CN113971351B - Method and device for determining porosity of crack - Google Patents
Method and device for determining porosity of crack Download PDFInfo
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Abstract
The invention provides a method and a device for determining fracture porosity, wherein the method comprises the following steps: obtaining a logging curve of a target research area, wherein the logging curve comprises a depth lateral resistivity curve; calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves; weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves; training a probabilistic neural network model according to the logging curve and the weighted fracture porosity curve to obtain a probabilistic neural network fracture porosity curve; and determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve. The method can calculate the porosity of the crack and has high precision.
Description
Technical Field
The invention relates to the technical field of oil and gas logging, in particular to a method and a device for determining fracture porosity.
Background
The fracture is used as an oil and gas storage space and a channel for oil and gas migration, and is important content for research of a fracture type reservoir. The fracture porosity parameter is one of important parameters in the logging evaluation of fractured reservoirs, and the accuracy of the value directly influences the fracture effectiveness evaluation quality. The direct identification method of the cracks comprises the steps of observing field geological outcrop cracks and identifying core cracks under the macro scale, analyzing conventional sheets and casting body sheets, analyzing a scanning electron microscope, scanning CT (computed tomography) and identifying the cracks based on core hole seepage analysis under the micro scale, and reflecting the effectiveness of the cracks by dynamic information in engineering, such as slurry loss, gas logging information, drilling tool information during drilling, well cementation quality information, fracturing construction information, test oil and test production dynamic information, test well information and the like.
The method for identifying and evaluating the cracks based on the logging data comprises an imaging logging crack identification method, a nuclear magnetic resonance evaluation method, a conventional logging crack identification method, a multi-information comprehensive evaluation method and other research methods. Imaging logging is the most effective means for fracture evaluation, and the application range is limited by higher cost. In production practice, the depth lateral logging in conventional logging has strong focusing capability, and a good corresponding relationship also exists between the development degree of the crack and the resistivity, for example, carbonate rock generally has the characteristic of high resistivity, while a crack zone has the characteristic of relatively low resistivity, and in the crack zone, the depth lateral resistivity and the shallow lateral resistivity show obvious positive and negative amplitude differences, and the positive difference reflects the high-angle seam of reservoir development; negative differences reflect low angle seams in reservoir development. Therefore, the study of fractured reservoirs using conventional deep and shallow laterolog is one of the more widely used methods, and a great deal of research work has been carried out in this regard. In practical application, due to the difference of geological conditions, the occurrence and distribution density of cracks are difficult to accurately and effectively identify, and in addition, the difficulty of extracting crack parameters is high, so that the precision of calculating the porosity of the cracks is difficult to meet.
Disclosure of Invention
The embodiment of the invention provides a determination method of fracture porosity, which is used for calculating the fracture porosity and has high precision, and the method comprises the following steps:
obtaining a logging curve of a target research area, wherein the logging curve comprises a depth lateral resistivity curve;
calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves;
weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves;
training a probabilistic neural network model according to the logging curve and the weighted fracture porosity curve to obtain a probabilistic neural network fracture porosity curve;
and determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve.
The embodiment of the invention provides a device for determining the porosity of a crack, which is used for calculating the porosity of the crack and has high precision, and the device comprises:
the logging curve obtaining module is used for obtaining logging curves of a target research area, and the logging curves comprise depth lateral resistivity curves;
the calculation module is used for calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves;
the weighting module is used for weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves;
the probability neural network fracture porosity curve obtaining module is used for training a probability neural network model according to the logging curve and the fracture porosity curve after weighting processing to obtain a probability neural network fracture porosity curve;
and the fracture porosity determination module is used for determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the determination method of the fracture porosity when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program for executing the method for determining the porosity of the crack.
In the embodiment of the invention, a logging curve of a target research area is obtained, wherein the logging curve comprises a depth lateral resistivity curve; calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves; weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves; training a probabilistic neural network model according to the logging curve and the fracture porosity curve after weighting treatment to obtain a probability neural network fracture porosity curve; and determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve. In the process, the depth lateral resistivity curves are adopted to calculate the fracture porosity curves in various resistivity models, namely various different resistivity models are considered, and in addition, the probability neural network model is trained, so that the precision of the obtained probability neural network fracture porosity curves is very high, and the precision of the finally determined fracture porosity of the target research area is very high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts. In the drawings:
FIG. 1 is a flow chart of a method of determining fracture porosity in an embodiment of the invention;
FIG. 2 is a schematic representation of fracture porosity curves in four resistivity models in an example of the invention;
FIG. 3 is a plot of the fracture porosity after weighting treatment in an example of the invention;
FIG. 4 is a schematic diagram of a learning model and a verification model corresponding to four single wells in an embodiment of the invention;
FIG. 5 is a graphical representation of a probabilistic neural network fracture porosity curve for a second study region obtained in an embodiment of the present invention;
FIG. 6 is a comparison of fracture porosity curves calculated by various algorithms;
FIG. 7 is a schematic view of a fracture porosity determination apparatus according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a computer device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are used in an open-ended fashion, i.e., to mean including, but not limited to. Reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is for illustrative purposes to illustrate the implementation of the present application, and the sequence of steps is not limited and can be adjusted as needed.
Fig. 1 is a flow chart of a method for determining fracture porosity in an embodiment of the invention, as shown in fig. 1, the method comprising:
101, obtaining a logging curve of a target research area, wherein the logging curve comprises a depth lateral resistivity curve;
102, calculating fracture porosity curves in various resistivity models by using the deep and shallow lateral resistivity curves;
103, weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves;
104, training a probabilistic neural network model according to the well logging curve and the weighted fracture porosity curve to obtain a probabilistic neural network fracture porosity curve;
and 105, determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve.
In the method provided by the embodiment of the invention, the depth lateral resistivity curves are adopted to calculate the fracture porosity curves in various resistivity models, namely various different resistivity models are considered, and in addition, the probabilistic neural network model is trained, so that the precision of the obtained probabilistic neural network fracture porosity curves is very high, and the precision of the finally determined fracture porosity of the target research area is very high.
In specific implementation, the embodiment of the invention can finally determine the high-precision fracture porosity by a training method of a probabilistic neural network model based on a logging curve, thereby solving the problem of single-well fracture identification and calculation without single-well imaging logging information, achieving the technical problem of low accuracy of fracture porosity calculation, realizing the calculation of the fracture porosity of a well without single-well imaging logging information, improving the accuracy of fracture porosity calculation, and providing reliable well point data for subsequent fracture research.
In an embodiment, the well log further comprises at least one of a sonic moveout curve, a density curve, and a neutron curve. In fact, well logs are often used for the subsequent training of probabilistic neural network models, and four types of well logs are often used.
In one embodiment, the resistivity model is one or any combination of a SIBBIT model, a P-A model, a mesh fracture model, and a three-dimensional finite element model. Wherein, the P-A model, namely the Philippe A.Pezard-Anderon model, is used for respectively calculating fracture porosity curves in the four resistivity models to form four curves. The basic principle of the resistivity models is mainly that actual irregular and non-uniformly distributed cracks and matrix geological bodies are simplified into various regularly and uniformly distributed idealized models according to a volume model, then the serial-parallel connection relation of the cracks and the matrix rock under different conditions is determined through certain assumed conditions, the relation between the crack initiation porosity and the resistivity is established, and then the crack porosity is solved. The main considered influence factors of each resistivity model parameter include parameters such as slurry filtrate resistivity, formation water resistivity, fracture dip angle and the like. These parameters vary in value depending on the particular work area.
In an embodiment, weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves includes:
weighting the multiple fracture porosity curves by adopting the following weighting factors to obtain weighted fracture porosity curves:
wherein δ is a weighting factor; r ismfIs the mud filtrate resistivity; LLD and LLS are deep and shallow lateral resistivity; a is the empirical value of the target study area.
In the embodiment, the weighting factor mainly considers the situation that the depth resistivity amplitude difference and the depth resistivity ratio reflect the fracture, the weighting factor is a curve, the error between the weighting calculation result and the fracture porosity explained by the imaging logging is small, and the weighting calculation fracture porosity is higher than the fracture porosity coincidence rate calculated by the basic resistivity model.
In one embodiment, the probabilistic neural network model includes a learning model and a verification model;
the learning model is used for taking the logging curve as input, taking the weighted fracture porosity curve as output, and adopting a probabilistic neural network algorithm to construct a probabilistic neural network model to obtain parameters of the probabilistic neural network model;
the verification model is used for obtaining a probability neural network fracture porosity curve based on the constructed probability neural network model.
To illustrate the effectiveness of the method for determining fracture porosity, a specific example is given below, which includes a verification of the above method.
Firstly, acquiring fracture data, single-well imaging logging data and a logging curve of a second research area, wherein the logging curve comprises a depth lateral resistivity curve, namely the second research area can acquire the single-well imaging logging data, and the fracture data comprises crack development characteristics, crack distribution rule data and core data on the macro layer section of a target research area.
Then obtaining the fracture porosity of the single well according to the fracture data and the single well imaging logging data, and the method specifically comprises the following steps: according to the single-well imaging logging information, the macroscopic crack development characteristics and the crack distribution rule data of the target interval of the second research area, interpreting to obtain the crack parameter data in the target interval of the second research area, wherein the crack parameter data comprise the number of crack development strips, the crack development direction and the crack porosity; and correlating and calibrating the core data and the fracture parameter data, and interpreting to obtain the fracture porosity of the single well, wherein the value range of the fracture porosity of the single well is generally 0-0.03%.
Then, a deep lateral resistivity curve and a shallow lateral resistivity curve of the second study area are used for calculating a fracture porosity curve in the four resistivity models, the deep lateral resistivity curve and the shallow lateral resistivity curve are divided into a deep resistivity curve and a shallow lateral resistivity curve, fig. 2 is a schematic diagram of the fracture porosity curve in the four resistivity models in the embodiment of the invention, RT at the leftmost side of the diagram is the deep resistivity curve, RXO represents the shallow lateral resistivity curve, a black bar shows imaging logging fracture porosity and is discontinuous, namely the fracture porosity of the single well (mark 1), calculating the fracture porosity by the sibbi model in fig. 2 refers to calculating the fracture porosity by the sibbi model (mark 2), calculating the fracture porosity by the P-a model refers to calculating the fracture porosity by the P-a model (mark 3), calculating the fracture porosity by the network model refers to calculating the fracture porosity by the mesh fracture model (mark 4), and calculating the fracture porosity by the finite element method refers to calculating the fracture porosity by the three-dimensional finite element model (mark 5).
And then, weighting the four fracture porosity curves to obtain weighted fracture porosity curves, wherein as shown in fig. 3, the weighting factor is one curve, and the mark 6 is the weighted fracture porosity curve.
In the embodiment of the invention, the probabilistic neural network model comprises a learning model and a verification model, the learning model takes a logging curve as input, a fracture porosity curve after weighting treatment as output, a probabilistic neural network model is constructed by adopting a probabilistic neural network algorithm to obtain parameters of the probabilistic neural network model, so that the probabilistic neural network model is determined, the verification model obtains the fracture porosity curve of the probabilistic neural network based on the constructed probabilistic neural network model, fig. 4 is a schematic diagram of the learning model and the verification model corresponding to four single wells in the embodiment of the invention, and the four single wells are W1, W2 and W3. Fig. 5 is a schematic diagram of a probability neural network fracture porosity curve of the second research area obtained in the embodiment of the present invention, where a reference numeral 7 is fracture porosity calculated by using a conventional neural network model, and a reference numeral 8 represents probability weighted fracture porosity, that is, a probability neural network fracture porosity curve of the second research area, and it can be seen that the probability neural network fracture porosity curve of the second research area is closer to the fracture porosity of the imaging log, that is, a fracture porosity curve of a single well. FIG. 6 is a comparison of fracture porosity curves calculated by various algorithms, VPV01 is the fracture porosity curve (i.e., the actual curve) of a single well, POR _ DEN _ DT is the fracture porosity curve obtained by the total porosity-matrix porosity method, POR _ F is the fracture porosity curve obtained by the resistivity weighting algorithm, POR _ FZ is the fracture porosity curve obtained by the normal neural network algorithm, POR _ SM _ F is the fracture porosity curve obtained by the method of the present invention, it can be seen that the fracture porosity curve obtained by the method of the present invention is closest to the fracture porosity curve of a single well in the second study area, demonstrating the effectiveness of the method of the present invention.
Thereafter, the method of the present invention may be used to determine the fracture porosity of the target region of interest, which is not described in detail herein.
In summary, in the method provided by the embodiment of the present invention, a well log of a target research area is obtained, where the well log includes a depth lateral resistivity curve; calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves; weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves; training a probabilistic neural network model according to the logging curve and the weighted fracture porosity curve to obtain a probabilistic neural network fracture porosity curve; and determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve. In the process, the depth lateral resistivity curves are adopted to calculate the fracture porosity curves in various resistivity models, namely various different resistivity models are considered, and in addition, the probability neural network model is trained, so that the precision of the obtained probability neural network fracture porosity curves is very high, and the precision of the finally determined fracture porosity of the target research area is very high.
The embodiment of the invention also provides a device for determining the porosity of the fracture, the principle of which is similar to that of a method for determining the porosity of the fracture, and details are not repeated here. Fig. 7 is a schematic diagram of a determination apparatus of fracture porosity in an embodiment of the present invention, including:
a logging curve obtaining module 701, configured to obtain a logging curve of a target research area, where the logging curve includes a deep lateral resistivity curve and a shallow lateral resistivity curve;
a calculating module 702, configured to calculate a fracture porosity curve in multiple resistivity models by using the depth lateral resistivity curve;
the weighting module 703 is configured to perform weighting processing on the multiple fracture porosity curves to obtain weighted fracture porosity curves;
a probabilistic neural network fracture porosity curve obtaining module 704, configured to train a probabilistic neural network model according to the well logging curve and the weighted fracture porosity curve, so as to obtain a probabilistic neural network fracture porosity curve;
a fracture porosity determination module 705 configured to determine a fracture porosity of the target study area based on the probabilistic neural network fracture porosity curve.
In an embodiment, the well log further comprises at least one of a sonic moveout curve, a density curve, and a neutron curve.
In one embodiment, the resistivity model is one or any combination of a SIBBIT model, a P-A model, a mesh fracture model, and a three-dimensional finite element model.
In an embodiment, the weighting module 703 is specifically configured to:
weighting the multiple fracture porosity curves by adopting the following weighting factors to obtain weighted fracture porosity curves:
wherein δ is a weighting factor; r ismfIs the mud filtrate resistivity; LLD and LLS are deep and shallow lateral resistivity; a is the empirical value for the target study area.
In one embodiment, the probabilistic neural network model includes a learning model and a verification model;
the learning model is used for taking the logging curve as input, taking the weighted fracture porosity curve as output, and adopting a probabilistic neural network algorithm to construct a probabilistic neural network model to obtain parameters of the probabilistic neural network model;
the verification model is used for obtaining a probability neural network fracture porosity curve based on the constructed probability neural network model.
In summary, in the apparatus provided in the embodiment of the present invention, a well logging curve of a target research area is obtained, where the well logging curve includes a deep lateral resistivity curve and a shallow lateral resistivity curve; calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves; weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves; training a probabilistic neural network model according to the logging curve and the weighted fracture porosity curve to obtain a probabilistic neural network fracture porosity curve; and determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve. In the process, the depth lateral resistivity curves are adopted to calculate the fracture porosity curves in various resistivity models, namely various different resistivity models are considered, and in addition, the probability neural network model is trained, so that the precision of the obtained probability neural network fracture porosity curves is very high, and the precision of the finally determined fracture porosity of the target research area is very high.
An embodiment of the present application further provides a computer device, and fig. 8 is a schematic diagram of the computer device in the embodiment of the present invention, where the computer device is capable of implementing all steps in the determination method of crack porosity in the foregoing embodiment, and the electronic device specifically includes the following contents:
a processor (processor) 801, a memory (memory) 802, a communication Interface (Communications Interface) 803, and a bus 804;
the processor 801, the memory 802 and the communication interface 803 complete mutual communication through the bus 804; the communication interface 803 is used to implement information transmission between related devices, such as a server-side device, a detection device, and a user-side device;
the processor 801 is used to call a computer program in the memory 802, and when the processor executes the computer program, the processor realizes all the steps in the determination method of fracture porosity in the above embodiment.
Embodiments of the present application also provide a computer-readable storage medium, which can implement all the steps of the determination method of fracture porosity in the above embodiments, and the computer-readable storage medium stores thereon a computer program, which when executed by a processor implements all the steps of the determination method of fracture porosity in the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for determining fracture porosity, comprising:
obtaining a logging curve of a target research area, wherein the logging curve comprises a depth lateral resistivity curve;
calculating fracture porosity curves in various resistivity models by adopting the depth lateral resistivity curves;
weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves;
training a probabilistic neural network model according to the logging curve and the weighted fracture porosity curve to obtain a probabilistic neural network fracture porosity curve;
determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve;
wherein the well log further comprises at least one of a sonic moveout curve, a density curve, and a neutron curve;
wherein, weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves comprises:
weighting the multiple fracture porosity curves by adopting the following weighting factors to obtain weighted fracture porosity curves:
wherein δ is a weighting factor; rmfIs the mud filtrate resistivity; LLD and LLS are deep and shallow lateral resistivity; a is the empirical value for the target study area.
2. The method for determining fracture porosity of claim 1, wherein the resistivity model is one or any combination of a sibbi model, a P-a model, a mesh fracture model, and a three-dimensional finite element model.
3. The method of determining fracture porosity of claim 1, wherein the probabilistic neural network model includes a learning model and a verification model;
the learning model is used for taking the logging curve as input, taking the weighted fracture porosity curve as output, and adopting a probabilistic neural network algorithm to construct a probabilistic neural network model to obtain parameters of the probabilistic neural network model;
the verification model is used for obtaining a probability neural network fracture porosity curve based on the constructed probability neural network model.
4. A device for determining fracture porosity, comprising:
the logging curve acquisition module is used for acquiring logging curves of a target research area, wherein the logging curves comprise deep and shallow lateral resistivity curves;
the calculation module is used for calculating fracture porosity curves in the resistivity models by adopting the deep and shallow lateral resistivity curves;
the weighting module is used for weighting the plurality of fracture porosity curves to obtain weighted fracture porosity curves;
the probability neural network fracture porosity curve obtaining module is used for training a probability neural network model according to the logging curve and the fracture porosity curve after weighting processing to obtain a probability neural network fracture porosity curve;
the fracture porosity determination module is used for determining the fracture porosity of the target research area based on the probability neural network fracture porosity curve;
wherein the well log further comprises at least one of a sonic moveout curve, a density curve, and a neutron curve;
wherein the weighting module is specifically configured to:
weighting the multiple fracture porosity curves by adopting the following weighting factors to obtain weighted fracture porosity curves:
wherein δ is a weighting factor; r ismfIs the mud filtrate resistivity; LLD and LLS are deep and shallow lateral resistivities; a is the empirical value of the target study area.
5. The determination apparatus of fracture porosity according to claim 4, wherein the resistivity model is one or any combination of a sibbi model, a P-a model, a reticular fracture model, and a three-dimensional finite element model.
6. The determination of fracture porosity according to claim 4, wherein the probabilistic neural network model comprises a learning model and a verification model;
the learning model is used for taking a logging curve as input, taking the fracture porosity curve after weighting treatment as output, and adopting a probabilistic neural network algorithm to construct a probabilistic neural network model to obtain parameters of the probabilistic neural network model;
the verification model is used for obtaining a probability neural network fracture porosity curve based on the constructed probability neural network model.
7. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 3 when executing the computer program.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 3.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106526693A (en) * | 2016-09-30 | 2017-03-22 | 中国石油天然气股份有限公司 | crack identification method and device |
| CN108897066A (en) * | 2018-08-23 | 2018-11-27 | 中国石油天然气股份有限公司 | Carbonate rock crack density quantitative prediction method and device |
| CN110927794A (en) * | 2019-12-06 | 2020-03-27 | 中国地质大学（北京） | Method for identifying tight reservoir cracks and quantitatively calculating porosity |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106526693A (en) * | 2016-09-30 | 2017-03-22 | 中国石油天然气股份有限公司 | crack identification method and device |
| CN108897066A (en) * | 2018-08-23 | 2018-11-27 | 中国石油天然气股份有限公司 | Carbonate rock crack density quantitative prediction method and device |
| CN110927794A (en) * | 2019-12-06 | 2020-03-27 | 中国地质大学（北京） | Method for identifying tight reservoir cracks and quantitatively calculating porosity |
Non-Patent Citations (2)
| Title |
|---|
| 五百梯气田石炭系储层裂缝测井识别研究;周平等;《河北地质大学学报》;20180723(第04期);全文 * |
| 利用常规测井资料评价碳酸盐岩裂缝-孔隙性储层;何伶;《石油天然气学报》;20101215(第06期);全文 * |
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