CN111175817A - Micro-seismic data-assisted compact oil and gas reservoir fracture distribution inversion method - Google Patents
Micro-seismic data-assisted compact oil and gas reservoir fracture distribution inversion method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
- E21B43/25—Methods for stimulating production
- E21B43/26—Methods for stimulating production by forming crevices or fractures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
Abstract
The invention discloses a micro-seismic data-assisted compact oil and gas reservoir fracture distribution inversion method. According to the method, Hough transformation is adopted to transform microseism data into Hough space, a Hough space function is obtained, and an initial mean value of a Hough space random field is formed; generating samples of crack distribution in a physical space by adopting Hough inverse transformation; predicting production prediction data corresponding to sampling of crack distribution in a physical space through numerical simulation; obtaining a correlation matrix of the Hough space random field and production prediction data by adopting an algebraic statistical method; maximizing the posterior probability of the Hough space random field by using a correlation matrix and adopting an iterative algorithm based on a sample set to form a crack distribution inversion method; the method can improve the precision of compact oil and gas reservoir fracture distribution inversion and production prediction of a development well.
Description
Technical Field
The invention relates to an oil exploitation technology, in particular to a micro-seismic data-assisted compact oil and gas reservoir fracture distribution inversion method.
Background
Because of the extremely low permeability of reservoirs, economic development of tight hydrocarbon reservoirs typically requires the assistance of horizontal drilling and multi-stage fracturing techniques. The fracture formed by fracturing has high flow conductivity, can provide an effective channel for oil and gas to flow, and is the key for developing a compact oil and gas reservoir. Therefore, accurate inversion of the tight reservoir fracture distribution is key to predicting well production.
In the prior art, an automatic history fitting technique using production data of an oil and gas well is a main tool for inverting the properties of an oil and gas reservoir. Automated history-fitting techniques are commonly used to invert the hydrocarbon reservoir property parameters such as permeability and porosity, and generally assume that the reservoir properties satisfy a gaussian distribution. The fracture distribution of the compact oil and gas reservoir formed by fracturing does not meet Gaussian distribution statistically and is difficult to parameterize, which hinders the application of automatic history fitting technology. On the other hand, inversion of the fracture distribution of tight reservoirs usually relies on production data from a single well, taking into account the locality of the fracture. Thus, the application of the automatic history fitting method is generally poor due to the limitation of the data quantity. Besides production data, microseism data are important data for mastering the crack distribution of a compact oil and gas reservoir, and the data can prompt the position of the crack. But since the microseismic data are discrete spatial points, the fracture distribution cannot be directly given. In addition, due to the large data error, the fracture distribution constructed using microseismic data is generally less accurate and requires further correction. Existing techniques for constructing fracture distributions based on microseismic data generally lack a correction function.
Disclosure of Invention
In order to solve the inversion problem of the compact hydrocarbon reservoir fracture distribution (the inversion refers to the fact that unknown parameters are deduced by using an inverse problem modeling technology based on observation data), the invention provides a micro-seismic data-assisted compact hydrocarbon reservoir fracture distribution inversion method.
The invention discloses a micro-seismic data assisted compact hydrocarbon reservoir fracture distribution inversion method, which comprises the following steps:
1) acquiring micro-seismic data:
acquiring microseism data indicating the crack occurrence position of the compact oil and gas reservoir through a surface microseism signal monitor and a data interpretation system;
2) obtaining initial statistical properties of the Hough space random field:
transforming the microseism data obtained in the step 1) to a Hough space by adopting a Hough transformation method to obtain a Hough space function, setting the Hough space function as an initial mean value of a Hough space random field, further setting an initial covariance of the Hough space random field, taking the initial mean value and the initial covariance as initial statistical properties of the Hough space random field, and representing fracture distribution by using the Hough space random field;
3) generating a sample of fracture distribution in physical space:
sampling the Hough space random field according to the initial statistical properties of the Hough space random field obtained in the step 2), and generating samples of crack distribution in a physical space by adopting a Hough inverse transformation method;
4) obtaining production prediction data:
sampling the crack distribution in the physical space obtained in the step 3), and obtaining production prediction data corresponding to the sampling of the crack distribution in the physical space through numerical simulation;
5) calculating statistical correlation:
calculating a correlation matrix of the Hough space random field and the production prediction data by adopting an algebraic statistical method based on the production prediction data corresponding to the samples of the crack distribution in the physical space obtained in the step 4);
6) actual production data were obtained:
obtaining actual production data of a production well of the compact oil and gas reservoir;
7) updating Hough space random fields:
constructing a posterior probability distribution expression of the Hough space random field, wherein the expression comprises a prior probability distribution item and a likelihood function item of the Hough space random field, the prior probability distribution item is determined by a mean value and covariance, the likelihood function item is related to actual production data, and the updated Hough space random field is obtained by selecting the posterior probability distribution of the Hough space random field to be maximized based on an iterative algorithm of a sample set by utilizing a correlation matrix;
8) generating an updated physical spatial fracture distribution:
and 7) adopting Hough inverse transformation to obtain the updated crack distribution in the physical space for the updated Hough space random field obtained in the step 7).
In step 2), setting the principle of initial covariance of the Hough space random field: selecting an exponential type or a Gaussian type for the covariance type; the direction correlation length is set to be 0.05 to 0.5 times of the length of the direction interval; the standard deviation was set to 0.01 to 0.2 times the mean.
In step 3), the specific implementation procedure of the hough inverse transform is as follows: searching all local extreme points sampled by the Hough space random field; reading the coordinates of the local extreme point, namely the distance rho and the angle theta; determining a straight line in the physical space by using the coordinates of the local extreme points; the resulting straight lines are used to characterize the fracture.
In step 4), the numerical simulation follows the following principle: solving the equation by using a multiphase seepage equation; and carrying out fracture modeling by utilizing a discrete fracture model or an embedded discrete fracture model.
In step 5), calculating the correlation matrix by using an algebraic statistical method specifically includes: calculating the respective mean values of the two vectors of the analyzed Hough space random field and the production prediction data; calculating the disturbance obtained by subtracting the mean value from the samples of the two vectors to obtain the sampled disturbance; calculating a covariance matrix between the two vectors using the sampled perturbations; the correlation matrix is characterized by a covariance matrix.
In step 6), the actual production data adopts one or more of gas production rate, oil production rate, water production rate and bottom hole pressure; wherein, the gas production rate, the oil production rate and the water production rate are obtained by measuring through a surface volume measurer; the bottom hole pressure is measured by a pressure monitor.
In the step 7), the prior probability distribution refers to the probability distribution meeting Gaussian distribution, the type of probability distribution is determined by mean and covariance, and the initial mean and initial covariance of the Hough space random field can determine the prior probability distribution item of the Hough space random field; the likelihood function term is associated with actual production data, and the calculation process comprises: calculating a 2-norm of a difference between the production prediction data and the actual production data; taking the negative value of the obtained 2-norm as a variable and taking an exponential function with a natural constant as a base for operation; the resulting function is the likelihood function term.
The iterative algorithm based on the sample set adopts one of an iterative set Kalman filtering method, an iterative set maximum likelihood estimation method and an iterative set data smoothing method. And when the posterior probability distribution of the Hough space random field is maximized by using an iterative algorithm based on the sample set, continuously updating the Hough space random field in the iterative process to obtain the updated Hough space random field.
The invention has the advantages that:
according to the method, Hough transformation is adopted to transform microseism data into Hough space, a Hough space function is obtained, and an initial mean value of a Hough space random field is formed; generating samples of crack distribution in a physical space by adopting Hough inverse transformation; predicting production prediction data corresponding to sampling of crack distribution in a physical space through numerical simulation; obtaining a correlation matrix of the Hough space random field and production prediction data by adopting an algebraic statistical method; maximizing the posterior probability of the Hough space random field by using a correlation matrix and adopting an iterative algorithm based on a sample set to form a crack distribution inversion method; the method can improve the precision of compact oil and gas reservoir fracture distribution inversion and production prediction of a development well.
Drawings
FIG. 1 is a schematic diagram of a reservoir block containing a fracture profile;
FIG. 2 is a diagram of microseismic data obtained by one embodiment of a microseismic data assisted inversion method of fracture distribution of a tight reservoir in accordance with the present invention;
FIG. 3 is an initial mean value of a Hough space random field obtained by an embodiment of a microseismic data assisted inversion method of fracture distribution of a tight hydrocarbon reservoir according to the present invention;
FIG. 4 is a graph of production prediction data obtained by an embodiment of the microseismic data assisted inversion method of tight reservoir fracture distribution in which (a) is a graph of oil production and (b) is a graph of water production, according to the present invention;
FIG. 5 is a plot of the results of fracture distributions obtained by one embodiment of a microseismic data assisted inversion method of fracture distribution of a tight hydrocarbon reservoir in accordance with the present invention;
FIG. 6 is a flow chart of a microseismic data assisted inversion method of the fracture distribution of a tight reservoir according to the present invention.
Detailed Description
The invention will be further elucidated by means of specific embodiments in the following with reference to the drawing.
The micro-seismic data assisted inversion method for the fracture distribution of the tight hydrocarbon reservoir in the embodiment is shown in fig. 6, and comprises the following steps:
1) acquiring micro-seismic data:
the schematic diagram of a reservoir block containing fracture distribution is shown in figure 1, and micro-seismic data indicating the occurrence positions of the fractures of the compact oil and gas reservoir are obtained through an earth surface micro-seismic signal monitor and a data interpretation system, as shown in figure 2; the crack occurrence locations are continuous, but as can be seen from fig. 2, the micro-seismic data are discrete, and although the crack occurrence locations cannot be directly obtained from the micro-seismic data, the initial probability of the crack occurrence locations can be obtained;
2) obtaining initial statistical properties of the Hough space random field:
transforming the micro-seismic data obtained in the step 1) to a Hough space by adopting a Hough transformation method to obtain a Hough space function, wherein as shown in FIG. 3, the micro-seismic data are points of an actual physical space, and the Hough space function takes a distance rho and an angle theta as variables; setting a Hough space function as an initial mean value of a Hough space random field, further setting an initial covariance of the Hough space random field, selecting an exponential type for the covariance type, and setting the direction correlation length as 0.25 times of the length of a direction interval; setting the standard deviation as 0.1 time of the mean value, taking the initial mean value and the initial covariance as the initial statistical properties of the Hough space random field, and representing the fracture distribution by the Hough space random field;
3) generating a sample of fracture distribution in physical space:
sampling the Hough space random field according to the initial statistical properties of the Hough space random field obtained in the step 2) to obtain samples of the Hough space random field, and searching all local extreme points sampled by the Hough space random field by adopting a Hough inverse transformation method for the samples of the Hough space random field; reading the coordinates of the local extreme point, namely the distance rho and the angle theta; determining a straight line in the physical space by using the coordinates of the local extreme points; representing the cracks by using the obtained straight line, and generating samples of crack distribution in a physical space;
4) obtaining production prediction data:
sampling the crack distribution in the physical space obtained in the step 3), running numerical simulation by using oil reservoir numerical simulation software, solving an equation by using a multiphase seepage equation, and performing crack modeling by using a discrete crack model to obtain production prediction data corresponding to the sampling of the crack distribution in the physical space;
5) calculating statistical correlation:
calculating the respective mean values of two vectors of the analyzed Hough space random field and the production prediction data by adopting an algebraic statistical method based on the production prediction data corresponding to the samples of the crack distribution in the physical space obtained in the step 4);
calculating the disturbance obtained by subtracting the mean value from the samples of the two vectors to obtain the sampled disturbance; calculating a covariance matrix between the two vectors using the sampled perturbations; characterizing a correlation matrix by using a covariance matrix;
6) actual production data were obtained:
measuring the oil production rate and the water production rate of the production well of the compact oil and gas reservoir by using a surface volume measurer, wherein the oil production rate and the water production rate are used as actual production data and are respectively shown in fig. 4(a) and (b);
7) updating Hough space random fields:
constructing a posterior probability distribution expression of the Hough space random field, wherein the expression comprises a prior probability distribution item and a likelihood function item of the Hough space random field, the prior probability distribution item is determined by a mean value and covariance, the likelihood function item is related to actual production data, and the updated Hough space random field is obtained by selecting the posterior probability distribution of the Hough space random field to be maximized based on an iterative algorithm of a sample set by utilizing a correlation matrix;
8) generating an updated physical spatial fracture distribution:
and 7) adopting the Hough inverse transformation to obtain the updated crack distribution in the physical space for the updated Hough space random field obtained in the step 7), as shown in FIG. 5.
Finally, it is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and the appended claims. Therefore, the invention should not be limited to the embodiments disclosed, but the scope of the invention is defined by the appended claims.
Claims (10)
1. The method for inverting the fracture distribution of the tight hydrocarbon reservoir assisted by microseismic data is characterized by comprising the following steps of:
1) acquiring micro-seismic data:
acquiring microseism data indicating the crack occurrence position of the compact oil and gas reservoir through a surface microseism signal monitor and a data interpretation system;
2) obtaining initial statistical properties of the Hough space random field:
transforming the microseism data obtained in the step 1) to a Hough space by adopting a Hough transformation method to obtain a Hough space function, setting the Hough space function as an initial mean value of a Hough space random field, further setting an initial covariance of the Hough space random field, taking the initial mean value and the initial covariance as initial statistical properties of the Hough space random field, and representing fracture distribution by using the Hough space random field;
3) generating a sample of fracture distribution in physical space:
sampling the Hough space random field according to the initial statistical properties of the Hough space random field obtained in the step 2), and generating samples of crack distribution in a physical space by adopting a Hough inverse transformation method;
4) obtaining production prediction data:
sampling the crack distribution in the physical space obtained in the step 3), and running numerical simulation by using numerical reservoir simulation software to obtain production prediction data corresponding to the sampling of the crack distribution in the physical space;
5) calculating statistical correlation:
calculating a correlation matrix of the Hough space random field and the production prediction data by adopting an algebraic statistical method based on the production prediction data corresponding to the samples of the crack distribution in the physical space obtained in the step 4);
6) actual production data were obtained:
obtaining actual production data of a production well of the compact oil and gas reservoir;
7) updating Hough space random fields:
constructing a posterior probability distribution expression of the Hough space random field, wherein the expression comprises a prior probability distribution item and a likelihood function item of the Hough space random field, the prior probability distribution item is determined by a mean value and covariance, the likelihood function item is related to actual production data, and the updated Hough space random field is obtained by selecting the posterior probability distribution of the Hough space random field to be maximized based on an iterative algorithm of a sample set by utilizing a correlation matrix;
8) generating an updated physical spatial fracture distribution:
and 7) adopting Hough inverse transformation to obtain the updated crack distribution in the physical space for the updated Hough space random field obtained in the step 7).
2. The inversion method for the fracture distribution of the tight hydrocarbon reservoir as claimed in claim 1, wherein in the step 2), the principle of initial covariance of the Hough space random field is set as follows: selecting an exponential type or a Gaussian type for the covariance type; the direction correlation length is set to be 0.05 to 0.5 times of the length of the direction interval; the standard deviation was set to 0.01 to 0.2 times the mean.
3. The inversion method for fracture distribution of tight hydrocarbon reservoir according to claim 1, wherein in the step 3), the inverse Hough transform specifically comprises: searching all local extreme points sampled by the Hough space random field; reading the coordinates of the local extreme point, namely the distance rho and the angle theta; determining a straight line in the physical space by using the coordinates of the local extreme points; the resulting straight lines are used to characterize the fracture.
4. The inversion method of fracture distribution in tight hydrocarbon reservoir as claimed in claim 1, wherein in step 4), the numerical simulation follows the principle: solving the equation by using a multiphase seepage equation; and carrying out fracture modeling by utilizing a discrete fracture model or an embedded discrete fracture model.
5. The inversion method of fracture distribution in tight hydrocarbon reservoirs according to claim 1, wherein in the step 5), the calculating the correlation matrix by using an algebraic statistical method specifically comprises: calculating the respective mean values of the two vectors of the analyzed Hough space random field and the production prediction data; calculating the disturbance obtained by subtracting the mean value from the samples of the two vectors to obtain the sampled disturbance; calculating a covariance matrix between the two vectors using the sampled perturbations; the correlation matrix is characterized by a covariance matrix.
6. The tight reservoir fracture distribution inversion method of claim 1, wherein in step 6), the actual production data is one or more of gas production rate, oil production rate, water production rate, and bottom hole pressure; wherein, the gas production rate, the oil production rate and the water production rate are obtained by measuring through a surface volume measurer; the bottom hole pressure is measured by a pressure monitor.
7. The inversion method for fracture distribution of tight hydrocarbon reservoir as claimed in claim 1, wherein in step 7), the prior probability distribution refers to a probability distribution satisfying a gaussian distribution, the type of probability distribution is determined by mean and covariance, and then the initial mean and initial covariance of the hough space random field can determine the prior probability distribution term of the hough space random field.
8. The inversion method of fracture distribution in tight hydrocarbon reservoir as claimed in claim 1, wherein in step 7), the likelihood function term is correlated with actual production data, and the calculation process comprises: calculating a 2-norm of a difference between the production prediction data and the actual production data; taking the negative value of the obtained 2-norm as a variable and taking an exponential function with a natural constant as a base for operation; the resulting function is the likelihood function term.
9. The inversion method of tight reservoir fracture distribution according to claim 1, wherein in step 7), the sample set-based iterative algorithm employs one of an iterative set kalman filtering method, an iterative set maximum likelihood estimation method, and an iterative set data smoothing method.
10. The inversion method for fracture distribution of tight hydrocarbon reservoir as claimed in claim 1, wherein in step 7), when the posterior probability distribution of the hough space random field is maximized by using an iterative algorithm based on a sample set, the hough space random field is continuously updated in an iterative process to obtain an updated hough space random field.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270093A (en) * | 2020-10-28 | 2021-01-26 | 成都北方石油勘探开发技术有限公司 | Reservoir natural fracture modeling method based on microseism moment tensor inversion |
| CN112539049A (en) * | 2020-11-25 | 2021-03-23 | 中国石油大学(华东) | Fracturing fracture parameter inversion method based on production dynamic automatic history fitting |
| CN115526114A (en) * | 2022-10-20 | 2022-12-27 | 中国地质大学（北京） | Hydraulic fracture morphology inversion method and system based on ensemble Kalman filtering method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5960371A (en) * | 1997-09-04 | 1999-09-28 | Schlumberger Technology Corporation | Method of determining dips and azimuths of fractures from borehole images |
| CN103592690A (en) * | 2013-10-24 | 2014-02-19 | 长江大学 | Method for automatically recognizing reservoir cracks based on electric imaging logging porosity spectrum information |
| US10139508B1 (en) * | 2016-03-24 | 2018-11-27 | EMC IP Holding Company LLC | Methods and apparatus for automatic identification of faults on noisy seismic data |
| CN109061734A (en) * | 2018-05-24 | 2018-12-21 | 中国石油天然气集团有限公司 | A kind of processing method and processing device of seismic data |
| CN109086773A (en) * | 2018-08-29 | 2018-12-25 | 电子科技大学 | Fault plane recognition methods based on full convolutional neural networks |
| CN109655895A (en) * | 2019-01-29 | 2019-04-19 | 中国科学院地质与地球物理研究所 | A kind of arbitrarily complicated fracture medium grid method elasticity modeling method |
-
2020
- 2020-01-06 CN CN202010009416.5A patent/CN111175817B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5960371A (en) * | 1997-09-04 | 1999-09-28 | Schlumberger Technology Corporation | Method of determining dips and azimuths of fractures from borehole images |
| CN103592690A (en) * | 2013-10-24 | 2014-02-19 | 长江大学 | Method for automatically recognizing reservoir cracks based on electric imaging logging porosity spectrum information |
| US10139508B1 (en) * | 2016-03-24 | 2018-11-27 | EMC IP Holding Company LLC | Methods and apparatus for automatic identification of faults on noisy seismic data |
| CN109061734A (en) * | 2018-05-24 | 2018-12-21 | 中国石油天然气集团有限公司 | A kind of processing method and processing device of seismic data |
| CN109086773A (en) * | 2018-08-29 | 2018-12-25 | 电子科技大学 | Fault plane recognition methods based on full convolutional neural networks |
| CN109655895A (en) * | 2019-01-29 | 2019-04-19 | 中国科学院地质与地球物理研究所 | A kind of arbitrarily complicated fracture medium grid method elasticity modeling method |
Non-Patent Citations (2)
| Title |
|---|
| MENGBI YAO ELC: ""Tuning Fractures With Dynamic Data"", 《WATER RESOURCES RESEARCH》 * |
| 李昱垚 等: "《致密油体积压裂缝网形态和储层物性反演》", 《2019 INTERNATIONAL FIELD EXPLORATION AND DEVELOPMENT CONFERENCE IN XI’AN》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112270093A (en) * | 2020-10-28 | 2021-01-26 | 成都北方石油勘探开发技术有限公司 | Reservoir natural fracture modeling method based on microseism moment tensor inversion |
| CN112270093B (en) * | 2020-10-28 | 2022-11-08 | 成都北方石油勘探开发技术有限公司 | Reservoir natural fracture modeling method based on microseism moment tensor inversion |
| CN112539049A (en) * | 2020-11-25 | 2021-03-23 | 中国石油大学(华东) | Fracturing fracture parameter inversion method based on production dynamic automatic history fitting |
| CN115526114A (en) * | 2022-10-20 | 2022-12-27 | 中国地质大学（北京） | Hydraulic fracture morphology inversion method and system based on ensemble Kalman filtering method |
| CN115526114B (en) * | 2022-10-20 | 2023-07-14 | 中国地质大学（北京） | Hydraulic fracture morphology inversion method and system based on ensemble Kalman filtering method |
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