CN110927794A - Method for identifying tight reservoir cracks and quantitatively calculating porosity - Google Patents
Method for identifying tight reservoir cracks and quantitatively calculating porosity Download PDFInfo
- Publication number
- CN110927794A CN110927794A CN201911241396.8A CN201911241396A CN110927794A CN 110927794 A CN110927794 A CN 110927794A CN 201911241396 A CN201911241396 A CN 201911241396A CN 110927794 A CN110927794 A CN 110927794A
- Authority
- CN
- China
- Prior art keywords
- porosity
- den
- logging
- curve
- density
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 206010017076 Fracture Diseases 0.000 claims abstract description 102
- 208000010392 Bone Fractures Diseases 0.000 claims abstract description 89
- 238000011161 development Methods 0.000 claims abstract description 65
- 238000005520 cutting process Methods 0.000 claims abstract description 28
- 238000004364 calculation method Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims description 16
- 239000000758 substrate Substances 0.000 claims description 15
- 230000015572 biosynthetic process Effects 0.000 claims description 13
- 239000012530 fluid Substances 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 7
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 6
- 239000011707 mineral Substances 0.000 claims description 6
- 239000011435 rock Substances 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 3
- 238000005755 formation reaction Methods 0.000 description 10
- 238000003384 imaging method Methods 0.000 description 9
- 238000011156 evaluation Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 230000009977 dual effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- BVKZGUZCCUSVTD-UHFFFAOYSA-L Carbonate Chemical compound [O-]C([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-L 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000009545 invasion Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a method for identifying tight reservoir fractures and quantitatively calculating porosity, which comprises the following steps: respectively cutting the acoustic logging curve and the density logging curve according to the acoustic waves and the density, cutting extreme points of the curves, and taking the middle point of the curves as a cutting point when the curves meet a straight line segment; according to the principle of the correlation coefficient, calculating the correlation coefficient of the cut curve segment; calculating an AC-DEN correlation coefficient, and identifying a tight reservoir fracture development section and a non-fracture development section; evaluating the fractures, establishing an equation set by using logging information to enable a target function to reach a minimum value, and solving a comprehensive framework of the acoustic time difference and the density of the reservoir; calculating the shale content of the compact reservoir; and establishing a calculation model of the porosity of the fracture by using the acoustic time difference and the density logging data. Compared with the calculation result of FMI, the crack porosity calculation result is improved in crack identification precision, and conventional logging information is used for identifying and evaluating cracks, so that the logging interpretation efficiency is greatly improved.
Description
Technical Field
The invention belongs to the field of oil and gas exploration and development, and particularly relates to a method for identifying compact reservoir fractures and quantitatively calculating porosity.
Background
The cracks are key migration channels and important storage spaces of the compact oil and gas, and play an important role in the exploration and development of compact reservoirs. The existing well logging evaluation technology of the cracks has a good application effect on the conventional reservoir stratum.
The well logging curves of natural potential (SP) and natural Gamma (GR) are mainly changed under the influence of mud, and the cracks have no influence on the SP and the GR basically; the well diameter logging Curve (CAL) is mainly influenced by the lithology of the stratum to change, and the fracture has no influence on the well diameter; the dual lateral resistivity is changed by the change of the properties of the stratum and the fluid, the development of the fracture has certain influence on the resistivity, mainly in the development section of the fracture, and the invasion of mud causes some differences of the resistivity logging value. The response characteristics of the dual lateral resistivity logging in high and low angle fractures are different, namely when the fracture angle is larger than 75 degrees, the deep and shallow lateral resistivities are shown as abnormal positive amplitude difference; when the crack angle is less than 45 °, the deep and shallow lateral resistivities show negative amplitude difference anomalies. Therefore, the fluid and the fracture can be identified by utilizing the characteristic of the logging amplitude difference of the depth resistivity of the fracture. It is common to use finite element methods to perform simulation calculations to build models of the relationship between bi-lateral and fracture porosity, fluid conductivity, and formation resistivity to identify and evaluate fractures in carbonate formations. However, in complex tight reservoirs such as tight sandstone and shale formations, the effect of identifying fractures by using a resistivity logging response difference method of high-angle fractures and low-angle fractures is not good.
The imaging logging in the new logging technology has high sampling rate, high resolution, direct observability and continuity, and can directly reflect the development condition and geological features of cracks around the well. The method can analyze the macroscopic structure vertically and the detailed structural characteristics inside the stratum microscopically, can provide more reliable and powerful evidence than conventional logging information for describing the characteristics of the stratum, and directly and finely reflects the complexity and the nonuniformity of the stratum. The rock type, rock structure, sedimentary structure, fracture and the like of the stratum can be described finely, and particularly, the position, inclination angle, strike, type and other characteristics of the fracture can be effectively indicated, so that the effectiveness and feasibility of researching and evaluating the fracture of the reservoir by using the imaging logging information are realized. In the past, stratum microresistivity scanning imaging logging (FMI) is commonly used for describing cracks and extracting crack parameters, but imaging logging cost is high, imaging logging information is often lacked in a research area, imaging logging can only identify cracks on a well wall, and radial detection depth is shallow, so that imaging logging has limitation.
The well logging interpretation and evaluation work mainly aims at reservoir physical properties, fluid identification and related parameter calculation, and well logging research is continuously developed to a great extent. However, in a complex compact reservoir, the existing well logging interpretation and evaluation technology often has great problems, such as the problems of parameter calculation, fracture identification, fracture porosity calculation and evaluation of the compact reservoir, and the like, and the requirements of oil and gas exploration and development cannot be met. Identification of a fracture development section and characterization of fracture porosity are problems to be overcome in well logging evaluation in oil and gas exploration and development at present. Because the imaging logging cost is high, the radial detection depth is shallow, and the research area is often lack of imaging logging information, the method has great significance in researching and identifying the cracks and performing calculation evaluation on crack parameters, particularly on the porosity of the cracks by using the conventional logging information.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for identifying compact reservoir fractures and quantitatively calculating porosity.
The purpose of the invention is realized by the following technical scheme: a method for identifying tight reservoir fractures and quantitatively calculating porosity comprises the following steps:
s1, respectively cutting an acoustic logging curve and a density logging curve, cutting an extreme point of the curves, and taking the middle point of the curves as a cutting point when a straight line segment is encountered, wherein the acoustic logging curve, namely an AC logging curve, is a line formed by acoustic time difference logging data, and the density logging curve, namely a DEN logging curve, is a line formed by density logging data;
s2, according to the principle of correlation coefficients, cutting the acoustic logging curve and the density logging curve sequentially from top to bottom by adopting a certain window length, and calculating the correlation coefficients of the cut curve sections:
wherein x and y respectively represent the acoustic transit time difference AC and the density logging DEN curve of the fracture development section,
and
the average of the AC and DEN log cut segment data, respectively. Firstly, cutting the AC curve, and substituting the formula to obtain a correlation coefficient r of the AC curve cutting treatmentACThen, cutting the DEN curve, and substituting the formula to obtain the correlation coefficient r of the DEN curve cutting treatmentDEN；
S3, calculating the AC-DEN correlation coefficient rAC-DEN：
rAC-DEN＝rAC·rDEN；
According to the method for logging by core scales, firstly, a crack development section and a non-crack development section on a core are identified, the crack development section and the non-crack development section on a logging curve are scaled to a sound wave time difference and density logging curve, the crack development section and the non-crack development section on the logging curve are calibrated and used as sample points, the correlation coefficient of the crack development section and the non-crack development section is used as a standard for crack identification, the correlation coefficient is distributed between-0.8 and is a non-crack development section, the correlation coefficient is distributed between [ -10.8 ] and [ 0.81 ] and is a crack development section, and the correlation coefficient size distribution sections of the cracks and the non-crack development section are actually verified, so that the crack development section and the non-crack development section of a compact reservoir can be well identified according to the correlation coefficient;
s4, after identification of a crack development section and a non-crack development section is completed, evaluating the crack, and establishing an equation set by using logging data:
so that the objective function
When the minimum value is reached, the comprehensive framework value of the acoustic time difference and the density of the reservoir is obtained;
in the formula, ACmIs a reservoir acoustic time difference comprehensive framework with the unit of mu s/m; ACfIs the acoustic time difference of the fluid, in μ s/m; DENmIs a reservoir density comprehensive framework with the unit of g/cm3；DENfIs the fluid density in g/cm3；VmIs the comprehensive volume content of the rock matrix and the argillaceous substances; vφIs the total porosity of the formation; f (v) is an objective function; pjThe jth weight factor; k is a norm; a. theijLog response for the ith x j minerals; y isjIs the jth log data; viIs the ith mineral content or porosity;
s5, calculating the shale content of the compact reservoir
Wherein, VshIs the argillaceous content; GCUR is a formation factor, GRminAnd GRmaxIs the minimum and maximum of one formation of the GR curve;
and S6, establishing a calculation model of the fracture porosity by using the acoustic time difference and the density logging data so as to realize quantitative calculation of the fracture porosity.
Wherein the step S6 includes:
acoustic porosity phi if tight reservoir develops with low angle seamsACComprising a matrix porosity phiSubstratePorosity of crackφfr(ii) a Density porosity phiDENOnly matrix porosity is included and fracture porosity is ignored because low angle seams have substantially no effect on density logs, such that density porosity does not include fracture porosity; if tight reservoirs develop high angle seams, then phiACOnly the porosity of the matrix is included, since the high angle slots have no or negligible effect on the acoustic moveout, so that the acoustic porosity phi isACHigh angle seams have an effect on density, including only matrix porosity, and the effect is significant, making phiDENIncluding matrix porosity and fracture porosity, namely:
low angle seam development zone:
φAC＝φsubstrate+φfr，φDEN＝φSubstrate
High angle seam development area:
φAC＝φsubstrate，φDEN＝φSubstrate+φfr
The calculation formula for fracture porosity can be expressed as:
this formula is a willi difference model for quantitative calculation of fracture porosity.
The calculation formula of the tight reservoir fracture porosity is subjected to shale correction (1-V)sh) Then, the following are obtained:
this formula is an improved Weili difference model for quantitative calculation of fracture porosity.
The invention has the beneficial effects that: (1) the invention uses the conventional logging information to identify and evaluate the cracks, thereby greatly improving the logging interpretation efficiency and reducing the logging interpretation cost. (2) The invention uses the compensating acoustic wave time difference and the compensating density to have different sensibility and difference to the fracture, establishes a correlation coefficient method to identify the fracture development area and the non-fracture development area, and provides guidance and basis for further exploration and development. (3) The method uses conventional logging information to calculate the fracture porosity of the compact reservoir, the result is well matched with the fracture porosity extracted by FMI, the application prospect is wide, and the fracture evaluation method can be well applied to fracture evaluation of compact reservoirs such as compact sandstone, shale and the like.
Drawings
FIG. 1 is a schematic diagram of the distribution of acoustic-density correlation coefficients of a fracture development section and a non-fracture development section of a tight reservoir.
FIG. 2 is a schematic diagram of the logging response characteristics of tight reservoir sonic and density curves to high and low angle seams;
FIG. 3 is a flow chart of a method of the present invention;
FIG. 4 is a schematic view of a cutting process for sonic and density logs;
FIG. 5 is a schematic diagram of an example of identifying tight sandstone fracture development zones and non-fracture development zones using a correlation coefficient method;
FIG. 6 is a schematic diagram of an example of tight sandstone logging curve cutting process and correlation coefficient fracture identification;
FIG. 7 is a schematic diagram of an example of a tight sandstone reservoir fracture porosity calculation;
FIG. 8 is a plot of Weili Difference model calculated fracture porosity versus FMI calculated fracture porosity;
FIG. 9 is a graph of improved Willi difference model calculated fracture porosity compared to FMI calculated fracture porosity.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
The invention is based on the research and invention of the distribution difference between the crack development section and the non-crack development section (as shown in figure 1, the absolute value of the correlation coefficient of the crack development section is large, but the absolute value of the correlation coefficient of the non-crack development section is small) of the correlation coefficient of the acoustic logging and the density logging. The correlation relationship between the acoustic wave time difference (AC) in the fracture development section and the density logging (DEN) curve is very obvious, and the acoustic wave time difference value is increased and the density is basically unchanged or slightly changed in the low-angle fracture development section, and the acoustic wave time difference value is basically unchanged or slightly changed and the density is increased in the high-angle fracture development section, which show strong correlation (as shown in fig. 2). The correlation coefficient is a quantity for determining the degree of closeness of the relationship between two variables, and the strength of the correlation is described by the correlation coefficient. Extracting fracture information of a compact reservoir well, and analyzing a correlation coefficient by adopting sound wave-density, specifically:
as shown in fig. 3, a method for identifying tight reservoir fractures and quantitatively calculating porosity includes the following steps:
s1, in order to reduce the absolute value of a correlation coefficient caused by a large amount of data to be smaller, as shown in FIG. 4, cutting a sound wave curve and a density curve respectively, cutting extreme points of the curves, and taking the middle point of the curve as a cutting point when a straight line segment is encountered; wherein the acoustic logging curve, i.e. the AC logging curve, is a line formed by acoustic time difference (AC) logging data, and the density logging curve, i.e. the DEN logging curve, is a line formed by Density (DEN) logging data; acquiring acoustic time difference logging data and density logging data from conventional logging data;
s2, according to the principle of correlation coefficients, cutting the acoustic logging curve and the density logging curve sequentially from top to bottom by adopting a certain window length, and calculating the correlation coefficients of the cut curve sections:
wherein x and y respectively represent the acoustic transit time difference AC and the density logging DEN curve of the fracture development section,
and
the average of the AC and DEN log cut segment data, respectively. Firstly, cutting the AC curve, and substituting the formula to obtain a correlation coefficient r of the AC curve cutting treatmentACThen the DEN curve is carried outCutting, and obtaining the correlation coefficient r of DEN curve cutting treatmentDEN；
S3, calculating the AC-DEN correlation coefficient rAC-DEN：
rAC-DEN＝rAC·rDEN；
The correlation coefficient values are distributed between-0.8 and 0.8 to form non-fracture development sections, the correlation coefficient values are distributed between-10.8 and 0.81 to form fracture development sections, and the correlation coefficient value distribution intervals of the fracture and non-fracture development sections are actually verified, so that the dense reservoir fracture development sections and the non-fracture development sections can be well identified according to the correlation coefficient values, namely the dense reservoir fracture development sections and the non-fracture development sections can be well identified by the correlation coefficients, as shown in fig. 5;
s4, after identification of a crack development section and a non-crack development section is completed, evaluating the crack, and establishing an equation set by using logging data:
so that the objective function
When the minimum value is reached, the comprehensive framework value of the acoustic time difference and the density of the reservoir is obtained;
in the formula, ACmIs a reservoir acoustic time difference comprehensive framework with the diameter of mu s/m; ACfIs the acoustic time difference of the fluid, μ s/m; DENmIs a reservoir density comprehensive framework of g/cm3；DENfIs the fluid density, g/cm3；VmIs the comprehensive volume content of rock matrix and argillaceous substance,%; vφIs the total porosity of the formation,%; f (v) is an objective function; pjThe jth weight factor; k is a norm; a. theijLog response for the ith x j minerals; y isjIs the jth log data; viIs the ith mineral content or porosity;
s5, calculating the shale content of the compact reservoir
Wherein, VshIs the argillaceous content; GCUR is a stratum factor, the old stratum is 2 generally, and the new stratum is 3.7-4.0; GRminAnd GRmaxIs the minimum and maximum of one formation of the GR curve;
and S6, establishing a calculation model of the fracture porosity by using the acoustic time difference and the density logging data so as to realize quantitative calculation of the fracture porosity.
According to the well logging response of the cracks, the influence of the high-angle cracks of the compact reservoir on the sound wave time difference can be ignored, the influence on the density is large, the influence of the low-angle cracks on the sound wave time difference is large, and the influence on the density can be ignored, namely, the sound waves are insensitive to the high-angle cracks, the density is sensitive to the high-angle cracks, the sound waves are sensitive to the low-angle cracks, and the density is insensitive to the low-angle cracks. Therefore, whatever the type of fracture, the difference between the acoustic moveout porosity and the density porosity reflects the information of the fracture, namely the difference between the AC and DEN porosities represents the porosity of the fracture.
Acoustic porosity phi if tight reservoir develops with low angle seamsACComprising a matrix porosity phiSubstratePorosity phi of crackfr(ii) a Density porosity phiDENOnly matrix porosity is included, fracture porosity is negligible; if tight reservoirs develop high angle seams, then phiACComprising only the porosity of the matrix,. phiDENIncluding matrix porosity and fracture porosity, namely:
low angle seam development zone:
φAC＝φsubstrate+φfr，φDEN＝φSubstrate
High angle seam development area:
φAC＝φsubstrate，φDEN＝φSubstrate+φfr
The calculation formula for fracture porosity can be expressed as:
the willi-difference method;
the calculation formula of the tight reservoir fracture porosity is subjected to shale correction (1-V)sh) After that, the expression is improved Weili Difference method model:
in an example of the present application, tight sandstone reservoir AC and DEN logs were cut, as shown in fig. 6. Coefficient of correlation rAC、rDENIs distributed in the range of [ -0.972, -0.73 [)]&[0.73,0.972]And the reservoir fracture development section and the non-fracture development section can be well identified.
The fracture porosity calculated by the willi difference model is greatly affected by the GR curve change, and at the GR low value peak, the fracture porosity value also reaches the peak, as in the circle of fig. 7. The FMI direct identification method verifies that there are low angle seams developed, but the fracture porosity calculated here is abnormally high, which is not in line with reality. Comparing the calculated fracture porosity value with the fracture porosity value calculated by FMI extraction, finding that the fracture porosity calculated by the Weiley difference method is generally higher than the fracture porosity value calculated by FMI and a rock core due to the influence of the shale content. After the shale correction, the calculated fracture porosity at that point is reduced, which corresponds to the true size of the actual formation fracture porosity.
In fig. 8, the fracture porosity calculated by the willi difference model is compared with the fracture porosity calculated by the FMI, and the porosity distribution calculated by the willi difference method is more dispersed; in fig. 9, the fracture porosity calculated by the improved willingness difference model is compared with the fracture porosity calculated by the FMI, so that the pore accuracy calculated by the improved willingness difference method is improved, and the actual situation of the fracture porosity of the formation is better reflected.
The foregoing is a preferred embodiment of the present invention, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as excluding other embodiments, and is capable of other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. A method for identifying compact reservoir fractures and quantitatively calculating porosity is characterized by comprising the following steps: the method comprises the following steps:
s1, respectively cutting an acoustic logging curve and a density logging curve, cutting an extreme point of the curves, and taking the middle point of the curves as a cutting point when a straight line segment is encountered, wherein the acoustic logging curve, namely an AC logging curve, is a line formed by acoustic time difference logging data, and the density logging curve, namely a DEN logging curve, is a line formed by density logging data;
s2, according to the principle of correlation coefficients, cutting the acoustic logging curve and the density logging curve sequentially from top to bottom by adopting a certain window length, and calculating the correlation coefficients of the cut curve sections:
wherein x and y respectively represent the acoustic transit time difference AC and the density logging DEN curve of the fracture development section,
and
average values of the AC and DEN log processing segment data, respectively; firstly, cutting the AC curve, and substituting the formula to obtain a correlation coefficient r of the AC curve cutting treatmentACThen, cutting the DEN curve, and substituting the formula to obtain the correlation coefficient r of the DEN curve cutting treatmentDEN；
S3, calculating the AC-DEN correlation coefficient rAC-DEN：
rAC-DEN＝rAC·rDEN；
The correlation coefficient value is distributed between-0.8 and 0.8 to be a non-crack development section, the correlation coefficient value is distributed between [ -10.8 ] and [ 0.81 ] to be a crack development section, and the dense reservoir crack development section and the non-crack development section are identified according to the correlation coefficient value;
s4, after identification of a crack development section and a non-crack development section is completed, evaluating the crack, and establishing an equation set by using logging data:
so that the objective function
When the minimum value is reached, the comprehensive framework value of the acoustic time difference and the density of the reservoir is obtained;
in the formula, ACmIs a reservoir acoustic time difference comprehensive framework with the unit of mu s/m; ACfIs the acoustic time difference of the fluid, in μ s/m; DENmIs a reservoir density comprehensive framework with the unit of g/cm3；DENfIs the fluid density in g/cm3；VmIs the comprehensive volume content of the rock matrix and the argillaceous substances; vφIs the total porosity of the formation; f (v) is an objective function; pjThe jth weight factor; k is a norm; a. theijLog response for the ith x j minerals; y isjIs the jth log data; viIs the ith mineral content or porosity;
s5, calculating the shale content of the compact reservoir
Wherein, VshIs the argillaceous content; GCUR is a formation factor, GRminAnd GRmaxIs the minimum and maximum of one formation of the GR curve;
and S6, establishing a calculation model of the fracture porosity by using the acoustic time difference and the density logging data so as to realize quantitative calculation of the fracture porosity.
2. The method for identifying tight reservoir fractures and quantitatively calculating porosity as claimed in claim 1, wherein the method comprises the following steps: the step S6 includes:
acoustic porosity phi if tight reservoir develops with low angle seamsACComprising a matrix porosity phiSubstratePorosity phi of crackfr(ii) a Density porosity phiDENOnly matrix porosity is included, fracture porosity is negligible; if tight reservoirs develop high angle seams, then phiACComprising only the porosity of the matrix,. phiDENIncluding matrix porosity and fracture porosity, namely:
low angle seam development zone:
φAC＝φsubstrate+φfr，φDEN＝φSubstrate
High angle seam development area:
φAC＝φsubstrate，φDEN＝φSubstrate+φfr
The calculation formula for fracture porosity can be expressed as:
the calculation formula of the tight reservoir fracture porosity is subjected to shale correction (1-V)sh) Then, the following are obtained:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911241396.8A CN110927794A (en) | 2019-12-06 | 2019-12-06 | Method for identifying tight reservoir cracks and quantitatively calculating porosity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911241396.8A CN110927794A (en) | 2019-12-06 | 2019-12-06 | Method for identifying tight reservoir cracks and quantitatively calculating porosity |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110927794A true CN110927794A (en) | 2020-03-27 |
Family
ID=69858128
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911241396.8A Pending CN110927794A (en) | 2019-12-06 | 2019-12-06 | Method for identifying tight reservoir cracks and quantitatively calculating porosity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110927794A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112392476A (en) * | 2020-12-02 | 2021-02-23 | 西南石油大学 | Method for determining hole permeability parameters of low-permeability fracture by using conventional logging data |
| CN112682034A (en) * | 2020-12-04 | 2021-04-20 | 中国地质大学（北京） | Method and device for crack identification and dip angle characterization based on tight sandstone reservoir |
| CN112835124A (en) * | 2021-03-10 | 2021-05-25 | 长江大学 | Fracture effectiveness evaluation method based on imaging logging and array acoustic logging data |
| CN113050168A (en) * | 2021-03-10 | 2021-06-29 | 长江大学 | Fracture effectiveness evaluation method based on array acoustic logging and acoustic remote detection logging data |
| CN113971351A (en) * | 2020-07-24 | 2022-01-25 | 中国石油天然气股份有限公司 | Method and device for determining porosity of crack |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107923240A (en) * | 2015-09-16 | 2018-04-17 | 因格瑞恩股份有限公司 | Method for determining the porosity associated with the organic matter in well or stratum |
-
2019
- 2019-12-06 CN CN201911241396.8A patent/CN110927794A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107923240A (en) * | 2015-09-16 | 2018-04-17 | 因格瑞恩股份有限公司 | Method for determining the porosity associated with the organic matter in well or stratum |
Non-Patent Citations (3)
| Title |
|---|
| FENQIANG WU ET AL.: "A Developed Optimization Method of Tight Reservoir Porosity", 《MATHEMATICAL PROBLEMS IN ENGINEERING》 * |
| JINGLING XU ET AL.: "Method for calculating the fracture porosity of tight-fracture reservoirs", 《GEOPHYSICS》 * |
| JINGLING XU ET AL.: "Predicting the Porosity of Natural Fractures in Tight Reservoirs", 《ARAB J SCI ENG》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113971351A (en) * | 2020-07-24 | 2022-01-25 | 中国石油天然气股份有限公司 | Method and device for determining porosity of crack |
| CN113971351B (en) * | 2020-07-24 | 2022-11-01 | 中国石油天然气股份有限公司 | Method and device for determining porosity of crack |
| CN112392476A (en) * | 2020-12-02 | 2021-02-23 | 西南石油大学 | Method for determining hole permeability parameters of low-permeability fracture by using conventional logging data |
| CN112682034A (en) * | 2020-12-04 | 2021-04-20 | 中国地质大学（北京） | Method and device for crack identification and dip angle characterization based on tight sandstone reservoir |
| CN112835124A (en) * | 2021-03-10 | 2021-05-25 | 长江大学 | Fracture effectiveness evaluation method based on imaging logging and array acoustic logging data |
| CN113050168A (en) * | 2021-03-10 | 2021-06-29 | 长江大学 | Fracture effectiveness evaluation method based on array acoustic logging and acoustic remote detection logging data |
| CN113050168B (en) * | 2021-03-10 | 2024-01-26 | 长江大学 | Crack effectiveness evaluation method based on array acoustic logging and acoustic remote detection logging data |
| CN112835124B (en) * | 2021-03-10 | 2024-01-26 | 长江大学 | Crack effectiveness evaluation method based on imaging logging and array acoustic logging data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110927794A (en) | Method for identifying tight reservoir cracks and quantitatively calculating porosity | |
| Lai et al. | Geophysical well-log evaluation in the era of unconventional hydrocarbon resources: a review on current status and prospects | |
| US20090260883A1 (en) | Continuous measurement of heterogeneity of geomaterials | |
| CN106842301B (en) | Quantitative identification and prediction method for favorable reservoir of tufaceous sandstone | |
| CN105089663B (en) | A kind of High angle/horizontal well formation resistivity anisotropy bearing calibration | |
| CN105277982B (en) | A kind of mud shale total content of organic carbon earthquake prediction method | |
| CN104213899B (en) | A kind of Logging Identification Method of formation rock skeleton | |
| CN107829731B (en) | Clay alteration volcanic porosity correction method | |
| CN107784159B (en) | Method for determining anisotropic coefficient of reservoir resistivity | |
| US8681582B2 (en) | Method for sonic indication of formation porosity and lithology | |
| WO2004095077A1 (en) | Method for predicting pore pressure | |
| US8005619B2 (en) | Method of determining reservoir parameters | |
| Sharifi et al. | Application of extended elastic impedance in seismic geomechanics | |
| US20220351037A1 (en) | Method and system for spectroscopic prediction of subsurface properties using machine learning | |
| CN105317435A (en) | Horizontal well crack recognition method | |
| Fan et al. | Quantitative prediction and spatial analysis of structural fractures in deep shale gas reservoirs within complex structural zones: A case study of the Longmaxi Formation in the Luzhou area, southern Sichuan Basin, China | |
| US20130292111A1 (en) | Method of constructing a well log of a quantitative property from sample measurements and log data | |
| CN112946782B (en) | Earthquake fine depicting method for dense oil-gas storage seepage body | |
| CN113216945B (en) | Quantitative evaluation method for permeability of tight sandstone reservoir | |
| CN112835124B (en) | Crack effectiveness evaluation method based on imaging logging and array acoustic logging data | |
| CN109826623B (en) | Geophysical well logging identification method for tight sandstone reservoir bedding joints | |
| Petrik et al. | Quantitative characterisation of fracture connectivity from high-resolution borehole image logs | |
| CN110529106B (en) | Method for determining content of coal seam micro-components by using logging information | |
| CN112746835A (en) | Optimized deep shale gas geology dessert logging comprehensive evaluation method | |
| CN110320562B (en) | Method for correcting acoustic time difference in horizontal well of shale gas reservoir |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200327 |
|
| RJ01 | Rejection of invention patent application after publication |