CN109143397B - Carbonate reservoir fracture-cave filling identification method and system - Google Patents
Carbonate reservoir fracture-cave filling identification method and system Download PDFInfo
- Publication number
- CN109143397B CN109143397B CN201710506999.0A CN201710506999A CN109143397B CN 109143397 B CN109143397 B CN 109143397B CN 201710506999 A CN201710506999 A CN 201710506999A CN 109143397 B CN109143397 B CN 109143397B
- Authority
- CN
- China
- Prior art keywords
- wave impedance
- equivalent medium
- modulus
- rock
- equivalent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
A carbonate reservoir fracture-cave filling identification method and system are disclosed. The method can comprise the following steps: acquiring the elastic modulus of the rock matrix based on the logging information; acquiring the elastic modulus of the dry rock based on the elastic modulus of the rock matrix; acquiring equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock; identifying components of a carbonate reservoir fracture-cave filler based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance; and acquiring the filling rate of the carbonate reservoir seam hole based on the empirical relationship between the filling rate of the seam hole and the density established by the logging data. The method can identify the components of the carbonate reservoir fracture-cavity filler, obtain the carbonate reservoir fracture-cavity filling rate and provide a basis for measuring the oil-gas storage capacity and the seepage capacity of the fracture-cavity.
Description
Technical Field
The invention relates to the field of oil and gas geophysical exploration, in particular to a carbonate reservoir fracture-cave filler identification method and system.
Background
The Tahe oil field is an ancient sea-facies large oil field mainly comprising an Ordovician carbonate ancient karst reservoir, a cave and cracks at the periphery of the cave are main storage spaces of the oil field, but not all cracks and erosion holes are effective open cracks and erosion holes, various fillers are often mixed in the cave, the properties of the fillers in the cracks and the holes of the carbonate reservoir are researched, and the Tahe oil field has important significance for measuring the oil-gas storage capacity and the seepage capacity of the cracks and the holes.
Previous research results mostly focus on research on effectively opening cracks and erosion cavities, and the research on identifying properties of filling cracks and cavities, particularly filling materials in the cracks and cavities, is less. The key to the study of the properties of the fill is to identify the type and degree of fill of the fill. Common fracture-cave filling materials comprise mud, silt, organic matters, heavy minerals, collapsed cobbles, crystalline calcite and the like, and most filling materials are complex in components and are a mixture of a plurality of materials. The filling degree can be quantitatively obtained on the basis of identifying the top and bottom of the hole and the filling thickness, and can also be roughly divided into three types of full filling, half filling and unfilled.
The identification of the properties of a pack using geophysical methods can be divided into well logging identification methods and seismic identification methods. The basic means of the method is to utilize the rock core and the imaging logging information to calibrate the conventional logging information, analyze logging response characteristics corresponding to different fillings, extract sensitive logging parameters, and determine the threshold value of a sensitive logging curve for identifying different types of fillings through cross plot analysis. However, the cross-plot method has the advantages of simplicity and rapidness, different filling material types are often difficult to completely separate on the cross-plot, and the identification effect is not accurate. The researchers also propose a filling material well logging identification method based on a neural network, a mapping relation between the characteristics of the fracture-cavity filling material and well logging information is sought, and the quality of an identification result is determined by well logging characteristic vectors of selected samples to a great extent. The feature vector recognition of the more typical response features corresponding to each fill type is very prominent, while the log values with larger differences from the typical response are not good. Therefore, the accuracy of the model identification is also greatly dependent on the accuracy of the logging data and the identification capability of each filling type; relative to a well logging method, seismic identification belongs to real pre-drilling prediction, and can provide space distribution characteristics of filled karst caves, search unfilled favorable seismic characteristic parts and guide well position deployment. The existing seismic method for identifying the filling materials is mainly to disclose and establish a geological model of a karst cave and real parameters of different filling materials through a typical well on the basis of geological summary of the karst cave filling materials, simulate actual seismic acquisition parameters to carry out forward modeling, analyze the influence of different filling properties on seismic reflection characteristics, and use seismic facies to carry out filling material characterization description. However, it is not easy to establish a real geological model based on forward modeling, the model can only simplify the underground medium to a certain extent, the change of parameters such as fluid type, saturation, reservoir structure and the like may have a larger influence on the simulation result than the filling material property, and the difference between the factors and the actual medium is difficult to completely eliminate in the model, so that the accuracy of identifying the filling material by using a seismic facies method is influenced.
Therefore, it is necessary to develop a method and a system for identifying the fracture-cavity filling material of the carbonate reservoir, which can predict the components and filling rate of the fracture-cavity filling material of the carbonate reservoir with high precision.
Disclosure of Invention
The invention provides a carbonate reservoir fracture-cave filler identification method and system, which can identify the components of the carbonate reservoir fracture-cave filler, obtain the fracture-cave filling rate of the carbonate reservoir and provide a basis for measuring the oil-gas storage capacity and the seepage capacity of the fracture-cave.
In order to achieve the above object, according to an aspect of the present invention, there is provided a carbonate reservoir fracture-cave filling identification method, including:
acquiring the elastic modulus of the rock matrix based on the logging information;
acquiring the elastic modulus of the dry rock based on the elastic modulus of the rock matrix;
acquiring equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock;
identifying components of a carbonate reservoir fracture-cave filler based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance;
and acquiring the filling rate of the carbonate reservoir seam hole based on the empirical relationship between the filling rate of the seam hole and the density established by the logging data.
Preferably, based on the well log data, the elastic modulus of the rock matrix is obtained: based on logging information, the elastic modulus of the rock matrix is obtained through Voight-Reuss-Hill model calculation, and the specific formula is
Wherein M isHIs the elastic modulus of the rock matrix, fiDenotes the volume content of the mineral constituents, MiRepresents the elastic modulus of the mineral constituent; the Voigt and reus models provide the upper limit M of the equivalent rock modulus, respectivelyVAnd a lower limit MRAnd carrying out arithmetic mean on the upper limit and the lower limit to obtain the elastic modulus of the rock matrix.
Preferably, the method comprises the following steps of obtaining the elastic modulus of dry rock based on the elastic modulus of rock matrix: the method comprises the following steps of (1) enabling rock pores to be equivalent to ideal ellipsoid pores with a single aspect ratio, adding dry equivalent pores into a rock matrix by using a differential equivalent medium model, and obtaining the elastic modulus of the dry rock, wherein the specific formula is as follows:
Kd(0)＝Km,μd(0)＝μm(4)
wherein, KdIs the dry rock bulk modulus, μdIs the dry rock shear modulus, KmIs the bulk modulus of rock matrix, mumIs the rock matrix shear modulus, KfIs the pore fluid bulk modulus, μfis the pore fluid shear modulus, y is the porosity, P and Q are shape factors and are the equivalent aspect ratio αeFunction of, for dry porosity, KfAnd mufAll take 0.
Preferably, the obtaining of the equivalent medium shear wave impedance and the equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock comprises: obtaining an equivalent medium elastic modulus through Gassmann equation calculation, and obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance through the equivalent medium elastic modulus, wherein the specific formula is as follows:
μs＝μd(6)
wherein, KsIs an equivalent medium bulk modulus, μsIs equivalent medium shear modulus, KdIs the dry rock bulk modulus, mudIs the dry rock shear modulus, KfIs the pore fluid bulk modulus, KmIs the bulk modulus of the matrix mineral, phi is the porosity.
Wherein Ip Is equivalent medium longitudinal wave impedance, Is equivalent medium transverse wave impedance, and ρ Is density, and the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained by formula (6) and formula (7).
Specifically, a mathematical relationship between the filler properties and the geophysical parameters is established through a formula (5), a formula (6) and a formula (7), and a bridge is provided for directly inverting the filler components and identifying the type of the filler by using seismic data.
Preferably, the identifying the composition of the carbonate reservoir fracture-cavity filler based on the equivalent medium shear wave impedance and the equivalent medium compressional wave impedance comprises: and inverting under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance and the logging density, inverting the mineral component parameters of the karst cave section, and identifying the components of the carbonate reservoir fracture-cave filler.
Preferably, the obtaining of the carbonate reservoir fracture-cavity filling rate based on the empirical relationship between fracture-cavity filling rate and density established by the logging data includes: and constructing a regression formula between the karst cave filling rate and the logging density according to the existing karst cave interpretation result, and obtaining the spatial distribution of the karst cave filling rate through the calculation of the components of the carbonate reservoir fracture-cave filling material.
According to another aspect of the present invention, there is provided a carbonate reservoir fracture-cavity filler identification system, comprising:
a unit for obtaining the elastic modulus of the rock matrix based on the logging information;
a unit that obtains an elastic modulus of the dry rock based on the elastic modulus of the rock matrix;
a unit for obtaining equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock;
identifying a unit of a carbonate reservoir fracture-cave filler component based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance;
and acquiring a unit of the filling rate of the carbonate reservoir fracture-cavity based on the empirical relationship between the filling rate of the fracture-cavity and the density established by the logging information.
Preferably, the unit for obtaining the elastic modulus of the dry rock based on the elastic modulus of the rock matrix comprises: the method comprises the following steps of (1) enabling rock pores to be equivalent to ideal ellipsoid pores with a single aspect ratio, adding dry equivalent pores into a rock matrix by using a differential equivalent medium model, and obtaining the elastic modulus of the dry rock, wherein the specific formula is as follows:
Kd(0)＝Km,μd(0)＝μm(4)
wherein, KdIs the dry rock bulk modulus, μdIs the dry rock shear modulus, KmIs the bulk modulus of rock matrix, mumIs the rock matrix shear modulus, KfIs the pore fluid bulk modulus, μfis the pore fluid shear modulus, y is the porosity, P and Q are shape factors and are the equivalent aspect ratio αeFunction of, for dry porosity, KfAnd mufAll take 0.
Preferably, the unit for obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock comprises: obtaining an equivalent medium elastic modulus through Gassmann equation calculation, and obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance through the equivalent medium elastic modulus, wherein the specific formula is as follows:
μs＝μd(6)
wherein, KsIs an equivalent medium bulk modulus, μsIs equivalent medium shear modulus, KdIs the dry rock bulk modulus, mudIs the dry rock shear modulus, KfIs the pore fluid bulk modulus, KmIs the bulk modulus of the matrix mineral, phi is the porosity.
Wherein Ip Is equivalent medium longitudinal wave impedance, Is equivalent medium transverse wave impedance, and ρ Is density, and the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained by formula (7).
Preferably, the unit for identifying the components of the carbonate reservoir fracture-cavity filler based on the equivalent medium shear wave impedance and the equivalent medium longitudinal wave impedance comprises: and inverting under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance and the logging density, inverting the mineral component parameters of the karst cave section, and identifying the components of the carbonate reservoir fracture-cave filler.
The invention has the beneficial effects that: the conventional carbonate bedrock section rock physical model is improved to be suitable for a fracture-cavity carbonate rock reservoir, a mathematical relation between the property of a filler and geophysical parameters is established, the components of the fracture-cavity filler of the carbonate rock reservoir are identified, and meanwhile, the fracture-cavity filling rate of the carbonate rock reservoir is obtained based on an empirical relation between the fracture-cavity filling rate and the density established by logging data.
The method and apparatus of the present invention have other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts.
Fig. 1 shows a flow chart of a carbonate reservoir fracture hole filling identification method according to one embodiment of the invention.
Fig. 2 shows a schematic diagram of modeling effects of identifying carbonate reservoir fracture hole filler composition according to one embodiment of the invention.
Figure 3 shows a plot of the carbonate reservoir fracture hole fill rate versus its density for one embodiment of the present invention.
Detailed Description
The invention will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Embodiment mode 1
In this embodiment, the carbonate reservoir fracture-cave filling identification method according to the present invention includes: acquiring the elastic modulus of the rock matrix based on the logging information; acquiring the elastic modulus of the dry rock based on the elastic modulus of the rock matrix; acquiring equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock; identifying components of a carbonate reservoir fracture-cave filler based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance; and acquiring the filling rate of the carbonate reservoir seam hole based on the empirical relationship between the filling rate of the seam hole and the density established by the logging data.
According to the embodiment, the elastic modulus of a rock matrix and the elastic modulus of dry rock are obtained through the logging quality, the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained, the components of the carbonate reservoir fracture-cave filling material are identified based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance, the carbonate reservoir fracture-cave filling rate is obtained based on the empirical relationship between the fracture-cave filling rate and the density established by logging information, and a basis is provided for measuring the oil-gas storage capacity and the seepage capacity of the fracture-cave.
The concrete steps of the carbonate reservoir fracture-cave filling identification method according to the invention are explained in detail below.
And acquiring the elastic modulus of the rock matrix based on the logging information.
In one example, the obtaining, based on the well log data, an elastic modulus of the rock matrix: based on logging information, the elastic modulus of the rock matrix is obtained through Voight-Reuss-Hill model calculation, and the specific formula is
Wherein M isHIs the elastic modulus of the rock matrix, fiDenotes the volume content of the mineral constituents, MiRepresents the elastic modulus of the mineral constituent; the Voigt and reus models provide the upper limit M of the equivalent rock modulus, respectivelyVAnd a lower limit MRAnd carrying out arithmetic mean on the upper limit and the lower limit to obtain the elastic modulus of the rock matrix.
In particular, the rock matrix mineral may be calcite, dolomite, quartz, clay, etc., the elastic modulus includes bulk modulus K, shear modulus μ, etc. other elastic modulus parameters, and the Voigt and Reuss models provide the upper limit M of the equivalent rock modulus, respectivelyVAnd a lower limit MRThe upper and lower limits are arithmetically averaged to obtain MH。
The elastic modulus of the dry rock is obtained based on the elastic modulus of the rock matrix.
In one example, based on the elastic modulus of the rock matrix, the elastic modulus of the dry rock is taken: the method comprises the following steps of (1) enabling rock pores to be equivalent to ideal ellipsoid pores with a single aspect ratio, adding dry equivalent pores into a rock matrix by using a differential equivalent medium model, and obtaining the elastic modulus of the dry rock, wherein the specific formula is as follows:
Kd(0)＝Km,μd(0)＝μm(4)
wherein, KdIs the dry rock bulk modulus, μdIs the dry rock shear modulus, KmIs the bulk modulus of rock matrix, mumIs the rock matrix shear modulus, KfIs the pore fluid bulk modulus, μfis the pore fluid shear modulus, y is the porosity, P and Q are shape factors and are the equivalent aspect ratio αeFunction of, for dry porosity, KfAnd mufAll take 0.
specifically, the equivalent aspect ratio αeThe ratio of the width to the diameter of the pore is called the pore aspect ratio, which is a parameter that defines the shape of the pore. When numerical simulation is carried out, the complicated pore types of the rock can be simplified into equivalent pores with a single shape, and the shape of the equivalent pores is expressed by an equivalent aspect ratio. P and Q are functions of pore aspect ratio, different aspect ratios corresponding to different P, Q values, implying the effect of pore shape on rock elastic properties, and may be referred to as a shape factor.
And acquiring equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock.
In one example, obtaining the equivalent dielectric shear wave impedance and the equivalent dielectric longitudinal wave impedance based on the elastic modulus of the dry rock comprises: obtaining an equivalent medium elastic modulus through Gassmann equation calculation, and obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance through the equivalent medium elastic modulus, wherein the specific formula is as follows:
μs＝μd(6)
wherein, KsIs an equivalent medium bulk modulus, μsIs equivalent medium shear modulus, KdIs the dry rock bulk modulus, mudIs the dry rock shear modulus, KfIs the pore fluid bulk modulus, KmIs the bulk modulus of the matrix mineral, phi is the porosity.
Wherein Ip Is equivalent medium longitudinal wave impedance, Is equivalent medium transverse wave impedance, and ρ Is density, and the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained by formula (7).
In particular, porosity is provided as an input parameter to the petrophysical model from a total porosity curve in the well log interpretation effort data.
the model is built for the bedrock section, the influence of the karst cave and filling materials is not considered, the model is applied to the karst cave section, large errors are generated, the prediction result of the karst cave section can be corrected only by debugging model parameters, and the debugging process has no theoretical basiseThe single pore form of the fracture-cavity carbonate reservoir is constructed.
And identifying the components of the carbonate reservoir fracture-cave filling material based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance.
In one example, identifying a carbonate reservoir fracture-cavity filler composition based on the equivalent dielectric shear wave impedance and the equivalent dielectric compressional wave impedance comprises: and inverting under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance and the logging density, inverting the mineral component parameters of the karst cave section, and identifying the components of the carbonate reservoir fracture-cave filler.
Specifically, mineral component parameters such as the shale content Vcl and the sand content Vqu of the karst cave section are inverted under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance, the logging density and the fracture-cave carbonate rock physical relation { Ip, Is, ρ } ═ f (Vcl, Vqu, …), and the filling lithology Is identified.
And acquiring the filling rate of the carbonate reservoir seam hole based on the empirical relationship between the filling rate of the seam hole and the density established by the logging data.
In one example, obtaining the fracture-cavity filling rate of the carbonate reservoir based on the empirical relationship between the fracture-cavity filling rate and the density established by the logging data comprises: and constructing a regression formula between the karst cave filling rate and the logging density according to the existing karst cave interpretation result, and obtaining the spatial distribution of the karst cave filling rate through the calculation of the components of the carbonate reservoir fracture-cave filling material.
Specifically, a regression formula fd (f (rho)) between the karst cave filling rate fd and the logging density rho is constructed according to the existing karst cave interpretation result, and the spatial distribution of the karst cave filling rate is calculated by utilizing the density term in the mineral component parameters of the inversion karst cave section.
Specifically, the filling material identification method based on the rock physical model and the seismic inversion can quantitatively calculate the filling material components and the filling degree, enclose different lithologies, and spatially distribute the filling materials with different degrees, and has certain guiding significance for judging the effectiveness of the fracture hole and providing a well location deployment scheme.
Specifically, the fracture-cavity filling rate is calculated from the density of seismic inversion based on an empirical relationship between the fracture-cavity filling rate and the density established by logging data. The filling rate is obtained by the density of seismic inversion on the basis of the empirical relationship between the filling rate and the density counted by logging data.
Examples
Fig. 1 shows a flow chart of a carbonate reservoir fracture hole filling identification method according to one embodiment of the invention. Fig. 2 shows a schematic diagram of modeling effects of identifying carbonate reservoir fracture hole filler composition according to one embodiment of the invention. Figure 3 shows a plot of the carbonate reservoir fracture hole fill rate versus its density for one embodiment of the present invention.
Taking the Ordovician carbonate rock oil reservoir in a certain work area of the Tahe oil field as an example, development and practice show that the karst cave type reservoir is the main reservoir type and is influenced by the size of the karst cave, the type of fillers in the karst cave and the filling degree, the difference of the yield of the karst cave type reservoirs with different filling characteristics is large, and the recognition of the karst cave filling characteristics before drilling has important significance.
As shown in figure 1, according to the obtained rock physical mineral components and contents explained by well logging information, the elastic modulus of the rock matrix is calculated by using a Voight-Reuss-Hill model; the rock pores are equivalent to ideal ellipsoid pores with a single aspect ratio, and the elastic modulus of the dry rock is calculated by using a differential equivalent medium model; calculating the elastic modulus of the equivalent medium by using a Gassmann equation; performing prestack elastic parameter inversion on the seismic data volume of the work area, and inverting mineral component parameters of a karst cave section according to a rock physical relation established by equivalent medium elastic modulus under a Bayes inversion framework to realize the identification of lithology of the filler; and constructing a regression formula between the karst cave filling rate and the logging density according to the existing karst cave interpretation result, and calculating the karst cave filling rate by utilizing the density term in the mineral component parameters of the inversion karst cave section.
The key steps of the process are that a rock physical model suitable for the fracture-cavity carbonate reservoir is accurately constructed, and according to the mathematical relationship between the lithology of the filler and the geophysical parameters provided by the model, the components of the filler are solved by means of a Bayesian inversion framework, and the type of the filler is identified. On the other hand, the filling rate data of the existing karst cave section in the well and the corresponding logging density are counted, a regression relation between the filling rate and the density can be established, and then a karst cave section filling rate data volume is obtained by utilizing the density term seismic inversion result.
The accuracy of the inversion of the mineral composition from the seismic data depends greatly on the adaptability of the established petrophysical model in the work area. Four typical wells in the work area are selected for testing the model, and the test result is shown in figure 2. In the figure, a black curve and a gray curve respectively represent the longitudinal wave velocity and the logging acoustic wave velocity calculated by the model, and a black strip beside a vertical coordinate represents a depth range corresponding to a karst cave section. The core data shows that the karst cave section in the well is mainly filled with sand and mud rocks and is represented as full filling or half filling. The method has the advantages that the predicted longitudinal wave speeds of the four wells can be well matched with the actually measured longitudinal wave speeds in both the bedrock section and the karst cave section, the prediction error is small, the precision meets the production requirement, the rock physical model constructed by the method has good adaptability in the work area, and a solid foundation is laid for the subsequent model-based physical parameter inversion.
In addition, data mining is carried out on the existing karst cave section interpretation results in the work area, and the correlation degree between the karst cave filling rate and the logging density at the corresponding depth is found to be the highest, as shown in fig. 3. And (3) collecting 48 filling rate data points in total, performing intersection analysis on the 48 filling rate data points and corresponding density data, wherein the correlation coefficient can reach 0.95, the density data can be used for calculating the filling rate of the karst cave of the work area, and after a density data volume is obtained through pre-stack seismic inversion, the spatial distribution of the filling rate can be estimated based on a regression formula in the graph. FIG. 3 is a statistical filling rate versus density based on log interpretation of actual carbonate reservoirs. The method aims to establish an empirical relationship between the filling rate and the density according to actual data, so that a foundation is laid for predicting the filling rate by using the density after the density is inverted in an earthquake.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
According to an embodiment of the present invention, there is provided a carbonate reservoir fracture-cavity filler identification system including:
a unit for obtaining the elastic modulus of the rock matrix based on the logging information;
a unit that obtains an elastic modulus of the dry rock based on the elastic modulus of the rock matrix;
a unit for obtaining equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock;
identifying a unit of a carbonate reservoir fracture-cave filler component based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance;
and acquiring a unit of the filling rate of the carbonate reservoir fracture-cavity based on the empirical relationship between the filling rate of the fracture-cavity and the density established by the logging information.
In one example, the unit for obtaining the elastic modulus of the dry rock based on the elastic modulus of the rock matrix comprises: the method comprises the following steps of (1) enabling rock pores to be equivalent to ideal ellipsoid pores with a single aspect ratio, adding dry equivalent pores into a rock matrix by using a differential equivalent medium model, and obtaining the elastic modulus of the dry rock, wherein the specific formula is as follows:
Kd(0)＝Km,μd(0)＝μm(4)
wherein, KdIs the dry rock bulk modulus, μdIs the dry rock shear modulus, KmIs the bulk modulus of rock matrix, mumIs the rock matrix shear modulus, KfIs the pore fluid bulk modulus, μfis the pore fluid shear modulus, y is the porosity, P and Q are shape factors and are the equivalent aspect ratio αeFunction of, for dry porosity, KfAnd mufAll take 0.
In one example, the unit for obtaining the equivalent dielectric shear wave impedance and the equivalent dielectric longitudinal wave impedance based on the elastic modulus of the dry rock comprises: obtaining an equivalent medium elastic modulus through Gassmann equation calculation, and obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance through the equivalent medium elastic modulus, wherein the specific formula is as follows:
μs＝μd(6)
wherein, KsIs an equivalent medium bulk modulus, μsIs equivalent medium shear modulus, KdIs the dry rock bulk modulus, mudIs the dry rock shear modulus, KfIs the pore fluid bulk modulus, KmIs the bulk modulus of the matrix mineral, phi is the porosity.
Wherein Ip Is equivalent medium longitudinal wave impedance, Is equivalent medium transverse wave impedance, and ρ Is density, and the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained by formula (7).
In one example, identifying a carbonate reservoir fracture-cavity filler composition based on the equivalent dielectric shear wave impedance and the equivalent dielectric compressional wave impedance comprises: and inverting under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance and the logging density, inverting the mineral component parameters of the karst cave section, and identifying the components of the carbonate reservoir fracture-cave filler.
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A carbonate reservoir fracture-cave filling identification method comprises the following steps:
acquiring the elastic modulus of the rock matrix based on the logging information;
acquiring the elastic modulus of the dry rock based on the elastic modulus of the rock matrix;
acquiring equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock;
identifying components of a carbonate reservoir fracture-cave filler based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance;
and acquiring the filling rate of the carbonate reservoir seam holes according to the density of seismic inversion based on the empirical relationship between the filling rate of the seam holes and the density established by the logging data.
2. A carbonate reservoir fracture-hole pack identification method according to claim 1, wherein the obtaining, based on the well log data, the elastic modulus of the rock matrix: based on logging information, the elastic modulus of the rock matrix is obtained through Voight-Reuss-Hill model calculation, and the specific formula is
Wherein M isHIs the elastic modulus of the rock matrix, fiDenotes the volume content of the mineral constituents, MiRepresents the elastic modulus of the mineral constituent; voigt and Reuss model provides the upper limit M of the equivalent rock modulusVAnd a lower limit MRAnd carrying out arithmetic mean on the upper limit and the lower limit to obtain the elastic modulus of the rock matrix.
3. A carbonate reservoir fracture hole pack identification method according to claim 1, wherein the obtaining of the modulus of elasticity of dry rock based on the modulus of elasticity of the rock matrix is: the method comprises the following steps of (1) enabling rock pores to be equivalent to ideal ellipsoid pores with a single aspect ratio, adding dry equivalent pores into a rock matrix by using a differential equivalent medium model, and obtaining the elastic modulus of the dry rock, wherein the specific formula is as follows:
Kd(0)＝Km,μd(0)＝μm(4)
wherein, KdIs the dry rock bulk modulus, μdIs the dry rock shear modulus, KmIs the bulk modulus of rock matrix, mumIs the rock matrix shear modulus, KfIs the pore fluid bulk modulus, μfis the pore fluid shear modulus, y is the porosity, P and Q are shape factors and are the equivalent aspect ratio αeFunction of, for dry porosity, KfAnd mufAll take 0.
4. A carbonate reservoir fracture-hole fill identification method according to claim 1, wherein said obtaining an equivalent medium shear wave impedance and an equivalent medium compressional wave impedance based on the elastic modulus of the dry rock comprises: obtaining an equivalent medium elastic modulus through Gassmann equation calculation, and obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance through the equivalent medium elastic modulus, wherein the specific formula is as follows:
μs＝μd
wherein, KsIs the equivalent medium bulk modulus, μsIs equivalent medium shear modulus, KdIs the dry rock bulk modulus, mudIs the dry rock shear modulus, KfIs the pore fluid bulk modulus, KmIs the bulk modulus of the matrix mineral, phi is the porosity;
wherein Ip Is equivalent medium longitudinal wave impedance, Is equivalent medium transverse wave impedance, and ρ Is density, and the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained by formula (6) and formula (7).
5. The carbonate reservoir fracture hole fill identification method of claim 1, wherein identifying carbonate reservoir fracture hole fill composition based on the equivalent medium shear wave impedance and the equivalent medium compressional wave impedance comprises: and inverting under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance and the logging density, inverting the mineral component parameters of the karst cave section, and identifying the components of the carbonate reservoir fracture-cave filler.
6. The carbonate reservoir fracture-cavity filler identification method according to claim 1, wherein the obtaining of the carbonate reservoir fracture-cavity filling rate based on the empirical relationship between fracture-cavity filling rate and density established by the well log data comprises: and constructing a regression formula between the karst cave filling rate and the logging density according to the existing karst cave interpretation result, and calculating the spatial distribution of the karst cave filling rate through the density term in the carbonate reservoir fracture-cave filling material component.
7. A carbonate reservoir fracture-cave filling identification system, comprising:
a unit for obtaining the elastic modulus of the rock matrix based on the logging information;
a unit that obtains an elastic modulus of the dry rock based on the elastic modulus of the rock matrix;
a unit for obtaining equivalent medium transverse wave impedance and equivalent medium longitudinal wave impedance based on the elastic modulus of the dry rock;
identifying a unit of a carbonate reservoir fracture-cave filler component based on the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance;
and acquiring the unit of the filling rate of the carbonate reservoir seam holes according to the density of seismic inversion based on the empirical relationship between the filling rate of the seam holes and the density established by the logging data.
8. A carbonate reservoir fracture hole pack identification system as claimed in claim 7, wherein the means for obtaining the modulus of elasticity of dry rock based on the modulus of elasticity of the rock matrix comprises: the method comprises the following steps of (1) enabling rock pores to be equivalent to ideal ellipsoid pores with a single aspect ratio, adding dry equivalent pores into a rock matrix by using a differential equivalent medium model, and obtaining the elastic modulus of the dry rock, wherein the specific formula is as follows:
Kd(0)＝Km,μd(0)＝μm(4)
wherein, KdIs the dry rock bulk modulus, μdIs the dry rock shear modulus, KmIs the bulk modulus of rock matrix, mumIs the rock matrix shear modulus, KfIs pore fluid volumeModulus, μfis the pore fluid shear modulus, y is the porosity, P and Q are shape factors and are the equivalent aspect ratio αeFunction of, for dry porosity, KfAnd mufAll take 0.
9. A carbonate reservoir fracture hole fill identification system as defined in claim 7 wherein said means for obtaining an equivalent medium shear wave impedance and an equivalent medium compressional wave impedance based on the modulus of elasticity of the dry rock comprises: obtaining an equivalent medium elastic modulus through Gassmann equation calculation, and obtaining the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance through the equivalent medium elastic modulus, wherein the specific formula is as follows:
μs＝μd
wherein, KsIs the equivalent medium bulk modulus, μsIs equivalent medium shear modulus, KdIs the dry rock bulk modulus, mudIs the dry rock shear modulus, KfIs the pore fluid bulk modulus, KmIs the bulk modulus of the matrix mineral, phi is the porosity;
wherein Ip Is equivalent medium longitudinal wave impedance, Is equivalent medium transverse wave impedance, and ρ Is density, and the equivalent medium transverse wave impedance and the equivalent medium longitudinal wave impedance are obtained by formula (6) and formula (7).
10. The carbonate reservoir fracture hole fill identification system of claim 7, wherein the means for identifying carbonate reservoir fracture hole fill components based on the equivalent medium shear wave impedance and the equivalent medium compressional wave impedance comprises: and inverting under a Bayes inversion framework through the equivalent medium transverse wave impedance, the equivalent medium longitudinal wave impedance and the logging density, inverting the mineral component parameters of the karst cave section, and identifying the components of the carbonate reservoir fracture-cave filler.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710506999.0A CN109143397B (en) | 2017-06-28 | 2017-06-28 | Carbonate reservoir fracture-cave filling identification method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710506999.0A CN109143397B (en) | 2017-06-28 | 2017-06-28 | Carbonate reservoir fracture-cave filling identification method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109143397A CN109143397A (en) | 2019-01-04 |
| CN109143397B true CN109143397B (en) | 2020-05-19 |
Family
ID=64805636
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710506999.0A Active CN109143397B (en) | 2017-06-28 | 2017-06-28 | Carbonate reservoir fracture-cave filling identification method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109143397B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112649870A (en) * | 2019-10-12 | 2021-04-13 | 中国石油化工股份有限公司 | Method and system for determining mineral elastic parameters in rock physical modeling |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103064118A (en) * | 2013-01-08 | 2013-04-24 | 西南石油大学 | Method of acoustic logging and quantifying cavern filling degree |
| CN104007485A (en) * | 2014-04-16 | 2014-08-27 | 孙赞东 | Method for establishing complex pore shale rock physical model |
| CN104834007A (en) * | 2015-05-04 | 2015-08-12 | 中国石油天然气股份有限公司 | Method for calculating filling degrees of carbonate fracture-cave reservoir during seismic inversion process |
| CN106772608A (en) * | 2017-02-16 | 2017-05-31 | 甘肃省地震局 | The elastic impedance and Generalized Fluid factor-analysis approach of equivalent aperture gap fracture medium |
-
2017
- 2017-06-28 CN CN201710506999.0A patent/CN109143397B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103064118A (en) * | 2013-01-08 | 2013-04-24 | 西南石油大学 | Method of acoustic logging and quantifying cavern filling degree |
| CN104007485A (en) * | 2014-04-16 | 2014-08-27 | 孙赞东 | Method for establishing complex pore shale rock physical model |
| CN104834007A (en) * | 2015-05-04 | 2015-08-12 | 中国石油天然气股份有限公司 | Method for calculating filling degrees of carbonate fracture-cave reservoir during seismic inversion process |
| CN106772608A (en) * | 2017-02-16 | 2017-05-31 | 甘肃省地震局 | The elastic impedance and Generalized Fluid factor-analysis approach of equivalent aperture gap fracture medium |
Non-Patent Citations (2)
| Title |
|---|
| 基于等效各向异性和流体替换的地下裂缝地震预测方法;陈怀震;《中国科学》;20151231;第45卷(第5期);全文 * |
| 贝叶斯框架下联合AVA反演与统计岩石物理的储层参数估计;滕龙;《石油地球物理勘探》;20140831;第49卷(第4期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109143397A (en) | 2019-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10502863B2 (en) | Diagenetic and depositional rock analysis | |
| US10083258B2 (en) | Combining downhole fluid analysis and petroleum systems modeling | |
| US8768672B2 (en) | Method for predicting time-lapse seismic timeshifts by computer simulation | |
| US20180238148A1 (en) | Method For Computing Lithofacies Probability Using Lithology Proximity Models | |
| CN106368691B (en) | Three-dimensional abnormal pore pressure prediction method based on rock physics seismic information | |
| Asadi | Application of artificial neural networks in prediction of uniaxial compressive strength of rocks using well logs and drilling data | |
| CN103760081A (en) | Gas pool prediction method and system of carbonate reservoir based on pore structure characteristics | |
| CA2754067A1 (en) | Method for integrating reservoir charge modeling and downhole fluid analysis | |
| CN102656480A (en) | Method for modeling a reservoir basin | |
| CN105445791A (en) | Stratum aperture pressure prediction method based on variety earthquake attributes | |
| US11346833B2 (en) | Reservoir fluid characterization system | |
| CN105629308A (en) | Phase control heterogeneous mechanical parameter crustal stress prediction method | |
| CN109667573A (en) | Three-dimensional shale reservoir pore pressure prediction method, apparatus and electronic equipment | |
| CN109709608B (en) | Method for measuring TOC content and lithologic components of mixed accumulated hydrocarbon source rock and application | |
| Askari et al. | A fully integrated method for dynamic rock type characterization development in one of Iranian off-shore oil reservoir | |
| Esmaeili et al. | Developing a saturation-height function for reservoir rock types and comparing the results with the well log-derived water saturation, a case study from the Fahliyan formation, Dorood oilfield, Southwest of Iran | |
| Venieri et al. | The interplay between cm-and m-scale geological and geomechanical heterogeneity in organic-rich mudstones: implications for reservoir characterization of unconventional shale plays | |
| Holden et al. | Integration of production logs helps to understand heterogeneity of Mishrif reservoir in Rumaila | |
| CN109323954A (en) | A kind of predicting method of formation pore pressure for car-bonate rock | |
| CN109143397B (en) | Carbonate reservoir fracture-cave filling identification method and system | |
| Jasim et al. | Specifying quality of a tight oil reservoir through 3-d reservoir modeling | |
| CN104965227B (en) | A kind of computational methods and device of compact reservoir well logging stiffness coefficient | |
| US20220196862A1 (en) | Method and system for determining a location of hydrocarbon reservoirs | |
| CN109118019A (en) | A kind of coal bed gas content prediction technique and device | |
| Zhou et al. | Reservoir geomechanic heterogeneity index (RGHI): Concept, methodology, and application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |