CN113887076A - Method and device for comprehensively evaluating and analyzing shale geological conditions based on mathematical model - Google Patents
Method and device for comprehensively evaluating and analyzing shale geological conditions based on mathematical model Download PDFInfo
- Publication number
- CN113887076A CN113887076A CN202111242770.3A CN202111242770A CN113887076A CN 113887076 A CN113887076 A CN 113887076A CN 202111242770 A CN202111242770 A CN 202111242770A CN 113887076 A CN113887076 A CN 113887076A
- Authority
- CN
- China
- Prior art keywords
- level
- shale
- list
- parameters
- parameter
- 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.)
- Granted
Links
- 238000013178 mathematical model Methods 0.000 title claims abstract description 114
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000011156 evaluation Methods 0.000 claims abstract description 119
- 238000004458 analytical method Methods 0.000 claims abstract description 48
- 238000012512 characterization method Methods 0.000 claims description 73
- 238000011161 development Methods 0.000 claims description 42
- 239000005416 organic matter Substances 0.000 claims description 39
- 239000011148 porous material Substances 0.000 claims description 39
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 28
- 239000011707 mineral Substances 0.000 claims description 28
- 238000012216 screening Methods 0.000 claims description 28
- 230000003993 interaction Effects 0.000 claims description 24
- 238000007789 sealing Methods 0.000 claims description 18
- 238000001179 sorption measurement Methods 0.000 claims description 18
- 238000005259 measurement Methods 0.000 claims description 16
- 238000004422 calculation algorithm Methods 0.000 claims description 15
- 229910001748 carbonate mineral Inorganic materials 0.000 claims description 15
- 230000035699 permeability Effects 0.000 claims description 15
- 238000009933 burial Methods 0.000 claims description 13
- 239000011435 rock Substances 0.000 claims description 12
- 230000008021 deposition Effects 0.000 claims description 6
- 238000004321 preservation Methods 0.000 claims description 6
- 238000000691 measurement method Methods 0.000 claims description 4
- 206010017076 Fracture Diseases 0.000 claims 4
- 208000010392 Bone Fractures Diseases 0.000 claims 3
- 208000013201 Stress fracture Diseases 0.000 claims 2
- 238000010276 construction Methods 0.000 abstract description 18
- 230000010354 integration Effects 0.000 abstract description 8
- 238000004364 calculation method Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 8
- 238000013210 evaluation model Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
- 239000004215 Carbon black (E152) Substances 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 229930195733 hydrocarbon Natural products 0.000 description 4
- 150000002430 hydrocarbons Chemical class 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 206010011416 Croup infectious Diseases 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 201000010549 croup Diseases 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 235000015076 Shorea robusta Nutrition 0.000 description 1
- 244000166071 Shorea robusta Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010494 dissociation reaction Methods 0.000 description 1
- 230000005593 dissociations Effects 0.000 description 1
- 238000004090 dissolution Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 238000012821 model calculation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and a device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, belonging to the technical field of shale gas exploration, wherein a hierarchical list construction and an influential list construction are carried out by reflecting multistage parameters of the shale geological conditions, so that shale gas exploration personnel can comprehensively explore shale in combination with a computer conveniently; by constructing and providing a mathematical model matched with the multi-level parameters, a characteristic value is obtained, and comprehensive evaluation and analysis are carried out on shale geological conditions, so that the shale geological conditions are more intelligent; the method for comprehensively evaluating and analyzing the shale geological condition based on the multi-level parameters is constructed by performing grading integration and influential integration on the multi-parameter, avoids the problem that a plurality of different models are required to be selected for evaluation and analysis aiming at different parameters for many times in the prior art, avoids the complexity of selecting a calculation model, reduces the workload of exploration testers, and is convenient for performing unified digital comprehensive analysis on the shale geological condition.
Description
Technical Field
The application relates to the technical field of shale gas exploration, in particular to a method, a device, equipment and a storage medium for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model.
Background
The gas content is an important index for shale gas geological evaluation, and directly influences the evaluation on whether a shale gas block has industrial exploitation value. The existing state of shale gas comprises three parts of dissociation, adsorption and dissolution, the influence factors of gas content are many, the shale has the self factors (such as shale organic matter type, TOC, thermal evolution degree, crack, porosity, mineral composition, thickness and the like) and also has external factors (such as burial depth, temperature, pressure, humidity and the like of the shale), the gas content of the shale is generally evaluated by adopting a method for measuring the gas adsorption and (or) total gas content by experiments at home and abroad at present, but the problem of determining the gas content of the shale by using the experiment method is as follows: firstly, the adsorption gas is an important component in the shale, but cannot completely represent the gas content of the shale; secondly, the experimental research cost is high, and the period is long; and thirdly, to evaluate the shale gas content in the basin area, a certain amount of experimental samples which are distributed uniformly in a large area are needed, and the shale gas content evaluation is not facilitated due to the limitation of multiple aspects. Although there are many characterization methods for gas bearing, the evaluation indexes and standards for gas bearing of shale are still in exploration, and no evaluation method for gas bearing of shale horizontal wells is available at present.
The method is characterized in that a mathematical model which is more in line with the comprehensive evaluation and analysis of the shale geological conditions is designed according to the grades of the multilevel parameters and the influence of the parameters on the shale geological conditions, the multilevel parameters are comprehensively evaluated through the mathematical model, and the problem that the conditions are not comprehensive enough and lack enough when the shale geological conditions are comprehensively evaluated and analyzed in the prior art is solved.
Disclosure of Invention
The embodiment of the application aims to provide a method, a device, equipment and a storage medium for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, so as to solve the problems in the prior art.
In order to solve the technical problems, the application provides a method for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, and the following technical scheme is adopted: the method comprises the following steps:
acquiring multi-level parameters for evaluating and analyzing shale geological conditions and the relevance among the parameters based on human-computer interaction;
constructing a multi-level list of the multi-level parameters according to the relevance among the parameters, and storing the multi-level list into a preset parameter database;
obtaining measured values corresponding to the multilevel parameters respectively based on human-computer interaction, transmitting the measured values into the preset parameter database, and establishing a one-to-one correspondence relationship with each list in the parameter database respectively;
and constructing a mathematical model, inputting measured values of the elements in the multilevel list, which correspond to the elements in the multilevel list one by one, into the mathematical model as input parameters, and acquiring an output result as a characteristic value for comprehensively evaluating and analyzing the shale geological condition.
Further, the obtaining of the association between the multilevel parameters and the parameters for evaluating and analyzing the shale geological conditions based on the human-computer interaction includes:
providing an operation interface for the researchers to input parameters and relevance;
and acquiring multi-level parameter information and relevance information among the parameters, which are input by researchers and used for evaluating and analyzing the shale geological condition, by adopting a heartbeat reporting mechanism, and reporting the parameter information and the relevance information to a list construction module respectively.
Further, the multi-stage parameters for evaluating and analyzing the shale geological condition at least comprise:
a first level parameter, a second level parameter and a third level parameter,
the primary parameters at least include: the shale has a sedimentary type, a tectonic pattern, a source rock characteristic, a brittle mineral content, a reservoir characteristic and a preservation condition;
the secondary parameters at least comprise: the lithofacies, the effective shale thickness, the organic matter abundance, the thermal evolution degree, the organic matter type, the development condition of the lithostrip, the long-English mineral content, the carbonate mineral content, the porosity, the permeability, the fault condition, the top and bottom plate condition, the self-sealing property, the crack development condition and the burial depth of the shale;
the three-level parameters at least comprise: organic matter pores, inorganic matter pores, microcracks, fault properties, fault scale, fault development conditions, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, pore roar and adsorption performance of the shale.
Further, the relevance between the parameters specifically includes:
the characteristics of the hydrocarbon source rocks in the primary parameters comprise lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter types and lithoid zone development conditions in the secondary parameters;
the brittle mineral content in the first-level parameter comprises the long-english class mineral content and the carbonate mineral content in the second-level parameter;
reservoir characteristics in the primary parameter include porosity and permeability in the secondary parameter;
the storage conditions in the first-level parameters comprise fault conditions, top and bottom plate conditions, self-sealing property, crack development conditions and burial depth in the second-level parameters;
the porosity in the secondary parameters comprises organic porosity, inorganic porosity and microcracks in the tertiary parameters;
fault conditions in the secondary parameters comprise fault properties, fault scale and fault development conditions in the tertiary parameters;
the top and bottom plate conditions in the secondary parameters comprise top and bottom plate lithology, top and bottom plate thickness and top and bottom plate conductivity in the tertiary parameters;
the self-sealing property in the secondary parameters comprises the thickness, the pore throat and the adsorption performance in the tertiary parameters.
Further, the establishing of the multi-level list of the multi-level parameters according to the relevance among the parameters specifically includes:
constructing a primary list by taking the primary parameters as each element in the primary list;
taking the lithofacies, the effective shale thickness, the organic matter abundance, the thermal evolution degree, the organic matter type and the development condition of the lithoid zone in the secondary parameters as elements in a first secondary list, and constructing the first secondary list;
taking the long and English mineral content and the carbonate mineral content in the secondary parameters as each element in a second secondary list to construct a second secondary list;
taking the porosity and permeability in the secondary parameters as elements in a third secondary list, and constructing the third secondary list;
taking fault conditions, top and bottom plate conditions, self-sealing property, crack development conditions and buried depth in the secondary parameters as elements in a fourth secondary list, and constructing the fourth secondary list;
taking organic matter pores, inorganic matter pores and microcracks in the third-level parameters as elements in a first third-level list, and constructing a first third-level list;
taking the fault property, the fault scale and the fault development condition in the three-level parameters as each element in a second three-level list, and constructing a second three-level list;
constructing a third level list by taking the top and bottom plate lithology, the top and bottom plate thickness and the top and bottom plate conductivity in the third level parameters as each element in the third level list;
and constructing a fourth-level list by taking the thickness, the croup and the adsorption performance in the third-level parameters as elements in the fourth-level list.
Further, the constructing a multi-level list of the multi-level parameters according to the relevance between the parameters further includes:
based on the influence of each multi-stage parameter on evaluation and analysis of shale geological conditions, an influence list is constructed, and the influence list comprises: a positive correlation evaluation list, a negative correlation evaluation list and an intermediate evaluation list,
wherein the more the measurement value corresponding to the element in the positive correlation evaluation list is greater than a preset first threshold, the better the shale geological condition is, the more the measurement value corresponding to the element in the negative correlation evaluation list is less than a preset second threshold, the better the shale geological condition is, the more the measurement value corresponding to the element in the intermediate correlation evaluation list is close to a preset third threshold, the better the shale geological condition is,
wherein the elements in the positive correlation evaluation list comprise brittle mineral content, reservoir characteristics, storage conditions, effective shale thickness, organic matter abundance, lithoid zone development, long-English class mineral content, porosity, permeability, top and bottom plate conditions, self-sealing property, burial depth, organic matter pores, inorganic matter pores, microcracks, top and bottom plate lithology, top and bottom plate thickness, pore throat and adsorption performance;
elements in the negative correlation evaluation list comprise crack development condition and top and bottom plate conductivity;
the elements in the inter-type evaluation list comprise deposition types, construction styles, hydrocarbon source rock characteristics, lithofacies, thermal evolution degrees, organic matter types, carbonate mineral content, fault conditions, fault properties and fault scales.
Further, the measured values corresponding to the multiple levels of parameters respectively include:
the mathematical type values obtained based on the existing measurement method for representing the multi-stage parameters at least should include:
the primary parameters without the corresponding secondary parameters comprise deposition types and construction patterns;
the second-level parameters of the shale without the corresponding third-level parameters respectively correspond to mathematical type values, and the second-level parameters of the shale without the corresponding third-level parameters respectively correspond to mathematical type values of lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type, chalkboard development condition, long-English mineral content, carbonate mineral content, permeability, crack development condition and burial depth;
and the three-level parameters of the shale comprise mathematical type values corresponding to organic matter pores, inorganic matter pores, microcracks, fault properties, fault scale, fault development conditions, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, pore roar and adsorption performance respectively.
Further, the building of the mathematical model specifically includes:
based on a preset first algorithm formula:
constructing a shale sample screening model, wherein the shale sample screening model is used for screening out proper shale samples, z represents the sample number of the shale to be analyzed, and x represents1To xzThe measured values which respectively correspond to any one of the same multi-stage parameters of the z shales to be analyzed are represented;
based on a preset second algorithm formula:
constructing a shale positive correlation characterization model, wherein the shale positive correlation characterization model is used for obtaining a first characterization value, x represents a measured value of any element in a positive correlation evaluation list corresponding to any shale sample to be analyzed, and xlRepresenting a preset first threshold value, xbTo xaI.e. [ x ]b，xa]Representing the range of the error value allowable between the measured value of any element in the positive correlation evaluation list and a preset first threshold value;
based on a preset third algorithm formula:
constructing a shale negative correlation characterization model, wherein the shale negative correlation characterization model is used for obtaining a second characterization value, x represents a measured value of any element in a negative correlation evaluation list corresponding to any shale sample to be analyzed, and xmRepresenting a preset second threshold value, xcTo xdI.e. [ x ]c，xd]Representing the allowable error value range between the measured value of any element in the negative correlation evaluation list and a preset second threshold value;
based on a preset fourth algorithm formula:
constructing a shale intermediate type characterization model, wherein the shale intermediate type characterization model is used for obtaining a third characterization value, x represents a measured value of any element in an intermediate type evaluation list corresponding to any shale sample to be analyzed, and xnRepresenting a preset third threshold value, xeTo xfI.e. by
Indicating an allowable error value range between a measured value of any element in the intermediate type evaluation list and a preset third threshold value;
based on a preset fifth algorithm formula:
and constructing a shale comprehensive evaluation and analysis model, wherein the shale comprehensive evaluation and analysis model is used for obtaining a final characteristic value based on the first characteristic value, the second characteristic value and the third characteristic value, wherein z represents the number of samples of the shale to be analyzed, i represents the number of elements in a positive correlation evaluation list corresponding to any one shale sample to be analyzed, j represents the number of elements in a negative correlation evaluation list corresponding to any one shale sample to be analyzed, and k represents the number of elements in an intermediate type evaluation list corresponding to any one shale sample to be analyzed.
Further, the method includes the following specific steps of inputting measured values of the elements in the multilevel list, which correspond to the elements in the multilevel list one by one, into the mathematical model as input parameters, and obtaining output results as characteristic values for comprehensive evaluation and analysis of shale geological conditions:
step 301-1, identifying whether parameters corresponding to elements input into the mathematical model are three-level parameters based on the multi-level list;
step 302-1, if the parameter is a third-level parameter, acquiring a third-level list corresponding to the third-level parameter, and acquiring all elements in the third-level list and measurement values corresponding to all the elements;
305-1, if an influence list corresponding to an element in the mathematical model is a positive correlation evaluation list, transmitting the element to a shale positive correlation characterization model to obtain a first characterization value;
305-2, if the influence list corresponding to the elements in the mathematical model is a negative correlation evaluation list, transmitting the elements to a shale negative correlation characterization model to obtain a second characterization value;
305-3, if the influence list corresponding to the elements in the mathematical model is an intermediate evaluation list, transmitting the elements to a shale intermediate representation model to obtain a third representation value;
step 301-2, identifying whether the parameters corresponding to the elements input into the mathematical model are secondary parameters without corresponding tertiary parameters based on the multi-level list;
step 302-2, if the parameter is a secondary parameter without a corresponding tertiary parameter, acquiring a secondary list corresponding to the secondary parameter without the corresponding tertiary parameter, and simultaneously acquiring all elements in the secondary list and measured values corresponding to all the elements;
307, transmitting all the elements in the secondary list obtained in the step 302-2 and the measured values corresponding to all the elements into the mathematical model, repeatedly executing the steps 303 to 306, and obtaining a final characterization value as a mathematical type value of the primary parameter corresponding to all the elements in the secondary list;
step 301-3, identifying whether the parameters corresponding to the elements input into the mathematical model are first-level parameters without corresponding second-level parameters based on the multi-level list;
step 302-3, if the first-level parameter is a first-level parameter without a corresponding second-level parameter, acquiring a first-level list corresponding to the first-level parameter without the corresponding second-level parameter, and simultaneously acquiring all elements in the first-level list and measured values corresponding to all the elements;
step 308, transmitting all the elements in the primary list obtained in the step 302-3 and the measured values corresponding to all the elements into the mathematical model, repeatedly executing the steps 303 to 306, and obtaining a final characteristic value as a mathematical type value corresponding to all the elements in the primary list, namely a characteristic value for comprehensively evaluating and analyzing the shale geological conditions.
Further, when the shale geological conditions are comprehensively evaluated and analyzed through the mathematical model, if a secondary parameter without a corresponding tertiary parameter or a primary parameter without a corresponding secondary parameter is preferentially obtained, and a secondary parameter with a corresponding tertiary parameter or a mathematical measurement value corresponding to the primary parameter with a corresponding secondary parameter is not measured through the mathematical model or is not input in a human-computer interaction manner, firstly, the step 307 or the step 308 is suspended, and a program is waited until the secondary parameter with a corresponding tertiary parameter or the mathematical measurement value corresponding to the primary parameter with a corresponding secondary parameter is measured through the mathematical model or is input in a human-computer interaction manner, and the step 307 or the step 308 is executed again, wherein the secondary parameter with a corresponding tertiary parameter comprises: porosity, fault conditions, top and bottom plate conditions and self-sealing property, wherein the first-level parameters corresponding to the second-level parameters comprise: source rock characteristics, brittle mineral content, reservoir characteristics, and storage conditions.
In order to solve the above technical problem, an embodiment of the present application further provides a device for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, including:
the parameter input module is used for acquiring multi-level parameters for evaluating and analyzing shale geological conditions and the relevance among the parameters based on human-computer interaction; obtaining measured values respectively corresponding to the multilevel parameters based on human-computer interaction;
the database construction module is used for constructing a multi-level list of the multi-level parameters according to the relevance among the parameters and storing the multi-level list into a preset parameter database; transmitting the measured values into the preset parameter database, and respectively establishing a one-to-one corresponding relation with each list in the parameter database;
and the mathematical model construction, evaluation and analysis module is used for constructing a mathematical model, inputting measured values of the elements in the multilevel list, which correspond to the elements in the multilevel list one by one, into the mathematical model as input parameters, and acquiring an output result as a characterization value for comprehensively evaluating and analyzing the shale geological conditions.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions: the shale geological condition comprehensive evaluation and analysis method based on the mathematical model comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to realize the steps of the method for comprehensively evaluating and analyzing the shale geological condition based on the mathematical model.
In order to solve the above technical problem, an embodiment of the present application further provides a nonvolatile computer-readable storage medium, which adopts the following technical solutions: the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of a method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model as set forth in the embodiments of the present application.
Compared with the prior art, the application mainly has the following beneficial effects:
the embodiment of the application discloses a method, a device, equipment and a storage medium for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, wherein a hierarchical list is constructed by reflecting multistage parameters of the shale geological conditions, and an influence list is constructed, so that shale gas exploration personnel can comprehensively explore shale by combining a computer conveniently; by constructing and providing a mathematical model matched with the multi-level parameters, a characteristic value is obtained, comprehensive evaluation and analysis are carried out on shale geological conditions, the shale gas exploration device is more intelligent, and meanwhile, the manual calculation amount of shale gas exploration personnel is reduced to a certain extent; the method for comprehensively evaluating and analyzing the shale geological condition based on the multi-level parameters is constructed by performing grading integration and influential integration on the multi-parameter, avoids the problem that a plurality of different models are required to be selected for evaluation and analysis aiming at different parameters for many times in the prior art, avoids the complexity of model calculation, reduces the workload of explorations, and is convenient for performing unified digital comprehensive analysis on the shale geological condition.
Drawings
FIG. 1 is a diagram of an exemplary system architecture to which embodiments of the present application may be applied;
FIG. 2 is a flow chart of an embodiment of a method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model as described in the embodiments of the present application;
FIGS. 3, 4 and 5 are flow charts of one embodiment of the mathematical model for program execution after input parameters are entered in the embodiments of the present application;
FIGS. 6a and 6b are logic diagrams of an embodiment of a method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model according to the embodiment of the present application;
FIG. 7 is a schematic structural diagram of an embodiment of an apparatus for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model according to the embodiment of the present application;
fig. 8 is a schematic structural diagram of an embodiment of a computer device in an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the method for comprehensively evaluating and analyzing the shale geological condition based on the mathematical model provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the device for comprehensively evaluating and analyzing the shale geological condition based on the mathematical model is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to fig. 2, a flow chart of an embodiment of the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model according to the present application is shown, and the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model comprises the following steps:
In an embodiment of the present application, the obtaining of the association between the multiple stages of parameters and the parameters for evaluating and analyzing the shale geological conditions based on human-computer interaction includes: providing an operation interface for the researchers to input parameters and relevance; and acquiring multi-level parameter information and relevance information among the parameters, which are input by researchers and used for evaluating and analyzing the shale geological condition, by adopting a heartbeat reporting mechanism, and reporting the parameter information and the relevance information to a list construction module respectively.
In an embodiment of the present application, the multi-stage parameters for evaluating and analyzing shale geological conditions at least include: the method comprises the following steps of primary parameters, secondary parameters and tertiary parameters, wherein the primary parameters at least comprise: the shale has a sedimentary type, a tectonic pattern, a source rock characteristic, a brittle mineral content, a reservoir characteristic, and a preservation condition; the secondary parameters at least comprise: the lithofacies, the effective shale thickness, the organic matter abundance, the thermal evolution degree, the organic matter type, the development condition of the lithostrip, the long-English mineral content, the carbonate mineral content, the porosity, the permeability, the fault condition, the top and bottom plate condition, the self-sealing property, the crack development condition and the burial depth of the shale; the three-level parameters at least comprise: organic matter pores, inorganic matter pores, microcracks, fault properties, fault scale, fault development conditions, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, pore roar and adsorption performance of the shale.
In the embodiment of the present application, the relevance between the parameters specifically includes: the characteristics of the hydrocarbon source rocks in the primary parameters comprise lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter types and lithoid zone development conditions in the secondary parameters; the brittle mineral content in the first-level parameter comprises the long-english class mineral content and the carbonate mineral content in the second-level parameter; reservoir characteristics in the primary parameter include porosity and permeability in the secondary parameter; the storage conditions in the first-level parameters comprise fault conditions, top and bottom plate conditions, self-sealing property, crack development conditions and burial depth in the second-level parameters; the porosity in the secondary parameters comprises organic porosity, inorganic porosity and microcracks in the tertiary parameters; fault conditions in the secondary parameters comprise fault properties, fault scale and fault development conditions in the tertiary parameters; the top and bottom plate conditions in the secondary parameters comprise top and bottom plate lithology, top and bottom plate thickness and top and bottom plate conductivity in the tertiary parameters; the self-sealing property in the secondary parameters comprises the thickness, the pore throat and the adsorption performance in the tertiary parameters.
In this embodiment of the present application, the establishing a multi-level list of the multi-level parameters according to the relevance between the parameters specifically includes: constructing a primary list by taking the primary parameters as each element in the primary list; taking the lithofacies, the effective shale thickness, the organic matter abundance, the thermal evolution degree, the organic matter type and the development condition of the lithoid zone in the secondary parameters as elements in a first secondary list, and constructing the first secondary list; taking the long and English mineral content and the carbonate mineral content in the secondary parameters as each element in a second secondary list to construct a second secondary list; taking the porosity and permeability in the secondary parameters as elements in a third secondary list, and constructing the third secondary list; taking fault conditions, top and bottom plate conditions, self-sealing property, crack development conditions and buried depth in the secondary parameters as elements in a fourth secondary list, and constructing the fourth secondary list; taking organic matter pores, inorganic matter pores and microcracks in the third-level parameters as elements in a first third-level list, and constructing a first third-level list; taking the fault property, the fault scale and the fault development condition in the three-level parameters as each element in a second three-level list, and constructing a second three-level list; constructing a third level list by taking the top and bottom plate lithology, the top and bottom plate thickness and the top and bottom plate conductivity in the third level parameters as each element in the third level list; and constructing a fourth-level list by taking the thickness, the croup and the adsorption performance in the third-level parameters as elements in the fourth-level list.
In this embodiment of the present application, the constructing a multi-level list of the multi-level parameters according to the relevance between the parameters further includes: based on the influence of each multi-stage parameter on evaluation and analysis of shale geological conditions, an influence list is constructed, and the influence list comprises: the shale geological condition is better when the measured value corresponding to the element in the positive correlation evaluation list is larger than a preset first threshold value, the shale geological condition is better when the measured value corresponding to the element in the negative correlation evaluation list is smaller than a preset second threshold value, and the shale geological condition is better when the measured value corresponding to the element in the intermediate evaluation list is close to a preset third threshold value, wherein the element in the positive correlation evaluation list comprises brittle mineral content, storage characteristics, storage conditions, effective shale thickness, organic matter abundance, rubble belt development condition, long-English mineral content, porosity, permeability, top and bottom plate conditions, self-sealing property, burial depth, organic matter pore, inorganic matter pore, microcrack, top and bottom plate lithology, Top and bottom plate thickness, pore throat and adsorption performance; elements in the negative correlation evaluation list comprise crack development condition and top and bottom plate conductivity; the elements in the inter-type evaluation list comprise deposition types, construction styles, hydrocarbon source rock characteristics, lithofacies, thermal evolution degrees, organic matter types, carbonate mineral content, fault conditions, fault properties and fault scales.
In an embodiment of the present application, the measured values corresponding to the multiple levels of parameters respectively include: the mathematical type values obtained based on the existing measurement method for representing the multi-stage parameters at least should include: the primary parameters without the corresponding secondary parameters comprise deposition types and construction patterns; the second-level parameters of the shale without the corresponding third-level parameters respectively correspond to mathematical type values, and the second-level parameters of the shale without the corresponding third-level parameters respectively correspond to mathematical type values of lithofacies, effective shale thickness, organic matter abundance, thermal evolution degree, organic matter type, chalkboard development condition, long-English mineral content, carbonate mineral content, permeability, crack development condition and burial depth; and the three-level parameters of the shale comprise mathematical type values corresponding to organic matter pores, inorganic matter pores, microcracks, fault properties, fault scale, fault development conditions, top and bottom plate lithology, top and bottom plate thickness, top and bottom plate conductivity, thickness, pore roar and adsorption performance respectively.
And 204, constructing a mathematical model, inputting measured values of the elements in the multilevel list, which correspond to the elements in the multilevel list one by one, into the mathematical model as input parameters, and acquiring an output result as a characteristic value for comprehensively evaluating and analyzing the shale geological condition.
In an embodiment of the present application, the constructing a mathematical model specifically includes:
based on a preset first algorithm formula:
constructing a shale sample screening model, wherein the shale sample screening model is used for screening out proper shale samples, z represents the sample number of the shale to be analyzed, and x represents1To xzAny of the various multi-level parameters representing z of said shale to be analyzedMeasured values corresponding to the same parameters respectively;
based on a preset second algorithm formula:
constructing a shale positive correlation characterization model, wherein the shale positive correlation characterization model is used for obtaining a first characterization value, x represents a measured value of any element in a positive correlation evaluation list corresponding to any shale sample to be analyzed, and xlRepresenting a preset first threshold value, xbTo xaI.e. [ x ]b，xa]Representing the range of the error value allowable between the measured value of any element in the positive correlation evaluation list and a preset first threshold value;
based on a preset third algorithm formula:
constructing a shale negative correlation characterization model, wherein the shale negative correlation characterization model is used for obtaining a second characterization value, x represents a measured value of any element in a negative correlation evaluation list corresponding to any shale sample to be analyzed, and xmRepresenting a preset second threshold value, xcTo xdI.e. [ x ]c，xd]Representing the allowable error value range between the measured value of any element in the negative correlation evaluation list and a preset second threshold value;
based on a preset fourth algorithm formula:
constructing a shale intermediate type characterization model, wherein the shale intermediate type characterization model is used for obtaining a third characterization value, x represents a measured value of any element in an intermediate type evaluation list corresponding to any shale sample to be analyzed, and xnRepresenting a preset third threshold value, xeTo xfI.e. by
Indicating an allowable error value range between a measured value of any element in the intermediate type evaluation list and a preset third threshold value;
based on a preset fifth algorithm formula:
and constructing a shale comprehensive evaluation and analysis model, wherein the shale comprehensive evaluation and analysis model is used for obtaining a final characteristic value based on the first characteristic value, the second characteristic value and the third characteristic value, wherein z represents the number of samples of the shale to be analyzed, i represents the number of elements in a positive correlation evaluation list corresponding to any one shale sample to be analyzed, j represents the number of elements in a negative correlation evaluation list corresponding to any one shale sample to be analyzed, and k represents the number of elements in an intermediate type evaluation list corresponding to any one shale sample to be analyzed.
Explanation: in specific use, assuming that the geological condition (gas-containing performance) of shale in a certain (A) region range is measured, firstly, selecting a plurality of shale samples in the A region range, measuring values of three-level parameters corresponding to the shale samples, two-level parameters without corresponding three-level parameters and one-level parameters without corresponding two-level parameters, respectively setting reference thresholds of each three-level parameter, two-level parameters without corresponding three-level parameters and one-level parameters without corresponding two-level parameters, then, screening out proper samples by using a shale sample screening model, respectively performing mathematical measurement on the three-level parameters, the two-level parameters with corresponding three-level parameters and the one-level parameters with corresponding two-level parameters in the proper samples through a shale positive correlation evaluation model, a shale negative correlation evaluation model and a shale intermediate evaluation model, and obtaining a shale sample comprehensive evaluation and analysis model for comprehensively evaluating and classifying the shale geological condition And analyzing the characteristic value, taking the characteristic value as a reference value, comparing the characteristic value of the new shale sample determined by the method with the reference value after the reference value is determined, determining the geological condition corresponding to the new shale sample according to the comparison result, and judging the gas storage or gas containing capacity of the shale in the area A according to the geological condition.
In addition, in the embodiment of the present application, the method may further include: and taking the final characterization value obtained based on the first characterization value, the second characterization value and the third characterization value as a reference value, namely a characterization threshold value, taking a new shale sample as a test sample, obtaining a new characterization value through steps 201 to 204, and if the new characterization value is greater than the characterization threshold value, then in terms of gas storage performance, the geological condition of the new shale sample is superior to that of the shale sample corresponding to the characterization threshold value, otherwise, in terms of gas storage performance, the geological condition of the new shale sample is inferior to that of the shale sample corresponding to the characterization threshold value, and if the new characterization value is equal to the characterization threshold value, then in terms of gas storage performance, the geological condition of the new shale sample is substantially the same as that of the shale sample corresponding to the characterization threshold value.
In an embodiment of the present application, with reference to fig. 3, fig. 4, and fig. 5 in particular, fig. 3, fig. 4, and fig. 5 collectively show a flowchart of an embodiment of program execution performed by the mathematical model after input parameters are introduced in the embodiment of the present application, and the method inputs measurement values of elements in the multilevel list and elements in the multilevel list, which correspond to each other one by one, into the mathematical model as input parameters, and obtains an output result as a characteristic value for performing comprehensive evaluation and analysis on shale geological conditions, and specifically includes the steps of:
step 301-1, identifying whether parameters corresponding to elements input into the mathematical model are three-level parameters based on the multi-level list;
step 302-1, if the parameter is a third-level parameter, acquiring a third-level list corresponding to the third-level parameter, and acquiring all elements in the third-level list and measurement values corresponding to all the elements;
305-1, if an influence list corresponding to an element in the mathematical model is a positive correlation evaluation list, transmitting the element to a shale positive correlation characterization model to obtain a first characterization value;
305-2, if the influence list corresponding to the elements in the mathematical model is a negative correlation evaluation list, transmitting the elements to a shale negative correlation characterization model to obtain a second characterization value;
305-3, if the influence list corresponding to the elements in the mathematical model is an intermediate evaluation list, transmitting the elements to a shale intermediate representation model to obtain a third representation value;
step 301-2, identifying whether the parameters corresponding to the elements input into the mathematical model are secondary parameters without corresponding tertiary parameters based on the multi-level list;
step 302-2, if the parameter is a secondary parameter without a corresponding tertiary parameter, acquiring a secondary list corresponding to the secondary parameter without the corresponding tertiary parameter, and simultaneously acquiring all elements in the secondary list and measured values corresponding to all the elements;
307, transmitting all the elements in the secondary list obtained in the step 302-2 and the measured values corresponding to all the elements into the mathematical model, repeatedly executing the steps 303 to 306, and obtaining a final characterization value as a mathematical type value of the primary parameter corresponding to all the elements in the secondary list;
step 301-3, identifying whether the parameters corresponding to the elements input into the mathematical model are first-level parameters without corresponding second-level parameters based on the multi-level list;
step 302-3, if the first-level parameter is a first-level parameter without a corresponding second-level parameter, acquiring a first-level list corresponding to the first-level parameter without the corresponding second-level parameter, and simultaneously acquiring all elements in the first-level list and measured values corresponding to all the elements;
step 308, transmitting all the elements in the primary list obtained in the step 302-3 and the measured values corresponding to all the elements into the mathematical model, repeatedly executing the steps 303 to 306, and obtaining a final characteristic value as a mathematical type value corresponding to all the elements in the primary list, namely a characteristic value for comprehensively evaluating and analyzing the shale geological conditions.
In this embodiment of the application, when the shale geological conditions are comprehensively evaluated and analyzed through the mathematical model, if a secondary parameter without a corresponding tertiary parameter or a primary parameter without a corresponding secondary parameter is preferentially obtained, and a secondary parameter with a corresponding tertiary parameter or a mathematical measurement value corresponding to a primary parameter with a corresponding secondary parameter is not yet measured through the mathematical model or is not input in a human-computer interaction manner, the step 307 or the step 308 is terminated in advance, and a program is waited until the secondary parameter with a corresponding tertiary parameter or the mathematical measurement value corresponding to the primary parameter with a corresponding secondary parameter is measured through the mathematical model or is input in a human-computer interaction manner, and the step 307 or the step 308 is executed again, where the secondary parameter with a corresponding tertiary parameter includes: porosity, fault conditions, top and bottom plate conditions and self-sealing property, wherein the first-level parameters corresponding to the second-level parameters comprise: source rock characteristics, brittle mineral content, reservoir characteristics, and storage conditions.
With continuing reference to fig. 6a and 6b, fig. 6a and 6b together illustrate a logical schematic diagram of an embodiment of the method for comprehensive evaluation and analysis of shale geological conditions based on a mathematical model in the embodiment of the present application,
wherein the execution logic diagram shown in fig. 6a shows: the method comprises the steps of obtaining a first parameter, a second parameter and a third parameter which are input by a user in a man-machine interaction mode, establishing a grade list of the first parameter, the second parameter and the third parameter based on relevance input by the user, determining a secondary parameter without a corresponding tertiary parameter, a primary parameter without a corresponding secondary parameter, a secondary parameter with a corresponding tertiary parameter and a primary parameter with a corresponding secondary parameter, establishing a classification list of the first parameter, the second parameter and the third parameter based on the influence of the first parameter, the second parameter and the third parameter on shale geological conditions, establishing a positive correlation evaluation list, a negative correlation evaluation list and an intermediate evaluation list, and establishing a mathematical model for evaluating and analyzing the shale geological conditions, wherein the mathematical model comprises the following steps of: the shale sample screening model is used for screening shale samples, screening out proper shale samples, selecting a shale positive correlation evaluation model, a shale negative correlation evaluation model or a shale intermediate evaluation model for the first-level parameter, the second-level parameter and the third-level parameter respectively for the proper shale samples to evaluate measured values based on the positive correlation evaluation list, the negative correlation evaluation list and the intermediate evaluation list, and finally obtaining a characterization value for comprehensively evaluating and analyzing the shale geological conditions based on the mathematical model;
the execution logic diagram shown in FIG. 6b shows: identifying whether the parameters corresponding to the elements input into the mathematical model are three-level parameters, two-level parameters without corresponding three-level parameters and one-level parameters without corresponding two-level parameters based on the multi-level list; if the parameters are three-level parameters, two-level parameters without corresponding three-level parameters and one-level parameters without corresponding two-level parameters, acquiring a three-level list corresponding to the three-level parameters, and simultaneously acquiring all elements in the three-level list and measured values corresponding to all the elements; if the parameter is a secondary parameter without a corresponding tertiary parameter, acquiring a secondary list corresponding to the secondary parameter without the corresponding tertiary parameter, and simultaneously acquiring all elements in the secondary list and measured values corresponding to all the elements; if the first-level parameters are the first-level parameters without the corresponding second-level parameters, acquiring a first-level list corresponding to the first-level parameters without the corresponding second-level parameters, and simultaneously acquiring all elements in the first-level list and measured values corresponding to all the elements; transmitting the measured values corresponding to all the elements into a shale sample screening model, and screening out a proper shale sample; after screening out a proper shale sample based on the shale sample screening model, identifying an influence list corresponding to elements input into the mathematical model; if the influence list corresponding to the elements in the mathematical model is a positive correlation evaluation list, transmitting the elements to a shale positive correlation characterization model to obtain a first characterization value; if the influence list corresponding to the elements in the mathematical model is a negative correlation evaluation list, transmitting the elements to a shale negative correlation characterization model to obtain a second characterization value; if the influence list corresponding to the elements in the mathematical model is an intermediate evaluation list, transmitting the elements to a shale intermediate representation model to obtain a third representation value; after the first characteristic value, the second characteristic value and the third characteristic value are obtained, the first characteristic value, the second characteristic value and the third characteristic value are transmitted to the shale comprehensive evaluation and analysis model to obtain final characteristic values which are respectively used as a mathematical type value of a secondary parameter corresponding to all elements in the tertiary list, a mathematical type value of a primary parameter corresponding to all elements in the secondary list and a characteristic value for comprehensively evaluating and analyzing the shale geological condition, when the shale geological condition is comprehensively evaluated and analyzed through the mathematical model, if a secondary parameter without corresponding to a tertiary parameter or a primary parameter without corresponding to the secondary parameter is preferentially obtained, and a secondary parameter with corresponding to the tertiary parameter or a mathematical measurement value corresponding to the primary parameter with corresponding to the secondary parameter is not measured through the mathematical model or is not input in a man-machine interaction mode, and stopping the procedure, waiting until the second-level parameters corresponding to the third-level parameters or the mathematical measurement values corresponding to the first-level parameters corresponding to the second-level parameters are measured through the mathematical model or input in a man-machine interaction mode, and executing the procedure again to obtain the mathematical type values of the first-level parameters corresponding to all elements in the second-level list and the characterization values for comprehensively evaluating and analyzing the shale geological conditions.
According to the method for comprehensively evaluating and analyzing the shale geological condition based on the mathematical model, the hierarchical list construction and the influential list construction can be performed by reflecting the multi-level parameters of the shale geological condition, so that shale gas exploration personnel can comprehensively explore shale by combining a computer conveniently; by constructing and providing a mathematical model matched with the multi-level parameters, a characteristic value is obtained, and comprehensive evaluation and analysis are carried out on shale geological conditions, so that the shale geological conditions are more intelligent; the method for comprehensively evaluating and analyzing the shale geological condition based on the multi-level parameters is constructed by performing grading integration and influential integration on the multi-parameter, avoids the problem that a plurality of different models are required to be selected for evaluation and analysis aiming at different parameters for many times in the prior art, avoids the complexity of selecting a calculation model, reduces the workload of exploration and test personnel, and is convenient for performing unified digital comprehensive analysis on the shale geological condition.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 7, as an implementation of the method shown in fig. 2, the present application provides an embodiment of an apparatus for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 7, the device 7 for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model according to the present embodiment includes: a parameter input module 701, a database construction module 702, and a mathematical model construction and evaluation and analysis module 703. Wherein:
the parameter input module 701 is used for acquiring multi-level parameters for evaluating and analyzing shale geological conditions and the relevance among the parameters based on human-computer interaction; obtaining measured values respectively corresponding to the multilevel parameters based on human-computer interaction;
a database construction module 702, configured to perform multilevel list construction on the multilevel parameters according to the relevance between the parameters, and store the multilevel list in a preset parameter database; transmitting the measured values into the preset parameter database, and respectively establishing a one-to-one corresponding relation with each list in the parameter database;
and the mathematical model constructing, evaluating and analyzing module 703 is configured to construct a mathematical model, input measurement values of the elements in the multilevel list, which correspond to the elements in the multilevel list one to one, into the mathematical model as input parameters, and obtain an output result as a characterization value for performing comprehensive evaluation and analysis on the shale geological condition.
According to the device for comprehensively evaluating and analyzing the shale geological condition based on the mathematical model, the hierarchical list construction and the influential list construction are carried out by reflecting the multi-level parameters of the shale geological condition, so that shale gas exploration personnel can comprehensively explore shale by combining a computer conveniently; by constructing and providing a mathematical model matched with the multi-level parameters, a characteristic value is obtained, and comprehensive evaluation and analysis are carried out on shale geological conditions, so that the shale geological conditions are more intelligent; the method for comprehensively evaluating and analyzing the shale geological condition based on the multi-level parameters is constructed by performing grading integration and influential integration on the multi-parameter, avoids the problem that a plurality of different models are required to be selected for evaluation and analysis aiming at different parameters for many times in the prior art, avoids the complexity of selecting a calculation model, reduces the workload of exploration and test personnel, and is convenient for performing unified digital comprehensive analysis on the shale geological condition.
To solve the above technical problem, an embodiment of the present application further provides a computer device, and specifically refer to fig. 8, where fig. 8 is a block diagram of a basic structure of the computer device according to the embodiment.
The computer device 8 comprises a memory 8a, a processor 8b, a network interface 8c communicatively connected to each other via a system bus. It is noted that only a computer device 8 having components 8a-8c is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 8a includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 8a may be an internal storage unit of the computer device 8, such as a hard disk or a memory of the computer device 8. In other embodiments, the memory 8a may also be an external storage device of the computer device 8, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 8. Of course, the memory 8a may also comprise both an internal storage unit of the computer device 8 and an external storage device thereof. In this embodiment, the memory 8a is generally used for storing an operating system installed in the computer device 8 and various application software, such as program codes of a method for comprehensively evaluating and analyzing shale geological conditions based on a mathematical model. In addition, the memory 8a may also be used to temporarily store various types of data that have been output or are to be output.
The processor 8b may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data Processing chip in some embodiments. The processor 8b is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 8b is configured to run the program code stored in the memory 8a or process data, for example, the program code of the method for comprehensively evaluating and analyzing the shale geological condition based on the mathematical model.
The network interface 8c may comprise a wireless network interface or a wired network interface, and the network interface 8c is typically used to establish a communication connection between the computer device 8 and other electronic devices.
The present application further provides a non-transitory computer readable storage medium storing a program for performing a mathematical model-based comprehensive evaluation and analysis of shale geological conditions, where the program is executable by at least one processor to cause the at least one processor to perform the steps of the method for performing the mathematical model-based comprehensive evaluation and analysis of shale geological conditions as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111242770.3A CN113887076B (en) | 2021-10-25 | 2021-10-25 | Method and device for comprehensive evaluation and analysis of shale geological conditions based on mathematical model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111242770.3A CN113887076B (en) | 2021-10-25 | 2021-10-25 | Method and device for comprehensive evaluation and analysis of shale geological conditions based on mathematical model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113887076A true CN113887076A (en) | 2022-01-04 |
| CN113887076B CN113887076B (en) | 2025-02-14 |
Family
ID=79014034
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111242770.3A Active CN113887076B (en) | 2021-10-25 | 2021-10-25 | Method and device for comprehensive evaluation and analysis of shale geological conditions based on mathematical model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113887076B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114781040A (en) * | 2022-05-10 | 2022-07-22 | 青岛理工大学 | Building blasting vibration safety evaluation method, device, equipment and medium |
| CN114840970A (en) * | 2022-03-16 | 2022-08-02 | 中国石油化工股份有限公司 | Method for evaluating shale oil reservoir resources |
| CN118897972A (en) * | 2024-07-15 | 2024-11-05 | 中国地质调查局油气资源调查中心 | A method for identifying shale gas sweet spots based on radioactive elements |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN107102354A (en) * | 2016-12-21 | 2017-08-29 | 中国石油化工股份有限公司江汉油田分公司物探研究院 | A kind of shale dessert seismic Integrated Evaluation method |
| CN110245855A (en) * | 2019-06-06 | 2019-09-17 | 中国石油化工股份有限公司 | The normal pressure shale gas preservation condition quantitative analysis method of low exploration activity field area |
| CN111691878A (en) * | 2019-03-13 | 2020-09-22 | 中国石油化工股份有限公司 | Lithofacies quality grading evaluation method and device for shale |
-
2021
- 2021-10-25 CN CN202111242770.3A patent/CN113887076B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016041189A1 (en) * | 2014-09-19 | 2016-03-24 | 杨顺伟 | Method for evaluating shale gas reservoir and seeking desert area |
| CN107102354A (en) * | 2016-12-21 | 2017-08-29 | 中国石油化工股份有限公司江汉油田分公司物探研究院 | A kind of shale dessert seismic Integrated Evaluation method |
| CN111691878A (en) * | 2019-03-13 | 2020-09-22 | 中国石油化工股份有限公司 | Lithofacies quality grading evaluation method and device for shale |
| CN110245855A (en) * | 2019-06-06 | 2019-09-17 | 中国石油化工股份有限公司 | The normal pressure shale gas preservation condition quantitative analysis method of low exploration activity field area |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114840970A (en) * | 2022-03-16 | 2022-08-02 | 中国石油化工股份有限公司 | Method for evaluating shale oil reservoir resources |
| CN114781040A (en) * | 2022-05-10 | 2022-07-22 | 青岛理工大学 | Building blasting vibration safety evaluation method, device, equipment and medium |
| CN114781040B (en) * | 2022-05-10 | 2023-07-07 | 青岛理工大学 | Building blasting vibration safety evaluation method, device, equipment and medium |
| CN118897972A (en) * | 2024-07-15 | 2024-11-05 | 中国地质调查局油气资源调查中心 | A method for identifying shale gas sweet spots based on radioactive elements |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113887076B (en) | 2025-02-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113887076B (en) | Method and device for comprehensive evaluation and analysis of shale geological conditions based on mathematical model | |
| EP3397833B1 (en) | Machine learning for production prediction | |
| CN113792936A (en) | A method, system, equipment and storage medium for intelligent identification of lithology while drilling | |
| CN113065279A (en) | Method, device, equipment and storage medium for predicting total organic carbon content | |
| Chen et al. | Prediction of shale gas horizontal wells productivity after volume fracturing using machine learning–an LSTM approach | |
| CN114638300A (en) | Method, device and storage medium for identifying desserts of shale oil and gas reservoir | |
| CN115358285B (en) | Method, device, and equipment for selecting key geological parameters of blocks to be surveyed | |
| Thiele et al. | Evolve: A linear workflow for quantifying reservoir uncertainty | |
| CN116927771A (en) | A method, device, equipment and medium for predicting total organic carbon data of shale reservoirs | |
| CN115757075A (en) | Task abnormity detection method and device, computer equipment and storage medium | |
| CN115146976A (en) | Method and device for selecting main geological parameters influencing evaluation of block to be surveyed | |
| CN106338778A (en) | Shale lithofacies continuity prediction method based on logging information | |
| CN113738353B (en) | Method, system, equipment and storage medium for predicting movable oil quantity of oil-containing shale | |
| CN114398828A (en) | Drilling rate intelligent prediction and optimization method, system, equipment and medium | |
| CN110083930A (en) | The construction method and device of shale weathering index | |
| He et al. | Adaptive material balance method for reserve evaluation: A combination of machine learning and reservoir engineering | |
| Anifowose et al. | A hybrid of functional networks and support vector machine models for the prediction of petroleum reservoir properties | |
| CN106285661B (en) | Low-resistance oil layer identification method and device based on judgment index | |
| Nematolahi et al. | Improved Kaplan‐Meier Estimator in Survival Analysis Based on Partially Rank‐Ordered Set Samples | |
| Sharma et al. | Classification of oil and gas reservoirs based on recovery factor: a data-mining approach | |
| CN111580179B (en) | Method, device and system for determining organic carbon content | |
| CN117875494A (en) | Single well production prediction method and device for low permeability oilfield based on WOA | |
| CN114755744B (en) | Total organic carbon logging interpretation method and system based on shale heterogeneity characteristics | |
| Palmer | Using AI and Machine Learning to Indicate Shale Anisotropy and Assist in Completions Design | |
| Nie et al. | Bagging machine learning algorithms for rapid identification, classification, evaluation and upscaling in unconventional reservoir |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |