good oiliness, indicating a high degree of oil and gas charging during accumulation, which can reflect its relatively good permeability
  • Am represents the pore structure, and a high value represents a complex conductive path, which is a typical Large-pore and small-throat coarse-grained sedimentary reservoirs.
  • the qualitative identification of logging facies is carried out according to the qualitative identification mode of high-permeability strip logging facies, including: according to the "T2 spectrum double peak or multi-peak characteristics and T2 time and energy spectrum peaks are both high” and imaging log Further identify and confirm high-energy sedimentary high-permeability belts based on the display characteristics of "larger scale and stronger dissolution vugs" in the well;
  • karst reservoirs have the characteristics of "thieves". According to the "sinusoidal well-passing fractures or irregular micro-fractures, black patch-shaped caves or large dissolution pores" on the FMI imaging map and the “double peak or multi-peak characteristics" for qualitative identification;
  • the constraints and mutual verification of geological laws include: sedimentary high-permeability belts are controlled by sedimentary facies and developed in relatively fixed depositional positions, karst-type high-permeability belts are controlled by sequence stratigraphy and paleostructures and developed in the area near the sequence boundary,
  • the tidal channel type hyperpermeable strips mostly develop in the lower part of the tidal channel
  • the bioclastic beach type hyperpermeable strips mostly develop in the upper part of the beach body
  • the beach wing facies hyperpermeable strips mostly develop near the beach body.
  • the geological rule that the karst-type hyperpermeable belts mostly develop near the sequence boundary proves or picks up the hyperpermeable belts again.
  • the method also includes: using core well core test results, production logging data (such as PLT test data) and dynamic permeability data, and injection-production well
  • core well core test results such as PLT test data
  • production logging data such as PLT test data
  • dynamic permeability data such as PLT test data
  • injection-production well The water breakthrough zone of the water breakthrough well in the formation and the development of the high permeability zone in the water breakthrough zone were used to test the identification results of the high permeability zone in the marine carbonate bioclastic limestone reservoir.
  • the method described above in the present invention wherein, using the results of core experiments to verify the identification results of high-permeability bands in marine carbonate bioclastic limestone reservoirs, includes: in the identified high-permeability bands , to analyze whether the high-energy sedimentary hyperpermeability zone (sedimentary hyperpermeability zone) has a good corresponding relationship with the hyperpermeable part of the core permeability.
  • the karst hyperpermeability zone and the core permeability The correlation is unstable, while the correlation between the sedimentary hyperpermeability zone and the core permeability is relatively stable.
  • the water seepage layer of the water seepage well of the injection-production well group and the situation of the development of a hyperpermeable band in the water seepage layer are used to test the marine carbonate bioclastic limestone oil.
  • the identification results of high-permeability zones in reservoirs including: in the established injection-production well groups, analyze the corresponding relationship and statistical relationship between the identified high-permeability zones and the wells that have seen water (water wells in advance), and verify the reliability of the method .
  • the present invention also provides a device for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs, wherein the device includes:
  • the reservoir dynamic parameter determination module is used to determine the single-layer dynamic permeability and single-layer specific liquid production index of the single-well flow unit according to the well test data and production logging data;
  • the high-permeability strip extraction module is used to analyze the interlayer interference of multi-layer commingled production according to the production logging data, and comprehensively utilize the single-layer production in the production logging data and the single-layer dynamic permeability of the single-well flow unit and the single-layer dynamic permeability of the single-layer Establish the dynamic standard of the hypertonic band by comparing the liquid production index and extract the hypertonic band of the single well;
  • the genetic type division module of hyperpermeable bands is used to classify the genetic types of hyperpermeable reservoirs according to the research results of sequence stratigraphy, lithofacies, sedimentary facies and petrophysical facies in the coring wells, and determine the hyperpermeable bands in Distribution laws in lithofacies, sedimentary facies and sequence boundaries;
  • the high-permeability band logging identification module is used to establish a high-permeability band logging identification mode, and according to the hyper-permeability band logging identification mode, the high-permeability bands are identified well by well within the entire reservoir range, so as to realize the full-reservoir Log interpretation.
  • the present invention also provides a device for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs, wherein the device includes: a processor, wherein the processor is used to execute the following programs stored in the memory module:
  • the reservoir dynamic parameter determination module is used to determine the single-layer dynamic permeability and single-layer specific liquid production index of the single-well flow unit according to the well test data and production logging data;
  • the high-permeability strip extraction module is used to analyze the interlayer interference of multi-layer commingled production according to the production logging data, and comprehensively utilize the single-layer production in the production logging data and the single-layer dynamic permeability of the single-well flow unit and the single-layer dynamic permeability of the single-layer Establish the dynamic standard of the hypertonic band by comparing the liquid production index and extract the hypertonic band of the single well;
  • the genetic type division module of hyperpermeable bands is used to classify the genetic types of hyperpermeable reservoirs according to the research results of sequence stratigraphy, lithofacies, sedimentary facies and petrophysical facies in the coring wells, and determine the hyperpermeable bands in Distribution laws in lithofacies, sedimentary facies and sequence boundaries;
  • the high-permeability band logging identification module is used to establish a high-permeability band logging identification mode, and according to the hyper-permeability band logging identification mode, the high-permeability bands are identified well by well within the entire reservoir range, so as to realize the full-reservoir Log interpretation.
  • the well test data is PBU data
  • the production logging data is PLT test data
  • the reservoir dynamic parameter determination module includes a single-layer specific fluid production index determination unit, and the single-layer specific fluid production index determination unit is used to determine the unit according to well test data and Production logging data is determined according to the following formula (1):
  • SPI is the specific fluid production index, the unit is bbl/(d psi m); q o is the daily fluid production, the unit is bbl/d; ⁇ P is the bottom hole pressure difference, the unit is psi; h is the thickness of the fluid-producing layer, in m.
  • the reservoir dynamic parameter determination module also includes a single-layer dynamic permeability determination unit, for the case of a single-layer well test, when the well test data is PBU data
  • the single-layer dynamic permeability determining unit is used to determine the single-layer dynamic permeability of the single-well flow unit according to the PBU data, including:
  • kh is the formation coefficient of the single well interpreted by PBU data, in mD m;
  • q is the PBU test production, in m 3 /ks;
  • ⁇ is fluid viscosity, in mPa s;
  • m is the slope of the Horner curve used in the interpretation of PBU data, dimensionless;
  • h is the thickness of the flow unit, in m;
  • k is the single-layer dynamic permeability of the flow unit in a single well, in mD;
  • the single-layer dynamic permeability determination unit is used to determine the single-layer dynamic permeability according to the PBU data and PLT test data.
  • Single-layer dynamic permeability of well flow cells including:
  • k l is the single-layer dynamic permeability of the flow unit, in mD; (kh) l is the single-layer formation coefficient of each flow unit, in mD m; h l is the flow rate in the PLT test data The actual thickness of the unit liquid-producing section, in m; l is the number of actual liquid-producing sections in the PLT test data, which is a positive integer.
  • the reservoir dynamic parameter determination module further includes a flow unit thickness determination unit, which is used to determine the thickness of the flow unit, including:
  • hypertonic band extraction module is specifically used for:
  • the hypertonic band extraction module further includes a single-well hypertonic band extraction unit, and the single-well hypertonic band extraction unit is used to extract the hypertonic band according to the dynamic
  • the identification standard extracts hypertonic bands layer by layer in the single well, specifically for:
  • the dynamic permeability and specific fluid production index data of the single well flow unit determined in sections are used to draw the dynamic permeability and specific fluid production index rectangular curves respectively, and
  • the dynamic permeability of the single layer and the specific production index of the single layer in the dynamic standard of the hypertonic strip are set as the baseline of the rectangular curve and filled with the right side of the rectangular curve, when the dynamic permeability of the single layer and the specific production index of the single layer
  • the single layer is determined to be a hyperpermeable strip.
  • hypertonic band formation type division module is specifically used for:
  • the development of the high-permeability reservoir or the hyperpermeability band is analyzed by using the method of scatter crossplot or frequency histogram Types of lithofacies and sedimentary facies; combined with sedimentary microfacies and sequence stratigraphy division results, the position of hyperpermeable belts in sedimentary facies and stratigraphic sequences is analyzed.
  • hyperpermeable strip logging identification module is specifically used for:
  • the hyperpermeable band logging identification module includes a hyperpermeable band logging facies qualitative identification mode establishment unit, and the hyperpermeable band logging facies qualitative identification
  • the schema building unit is used for:
  • the hyperpermeable zone logging identification module further includes a sedimentary hyperpermeable zone conventional logging quantitative identification mode establishment unit, the sedimentary hyperpermeable zone
  • the conventional logging quantitative identification mode building unit is used for:
  • the obvious non-permeable interbeds and low-permeability reservoirs are deducted, and the curve values of the logging curve are obtained in segments according to the sampling interval of the conventional logging curve, and then according to the dynamic permeability
  • the scatter-point intersection diagram of double logging curve classification was made in the rate distribution interval, and the distribution law and identification boundary of the logging curve value of the high-permeability zone were analyzed in combination with geological laws, and the conventional logging quantitative identification mode of the sedimentary high-permeability zone was established based on this.
  • the high-permeability zone logging identification module further includes a curve value calculation unit of the pore structure index curve, and the curve value calculation unit of the pore structure index curve is used for :
  • the curve value of the pore structure index curve is calculated according to the following formula (7):
  • R w is the resistivity of formation water, in ohm m
  • R t is the resistivity of undisturbed formation, and the resistivity corresponding to the deep detection resistivity curve is shown on the logging curve, in ohm m
  • POR reservoir porosity, unit is dimensionless decimal
  • Sw is formation water saturation, unit is dimensionless decimal
  • n saturation index, unit is dimensionless decimal.
  • the device also includes: an identification result verification module, which is used to utilize core test results, production logging data and dynamic permeability data, and injection-production well group see The water breakthrough zone of the water well and the development of high permeability bands in the water breakthrough zone are used to test the identification results of high permeability bands in marine carbonate bioclastic limestone reservoirs.
  • an identification result verification module which is used to utilize core test results, production logging data and dynamic permeability data, and injection-production well group see The water breakthrough zone of the water well and the development of high permeability bands in the water breakthrough zone are used to test the identification results of high permeability bands in marine carbonate bioclastic limestone reservoirs.
  • the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein, when the processor executes the computer program, the above-mentioned
  • a computer device including a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein, when the processor executes the computer program, the above-mentioned
  • the present invention also provides a computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the marine carbonate bioclastic limestone oil described above is realized.
  • the present invention takes dynamics as the key point, and provides a dynamic and static integrated marine carbonate bioclastic limestone reservoir high-permeability strip identification method and device under the constraints of geological laws.
  • the method and device provided by the present invention The theoretical basis and technical logic are solid and reliable, and the method is simple and easy to operate; the identification objects of the method and device provided by the present invention include but not limited to thief beds, which effectively solve the problem of internal bioclastic limestone reservoirs of marine carbonate rocks.
  • Fig. 1 is a flowchart of a method for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs provided by an embodiment of the present invention.
  • Fig. 2 is a schematic diagram of the PBU test results of well X in an oilfield in a specific embodiment of the present invention.
  • Fig. 3 is a schematic diagram of the dynamic test and high-permeability strip picking results of Y data wells in an oilfield in a specific embodiment of the present invention.
  • Fig. 4 is a schematic diagram of the Horner curve of well X pressure recovery test in a specific embodiment of the present invention.
  • Fig. 5 is a schematic diagram of identification of interlayer interference in well Z in a specific embodiment of the present invention.
  • Fig. 6 is a schematic diagram of determining the dynamic identification standard of a hypertonic band in a specific embodiment of the present invention.
  • Fig. 7 is a schematic diagram of research results of single well sequence stratigraphy, lithofacies and sedimentary facies in a specific embodiment of the present invention.
  • Fig. 8 is a schematic diagram of petrophysical classification of effective reservoirs in a specific embodiment of the present invention.
  • Fig. 9 is a schematic diagram of lithofacies analysis of the development of hyperpermeable reservoirs in a specific embodiment of the present invention.
  • Fig. 10a is a statistical histogram of lithofacies types developed in high permeability reservoirs in a specific embodiment of the present invention.
  • Fig. 10b is a statistical histogram of sedimentary facies types developed in hyperpermeable reservoirs in specific examples of the present invention.
  • Fig. 11a is a schematic diagram of charging characteristics and longitudinal distribution of tidal channel deposition type hypertonic strips in a specific embodiment of the present invention.
  • Fig. 11b is a schematic diagram of charging characteristics and longitudinal distribution of bioclastic shoal-type sedimentary hypertonic strips in a specific embodiment of the present invention.
  • Fig. 11c is a schematic diagram of the charging characteristics and longitudinal distribution of the shoal airfoil deposition type hypertonic strip in a specific embodiment of the present invention.
  • Fig. 12a is a schematic diagram of charging characteristics and longitudinal distribution of karst-type hyperpermeable strips in well D in a specific embodiment of the present invention.
  • Fig. 12b is a schematic diagram of the charging characteristics and longitudinal distribution of karst-type hypertonic strips in Well E in a specific embodiment of the present invention.
  • Fig. 13a is a schematic diagram of FMI and CMR logging characteristics of a sedimentary hyperpermeable zone in a specific embodiment of the present invention.
  • Fig. 13b is a schematic diagram of FMI and CMR logging characteristics of a karst-type hyperpermeable zone in a specific embodiment of the present invention.
  • Fig. 14 is a schematic diagram of sample extraction for establishing a logging interpretation model for a hyperpermeable zone in a specific embodiment of the present invention.
  • Fig. 15a is a scatter-point intersection diagram of dual logging curves RHOB-Rt based on K_PBU classification in a specific embodiment of the present invention.
  • Fig. 15b is a scatter-point intersection diagram of dual logging curves GR-Am based on K_PBU classification in a specific embodiment of the present invention.
  • Fig. 16a is a schematic diagram of the identification results of a single well in a hyperpermeable strip in a specific embodiment of the present invention.
  • Fig. 16b is the FMI image and CMR logging T2 energy spectrum of the karst - type hyperpermeable zone corresponding to 76 in Fig. 16a.
  • Fig. 16c is the FMI image and CMR logging T2 energy spectrum of the karst - type hyperpermeable zone corresponding to 77 in Fig. 16a.
  • 79 in Fig. 17a is the well location distribution diagram of the injection-production well group.
  • Fig. 17b is a well connection comparison diagram of two water injection wells (INJ1, INJ2) and one production well (PROD1).
  • Figure 17c is an illustration of the sedimentary microfacies corresponding to 84 in Figure 17b.
  • Fig. 18 is a schematic structural diagram of a device for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs provided by an embodiment of the present invention.
  • Fig. 19 is a schematic structural diagram of a device for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs according to another embodiment of the present invention.
  • Fig. 1 is the flowchart of the method for identifying hyperpermeable bands in marine carbonate bioclastic limestone reservoirs provided by the embodiment of the present invention.
  • the marine carbonate bioclastic limestone oil The Vietnamese hypertonic band identification method includes the following specific steps:
  • S101 Determine the single-layer dynamic permeability and single-layer specific liquid production index of the single-well flow unit according to the well test data and production logging data;
  • S102 According to the production logging data, analyze the interlayer interference of multi-layer commingled production, comprehensively use the single-layer production in the production logging data, the single-layer dynamic permeability of the single-well flow unit and the single-layer specific production index to establish high permeability Band dynamic standard and extract the single well hypertonic band;
  • S104 Establish a high-permeability band logging identification mode, and identify high-permeability bands well by well within the entire reservoir range according to the high-permeability band logging identification mode, so as to realize full-reservoir logging interpretation.
  • the well test data is PBU data
  • the production logging data is PLT test data
  • the single-layer specific liquid production index is determined according to the following formula (1):
  • SPI is the specific fluid production index, the unit is bbl/(d psi m); q o is the daily fluid production, the unit is bbl/d; ⁇ P is the bottom hole pressure difference, the unit is psi; h is the thickness of the fluid-producing layer, in m.
  • determining the single-layer dynamic permeability of the single-well flow unit according to the PBU data includes:
  • kh is the formation coefficient of the single well interpreted by PBU data, in mD m;
  • q is the PBU test production, in m 3 /ks;
  • ⁇ is fluid viscosity, in mPa s;
  • m is the slope of the Horner curve used in the interpretation of PBU data, dimensionless;
  • h is the thickness of the flow unit, in m;
  • k is the single-layer dynamic permeability of the flow unit in a single well, in mD;
  • the single-layer dynamic permeability of the single well flow unit is determined according to the PBU data and PLT test data, including :
  • k l is the single-layer dynamic permeability of the flow unit, in mD; (kh) l is the single-layer formation coefficient of each flow unit, in mD m; h l is the flow rate in the PLT test data The actual thickness of the unit liquid-producing section, in m; l is the number of actual liquid-producing sections in the PLT test data, which is a positive integer.
  • determining the thickness of the flow cell includes:
  • the single-layer production in the production logging data, the single-layer dynamic permeability of the single-well flow unit and the single-layer specific liquid production index are comprehensively used to establish the dynamic standard of the hyperpermeable strip, including:
  • extracting the single-well hypertonic band includes: layer-by-layer extracting the hypertonic band in the single well according to the dynamic identification standard, specifically including:
  • the dynamic permeability and specific fluid production index data of the single well flow unit determined in sections are used to draw the dynamic permeability and specific fluid production index rectangular curves respectively, and
  • the dynamic permeability of the single layer and the specific production index of the single layer in the dynamic standard of the hypertonic strip are set as the baseline of the rectangular curve and filled with the right side of the rectangular curve, when the dynamic permeability of the single layer and the specific production index of the single layer
  • the single layer is determined to be a hyperpermeable strip.
  • the genetic types of hyperpermeable reservoirs are classified, and the hyperpermeable bands are determined at the lithofacies, sedimentary facies, and sequence boundaries.
  • the distribution rules in including:
  • the development of the high-permeability reservoir or the hyperpermeability band is analyzed by using the method of scatter crossplot or frequency histogram Types of lithofacies and sedimentary facies; combined with sedimentary microfacies and sequence stratigraphy division results, the position of hyperpermeable belts in sedimentary facies and stratigraphic sequences is analyzed.
  • the establishment of a high-permeability strip logging identification mode includes:
  • the establishment of a qualitative identification pattern of logging facies in hyperpermeable strips includes:
  • the high-permeability strips are mostly developed in the high-energy depositional environment, and most of them belong to the pore-type high-permeability strips with pure lithology, coarse grain size, high porosity, and high permeability; conventional Logging curves show low gamma, high porosity, and high resistivity; FMI images show light color, regularly distributed or irregular black patches, indicating the development of intergranular pores or large dissolution pores; nuclear magnetic logging T2 spectrum shows The T2 value is bimodal or multimodal, and the T2 time and energy spectrum peaks both show high values;
  • the high-permeability zone is thin, and the conventional logging features are not obvious, but the productivity is high, and the "thief layer” feature is often displayed;
  • FMI imaging shows that sinusoidal fractures or irregular micro-fractures are developed. See caves or large dissolution pores; nuclear magnetic logging T2 spectrum shows bimodal or multi-peak characteristics, T2 time and energy spectrum peaks are medium or low, and hyperpermeable strips with caves or large dissolution pores have large T2 values.
  • a conventional logging quantitative identification mode for sedimentary hyperpermeable belts including:
  • the obvious non-permeable interbeds and low-permeability reservoirs are deducted, and the curve values of the logging curve are obtained in segments according to the sampling interval of the conventional logging curve, and then according to the dynamic permeability
  • the scatter-point intersection diagram of double logging curve classification was made in the rate distribution interval, and the distribution law and identification boundary of the logging curve value of the high-permeability zone were analyzed in combination with geological laws, and the conventional logging quantitative identification mode of the sedimentary high-permeability zone was established based on this.
  • the curve value of the pore structure index curve is calculated according to the following formula (7):
  • R w is the resistivity of formation water, in ohm m
  • R t is the resistivity of undisturbed formation, and the resistivity corresponding to the deep detection resistivity curve is shown on the logging curve, in ohm m
  • POR reservoir porosity, unit is dimensionless decimal
  • Sw is formation water saturation, unit is dimensionless decimal
  • n saturation index, unit is dimensionless decimal.
  • the method further includes: using the core test results of the core well, the production logging data and the dynamic permeability data, and the water breakthrough layer of the water breakthrough well in the injection-production well group and the development of the water breakthrough layer
  • the high-permeability strips are used to test the identification results of high-permeability strips in marine carbonate bioclastic limestone reservoirs.
  • Fig. 2 is a schematic diagram of the PBU well test test results of well X in the oil field.
  • the well test period has reached the pseudo-steady flow stage, and the reservoir parameters explained by the double logarithmic curve of the well test It can more truly reflect the reservoir characteristics of the flow unit encountered in a single well drilling, and the formation coefficient explained by it can better reveal the production capacity of the flow unit;
  • Fig. 3 is a schematic diagram of the dynamic test and high-permeability strip picking results of well Y in the oilfield.
  • 1 is the logging depth track
  • 2 is the vertical depth track after the core height correction
  • 3 is natural gamma ray (GR) and rock density (RHOB) curve overlapping trace
  • 4 is natural gamma ray (GR) and deep sounding resistivity (LLD) curve overlapping trace
  • 5 is PLT test trace
  • 6 K_PBU calculation result trace
  • 7 is SPI calculation Result track
  • 8 is the perforation section indicator track
  • 9 is the baseline of K_PBU
  • 10 is the rectangular curve of K_PBU
  • 11 is the baseline of SPI
  • 12 is the rectangular curve of SPI
  • 13 is the filling of SPI baseline to the right and the rectangular curve of SPI (Fig.
  • the black area represented by 13 can only be filled when the SPI value is greater than the SPI baseline value, 14-15 is the interlayer in the perforation section, as shown in 5 in Figure 3, within the range of the perforation section, the PLT test liquid section Discontinuously distributed, among which the top of MB1-2C is the strongest, that is, the PLT test fluid production section in the perforation section shows the flow unit of the tested well under the current production conditions;
  • the determining the thickness of the flow unit includes:
  • the well logging curve to deduct the interlayer and interlayer in the unit (14, 15 in Fig. 3); then, through the well trajectory calculation, the thickness of the flow unit is corrected to the vertical depth corrected by the height of the core (Fig. 2) in 3), that is, obtain the thickness of the flow cell;
  • the single-layer specific liquid production index of Y well is determined: as shown in Fig. 3, 7 and table 1, the production pressure difference (bottom hole pressure) of the 5 PLT test liquid sections of this well difference) is 453.7psi, the daily oil production is 196.93-1015.70bbl/d, and the thickness of the flow unit (vertical depth thickness of the PLT liquid production section) is 1.30-7.23m. Based on this, the range of SPI calculated according to formula (1) is 0.064 -0.525bbl/(d ⁇ psi ⁇ m);
  • the dynamic permeability is calculated according to the above-mentioned formula (4)-formula (6), and the intermediate parameters involved in the calculation process and the result data obtained by calculation are shown in Fig.
  • the K_PBU values of different flow units obtained through the comprehensive interpretation of PBU data and PLT test data range from 26.00 to 328.54mD, with the highest flow units at the top of MB1-2A and upper part of MB1-2C, MB1
  • the calculation results of the upper part of -2C have a good correlation with the geological characteristics of the reservoir and the logging response characteristics.
  • the logging response characteristics of the ultra-thin layer at the top of MB1-2A are not obvious, but because it is near the third-order sequence boundary, it is related to the dissolution exposure , which is difficult to identify except for imaging logging, and has typical "thief layer" characteristics.
  • the production logging data analyze the interlayer interference of multi-layer co-production, and comprehensively use the single-layer production in the production logging data, the single-layer dynamic permeability of the single-well flow unit and the single-layer specific fluid production index to establish a high-permeability strip Dynamic standard and extract the single well hypertonic band:
  • the analysis of the interlayer interference of multi-layer commingled production according to the production logging data includes: analyzing the production suppression and interference of the possible high-yield layers to the relatively poor reservoirs according to the production logging data (such as PLT test data) happening;
  • Fig. 5 is a schematic diagram of identification of interlayer interference in well Z in a specific embodiment.
  • 16 is the stratum division information of a single well
  • 17-19 are conventional logging natural gamma ray, rock density, and deep detection resistivity curves respectively
  • 20 is PLT test curve
  • 21 is the MDT test data
  • 22 is the perforation profile
  • 23-25 is the production data of PLT test of different layers
  • 26-27 is the PLT non-production interval. It can be seen from Fig.
  • the reservoirs in the central MB1-2A and MB1-2B reservoirs with similar characteristics to the MB1-2C production layer are low-yielding (24) or even non-yielding (26-27) , indicating that there are obvious yield suppression and interlayer interference;
  • the single-layer production in the production logging data, the single-layer dynamic permeability of the single-well flow unit and the single-layer specific liquid production index are comprehensively used to establish the dynamic standard of the high-permeability strip, including:
  • the high-permeability band is extracted layer by layer, providing a basis for establishing an identification model extended to the entire reservoir. sample.
  • a total of 200 flow units in 31 wells were analyzed, and a total of 62 layers of high-permeability strips were extracted, which effectively covered all sweet spot sedimentary facies in the reservoir. Identifying patterns provides a good foundation.
  • the research on sequence stratigraphy includes: based on the theory of sequence stratigraphy that sea level change controls the formation and development of sequences, establishing a hierarchical sub-marine stratigraphy division scheme according to lithofacies cycles and seismic sequence structures, It can be extended to the whole reservoir through single well logging curve and 3D seismic.
  • the third- and fourth-order sequences divided into the target oil reservoirs in the specific examples all have the characteristics of rapid transgression, slow regression, and exposed tops, which are highly comparable. Tidal channels and incised valleys are developed on the tops of some sequences (as shown in Fig. 7 out of 30).
  • the research on lithofacies includes: according to the grain structure of carbonate rock, according to the Dunham scheme to classify the lithofacies type; in the coring well, according to the core observation and thin section analysis, classify the lithofacies, scale logging and establish identification Standard; use of well logs to identify lithofacies in non-cored wells and generalize to the entire reservoir.
  • the oil reservoir is developed on a gentle slope platform, and the grain structure of carbonate rock is mainly composed of bioclastic particles, stucco matrix, and bright crystal cement.
  • the study of sedimentary facies includes: determining the sedimentary environment of the reservoir according to the regional geological background, and establishing a sedimentary model; using cores and thin sections to analyze sedimentary characteristics, dividing sedimentary microfacies, calibration logging, and establishing sedimentary facies in cored wells.
  • Microfacies logging identification mode In non-coring wells, the characteristics of logging curve shape, combination and vertical rhythmic changes are used to identify sedimentary microfacies, so as to be extended to the entire reservoir.
  • the oil reservoir in the specific example is a weakly edged gentle slope carbonate platform environment, and eight types of sedimentary microfacies are divided in total, including tidal channel, incised valley, intraplatform bioclastic beach, front bioclastic beach, beach wing,
  • tidal channel incised valley
  • intraplatform bioclastic beach front bioclastic beach
  • beach wing beach wing
  • sedimentary microfacies in high-energy environments such as tidal channels and bioclastic beaches
  • sedimentary microfacies in low-energy environments are like tidal
  • the lower zone and the lagoon mainly develop micritic limestone, marlstone and marlstone dominated by stucco.
  • the research on petrophysical phases includes: according to the Petroleum and Natural Gas Industry Standard of the People's Republic of China "Evaluation Method for Oil and Gas Reservoirs" (SY/T 6285-1997), the porosity of carbonate rock reservoirs is divided into high porosity ( ⁇ 20%), medium porosity (12%-20%), low porosity (4%-12%) and ultra-low porosity ( ⁇ 4%), the permeability is divided into hypertonic ( ⁇ 100mD) , medium permeability (10-100mD), low permeability (1-10mD) and ultra-low permeability ( ⁇ 1mD) four levels;
  • the permeability classification standard may not be completely consistent with the national standard.
  • the porosity is determined by using the standard core physical property experiment of the core well and the porosity and permeability scatter plot constrained by the main controlling factors of the reservoir (such as sedimentary microfacies) and permeability classification standards. As shown in Fig. 8, considering the genesis of effective reservoirs and the clustering characteristics of pores and seepage points, the adjusted porosity classification standards are 22% (33 in Fig. 8), 15% (34 in Fig. 8), and 8%. (35 in Figure 8), the adjusted permeability grading standard limits are 30mD (36 in Figure 8), 3mD (37 in Figure 8) and 0.3mD (38 in Figure 8), slightly different from the industry standard.
  • the method of scatter crossplot or frequency histogram is used , to analyze the types of lithofacies and sedimentary facies developed in hyperpermeable reservoirs or hyperpermeable strips; as shown in Fig. 0.15) are mainly developed in grainstone and grain-dominated marlstone; at the same time, combined with the results of sedimentary microfacies and sequence stratigraphy, the position of the high-permeability belt in the sedimentary facies and stratigraphic Well-identified hypertonic bands provide regularity constraints.
  • Fig. 10a is a statistical histogram of lithofacies types developed in high-permeability reservoirs in specific examples
  • Fig. 10b is a statistical histogram of sedimentary facies types in hyper-permeable reservoirs developed in specific examples. It can be seen from Fig. 10a that high-permeability The lithofacies developed in the reservoirs are mainly grainstone and grain-dominated marlstone. It can be seen from Fig.
  • the sedimentary facies developed in the high-permeability reservoirs are mainly tidal channels and frontal bioclastic beaches, and a small amount of Beach wings and lagoons, among which sweet spot sedimentary tidal channels, bioclastic beaches and some beach wings are the main body of sedimentary hyperpermeable belts, while lagoons, swamps and beach wings and other non-sweet facies are mostly formed by karst reformation, and karst high Infiltration strips (see table 3 below).
  • FIGS 11a-11c are schematic diagrams of the logging response characteristics and longitudinal distribution of the three types of sedimentary high-permeability strips in specific examples.
  • Gamma curve and porosity curve 43 is the deep detection resistivity and natural gamma curve
  • 44 is the HPS identification result
  • Fig. 11b-Fig. 11c have the same drawing structure. From Fig. 11a-Fig. 11c and Table 3, it can be seen that the tidal channel has the characteristics of weakened upward depositional energy and reduced grain size, and most of them are normal-sequenced structures, and high-permeability bands are mostly developed in the lower part (well A in Fig.
  • bioclastic shoals as an example
  • bioclastic shoals on the contrary, mostly exhibit anti-grained structure, and high-permeability strips are mostly developed in the upper part (well B in Fig. (well C in Figure 11c as an example).
  • Figure 12a- Figure 12b is a schematic diagram of the logging response characteristics and longitudinal distribution of the karst-type high-permeability zone in the specific embodiment
  • 45 in Figure 12a- Figure 12b is the sequence stratigraphic division track
  • 46 is the FMI static imaging track
  • 47 is FMI dynamic imaging track
  • 48 is the nuclear magnetic logging T2 spectrum track
  • 49 is the PLT test data track
  • 50 is the dynamic permeability calculation result track.
  • K_PBU hyperpermeable zone
  • K_PBU ⁇ 100mD hyperpermeable zone
  • K_PBU ⁇ 100mD non-hyperpermeable zone
  • the establishment of the qualitative identification mode of the hyperpermeable band logging phase includes: analyzing the difference between the hyperpermeable band and the non-hyperpermeable band on the conventional logging curve, the FMI imaging logging map, and the nuclear magnetic logging T2 spectrum , to establish a qualitative identification pattern of hyperpermeable strip logging facies in two types.
  • hyperpermeable bands are controlled by sedimentary facies, mostly developed in high-energy depositional environments, and mostly belong to pore-type hyperpermeable bands with pure lithology, coarse grain size, high porosity, and high permeability; It shows low gamma, high porosity, and high resistivity, as shown in wells A, B, and C in Fig. 11a-Fig.
  • nuclear magnetic logging T2 spectrum shows that the T2 value is double-peaked or multi-peaked, and the T2 time and energy spectrum peaks both show high values, as shown in Figure 13a.
  • 51 is FMI static imaging trace
  • 52 is the FMI dynamic imaging trace
  • 53 is the NMR T2 spectrum trace.
  • well D shows regular dissolution pores (black patches) in the interval
  • CMR Well logging T2 spectrum shows bimodal or multimodal features.
  • the high-permeability zone is thin, and the conventional logging features are not obvious, but the productivity is high, and it often shows the characteristics of "thief layer" (see well E in Fig.
  • the FMI image shows the development of sinusoidal well Fractures or irregular micro-fractures (see Well D in Figure 12a), and some wells see dissolved caves or large dissolution pores (see Figure 13b); NMR T2 spectrum shows double or multi-peak features, T2 time and energy spectrum fractures The peak value is medium or low (see well D in Fig. 13b), and the T2 value of hyperpermeable strips with caves or large dissolution pores is large (see Fig. 13b).
  • the establishment of a sedimentary-type high-permeability strip conventional logging quantitative identification mode includes:
  • the pore structure index curve reflecting the storage and seepage characteristics of the reservoir is introduced, and the formation water salinity, capillary pressure curve, phase permeability curve, displacement resistivity experiment, etc. are comprehensively analyzed, and the formula ( In 7), the value of Rw in 7) is 0.05ohm ⁇ m, the value of n is 2.05, the value of Sw in pure oil layer is 0.1-0.3, and it increases with the deterioration of physical properties, the value of Sw in pure water layer is 1, and the value of oil and water in the same layer According to the interpolation of oil column height and physical properties, the pore structure index curve (Am) is calculated by using the porosity curve and the resistivity curve of deep logging logging. Fig.
  • FIG. 14 is a schematic diagram of sample extraction for establishing a logging identification pattern in a hyperpermeable zone in a specific embodiment of the present invention.
  • 54 is a natural gamma ray curve
  • 55 is three porosity curves (density RHOB, neutron NPHI, acoustic wave DT)
  • 56 is the deep detection resistivity curve
  • 57 is the calculated pore structure index curve Am
  • 58 is the PLT test data
  • 59 is the dynamic permeability calculation result
  • 60-61 is the deduction interval of the PLT test liquid production section Histogram of dynamic permeability after interlayer.
  • the value of Am increases as the reservoir permeability becomes better.
  • K_PBU value distribution interval (specific example The K_PBU value is divided into 0mD, 0-1mD, 1-10mD, 10-50mD, 50-100mD, 100-200mD, 200-500mD, 500-1000, >1000mD, a total of 9 intervals) to make a double log curve classification
  • the point intersection diagram combined with geological laws, analyzes the distribution of log curve values and identification boundaries of high-permeability zones (K_PBU ⁇ 100mD), and establishes a conventional logging quantitative identification model for sedimentary-type high-permeability zones. As shown in Fig. 15a-Fig. 15b, it is the scatter-point intersection diagram of double logging curves based on K_PBU classification in this specific embodiment.
  • Fig. 15a-Fig. 64 is the lower limit of the pore structure index curve of the hyperpermeable zone, and 65 is the upper limit of the natural gamma ray curve of the hyperpermeable zone.
  • the reservoirs with K_PBU greater than 100mD are concentrated in low density/porosity ( ⁇ 2.4g/cm 3 ), high resistivity ( ⁇ 20ohm ⁇ m), low gamma ( ⁇ 20API) , high Am ( ⁇ 2.5) area, which is consistent with the electrical characteristics of high-energy sedimentary high-permeability reservoirs, based on which a quantitative identification model for sedimentary-type high-permeability strips is established, and the identification standards are shown in Table 4.
  • the high-permeability bands are identified well by well within the entire reservoir range, so as to realize the logging interpretation of the whole reservoir, including:
  • GR represents lithology and depositional environment, and its low value represents pure lithology, coarse particles, and relatively high depositional energy
  • RHOB represents total rock porosity, and its A low value represents high porosity and good physical properties
  • Rt represents oiliness and permeability, and a high value in a pure oil layer represents good oiliness, indicating a high degree of oil and gas charging during accumulation, which can reflect relatively good permeability
  • Am Re represents the pore structure, and its high value represents the complex conduction path, which belongs to the typical large-pore and small-throat coarse-grained sedimentary reservoir.
  • sedimentary hyperpermeable belts are controlled by sedimentary facies and developed in relatively fixed depositional positions, and karst hyperpermeable belts are restricted by sequence stratigraphy and paleostructure to develop near sequence boundaries.
  • the tidal channel type hyperpermeable bands mostly develop in the lower part of the tidal channel
  • the bioclastic beach type hyperpermeable bands mostly develop in the upper part of the beach body
  • the beach wing facies hyperpermeable bands mostly develop near the beach body
  • the geological law that karst-type hyperpermeable belts are mostly developed near the sequence boundary supports or re-picks the high-permeability belts;
  • Figure 16a is a schematic diagram of the identification results of a single well in a high-permeability zone in this specific example
  • 66 in Figure 16a is the conventional logging GR curve
  • 67 is the RHOB curve and core porosity
  • 68 is the Rt curve and core permeability
  • 69 is the calculated Am curve
  • 70 is the PLT test result
  • 71 is the K_PBU calculation result
  • 72-74 are the evaluation results of lithofacies, sedimentary facies and sequence stratigraphy respectively
  • 75 is the identification result of hyperpermeable band
  • 76-77 is the karst 78 is a high-energy sedimentary hyperpermeable band (displayed by a gray band that is not fully filled in the well logging);
  • 16b is the 76 in Fig. 16a
  • the high-energy sedimentary hyperpermeable belt strictly meets the logging identification conditions of low gamma, low density, high resistivity, and high Am, while the karst hyperpermeable belt does not strictly meet the above conditions, but Its development is controlled by sequence boundaries, which can be effectively identified by using FMI imaging logging combined with CMR logging T2 energy spectrum.
  • the method further includes: using the core test results of the coring well, the production logging data and the dynamic permeability data, as well as the water breakthrough layer of the water breakthrough well in the injection-production well group and the development height of the water breakthrough layer.
  • the identification results of the high-permeability bands in marine carbonate bioclastic limestone reservoirs were tested by using the core test results of core wells, including: in the identified hyper-permeable bands (75 in Fig. 16a), high-energy sediment
  • the karst-type hyperpermeable zone has a good corresponding relationship with the high-permeability part of the core permeability, and the karst-type hyperpermeable zone has a poor correlation with the core permeability, as shown in 68 in Fig. 16a for details;
  • Fig. 17a-Fig. 17c are the schematic diagrams of water well analysis of the PROD1 water injection well group in the specific embodiment, wherein, 79 in Fig. 17a is the well position distribution map of the injection and production well group; Fig.
  • FIG. 17b is two water injection wells (INJ1, INJ2) and one Well-tie comparison diagram of production well (PROD1)
  • 80-81 in Fig. 17b are the conventional three-porosity curve, natural gamma ray curve and resistivity curve respectively
  • 82 is the oil and water production profile tested by PLT
  • 83 is the water absorption of the injection well Section
  • 84 is the sedimentary microfacies section
  • 85 is the identification result of HPS
  • Fig. 17c is the legend of 84 sedimentary microfacies in Fig. 17b; it can be seen from Fig. 17a-Fig. 17c that there are two water injection wells around the production well PROD1, and the water injection layer Locations include MB1-2A to MB1-2C.
  • the water breakthrough layer is located in the upper part of MB1-2B.
  • This layer and the injection well INJ1 develop tidal channel type hyperpermeable strips at the same time, and they are connected to each other to form a dominant seepage channel, leading to the earliest water breakthrough.
  • the production wells that inject water involve 16 wells in 9 well groups, and the water injection wells related to the high-permeability zone account for 85%.
  • the coincidence rate of the injection-production relationship of the identified high-permeability zone is as high as 85%, which is basically consistent with the PLT and PBU test verification results.
  • the embodiment of the present invention also provides a device for identifying high-permeability bands in marine carbonate bioclastic limestone reservoirs.
  • the method for identifying high-permeability bands in reservoirs is similar, so the implementation of this device can refer to the implementation of the method, and the repetition will not be repeated.
  • the term "unit” or “module” may be a combination of software and/or hardware that realizes a predetermined function.
  • the devices described in the following embodiments are preferably implemented in hardware, but implementation in software or a combination of software and hardware is also possible and conceivable.
  • Fig. 18 is a schematic structural diagram of a device for identifying high-permeability bands in marine carbonate bioclastic limestone reservoirs provided by an embodiment of the present invention. It can be seen from Fig. 18 that the device includes:
  • Reservoir dynamic parameter determination module 101 used to determine single-layer dynamic permeability and single-layer specific liquid production index of single-well flow unit according to well test data and production logging data;
  • the high-permeability strip extraction module 102 is used to analyze the interlayer interference of multi-layer commingled production according to the production logging data, and comprehensively utilize the single-layer production in the production logging data and the single-layer dynamic permeability and single-layer dynamic permeability of the single-well flow unit.
  • the layer ratio liquid production index establishes the dynamic standard of the hypertonic band and extracts the hypertonic band of the single well;
  • the genetic type classification module 103 of hyperpermeable bands is used to classify the genetic types of hyperpermeable reservoirs and determine the hyperpermeable bands according to the research results of sequence stratigraphy, lithofacies, sedimentary facies and petrophysical facies in coring wells Distribution law in lithofacies, sedimentary facies and sequence boundaries;
  • the high-permeability band logging identification module 104 is used to establish a high-permeability band logging identification mode, and identify high-permeability bands well by well within the entire reservoir range according to the hyper-permeability band logging identification mode, so as to realize full oil recovery.
  • the high-permeability band logging identification module 104 is used to establish a high-permeability band logging identification mode, and identify high-permeability bands well by well within the entire reservoir range according to the hyper-permeability band logging identification mode, so as to realize full oil recovery.
  • Tibetan logging interpretation is used to establish a high-permeability band logging identification mode, and identify high-permeability bands well by well within the entire reservoir range according to the hyper-permeability band logging identification mode, so as to realize full oil recovery.
  • the well test data is PBU data
  • the production logging data is PLT test data
  • the reservoir dynamic parameter determination module includes a single-layer specific fluid production index determination unit, and the single-layer specific fluid production index determination unit is used for according to the following formula (1 ) to determine the monolayer specific fluid extraction index:
  • SPI is the specific fluid production index, the unit is bbl/(d psi m); q o is the daily fluid production, the unit is bbl/d; ⁇ P is the bottom hole pressure difference, the unit is psi; h is the thickness of the fluid-producing layer, in m.
  • the reservoir dynamic parameter determination module also includes a single-layer dynamic permeability determination unit.
  • the single-layer dynamic permeability determination unit is used to determine the single-layer dynamic permeability of the single-well flow unit according to the PBU data, including:
  • kh is the formation coefficient of the single well interpreted by PBU data, in mD m;
  • q is the PBU test production, in m 3 /ks;
  • ⁇ is fluid viscosity, in mPa s;
  • m is the slope of the Horner curve used in the interpretation of PBU data, dimensionless;
  • h is the thickness of the flow unit, in m;
  • k is the single-layer dynamic permeability of the flow unit in a single well, in mD;
  • the single-layer dynamic permeability determination unit is used to determine the single-layer dynamic permeability according to the PBU data and PLT test data.
  • Single-layer dynamic permeability of well flow cells including:
  • k l is the single-layer dynamic permeability of the flow unit, in mD; (kh) l is the single-layer formation coefficient of each flow unit, in mD m; h l is the flow rate in the PLT test data The actual thickness of the unit liquid-producing section, in m; l is the number of actual liquid-producing sections in the PLT test data, which is a positive integer.
  • the reservoir dynamic parameter determination module further includes a flow unit thickness determination unit, configured to determine the thickness of the flow unit, including:
  • the hypertonic band extraction module is specifically used for:
  • the hypertonic band extraction module further includes a single-well hypertonic band extraction unit, and the single-well hypertonic band extraction unit is used to stratify in the single well according to the dynamic identification standard Extract hypertonic bands, specifically for:
  • the dynamic permeability and specific fluid production index data of the single well flow unit determined in sections are used to draw the dynamic permeability and specific fluid production index rectangular curves respectively, and
  • the dynamic permeability of the single layer and the specific production index of the single layer in the dynamic standard of the hypertonic strip are set as the baseline of the rectangular curve and filled with the right side of the rectangular curve, when the dynamic permeability of the single layer and the specific production index of the single layer
  • the single layer is determined to be a hyperpermeable strip.
  • the hypertonic band formation type division module is specifically used for:
  • the development of the high-permeability reservoir or the hyperpermeability band is analyzed by using the method of scatter crossplot or frequency histogram Types of lithofacies and sedimentary facies; combined with sedimentary microfacies and sequence stratigraphy division results, the position of hyperpermeable belts in sedimentary facies and stratigraphic sequences is analyzed.
  • the hyperpermeable strip logging identification module is specifically used for:
  • the high-permeability strip logging identification module includes a high-permeability strip logging facies qualitative identification mode establishment unit, and the high-permeability strip logging facies qualitative identification mode establishment unit is used for:
  • the hyperpermeable zone logging identification module further includes a sedimentary hyperpermeable zone conventional logging quantitative identification mode establishment unit, and the sedimentary hyperpermeable conventional logging quantitative identification mode establishment unit uses At:
  • the obvious non-permeable interbeds and low-permeability reservoirs are deducted, and the curve values of the logging curve are obtained in segments according to the sampling interval of the conventional logging curve, and then according to the dynamic permeability
  • the scatter-point intersection diagram of double logging curve classification was made in the rate distribution interval, and the distribution law and identification boundary of the logging curve value of the high-permeability zone were analyzed in combination with geological laws, and the conventional logging quantitative identification mode of the sedimentary high-permeability zone was established based on this.
  • the high-permeability strip logging identification module further includes a curve value calculation unit of the pore structure index curve, and the curve value calculation unit of the pore structure index curve is used for:
  • the curve value of the pore structure index curve is calculated according to the following formula (7):
  • R w is the resistivity of formation water, in ohm m
  • R t is the resistivity of undisturbed formation, and the resistivity corresponding to the deep detection resistivity curve is shown on the logging curve, in ohm m
  • POR reservoir porosity, unit is dimensionless decimal
  • Sw is formation water saturation, unit is dimensionless decimal
  • n saturation index, unit is dimensionless decimal.
  • the device (as shown in FIG. 19 ) further includes: an identification result inspection module 105, which is used to utilize core test results, production logging data and dynamic permeability data, and water seepage wells of injection-production well groups.
  • an identification result inspection module 105 which is used to utilize core test results, production logging data and dynamic permeability data, and water seepage wells of injection-production well groups.
  • the water breakthrough zone and the development of high permeability bands in the water breakthrough zone were used to test the identification results of high permeability bands in marine carbonate bioclastic limestone reservoirs.
  • An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and operable on the processor, wherein, when the processor executes the computer program, the above sea Steps of identification method for high permeability bands in carbonate bioclastic limestone reservoirs.
  • An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, the above-mentioned marine carbonate bioclastic limestone reservoir high The steps of the infiltration band identification method.
  • the embodiment of the present invention takes dynamics as the key point, and provides a dynamic and static integrated marine carbonate bioclastic limestone reservoir high-permeability band identification method and device under the constraints of geological laws.
  • the embodiment of the present invention The theoretical basis and technical logic of the method and device provided are solid and reliable, and the method is simple and easy to operate; the identification objects of the method and device provided by the embodiment of the present invention include but not limited to thief layers, effectively solving the problem of marine carbon It is difficult to identify high-energy sedimentary high-permeability bands that have the greatest impact on water flooding development in salt rock bioclastic limestone reservoirs; after the method and device provided by the embodiments of the present invention are popularized and applied in oil fields, they can provide a set of effective coverage of the entire reservoir.
  • the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

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Abstract

一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置，方法包括：根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取单井高渗条带；在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定高渗条带在岩石相、沉积相和层序界面中的分布规律；建立高渗条带测井识别模式，根据高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。

Description

一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置 技术领域
本发明涉及一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置，属于油气田勘探开发领域中的储层非均质性评价技术领域。
背景技术
高渗条带又称高渗透率条带，英文名字简称为HPS(High Permeability Streak)，根据O.M.Adeoye(SPE-177824)的观点，它是相对较高渗透率储层导致的油藏非均质性的一种表现，也是水驱前缘提前和不均匀提前导致部分井因含水增加而失去控制的原因。因此，在非均质性极强的海相碳酸盐岩油藏开发中，搞清高渗条带的成因、类型和分布对部署高产开发井、设计注采井网、提高注入水驱油效率、延长无水或低含水采油期、提高开井时率非常重要。
目前与高渗条带相关的研究内容丰富，本发明不同之处包括：
一是研究对象不同，已公开文献大多以国际上广泛使用的“贼层(Thief zone)”和“超高渗层(Super-K)”为主，二者是一种异常高渗透率的薄层，通常由淋滤面的侵蚀和粗粒岩相的高能量沉积(如鲕粒灰岩)及裂缝走廊形成，是导致油田见水早、含水上升快、驱油效率低、采收率低的最具传导性的通道；本发明提出的高渗条带既包括“贼层”和“超高渗层”，也包括不同于典型鲕粒灰岩的高能生屑灰岩沉积型高渗条带，实际研究证明，在孔隙型碳酸盐岩油藏中，“贼层”和“超高渗层”受地层层序控制，多发育于三级层序界面或局部暴露面附近及与正常沉积明显不同的异常高能沉积带，分布规律性强，容易识别和管理，对注水开发管理影响反而较小，而生屑灰岩内部大量发育的高能沉积型高渗条带受沉积作用控制，分布规律差，识别难度大，对注水效果和开发管理影响大。
二是研究内容不同，已公开文献在贼层和超高渗层的地质成因、识别、表征和应用方面已有较为深入的研究(如SPE-64989、IPTC-16632、SEG-2008-3184、WPC-30164及中国专利CN106121641A等)，其中贼层或超高渗层识别主要集中于地质、测井、地震和动态单学科识别，识别结果都具有一定的符合率但受方法影响应用受限；在与高渗条带(HPS)相关的文献中，其中一部分文献的研究内容主要集中在基于野外露 头、取芯、测井及地质分析建立地质模型，揭示并表征高渗条带的空间分布(如IPTC-13385)，一部分文献的研究内容主要集中在分析高渗条带的动态特征、用示踪剂追踪条带方向、实现水平井注水开发技术的整体管理和应用(如SPE-31149、SPE-176110等)，目前已公开的涉及高渗条带的专利文献涉及高渗条带岩芯实验(如中国专利CN108535161A)、动态识别和监测(如中国专利CN110821485A)、优势通道物性参数计算(如中国专利CN110821486A)等方面，但是关于静动态一体化识别方面目前未见相关报道。
针对高渗条带相关的研究现状和生屑灰岩油藏开发面临的难题，创新地提供一套基于静动态一体化的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置既是生产亟需，也是提高高渗条带研究内涵、提升注水开发管理水平的关键。
背景技术部分所引用的参考文献说明：
1)期刊名称缩写说明：
SPE为Society of Petroleum Engineers的简称；
IPTC为International Petroleum Technology Conference的简称；
SEG为Society of Exploration Geophysicists International Exhibition and Annual Meeting的简称；
WPC为World Petroleum Council的简称；
2)参考文献详细说明：
SPE-177824:O.M.Adeoye,etc.Integrated Evaluation Approach and Implications of High Permeability Streaks in Giant Carbonate OilField.2015.11；
SPE-64989:M.H.Alqam,etc.Treatment of Super-K Zones Using Gelling Polymers.2001.02；
IPTC-16632:Diah Agustina,etc.Drilling with Casing Technique Successfully Overcome Massive Thief Zone.2013.03；
SEG-2008-3184:Alexandre W.Araman,etc.Thief Zone Identification Through Seismic Monitoring of a CO2Flood,Weyburn Field,Saskatchewan.2008；
WPC-30164:C.Ravenne,etc.Characterisation of Reservoir Heterogeneities and Super Permeability Thief Zones in a Major Oilfield in The Middle East.2000；
IPTC-13385:Agus Sudarsana,etc.High Permeability Streaks Characterisations in  Middle East Carbonate.2009.12；
SPE-31149:P.valko.Performance of Fractured Horizontal Wells in High-Permeability Reservoirs.1996.01；
SPE-176110:Hu Dandan,etc.Integrated management and Application of Horizontal Well Water Flooding Technology in a Large-scale Complicated Carbonate Oilfield Containing High permeability Streaks.2015.08。
发明内容
为了解决上述的缺点和不足，本发明的一个目的在于提供一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法。
本发明的另一个目的还在于提供一种海相碳酸盐岩生屑灰岩油藏高渗条带识别装置。
又一方面，本发明还提供了一种计算机设备。
再一方面，本发明还提供了一种计算机可读存储介质。本发明解决了强非均质性生屑灰岩油藏内部注采井组见水顺序及注采管理的难题，为油藏高效开发和均衡动用、提高最终采收率提供了技术支撑。
为了实现以上目的，一方面，本发明提供了一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法，其中，所述方法包括：
根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究所获得的成果，划分高渗储集层(高渗层)成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
本发明中，所述单井流动单元是指在一定的生产条件(生产压差)下沿某个途径形成的、具有相似渗流特征的储集层，表现为单井纵向上具有一定的产液量。
作为本发明以上所述方法的一具体实施方式，其中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料。
作为本发明以上所述方法的一具体实施方式，其中，根据试井资料和生产测井资料按照如下公式(1)确定单层比采液指数(SPI)：
Figure PCTCN2022070683-appb-000001
公式(1)中：SPI为比采液指数，单位为bbl/(d·psi·m)；q o为日产液量，单位为bbl/d；ΔP为井底压力差(生产压差)，单位为psi；h为产液层厚度，单位为m。
本发明公式(1)中，需要利用试井资料确定井底压力差(生产压差)ΔP。
本发明中，所述比采液指数(SPI)又称米采液指数，是反映流动单元生产能力的重要参数，定义为单位厚度、单位生产压差下的日产液量。
作为本发明以上所述方法的一具体实施方式，其中，对于单层试井的情况，当所述试井资料为PBU资料时，根据PBU资料确定单井流动单元的单层动态渗透率(K_PBU)，包括：
1)根据PBU资料解释按照如下公式(2)确定该单井的地层系数；
Figure PCTCN2022070683-appb-000002
公式(2)中：kh为PBU资料解释的该单井的地层系数，单位为mD·m；q为PBU测试产量，单位为m 3/ks；μ为流体粘度，单位为mPa·s；m为PBU资料解释时所用Horner曲线的斜率，无因次；h为流动单元厚度(通常为试井测试射孔段段内具有渗流能力的有效储层厚度)，单位为m；k为单井流动单元的单层动态渗透率，单位为mD；
2)在测井曲线上确定流动单元厚度，再用单井的地层系数除以所述流动单元厚度即得到所述单井流动单元的单层动态渗透率；
对于合层试井的情况，当所述试井资料为PBU资料、所述生产测井资料为PLT测试资料时，根据PBU资料和PLT测试资料确定单井流动单元的单层动态渗透率(K_PBU)，包括：
1)根据PBU资料解释按照以上公式(2)确定该单井的综合地层系数(即所有流动单元地层系数的总和)；
2)利用PLT测试资料中的分层产液量按照如下公式(3)-公式(5)将该单井的 综合地层系数批分到每个流动单元，得到每个流动单元的单层地层系数；
q t＝q 1+q 2+q 3+...+q l      公式(3)；
Figure PCTCN2022070683-appb-000003
(kh) l＝(kh)×ration l       公式(5)；
公式(3)-公式(5)中，q t为PLT测试资料中的累计产液量，单位为bbl/d；q 1、q 2、q 3、……q l分别为PLT测试资料中不同产液段的实际产液量，单位均为bbl/d；ration l为PLT测试资料中不同产液段的实际产液量占PLT测试资料中累计产液量的比值，单位为无因次；kh为PBU资料解释的地层系数，即该单井的综合地层系数，单位为mD·m；(kh) l为每个流动单元的单层地层系数，单位为mD·m；
3)再根据每个流动单元的单层地层系数和每个流动单元的厚度按照如下公式(6)确定单井每个流动单元的单层动态渗透率；
Figure PCTCN2022070683-appb-000004
公式(6)中，k l为流动单元的单层动态渗透率，单位mD；(kh) l为每个流动单元的单层地层系数，单位为mD·m；h l为PLT测试资料中流动单元产液段的实际厚度，单位为m；l为PLT测试资料中实际产液段的段数，为正整数。
其中，通常情况下，本发明所测试的对象为油层，因此公式(2)中的流体粘度μ一般为油在地下条件下的粘度。
作为本发明以上所述方法的一具体实施方式，其中，对于单层试井的情况，当所述试井资料为PBU资料时，根据PBU资料确定单井流动单元的单层动态渗透率时，在测井曲线上确定流动单元厚度为利用单层PBU测试确定流动单元厚度h，具体为：根据射孔段厚度h1，利用测井曲线扣除夹层h2后，再通过井斜校正后确定流动单元厚度h。
由此可见，对单层试井(单层PBU测试)的情况，通过PBU测试所获得的PBU资料解释可以得到该层地层系数kh，再结合流动单元厚度(通常是射孔段内相对较好的有效储层厚度)h就可以确定该层动态渗透率；其中，kh的获取依赖于PBU资料解释时所用Horner曲线，图4为本发明实施例中东某油田X井压力恢复试井Horner曲线，图4中t p为压力恢复试井测试前油井生产时间，单位ks；dt为油井压力恢复 时间，单位ks；
但对于合层开采的井，PBU资料解释获得的kh为该井所有流动单元的综合地层系数，只有结合PLT测试资料，将kh批分到每个流动单元，才能确定不同流动单元的动态渗透率；因此，同时有试井资料和生产测井资料是对合层开采井进行储层参数解释的必要条件。
根据中华人民共和国石油天然气行业标准“油田试井技术规范”(SY/T6172-2006)，本发明中所述试井资料是为了确定油藏的地层压力、地层温度、流动压力以及单井的生产能力、研究储层参数、探测油、气、水层及其间连通性、监测储层动态而获取的对油水井开展的专门测试资料。其中，PBU又称压力恢复试井，是一种不稳定试井，压力恢复试井所得到的资料成为PBU资料。
本发明中，所述生产测井资料是在套管中测量的生产层流体密度、持水率、温度、压力、产量等数据，可直接确定在相同生产条件下不同层位生产能力的差异。其中，PLT是斯伦贝谢公司生产的测井仪，主要用于测量不同射孔层段的实际出液量并识别、区分产油量、产水量和产气量，量化不同射孔层段在某一工作制度条件下的供液能力。通过PLT获得的生产测井资料称为PLT测试资料。
作为本发明以上所述方法的一具体实施方式，其中，确定所述流动单元的厚度包括：
在PLT测试资料中显示的流动单元内部，利用测井曲线扣除单元内部隔、夹层；再通过井轨迹计算将流动单元厚度校正到垂深，即得到所述流动单元的厚度。
作为本发明以上所述方法的一具体实施方式，其中，根据生产测井资料分析多层合采的层间干扰情况，包括：根据生产测井资料(如PLT测试资料)分析可能存在的高产层对相对差储层的产能压制和干扰情况。
本发明中，层间干扰定义为油、气生产层段中，各小层间由于渗透率、流体性质及压力的差别，导致层间流体流动产生相互干扰的现象，通常是生产能力最好的流动单元对能力相对较差的流动单元产生干扰，多通过不稳定试井和动态测井进行判定。
对于多层合采井，物性较好、压力较大的储层PLT测试产液量贡献大，当整体产液量较大时，物性较差、压力较小的储层产液能力无法完全释放，PLT测试产液量低于实际生产能力，因此综合对比不同射孔层段储层物性、地层压力及PLT产量的匹配度，可对明显的层间干扰现象进行判定。
本发明中，当确定某流动单元存在层间干扰情况时，表明其生产数据及计算的动态渗透率未能反映其真实的生产能力，如果其它井中具有与上述存在层间干扰情况的层(记为层A)之间储层特征相同的层(记为层B)，并且层B在相同生产条件下未受层间干扰影响，此时可以将层B的动态渗透率标定为代表具有类似储层特征的单层动态渗透率，层A的动态渗透率数据作为不可靠数据，不进入下步操作使用的数据库。
作为本发明以上所述方法的一具体实施方式，其中，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准，包括：
建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，分析流动单元生产能力，即比采液指数随动态渗透率的变化趋势并确定拐点，将拐点所对应的动态渗透率和采液指数确定为海相碳酸盐岩生屑灰岩油藏高渗条带的动态识别标准。
作为本发明以上所述方法的一具体实施方式，其中，提取所述单井高渗条带，包括：根据所述动态识别标准在所述单井中分层提取高渗条带，具体包括：
在扣除流动单元内部隔、夹层的基础上，利用分段确定的单井流动单元的单层动态渗透率和单层比采液指数数据分别绘制动态渗透率和比采液指数矩形曲线，并将高渗条带动态标准中的单层动态渗透率和单层比采液指数设置为所述矩形曲线的基线并与所述矩形曲线右充填，当该单层的动态渗透率和比采液指数均大于矩形曲线的基线所对应的动态渗透率和比采液指数时，确定该单层为高渗条带。
本发明中，根据所述动态识别标准在所述单井中分层提取高渗条带可为建立推广到全油藏的识别模型提供样本。
本发明中，所述层序地层研究包括：以海平面变化控制层序形成与发育的层序地层学理论为基础，根据岩相旋回和地震层序结构建立分级次海相地层划分方案，通过单井测井曲线及三维地震推广到整个油藏。
本发明中，所述岩石相研究包括：根据碳酸盐岩粒屑结构，按照Dunham方案划分岩石相类型；在取芯井，根据岩芯观察和薄片分析划分岩石相、刻度测井并建立识别标准；在非取芯井利用测井曲线识别岩石相，从而推广到整个油藏。
本发明中，所述沉积相研究包括：根据区域地质背景确定油藏沉积环境，建立沉积模式；在取芯井，利用岩芯、薄片分析沉积特征、划分沉积微相、刻度测井并建立 沉积微相测井识别模式；在非取芯井，利用测井曲线形态、组合及纵向韵律性变化等特征识别沉积微相，从而推广到整个油藏。
本发明中，所述岩石物理相研究，包括：按照中华人民共和国石油天然气行业标准“油气储层评价方法”(SY/T 6285-1997)”的规定，将碳酸盐岩储层按照岩芯孔隙度划分为高孔(≥20％)、中孔(12％-20％)、低孔(4％-12％)和特低孔(<4％)四个级次，按照岩芯渗透率划分为高渗(≥100mD)、中渗(10-100mD)、低渗(1-10mD)和特低渗(<1mD)四个级次；
考虑到不同油田由于油藏、储层成因差异导致孔隙度和渗透率分级标准可能与国家标准不完全一致，本发明中，可根据取芯井标准岩芯物性实验，采用储层主控因素(如沉积微相)约束的孔隙度与渗透率散点交会图，来确定孔隙度和渗透率等级划分标准。
作为本发明以上所述方法的一具体实施方式，其中，根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律，包括：
利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相与地层层序中的位置，为利用测井资料井识别高渗条带提供规律性约束。
本发明中，利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；此处涉及静态和动态两条获取路线，具体而言：对于通过岩心测试获得的1cm小岩样的孔隙度和渗透率与PBU和PLT测试井周围可达数十至数百米范围的渗流能力和动态渗透率，二者的趋势一致但具体数值有较大差异。最可靠的方法是在同时具有取芯、PBU和PLT测试的井中进行该项工作，但一般取芯井少(成本高)，PBU和PLT测试井多，既有取芯又有测试PBU和PLT的井极少。因此，利用二者的一致性，分两条路走：一是利用岩心实验数据，按照行业标准(SY/T 6285-1997)划分高、中、低渗储集层，再根据岩心建立的层序地层、岩石相、沉积相结果寻找规律，其优点是数据都来自岩芯，匹配性好，高渗与中低渗对比性好；二是利用PBU、PLT确定的高渗条带及由测井曲线识别的层序地层、岩石相、沉积相建立对应关系，由于数 据来自于测井解释，其数据可靠性、不同属性一致性略有不足。本发明中，优选利用第一条路线确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律，其中高渗储集层来自岩芯分析数据，其与动态数据趋势一致。
作为本发明以上所述方法的一具体实施方式，其中，根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律，具体包括：
利用取芯井地质研究成果，提取单井地层层序、沉积相和岩石相划分结果；
利用取芯井岩石物性实验数据，结合地质相研究成果，建立岩石物理相分类标准并在取芯井提取岩石物理相；
利用沉积相、岩石相和岩石物理相划分高渗条带成因类型，为揭示高渗条带分布规律奠定基础；
综合分析高渗条带在岩石相、沉积相和层序界面中的分布规律。
作为本发明以上所述方法的一具体实施方式，其中，所述建立高渗条带测井识别模式，包括：
以单井生产测井测试段(如进行了PLT测试，则此处的测试段为PLT测试的测试段)为样本，以高渗条带动态标准所对应的单层动态渗透率和单层比采液指数为界，分析高渗条带与非高渗条带的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式。
其中，若高渗条带动态标准所对应的单层动态渗透率为a，则动态渗透率≥a对应高渗条带，动态渗透率<a对应非高渗条带。
作为本发明以上所述方法的一具体实施方式，其中，所述建立高渗条带测井相定性识别模式，包括：
分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，根据分析所得结果在沉积型储层和岩溶型储层中分别建立高渗条带测井相定性识别模式。
作为本发明以上所述方法的一具体实施方式，其中，在沉积型储层中，高渗条带多发育于高能沉积环境，多属于岩性纯、粒度粗、高孔隙度、高渗的孔隙型高渗条带；常规测井曲线显示低伽马、高孔隙度、高电阻率；FMI成像图色浅、分布规则或不规则的黑色斑块，显示粒间孔或较大的溶蚀孔发育；核磁测井T2谱显示T2值呈双峰或多峰特征，T2时间和能谱峰都显示高值；
在岩溶型储层中，高渗条带厚度薄，常规测井特征不明显但产能高，多显示“贼层”特征；FMI成像图显示发育正弦型过井裂缝或不规则微裂缝，部分井见溶洞或大的溶蚀孔；核磁测井T2谱显示双峰或多峰特征，T2时间和能谱峰值中等或偏低，发育溶洞或大溶蚀孔的高渗条带T2值大。
其中，本领域技术人员可以根据目标油田的实际情况，并结合该油田中某参数的正态分布来合理确定该参数所对应的程度，如粒度粗细、低伽马或者高伽马、高电阻率或者低电阻率、高孔隙度或者低孔隙度等等。
作为本发明以上所述方法的一具体实施方式，其中，建立沉积型高渗条带常规测井定量识别模式，包括：
在生产测井测试段(如进行了PLT测试，则此处的测试段为PLT测试的测试段)，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层(高伽马、高密度、油层中的低电阻率和低Am层)，按照常规测井曲线采样间隔分别分段获取测井曲线的曲线值，再根据动态渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测井曲线值的分布规律和识别界限(包括上限或者下限)，并据此建立沉积型高渗条带常规测井定量识别模式。
其中，所述双测井曲线分类散点交会图包括所有测井曲线中任意两种曲线的散点交会图，如：孔隙度曲线值-电阻率曲线值散点交会图和自然伽马曲线值-孔隙结构指数曲线值散点交会图。
作为本发明以上所述方法的一具体实施方式，其中，建立沉积型高渗条带常规测井定量识别模式过程中，所述测井曲线包括：自然伽马曲线(GR)、孔隙度曲线(RHOB)、电阻率曲线(Rt)和孔隙结构指数曲线(Am)。相应地，所述测井曲线的曲线值包括自然伽马曲线值、孔隙度曲线值、电阻率曲线值和孔隙结构指数曲线值。
在本发明一具体实施例中，建立沉积型高渗条带常规测井定量识别模式，具体包括以下步骤：
在生产测井测试段，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层，按照常规测井曲线采样间隔分别分段获取自然伽马曲线(GR)、孔隙度曲线(RHOB)、电阻率曲线(Rt)和孔隙结构指数曲线(Am)的曲线值，再根据动态渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测井曲线值分布规律和识别界线并据此建立沉积型高渗条带常规测井定量识别模式；
其中，所述曲线值包括自然伽马曲线值、孔隙度曲线值、电阻率曲线值和孔隙结 构指数曲线值；
所述双测井曲线分类散点交会图包括孔隙度曲线值-电阻率曲线值散点交会图和自然伽马曲线值-孔隙结构指数曲线值散点交会图。
由于动态渗透率代表测试结果，受层间干扰等影响，其并不一定完全反映地层渗流特征，因此本发明根据以下具体原则确定动态渗透率分布区间：
0mD代表测试期间不产液；其它动态渗透率分布区间则根据最大值和正态型分布区间，按照对数分区原则，考虑可能的高渗条带下限值确定分布区间，如本发明一具体实施例中，渗透率大于100mD可能为高渗条带，大于500mD则可能与超高渗或贼层有关。因此，在本发明一具体实施例中，所确定的动态渗透率分布区间为：0mD、0-1mD、1-10mD、10-50mD、50-100mD、100-200mD、200-500mD、500-1000及>1000mD。
作为本发明以上所述方法的一具体实施方式，其中，当所述测井曲线包括孔隙结构指数曲线时，根据如下公式(7)计算得到用于反映储层储渗特征的孔隙结构指数曲线的曲线值：
Figure PCTCN2022070683-appb-000005
公式(7)中，R w为地层水电阻率，单位为ohm·m；R t为原状地层电阻率，测井曲线上为深探测电阻率曲线所对应的电阻率，单位为ohm·m；POR为储层孔隙度，单位为无因次小数；S w为地层水饱和度，单位为无因次小数；n为饱和度指数，单位为无因次小数。
在本发明以上所述方法的一具体实施方式中，其中，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释，包括：根据沉积型高渗条带常规测井定量识别模式进行高能沉积型高渗条带测井定量识别、根据高渗条带测井相定性识别模式进行测井相定性识别以及地质规律的约束和相互印证；
其中，根据沉积型高渗条带常规测井定量识别模式进行高能沉积型高渗条带测井定量识别，包括：在自然伽马曲线、孔隙度曲线、电阻率曲线和孔隙结构指数曲线这四条曲线中，GR代表岩性和沉积环境，其值低代表岩性纯、颗粒粗，沉积能量相对较高；RHOB代表岩石总孔隙度，其值低代表孔隙度高、物性好；Rt代表含油性和渗透性，在纯油层其值高代表含油性好，说明成藏时油气充注程度高，可侧面反映其 渗透性相对较好；Am代表孔隙结构，其值高代表导电路径复杂，属于典型的大孔小喉型粗颗粒沉积储层。因此，作为能够形成渗流优势通道的高渗条带，必须满足上述所有条件，以测井曲线同时满足高渗条带标准来逐点筛选高能沉积型高渗条带；
其中根据高渗条带测井相定性识别模式进行测井相定性识别，包括：根据核磁测井中“T2谱双峰或多峰特征及T2时间和能谱峰均为高值”及成像测井“较大尺度及较强程度溶蚀孔洞”的显示特征进一步识别和确定高能沉积型高渗条带；
岩溶型储层中多具有“贼层”特征，根据FMI成像图上“发育正弦型过井裂缝或不规则微裂缝、黑色斑块状溶洞或大的溶蚀孔”以及核磁测井T2谱“双峰或多峰特征”进行定性识别；
地质规律的约束和相互印证，包括：沉积型高渗条带受沉积相控制发育于相对固定的沉积部位，岩溶型高渗条带受层序地层及古构造约束发育于层序界面附近区域，在上述识别过程中，根据“潮道型高渗条带多发育于潮道下部、生屑滩型高渗条带多发育于滩体上部、滩翼相高渗条带多发育于靠近滩体部位、岩溶型高渗条带多发育于层序界面附近”的地质规律，佐证或重新拾取高渗条带。
作为本发明以上所述方法的一具体实施方式，其中，所述方法还包括：利用取芯井岩芯实验结果、生产测井资料(如PLT测试资料)和动态渗透率数据，以及注采井组中见水井的见水层及所述见水层发育高渗条带的情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果。
作为本发明以上所述方法的一具体实施方式，其中，利用岩芯实验结果检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果，包括：在识别出的高渗条带中，分析高能沉积型高渗条带(沉积型高渗条带)与岩芯渗透率中的高渗部分是否具有较好的对应关系，一般情况下，岩溶型高渗条带与岩芯渗透率相关性不稳定，而沉积型高渗条带与岩芯渗透率相关性相对较为稳定。
作为本发明以上所述方法的一具体实施方式，其中，利用生产测井资料(如PLT测试资料)和动态渗透率数据检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果，包括：分析识别出的高渗条带中其K_PBU是否高于下限值(即高渗条带动态标准所对应的单层动态渗透率)、PLT测试资料中是否具有高产量，以及高渗条带与高K_PBU、高PLT产量之间是否具有统计关系，以验证方法的可靠性。
作为本发明以上所述方法的一具体实施方式，其中，利用注采井组见水井的见水层及所述见水层发育高渗条带的情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别 结果，包括：在已建立的注采井组中，分析已识别的高渗条带与目前已见水井(提前见水井)的对应关系及统计关系，验证方法的可靠性。
另一方面，本发明还提供了一种海相碳酸盐岩生屑灰岩油藏高渗条带识别装置，其中，所述装置包括：
储层动态参数确定模块，用于根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
高渗条带提取模块，用于根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
高渗条带成因类型划分模块，用于在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
高渗条带测井识别模块，用于建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
本发明还提供了一种海相碳酸盐岩生屑灰岩油藏高渗条带识别装置，其中，所述装置包括：处理器，其中，所述处理器用于执行存储在存储器中的以下程序模块：
储层动态参数确定模块，用于根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
高渗条带提取模块，用于根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
高渗条带成因类型划分模块，用于在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
高渗条带测井识别模块，用于建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
作为本发明以上所述装置的一具体实施方式，其中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料。
作为本发明以上所述装置的一具体实施方式，其中，所述储层动态参数确定模块 包括单层比采液指数确定单元，所述单层比采液指数确定单元用于根据试井资料和生产测井资料按照如下公式(1)确定单层比采液指数：
Figure PCTCN2022070683-appb-000006
公式(1)中：SPI为比采液指数，单位为bbl/(d·psi·m)；q o为日产液量，单位为bbl/d；ΔP为井底压力差，单位为psi；h为产液层厚度，单位为m。
作为本发明以上所述装置的一具体实施方式，其中，所述储层动态参数确定模块还包括单层动态渗透率确定单元，对于单层试井的情况，当所述试井资料为PBU资料时，所述单层动态渗透率确定单元用于根据PBU资料确定单井流动单元的单层动态渗透率，包括：
1)根据PBU资料解释按照如下公式(2)确定该单井的地层系数；
Figure PCTCN2022070683-appb-000007
公式(2)中：kh为PBU资料解释的该单井的地层系数，单位为mD·m；q为PBU测试产量，单位为m 3/ks；μ为流体粘度，单位为mPa·s；m为PBU资料解释时所用Horner曲线的斜率，无因次；h为流动单元厚度，单位为m；k为单井流动单元的单层动态渗透率，单位为mD；
2)在测井曲线上确定流动单元的厚度，再用单井的地层系数除以所述流动单元的厚度即得到所述单井流动单元的单层动态渗透率；
对于合层试井的情况，当所述试井资料为PBU资料、所述生产测井资料为PLT测试资料时，所述单层动态渗透率确定单元用于根据PBU资料和PLT测试资料确定单井流动单元的单层动态渗透率，包括：
1)根据PBU资料解释按照以上公式(2)确定该单井的综合地层系数；
2)利用PLT测试资料中的分层产液量按照如下公式(3)-公式(5)将该单井的综合地层系数批分到每个流动单元，得到每个流动单元的单层地层系数；
q t＝q 1+q 2+q 3+...+q l       公式(3)；
Figure PCTCN2022070683-appb-000008
(kh) l＝(kh)×ration l      公式(5)；
公式(3)-公式(5)中，q t为PLT测试资料中的累计产液量，单位为bbl/d；q 1、 q 2、q 3、……q l分别为PLT测试资料中不同产液段的实际产液量，单位均为bbl/d；ration l为PLT测试资料中不同产液段的实际产液量占PLT测试资料中累计产液量的比值，单位为无因次；kh为PBU资料解释的地层系数，即该单井的综合地层系数，单位为mD·m；(kh) l为每个流动单元的单层地层系数，单位为mD·m；
3)再根据每个流动单元的单层地层系数和每个流动单元的厚度按照如下公式(6)确定单井每个流动单元的单层动态渗透率；
Figure PCTCN2022070683-appb-000009
公式(6)中，k l为流动单元的单层动态渗透率，单位mD；(kh) l为每个流动单元的单层地层系数，单位为mD·m；h l为PLT测试资料中流动单元产液段的实际厚度，单位为m；l为PLT测试资料中实际产液段的段数，为正整数。
作为本发明以上所述装置的一具体实施方式，其中，所述储层动态参数确定模块还包括流动单元的厚度确定单元，用于确定所述流动单元的厚度，包括：
在PLT测试资料中显示的流动单元内部，利用测井曲线扣除单元内部隔、夹层；再通过井轨迹计算将流动单元厚度校正到垂深，即得到所述流动单元的厚度。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带提取模块具体用于：
建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，分析流动单元生产能力，即比采液指数随动态渗透率的变化趋势并确定拐点，将拐点所对应的动态渗透率和采液指数确定为海相碳酸盐岩生屑灰岩油藏高渗条带的动态识别标准。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带提取模块还包括单井高渗条带提取单元，所述单井高渗条带提取单元用于根据所述动态识别标准在所述单井中分层提取高渗条带，具体用于：
在扣除流动单元内部隔、夹层的基础上，利用分段确定的单井流动单元的单层动态渗透率和单层比采液指数数据分别绘制动态渗透率和比采液指数矩形曲线，并将高渗条带动态标准中的单层动态渗透率和单层比采液指数设置为所述矩形曲线的基线并与所述矩形曲线右充填，当该单层的动态渗透率和比采液指数均大于矩形曲线的基线所对应的动态渗透率和比采液指数时，确定该单层为高渗条带。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带成因类型划分模块具体用于：
利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相与地层层序中的位置。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带测井识别模块具体用于：
以单井生产测井测试段为样本，以高渗条带动态标准所对应的单层动态渗透率和单层比采液指数为界，分析高渗条带与非高渗条带的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带测井识别模块包括高渗条带测井相定性识别模式建立单元，所述高渗条带测井相定性识别模式建立单元用于：
分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，根据分析所得结果在沉积型储层和岩溶型储层中分别建立高渗条带测井相定性识别模式。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带测井识别模块还包括沉积型高渗条带常规测井定量识别模式建立单元，所述沉积型高渗条带常规测井定量识别模式建立单元用于：
在生产测井测试段，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层，按照常规测井曲线采样间隔分别分段获取测井曲线的曲线值，再根据动态渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测井曲线值分布规律和识别界线并据此建立沉积型高渗条带常规测井定量识别模式。
作为本发明以上所述装置的一具体实施方式，其中，所述高渗条带测井识别模块还包括孔隙结构指数曲线的曲线值计算单元，所述孔隙结构指数曲线的曲线值计算单元用于：
当所述测井曲线包括孔隙结构指数曲线时，根据如下公式(7)计算得到孔隙结构指数曲线的曲线值：
Figure PCTCN2022070683-appb-000010
公式(7)中，R w为地层水电阻率，单位为ohm·m；R t为原状地层电阻率，测井 曲线上为深探测电阻率曲线所对应的电阻率，单位为ohm·m；POR为储层孔隙度，单位为无因次小数；S w为地层水饱和度，单位为无因次小数；n为饱和度指数，单位为无因次小数。
作为本发明以上所述装置的一具体实施方式，其中，所述装置还包括：识别结果检验模块，用于利用岩芯实验结果、生产测井资料和动态渗透率数据，以及注采井组见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果。
又一方面，本发明还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现以上所述海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的步骤。
再一方面，本发明还提供了一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现以上所述的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的步骤。
本发明以动态为纲，在地质规律约束下提供了一种动静态一体化的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置，本发明所提供的该方法及装置的理论基础和技术逻辑扎实可靠，方法简单易操作；本发明所提供的该方法及装置的识别对象包括贼层但不限于贼层，有效解决了海相碳酸盐岩生屑灰岩油藏内部对注水开发影响最大的高能沉积型高渗条带识别难题；本发明所提供的方法及装置在油田推广应用后，可提供一套有效覆盖全油藏的可靠的高渗条带数据，验证了目前注采井组提前见水井都与本发明方法及装置所识别出的高渗条带有关，可为新的注采井组优化设计提供技术支撑。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例提供的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的流程图。
图2为本发明具体实施例中某油田X井PBU测试结果示意图。
图3为本发明具体实施例中某油田Y资料井动态测试及高渗条带拾取结果示意图。
图4为本发明具体实施例中X井压力恢复试井Horner曲线示意图。
图5为本发明具体实施例中Z井层间干扰识别示意图。
图6为本发明具体实施例中高渗条带动态识别标准确定示意图。
图7为本发明具体实施例中单井层序地层、岩石相、沉积相研究成果示意图。
图8为本发明具体实施例中油藏有效储层岩石物理分类示意图。
图9为本发明具体实施例中高渗储集层发育的岩石相分析示意图。
图10a为本发明具体实施例中高渗储集层发育的岩石相类型统计直方图。
图10b为本发明具体实施例中高渗储集层发育的沉积相类型统计直方图。
图11a为本发明具体实施例中潮道型沉积型高渗条带电性特征及纵向分布示意图。
图11b为本发明具体实施例中生屑滩型沉积型高渗条带电性特征及纵向分布示意图。
图11c为本发明具体实施例中滩翼型沉积型高渗条带电性特征及纵向分布示意图。
图12a为本发明具体实施例中D井岩溶型高渗条带电性特征及纵向分布示意图。
图12b为本发明具体实施例中E井岩溶型高渗条带电性特征及纵向分布示意图。
图13a为本发明具体实施例中沉积型高渗条带的FMI及CMR测井特征示意图。
图13b为本发明具体实施例中岩溶型高渗条带的FMI及CMR测井特征示意图。
图14为本发明具体实施例中高渗条带建立测井解释模型的样本提取示意图。
图15a为本发明具体实施例中基于K_PBU分类的双测井曲线RHOB-Rt散点交会图。
图15b为本发明具体实施例中基于K_PBU分类的双测井曲线GR-Am散点交会图。
图16a为本发明具体实施例中高渗条带单井识别结果示意图。
图16b为图16a中76对应的岩溶型高渗条带的FMI成像图和CMR测井T 2能谱图。
图16c为图16a中77对应的岩溶型高渗条带的FMI成像图和CMR测井T 2能谱 图。
图17a中的79为注采井组井位分布图。
图17b为两口注水井(INJ1、INJ2)及1口采油井(PROD1)的连井对比图。
图17c为图17b中84所对应的沉积微相图例。
图18为本发明一实施例所提供的海相碳酸盐岩生屑灰岩油藏高渗条带识别装置的结构示意图。
图19为本发明另一实施例所提供的海相碳酸盐岩生屑灰岩油藏高渗条带识别装置的结构示意图。
具体实施方式
为了对本发明的技术特征、目的和有益效果有更加清楚的理解，现结合以下具体实施例对本发明的技术方案进行以下详细说明，但不能理解为对本发明的可实施范围的限定。
图1为本发明实施例提供的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的流程图，从图1中可以看出，所述海相碳酸盐岩生屑灰岩油藏高渗条带识别方法包括以下具体步骤：
S101：根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
S102：根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
S103：在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
S104：建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
在一实施例中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料。
在一实施例中，根据试井资料和生产测井资料按照如下公式(1)确定单层比采液指数：
Figure PCTCN2022070683-appb-000011
公式(1)中：SPI为比采液指数，单位为bbl/(d·psi·m)；q o为日产液量，单位为bbl/d；ΔP为井底压力差，单位为psi；h为产液层厚度，单位为m。
在一实施例中，对于单层试井的情况，当所述试井资料为PBU资料时，根据PBU资料确定单井流动单元的单层动态渗透率，包括：
1)根据PBU资料解释按照如下公式(2)确定该单井的地层系数；
Figure PCTCN2022070683-appb-000012
公式(2)中：kh为PBU资料解释的该单井的地层系数，单位为mD·m；q为PBU测试产量，单位为m 3/ks；μ为流体粘度，单位为mPa·s；m为PBU资料解释时所用Horner曲线的斜率，无因次；h为流动单元厚度，单位为m；k为单井流动单元的单层动态渗透率，单位为mD；
2)在测井曲线上确定流动单元的厚度，再用单井的地层系数除以所述流动单元的厚度即得到所述单井流动单元的单层动态渗透率；
对于合层试井的情况，当所述试井资料为PBU资料、所述生产测井资料为PLT测试资料时，根据PBU资料和PLT测试资料确定单井流动单元的单层动态渗透率，包括：
1)根据PBU资料解释按照以上公式(2)确定该单井的综合地层系数；
2)利用PLT测试资料中的分层产液量按照如下公式(3)-公式(5)将该单井的综合地层系数批分到每个流动单元，得到每个流动单元的单层地层系数；
q t＝q 1+q 2+q 3+...+q l      公式(3)；
Figure PCTCN2022070683-appb-000013
(kh) l＝(kh)×ration l       公式(5)；
公式(3)-公式(5)中，q t为PLT测试资料中的累计产液量，单位为bbl/d；q 1、q 2、q 3、……q l分别为PLT测试资料中不同产液段的实际产液量，单位均为bbl/d；ration l为PLT测试资料中不同产液段的实际产液量占PLT测试资料中累计产液量的比值，单位为无因次；kh为PBU资料解释的地层系数，即该单井的综合地层系数，单位为mD·m；(kh) l为每个流动单元的单层地层系数，单位为mD·m；
3)再根据每个流动单元的单层地层系数和每个流动单元的厚度按照如下公式(6)确定单井每个流动单元的单层动态渗透率；
Figure PCTCN2022070683-appb-000014
公式(6)中，k l为流动单元的单层动态渗透率，单位mD；(kh) l为每个流动单元的单层地层系数，单位为mD·m；h l为PLT测试资料中流动单元产液段的实际厚度，单位为m；l为PLT测试资料中实际产液段的段数，为正整数。
在一实施例中，确定所述流动单元的厚度包括：
在PLT测试资料中显示的流动单元内部，利用测井曲线扣除单元内部隔、夹层；再通过井轨迹计算将流动单元厚度校正到垂深，即得到所述流动单元的厚度。
在一实施例中，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准，包括：
建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，分析流动单元生产能力，即比采液指数随动态渗透率的变化趋势并确定拐点，将拐点所对应的动态渗透率和采液指数确定为海相碳酸盐岩生屑灰岩油藏高渗条带的动态识别标准。
在一实施例中，提取所述单井高渗条带，包括：根据所述动态识别标准在所述单井中分层提取高渗条带，具体包括：
在扣除流动单元内部隔、夹层的基础上，利用分段确定的单井流动单元的单层动态渗透率和单层比采液指数数据分别绘制动态渗透率和比采液指数矩形曲线，并将高渗条带动态标准中的单层动态渗透率和单层比采液指数设置为所述矩形曲线的基线并与所述矩形曲线右充填，当该单层的动态渗透率和比采液指数均大于矩形曲线的基线所对应的动态渗透率和比采液指数时，确定该单层为高渗条带。
在一实施例中，根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律，包括：
利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相与地层层序中的位置。
在一实施例中，所述建立高渗条带测井识别模式，包括：
以单井生产测井测试段为样本，以高渗条带动态标准所对应的单层动态渗透率和单层比采液指数为界，分析高渗条带与非高渗条带的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式。
在一实施例中，所述建立高渗条带测井相定性识别模式，包括：
分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，根据分析所得结果在沉积型储层和岩溶型储层中分别建立高渗条带测井相定性识别模式。
在一实施例中，其中，在沉积型储层中，高渗条带多发育于高能沉积环境，多属于岩性纯、粒度粗、高孔隙度、高渗的孔隙型高渗条带；常规测井曲线显示低伽马、高孔隙度、高电阻率；FMI成像图色浅、分布规则或不规则的黑色斑块，显示粒间孔或较大的溶蚀孔发育；核磁测井T2谱显示T2值呈双峰或多峰特征，T2时间和能谱峰都显示高值；
在岩溶型储层中，高渗条带厚度薄，常规测井特征不明显但产能高，多显示“贼层”特征；FMI成像图显示发育正弦型过井裂缝或不规则微裂缝，部分井见溶洞或大的溶蚀孔；核磁测井T2谱显示双峰或多峰特征，T2时间和能谱峰值中等或偏低，发育溶洞或大溶蚀孔的高渗条带T2值大。
在一实施例中，建立沉积型高渗条带常规测井定量识别模式，包括：
在生产测井测试段，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层，按照常规测井曲线采样间隔分别分段获取测井曲线的曲线值，再根据动态渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测井曲线值分布规律和识别界线并据此建立沉积型高渗条带常规测井定量识别模式。
在一实施例中，当所述测井曲线包括孔隙结构指数曲线时，根据如下公式(7)计算得到孔隙结构指数曲线的曲线值：
Figure PCTCN2022070683-appb-000015
公式(7)中，R w为地层水电阻率，单位为ohm·m；R t为原状地层电阻率，测井曲线上为深探测电阻率曲线所对应的电阻率，单位为ohm·m；POR为储层孔隙度，单位为无因次小数；S w为地层水饱和度，单位为无因次小数；n为饱和度指数，单位为无因次小数。
在一实施例中，所述方法还包括：利用取芯井岩芯实验结果、生产测井资料和动 态渗透率数据，以及注采井组中见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果。
下面将以海相碳酸盐岩生屑灰岩油藏某油田为例，具体说明本发明所提供的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法，其包括：
根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数：其中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料；
具体实施例中，图2为该油田X井PBU试井测试结果示意图，从图2中可以看出，该井试井期间已达拟稳定流阶段，试井双对数曲线解释的储层参数可以较为真实地反映单井钻遇流动单元的储层特性，利用其解释的地层系数可以较好地揭示流动单元的生产能力；
具体实施例中，图3为该油田Y井动态测试及高渗条带拾取结果示意图，图3中1为测井深度道、2为补芯高校正后的垂深道、3为自然伽马(GR)与岩石密度(RHOB)曲线重叠道、4为自然伽马(GR)与深探测电阻率(LLD)曲线重叠道、5为PLT测试道、6为K_PBU计算结果道、7为SPI计算结果道、8为射孔段指示道、9为K_PBU的基线、10为K_PBU矩形曲线、11为SPI的基线、12为SPI矩形曲线、13为SPI基线向右与SPI矩形曲线充填(图3中13所代表的黑色区域)，只有当SPI值大于SPI基线值时才能充填，14-15为射孔段隔夹层，如图3中5所示，在射孔段范围内，PLT测试出液段呈断续分布，其中以MB1-2C顶部最强，即射孔段内PLT测试出液段显示了在当前生产条件下过测试井的流动单元；
具体实施例中，所述确定流动单元的厚度包括：
首先在PLT揭示的流动单元内部，利用测井曲线扣除单元内部隔、夹层(图3中14、15)；然后通过井轨迹计算，将流动单元厚度校正到补芯高校正后的垂深(图3中2)，即得到所述流动单元的厚度；
具体实施例中，按照上述公式(1)确定Y井的单层比采液指数：如图3中7及表1所示，该井5个PLT测试出液段的生产压差(井底压力差)为453.7psi，日产油量为196.93-1015.70bbl/d，流动单元厚度(PLT产液段垂深厚度)为1.30-7.23m，据此按照公式(1)计算得到的SPI值范围为0.064-0.525bbl/(d·psi·m)；
表1 Y井比采液指数计算表
层号 垂深厚度，m 油产量，bbl/d 生产压差，MPa SPI，bbl/(d·psi·m)
9 1.30 309.51 453.7 0.525
10 6.78 196.93 453.7 0.064
11 7.23 406.63 453.7 0.124
12 5.70 1015.70 453.7 0.393
13 3.00 703.81 453.7 0.517
具体实施例中，由于Y井为合层试井的情况，按照如上所述公式(4)-公式(6)计算动态渗透率，计算过程中所涉及的中间参数以及计算得到的结果数据见图3中6及表2所示，可见，利用PBU资料和PLT测试资料综合解释获取的不同流动单元的K_PBU值介于26.00-328.54mD，以MB1-2A顶部和MB1-2C上部流动单元最高，MB1-2C上部计算结果与储层地质特征及测井响应特征具有较好的相关性，MB1-2A顶部极薄层测井响应特征不明显，但由于处于三级层序界面附近，与溶蚀暴露有关，除成像测井外难以识别，具有典型“贼层”特征。
表2 Y井动态渗透率计算表
Figure PCTCN2022070683-appb-000016
根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带：
具体实施例中，根据生产测井资料分析多层合采的层间干扰情况，包括：根据生产测井资料(如PLT测试资料)分析可能存在的高产层对相对差储层的产能压制和干扰情况；
图5为具体实施例中Z井层间干扰识别示意图，图5中16为单井层系划分信息，17-19分别为常规测井自然伽马、岩石密度、深探测电阻率曲线，20为PLT测试曲线，21为MDT测试数据，22为射孔剖面，23-25为不同层位PLT测试的产量数据，26-27 为PLT无产量层段，从图5中可以看出，由于MA2高产层(23)和MB1-2C高产层(25)的影响，中部MB1-2A和MB1-2B储层特征与MB1-2C产层相似的储层表现为低产(24)甚至无产(26-27)，表明存在明显的产量压制和层间干扰作用；
具体实施例中，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准，包括：
建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，所得交会图(即高渗条带动态识别标准确定示意图)如图6所示，从图6中可以看出，SPI随K_PBU单调增加，相关性好，在K_PBU＝100mD处SPI明显分叉，将拐点设为K_PBU＝100mD(28)、SPI＝0.3bbl/(d·psi·m)(29)，在拐点之上，流动单元生产能力随渗透率增加快速增强，因此定义K_PBU＝100mD、SPI＝0.3bbl/(d·psi·m)为具体实施例中生屑灰岩油藏高渗条带的动态识别标准；
再根据所述生屑灰岩油藏高渗条带的动态识别标准，在同时具有PBU资料和PLT测试资料的井中，分层提取高渗条带，为建立推广到全油藏的识别模型提供样本。如图3所示，在扣除流动单元内部隔、夹层的基础上，利用分段确定的K_PBU、SPI数据绘制动态渗透率矩形曲线，并将高渗条带动态渗透率下限(K_PBU＝100mD、SPI＝0.3bbl/(d·psi·m))设置为矩形曲线的基线(Baseline，图3中6、7、9及11)并与矩形曲线右充填，当K_PBU和SPI大于基线所对应的动态渗透率和比采液指数时，说明该层为高渗条带。本具体实施例共计对31口井200个流动单元进行了分析，共提取高渗条带62层，有效覆盖了油藏所有甜点沉积相，样本数量大、代表性好，为建立地质、测井识别模式奠定了良好基础。
在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
具体实施例中，层序地层的研究，包括：以海平面变化控制层序形成与发育的层序地层学理论为基础，根据岩相旋回和地震层序结构建立分级次海相地层划分方案，通过单井测井曲线及三维地震推广到整个油藏。具体实施例中的目标油藏被划分出的三、四级层序都具有快速海侵缓慢海退、顶部暴露的特点，可对比性强，部分层序顶部发育潮道和下切谷(如图7中30)。
具体实施例中，岩石相的研究，包括：根据碳酸盐岩粒屑结构，按照Dunham方案划分岩石相类型；在取芯井根据岩芯观察和薄片分析划分岩石相、刻度测井并建立识别标准；在非取芯井利用测井曲线识别岩石相，从而推广到整个油藏。具体实施例 中油藏发育于缓坡台地，碳酸盐岩粒屑结构以生屑颗粒、灰泥基质、亮晶胶结物为主，由于有效储层弱胶结，因此划分出颗粒灰岩(颗粒含量>75％)、颗粒主导的泥粒灰岩(颗粒含量为50％-75％)、灰泥主导的泥粒灰岩(颗粒含量为25％-50％)、粒泥灰岩(颗粒含量为10％-25％)和泥晶灰岩(颗粒含量<10％)五种岩石相，如图7中31所示，随着颗粒含量减少，相同条件下测井曲线具有伽马(特别是去铀伽马)值增高、电阻率值降低的特点。
具体实施例中，沉积相的研究，包括：根据区域地质背景确定油藏沉积环境，建立沉积模式；在取芯井利用岩芯、薄片分析沉积特征、划分沉积微相、刻度测井并建立沉积微相测井识别模式；在非取芯井利用测井曲线形态、组合及纵向韵律性变化等特征识别沉积微相，从而推广到整个油藏。具体实施例中的油藏为弱镶边缓坡碳酸盐岩台地环境，共划分出八种沉积微相，包括潮道、下切谷、台内生屑滩、前缘生屑滩、滩翼、潟湖、沼泽和潮下带，从图7中32可以看出，高能环境沉积微相如潮道、生屑滩多发育颗粒灰岩和颗粒主导的泥粒灰岩，低能环境沉积微相如潮下带、潟湖则主要发育泥晶灰岩、粒泥灰岩和灰泥主导的泥粒灰岩。
具体实施例中，岩石物理相的研究，包括：按照中华人民共和国石油天然气行业标准“油气储层评价方法”(SY/T 6285-1997)，将碳酸盐岩储层孔隙度划分为高孔(≥20％)、中孔(12％-20％)、低孔(4％-12％)和特低孔(<4％)四个级次，将渗透率划分为高渗(≥100mD)、中渗(10-100mD)、低渗(1-10mD)和特低渗(<1mD)四个级次；具体实施例中，考虑到不同油田由于油藏、储层成因差异导致孔隙度和渗透率分级标准可能与国家标准不完全一致，利用取芯井标准岩芯物性实验，采用储层主控因素(如沉积微相)约束的孔隙度与渗透率散点交会图，来确定孔隙度和渗透率等级划分标准。如图8所示，综合考虑有效储层成因和孔、渗散点聚类特征，调整孔隙度分级标准界限分别为22％(图8中33)、15％(图8中34)、8％(图8中35)，调整渗透率分级标准界限为30mD(图8中36)、3mD(图8中37)和0.3mD(图8中38)，与行业标准略有差异。
具体实施例中，利用取芯井岩石物理相划分的高渗储集层或利用资料井PBU资料及PLT测试资料拾取的单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；如图9中39所示，岩石物理相中的高渗储集层(岩芯渗透率>30mD、孔隙度>0.15)主要发育于颗粒灰岩和颗粒主导的泥粒灰岩；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相 与地层层序中的位置，为利用测井资料井识别高渗条带提供规律性约束。图10a为具体实施例中高渗储集层发育的岩石相类型统计直方图，图10b为具体实施例中高渗储集层发育的沉积相类型统计直方图，从图10a中可以看出，高渗储集层发育的岩石相主要为颗粒灰岩和颗粒主导的泥粒灰岩，从图10b中可以看出，高渗储集层发育的沉积相主要为潮道和前缘生屑滩、少量滩翼和潟湖，其中甜点沉积相潮道、生屑滩和部分滩翼是沉积型高渗条带的主体，潟湖、沼泽和滩翼等其它非甜点相多由岩溶改造形成，发育岩溶型高渗条带(见如下表3所示)。
图11a-图11c为具体实施例中三类沉积型高渗条带的测井响应特征及纵向分布示意图，在图11a中，40代表地层分层数据道，41为钻井深度道，42为自然伽马曲线和孔隙度曲线道，43为深探测电阻率和自然伽马曲线道，44为HPS识别结果道；图11b-图11c具有相同的绘图结构。从图11a-图11c及表3可以看出，潮道具有向上沉积能量减弱、颗粒变细减少的特点，多为正粒序结构，高渗条带多发育于下部(图11a中的A井为例)；生屑滩则相反，多表现反粒序结构，高渗条带多发育于上部(图11b中的B井为例)；滩翼相高渗条带多发育于靠近滩体部分(图11c中的C井为例)。
图12a-图12b为具体实施例中岩溶型高渗条带的测井响应特征及纵向分布示意图，图12a-图12b中45为层序地层划分道，46为FMI静态成像图道，47为FMI动态成像图道，48为核磁测井T2谱图道，49为PLT测试数据道，50为动态渗透率计算结果道。从图12a-图12b中可以看出，岩溶型高渗条带多受层序结构和沉积暴露控制，多发育于层序界面附近(见如下表3所示)，其中D井的FMI成像测井显示在层序界面附近发育溶蚀型微裂缝，E井层序界面附近虽然储层基质物性差，但PLT测试获得较高产能和较高的动态渗透率(328mD)。
表3 高渗条带成因类型及其特征表
Figure PCTCN2022070683-appb-000017
建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐 井识别高渗条带，实现全油藏测井解释：
以资料井PLT测试段为样本，以K_PBU＝100mD为界，分析高渗条带(K_PBU≥100mD)与非高渗条带(K_PBU<100mD)的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式；
其中，所述建立高渗条带测井相定性识别模式，包括：分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，分两种类型建立高渗条带测井相定性识别模式。在沉积型储层中，高渗条带受沉积相控制，多发育于高能沉积环境，多属于岩性纯、粒度粗、高孔隙度、高渗的孔隙型高渗条带；常规测井曲线显示低伽马、高孔隙度、高电阻率，如图11a-图11c中的A井、B井、C井所示；FMI成像图色浅、分布规则或不规则的黑色斑块，显示粒间孔或较大的溶蚀孔发育；核磁测井T2谱显示T2值呈双峰或多峰特征，T2时间和能谱峰都显示高值，如图13a所示，在图13a中，51为FMI静态成像图道，52为FMI动态成像图道，53为核磁测井T2谱图道，从图13a中可以看出，D井图示层段发育规则的溶蚀孔(黑色斑块)，CMR测井T2谱显示双峰或多峰特征。在岩溶型储层中，高渗条带厚度薄、常规测井特征不明显但产能高，多显示“贼层”特征(见图12b中的E井)；FMI成像图显示发育正弦型过井裂缝或不规则微裂缝(见图12a中的D井)，部分井见溶洞或大的溶蚀孔(见图13b)；核磁测井T2谱显示双峰或多峰特征，T2时间和能谱缝峰值中等或偏低(见图13b中的D井)，发育溶洞或大溶蚀孔的高渗条带T2值大(见图13b)。
其中，所述建立沉积型高渗条带常规测井定量识别模式，包括：
首先按照如上公式(7)引入反映储层储渗特征的孔隙结构指数曲线，综合分析地层水矿化度、毛管压力曲线、相渗曲线、驱替电阻率实验等，具体实施例中确定公式(7)中的R w取值为0.05ohm·m，n取值为2.05，纯油层Sw取值为0.1-0.3，随物性变差而增大，纯水层Sw取值为1，油水同层根据油柱高度及物性插值，因此利用孔隙度曲线和深探测测井电阻率曲线计算得到孔隙结构指数曲线(Am)。图14为本发明具体实施例中高渗条带建立测井识别模式的样本提取示意图，图14中，54为自然伽马曲线道，55为三条孔隙度曲线道(密度RHOB、中子NPHI、声波DT)，56为深探测电阻率曲线道，57为计算的孔隙结构指数曲线Am道，58为PLT测试数据道，59为动态渗透率计算结果道，60-61为PLT测试产液段扣除隔夹层后的动态渗透率矩形曲线图。如图14中57所示，在纯油层中，Am值随储层渗透性变好而增大。
其次，在PLT测试段(图14中57)，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层(高伽马、高密度、油层中的低电阻率和低Am层，如图14中61所示)，按照常规测井曲线采样间隔分段获取自然伽马曲线(GR，图14中54)、代表性的孔隙度曲线(RHOB，图14中55)、代表性的电阻率曲线(Rt，图14中56)和孔隙结构指数曲线(Am，图14中57)等测井曲线值(图14中60-61)；再根据K_PBU值分布区间(具体实施例中将K_PBU值划分为0mD、0-1mD、1-10mD、10-50mD、50-100mD、100-200mD、200-500mD、500-1000、>1000mD共9个区间)制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带(K_PBU≥100mD)的测井曲线值分布规律和识别界线，据此建立沉积型高渗条带常规测井定量识别模式。如图15a-图15b所示为本具体实施例中基于K_PBU分类的双测井曲线散点交会图，图15a-图15b中，62为高渗条带的电阻率下限值，63为高渗条带的孔隙度曲线RHOB上限值，64为高渗条带的孔隙结构指数曲线下限值，65为高渗条带的自然伽马曲线上限值。从图15a-图15b中可以看出，K_PBU大于100mD的储层集中位于低密度/孔隙度(<2.4g/cm 3)、高电阻率(≥20ohm·m)、低伽马(<20API)、高Am(≥2.5)区域，与高能沉积型高渗储层的电性特征认识一致，据此建立沉积型高渗条带常规测井定量识别模式，识别标准如表4所示。
表4 高能沉积型高渗条带测井识别标准表
GR/(API) RHOB/(g/cm 3) Rt/(Ω.m) Am
<20 <2.4 ≥20 ≥2.5
具体实施例中，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释，包括：
高能沉积型高渗条带测井定量识别，在四条曲线中，GR代表岩性和沉积环境，其值低代表岩性纯、颗粒粗，沉积能量相对较高；RHOB代表岩石总孔隙度，其值低代表孔隙度高、物性好；Rt代表含油性和渗透性，在纯油层其值高代表含油性好，说明成藏时油气充注程度高，可侧面反映其渗透性相对较好；Am代表孔隙结构，其值高代表导电路径复杂，属于典型的大孔小喉型粗颗粒沉积储层。因此，作为能够形成渗流优势通道的高渗条带，必须满足上述所有条件，以测井曲线同时满足“GR<20API、RHOB<2.4g/cm 3、Rt≥20ohm·m、Am≥2.5”来逐点筛选高能沉积型高渗条带；
测井相定性识别，根据核磁测井“T2谱双峰或多峰特征及T2时间和谱峰高值” 及成像测井“较大尺度及较强程度溶蚀孔洞”的显示特征进一步识别和确定高能沉积型高渗条带；岩溶型储层中多具有“贼层”特征，根据FMI成像图上“正弦型过井裂缝或不规则微裂缝、黑色斑块状溶洞或大的溶蚀孔”以及核磁测井T2谱“双峰或多峰特征”进行定性识别；
地质规律的约束和相互印证，沉积型高渗条带受沉积相控制发育于相对固定的沉积部位，岩溶型高渗条带受层序地层及古构造约束发育于层序界面附近区域，在上述识别过程中，根据“潮道型高渗条带多发育于潮道下部、生屑滩型高渗条带多发育于滩体上部、滩翼相高渗条带多发育于靠近滩体部位、岩溶型高渗条带多发育于层序界面附近”的地质规律，佐证或重新拾取高渗条带；
图16a为本具体实施例中高渗条带单井识别结果示意图，图16a中的66为常规测井GR曲线，67为RHOB曲线和岩芯孔隙度，68为Rt曲线和岩芯渗透率，69为计算得到的Am曲线，70为PLT测试结果，71为K_PBU计算结果，72-74分别为岩石相、沉积相和层序地层评价结果，75为高渗条带识别结果，76-77为岩溶型高渗条带(用测井道满充填的黑色条带显示)，78为高能沉积型高渗条带(用测井道内未满充填的灰色条带显示)；图16b为图16a中76对应的岩溶型高渗条带的FMI成像图和CMR测井T 2能谱图，其中761为FMI静态成像图，762为FMI动态成像图，763为CMR测井T 2能谱图，从图16b中可以看出，该岩溶型高渗条带可见微裂缝和较大的溶蚀孔洞，CMR测井T 2能谱图上见多峰及T 2时间谱峰高值特征；图16c为图16a中77对应的岩溶型高渗条带的FMI成像图和CMR测井T 2能谱图，其中771为FMI静态成像图，772为FMI动态成像图，773为CMR测井T 2能谱图，从图16c中可以看出，该岩溶型高渗条带以尺度相对较小的溶蚀孔洞为主，CMR测井T 2能谱图上见双峰及T 2时间谱峰高值特征；此外从图16c中还可以看出，高能沉积型高渗条带严格满足低伽马、低密度、高电阻率、高Am的测井识别条件，岩溶型高渗条带并不严格满足上述条件，但其发育受层序界面控制，利用FMI成像测井结合CMR测井T 2能谱图可有效识别。
具体实施例中，所述方法还包括：利用取芯井岩芯实验结果、生产测井资料和动态渗透率数据，以及注采井组中见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果，具体如图16a-图16c及图17a-图17c所示：
其中，利用取芯井岩芯实验结果检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果，包括：在识别出的高渗条带中(图16a中的75)，高能沉积型高渗条带与岩芯渗透率中的高渗部分具有较好的对应关系，岩溶型高渗条带与岩芯渗透率相关性较差，具体可参见图16a中68所示；
其中，利用生产测井资料和动态渗透率数据检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果，包括：识别出的高渗条带(图16a中的75)与高K_PBU(>100mD)(图16a中的71)和PLT高产量(图16a中的70)具有较好的对应关系，如图16a-图16c所示；根据60口PLT测试井及24口PBU测试井对比分析，HPS识别结果动态符合率约为89％；
其中，利用注采井组中见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果，包括：高渗条带形成注水优势通道，通常是注采井组中最先实现注水突破，从而导致生产井见注入水的主要原因。图17a-图17c为具体实施例中PROD1注水井组见水井分析示意图，其中，图17a中的79为注采井组井位分布图；图17b为两口注水井(INJ1、INJ2)及1口采油井(PROD1)的连井对比图，图17b中80-81分别为常规三孔隙度曲线、自然伽马曲线和电阻率曲线，82为PLT测试的油、水产量剖面，83为注水井吸水剖面，84为沉积微相剖面，85为HPS识别结果；图17c为图17b中84沉积微相图例；从图17a-图17c中可以看出，生产井PROD1井周围有两口注水井，注水层位包括MB1-2A至MB1-2C，目前见水层位位于MB1-2B上部，该层位与注水井INJ1同时发育潮道型高渗条带，相互连通组成优势渗流通道，导致最早见水。目前见注入水的生产井涉及9个井组16口井，与高渗条带相关的注水井占比达到85％，一方面表明目前注采井组见水主要与高渗条带相关，另一方面则表明本次识别的高渗条带注采关系符合率高达85％以上，与PLT及PBU测试验证结果基本一致。
基于同一发明构思，本发明实施例还提供了一种海相碳酸盐岩生屑灰岩油藏高渗条带识别的装置，由于该装置解决问题的原理与海相碳酸盐岩生屑灰岩油藏高渗条带识别的方法相似，因此该装置的实施可以参见方法的实施，重复之处不再赘述。以下所使用的，术语“单元”或者“模块”可以实现预定功能的软件和/或硬件的组合。以下实施例所描述的装置较佳地以硬件来实现，但是软件或者软件和硬件的组合的实现也是可能并被构想的。
图18为本发明实施例所提供的海相碳酸盐岩生屑灰岩油藏高渗条带识别的装置的结构示意图，从图18中可以看出，所述装置包括：
储层动态参数确定模块101，用于根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
高渗条带提取模块102，用于根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
高渗条带成因类型划分模块103，用于在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
高渗条带测井识别模块104，用于建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
在一实施例中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料。
在一实施例中，所述储层动态参数确定模块包括单层比采液指数确定单元，所述单层比采液指数确定单元用于根据试井资料和生产测井资料按照如下公式(1)确定单层比采液指数：
Figure PCTCN2022070683-appb-000018
公式(1)中：SPI为比采液指数，单位为bbl/(d·psi·m)；q o为日产液量，单位为bbl/d；ΔP为井底压力差，单位为psi；h为产液层厚度，单位为m。
在一实施例中，所述储层动态参数确定模块还包括单层动态渗透率确定单元，对于单层试井的情况，当所述试井资料为PBU资料时，所述单层动态渗透率确定单元用于根据PBU资料确定单井流动单元的单层动态渗透率，包括：
1)根据PBU资料解释按照如下公式(2)确定该单井的地层系数；
Figure PCTCN2022070683-appb-000019
公式(2)中：kh为PBU资料解释的该单井的地层系数，单位为mD·m；q为PBU测试产量，单位为m 3/ks；μ为流体粘度，单位为mPa·s；m为PBU资料解释时所用Horner曲线的斜率，无因次；h为流动单元厚度，单位为m；k为单井流动单元的单层动态渗透率，单位为mD；
2)在测井曲线上确定流动单元的厚度，再用单井的地层系数除以所述流动单元的厚度即得到所述单井流动单元的单层动态渗透率；
对于合层试井的情况，当所述试井资料为PBU资料、所述生产测井资料为PLT测试资料时，所述单层动态渗透率确定单元用于根据PBU资料和PLT测试资料确定单井流动单元的单层动态渗透率，包括：
1)根据PBU资料解释按照以上公式(2)确定该单井的综合地层系数；
2)利用PLT测试资料中的分层产液量按照如下公式(3)-公式(5)将该单井的综合地层系数批分到每个流动单元，得到每个流动单元的单层地层系数；
q t＝q 1+q 2+q 3+...+q l      公式(3)；
Figure PCTCN2022070683-appb-000020
(kh) l＝(kh)×ration l      公式(5)；
公式(3)-公式(5)中，q t为PLT测试资料中的累计产液量，单位为bbl/d；q 1、q 2、q 3、……q l分别为PLT测试资料中不同产液段的实际产液量，单位均为bbl/d；ration l为PLT测试资料中不同产液段的实际产液量占PLT测试资料中累计产液量的比值，单位为无因次；kh为PBU资料解释的地层系数，即该单井的综合地层系数，单位为mD·m；(kh) l为每个流动单元的单层地层系数，单位为mD·m；
3)再根据每个流动单元的单层地层系数和每个流动单元的厚度按照如下公式(6)确定单井每个流动单元的单层动态渗透率；
Figure PCTCN2022070683-appb-000021
公式(6)中，k l为流动单元的单层动态渗透率，单位mD；(kh) l为每个流动单元的单层地层系数，单位为mD·m；h l为PLT测试资料中流动单元产液段的实际厚度，单位为m；l为PLT测试资料中实际产液段的段数，为正整数。
在一实施例中，所述储层动态参数确定模块还包括流动单元的厚度确定单元，用于确定所述流动单元的厚度，包括：
在PLT测试资料中显示的流动单元内部，利用测井曲线扣除单元内部隔、夹层；再通过井轨迹计算将流动单元厚度校正到垂深，即得到所述流动单元的厚度。
在一实施例中，所述高渗条带提取模块具体用于：
建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，分析流动单元生产能力，即比采液指数随动态渗透率的变化趋势并确定拐点，将拐点所对应的动态渗透率和采液指数确定为海相碳酸盐岩生屑灰岩油藏高渗条带的动态识别标准。
在一实施例中，所述高渗条带提取模块还包括单井高渗条带提取单元，所述单井高渗条带提取单元用于根据所述动态识别标准在所述单井中分层提取高渗条带，具体用于：
在扣除流动单元内部隔、夹层的基础上，利用分段确定的单井流动单元的单层动态渗透率和单层比采液指数数据分别绘制动态渗透率和比采液指数矩形曲线，并将高渗条带动态标准中的单层动态渗透率和单层比采液指数设置为所述矩形曲线的基线并与所述矩形曲线右充填，当该单层的动态渗透率和比采液指数均大于矩形曲线的基线所对应的动态渗透率和比采液指数时，确定该单层为高渗条带。
在一实施例中，所述高渗条带成因类型划分模块具体用于：
利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相与地层层序中的位置。
在一实施例中，所述高渗条带测井识别模块具体用于：
以单井生产测井测试段为样本，以高渗条带动态标准所对应的单层动态渗透率和单层比采液指数为界，分析高渗条带与非高渗条带的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式。
在一实施例中，所述高渗条带测井识别模块包括高渗条带测井相定性识别模式建立单元，所述高渗条带测井相定性识别模式建立单元用于：
分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，根据分析所得结果在沉积型储层和岩溶型储层中分别建立高渗条带测井相定性识别模式。
在一实施例中，所述高渗条带测井识别模块还包括沉积型高渗条带常规测井定量识别模式建立单元，所述沉积型高渗条带常规测井定量识别模式建立单元用于：
在生产测井测试段，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层，按照常规测井曲线采样间隔分别分段获取测井曲线的曲线值，再根据动态渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测 井曲线值分布规律和识别界线并据此建立沉积型高渗条带常规测井定量识别模式。
在一实施例中，所述高渗条带测井识别模块还包括孔隙结构指数曲线的曲线值计算单元，所述孔隙结构指数曲线的曲线值计算单元用于：
当所述测井曲线包括孔隙结构指数曲线时，根据如下公式(7)计算得到孔隙结构指数曲线的曲线值：
Figure PCTCN2022070683-appb-000022
公式(7)中，R w为地层水电阻率，单位为ohm·m；R t为原状地层电阻率，测井曲线上为深探测电阻率曲线所对应的电阻率，单位为ohm·m；POR为储层孔隙度，单位为无因次小数；S w为地层水饱和度，单位为无因次小数；n为饱和度指数，单位为无因次小数。
在一实施例中，所述装置(如图19所示)还包括：识别结果检验模块105，用于利用岩芯实验结果、生产测井资料和动态渗透率数据，以及注采井组见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果。
本发明实施例还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现以上所述海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的步骤。
本发明实施例还提供了一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现以上所述的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的步骤。
综上，本发明实施例以动态为纲，在地质规律约束下提供了一种动静态一体化的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法及装置，本发明实施例所提供的该方法及装置的理论基础和技术逻辑扎实可靠，方法简单易操作；本发明实施例所提供的该方法及装置的识别对象包括贼层但不限于贼层，有效解决了海相碳酸盐岩生屑灰岩油藏内部对注水开发影响最大的高能沉积型高渗条带识别难题；本发明实施例所提供的方法及装置在油田推广应用后，可提供一套有效覆盖全油藏的可靠的高渗条带数据，验证了目前注采井组提前见水井都与本发明方法及装置所识别出的高渗条带有关，可为新的注采井组优化设计提供技术支撑。
本领域内的技术人员应明白，本发明的实施例可提供为方法、***、或计算机程 序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(***)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述，仅为本发明的具体实施例，不能以其限定发明实施的范围，所以其等同组件的置换，或依本发明专利保护范围所作的等同变化与修饰，都应仍属于本专利涵盖的范畴。另外，本发明中的技术特征与技术特征之间、技术特征与技术发明之间、技术发明与技术发明之间均可以自由组合使用。

Claims (28)

  1. 一种海相碳酸盐岩生屑灰岩油藏高渗条带识别方法，其中，所述方法包括：
    根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
    根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
    在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
    建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
  2. 根据权利要求1所述的方法，其中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料。
  3. 根据权利要求1或2所述的方法，其中，根据试井资料和生产测井资料按照如下公式(1)确定单层比采液指数：
    Figure PCTCN2022070683-appb-100001
    公式(1)中：SPI为比采液指数，单位为bbl/(d·psi·m)；q o为日产液量，单位为bbl/d；ΔP为井底压力差，单位为psi；h为产液层厚度，单位为m。
  4. 根据权利要求1或2所述的方法，其中，对于单层试井的情况，当所述试井资料为PBU资料时，根据PBU资料确定单井流动单元的单层动态渗透率，包括：
    1)根据PBU资料解释按照如下公式(2)确定该单井的地层系数；
    Figure PCTCN2022070683-appb-100002
    公式(2)中：kh为PBU资料解释的该单井的地层系数，单位为mD·m；q为PBU测试产量，单位为m 3/ks；μ为流体粘度，单位为mPa·s；m为PBU资料解释时所用Horner曲线的斜率，无因次；h为流动单元厚度，单位为m；k为单井流动单元的单层动态渗透率，单位为mD；
    2)在测井曲线上确定流动单元的厚度，再用单井的地层系数除以所述流动单元的厚度即得到所述单井流动单元的单层动态渗透率；
    对于合层试井的情况，当所述试井资料为PBU资料、所述生产测井资料为PLT测试资料时，根据PBU资料和PLT测试资料确定单井流动单元的单层动态渗透率，包括：
    1)根据PBU资料解释按照以上公式(2)确定该单井的综合地层系数；
    2)利用PLT测试资料中的分层产液量按照如下公式(3)-公式(5)将该单井的综合地层系数批分到每个流动单元，得到每个流动单元的单层地层系数；
    q t＝q 1+q 2+q 3+…+q l  公式(3)；
    Figure PCTCN2022070683-appb-100003
    (kh) l＝(kh)×ration l  公式(5)；
    公式(3)-公式(5)中，q t为PLT测试资料中的累计产液量，单位为bbl/d；q 1、q 2、q 3、……q l分别为PLT测试资料中不同产液段的实际产液量，单位均为bbl/d；ration l为PLT测试资料中不同产液段的实际产液量占PLT测试资料中累计产液量的比值，单位为无因次；kh为PBU资料解释的地层系数，即该单井的综合地层系数，单位为mD·m；(kh) l为每个流动单元的单层地层系数，单位为mD·m；
    3)再根据每个流动单元的单层地层系数和每个流动单元的厚度按照如下公式(6)确定单井每个流动单元的单层动态渗透率；
    Figure PCTCN2022070683-appb-100004
    公式(6)中，k l为流动单元的单层动态渗透率，单位mD；(kh) l为每个流动单元的单层地层系数，单位为mD·m；h l为PLT测试资料中流动单元产液段的实际厚度，单位为m；l为PLT测试资料中实际产液段的段数，为正整数。
  5. 根据权利要求4所述的方法，其中，确定所述流动单元的厚度包括：
    在PLT测试资料中显示的流动单元内部，利用测井曲线扣除单元内部隔、夹层；再通过井轨迹计算将流动单元厚度校正到垂深，即得到所述流动单元的厚度。
  6. 根据权利要求1或2所述的方法，其中，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准，包括：
    建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，分析流动 单元生产能力，即比采液指数随动态渗透率的变化趋势并确定拐点，将拐点所对应的动态渗透率和采液指数确定为海相碳酸盐岩生屑灰岩油藏高渗条带的动态识别标准。
  7. 根据权利要求1或6所述的方法，其中，提取所述单井高渗条带，包括：根据所述动态识别标准在所述单井中分层提取高渗条带，具体包括：
    在扣除流动单元内部隔、夹层的基础上，利用分段确定的单井流动单元的单层动态渗透率和单层比采液指数数据分别绘制动态渗透率和比采液指数矩形曲线，并将高渗条带动态标准中的单层动态渗透率和单层比采液指数设置为所述矩形曲线的基线并与所述矩形曲线右充填，当该单层的动态渗透率和比采液指数均大于矩形曲线的基线所对应的动态渗透率和比采液指数时，确定该单层为高渗条带。
  8. 根据权利要求1或2所述的方法，其中，根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律，包括：
    利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相与地层层序中的位置。
  9. 根据权利要求1或2所述的方法，其中，所述建立高渗条带测井识别模式，包括：
    以单井生产测井测试段为样本，以高渗条带动态标准所对应的单层动态渗透率和单层比采液指数为界，分析高渗条带与非高渗条带的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式。
  10. 根据权利要求9所述的方法，其中，所述建立高渗条带测井相定性识别模式，包括：
    分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，根据分析所得结果在沉积型储层和岩溶型储层中分别建立高渗条带测井相定性识别模式。
  11. 根据权利要求9所述的方法，其中，建立沉积型高渗条带常规测井定量识别模式，包括：
    在生产测井测试段，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层，按照常规测井曲线采样间隔分别分段获取测井曲线的曲线值，再根据动态 渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测井曲线值分布规律和识别界线并据此建立沉积型高渗条带常规测井定量识别模式。
  12. 根据权利要求11所述的方法，其中，当所述测井曲线包括孔隙结构指数曲线时，根据如下公式(7)计算得到孔隙结构指数曲线的曲线值：
    Figure PCTCN2022070683-appb-100005
    公式(7)中，R w为地层水电阻率，单位为ohm·m；R t为原状地层电阻率，测井曲线上为深探测电阻率曲线所对应的电阻率，单位为ohm·m；POR为储层孔隙度，单位为无因次小数；S w为地层水饱和度，单位为无因次小数；n为饱和度指数，单位为无因次小数。
  13. 根据权利要求1-12任一项所述的方法，其中，所述方法还包括：利用取芯井岩芯实验结果、生产测井资料和动态渗透率数据，以及注采井组中见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果。
  14. 一种海相碳酸盐岩生屑灰岩油藏高渗条带识别装置，其中，所述装置包括：
    储层动态参数确定模块，用于根据试井资料和生产测井资料确定单井流动单元的单层动态渗透率和单层比采液指数；
    高渗条带提取模块，用于根据生产测井资料分析多层合采的层间干扰情况，综合利用生产测井资料中的单层产量及单井流动单元的单层动态渗透率和单层比采液指数建立高渗条带动态标准并提取所述单井高渗条带；
    高渗条带成因类型划分模块，用于在取芯井中根据层序地层、岩石相、沉积相及岩石物理相研究成果，划分高渗储集层成因类型，并确定所述高渗条带在岩石相、沉积相和层序界面中的分布规律；
    高渗条带测井识别模块，用于建立高渗条带测井识别模式，根据所述高渗条带测井识别模式在全油藏范围内逐井识别高渗条带，实现全油藏测井解释。
  15. 根据权利要求14所述的装置，其中，所述试井资料为PBU资料，所述生产测井资料为PLT测试资料。
  16. 根据权利要求14或15所述的装置，其中，所述储层动态参数确定模块包括单层比采液指数确定单元，所述单层比采液指数确定单元用于根据试井资料和生产测井资料按照如下公式(1)确定单层比采液指数：
    Figure PCTCN2022070683-appb-100006
    公式(1)中：SPI为比采液指数，单位为bbl/(d·psi·m)；q o为日产液量，单位为bbl/d；ΔP为井底压力差，单位为psi；h为产液层厚度，单位为m。
  17. 根据权利要求14或15所述的装置，其中，所述储层动态参数确定模块还包括单层动态渗透率确定单元，对于单层试井的情况，当所述试井资料为PBU资料时，所述单层动态渗透率确定单元用于根据PBU资料确定单井流动单元的单层动态渗透率，包括：
    1)根据PBU资料解释按照如下公式(2)确定该单井的地层系数；
    Figure PCTCN2022070683-appb-100007
    公式(2)中：kh为PBU资料解释的该单井的地层系数，单位为mD·m；q为PBU测试产量，单位为m 3/ks；μ为流体粘度，单位为mPa·s；m为PBU资料解释时所用Horner曲线的斜率，无因次；h为流动单元厚度，单位为m；k为单井流动单元的单层动态渗透率，单位为mD；
    2)在测井曲线上确定流动单元的厚度，再用单井的地层系数除以所述流动单元的厚度即得到所述单井流动单元的单层动态渗透率；
    对于合层试井的情况，当所述试井资料为PBU资料、所述生产测井资料为PLT测试资料时，所述单层动态渗透率确定单元用于根据PBU资料和PLT测试资料确定单井流动单元的单层动态渗透率，包括：
    1)根据PBU资料解释按照以上公式(2)确定该单井的综合地层系数；
    2)利用PLT测试资料中的分层产液量按照如下公式(3)-公式(5)将该单井的综合地层系数批分到每个流动单元，得到每个流动单元的单层地层系数；
    q t＝q 1+q 2+q 3+…+q l  公式(3)；
    Figure PCTCN2022070683-appb-100008
    (kh) l＝(kh)×ration l  公式(5)；
    公式(3)-公式(5)中，q t为PLT测试资料中的累计产液量，单位为bbl/d；q 1、q 2、q 3、……q l分别为PLT测试资料中不同产液段的实际产液量，单位均为bbl/d；ration l为PLT测试资料中不同产液段的实际产液量占PLT测试资料中累计产液量的比值， 单位为无因次；kh为PBU资料解释的地层系数，即该单井的综合地层系数，单位为mD·m；(kh) l为每个流动单元的单层地层系数，单位为mD·m；
    3)再根据每个流动单元的单层地层系数和每个流动单元的厚度按照如下公式(6)确定单井每个流动单元的单层动态渗透率；
    Figure PCTCN2022070683-appb-100009
    公式(6)中，k l为流动单元的单层动态渗透率，单位mD；(kh) l为每个流动单元的单层地层系数，单位为mD·m；h l为PLT测试资料中流动单元产液段的实际厚度，单位为m；l为PLT测试资料中实际产液段的段数，为正整数。
  18. 根据权利要求17所述的装置，其中，所述储层动态参数确定模块还包括流动单元的厚度确定单元，用于确定所述流动单元的厚度，包括：
    在PLT测试资料中显示的流动单元内部，利用测井曲线扣除单元内部隔、夹层；再通过井轨迹计算将流动单元厚度校正到垂深，即得到所述流动单元的厚度。
  19. 根据权利要求14或15所述的装置，其中，所述高渗条带提取模块具体用于：
    建立单井流动单元的单层动态渗透率和单层比采液指数的散点交会图，分析流动单元生产能力，即比采液指数随动态渗透率的变化趋势并确定拐点，将拐点所对应的动态渗透率和采液指数确定为海相碳酸盐岩生屑灰岩油藏高渗条带的动态识别标准。
  20. 根据权利要求14或19所述的装置，其中，所述高渗条带提取模块还包括单井高渗条带提取单元，所述单井高渗条带提取单元用于根据所述动态识别标准在所述单井中分层提取高渗条带，具体用于：
    在扣除流动单元内部隔、夹层的基础上，利用分段确定的单井流动单元的单层动态渗透率和单层比采液指数数据分别绘制动态渗透率和比采液指数矩形曲线，并将高渗条带动态标准中的单层动态渗透率和单层比采液指数设置为所述矩形曲线的基线并与所述矩形曲线右充填，当该单层的动态渗透率和比采液指数均大于矩形曲线的基线所对应的动态渗透率和比采液指数时，确定该单层为高渗条带。
  21. 根据权利要求14或15所述的装置，其中，所述高渗条带成因类型划分模块具体用于：
    利用取芯井岩石物理相划分的高渗储集层或所述单井高渗条带数据，采用散点交会图或频率直方图的方法，分析高渗储集层或高渗条带发育的岩石相、沉积相类型；同时结合沉积微相和层序地层划分结果，分析高渗条带在沉积相与地层层序中的位 置。
  22. 根据权利要求14或15所述的装置，其中，所述高渗条带测井识别模块具体用于：
    以单井生产测井测试段为样本，以高渗条带动态标准所对应的单层动态渗透率和单层比采液指数为界，分析高渗条带与非高渗条带的测井响应特征，建立高渗条带测井相定性识别模式和沉积型高渗条带常规测井定量识别模式。
  23. 根据权利要求22所述的装置，其中，所述高渗条带测井识别模块包括高渗条带测井相定性识别模式建立单元，所述高渗条带测井相定性识别模式建立单元用于：
    分析高渗条带与非高渗条带在常规测井曲线、FMI成像测井图、核磁测井T2谱图上的差异，根据分析所得结果在沉积型储层和岩溶型储层中分别建立高渗条带测井相定性识别模式。
  24. 根据权利要求22所述的装置，其中，所述高渗条带测井识别模块还包括沉积型高渗条带常规测井定量识别模式建立单元，所述沉积型高渗条带常规测井定量识别模式建立单元用于：
    在生产测井测试段，根据渗流优势通道原则，扣除明显的非渗透性隔夹层和低渗透性储层，按照常规测井曲线采样间隔分别分段获取测井曲线的曲线值，再根据动态渗透率分布区间制作双测井曲线分类散点交会图，并结合地质规律分析高渗条带的测井曲线值分布规律和识别界线并据此建立沉积型高渗条带常规测井定量识别模式。
  25. 根据权利要求24所述的装置，其中，所述高渗条带测井识别模块还包括孔隙结构指数曲线的曲线值计算单元，所述孔隙结构指数曲线的曲线值计算单元用于：
    当所述测井曲线包括孔隙结构指数曲线时，根据如下公式(7)计算得到孔隙结构指数曲线的曲线值：
    Figure PCTCN2022070683-appb-100010
    公式(7)中，R w为地层水电阻率，单位为ohm·m；R t为原状地层电阻率，测井曲线上为深探测电阻率曲线所对应的电阻率，单位为ohm·m；POR为储层孔隙度，单位为无因次小数；S w为地层水饱和度，单位为无因次小数；n为饱和度指数，单位为无因次小数。
  26. 根据权利要求14-25任一项所述的装置，其中，所述装置还包括：识别结果 检验模块，用于利用岩芯实验结果、生产测井资料和动态渗透率数据，以及注采井组见水井的见水层及所述见水层发育高渗条带情况检验海相碳酸盐岩生屑灰岩油藏高渗条带识别结果。
  27. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其中，所述处理器执行所述计算机程序时实现权利要求1-13任一项所述海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的步骤。
  28. 一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现权利要求1-13任一项所述的海相碳酸盐岩生屑灰岩油藏高渗条带识别方法的步骤。
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