Clastic rock breaking joint dessert body identification method, device and equipment

Abstract

The invention discloses a clastic rock breaking joint dessert body identification method, device and equipment, and the method comprises the steps: building a breaking joint system model of a research region based on the physical parameters and geometric parameters of a fracture, an associated fracture zone and a crack zone in the research region; interpreting the breaking joint system model by taking breaking joint data obtained by interpreting seismic data in the research area and / or partial breaking joint data without constructing the breaking joint system model as constraint conditions so as to obtain breaking joint system three-dimensional space data of the research area; based on the seismic attribute corresponding to the stratum in the research area and a pre-constructed quantitative identification chart of rock physics-logging data, determining a distribution area of a clastic rock effective reservoir in the research area; and based on the three-dimensional spatial data of the breaking joint system and the distribution area of the effective reservoir of the clastic rock, identifying the development area of the clastic rock breaking joint dessert body. According to the method, the working efficiency and the clastic rock breaking joint dessert body recognition precision are greatly improved, and the method has practical guiding significance for clastic rock breaking joint dessert body exploration.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum exploration and sedimentary reservoir evaluation, and in particular to a method, a device and equipment for identifying a clastic rock fracture sweet spot. Background Art

[0002] A fault body is a tectonic unit composed of faults, associated brittle fracture zones and fracture zones, and is often developed in rigid clastic rocks in tectonic deformation zones. When its flanks and overlying strata are impermeable layers, they can form oil and gas storage space, thus forming a fault body reservoir. The sweet spot area formed by this type of reservoir is the clastic rock fault sweet spot body.

[0003] The existing fault body identification technology is to use artificial interpretation of faults as a means to predict the development range of fracture zones using the main control mode of fracture geological development, thereby realizing the identification of fault bodies. The existing fault body identification method mainly includes the following steps: determine the fault development zone and fracture development zone in the target area, which are recorded as the first fault development zone and the first fracture development zone respectively; extract multiple seismic attributes in the target area, and then use the value range distribution range of each seismic attribute to determine the fault development zone and fracture development zone represented by the corresponding seismic attribute respectively; compare the fault development zone and fracture development zone represented by each seismic attribute with the first fault development zone and the first fracture development zone respectively, and select the effective seismic attributes of the target area; determine the area in the target area where the values ​​of all effective seismic attributes are distributed within the value range distribution range of the corresponding effective seismic attributes as the fault body development zone.

[0004] The existing fault body identification methods do not involve the prediction of clastic rock fault sweet spots, and there is no systematic prediction method for this type of sweet spot identification in clastic rock faults. Summary of the invention

[0005] In order to accurately identify and predict sweet spots in clastic rock fractures, enrich process routes and increase selection space, a method, device and apparatus for identifying sweet spots in clastic rock fractures are provided in an embodiment of the present invention.

[0006] In a first aspect, an embodiment of the present invention provides a method for identifying a sweet spot in a clastic rock fracture, which may include:

[0007] Based on the physical and geometric parameters of the faults, associated fracture zones and fracture zones in the study area, a fault system model of the study area is constructed;

[0008] The fault system model is interpreted using the fault data obtained by interpreting the seismic data in the study area and / or the partial fault data for which the fault system model is not constructed as constraints to obtain the three-dimensional spatial data of the fault system in the study area;

[0009] Based on the seismic attributes corresponding to the strata in the study area and the quantitative identification plate of the pre-constructed rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined;

[0010] Based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective reservoir of the clastic rock, the development area of ​​the clastic rock fault sweet spot body is identified.

[0011] Optionally, the determining the distribution area of ​​the effective clastic reservoir in the study area based on the seismic attributes corresponding to the strata in the study area and the pre-constructed quantitative identification plate of rock physics-logging data may include:

[0012] Extracting formation seismic attribute parameters in the study area, wherein the seismic attribute parameters include geometric shape, amplitude value and frequency domain rate distribution of logging data;

[0013] Based on the sample optimization method, seismic attribute parameters sensitive to the sedimentary facies belt in the stratum are screened out;

[0014] The sedimentary facies belt of the stratum is identified by cluster analysis method to determine the distribution area of ​​the clastic sedimentary facies belt;

[0015] Based on the distribution area of ​​the clastic sedimentary facies belt and the pre-constructed quantitative identification plate of rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0016] Optionally, the determining the distribution area of ​​the effective clastic reservoir in the study area based on the distribution area of ​​the clastic sedimentary facies belt and the pre-constructed quantitative identification plate of rock physics-logging data may include:

[0017] Determine at least two sensitive electrical parameters of the reservoir based on the pre-built quantitative identification chart of rock physics and logging data;

[0018] Performing post-stack seismic data inversion based on at least two of the sensitive electrical parameters to obtain a sensitive electrical parameter data volume;

[0019] Determine the value range of at least two of the sensitive electrical parameters based on the quantitative identification chart of the rock physics-logging data;

[0020] Based on the value range of the sensitive electrical parameter and the sensitive electrical parameter data body, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0021] Optionally, determining the distribution area of ​​the effective clastic reservoir in the study area based on the value range of the sensitive electrical parameter and the sensitive electrical parameter data body may include:

[0022] Based on the value ranges of at least two of the sensitive electrical parameters, respectively assigning values ​​to the sensitive electrical parameter data bodies so that data outside the value ranges of the sensitive electrical parameter data bodies are zero;

[0023] At least two of the assigned sensitive electrical parameter data bodies are multiplied to determine the distribution area of ​​the effective clastic reservoir in the study area.

[0024] Optionally, before explaining the fault system model, the following steps may also be included:

[0025] The fault system model is forward modeled based on the seismic data in the study area to optimize the model parameters of the fault system model.

[0026] Optionally, the quantitative identification chart of the rock physics-logging data is pre-constructed by the following method:

[0027] A correlation analysis is performed based on the rock physics data and well logging data in the study area, so as to construct a quantitative identification plate of the rock physics-well logging data based on the correlation analysis results.

[0028] Optionally, the method may further include:

[0029] Based on the basic geological data of the study area, the geological data, seismic data and well logging data in the study area are preprocessed.

[0030] In a second aspect, an embodiment of the present invention provides a device for identifying a clastic rock fracture sweet spot object, which may include:

[0031] A construction module is used to construct a fault system model of the study area based on the physical parameters and geometric parameters of the faults, associated fracture zones and fracture zones in the study area;

[0032] An interpretation module, used to interpret the fault system model using the fault data obtained by interpreting the seismic data in the study area and / or the partial fault data for which the fault system model is not constructed as constraints, so as to obtain the three-dimensional spatial data of the fault system in the study area;

[0033] A determination module, for determining the distribution area of ​​effective clastic reservoirs in the study area based on seismic attributes corresponding to the strata in the study area and a pre-constructed quantitative identification chart of rock physics-logging data;

[0034] The identification module is used to identify the development area of ​​the clastic rock fault sweet spot body based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective reservoir of the clastic rock.

[0035] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying sweet spots in clastic rock fractures as described in the first aspect.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for identifying sweet spots in clastic rock fractures as described in the first aspect is implemented.

[0037] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0038] The embodiment of the present invention provides a method, device and equipment for identifying sweet objects in clastic rock fractures. The method realizes accurate identification of the fault system by combining three-dimensional seismic data with field data; on this basis, it uses multi-mode seismic attributes to identify dominant phase belts and multi-parameter joint inversion to predict high-quality reservoirs; superimposes the three-dimensional spatial fault system and the distribution of high-quality reservoirs of clastic rocks to predict sweet objects in clastic rock fractures, which greatly improves work efficiency and recognition accuracy, and realizes high-precision prediction of sweet objects in clastic rock fractures and improves the identification and prediction process for the prediction of sweet objects in clastic rock fractures. This method systematically proposes the identification technology and prediction method of sweet objects in clastic rock fractures for the first time, which is of pioneering significance. In addition, this method provides a complete technical path for the identification and prediction of sweet objects in clastic rock fractures, which has practical guiding significance for the exploration of sweet objects in clastic rock fractures.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0040] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 A flow chart of a method for identifying a clastic rock fracture sweet spot provided in an embodiment of the present invention;

[0043] Figure 2 A detailed flow chart of a method for identifying a clastic rock fracture sweet spot provided in an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of a fracture system model provided in an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of three-dimensional spatial data of a three-dimensional fracture system provided in an embodiment of the present invention;

[0046] Figure 5 A flowchart for specifically implementing step S26;

[0047] Figure 6 A schematic diagram of the distribution area of ​​effective clastic reservoirs in the study area provided in an embodiment of the present invention;

[0048] Figure 7 A schematic diagram of a clastic rock fracture sweet spot identified in an embodiment of the present invention;

[0049] Figure 8 It is a schematic diagram of the structure of a device for identifying a clastic rock fracture sweet spot provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0051] Since there is no method for identifying clastic rock fracture sweet spot bodies in the prior art, but clastic rock fracture sweet spot bodies, as storage spaces, have considerable economic value for fracture body oil reservoirs. In view of the above problems, the present invention is proposed to provide a method, device and equipment for identifying clastic rock fracture sweet spot bodies that overcome the above problems or at least partially solve the above problems.

[0052] The present invention provides a method for identifying sweet spots in clastic rock fractures, which integrates artificial intelligence seismic interpretation technology with multi-parameter seismic attributes and seismic inversion technology to solve the current problem of difficulty in identifying and predicting sweet spots in clastic rock fractures. Figure 1 As shown, the method may include the following steps:

[0053] Step S11: construct a fault system model of the study area based on the physical parameters and geometric parameters of the faults, associated fracture zones and fracture zones in the study area.

[0054] This step is to clarify the spatial distribution characteristics of earthquake faults in the study area and the scale and fracture elements of the associated fracture zones and fracture zones, and then construct a three-dimensional distribution model of the fault system, namely the fault system model, based on its physical parameters (physical parameters) and geometric parameters.

[0055] Step S12: interpreting the fault system model using the fault data obtained from the interpretation of seismic data in the study area and / or the partial fault data for which the fault system model has not been constructed as constraints to obtain the three-dimensional spatial data of the fault system in the study area.

[0056] When this step is implemented, the fault system model can be automatically interpreted by artificial intelligence based on the method of automatic identification and analysis, thereby generating three-dimensional spatial data of the fault system in the three-dimensional seismic work area (study area).

[0057] Step S13: Based on the seismic attributes corresponding to the strata in the study area and the pre-constructed quantitative identification chart of rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined. This step is based on the seismic multi-attribute model and multi-parameter joint inversion to predict the spatial distribution characteristics of the effective clastic reservoir.

[0058] It should be noted that the above steps S11 and S12 are for identifying the fault system in the study area, and step S13 is for identifying the stratigraphic system. There is no particular order in identifying the fault system and the stratigraphic system. The fault system may be identified first and then the stratigraphic system, or the stratigraphic system may be identified first and then the fault system, or both may be identified simultaneously. The embodiment of the present invention does not make any specific limitation on this.

[0059] Step S14: Identify the development area of ​​clastic rock fault sweet spot objects based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective clastic rock reservoir. This step is to predict the spatial distribution of clastic rock fault sweet spot objects by combining the spatial development characteristics of the fault system and its cutting relationship with the reservoir, so as to realize the prediction of clastic rock fault sweet spot objects.

[0060] The above-mentioned clastic rock fault sweet spot object identification method provided by the embodiment of the present invention realizes the accurate identification of the fault system by combining three-dimensional seismic data with field data; on this basis, it uses multi-mode seismic attributes to identify the dominant phase belt and multi-parameter joint inversion to realize the prediction of high-quality reservoirs; superimposes the three-dimensional spatial fault system and the distribution of clastic rock high-quality reservoirs to realize the prediction of clastic rock fault sweet spot objects, which greatly improves the work efficiency and recognition accuracy, and realizes the high-precision prediction of clastic rock fault sweet spot objects and improves the identification and prediction process for the prediction of clastic rock fault sweet spot objects. This method systematically proposes the identification technology and prediction method of clastic rock fault sweet spot objects for the first time, which is of pioneering and important significance. In addition, this method provides a complete technical path for the identification and prediction of clastic rock fault sweet spot objects, which has practical guiding significance for the exploration of clastic rock fault sweet spot objects.

[0061] In a detailed embodiment, the present invention also provides a detailed method for identifying a clastic rock fracture sweet spot. Figure 2As shown, the method may include the following steps:

[0062] Step S20: Acquire geological data, seismic data and well logging data in the study area.

[0063] This step is to collect all relevant data in the exploration process in the study area, including but not limited to geological data, seismic data and well logging data. On this basis, the collected geological data, seismic data and well logging data are sorted and analyzed to facilitate the establishment of the fault system model and stratigraphic system model in the study area in the subsequent steps.

[0064] Step S21: Based on the basic geological data of the study area, pre-process the geological data, seismic data and well logging data in the study area.

[0065] This step is based on basic geological data and is familiar with the tectonic activity characteristics of the work area (study area) in order to gain an in-depth understanding of the stratigraphic development characteristics and tectonic activity periods in the study area, and to pre-process the geological data, seismic data, and logging data obtained in the above step S20.

[0066] For example, the seismic data in this embodiment includes three-dimensional seismic data. By performing quality analysis on the three-dimensional seismic data, the bandwidth and main frequency of the seismic data can be determined, and the resolution limits of the vertical resolution and lateral resolution of the seismic data can be grasped. The logging data can be used to select a suitable marker layer, control a suitable correction amount for standardization, and achieve consistency of lithology and electrical properties.

[0067] Step S22: construct a fault system model of the study area based on the physical parameters and geometric parameters of the faults, associated fracture zones and fracture zones in the study area.

[0068] When this step is implemented, first, the description morphology, direction and period of the faults are clarified through the plane and longitudinal combination patterns, the development scale of the faults and the mechanical properties of the faults in the study area. Then, for the associated brittle fracture zones and fracture zones of the faults, the data collected from the field outcrops, including the scale of the associated fracture zones of fractures of different scales, mechanical properties and morphologies, the occurrence of fractures in the fracture zones, the density of fracture development, and the intersection relationship of fractures, are used to determine the physical and geometric parameters of all faults in the study area. Finally, a fault system model of the study area is constructed based on the physical and geometric parameters of the above-mentioned faults.

[0069] It should be noted that, in this embodiment, when establishing the above-mentioned fault system model, the fault system model can be constructed by the Geoeast software geoseis insight module, or in this embodiment, a computer program similar to the above-mentioned geoseisinsight module is embedded to realize the process of constructing the fault system model. That is, when constructing the model, the physical parameters and geometric parameters of the above-mentioned fracture are input into the geoseis insight module, and the fault system model is output through the module. Taking a certain study area as an example, the constructed fault system model of the study area is as follows: Figure 3 shown.

[0070] Step S23: forward modeling the fault system model based on the seismic data in the study area to optimize the model parameters of the fault system model. This step uses seismic forward modeling to optimize the model parameters so that the final model reaches the optimal state.

[0071] Step S24, using the fault data obtained from the interpretation of the seismic data in the study area and / or the partial fault data for which the fault system model has not been constructed as constraints, interpret the fault system model to obtain the three-dimensional spatial data of the fault system in the study area.

[0072] This step takes the fracture system model optimized in step S23 as a precondition, referring to Figure 4 As shown, using coherent data and fracture data as constraints, the fracture system model made by three-dimensional seismic data is automatically interpreted under the artificial intelligence fracture system interpretation technology, thereby generating three-dimensional spatial data of the fracture system of the three-dimensional seismic work area. The coherent data in this step is the fracture data obtained by interpreting the seismic data. It should be noted that when constructing the fracture system model, part of the collected field fracture data is used to construct the model, and part of the field fracture data is used to verify the constructed model.

[0073] Step S25: performing correlation analysis based on the rock physical parameters in the geological data and the well logging data to construct a quantitative identification chart of the rock physics-well logging data in the study area.

[0074] The quantitative identification chart of rock physics and logging established in this step provides the range of correlation parameters for the subsequent reservoir identification of clastic rock fracture sweet spots, such as selecting sensitive electrical parameters of the target formation / reservoir.

[0075] Step S26: Based on the seismic attributes corresponding to the strata in the study area and the pre-constructed quantitative identification chart of rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0076] In specific implementation, refer to Figure 5As shown, this step may specifically include the following contents:

[0077] Step S261: extracting formation seismic attribute parameters in the study area, where the seismic attribute parameters include the geometry, amplitude value and frequency domain rate distribution of the logging data.

[0078] Step S262: Based on the sample optimization method, the seismic attribute parameters in the stratum that are sensitive to the sedimentary facies are screened out. The sample optimization method in this step can be a method for optimizing based on a neural network model to select the seismic attribute parameters that are sensitive to Cheng Jixiang.

[0079] Step S263: Identify the sedimentary facies belt of the stratum by cluster analysis to determine the distribution area of ​​the clastic sedimentary facies belt.

[0080] The above steps S261 to S263 are for determining the distribution area of ​​the clastic sedimentary facies belt, but not all clastic sedimentary facies belts are effective reservoirs. The subsequent step S264 is to find the effective clastic reservoir in the clastic sedimentary facies belt.

[0081] Step S264: Based on the distribution area of ​​the clastic sedimentary facies belt and the pre-constructed quantitative identification plate of rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0082] First, based on the pre-constructed quantitative identification map of rock physics-logging data, at least two sensitive electrical parameters of the reservoir are determined; then, post-stack seismic data inversion is performed based on the at least two sensitive electrical parameters to obtain a sensitive electrical parameter data body; third, the value range of at least two sensitive electrical parameters is determined based on the quantitative identification map of rock physics-logging data; fourth, based on the value range of sensitive electrical parameters and the sensitive electrical parameter data body, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0083] In the specific implementation of the fourth step, based on the value range of at least two sensitive electrical parameters, the sensitive electrical parameter data body is assigned values ​​respectively so that the data outside the value range of the sensitive electrical parameter data body is zero; the at least two assigned sensitive electrical parameter data bodies are multiplied to determine the distribution area of ​​the effective clastic reservoir in the study area.

[0084] In a specific example, the parameter Figure 6As shown, the quantitative identification chart of the rock physics-logging data pre-established in the above step S25 is used to determine the sensitive electrical parameters of the reservoir. The sensitive parameters in this example are GR and P-wave impedance. Then, the GR and P-wave impedance parameter data bodies are inverted respectively through post-stack seismic inversion. The value range of the GR and P-wave impedance parameters on the quantitative identification chart is used to perform mathematical assignment operations on the inverted GR and P-wave impedance parameter data bodies, so that the data outside the value range is assigned to 0. Then, the GR and P-wave impedance parameter data bodies are multiplied, and finally the spatial distribution and planar distribution of the effective clastic reservoir are obtained.

[0085] It should be noted that the above steps S22 to S24 are the process of determining the three-dimensional spatial data of the fault system, which solves the technical problem of three-dimensional seismic identification of the fault system; the above steps S25 to S26 are the process of determining the distribution area of ​​the effective reservoir of clastic rocks, which solves the technical problem of multi-mode seismic attribute identification of the reservoir and multi-parameter reservoir prediction; the above two processes are executed in no particular order, and the embodiments of the present invention will not be repeated here.

[0086] Step S27: Based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective reservoir of the clastic rock, the development area of ​​the clastic rock fault sweet spot body is identified.

[0087] In this step, the above-mentioned three-dimensional spatial data of the fault system and the distribution area of ​​the effective reservoir of the clastic rock are superimposed to predict the development area of ​​the clastic rock fault sweet spot body. Figure 7 As shown in the figure, the area enclosed by the dotted line is the development area of ​​the clastic rock fault sweet spot.

[0088] The embodiment of the present invention systematically proposes for the first time a method for identifying and predicting clastic rock fracture sweet spot objects, namely, the above-mentioned method for identifying clastic rock fracture sweet spot objects, which is of pioneering significance. In addition, the method provides a complete technical path for identifying and predicting clastic rock fracture sweet spot objects, which has practical guiding significance for the exploration of clastic rock fracture sweet spot objects.

[0089] Based on the same inventive concept, an embodiment of the present invention also provides a device for identifying a clastic rock fracture sweet spot. Figure 8 As shown, the device may include: a construction module 82, an interpretation module 84, a determination module 86 and an identification module 87, and its working principle is as follows:

[0090] The construction module 82 is used to construct a fault system model of the study area based on the physical parameters and geometric parameters of the faults, associated fracture zones and fracture zones in the study area;

[0091] The interpretation module 84 is used to interpret the fault system model using the fault data obtained by interpreting the seismic data in the study area and / or the partial fault data for which the fault system model is not constructed as constraints, so as to obtain the three-dimensional spatial data of the fault system in the study area;

[0092] The determination module 86 is used to determine the distribution area of ​​the effective clastic reservoir in the study area based on the seismic attributes corresponding to the strata in the study area and the pre-constructed quantitative identification plate of rock physics-logging data;

[0093] The identification module 87 is used to identify the development area of ​​the clastic rock fault sweet spot body based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective clastic rock reservoir.

[0094] In an optional embodiment, the determination module 86 is specifically configured to:

[0095] Extracting formation seismic attribute parameters in the study area, wherein the seismic attribute parameters include geometric shape, amplitude value and frequency domain rate distribution of logging data;

[0096] Based on the sample optimization method, seismic attribute parameters sensitive to the sedimentary facies belt in the stratum are screened out;

[0097] The sedimentary facies belt of the stratum is identified by cluster analysis method to determine the distribution area of ​​the clastic sedimentary facies belt;

[0098] Based on the distribution area of ​​the clastic sedimentary facies belt and the pre-constructed quantitative identification plate of rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0099] In another optional embodiment, the determination module 86 is further configured to:

[0100] Determine at least two sensitive electrical parameters of the reservoir based on the pre-built quantitative identification chart of rock physics and logging data;

[0101] Performing post-stack seismic data inversion based on at least two of the sensitive electrical parameters to obtain a sensitive electrical parameter data volume;

[0102] Determine the value range of at least two of the sensitive electrical parameters based on the quantitative identification chart of the rock physics-logging data;

[0103] Based on the value range of the sensitive electrical parameter and the sensitive electrical parameter data body, the distribution area of ​​the effective clastic reservoir in the study area is determined.

[0104] In another optional embodiment, the determination module 86 is further configured to:

[0105] Based on the value ranges of at least two of the sensitive electrical parameters, respectively assigning values ​​to the sensitive electrical parameter data bodies so that data outside the value ranges of the sensitive electrical parameter data bodies are zero;

[0106] At least two of the assigned sensitive electrical parameter data bodies are multiplied to determine the distribution area of ​​the effective clastic reservoir in the study area.

[0107] In another alternative embodiment, referring to Figure 8 As shown, the device may further include: an optimization module 83, which is used to perform forward modeling on the fault system model based on the seismic data in the study area to optimize the model parameters of the fault system model.

[0108] In another alternative embodiment, referring to Figure 8 As shown, the device may also include: a pre-construction module 85, which is used to perform correlation analysis based on the petrophysical data and logging data in the study area, so as to construct a quantitative identification plate of the petrophysical-logging data based on the correlation analysis results.

[0109] In another alternative embodiment, referring to Figure 8 As shown, the device may further include: an acquisition module 80 and a preprocessing module 81;

[0110] The acquisition module 80 is used to acquire geological data, seismic data and well logging data in the study area.

[0111] The preprocessing module 81 is used to preprocess the geological data, seismic data and well logging data in the study area based on the basic geological data of the study area.

[0112] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for identifying sweet spots in clastic rock fractures is implemented.

[0113] Based on the same inventive concept, a computer device is also provided in an embodiment of the present invention, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for identifying sweet spots in clastic rock fractures when executing the program.

[0114] The principles of the problems solved by the above-mentioned devices, media, and related equipment in the embodiments of the present invention are similar to those of the above-mentioned methods, so their implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be repeated.

[0115] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0116] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0117] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0119] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for identifying sweet spots in clastic rock fractures, characterized in that: include: Based on the physical and geometric parameters of the faults, associated fracture zones and fracture zones in the study area, a fault system model of the study area is constructed; The fault system model is interpreted using the fault data obtained by interpreting the seismic data in the study area and / or the partial fault data for which the fault system model is not constructed as constraints to obtain the three-dimensional spatial data of the fault system in the study area; Based on the seismic attributes corresponding to the strata in the study area and the quantitative identification plate of the pre-constructed rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined; Based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective reservoir of the clastic rock, the development area of ​​the clastic rock fault sweet spot body is identified.

2. The method according to claim 1, characterized in that The method of determining the distribution area of ​​effective clastic reservoirs in the study area based on the seismic attributes corresponding to the strata in the study area and the quantitative identification chart of the pre-constructed rock physics-logging data includes: Extracting formation seismic attribute parameters in the study area, wherein the seismic attribute parameters include geometric shape, amplitude value and frequency domain rate distribution of logging data; Based on the sample optimization method, seismic attribute parameters sensitive to the sedimentary facies belt in the stratum are screened out; The sedimentary facies belt of the stratum is identified by cluster analysis method to determine the distribution area of ​​the clastic sedimentary facies belt; Based on the distribution area of ​​the clastic sedimentary facies belt and the pre-constructed quantitative identification plate of rock physics-logging data, the distribution area of ​​the effective clastic reservoir in the study area is determined.

3. The method according to claim 2, characterized in that The method of determining the distribution area of ​​the effective clastic reservoir in the study area based on the distribution area of ​​the clastic sedimentary facies belt and the pre-constructed quantitative identification chart of rock physics-logging data includes: Determine at least two sensitive electrical parameters of the reservoir based on the pre-built quantitative identification chart of rock physics and logging data; Performing post-stack seismic data inversion based on at least two of the sensitive electrical parameters to obtain a sensitive electrical parameter data volume; Determine the value range of at least two of the sensitive electrical parameters based on the quantitative identification chart of the rock physics-logging data; Based on the value range of the sensitive electrical parameter and the sensitive electrical parameter data body, the distribution area of ​​the effective clastic reservoir in the study area is determined.

4. The method according to claim 3, characterized in that The determining of the distribution area of ​​the effective clastic reservoir in the study area based on the value range of the sensitive electrical parameter and the sensitive electrical parameter data body includes: Based on the value ranges of at least two of the sensitive electrical parameters, respectively assigning values ​​to the sensitive electrical parameter data bodies so that data outside the value ranges of the sensitive electrical parameter data bodies are zero; At least two of the assigned sensitive electrical parameter data bodies are multiplied to determine the distribution area of ​​the effective clastic reservoir in the study area.

5. The method according to claim 1, characterized in that Before explaining the fault system model, it also includes: The fault system model is forward modeled based on the seismic data in the study area to optimize the model parameters of the fault system model.

6. The method according to claim 1, characterized in that The quantitative identification chart of the rock physics-logging data is pre-constructed by the following method: A correlation analysis is performed based on the rock physics data and well logging data in the study area, so as to construct a quantitative identification plate of the rock physics-well logging data based on the correlation analysis results.

7. The method according to any one of claims 1 to 6, characterized in that: Also includes: Based on the basic geological data of the study area, the geological data, seismic data and well logging data in the study area are preprocessed.

8. A device for identifying sweet spots in clastic rock fractures, characterized in that: include: A construction module is used to construct a fault system model of the study area based on the physical parameters and geometric parameters of the faults, associated fracture zones and fracture zones in the study area; An interpretation module, used to interpret the fault system model using the fault data obtained by interpreting the seismic data in the study area and / or the partial fault data for which the fault system model is not constructed as constraints, so as to obtain the three-dimensional spatial data of the fault system in the study area; A determination module, for determining the distribution area of ​​effective clastic reservoirs in the study area based on seismic attributes corresponding to the strata in the study area and a pre-constructed quantitative identification chart of rock physics-logging data; The identification module is used to identify the development area of ​​the clastic rock fault sweet spot body based on the three-dimensional spatial data of the fault system and the distribution area of ​​the effective reservoir of the clastic rock.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for identifying a clastic rock fracture sweet spot according to any one of claims 1 to 7 is implemented.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for identifying a clastic rock fracture sweet spot according to any one of claims 1 to 7 is implemented.

Images