CN108829950B - Unconventional reservoir permeability evaluation method based on core image - Google Patents
Unconventional reservoir permeability evaluation method based on core image Download PDFInfo
- Publication number
- CN108829950B CN108829950B CN201810546608.2A CN201810546608A CN108829950B CN 108829950 B CN108829950 B CN 108829950B CN 201810546608 A CN201810546608 A CN 201810546608A CN 108829950 B CN108829950 B CN 108829950B
- Authority
- CN
- China
- Prior art keywords
- sub
- pore
- permeability
- rock
- component
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention provides a method for evaluating permeability of an unconventional reservoir based on a rock core image, which comprises the following steps: scanning and identifying the real rock sample by using a rock imaging technology to obtain a corresponding image, and dividing the rock sample in the image into corresponding features; respectively extracting the frequency spectrum of each characteristic, and fitting the analysis result by a mixed fractal method to obtain a fractal parameter of each corresponding characteristic; combining the fitted fractal parameters to obtain a rock pore model based on a mixed fractal theory; and calculating the communication probability among different kinds of pores in the rock pore model after the communication probability is arranged according to the communication probability matrix through a pore connectivity algorithm, so that the evaluation result of the apparent permeability of the current unconventional reservoir can be obtained. The method starts from real rock sample data, combines a pore connectivity algorithm to obtain a connectivity probability matrix among different kinds of pores, and finally realizes rapid and accurate evaluation of the unconventional reservoir apparent permeability.
Description
Technical Field
The invention belongs to the field of oil exploitation, and particularly relates to an evaluation method capable of quickly and accurately evaluating the apparent permeability of an unconventional reservoir based on a core image.
Background
Shale gas is natural gas extracted from a shale reservoir, is mainly composed of methane, and is an important emerging energy source. However, successful development of shale gas requires resolution of a number of challenges, one of the underlying ones being rapid and efficient assessment of the apparent permeability of shale.
Factors influencing the apparent permeability of shale gas are many, and mainly include the following points: firstly, the shale is very compact, the porosity and permeability are low, the seepage space is small, and the diameter of the main pore is in the nanometer level; secondly, the seepage space has various types, poor connectivity and different sizes and comprises a nanoscale organic hole and a micron-sized crack inorganic hole; third, the gas seepage and occurrence mechanisms are complex, including free state (existing in rock pores and cracks in the form of free gas), adsorption state (adsorbed on the wall surface of rock pores), and dissolution state (dissolved in kerogen, asphaltene and liquid crude oil). The calculation amount of the permeability of unconventional reservoir samples in the conventional various technical schemes is huge, and a new method for quickly evaluating the permeability of the unconventional reservoir samples needs to be established.
The current methods for characterizing the pore space of the rock comprise a capillary bundle method and a pore space reconstruction method, wherein the capillary bundle method is one of the classical methods for characterizing the pore space of the rock. The method simplifies rock pores into a series of parallel capillary bundles so as to study the seepage process of fluid in the seepage process of high-porosity rock. The method has simple model and simple calculation. On the basis, a series of equations such as adding tortuosity, changing the diameter of a capillary bundle and increasing a slip term are developed to simulate different kinds of rock pore spaces and fluid flow processes. However, the disadvantage of the capillary bundle method is that the complex pore network structure in the rock and the pore throat connectivity are not considered, the model is established based on a capillary bundle theoretical model, and the pore network composition of the model has a certain difference with the real rock.
And the pore space reconstruction rule characterizes the rock pore space based on statistics, and the method firstly counts the pore distribution spectrum and the coordination number in the rock. And further establishing a network consisting of the mesh-shaped communication pipelines, simulating throats by the pipelines, simulating pores at the intersection points of the two pipelines, proportionally dispersing the pore diameters obtained by statistics into the intersection points of the network, randomly plugging the pipelines according to the coordinate numbers of the statistics, and finally completing the reconstruction of pore spaces. The method has the disadvantages that after the complicated pore space is simplified into a standard square net structure model, the complicated pore space is scattered and randomly dispersed into a network, and the independence of the pore space and the throat space distribution is eliminated. Further, the tortuosity of the throat, as well as the pore size distribution of the throat itself, are not considered.
Disclosure of Invention
The invention aims to provide an evaluation method capable of quickly and accurately evaluating the apparent permeability of an unconventional reservoir based on a core image.
Particularly, the invention provides an unconventional reservoir permeability evaluation method based on a core image, which comprises the following steps:
and 400, calculating the connection probability among different types of pores in the rock pore model after arranging according to a connection probability matrix through a pore connectivity algorithm, and obtaining the evaluation result of the apparent permeability of the current unconventional reservoir.
In one embodiment of the present invention, the pore characteristics in the step 100 refer to organic mass, organic pores in ore particles, and inter-or intra-granular pores in ore; the auxiliary features refer to natural fractures or artificial fracture features in the rock sample.
In one embodiment of the invention, the rock imaging technology comprises SEM, FIB-SEM, Micro-CT and nano-CT.
In one embodiment of the invention, the composition characteristic is subdivided into a plurality of corresponding sub-composition characteristics according to the type of ore particles; the pore feature is subdivided into a plurality of corresponding sub-pore features corresponding to the differences in the sub-component features; the assist feature is subdivided into a plurality of corresponding sub-assist features according to relationships with different ones of the sub-component features.
In one embodiment of the invention, the spectrum of the constituent features is established over a distribution spectrum of equivalent sizes of the respective sub-constituents in each sub-constituent feature, the spectrum of the pore features is established over a distribution spectrum of equivalent pore diameters of each sub-pore feature, and the spectrum of the assist feature is established over fracture density, seam length and seam width represented by the sub-assist features.
In an embodiment of the present invention, a size distribution spectrum formed by the equivalent size of each level of sub-component characteristics obtained by the fractal parameter of each sub-component characteristic and the corresponding number of the equivalent size is equal to the size distribution spectrum of the sub-component characteristics; the equivalent diameter of each level of pores obtained by the fractal parameters of each sub-pore characteristic and the corresponding number of the pores jointly form a pore size distribution spectrum which is equal to the pore size distribution spectrum of the sub-pore characteristic; and the fracture frequency spectrum which is equal to the sub-auxiliary characteristics are formed by the equivalent size of each level of sub-auxiliary characteristics obtained by the fractal parameters of each sub-auxiliary characteristic and the corresponding number of the sub-auxiliary characteristics.
In one embodiment of the present invention, the connected pore probability matrix is established using a connected pore probability algorithm as follows: and sorting the sub-features in the three features from large to small according to the equivalent side length or equivalent diameter, wherein the first column is the size of the equivalent side length or equivalent diameter, the second column is the number corresponding to the equivalent side length or equivalent diameter, the third column is the connection probability, and the fourth column is the classification type of the row group.
In one embodiment of the present invention, a merge operation is required before sorting.
In one embodiment of the invention, the step of calculating the permeability is as follows:
wherein, the iteration number i, the fractal scale F, and the length lambda of the pore/block side generated by the ith iterationiMaximum holeGap/block side length λmaxNumber of pores/blocks N generated in the ith iterationiNumber N of pores/blocks not participating in iteration in the ith iterationi-soildA is the sectional area of the characteristic unit surface of the rock core, CiIs the ith row bulk/pore form factor, ki(pavg) To be at the average pressure pavgPermeability of the lower ith row of blocks/pores.
In one embodiment of the present invention, ki(pavg) The calculation formula of (2) needs to be judged according to the types of each row in the matrix;
firstly, setting the component characteristics as X, the sub-component characteristics as B, the sub-pore characteristics as Y, and the sub-auxiliary characteristics as Y, wherein the sub-component characteristics are respectively numbered in the sequence of X1 and X2 … … Xn, the sub-pore characteristics as B1 and B2 … … Bn, and the sub-auxiliary characteristics as Y1 and Y2 … … Yn; in the component characteristic X, each block of the component sub-component characteristic is a porous medium; k when the component feature X is recorded in the fourth column of the ith row in the connected probability matrixi(pavg) The expression is as follows:
wherein k isnD(j,pavg) Is the average pressure pavgNext, permeability values for the jth sub-pore feature; the calculation formula is as follows:
when j is 1
When j is 2
When j is 3
And so on, when j is equal to n
Wherein λ is1,λ2,…,λj,…,λnIs the equivalent diameter of the pores contained in the porous medium represented by row i after the coalescence operation; n is a radical of1,N2,…,Nj,…,NnThe number of pores contained in the porous medium represented by the ith row after the coalescence operation; wherein λ1≥λ2≥…≥λj≥…≥λn；
When the fourth column of the ith row in the connected probability matrix records the hole of which the row is not merged, the permeability item ki(pavg) Is composed of
Thirdly, when the fourth column of the ith row in the connected probability matrix records the auxiliary characteristic Y1 of the row, the permeability item ki(pavg) Is composed of
(iv) the term k for the permeability of the matrix in the formula (1)matrixIs composed of
In the above formulas, μ is the gas viscosity, M is the gas mole number, R is the ideal gas constant, T is the temperature, ρavgIs the average density, pavgIs the average pressure, alpha is the tangential momentum accommodation coefficient,
is the percentage of n matrix minerals, k1,k2,…,knIs the apparent permeability of n matrix minerals.
The method is based on real rock sample data, extracts main composition components, uses fractal parameter fitting for each component based on a mixed fractal theory, and finally combines the components to obtain the rock pore model based on the mixed fractal theory. And further combining a pore connectivity algorithm to obtain a connectivity probability matrix among different kinds of pores, and finally constructing an unconventional reservoir permeability evaluation method to realize rapid and accurate evaluation of the unconventional reservoir apparent permeability.
Drawings
FIG. 1 is a flow chart of an evaluation method according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the unconventional reservoir permeability evaluation method disclosed by one embodiment of the present invention generally includes the following steps:
rock imaging techniques herein include, but are not limited to, SEM (scanning electron microscope), FIB-SEM (focused ion beam), Micro-CT (Micro-computed tomography), nano-CT, and the like.
In the imaging technologies, only one mode of imaging can be adopted for analysis, and more than two imaging technologies can be simultaneously adopted for combined analysis.
The rock may be shale, tight sandstone, or other unconventional reservoir rock sample.
During the division, the types of ores such as organic mass, pyrite mass, quartz, feldspar, montmorillonite and the like in the rock sample image are uniformly divided into component characteristics, and each specific ore in the component characteristics can be finely divided according to the numbers of subgroup characteristic X1, subgroup characteristic X2 and subgroup characteristic X3 … …; dividing the pores such as inter-granular pores and intra-granular pores in the image into pore characteristics, wherein the pores are classified finely according to the distribution positions of the pores such as organic pores in organic blocks, pores in pyrite blocks, inorganic pores in matrixes and the like according to the sub-pore characteristics B1, B2 and B3 … …; the assistant feature is other features than the two features described above, such as a crack feature and a density feature, and the present embodiment uses a crack as the assistant feature, and the crack is classified into a natural crack and an artificial crack according to the type, and the assistant feature is classified into a sub assistant feature Y1, a sub assistant feature Y2, a sub assistant feature Y3, … … according to information such as the density, length, width, and the like of the crack.
the method specifically comprises the following steps: based on the scanning results of the adopted core digital imaging technologies with different scales, frequency spectrums of component characteristics, pore characteristics and crack characteristics in the image are extracted, wherein the component characteristics are the size distribution frequency spectrums (taking organic matters as an example, the equivalent size frequency spectrum of the organic matter block) of the organic matter block, the pyrite block and the like in the type, the pore characteristics are the pore distribution frequency spectrum (taking organic holes in the organic matter block as an example, the equivalent aperture frequency spectrum of the organic holes in the organic matter block), and the auxiliary characteristics are the crack density and the long and wide crack frequency spectrums of the crack.
And fitting the block size distribution frequency spectrum of the component characteristics by using a mixed fractal method to obtain a fractal parameter AF1, wherein the equivalent size of each sub-component characteristic obtained by the fractal parameter AF1 and the block size distribution frequency spectrum jointly formed by the corresponding number of the sub-component characteristics have the same shape as the block size distribution frequency spectrum of the corresponding sub-component characteristics, namely the two are the same or close to each other.
And fitting the pore diameter size distribution frequency spectrum of the pore characteristics by using a mixed fractal method to obtain a fractal parameter BF1, wherein the pore size distribution frequency spectrum jointly formed by the equivalent diameter of each level of sub-pore characteristics obtained by using the fractal parameter BF1 and the corresponding number of the sub-pore characteristics is equal to the pore size distribution frequency spectrum of the corresponding sub-pore characteristics, namely the two are the same or close to each other.
And fitting the fracture density and the fracture length and fracture width frequency spectrum of the fracture characteristic by using a mixed fractal method to obtain a fractal parameter CF1, wherein the fracture frequency spectrum jointly formed by the equivalent size of each level of sub-auxiliary characteristics obtained by the fractal parameter CF1 and the corresponding number of the sub-auxiliary characteristics is equal to or close to the fracture frequency spectrum of the sub-fracture characteristic.
in this step, the composition characteristics necessarily include pore characteristics, but the pore characteristics are not necessarily included by the composition characteristics, for example, the organic mass a1 itself has a distribution of mass sizes, each organic mass a1 has organic pores B1 inside, and the organic pores B1 in each organic mass a1 conform to the pore distribution spectrum represented by the organic pores of B1 type.
And 400, calculating the connection probability among different types of pores in the rock pore model after the connection probability is arranged according to a matrix through a pore connectivity algorithm, and obtaining the evaluation result of the apparent permeability of the current unconventional reservoir.
The matrix is built using the connected pore probability algorithm as follows: sorting the sub-features in the three features from large to small according to the equivalent side length or equivalent diameter, wherein the first column is the size of the equivalent side length or equivalent diameter, the second column is the number corresponding to the equivalent side length or equivalent diameter, the third column is the connection probability, and the fourth column is the row type; wherein the equivalent side length or the equivalent diameter is as follows: lambda [ alpha ]A1,1>λB2,1>λA1,2. λ represents an equivalent side length or an equivalent diameter. Lambda [ alpha ]A1,1Representing the equivalent side length of the 1 st large sample in the blocks of the type A1 in the block A after the blocks are sorted from large to small; lambda [ alpha ]B2,1Represents species B2 in pore B (B2 is not represented by any of A)Block contains, i.e., pores not participating in the coalescence operation, such as inorganic pores) the equivalent diameter of the 1 st macropore in order from large to small; lambda [ alpha ]A1,2Table a equivalent edge length of the 2 nd large sample in the block of species a1 from large to small in block a.
The method comprises the steps of extracting main composition components from real rock sample data, respectively using fractal parameter fitting for each component based on a mixed fractal theory, and finally combining the components to obtain a rock pore model based on the mixed fractal theory. And further combining a pore connectivity algorithm to obtain a connectivity probability matrix among different kinds of pores, and finally constructing an unconventional reservoir permeability evaluation method to realize rapid and accurate evaluation of the unconventional reservoir apparent permeability.
2 pore equivalent diameters satisfy the first column from large to small. The capillary force ratio is used when the diameters are equal, the capillary force is arranged from high to low when the non-wetting phase displaces the wetting phase, and the capillary force is arranged from low to high when the wetting phase displaces the non-wetting phase.
In step 400, a merge operation is required before sorting.
The specific coalescence operation process is as follows: if the image contains organic block bodies A1, the holes in the organic block bodies A1 are organic holes B1, each organic block body A1 is used as a porous medium, the organic holes B1 in each organic block body A1 conform to fractal distribution, only the organic block bodies A1 are taken to participate in sorting, and the organic holes B1 in the organic block bodies are not taken to participate in sorting in the step; if one of the classes, for example, inorganic pore B2, does not belong to any of the component characteristics, then inorganic pore B2 participates in the ranking.
In one embodiment of the invention, the step of calculating the permeability is as follows:
wherein, the iteration number i, the fractal scale F, the length lambda of the pore or block side generated by the ith iterationiMaximum pore or block side length λmaxNumber of pores or blocks N generated in the ith iterationiThe number N of pores or blocks not participating in the iteration in the ith iterationi-soildA is the sectional area of the characteristic unit surface of the rock core, CiIs the ith row bulk or pore form factor, ki(pavg) To be at the average pressure pavgPermeability of the lower ith row of blocks or pores.
λ1,λ2,…,λi,…,λmThe equivalent diameter size of the blocks or pores in the first column and row of the matrix, N1,N2,…,Ni,…,NmFor a second column of blocks or number of apertures in the matrix, e.g. first row λ1Corresponding number of blocks or pores N1I line λiCorresponding number of blocks or pores Ni。
ki(pavg) The calculation formula of (2) needs to be judged according to the types of each row in the matrix;
firstly, setting the component characteristics as X, the sub-component characteristics as B, the sub-pore characteristics as Y, and the sub-auxiliary characteristics as Y, wherein the sub-component characteristics are respectively numbered in the sequence of X1 and X2 … … Xn, the sub-pore characteristics as B1 and B2 … … Bn, and the sub-auxiliary characteristics as Y1 and Y2 … … Yn; in the component characteristic X, each block of the component sub-component characteristic is a porous medium; k when the component feature X is recorded in the fourth column of the ith row in the connected probability matrixi(pavg) The expression is as follows:
wherein k isnD(j,pavg) Is the average pressure pavgNext, permeability values for the jth sub-pore feature; the calculation formula is as follows:
when j is 1
When j is 2
When j is 3
And so on, when j is equal to n
Wherein λ is1,λ2,…,λj,…,λnIs the equivalent diameter of the pores contained in the porous medium represented by row i after the coalescence operation; n is a radical of1,N2,…,Nj,…,NnThe number of pores contained in the porous medium represented by the ith row after the coalescence operation; wherein λ1≥λ2≥…≥λj≥…≥λn；
When the fourth column of the ith row in the connected probability matrix records the hole of the row which is merged, the permeability item ki(pavg) Is composed of
Thirdly, when the fourth column of the ith row in the connected probability matrix records the auxiliary characteristic Y1 of the row, the permeability item ki(pavg) Is composed of
(iv) the term k for the permeability of the matrix in the formula (1)matrixIs composed of
In the above formulas, μ is the gas viscosity, M is the gas mole number, R is the ideal gas constant, T is the temperature, ρavgIs the average density, pavgIs the average pressure, alpha is the tangential momentum accommodation coefficient,
is the percentage of n matrix minerals, k1,k2,…,knGas permeability for n matrix minerals.
The first embodiment is as follows:
1. taking a sample of shale of the Longmaxi group of the Dimochamian pillar zone of Sichuan basin, taking SEM as a rock imaging technology as an example, and scanning the sample.
2. And analyzing the scanning result, and taking the mixed fractal model with a square section and a side length of 380 mu m. The basic characterization unit of the pyrite is single pyrite, and the organic matter basic unit surface is a square with the side length of 2.08 mu m.
3. And combining the fitted fractal parameters to obtain a rock pore model based on a mixed fractal theory, and calculating the communication probability among different types of pores in the rock pore model after the communication probability is arranged according to a matrix through a pore connectivity algorithm to obtain the evaluation result of the apparent permeability of the current unconventional reservoir.
Wherein the organic matter accounts for 3% of the area of the mixed fractal model, 82 pyrites are in the mixed fractal model plane, the average diameter of the pyrites is 182nm, the width of the microcracks is between 20nm and 85nm, the average width is 40nm, the average length is 5.18 mu m, and the average crack density is 50.3 strips/10000 mu m2。
4. The permeability of the rock sample calculated by the method is 99.17nD, the permeability measured by experiments is 91.36nD, the error is 8.55%, the permeability calculation result is similar to the real core permeability calculation result, and the method is reliable.
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
Claims (8)
1. A unconventional reservoir permeability evaluation method based on a core image is characterized by comprising the following steps:
step 100, scanning and identifying a real rock sample by using a rock imaging technology to obtain a corresponding image, dividing organic mass and ore particle types of the rock sample in the image into component characteristics, dividing pore information in the organic mass, the ore particles and a rock matrix into pore characteristics, and dividing other characteristics in the rock sample into auxiliary characteristics;
step 200, respectively extracting frequency spectrums of each characteristic, and fitting an analysis result by a mixed fractal method to obtain fractal parameters of each corresponding characteristic;
step 300, combining the fitted fractal parameters to obtain a rock pore model based on a mixed fractal theory;
step 400, aiming at different types of pores in the rock pore model, establishing a connected probability matrix by using a pore connected probability algorithm, and calculating the permeability based on the connected probability matrix to obtain the evaluation result of the apparent permeability of the current unconventional reservoir;
the steps for calculating the permeability are as follows:
wherein, the iteration number i, the pore/block side length lambda generated by the ith iterationiNumber of pores/blocks N generated in the ith iterationiA is the sectional area of the characteristic unit surface of the rock core, CiIs the ith row bulk/pore form factor, ki(pavg) To be at the average pressure pavgPermeability of the blocks/pores of the lower i-th row, KmatrixPermeability as a matrixAn item;
ki(pavg) The calculation formula of (2) needs to be judged according to the types of each row in the matrix;
firstly, setting the component characteristics as X, the sub-component characteristics as B, the sub-pore characteristics as Y, and the sub-auxiliary characteristics as Y, wherein the sub-component characteristics are respectively numbered in the sequence of X1 and X2 … … Xn, the sub-pore characteristics as B1 and B2 … … Bn, and the sub-auxiliary characteristics as Y1 and Y2 … … Yn; in the component characteristic X, each block of the component sub-component characteristic is a porous medium; k when the component feature X is recorded in the fourth column of the ith row in the connected probability matrixi(pavg) The expression is as follows:
wherein k isnD(j,pavg) Is the average pressure pavgNext, permeability values for the jth sub-pore feature; the calculation formula is as follows:
when j is 1
When j is 2
When j is 3
And so on, when j is equal to n
Wherein λ is1,λ2,……λiIs the side length of the pores/blocks contained in the porous medium represented by the ith row after the coalescence operation; n is a radical of1,N2,……NiThe number of pores/blocks contained in the porous medium represented by the ith row after the coalescence operation; wherein λ1≥λ2≥……≥λi；
When the fourth column of the ith row in the connected probability matrix records the hole of which the row is not merged, the permeability item ki(pavg) Is composed of
Thirdly, when the fourth column of the ith row in the connected probability matrix records the auxiliary characteristic Y1 of the row, the permeability item ki(pavg) Is composed of
(iv) the term k for the permeability of the matrix in the formula (1)matrixIs composed of
In the above formulas, μ is the gas viscosity, M is the gas mole number, R is the ideal gas constant, T is the temperature, ρavgIs the average density, pavgIs the average pressure, alpha is the tangential momentum accommodation coefficient,
is the percentage of n matrix minerals, k1,k2,…,knIs the apparent permeability of n matrix minerals.
2. The unconventional reservoir permeability evaluation method based on core images as claimed in claim 1, wherein,
the pore characteristics in step 100 refer to organic mass, organic pores in the ore particles, and inter-or intra-granular pores in the ore; the auxiliary features refer to natural fractures or artificial fracture features in the rock sample.
3. The unconventional reservoir permeability evaluation method based on core images as claimed in claim 1, wherein,
the rock imaging technology comprises SEM, FIB-SEM, Micro-CT and nano-CT.
4. The unconventional reservoir permeability evaluation method based on core images as claimed in claim 3, wherein,
the composition characteristics are subdivided into a plurality of corresponding sub-composition characteristics according to different types of ore particles; the pore feature is subdivided into a plurality of corresponding sub-pore features corresponding to the differences in the sub-component features; the assist feature is subdivided into a plurality of corresponding sub-assist features according to relationships with different ones of the sub-component features.
5. The unconventional reservoir permeability evaluation method based on core images as claimed in claim 4, wherein,
the component feature spectrum is established over an equivalent size distribution spectrum of a respective sub-component of each sub-component feature, the pore feature spectrum is established over an equivalent pore size distribution spectrum of each sub-pore feature, and the assist feature spectrum is established over the fracture density, the fracture length, and the fracture width represented by the sub-assist feature.
6. The unconventional reservoir permeability evaluation method based on the core image according to claim 5, wherein the size distribution spectrum formed by the equivalent size of each level of the sub-component characteristics and the corresponding number of the equivalent size obtained by the fractal parameters of each sub-component characteristic is equal to the size distribution spectrum of the sub-component characteristics;
the equivalent diameter of each level of pores obtained by the fractal parameters of each sub-pore characteristic and the corresponding number of the pores jointly form a pore size distribution spectrum which is equal to the pore size distribution spectrum of the sub-pore characteristic;
and the equivalent size of each level of sub-auxiliary features obtained by the fractal parameters of each sub-auxiliary feature and the corresponding number of the sub-auxiliary features jointly form a crack spectrum, which is equal to the crack spectrum of the sub-auxiliary features.
7. The unconventional reservoir permeability evaluation method based on core images as claimed in claim 6, wherein,
the manner in which the connectivity probability matrix is established using the pore connectivity probability algorithm is as follows: and sorting the sub-features in the three features from large to small according to the equivalent side length or equivalent diameter, wherein the first column is the size of the equivalent side length or equivalent diameter, the second column is the number corresponding to the equivalent side length or equivalent diameter, the third column is the connection probability, and the fourth column is the classification type of the row group.
8. The unconventional reservoir permeability evaluation method based on core images as claimed in claim 7, wherein,
a merge operation is required before sorting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810546608.2A CN108829950B (en) | 2018-05-31 | 2018-05-31 | Unconventional reservoir permeability evaluation method based on core image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810546608.2A CN108829950B (en) | 2018-05-31 | 2018-05-31 | Unconventional reservoir permeability evaluation method based on core image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108829950A CN108829950A (en) | 2018-11-16 |
| CN108829950B true CN108829950B (en) | 2021-04-27 |
Family
ID=64145245
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810546608.2A Active CN108829950B (en) | 2018-05-31 | 2018-05-31 | Unconventional reservoir permeability evaluation method based on core image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108829950B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108956415B (en) * | 2018-05-31 | 2020-03-17 | 中国科学院力学研究所 | Method for calculating relative permeability curve of unconventional reservoir sample |
| CN109632604B (en) * | 2019-01-04 | 2021-06-15 | 中国海洋石油集团有限公司 | Method for coarsening relative permeability of polymer flooding from pore size to core size |
| CN109933939B (en) * | 2019-03-22 | 2019-10-18 | 西南石油大学 | The method for numerical simulation of the unconventional crack initiation of dual media reservoir multiple cracking and extension |
| CN110567858B (en) * | 2019-10-17 | 2020-11-03 | 西南石油大学 | Method for predicting shale nano-pore permeability based on fractal theory |
| CN112302606B (en) * | 2020-07-07 | 2021-08-24 | 西南石油大学 | Inversion interpretation method for output profile of low-permeability gas reservoir fractured horizontal well |
| CN112967147B (en) * | 2021-02-04 | 2024-04-02 | 中海石油(中国)有限公司海南分公司 | Bedrock yield contribution rate calculation method considering multi-scale cracks |
| CN113189122B (en) * | 2021-05-13 | 2023-12-22 | 中海石油(中国)有限公司海南分公司 | Perforation damage indoor comprehensive evaluation method |
| CN113297779B (en) * | 2021-06-23 | 2022-07-29 | 中国石油大学(华东) | Shale permeability interpretation method based on dual-medium pore network model |
| CN116342541B (en) * | 2023-03-29 | 2024-03-22 | 中国矿业大学 | Rock-soil body permeability calculation method based on adjacent image pore fusion reconstruction |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104018829A (en) * | 2014-05-23 | 2014-09-03 | 中国地质大学（北京） | Method for measuring gas and water relative permeability curve through coal-bed gas well production data |
| US8881587B2 (en) * | 2011-01-27 | 2014-11-11 | Schlumberger Technology Corporation | Gas sorption analysis of unconventional rock samples |
| CN105279790A (en) * | 2014-06-13 | 2016-01-27 | 中国石油化工股份有限公司 | Fracture network 3D digital core modeling method |
| CN107449706A (en) * | 2017-06-06 | 2017-12-08 | 湖北工业大学 | Deformation soil body saturation, Unsaturated Hydraulic Conductivity Forecasting Methodology based on fractal theory |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100089124A1 (en) * | 2008-09-26 | 2010-04-15 | North Dakota State University | Integrated porous rigid wall and flexible wall permeability test device for soils |
| US20160337444A1 (en) * | 2015-05-13 | 2016-11-17 | Lunatech, Llc | Social network for electronic vapor device users |
-
2018
- 2018-05-31 CN CN201810546608.2A patent/CN108829950B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8881587B2 (en) * | 2011-01-27 | 2014-11-11 | Schlumberger Technology Corporation | Gas sorption analysis of unconventional rock samples |
| CN104018829A (en) * | 2014-05-23 | 2014-09-03 | 中国地质大学（北京） | Method for measuring gas and water relative permeability curve through coal-bed gas well production data |
| CN105279790A (en) * | 2014-06-13 | 2016-01-27 | 中国石油化工股份有限公司 | Fracture network 3D digital core modeling method |
| CN107449706A (en) * | 2017-06-06 | 2017-12-08 | 湖北工业大学 | Deformation soil body saturation, Unsaturated Hydraulic Conductivity Forecasting Methodology based on fractal theory |
Non-Patent Citations (3)
| Title |
|---|
| 《A 3D coupled model of organic matter and inorganic matrix for calculating the permeability of shale》;GaoHui Cao;《ElSEVIER》;20171231;第129-143页 * |
| 《基于分形理论的页岩基质表现渗透率研究》;李玉丹 等;《油气地质和采收率》;20170201;第24卷(第1期);第92-99、105页 * |
| 《孔隙网络对页岩储层自吸特征的影响与应用》;李曹雄;《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》;20180415;第B019-411页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108829950A (en) | 2018-11-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108829950B (en) | Unconventional reservoir permeability evaluation method based on core image | |
| Goral et al. | Pore system characterization of organic-rich shales using nanoscale-resolution 3D imaging | |
| Schlüter et al. | Exploring the relationship between soil structure and soil functions via pore-scale imaging | |
| CN105115874B (en) | Multi-component three-dimensional digital core construction method based on multi-source information fusion | |
| CN107817199B (en) | A kind of construction method of tight sand multi-scale porosity model and application | |
| WO2018218702A1 (en) | Method for evaluating evolution of different types of pores in shale | |
| CN103869052B (en) | A kind of method of sandstone reservoir diagenesis electrofacies quantitatively characterizing | |
| US10620181B2 (en) | Method of analysing a drill core sample | |
| CN113609696B (en) | Multi-scale multi-component digital core construction method and system based on image fusion | |
| CN108956415B (en) | Method for calculating relative permeability curve of unconventional reservoir sample | |
| CN105551004A (en) | Core CT image processing-based remaining oil micro-occurrence representing method | |
| CN104360039A (en) | Quantitative lithogenous phase evaluation method for compact sandstone reservoir | |
| CN105891083B (en) | A kind of digital cores two-phase simulated flow result quantitatively characterizing method and its application based on erosion-expansion algorithm | |
| CN105649615A (en) | Method for testing occurrence state of compact oil in reservoir stratum through CT quantitative and three-dimensional visualization | |
| CN110487693A (en) | A kind of method of determining mud shale different type porosity | |
| CN109632604B (en) | Method for coarsening relative permeability of polymer flooding from pore size to core size | |
| CN108897906A (en) | A kind of reservoir sensitivity damage analogy method based on digital cores model | |
| Wang et al. | Semi-quantitative multiscale modelling and flow simulation in a nanoscale porous system of shale | |
| CN104569343A (en) | Shale reservoir brittle mineral logging quantitative characterization method | |
| Xu et al. | Rapid multiscale pore network modeling for drainage in tight sandstone | |
| CN111537419A (en) | Method and device for evaluating category of micro-pore structure of digital core | |
| CN113868942A (en) | Rapid prediction method for collapse peak flow of weir dam | |
| CN111257188A (en) | Multi-scale characterization method and device for carbonate rock pore system | |
| CN115526031A (en) | Multi-scale pore network model construction and solving method reflecting mineral composition | |
| CN115221792A (en) | Natural gas hydrate type dividing device and method based on machine learning |
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 |