CN116127675A - Prediction method for maximum recoverable reserve of shale oil horizontal well volume fracturing - Google Patents
Prediction method for maximum recoverable reserve of shale oil horizontal well volume fracturing Download PDFInfo
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Abstract
The invention provides a method for predicting ultimate recoverable reserves of shale oil horizontal well volume fracturing, which comprises the steps of firstly, establishing a productivity prediction model based on reservoir geological parameters of a horizontal well to be detected and adjacent horizontal wells of a zone block, calculating effective fracture network sweep coefficients by using an oil reservoir numerical simulation method, and establishing a prediction chart of correlation between the effective fracture network sweep coefficients and the maximum recoverable reserves; secondly, calculating a correlation coefficient between geologic and volumetric fracturing transformation parameters of a fracturing section of the horizontal well to be tested and the fracture network wave and volume by using a gray correlation analysis method, and definitely influencing key control parameters of the fracture network wave and volume; and finally, establishing a joint network sweep volume prediction model of the coupling key control parameters, obtaining effective joint network sweep coefficients by using the joint network sweep volume of the horizontal well to be measured, and measuring the maximum recoverable reserve of the horizontal well by using a correlation prediction drawing. The method can rapidly predict the maximum recoverable reserves of any horizontal well by establishing the correlation plate of the effective joint network sweep coefficient and the maximum recoverable reserves.
Description
Technical Field
The invention belongs to the field of petroleum and natural gas exploitation, and particularly relates to a method for predicting the maximum recoverable reserve of shale oil horizontal well volume fracturing.
Background
The available resource amount of the Chinese shale oil technology can reach 145 multiplied by 10 8 t is mainly distributed in large basins such as a Erdos basin, a quasi-Song basin, a Bohai Bay basin and a Songlao basin, has huge development potential, and realizes the commercial development of shale oil and gas by a horizontal well volume fracturing technology.
The final recoverable reserve of a single horizontal well is a direct evaluation index for evaluating the matching of a volume fracturing process and a non-conventional shale oil reservoir, and is an important foundation for optimizing parameters of the volume fracturing process, matching fracturing equipment and building productivity. However, the factors affecting the maximum recoverable reserves are numerous and have complex nonlinear relationships among the factors, affecting the evaluation effect to varying degrees. The main evaluation methods at present are as follows:
(1) Li Fang et al (Li Fang, wu Juan, jiang Xin et al. Shale gas well EUR determination methods, apparatus, devices and storage media, patent application number CN 202110002077.2) by fitting first historical production data for a plurality of sample gas wells, the method establishing a production prediction model for each sample gas well; predicting shale gas production of each sample gas well in a target time period through a production prediction model of each sample gas well; determining the sum of the total historical production of each sample gas well and the shale gas production within the target time period as the EUR of the sample gas well; establishing a mapping relationship between the estimated cumulative yield and the EUR based on the estimated cumulative yield and the EUR of the plurality of sample gas wells; for any target gas well that does not reach the boundary flow pattern, determining the EUR of the target gas well based on the estimated cumulative yield and the mapping relationship of the target gas well. The method establishes a single well yield prediction experience model only by a production dynamic fitting method, is a common prediction method, needs to collect a large amount of production history data, and takes a long time.
(2) Lian Hua (Lianhua, yu Zhichao, luo Xia, etc. shale oil and gas final recovery geological master-taking the U.S. bay basin hawk beach shale as an example [ J ]. Oil exploration and development 2021, 48 (3): 654-6664). According to the method, key geological main control factors influencing the final recoverable quantity are analyzed, a normalization method is further adopted, a single well final recoverable quantity and key geological parameter prediction model is established, and further the final recoverable quantity of a shale gas well single well is predicted. According to the method, only the influence of key geological parameters on the final recovery amount of the horizontal well is analyzed, the influence of volume fracturing engineering parameters is not considered, analysis and consideration factors are comprehensive, and accurate prediction of the final recoverable amount of shale oil gas is difficult to realize.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for predicting the maximum recoverable reserve of shale oil horizontal well volume fracturing.
In order to achieve the technical purpose, the invention provides the following technical scheme:
a method for predicting the maximum recoverable reserve of shale oil horizontal well volume fracturing comprises the following steps:
(4) Establishing a productivity prediction model based on geological parameters of a horizontal well to be detected and a reservoir where the horizontal well is located in the same block as the horizontal well to be detected, calculating effective fracture network wave and coefficient by adopting a method of energy production comparison in combination with an oil reservoir numerical simulation method, and establishing a correlation prediction chart of the effective fracture network wave and coefficient and the maximum recoverable reserve;
(5) Calculating the association coefficient between each fracturing section geological parameter and the volume fracturing transformation parameter of the horizontal well to be tested and the fracture network wave and volume by using a gray association analysis method, and definitely influencing the key control parameters of the fracture network wave and volume;
(6) On the basis of determining the key control parameters of the seam wave and the volume, a seam wave and volume prediction model coupled with the key control parameters is established, the seam wave and the volume of the horizontal well to be detected are predicted, the effective seam wave and coefficient is obtained, and the maximum recoverable reserve of the horizontal well to be detected is further predicted by utilizing the correlation prediction graph of the step (1).
Further, the step (1) establishes a productivity prediction model based on geological parameters of the horizontal well to be measured and a reservoir where the horizontal well is located in the same block adjacent to the horizontal well to be measured, calculates an effective fracture network wave and coefficient by adopting a method of energy comparison in combination with an oil reservoir numerical simulation method, and establishes a correlation prediction chart of the effective fracture network wave and coefficient and the maximum recoverable reserve, and the method specifically comprises the following steps:
step (101), basic database establishment: comprises the steps of obtaining basic parameters of adjacent horizontal wells and horizontal wells to be measured in the same block,
step (102), establishing a horizontal well geological model by utilizing reservoir numerical simulation software eclipse according to the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101), and introducing single-section fracture network sweep volumes in the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101) into the horizontal well geological model to obtain a productivity prediction model;
Step (103), predicting the 1 st year cumulative oil yield of the adjacent horizontal wells of the same block by utilizing the productivity prediction model established in the step (102), and calculating a difference coefficient by adopting a productivity comparison method in combination with the 1 st year cumulative oil yield in the basic parameters of the adjacent horizontal wells of the block obtained in the step (101);
step (104), according to the difference coefficient obtained in the step (103), combining the step (101) to obtain the full-well section joint network wave and volume in the basic parameters of the adjacent horizontal wells of the zones, and calculating the effective joint network wave and volume;
step (105), calculating the volume of a control oil reservoir of the horizontal well by using the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101), and simultaneously calculating the effective fracture network sweep coefficient of the adjacent horizontal well of the same platform by combining the effective fracture network sweep volume obtained in the step (104);
and (106) establishing a prediction fit formula according to the maximum recoverable reserves of all well sections in the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101) and the effective joint network wave and coefficient correlation prediction plate obtained in the step (105).
Further, the step (101) of building a basic database specifically includes:
step (1011) of obtaining basic parameters of adjacent horizontal wells of the block, wherein the basic parameters comprise two parts, and the first part is a parameter for calculating a difference coefficient, and the step comprises the following steps: reservoir burial depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation of the reservoir, horizontal segment length, number of fracturing segments, cumulative oil production in 1 st year, and single well each fracturing segment data of microseism monitoring including single segment fracture length, single Duan Fengkuan, single segment fracture height, and single segment fracture network sweep volume; the second part is a parameter for the effective seam net sweep coefficient, comprising: the horizontal section is long, the reservoir layer is thick, the well distance of the horizontal well is long, the whole well section is provided with a network wave and volume, and the whole well section has the maximum recoverable reserve;
Step (1012), obtaining basic parameters of the horizontal well to be tested, including obtaining geological parameters and volumetric fracturing modification parameters of each fracturing segment, wherein the geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, level stress difference, and fracture pressure; the volume fracturing modification parameters include: crack density, construction displacement, single stage fracturing fluid volume and sand volume.
Further, in the step (103), the calculation formula of the difference coefficient is as follows:
wherein: FI is a coefficient of difference, dimensionless;
Q H accumulating oil production for the 1 st year of historical production of adjacent horizontal wells of the block, t;
Q P and (3) predicting the accumulated oil yield of the 1 st year for the adjacent horizontal wells of the block, and t.
Further, in the step (104), the calculation formula of the effective mesh sweep volume is as follows:
ESRV＝FI·SRV
wherein: ESRV is effective seam net sweep volume, 10 4 m 3 ；
FI is a coefficient of difference, dimensionless;
SRV is the full well section seam net sweep volume, 10 4 m 3 。
Further, in the step (105),
the calculation formula of the oil reservoir volume controlled by the horizontal well is as follows: vr= Lhd;
the calculation formula of the effective seam net sweep coefficient of the horizontal well adjacent to the platform is as follows:
wherein: v (V) r Controlling reservoir volume for horizontal wells, m 3 ；
L is the length of the horizontal segment, m;
h is the reservoir thickness, m;
d is the well distance of the horizontal well, m;
e is the effective net-stitching wave coefficient,%;
ESRV is effective seam net sweep volume, 10 4 m 3 。
Further, in the step (106), the fitting formula is:
EUR＝2.0656ln(E)-6.4729
in the EUR, the maximum recoverable and recoverable reserve of a single well is 10 4 t；
E is the effective seam net wave coefficient,%.
Further, the step (2) calculates the association coefficient between the geological parameters and the volume fracturing transformation parameters of each fracturing section of the horizontal well to be detected and the fracture network wave and volume of the horizontal well to be detected monitored by microseism by utilizing a gray association analysis method, and specifically comprises the following steps of,
step (201), multi-factor comprehensive evaluation matrix establishment: according to the basic parameters of the horizontal well to be predicted obtained in the step (101), a multi-factor comprehensive evaluation matrix X is established, wherein the multi-factor comprehensive evaluation matrix elements are geological parameters and volume fracturing modification parameters of the volume fracturing measured fracture section of the horizontal well to be measured, and the expression of the multi-factor comprehensive evaluation matrix X is as follows
Wherein: x is a multi-factor comprehensive evaluation matrix;
X i (j) The matrix elements are comprehensively evaluated for multiple factors;
m is the number of the volume fracturing stages of the horizontal well to be measured;
n is the number of the seam net wave and volume influencing factors;
Step (202), evaluation reference column establishment: establishing an evaluation reference column X according to the seam net wave and volume of the measured pressure crack section of the horizontal well obtained by microseism monitoring 0 ；
X 0 ＝[X 1 (0)，…X i (0)，…X m (0)] T i＝1，2，…m
Wherein: x is X 0 A reference column for evaluation; t is a transposed symbol;
step (203), normalization processing: maximum value method is adopted to comprehensively evaluate the matrix X and the evaluation reference column X for multiple factors 0 And respectively carrying out standardization processing, wherein the standardization processing formula is as follows:
wherein:
standardized elements of a multi-factor comprehensive evaluation matrix or evaluation reference column;
X i (j) The method comprises the steps of comprehensively evaluating matrix elements or evaluating reference column elements for multiple factors;
(X i (j)) max for the j-th influenceMaximum value in factor parameter set;
step (204), calculating a correlation coefficient: calculating correlation coefficients between different influencing factors and volumes of all-well section fracture network waveforms and volumes of the regional adjacent horizontal well volume fracturing microseism monitoring on the basis of the step (203);
step (205), key control parameter determination: and (3) sequencing the calculation results of the correlation coefficients in the step (204), and defining the values of the correlation coefficients to be larger than 0.5 as key control parameters for influencing the volume fracture network sweep volume of the horizontal well to be measured.
Further, in step (204), the specific content of the association coefficient calculation includes,
step (2041), calculating standard deviation according to the multi-factor comprehensive evaluation matrix standardized data and the evaluation reference column standardized data, wherein the calculation formula is as follows:
Wherein: delta i (j) Standard deviation between the multi-factor comprehensive evaluation matrix standardized data and the evaluation reference column standardized data;
step (2042), further calculating correlation coefficients between different influencing factors and the stitch network wave and volume according to the standard deviation calculated in the step (2041), wherein the calculation formula is as follows:
wherein: r is (r) j Is the association coefficient; ρ is the resolution factor, lead toTaking 0.5;
and m is the number of the volume fracturing stages of the horizontal well to be measured.
Further, the step (2) establishes a seam net wave and volume prediction model coupled with the key control parameters on the basis of the determination of the seam net wave and volume key control parameters, predicts the seam net wave and volume of the horizontal well to obtain effective seam net wave and coefficients, and further predicts the maximum recoverable reserves of the horizontal well by using a correlation prediction plate, wherein the seam net wave and volume prediction model comprises prediction matrix establishment, prediction matrix standardization and similarity factor calculation, and concretely comprises,
step (301), creating a seam net wave and volume prediction matrix: establishing a seam network wave and volume prediction matrix according to the key control parameters for influencing the seam network wave and volume determined in the step (2), wherein the matrix formula is as follows:
Wherein: b is a seam net wave and volume prediction matrix;
B i (j) I=0, 1,2, …, h for the slotted-net-wave prediction matrix element; j=1, 2, …, k;
h is the number of the horizontal well fracturing segments, wherein i=0 is the number of the predicted fracturing segments, and i=2 to h are the numbers of the fracturing segments already measured;
k is the number of key parameters affecting the mesh-gap wave and the volume;
step (302), the prediction matrix is normalized: carrying out standardization treatment on the seam net wave and volume prediction matrix according to the value distribution range of key control parameters of the seam net wave and volume influence;
step (303), similarity factor calculation: according to the fracture network wave and volume prediction standardized matrix, calculating a similarity factor between a horizontal well pressure-measured fracture section and a predicted fracture section, wherein the calculation formula is as follows:
wherein: ID (identity) i Is measured asA similarity factor between the segment and the predicted segment;
step (304), sorting similarity factors between the predicted section and the measured section, and assigning the largest similarity factor to the predicted section corresponding to the measured section microseism monitoring seam network sweep volume to obtain the predicted horizontal well full-well section microseism monitoring seam network sweep volume, wherein the calculation formula is as follows:
Wherein: k is the number of fracturing sections of the horizontal well, and the sections;
and (305) further utilizing a calculation formula of the effective fracture network wave volume of the formula in the step (1), the volume of the reservoir controlled by the horizontal well and the effective fracture network wave coefficient of the horizontal well adjacent to the same platform to quantitatively calculate the effective fracture network wave coefficient of the horizontal well on the basis of the step (304), and predicting the maximum recoverable reserve of the horizontal well by utilizing a prediction fitting formula in the step (1).
Further, the step (302) includes the specific content of the prediction matrix standardization,
step (3021), carrying out standardized processing on the prediction matrix element according to the value distribution range of the key parameters of the mesh wave and volume influence, wherein the calculation formula is as follows:
wherein:
the average value of each parameter set of the vector array of the seam net wave and the volume prediction matrix is obtained;
B i (j) I=0, 1,2, …, h for the slotted-net-wave prediction matrix element; j=1, 2, …, k;
k is the number of key parameters affecting the mesh-gap wave and the volume;
step (3021), performing standardization processing on the seam network wave and volume prediction matrix by using the formula (I) and the formula (II), wherein the standardization matrix is as follows:
wherein: b (B) * The matrix is standardized for the seam net wave and volume prediction.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention provides a novel method for predicting the maximum recoverable reserves of shale oil horizontal well volume fracturing.
2. According to the method, the effective fracture network wave and volume affecting the maximum recoverable reserves of the horizontal well can be rapidly predicted by quantitatively calculating the similarity factors, and the microseism monitoring of the whole well section of the horizontal well is not needed, so that the microseism monitoring cost of a mine field is greatly saved, and the problem that the oil reservoir numerical simulation result is large due to the fact that the volume of the microseism monitoring fracture is large is solved. The prediction method is also applicable to the prediction of the maximum recoverable reserve of the volume fracturing of the horizontal well of the similar unconventional oil reservoir, has wide application prospect, and can provide powerful support for the evaluation of the volume fracturing effect and the optimization of fracturing parameters of the horizontal well.
The foregoing description is only an overview of the technical solution of the present invention, and in order to make the technical means of the present invention more clearly understood, it can be implemented according to the content of the specification, and the following detailed description of the preferred embodiments of the present invention will be given with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other designs and drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a graph of maximum recoverable reserves versus effective seam net wave and coefficient correlation in accordance with the present invention;
FIG. 2 is a histogram of the shale oil horizontal well volume fracturing prediction section St19 similarity factors of the present invention;
FIG. 3 is a histogram of the shale oil horizontal well volume fracturing prediction segment St20 similarity factors of the present invention;
fig. 4 is a histogram of the shale oil horizontal well volumetric fracture prediction section St21 similarity factor of the present invention.
In order to more clearly illustrate the present invention, the present invention will be further described with reference to preferred embodiments. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and that this invention is not limited to the details given herein.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
A method for predicting the maximum recoverable reserve of shale oil horizontal well volume fracturing comprises the following steps:
(1) Establishing a productivity prediction model based on geological parameters of a horizontal well to be detected and a reservoir where the horizontal well is located in the same block as the horizontal well to be detected, calculating effective fracture network wave and coefficient by adopting a method of energy production comparison in combination with an oil reservoir numerical simulation method, and establishing a correlation prediction chart of the effective fracture network wave and coefficient and the maximum recoverable reserve;
(2) Calculating the association coefficient between each fracturing section geological parameter and the volume fracturing transformation parameter of the horizontal well to be tested and the fracture network wave and volume by using a gray association analysis method, and definitely influencing the key control parameters of the fracture network wave and volume;
(3) On the basis of determining the key control parameters of the seam wave and the volume, a seam wave and volume prediction model coupled with the key control parameters is established, the seam wave and the volume of the horizontal well to be detected are predicted, the effective seam wave and coefficient is obtained, and the maximum recoverable reserve of the horizontal well to be detected is further predicted by utilizing the correlation prediction graph of the step (1).
Further, the step (1) establishes a productivity prediction model based on geological parameters of the horizontal well to be measured and a reservoir where the horizontal well is located in the same block adjacent to the horizontal well to be measured, calculates an effective fracture network wave and coefficient by adopting a method of energy comparison in combination with an oil reservoir numerical simulation method, and establishes a correlation prediction chart of the effective fracture network wave and coefficient and the maximum recoverable reserve, and the method specifically comprises the following steps:
step (101), basic database establishment: the method comprises the steps of obtaining basic parameters of adjacent horizontal wells and horizontal wells to be detected in the same block, and further comprises the following steps:
step (1011) of obtaining basic parameters of adjacent horizontal wells of the block, wherein the basic parameters comprise two parts, and the first part is a parameter for calculating a difference coefficient, and the step comprises the following steps: reservoir burial depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation of the reservoir, horizontal segment length, number of fracturing segments, cumulative oil production in 1 st year, and single well each fracturing segment data of microseism monitoring including single segment fracture length, single Duan Fengkuan, single segment fracture height, and single segment fracture network sweep volume; the second part is a parameter for the effective seam net sweep coefficient, comprising: the horizontal section is long, the reservoir layer is thick, the well distance of the horizontal well is long, the whole well section is provided with a network wave and volume, and the whole well section has the maximum recoverable reserve;
Step (1012), obtaining basic parameters of the horizontal well to be tested, including obtaining geological parameters and volumetric fracturing modification parameters of each fracturing segment, wherein the geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, level stress difference, and fracture pressure; the volume fracturing modification parameters include: crack density, construction displacement, single stage fracturing fluid volume and sand volume.
Step (102), establishing a horizontal well geological model by utilizing reservoir numerical simulation software eclipse according to the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101), and introducing single-section fracture network sweep volumes in the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101) into the horizontal well geological model to obtain a productivity prediction model;
step (103), predicting the 1 st year cumulative oil yield of the adjacent horizontal wells of the same block by using the productivity prediction model established in the step (102), and calculating a difference coefficient by using a formula (1) by combining the 1 st year cumulative oil yield in the basic parameters of the adjacent horizontal wells of the block obtained in the step (101);
wherein: FI is a coefficient of difference, dimensionless;
Q H accumulating oil production for the 1 st year of historical production of adjacent horizontal wells of the block, t;
Q P The 1 st year cumulative oil production, t, is predicted for the adjacent horizontal wells of the block;
step (104), according to the difference coefficient obtained in the step (103), combining the step (101) to obtain the full-well section joint net wave and volume in the basic parameters of the adjacent horizontal wells of the zones, and calculating the effective joint net wave and volume by using a formula (2);
ESRV＝FI·SRV (2)
wherein: ESRV is effective seam net sweep volume, 10 4 m 3 ；
FI is a coefficient of difference, dimensionless;
SRV is the full well section seam net sweep volume, 10 4 m 3 ；
Step (105), calculating the volume of the reservoir by using the basic parameters of the adjacent horizontal wells of the block obtained in the step (101) and using a formula (3), and simultaneously calculating the effective fracture-network sweep coefficient of the adjacent horizontal wells of the same platform by using a formula (4) in combination with the effective fracture-network sweep volume obtained in the step (104); in particular, the method comprises the steps of,
Vr＝Lhd (3)
wherein: v (V) r Controlling reservoir volume for horizontal wells, m 3 ；
L is the length of the horizontal segment, m;
h is the reservoir thickness, m;
d is the well distance of the horizontal well, m;
e is the effective net-stitching wave coefficient,%;
ESRV is effective seam net sweep volume, 10 4 m 3 ；
Step (106), according to the maximum recoverable reserve of the whole well section in the basic parameters of the adjacent horizontal wells of the block obtained in the step (101), establishing a prediction plate of the correlation between the maximum recoverable reserve of the single well and the effective seam net wave and coefficient obtained in the step (105), and obtaining a prediction fitting formula: eur= 2.0656ln (E) -6.4729;
In the EUR, the maximum recoverable and recoverable reserve of a single well is 10 4 t；
E is the effective seam net wave coefficient,%.
Further, the step (2) calculates the association coefficient between the geological parameters and the volume fracturing transformation parameters of each fracturing section of the horizontal well to be detected and the fracture network wave and volume of the horizontal well to be detected monitored by microseism by utilizing a gray association analysis method, and specifically comprises the following steps of,
step (201), multi-factor comprehensive evaluation matrix establishment: according to the basic parameters of the horizontal well to be predicted obtained in the step (101), a multi-factor comprehensive evaluation matrix X is established, wherein the multi-factor comprehensive evaluation matrix elements are geological parameters and volume fracturing transformation parameters of the measured pressure fracture section of the horizontal well to be detected, and the expression of the multi-factor comprehensive evaluation matrix X is as follows (5):
wherein: x is a multi-factor comprehensive evaluation matrix;
X i (j) The matrix elements are comprehensively evaluated for multiple factors;
m is the number of the volume fracturing stages of the horizontal well to be measured;
n is the number of the seam net wave and volume influencing factors;
step (202), evaluation reference column establishment: establishing an evaluation reference column X according to the seam net wave and volume of the measured pressure crack section of the horizontal well obtained by microseism monitoring 0 The following formula (6);
X 0 ＝[X 1 (0)，…X i (0)，…X m (0)] T i＝1，2，…m (6)
wherein: x is X 0 A reference column for evaluation; t is a transposed symbol;
step (203), normalization processing: maximum value method is adopted to comprehensively evaluate the matrix X and the evaluation reference column X for multiple factors 0 And respectively carrying out standardization treatment, wherein the standardization treatment formula is as follows (7):
wherein:
standardized elements of a multi-factor comprehensive evaluation matrix or evaluation reference column;
X i (j) The method comprises the steps of comprehensively evaluating matrix elements or evaluating reference column elements for multiple factors;
(X i (j)) max is the maximum value in the j-th influencing factor parameter set;
step (204), calculating a correlation coefficient: calculating correlation coefficients between different influencing factors and volumes of all-well section fracture network waveforms and volumes of the regional adjacent horizontal well volume fracturing microseism monitoring on the basis of the step (203); the specific content includes the specific content that,
step (2041), calculating standard deviation according to the multi-factor comprehensive evaluation matrix standardized data and the evaluation reference column standardized data, wherein the calculation formula is as follows (8):
wherein: delta i (j) Standard deviation between the multi-factor comprehensive evaluation matrix standardized data and the evaluation reference column standardized data;
step (2042), further calculating correlation coefficients between different influencing factors and the stitch network wave and volume according to the standard deviation calculated in the step (2041), wherein the calculation formula is as follows (9):
In the middle of：r j Is the association coefficient; ρ is the resolution factor, typically 0.5;
m is the number of the volume fracturing stages of the horizontal well to be measured;
step (205), key control parameter determination: and (3) sequencing the calculation results of the correlation coefficients in the step (204), and defining the values of the correlation coefficients to be larger than 0.5 as key control parameters for influencing the volume fracture network sweep volume of the horizontal well to be measured.
Further, the step (2) establishes a seam net wave and volume prediction model coupled with the key control parameters on the basis of the determination of the seam net wave and volume key control parameters, predicts the seam net wave and volume of the horizontal well to obtain effective seam net wave and coefficients, and further predicts the maximum recoverable reserves of the horizontal well by using a correlation prediction plate, wherein the seam net wave and volume prediction model comprises prediction matrix establishment, prediction matrix standardization and similarity factor calculation, and concretely comprises,
step (301), creating a seam net wave and volume prediction matrix: establishing a seam wave and volume prediction matrix according to the key control parameters for influencing the seam wave and volume determined in the step (2), wherein the matrix formula is as follows (10):
wherein: b is a seam net wave and volume prediction matrix;
B i (j) I=0, 1,2, …, h for the slotted-net-wave prediction matrix element; j=1, 2, …, k;
h is the number of the horizontal well fracturing segments, wherein i=0 is the number of the predicted fracturing segments, and i=2 to h are the numbers of the fracturing segments already measured;
k is the number of key parameters affecting the mesh-gap wave and the volume;
step (302), the prediction matrix is normalized: carrying out standardization treatment on the seam net wave and volume prediction matrix according to the value distribution range of key control parameters of the seam net wave and volume influence; the specific content includes the specific content that,
step (3021), carrying out standardized processing on the prediction matrix element according to the value distribution range of the key parameters of the stitch network wave and the volume influence, wherein the calculation formulas are as follows (11) and (12):
wherein:
the average value of each parameter set of the vector array of the seam net wave and the volume prediction matrix is obtained;
B i (j) I=0, 1,2, …, h for the slotted-net-wave prediction matrix element; j=1, 2, …, k;
k is the number of key parameters affecting the mesh-gap wave and the volume;
step (3021), performing standardization processing on the seam network wave and volume prediction matrix by using the formula (11) and the formula (12), wherein the standardization matrix is represented by the following formula (13):
wherein: b (B) * Standardized matrix for seam net wave and volume prediction
Step (303), similarity factor calculation: according to the fracture network wave and volume prediction standardized matrix, calculating a similarity factor between a horizontal well pressure-measured fracture section and a predicted fracture section, wherein the calculation formula is as follows (14):
wherein: ID (identity) i For the phase between the measured and predicted segmentsA factor-like;
step (304), sorting similarity factors between the predicted section and the measured section, assigning the largest similarity factor to the predicted section corresponding to the measured section microseism monitoring seam network sweep volume, and further obtaining the predicted horizontal well full-well section microseism monitoring seam network sweep volume, wherein the calculation formula is as follows (15):
wherein: k is the number of fracturing sections of the horizontal well, and the sections;
and (305) further utilizing a calculation formula of the effective fracture network wave volume of the formula in the step (1), the volume of the control oil reservoir of the horizontal well and the effective fracture network wave coefficient of the horizontal well adjacent to the same platform to quantitatively calculate the effective fracture network wave coefficient of the horizontal well on the basis of the step (304), and predicting the maximum recoverable reserve of the horizontal well by utilizing a prediction fitting formula of the step (106).
The following detailed description of the embodiments of the invention, with reference to the figures and the horizontal well of the erdos basin shale oil, illustrates the applicability of the method.
Example 1:
the basin shale oil has the characteristics of low rock brittleness index, low oil layer pressure coefficient and low single well yield, the advanced energy storage subdivision cutting volume fracturing technology of the long horizontal well is a key technology for shale oil benefit development, and the maximum recoverable reserve is a direct evaluation index of the volume fracturing effect. The example predicts that the horizontal well H1-1 is positioned in a basin shale oil main development test area, the reservoir burial depth is 1985m, the well completion depth is 4034m, the horizontal section length is 1980m, the reservoir thickness is 14.2m, the well spacing of the horizontal well is 500m, and the method has strong heterogeneity, so that the prediction of the maximum recoverable reserves after the volume fracturing of the horizontal well has great challenges.
The embodiment provides a complete method for predicting the maximum recoverable reserve of shale oil horizontal well volume fracturing, which comprises the following steps:
1. based on the geological parameters of the reservoir where the adjacent horizontal wells of the regional blocks of the predicted horizontal wells are located, a productivity prediction model is established, an energy production comparison method is adopted in combination with an oil reservoir numerical simulation method, effective fracture network wave and coefficients are calculated, and a correlation prediction chart of the effective fracture network wave and coefficients and the maximum recoverable reserves is established. The method specifically comprises the following steps:
(1) And (3) establishing a basic database: the method comprises two parts of adjacent platform horizontal wells of the blocks and basic parameters of a predicted horizontal well, and comprises the following specific contents:
(1) the basic parameters of the adjacent horizontal wells of the block comprise two parts, wherein one part is used for calculating the difference coefficient and comprises the following parts: the method comprises the steps of burying depth of a reservoir, thickness of the reservoir, pressure of the reservoir, temperature of the reservoir, fluid parameters of the reservoir, average porosity, average permeability and average oil saturation of the reservoir, length of a horizontal section, number of fracturing sections, network sweep and volume of a micro-seismic monitoring full-well section, and accumulated oil production in the 1 st year, and is shown in tables 1 and 2. The second parameters for the effective seam net sweep coefficient include: the horizontal section length, reservoir thickness, well spacing, microseism full-well section fracture network sweep volume and maximum recoverable reserves are shown in table 3.
Table 1 geological base parameter table for reservoir where adjacent horizontal well H1 well is located
| Parameters (parameters) | Numerical value | Parameters (parameters) | Numerical value |
| Reservoir burial depth (m) | 1985 | Average oil saturation (%) | 56.2 |
| Reservoir thickness (m) | 11.2 | Formation crude oil volume coefficient (/) | 1.28 |
| Reservoir pressure (MPa) | 18.7 | Crude oil viscosity (mPa.s) | 1.52 |
| Reservoir temperature (. Degree. C.) | 66.5 | Horizontal segment length (m) | 1543 |
| Average porosity (%) | 10.1 | Number of fracturing segments (segment) | 13 |
| Average permeability (mD) | 0.16 | Cumulative oil production (t) of 1 st year | 2078 |
TABLE 2H 1 microseism monitoring data table for adjacent horizontal wells with blocks
TABLE 3 basic parameters table for H2-H12 wells of adjacent horizontal wells with blocks
(2) The predicted horizontal well basic parameters include each fracturing segment geological parameters and volume fracturing modification parameters. Wherein the geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, level stress difference and fracture pressure of each fracturing section of the horizontal well. The volume fracturing modification parameters include: crack density, construction displacement, single stage fracturing fluid volume and sand volume are shown in tables 4 and 5.
Table 4 geological parameters table for predicting each fracturing segment of horizontal well H1-1
TABLE 5 prediction of fracture volume modification parameters and microseism monitoring data tables for each fracture zone of horizontal well H1-1
(2) Establishing a horizontal well geological model by utilizing reservoir numerical simulation software Eclipse according to reservoir geological parameters of the adjacent horizontal wells H1 of the zones, and introducing microseism monitoring seam network wave volumes (table 2) into the horizontal well geological model to obtain a productivity prediction model;
(3) And (3) predicting the cumulative oil yield of the 1 st year of the adjacent horizontal well H1 of the block to be 9856t by using the productivity prediction model established in the step (2), and calculating the difference coefficient to be 0.21 by using the formula (1) in combination with the cumulative oil yield of the 1 st year 2078t (table 1) of the historical production initial stage of the horizontal well H1.
(4) And (3) combining the micro-seismic monitoring of the joint network wave volume of the whole well section of the adjacent horizontal well in the area, calculating the effective joint network wave volume by using the formula (2), and calculating the result as shown in the table 6.
TABLE 6 effective seam net sweep volume and coefficient calculation table for adjacent horizontal wells H2-H12 of zone blocks
(5) According to the calculation result of the table 6, calculating the effective fracture net wave and coefficient of the horizontal well adjacent to the platform by using the formula (4), wherein the calculation result is shown in the table (6).
(6) And (3) establishing a correlation prediction chart of the maximum recoverable reserve EUR of a single well and the effective fracture network sweep coefficient E by using the data of the table (6), and obtaining a prediction fitting formula EUR= 2.0656ln (E) -6.4729, as shown in figure 1.
2. And calculating and predicting the correlation coefficient between the fracturing transformation parameters of the geology and the volume of each fracturing section of the horizontal well and the fracture network wave and volume by using a gray correlation analysis method, and clearly influencing the key control parameters of the fracture network wave and volume. The method specifically comprises the following steps:
(1) And (3) establishing a multi-factor comprehensive evaluation matrix according to geological parameters of the measured section of the volume fracturing of the predicted horizontal well and the transformation parameters of the volume fracturing (table 4 and table 5), as shown in an expression (16).
(2) The multi-factor comprehensive evaluation matrix is normalized by using the formula (7), and the correlation coefficients between the different influence parameters and the multi-factor evaluation reference column are calculated by using the formulas (8) and (9), as shown in table 7.
TABLE 7 correlation coefficient calculation Table between different influencing parameters and microseism monitoring seam network sweep volumes
(3) The correlation coefficient calculation results are ordered as shown in table 7. And defining the values of the fracture network parameters to be larger than 0.5 as key control parameters affecting the fracture network wave and volume of the horizontal well volume, and sequentially fracturing fluid quantity, fracture density, brittleness index, sand quantity, construction displacement, horizontal stress difference, fracture pressure, permeability and clay content.
2. On the basis of determining key impact parameters of the slotted network wave and the volume, a slotted network wave and volume prediction model coupled with key control parameters is established, the slotted network wave and the volume of the horizontal well are predicted, effective slotted network wave and coefficients are obtained, and the maximum recoverable reserve of the horizontal well is predicted by using a correlation prediction plate. The seam network wave and volume prediction model comprises prediction matrix establishment, prediction matrix standardization and similarity factor calculation, and specifically comprises the following contents:
(1) And (5) establishing a seam net wave and volume prediction matrix. And (3) establishing a prediction matrix according to the key control parameters of the influencing suture net wave and the volume determined in the step (1), and taking an unmeasured St19 segment as an example, as shown in an expression (17).
(2) The prediction matrix is normalized. And (3) normalizing the stitch net wave and volume matrix elements by using formulas (11) and (12) to obtain a stitch net wave and volume prediction normalization matrix.
(3) And (5) calculating a similarity factor. According to the standard matrix of the seam wave and volume prediction, calculating the similarity factor between the horizontal well prediction section St19 and the measured section by using a formula (11), wherein the maximum similarity factor of the measured section St8 is 1.0, and according to the determination method in the step (7) in the step 3), the seam wave and volume of the St19 is the same as St 8. In the same manner, the similarity factors between St20 and S21 and the measured sections St1 to St18 are calculated by repeating steps (1) to (3) in step 2), respectively, as shown in Table 8 and FIGS. 2 to 4.
TABLE 8 similarity factor calculation Table between predicted segments St 19-St 21 and measured segments St 1-St 18
(4) According to the similarity factors between the predicted sections St 19-St 21 and the measured sections St 1-St 18 in Table 8, the microseism monitoring slit net sweep volumes of the predicted sections St19, st20, st21 were 240.3X10, respectively 4 m 3 ，252.2×10 4 m 3 ，207.7×10 4 m 3 And calculating the total well section microseism monitoring joint net sweep volume of the pre-logging well to 4534.7 multiplied by 10 by using the formula (15) 4 m 3 。
(5) On the basis of the step (4), the effective net gap sweep coefficient E of the horizontal well is further quantitatively calculated to be 67.7% by utilizing the formulas (2) - (4) in the step (1), and the maximum recoverable reserve EUR of the horizontal well H1-1 is predicted to be 2.234 multiplied by 10 by utilizing the prediction fitting formula obtained in the step (6) in the step (1) 4 t。
The present invention has been described in detail by way of examples, which are not to be construed as limiting the invention. And the modifications and simple changes carried out by the person skilled in the art do not deviate from the technical idea and scope of the invention, and all belong to the protection scope of the technical scheme of the invention.
Claims (11)
1. The method for predicting the maximum recoverable reserve of the shale oil horizontal well volume fracturing is characterized by comprising the following steps of:
(1) Establishing a productivity prediction model based on geological parameters of a horizontal well to be detected and a reservoir where the horizontal well is located in the same block as the horizontal well to be detected, calculating effective fracture network wave and coefficient by adopting a method of energy production comparison in combination with an oil reservoir numerical simulation method, and establishing a correlation prediction chart of the effective fracture network wave and coefficient and the maximum recoverable reserve;
(2) Calculating the association coefficient between each fracturing section geological parameter and the volume fracturing transformation parameter of the horizontal well to be tested and the fracture network wave and volume by using a gray association analysis method, and definitely influencing the key control parameters of the fracture network wave and volume;
(3) On the basis of determining the key control parameters of the seam wave and the volume, a seam wave and volume prediction model coupled with the key control parameters is established, the seam wave and the volume of the horizontal well to be detected are predicted, the effective seam wave and coefficient is obtained, and the maximum recoverable reserve of the horizontal well to be detected is further predicted by utilizing the correlation prediction graph of the step (1).
2. The method for predicting the maximum recoverable reserve of shale oil horizontal well volume fracturing according to claim 1, wherein the step (1) is based on geological parameters of the horizontal well to be measured and the reservoir where the horizontal well located in the same block as the horizontal well to be measured is located, establishes a productivity prediction model, calculates an effective fracture network sweep coefficient by adopting a method of productivity comparison in combination with a reservoir numerical simulation method, and establishes a correlation prediction template of the effective fracture network sweep coefficient and the maximum recoverable reserve, and specifically comprises:
step (101), basic database establishment: comprises the steps of obtaining basic parameters of adjacent horizontal wells and horizontal wells to be measured in the same block,
step (102), establishing a horizontal well geological model by utilizing reservoir numerical simulation software eclipse according to the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101), and introducing single-section fracture network sweep volumes in the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101) into the horizontal well geological model to obtain a productivity prediction model;
step (103), predicting the 1 st year cumulative oil yield of the adjacent horizontal wells of the same block by utilizing the productivity prediction model established in the step (102), and calculating a difference coefficient by adopting a productivity comparison method in combination with the 1 st year cumulative oil yield in the basic parameters of the adjacent horizontal wells of the block obtained in the step (101);
Step (104), according to the difference coefficient obtained in the step (103), combining the step (101) to obtain the full-well section joint network wave and volume in the basic parameters of the adjacent horizontal wells of the zones, and calculating the effective joint network wave and volume;
step (105), calculating the volume of a control oil reservoir of the horizontal well by using the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101), and simultaneously calculating the effective fracture network sweep coefficient of the adjacent horizontal well of the same platform by combining the effective fracture network sweep volume obtained in the step (104);
and (106) establishing a prediction fit formula according to the maximum recoverable reserves of all well sections in the basic parameters of the adjacent horizontal wells of the blocks obtained in the step (101) and the effective joint network wave and coefficient correlation prediction plate obtained in the step (105).
3. The method for predicting the maximum recoverable reserve of volumetric fracturing of a horizontal well of shale oil according to claim 2, wherein said step (101) of establishing a base database comprises:
step (1011) of obtaining basic parameters of adjacent horizontal wells of the block, wherein the basic parameters comprise two parts, and the first part is a parameter for calculating a difference coefficient, and the step comprises the following steps: reservoir burial depth, reservoir thickness, reservoir pressure, reservoir temperature, reservoir fluid parameters, average porosity, average permeability, average oil saturation of the reservoir, horizontal segment length, number of fracturing segments, cumulative oil production in 1 st year, and single well each fracturing segment data of microseism monitoring including single segment fracture length, single Duan Fengkuan, single segment fracture height, and single segment fracture network sweep volume; the second part is a parameter for the effective seam net sweep coefficient, comprising: the horizontal section is long, the reservoir layer is thick, the well distance of the horizontal well is long, the whole well section is provided with a network wave and volume, and the whole well section has the maximum recoverable reserve;
Step (1012), obtaining basic parameters of the horizontal well to be tested, including obtaining geological parameters and volumetric fracturing modification parameters of each fracturing segment, wherein the geological parameters include: porosity, permeability, oil saturation, clay content, brittleness index, level stress difference, and fracture pressure; the volume fracturing modification parameters include: crack density, construction displacement, single stage fracturing fluid volume and sand volume.
4. The method for predicting the maximum recoverable reserve of volumetric fracturing of a horizontal well of shale oil according to claim 2, wherein in said step (103), the calculation formula of the difference coefficient is as follows:
wherein: FI is a coefficient of difference, dimensionless;
Q H accumulating oil production for the 1 st year of historical production of adjacent horizontal wells of the block, t;
Q P and (3) predicting the accumulated oil yield of the 1 st year for the adjacent horizontal wells of the block, and t.
5. The method for predicting the maximum recoverable capacity of a volumetric fracture of a shale oil horizontal well according to claim 1, wherein in the step (104), the calculation formula of the effective fracture sweep volume is as follows:
ESRV＝FI·SRV
wherein: ESRV is effective seam net sweep volume, 10 4 m 3 ；
FI is a coefficient of difference, dimensionless;
SRV is the full well section seam net sweep volume, 10 4 m 3 。
6. The method for predicting volumetric fracturing maximum recoverable reserves of a shale oil horizontal well according to claim 2, wherein in said step (105),
The calculation formula of the oil reservoir volume controlled by the horizontal well is as follows: vr= Lhd;
the calculation formula of the effective seam net sweep coefficient of the horizontal well adjacent to the platform is as follows:
wherein: v (V) r Controlling reservoir volume for horizontal wells, m 3 ；
L is the length of the horizontal segment, m;
h is the reservoir thickness, m;
d is the well distance of the horizontal well, m;
e is the effective net-stitching wave coefficient,%;
ESRV is effective seam net sweep volume, 10 4 m 3 。
7. The method for predicting the volumetric fracturing maximum recoverable reserve of a shale oil horizontal well according to claim 2, wherein: in the step (106), the fitting formula is:
EUR＝2.0656ln(E)-6.4729
in the EUR, the maximum recoverable and recoverable reserve of a single well is 10 4 t；
E is the effective seam net wave coefficient,%.
8. The method for predicting the maximum recoverable reserve of the volumetric fracturing of the shale oil horizontal well according to claim 2, wherein the step (2) is characterized in that a gray correlation analysis method is utilized to calculate the correlation coefficient between the geological parameters and the volumetric fracturing transformation parameters of each fracturing section of the horizontal well to be detected and the fracture network and volume of the horizontal well to be detected monitored by microseism, and the key control parameters which can explicitly influence the fracture network and volume are specifically included,
step (201), multi-factor comprehensive evaluation matrix establishment: according to the basic parameters of the horizontal well to be tested obtained in the step (101), a multi-factor comprehensive evaluation matrix X is established, wherein the multi-factor comprehensive evaluation matrix elements are geological parameters and volume fracturing transformation parameters of the volume fracturing already-tested fracture section of the horizontal well to be tested, and the expression of the multi-factor comprehensive evaluation matrix X is as follows
Wherein: x is a multi-factor comprehensive evaluation matrix;
X i (j) The matrix elements are comprehensively evaluated for multiple factors;
m is the number of the volume fracturing stages of the horizontal well to be measured;
n is the number of the seam net wave and volume influencing factors;
step (202), evaluation reference column establishment: establishing an evaluation reference column X according to the seam net wave and volume of the measured pressure crack section of the horizontal well obtained by microseism monitoring 0 ；
X 0 ＝[X 1 (0)，...X i (0)，...X m (0)] T i＝1，2，…m
Wherein: x is X 0 A reference column for evaluation; t is a transposed symbol;
step (203), normalization processing: maximum value method is adopted to comprehensively evaluate the matrix X and the evaluation reference column X for multiple factors 0 And respectively carrying out standardization processing, wherein the standardization processing formula is as follows:
wherein:
standardized elements of a multi-factor comprehensive evaluation matrix or evaluation reference column;
X i (j) The method comprises the steps of comprehensively evaluating matrix elements or evaluating reference column elements for multiple factors;
(X i (j)) max is the maximum value in the j-th influencing factor parameter set;
step (204), calculating a correlation coefficient: calculating correlation coefficients between different influencing factors and volumes of all-well section fracture network waveforms and volumes of the regional adjacent horizontal well volume fracturing microseism monitoring on the basis of the step (203);
step (205), key control parameter determination: and (3) sequencing the calculation results of the correlation coefficients in the step (204), and defining the values of the correlation coefficients to be larger than 0.5 as key control parameters for influencing the volume fracture network sweep volume of the horizontal well to be measured.
9. The method for predicting volumetric fracturing maximum recoverable reserves of a shale oil horizontal well of claim 7, wherein the step (204) comprises the specific content of the correlation coefficient calculation,
step (2041), calculating standard deviation according to the multi-factor comprehensive evaluation matrix standardized data and the evaluation reference column standardized data, wherein the calculation formula is as follows:
wherein: delta i (j) Standard deviation between the multi-factor comprehensive evaluation matrix standardized data and the evaluation reference column standardized data;
step (2042), further calculating correlation coefficients between different influencing factors and the stitch network wave and volume according to the standard deviation calculated in the step (2041), wherein the calculation formula is as follows:
wherein: r is (r) j Is the association coefficient; ρ is the resolution factor, typically 0.5;
and m is the number of the volume fracturing stages of the horizontal well to be measured.
10. The method for predicting the maximum recoverable reserves of the shale oil horizontal well volume fracturing of claim 2, wherein the step (2) establishes a joint network wave and volume prediction model coupled with key control parameters on the basis of the determination of the joint network wave and volume key control parameters, predicts the joint network wave and volume of the horizontal well to obtain effective joint network wave and coefficient, and further predicts the maximum recoverable reserves of the horizontal well by utilizing a correlation prediction graph, wherein the joint network wave and volume prediction model comprises the establishment of a prediction matrix, the standardization of the prediction matrix and the calculation of a similarity factor,
Step (301), creating a seam net wave and volume prediction matrix: establishing a seam network wave and volume prediction matrix according to the key control parameters for influencing the seam network wave and volume determined in the step (2), wherein the matrix formula is as follows:
wherein: b is a seam net wave and volume prediction matrix;
B i (j) I=0, 1,2, …, h for the slotted-net-wave prediction matrix element; j=1, 2, …, k;
h is the number of the horizontal well fracturing segments, wherein i=0 is the number of the predicted fracturing segments, and i=2 to h are the numbers of the fracturing segments already measured;
k is the number of key parameters affecting the mesh-gap wave and the volume;
step (302), the prediction matrix is normalized: carrying out standardization treatment on the seam net wave and volume prediction matrix according to the value distribution range of key control parameters of the seam net wave and volume influence;
step (303), similarity factor calculation: according to the fracture network wave and volume prediction standardized matrix, calculating a similarity factor between a horizontal well pressure-measured fracture section and a predicted fracture section, wherein the calculation formula is as follows:
wherein: ID (identity) i A similarity factor between the measured segment and the predicted segment;
step (304), sorting similarity factors between the predicted section and the measured section, and assigning the largest similarity factor to the predicted section corresponding to the measured section microseism monitoring seam network sweep volume to obtain the predicted horizontal well full-well section microseism monitoring seam network sweep volume, wherein the calculation formula is as follows:
Wherein: k is the number of fracturing sections of the horizontal well, and the sections;
and (305) further utilizing a calculation formula of the effective fracture network wave volume of the formula in the step (1), the volume of the reservoir controlled by the horizontal well and the effective fracture network wave coefficient of the horizontal well adjacent to the same platform to quantitatively calculate the effective fracture network wave coefficient of the horizontal well on the basis of the step (304), and predicting the maximum recoverable reserve of the horizontal well by utilizing a prediction fitting formula in the step (1).
11. The method for predicting volumetric fracturing maximum recoverable reserves of a shale oil horizontal well according to claim 9, wherein said step (302) comprises the specific contents of the prediction matrix standardization including,
step (3021), carrying out standardized processing on the prediction matrix element according to the value distribution range of the key parameters of the mesh wave and volume influence, wherein the calculation formula is as follows:
wherein:
the average value of each parameter set of the vector array of the seam net wave and the volume prediction matrix is obtained;
B i (j) I=0, 1,2, …, h for the slotted-net-wave prediction matrix element; j=1, 2, …, k;
k is the number of key parameters affecting the mesh-gap wave and the volume;
step (3021), performing standardization processing on the seam network wave and volume prediction matrix by using the formula (I) and the formula (II), wherein the standardization matrix is as follows:
Wherein: b (B) * The matrix is standardized for the seam net wave and volume prediction.
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| CN116861714A (en) * | 2023-09-05 | 2023-10-10 | 西南石油大学 | Method for determining water flooding sweep degree of fracture-cavity oil reservoir |
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