CN111597671B - Fracture network complexity determining method and system based on probability distribution - Google Patents
Fracture network complexity determining method and system based on probability distribution Download PDFInfo
- Publication number
- CN111597671B CN111597671B CN201910121314.XA CN201910121314A CN111597671B CN 111597671 B CN111597671 B CN 111597671B CN 201910121314 A CN201910121314 A CN 201910121314A CN 111597671 B CN111597671 B CN 111597671B
- Authority
- CN
- China
- Prior art keywords
- event
- dimensional space
- magnitude
- complexity
- probability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
-
- 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
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The invention provides a fracture network complexity determining method and system based on probability distribution, wherein the method comprises the following steps: establishing a three-dimensional space containing all microseism events according to event information of the microseism events formed during hydraulic fracturing; selecting event information of microseism events positioned in a preset three-dimensional space range at different positions of the three-dimensional space according to a preset rule; according to the event information obtained by selecting different positions in the three-dimensional space, the complexity of the fracture network distribution of the reservoir after fracturing can be quantitatively determined from the probability distribution angle of the microseism event.
Description
Technical Field
The invention relates to the technical field of geophysical exploration, in particular to a fracture network complexity determining method and system based on probability distribution.
Background
Hydraulic fracturing is an effective measure for improving the yield of dense oil and gas. Hydraulic fracturing is the squeezing of fracturing fluids with higher viscosity into hydrocarbon reservoirs through wellbores using surface high pressure pumps. When the rate of injection of the fracturing fluid exceeds the absorption capacity of the reservoir, a high pressure builds up in the reservoir downhole, and when this pressure exceeds the fracture pressure of the rock near the bottom of the well, the reservoir will be forced apart and a fracture will develop. At this point, the fracturing fluid continues to be squeezed into the reservoir, and the fracture continues to expand into the reservoir. In order to keep the pressed-open cracks in an open state, sand-carrying fluid with propping agents (quartz sand, ceramsite and the like) is squeezed into the reservoir, and after the sand-carrying fluid enters the cracks, the cracks can be continued to extend forwards on the one hand, and the pressed-open cracks can be supported on the other hand, so that the pressed-open cracks are not closed any more. And then injecting displacement fluid, displacing all sand-carrying fluid in the well bore into the cracks, and supporting the cracks by using propping agents. Finally, the injected fracturing fluid automatically degrades out of the wellbore, and proppants remain in these voids, leaving one or more fractures in the reservoir, creating a fluid path between the reservoir and the wellbore.
In order to increase the complexity of the fracture network and improve the oil and gas productivity, scholars have conducted intensive researches on influencing factors, fracturing processes and the like for forming the fracture network. Document 1 (Zhang, scientific and technical and engineering 2015.15 (5)) adopts large-size true triaxial hydraulic fracturing simulation, and researches the influence of factors such as horizontal ground stress difference, pumping displacement, shaft quantity and the like on the fracture propagation rule of a tight shale gas reservoir; document 2 (Zhang Shicheng, et al, petroleum journal 2014.35 (3)) developed a hydraulic fracture propagation simulation test on shale outcrop, and examined the internal fracture morphology of the core by using high-energy CT scanning, and studied the influence of various factors on the fracture propagation rule of a tight shale horizontal well; document 3 (Weng Ding is equal to natural gas earth science 2014.25 (7)) establishes a mathematical model of a tight sandstone reservoir fracture network based on the results of physical simulation experiments, and adopts a numerical simulation method to study a stress field and test various construction processes; literature 4 (Wang Xiaojun. Jiang Han university of petroleum institute report. 2016.29 (6)) developed a temporary plugging diverting fracturing process study, and gas production profile data verified that the temporary plugging fracturing process is an effective way to increase the complexity of the tight shale reservoir artificial fracture network; document 5 (Guo Tiankui et al, geotechnical mechanics 2013.34 (4)) discusses a new method for evaluating the ability of a fracture to form a fracture network by testing rock mechanics parameters for 10 cores and comparing and analyzing the accuracy of the commonly used 3 rock brittleness evaluation methods. However, current research on fracture networks generated at the time of fracturing is mainly focused on the fracturing process, i.e., how to optimize the fracturing process to increase the complexity of the fracture network. The detection method for the complexity of the fracture network mainly depends on the oil and gas yield difference after fracturing to make qualitative judgment, but cannot directly and quantitatively analyze the complexity of the fracture network.
Disclosure of Invention
The invention aims to provide a fracture network complexity determining method based on probability distribution, which quantitatively determines the complexity of fracture network distribution of a reservoir after fracturing from the probability distribution angle of microseism events. It is another object of the present invention to provide a fracture network complexity determination system based on probability distribution. It is a further object of the invention to provide a computer device. It is a further object of the invention to provide a readable medium.
In order to achieve the above object, the present invention discloses a method for determining complexity of a fracture network based on probability distribution, comprising:
establishing a three-dimensional space containing all microseism events according to event information of the microseism events formed during hydraulic fracturing;
selecting event information of microseism events positioned in a preset three-dimensional space range at different positions of the three-dimensional space according to a preset rule;
and determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space.
Preferably, the establishing a three-dimensional space containing all the microseismic events according to the event information of the microseismic events formed during hydraulic fracturing specifically includes:
Acquiring event information of a microseism event formed during hydraulic fracturing monitored by a microseism monitoring system;
obtaining the position and the magnitude of each microseism event according to the event information;
a three-dimensional space containing all microseismic events is formed from the locations of all microseismic events.
Preferably, the predetermined rule is that event information of microseism events located in a preset three-dimensional space range at different positions is sequentially selected along three mutually perpendicular coordinate axis directions of the three-dimensional space.
Preferably, the predetermined rule is that event information of the microseism event located in a preset three-dimensional space range is selected by taking each microseism event as a center in sequence.
Preferably, the determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space specifically includes:
determining event density distribution complexity and event magnitude distribution complexity of a three-dimensional space according to the event information obtained by each selection;
multiplying the event density distribution complexity and the event magnitude distribution complexity to obtain the fracture network complexity.
Preferably, determining the complexity of the event density distribution in the three-dimensional space according to the event information obtained by each selection specifically includes:
Obtaining the number of microseism events in a preset three-dimensional space when each selection is performed according to the event information obtained by each selection;
calculating event space probability of the number of microseism events in a preset three-dimensional space relative to the number of the microseism events in the three-dimensional space when each selection is performed;
dividing the preset three-dimensional space into a plurality of subspaces and determining the number of microseismic events of each subspace during each selection;
calculating the event probability of the number of the microseism events of each subspace relative to the number of the microseism events in a preset three-dimensional space when each subspace is selected;
and obtaining the event density distribution complexity according to the event probability and the event space probability.
Preferably, obtaining the complexity of the event density distribution according to the event probability and the event space probability specifically includes:
taking the logarithm of the event probability of each subspace, and then obtaining an absolute value to obtain a first absolute value;
multiplying the first absolute value of each subspace with an event probability to obtain the event density complexity of each subspace;
summing the event density complexity of all subspaces to obtain a first sum value;
and multiplying the first sum value with the event space probability and the density distribution correction coefficient to obtain the event density distribution complexity.
Preferably, determining the event magnitude distribution complexity of the three-dimensional space according to the event information obtained by each selection specifically includes:
obtaining the total number of the earthquake levels of all microseism events in a preset three-dimensional space when each selection is performed according to the event information obtained by each selection;
calculating the magnitude space probability of the total magnitude number of all microseism events in a preset three-dimensional space relative to the three-dimensional space during each selection;
dividing the preset three-dimensional space into a plurality of subspaces and determining the total number of the earthquake magnitudes of all microseism events in each subspace when each selection is performed;
calculating the magnitude probability of the total magnitude of each subspace relative to the total magnitude of all microseism events in the preset three-dimensional space during each selection;
and obtaining the event magnitude distribution complexity according to the magnitude probability and the magnitude space probability.
Preferably, obtaining the event magnitude distribution complexity according to the magnitude probability and the magnitude space probability specifically includes:
taking logarithm of the magnitude probability of each subspace, and then obtaining an absolute value to obtain a second absolute value;
multiplying the second absolute value of each subspace with the magnitude probability to obtain the event magnitude complexity of each subspace;
Summing the event magnitude complexity of all subspaces to obtain a second sum value;
and multiplying the second sum value with the magnitude space probability and the magnitude distribution correction coefficient to obtain the complexity of the magnitude distribution of the event.
The invention also discloses a crack network complexity determining system based on probability distribution, which comprises:
the space establishing unit is used for establishing a three-dimensional space containing all the microseism events according to the event information of the microseism events formed during hydraulic fracturing;
the space scanning unit is used for selecting event information of microseism events located in a preset three-dimensional space range at different positions of the three-dimensional space according to a preset rule;
and the complexity calculation unit is used for determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space.
Preferably, the predetermined rule is that event information of microseism events located in a preset three-dimensional space range at different positions is sequentially selected along three mutually perpendicular coordinate axis directions of the three-dimensional space.
Preferably, the predetermined rule is that event information of the microseism event located in a preset three-dimensional space range is selected by taking each microseism event as a center in sequence.
The invention also discloses a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor,
the processor, when executing the program, implements the method as described above.
The invention also discloses a computer readable medium, on which a computer program is stored,
the program, when executed by a processor, implements the method as described above.
Aiming at the problem of evaluating the complexity of a fracture network formed by the conventional reservoir fracturing transformation, the invention provides a scheme for determining the complexity of the fracture network from the probability distribution angle of microseism events, quantitatively describes the complexity of the fracture network distribution of the reservoir after fracturing, and quantitatively determines the advantages and disadvantages of the fracturing effect, so that the method can be used for quantitatively researching the fracturing process, optimizing the fracturing parameters, researching the modulus of the reservoir construction, and the like, and has great significance for the reservoir development.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of one embodiment of a probability distribution-based fracture network complexity determination method of the present invention;
FIG. 2 is a schematic diagram showing a second embodiment of a probability distribution-based fracture network complexity determination method according to the present invention;
FIG. 3 is a schematic diagram illustrating the operation of a predetermined rule according to one embodiment of a probability distribution-based fracture network complexity determination method of the present invention;
FIG. 4 is a schematic diagram illustrating the operation of another predetermined rule of one embodiment of a probability distribution based fracture network complexity determination method of the present invention;
FIG. 5 illustrates a third exemplary embodiment of a method for determining complexity of a fracture network based on probability distribution according to the present invention;
FIG. 6 shows a schematic diagram of one embodiment of a probability distribution based fracture network complexity determination method of the present invention;
FIG. 7 illustrates fifth exemplary embodiment of a probability distribution based fracture network complexity determination method of the present invention;
FIG. 8 is a graph of density distribution correction coefficients and magnitude distribution correction coefficients for one embodiment of a probability distribution based fracture network complexity determination method of the present invention;
FIG. 9 illustrates a sixth exemplary embodiment of a probability distribution based fracture network complexity determination method of the present invention;
FIG. 10 illustrates a seventh exemplary embodiment of a method for determining complexity of a fracture network based on probability distribution;
FIG. 11 is a block diagram of one embodiment of a fracture network complexity determination system based on probability distribution in accordance with the present invention;
fig. 12 shows a schematic structural diagram of a computer device suitable for use in implementing embodiments of the present invention.
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.
According to one aspect of the invention, the embodiment discloses a fracture network complexity determination method based on probability distribution. As shown in fig. 1, in this embodiment, the method includes:
S100: and establishing a three-dimensional space containing all the microseism events according to the event information of the microseism events formed during hydraulic fracturing.
S200: and selecting event information of microseism events positioned in a preset three-dimensional space range at different positions of the three-dimensional space according to a preset rule.
S300: and determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space.
Aiming at the problem of evaluating the complexity of a fracture network formed by the conventional reservoir fracturing transformation, the invention provides a scheme for determining the complexity of the fracture network from the probability distribution angle of microseism events, quantitatively describes the complexity of the fracture network distribution of the reservoir after fracturing, quantitatively determines the advantages and disadvantages of the fracturing effect, and can be used for quantitatively researching the fracturing process, optimizing the fracturing parameters, researching the modulus of the reservoir construction and the like.
In a preferred embodiment, as shown in fig. 2, the S100 specifically includes:
s110: and acquiring event information of microseism events formed during hydraulic fracturing monitored by a microseism monitoring system. The microseism monitoring technology is a new geophysical technology which has been rising in the last 20 years, is an effective monitoring means for hydraulic fracturing, and can monitor the spatial morphology of a fracture network generated during fracturing in real time. When the formation breaks to create a fracture, the elastic wave is released. An observation system is arranged near the reservoir, elastic fluctuation signals are collected, and the position of stratum fracture can be effectively positioned by adopting a geophysical method, so that the characteristics of the reservoir after fracturing are further analyzed. A monitoring system may be deployed near the reservoir to monitor event information of microseism events formed during the collected hydraulic fracturing.
S120: and obtaining the position and the magnitude of each microseism event according to the event information. The information such as the position and the magnitude of each microseism event formed by the stratum fracture can be positioned through the event information, wherein the position of each microseism event can be the space coordinate of the microseism event, and the space coordinate of each microseism event in the pre-defined coordinate can be obtained according to the pre-defined coordinate.
S130: a three-dimensional space containing all microseismic events is formed from the locations of all microseismic events. According to the space coordinates of each microseism event in the same coordinate system, a three-dimensional space D0 comprising all the microseism events can be formed, each microseism event is located at a corresponding coordinate position in the three-dimensional space, and a mark can be set for each microseism event so as to correspond to the unique information of the space coordinates, the earthquake magnitude and the like of the microseism event.
Given a preset three-dimensional space D1, the three-dimensional space D0 can be scanned through the preset three-dimensional space. The volume of the preset three-dimensional space D1 is smaller than the size of the three-dimensional space D0, the volume of the preset three-dimensional space D1 is V1, and the lengths of the preset three-dimensional space D1 in the X, Y, Z axial direction are lx, ly and lz respectively. The size of the volume V1 of the preset three-dimensional space D1 depends on the precision requirement of research, the smaller the volume is, the higher the precision is, the smaller the volume is, the fewer events in the research space are, and the precision is also reduced.
In one embodiment, the predetermined rule is to sequentially select event information of microseism events located in a preset three-dimensional space range at different positions along three mutually perpendicular coordinate axis directions of the three-dimensional space, as shown in fig. 3. Specifically, in one example, three mutually perpendicular coordinate axes of the three-dimensional space are X, Y, Z axes, respectively. When event information in which different positions in the three-dimensional space D0 are within the range of the preset three-dimensional space D1 is selected, the preset three-dimensional space D1 is sequentially moved to different positions along the directions of X, Y and Z in the D0 space to traverse the three-dimensional space D0, and the moving step length generally does not exceed the side length of the preset three-dimensional space in the moving direction according to the requirement of research precision, so that a certain overlapping area exists in the range of the preset three-dimensional space D1 after two adjacent times of movement, and the continuity and stability of a calculation result are improved. And taking out event information of the preset three-dimensional space D1 after each movement as a calculation object to calculate and obtain the complexity of the fracture network.
In another embodiment, the predetermined rule is to select event information of microseismic events located within a preset three-dimensional space range by taking each microseismic event as a center in sequence, as shown in fig. 4. Specifically, in one example, all microseism events in the three-dimensional space D0 are determined, the position of each microseism event is overlapped with the center point of the preset three-dimensional space D1 in sequence, and all the microseism events in the range of the preset three-dimensional space D1 when the position of the preset three-dimensional space D1 is overlapped with the position of the microseism event are selected as calculation objects to calculate and obtain the complexity of the fracture network. It will be appreciated that in other embodiments, other scanning methods may be used, and the preset three-dimensional space D1 may be moved in the three-dimensional space D0 according to other predetermined rules, so as to traverse the three-dimensional space D0.
In a preferred embodiment, as shown in fig. 5, the S300 specifically includes:
s310: and determining the event density distribution complexity FED and the event magnitude distribution complexity DEM of the three-dimensional space according to the event information obtained by each selection.
S320: and multiplying the event density distribution complexity FED and the event magnitude distribution complexity DEM to obtain the fracture network complexity CF.
In a preferred embodiment, as shown in fig. 6, determining the complexity FED of the event density distribution in the three-dimensional space according to the event information obtained by each selection in S310 specifically includes:
s311: and obtaining the number NT of the microseism events in the preset three-dimensional space D1 when each selection is performed according to the event information obtained by each selection.
S312: and calculating the event space probability PNT of the number NT of the microseism events in the preset three-dimensional space relative to the number of the microseism events of the three-dimensional space D0 when each selection is performed. Preferably, PNT is the ratio of the number of microseismic events NT to the number of microseismic events of the three-dimensional space D0.
S313: dividing the preset three-dimensional space D1 into a plurality of subspaces and determining the number of microseismic events of each subspace during each selection. Preferably, the preset three-dimensional space D1 may be divided into N subspaces, where NTi represents the number of microseismic events in the ith subspace, and N is a positive integer.
S314: and calculating the event probability PNTi of the number of the microseism events of each subspace relative to the number of the microseism events in the preset three-dimensional space D1. PNTi is the event probability of the i-th subspace, PNTi is the ratio of the number of events NTi in the i-th subspace to the total number of events NT.
S315: and obtaining the event density distribution complexity according to the event probability and the event space probability.
In a preferred embodiment, as shown in fig. 7, the step S315 specifically includes:
s3151: and taking the logarithm of the event probability of each subspace, and then obtaining an absolute value to obtain a first absolute value. In order to make FED always positive real numbers, the event probability is generally obtained by taking the logarithm and then obtaining the absolute value.
S3152: multiplying the first absolute value of each subspace with an event probability to obtain the event density complexity of each subspace.
S3153: the event density complexity of all subspaces is summed to obtain a first sum.
S3154: and multiplying the first sum value with the event space probability and the density distribution correction coefficient to obtain the event density distribution complexity FED. When N is different, the calculation results of FED have a certain difference, so that FED is not affected by N variation, and the FED needs to be multiplied by a density distribution correction coefficient. The density distribution correction coefficient may be obtained by a theoretical simulation, and is a function of the number N of subspaces, as shown in fig. 8.
In one specific example, the event density distribution complexity FED may be calculated by the following formula:
FED＝c1*PNT*sum(PNTi*abs(log(PNTi)))
where c1 is a density distribution correction coefficient, abs () is an absolute value operation.
In a preferred embodiment, as shown in fig. 9, determining the event magnitude distribution complexity DEM of the three-dimensional space according to the event information obtained by each selection in S310 specifically includes:
s316: and obtaining the total number MT of the earthquake magnitudes of all the microseism events in the preset three-dimensional space D1 in each selection according to the event information obtained in each selection.
S317: and calculating the magnitude space probability PMT of the total magnitude MT of all the microseism events in the preset three-dimensional space D1 relative to the three-dimensional space D0 when each selection is performed.
S318: dividing the preset three-dimensional space into a plurality of subspaces and determining the total number of the earthquake magnitudes of all the microseismic events in each subspace when each selection is performed. Preferably, the preset three-dimensional space D1 may be divided into N subspaces, MTi represents the total number of magnitudes of microseismic events for the ith subspace, and N is a positive integer.
S319: and calculating the magnitude probability PMTi of the magnitude total number of each subspace relative to the magnitude total number of all microseismic events in the preset three-dimensional space when each subspace is selected. PMTi is the ratio of the total number of magnitudes MTi to the total number of magnitudes MT in the ith subspace.
S3110: and obtaining the event magnitude distribution complexity DEM according to the magnitude probability PMTi and the magnitude space probability PMT.
In a preferred embodiment, as shown in fig. 10, the step S3110 specifically includes:
s3111: and taking the logarithm of the magnitude probability of each subspace, and then obtaining the absolute value to obtain a second absolute value. In order to make DEM always positive real numbers, the event magnitude probability is generally obtained by taking the logarithm and then obtaining the absolute value.
S3112: multiplying the second absolute value of each subspace by a magnitude probability to obtain the event magnitude complexity of each subspace.
S3113: the event magnitude complexity for all subspaces is summed to obtain a second sum.
S3114: and multiplying the second sum value with the magnitude space probability and the magnitude distribution correction coefficient to obtain the complexity of the magnitude distribution of the event. When N is different, the calculation results of DEM have certain difference, so that DEM is not affected by N variation, and the correction coefficient of magnitude distribution needs to be multiplied. The magnitude distribution correction factor may be obtained by a theoretical simulation, and is a function of the number N of subspaces, as shown in fig. 8.
In one specific example, the event magnitude distribution complexity DEM may be calculated by the following formula:
FED＝c1*PNT*sum(PNTi*abs(log(PNTi)))
Wherein c2 is a magnitude distribution correction coefficient, abs () is an absolute value operation.
Based on the same principle, the embodiment also discloses a fracture network complexity determining system based on probability distribution. As shown in fig. 11, the system includes a space creation unit 11, a space scanning unit 12, and a complexity calculation unit 13.
The space creating unit 11 is configured to create a three-dimensional space including all the microseism events according to event information of the microseism events formed during hydraulic fracturing.
The space scanning unit 12 is configured to select event information of microseism events located in a preset three-dimensional space range at different positions in the three-dimensional space according to a preset rule.
The complexity calculating unit 13 is configured to determine the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space.
Aiming at the problem of evaluating the complexity of a fracture network formed by the conventional reservoir fracturing transformation, the invention provides a scheme for determining the complexity of the fracture network from the probability distribution angle of microseism events, quantitatively describes the complexity of the fracture network distribution of the reservoir after fracturing, quantitatively determines the advantages and disadvantages of the fracturing effect, and can be used for quantitatively researching the fracturing process, optimizing the fracturing parameters, researching the modulus of the reservoir construction and the like.
In a preferred embodiment, the space creating unit 11 is further configured to obtain event information of microseism events formed during hydraulic fracturing monitored by the microseism monitoring system, obtain a position and a magnitude of each microseism event according to the event information, and form a three-dimensional space including all the microseism events according to the positions of all the microseism events.
The microseism monitoring technology is a new geophysical technology which has been rising in the last 20 years, is an effective monitoring means for hydraulic fracturing, and can monitor the spatial morphology of a fracture network generated during fracturing in real time. When the formation breaks to create a fracture, the elastic wave is released. An observation system is arranged near the reservoir, elastic fluctuation signals are collected, and the position of stratum fracture can be effectively positioned by adopting a geophysical method, so that the characteristics of the reservoir after fracturing are further analyzed. A monitoring system may be deployed near the reservoir to monitor event information of microseism events formed during the collected hydraulic fracturing.
The information such as the position and the magnitude of each microseism event formed by the stratum fracture can be positioned through the event information, wherein the position of each microseism event can be the space coordinate of the microseism event, and the space coordinate of each microseism event in the pre-defined coordinate can be obtained according to the pre-defined coordinate.
According to the space coordinates of each microseism event in the same coordinate system, a three-dimensional space D0 comprising all the microseism events can be formed, each microseism event is located at a corresponding coordinate position in the three-dimensional space, and a mark can be set for each microseism event so as to correspond to the unique information of the space coordinates, the earthquake magnitude and the like of the microseism event.
Given a preset three-dimensional space D1, the three-dimensional space D0 can be scanned through the preset three-dimensional space. The volume of the preset three-dimensional space D1 is smaller than the size of the three-dimensional space D0, the volume of the preset three-dimensional space D1 is V1, and the lengths of the preset three-dimensional space D1 in the X, Y, Z axial direction are lx, ly and lz respectively. The size of the volume V1 of the preset three-dimensional space D1 depends on the precision requirement of research, the smaller the volume is, the higher the precision is, the smaller the volume is, the fewer events in the research space are, and the precision is also reduced.
In one embodiment, the predetermined rule is to sequentially select event information of microseism events located in a preset three-dimensional space range at different positions along three mutually perpendicular coordinate axis directions of the three-dimensional space, as shown in fig. 3. Specifically, in one example, three mutually perpendicular coordinate axes of the three-dimensional space are X, Y, Z axes, respectively. When event information in which different positions in the three-dimensional space D0 are within the range of the preset three-dimensional space D1 is selected, the preset three-dimensional space D1 is sequentially moved to different positions along the directions of X, Y and Z in the D0 space to traverse the three-dimensional space D0, and the moving step length generally does not exceed the side length of the preset three-dimensional space in the moving direction according to the requirement of research precision, so that a certain overlapping area exists in the range of the preset three-dimensional space D1 after two adjacent times of movement, and the continuity and stability of a calculation result are improved. And taking out event information of the preset three-dimensional space D1 after each movement as a calculation object to calculate and obtain the complexity of the fracture network.
In another embodiment, the predetermined rule is to select event information of microseism events located in a preset three-dimensional space range by taking each microseism event as a center in sequence. Specifically, in one example, all microseism events in the three-dimensional space D0 are determined, the position of each microseism event is overlapped with the center point of the preset three-dimensional space D1 in sequence, and all the microseism events in the range of the preset three-dimensional space D1 when the position of the preset three-dimensional space D1 is overlapped with the position of the microseism event are selected as calculation objects to calculate and obtain the complexity of the fracture network. It will be appreciated that in other embodiments, other scanning methods may be used, and the preset three-dimensional space D1 may be moved in the three-dimensional space D0 according to other predetermined rules, so as to traverse the three-dimensional space D0.
In a preferred embodiment, the complexity calculating unit 13 is further configured to determine an event density distribution complexity FED and an event magnitude distribution complexity DEM of the three-dimensional space according to the event information obtained by each selection, and multiply the event density distribution complexity FED and the event magnitude distribution complexity DEM to obtain the fracture network complexity CF.
In a preferred embodiment, the complexity calculating unit 13 is further configured to obtain, according to the event information obtained by each selection, the number NT of microseismic events in the preset three-dimensional space D1 at each selection. Calculating event space probability PNT of the number of microseism events NT in the preset three-dimensional space relative to the number of microseism events of the three-dimensional space D0 when each selection is performed, wherein PNT is preferably the ratio of the number of microseism events NT to the number of microseism events of the three-dimensional space D0. Dividing the preset three-dimensional space D1 into a plurality of subspaces and determining the number of microseismic events of each subspace during each selection. Preferably, the preset three-dimensional space D1 may be divided into N subspaces, where NTi represents the number of microseismic events in the ith subspace, and N is a positive integer. And calculating the event probability PNTi of the number of the microseism events of each subspace relative to the number of the microseism events in the preset three-dimensional space D1. PNTi is the event probability of the i-th subspace, PNTi is the ratio of the number of events NTi in the i-th subspace to the total number of events NT. And further obtaining the complexity of the event density distribution according to the event probability and the event space probability.
In a preferred embodiment, the complexity calculation unit 13 is further configured to obtain a first absolute value by taking the logarithm of the event probability of each subspace and then obtaining an absolute value. In order to make FED always positive real numbers, the event probability is generally obtained by taking the logarithm and then obtaining the absolute value. Multiplying the first absolute value of each subspace with an event probability to obtain the event density complexity of each subspace. The event density complexity of all subspaces is summed to obtain a first sum. And multiplying the first sum value with the event space probability and the density distribution correction coefficient to obtain the event density distribution complexity FED. When N is different, the calculation results of FED have a certain difference, so that FED is not affected by N variation, and the FED needs to be multiplied by a density distribution correction coefficient. The density distribution correction coefficient can be obtained by a theoretical simulation mode, and is a function of the number N of subspaces.
In one specific example, the event density distribution complexity FED may be calculated by the following formula:
FED＝c1*PNT*sum(PNTi*abs(log(PNTi)))
where c1 is a density distribution correction coefficient, abs () is an absolute value operation.
In a preferred embodiment, the complexity calculation unit 13 is further configured to obtain, according to the event information obtained by each selection, a total number of magnitudes MT of all the microseismic events in the preset three-dimensional space D1 at each selection. And calculating the magnitude space probability PMT of the total magnitude MT of all the microseism events in the preset three-dimensional space D1 relative to the three-dimensional space D0 when each selection is performed. Dividing the preset three-dimensional space into a plurality of subspaces and determining the total number of the earthquake magnitudes of all the microseismic events in each subspace when each selection is performed. Preferably, the preset three-dimensional space D1 may be divided into N subspaces, MTi represents the total number of magnitudes of microseismic events for the ith subspace, and N is a positive integer. And calculating the magnitude probability PMTi of the magnitude total number of each subspace relative to the magnitude total number of all microseismic events in the preset three-dimensional space when each subspace is selected. PMTi is the ratio of the total number of magnitudes MTi to the total number of magnitudes MT in the ith subspace. And obtaining the event magnitude distribution complexity DEM according to the magnitude probability PMTi and the magnitude space probability PMT.
In a preferred embodiment, the complexity calculation unit 13 is further configured to take a logarithm of the magnitude probability of each subspace and then determine an absolute value to obtain a second absolute value. In order to make DEM always positive real numbers, the event magnitude probability is generally obtained by taking the logarithm and then obtaining the absolute value. Multiplying the second absolute value of each subspace by a magnitude probability to obtain the event magnitude complexity of each subspace. The event magnitude complexity for all subspaces is summed to obtain a second sum. And multiplying the second sum value with the magnitude space probability and the magnitude distribution correction coefficient to obtain the complexity of the magnitude distribution of the event. When N is different, the calculation results of DEM have certain difference, so that DEM is not affected by N variation, and the correction coefficient of magnitude distribution needs to be multiplied. The magnitude distribution correction coefficient can be obtained through a theoretical simulation mode, and is a function of the number N of subspaces.
In one specific example, the event magnitude distribution complexity DEM may be calculated by the following formula:
FED＝c1*PNT*sum(PNTi*abs(log(PNTi)))
wherein c2 is a magnitude distribution correction coefficient, abs () is an absolute value operation.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer device, which may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
In a typical example, the computer apparatus includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement a method performed by a client as described above, or where the processor executes the program to implement a method performed by a server as described above.
Referring now to FIG. 12, there is illustrated a schematic diagram of a computer device 600 suitable for use in implementing embodiments of the present application.
As shown in fig. 12, the computer apparatus 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate works and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM)) 603. In the RAM603, various programs and data required for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a liquid crystal feedback device (LCD), and the like, and a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 606 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on drive 610 as needed, so that a computer program read therefrom is mounted as needed as storage section 608.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (4)
1. A method for determining complexity of a fracture network based on probability distribution, comprising:
establishing a three-dimensional space containing all microseism events according to event information of the microseism events formed during hydraulic fracturing;
selecting event information of microseism events positioned in a preset three-dimensional space range at different positions of the three-dimensional space according to a preset rule;
determining the complexity of a fracture network according to event information obtained by selecting different positions in the three-dimensional space;
the establishing a three-dimensional space containing all the microseism events according to the event information of the microseism events formed during hydraulic fracturing specifically comprises the following steps:
acquiring event information of a microseism event formed during hydraulic fracturing monitored by a microseism monitoring system;
obtaining the position and the magnitude of each microseism event according to the event information;
forming a three-dimensional space containing all the microseism events according to the positions of all the microseism events;
the preset rule is that event information of microseism events in a preset three-dimensional space range at different positions is sequentially selected along three mutually perpendicular coordinate axis directions of the three-dimensional space;
the preset rule is to sequentially select event information of microseism events in a preset three-dimensional space range by taking each microseism event as a center;
The determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space specifically comprises the following steps:
determining event density distribution complexity and event magnitude distribution complexity of a three-dimensional space according to the event information obtained by each selection;
multiplying the event density distribution complexity and the event magnitude distribution complexity to obtain the fracture network complexity;
the determining the event density distribution complexity of the three-dimensional space according to the event information obtained by each selection specifically comprises the following steps:
obtaining the number of microseism events in a preset three-dimensional space when each selection is performed according to the event information obtained by each selection;
calculating event space probability of the number of microseism events in a preset three-dimensional space relative to the number of the microseism events in the three-dimensional space when each selection is performed;
dividing the preset three-dimensional space into a plurality of subspaces and determining the number of microseismic events of each subspace during each selection;
calculating the event probability of the number of the microseism events of each subspace relative to the number of the microseism events in a preset three-dimensional space when each subspace is selected;
obtaining event density distribution complexity according to the event probability and the event space probability;
The obtaining the event density distribution complexity according to the event probability and the event space probability specifically comprises the following steps:
taking the logarithm of the event probability of each subspace, and then obtaining an absolute value to obtain a first absolute value;
multiplying the first absolute value of each subspace with an event probability to obtain the event density complexity of each subspace;
summing the event density complexity of all subspaces to obtain a first sum value;
multiplying the first sum value with the event space probability and the density distribution correction coefficient to obtain event density distribution complexity;
the determining of the event magnitude distribution complexity of the three-dimensional space according to the event information obtained by each selection specifically comprises the following steps:
obtaining the total number of the earthquake levels of all microseism events in a preset three-dimensional space when each selection is performed according to the event information obtained by each selection;
calculating the magnitude space probability of the total magnitude number of all microseism events in a preset three-dimensional space relative to the three-dimensional space during each selection;
dividing the preset three-dimensional space into a plurality of subspaces and determining the total number of the earthquake magnitudes of all microseism events in each subspace when each selection is performed;
calculating the magnitude probability of the total magnitude of each subspace relative to the total magnitude of all microseism events in the preset three-dimensional space during each selection;
Obtaining event magnitude distribution complexity according to the magnitude probability and the magnitude space probability;
obtaining the event magnitude distribution complexity according to the magnitude probability and the magnitude space probability specifically comprises the following steps:
taking logarithm of the magnitude probability of each subspace, and then obtaining an absolute value to obtain a second absolute value;
multiplying the second absolute value of each subspace with the magnitude probability to obtain the event magnitude complexity of each subspace;
summing the event magnitude complexity of all subspaces to obtain a second sum value;
and multiplying the second sum value with the magnitude space probability and the magnitude distribution correction coefficient to obtain the complexity of the magnitude distribution of the event.
2. A fracture network complexity determination system based on probability distribution, comprising:
the space establishing unit is used for establishing a three-dimensional space containing all the microseism events according to the event information of the microseism events formed during hydraulic fracturing;
the space scanning unit is used for selecting event information of microseism events located in a preset three-dimensional space range at different positions of the three-dimensional space according to a preset rule;
the complexity calculation unit is used for determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space;
The establishing a three-dimensional space containing all the microseism events according to the event information of the microseism events formed during hydraulic fracturing specifically comprises the following steps:
acquiring event information of a microseism event formed during hydraulic fracturing monitored by a microseism monitoring system;
obtaining the position and the magnitude of each microseism event according to the event information;
forming a three-dimensional space containing all the microseism events according to the positions of all the microseism events;
the preset rule is that event information of microseism events in a preset three-dimensional space range at different positions is sequentially selected along three mutually perpendicular coordinate axis directions of the three-dimensional space;
the preset rule is to sequentially select event information of microseism events in a preset three-dimensional space range by taking each microseism event as a center;
the determining the complexity of the fracture network according to the event information obtained by selecting different positions in the three-dimensional space specifically comprises the following steps:
determining event density distribution complexity and event magnitude distribution complexity of a three-dimensional space according to the event information obtained by each selection;
multiplying the event density distribution complexity and the event magnitude distribution complexity to obtain the fracture network complexity;
The determining the event density distribution complexity of the three-dimensional space according to the event information obtained by each selection specifically comprises the following steps:
obtaining the number of microseism events in a preset three-dimensional space when each selection is performed according to the event information obtained by each selection;
calculating event space probability of the number of microseism events in a preset three-dimensional space relative to the number of the microseism events in the three-dimensional space when each selection is performed;
dividing the preset three-dimensional space into a plurality of subspaces and determining the number of microseismic events of each subspace during each selection;
calculating the event probability of the number of the microseism events of each subspace relative to the number of the microseism events in a preset three-dimensional space when each subspace is selected;
obtaining event density distribution complexity according to the event probability and the event space probability;
the obtaining the event density distribution complexity according to the event probability and the event space probability specifically comprises the following steps:
taking the logarithm of the event probability of each subspace, and then obtaining an absolute value to obtain a first absolute value;
multiplying the first absolute value of each subspace with an event probability to obtain the event density complexity of each subspace;
summing the event density complexity of all subspaces to obtain a first sum value;
Multiplying the first sum value with the event space probability and the density distribution correction coefficient to obtain event density distribution complexity;
the determining of the event magnitude distribution complexity of the three-dimensional space according to the event information obtained by each selection specifically comprises the following steps:
obtaining the total number of the earthquake levels of all microseism events in a preset three-dimensional space when each selection is performed according to the event information obtained by each selection;
calculating the magnitude space probability of the total magnitude number of all microseism events in a preset three-dimensional space relative to the three-dimensional space during each selection;
dividing the preset three-dimensional space into a plurality of subspaces and determining the total number of the earthquake magnitudes of all microseism events in each subspace when each selection is performed;
calculating the magnitude probability of the total magnitude of each subspace relative to the total magnitude of all microseism events in the preset three-dimensional space during each selection;
obtaining event magnitude distribution complexity according to the magnitude probability and the magnitude space probability;
obtaining the event magnitude distribution complexity according to the magnitude probability and the magnitude space probability specifically comprises the following steps:
taking logarithm of the magnitude probability of each subspace, and then obtaining an absolute value to obtain a second absolute value;
multiplying the second absolute value of each subspace with the magnitude probability to obtain the event magnitude complexity of each subspace;
Summing the event magnitude complexity of all subspaces to obtain a second sum value;
and multiplying the second sum value with the magnitude space probability and the magnitude distribution correction coefficient to obtain the complexity of the magnitude distribution of the event.
3. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that,
the processor, when executing the program, implements the method of claim 1.
4. A computer readable medium having a computer program stored thereon, characterized in that,
which when executed by a processor implements the method of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910121314.XA CN111597671B (en) | 2019-02-19 | 2019-02-19 | Fracture network complexity determining method and system based on probability distribution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910121314.XA CN111597671B (en) | 2019-02-19 | 2019-02-19 | Fracture network complexity determining method and system based on probability distribution |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111597671A CN111597671A (en) | 2020-08-28 |
| CN111597671B true CN111597671B (en) | 2023-08-22 |
Family
ID=72181281
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910121314.XA Active CN111597671B (en) | 2019-02-19 | 2019-02-19 | Fracture network complexity determining method and system based on probability distribution |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111597671B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015003028A1 (en) * | 2011-03-11 | 2015-01-08 | Schlumberger Canada Limited | Method of calibrating fracture geometry to microseismic events |
| CN106988739A (en) * | 2017-05-19 | 2017-07-28 | 中国石油集团川庆钻探工程有限公司 | Shale reservoir fracturing fracture is recognized and explanation evaluating method |
| WO2017155548A1 (en) * | 2016-03-11 | 2017-09-14 | Halliburton Energy Services, Inc. | Fracture network fluid flow simulation with enhanced fluid-solid interaction force determination |
| CN107220493A (en) * | 2017-05-24 | 2017-09-29 | 王欣 | Shale gas horizontal well network fracture modeling method based on micro-seismic event |
| CN109100790A (en) * | 2018-09-25 | 2018-12-28 | 中国石油天然气股份有限公司 | A kind of analogy method and device of man-made fracture |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10444389B2 (en) * | 2013-06-21 | 2019-10-15 | Schlumberger Technology Corporation | Determining change in permeability caused by a hydraulic fracture in reservoirs |
-
2019
- 2019-02-19 CN CN201910121314.XA patent/CN111597671B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015003028A1 (en) * | 2011-03-11 | 2015-01-08 | Schlumberger Canada Limited | Method of calibrating fracture geometry to microseismic events |
| WO2017155548A1 (en) * | 2016-03-11 | 2017-09-14 | Halliburton Energy Services, Inc. | Fracture network fluid flow simulation with enhanced fluid-solid interaction force determination |
| CN106988739A (en) * | 2017-05-19 | 2017-07-28 | 中国石油集团川庆钻探工程有限公司 | Shale reservoir fracturing fracture is recognized and explanation evaluating method |
| CN107220493A (en) * | 2017-05-24 | 2017-09-29 | 王欣 | Shale gas horizontal well network fracture modeling method based on micro-seismic event |
| CN109100790A (en) * | 2018-09-25 | 2018-12-28 | 中国石油天然气股份有限公司 | A kind of analogy method and device of man-made fracture |
Non-Patent Citations (1)
| Title |
|---|
| 赵超峰 ; 张伟 ; 田建涛 ; 徐海峰 ; 王海波 ; .微地震事件解释实例.石油地球物理勘探.2018,(第04期),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111597671A (en) | 2020-08-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2015210410B2 (en) | Simulating subterranean fracture propagation | |
| US8437962B2 (en) | Generating probabilistic information on subterranean fractures | |
| US9176245B2 (en) | Refining information on subterranean fractures | |
| Matsuki et al. | Determination of three dimensional in situ stress from core discing based on analysis of principal tensile stress | |
| US20190120022A1 (en) | Methods, systems and devices for modelling reservoir properties | |
| US10385659B2 (en) | Evaluation of production performance from a hydraulically fractured well | |
| Zhang et al. | Pore-structure characterization of the Eocene Sha-3 sandstones in the Bohai Bay Basin, China | |
| US9733373B2 (en) | Systems and methods for automated identification of surfaces for building of geologic hydrodynamic model of oil and gas deposit by seismic data | |
| CN112946783A (en) | Method, device and equipment for determining hydrate saturation | |
| CN110470581A (en) | Determine the method, apparatus and storage medium of reservoir stress sensitive degree | |
| Hossain et al. | Volumetric growth and hydraulic conductivity of naturally fractured reservoirs during hydraulic fracturing: a case study using Australian conditions | |
| CN111781662B (en) | Reservoir fracture parameter obtaining method, device and equipment | |
| CN111963149B (en) | Post-fracturing stratum pressure solving method taking earth stagnation amount pressurization into consideration | |
| CN111597671B (en) | Fracture network complexity determining method and system based on probability distribution | |
| CN111650640B (en) | Crack network complexity evaluation method and system | |
| Naugolnov et al. | New approach to estimate reservoir complexity index for West Siberian fields | |
| Schwalbert | Comprehensive Analysis of Acid Stimulation in Carbonates | |
| Chang et al. | Strength anisotropy of jointed rock slope considering mining damage: a numerical analysis method | |
| Zhang et al. | Fracability evaluation method of a fractured-vuggy carbonate reservoir in the shunbei block | |
| CN113946931A (en) | Method and device for determining dominant factors of logging encounter card | |
| CN115144929A (en) | Oil and gas reservoir reserve determining method and device, storage medium and electronic equipment | |
| CN107942400B (en) | A kind of method and device for predicting drilling risk | |
| CN112100796A (en) | Drilling track determination method and device for preventing casing in shale gas well from being damaged | |
| US20150301224A1 (en) | System, Method and Computer Program Product For Evaluating and Ranking GeoBodies Using A Euler Characteristic | |
| CN110907987A (en) | Taylor polygon constraint-based near-surface model modeling method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |