Reservoir classification evaluation method, device and equipment based on various geological elements and medium
By constructing a reservoir classification and evaluation method based on multiple geological elements, and combining planar maps of faults, fractures, and matrix parameters to generate a coupling strength index map, the problem of single parameters and strong subjectivity in traditional reservoir evaluation is solved. This enables accurate reservoir classification under the synergistic control of multiple media, thereby improving the accuracy and efficiency of reservoir evaluation.
Patent Information
- Application Number
- CN202511415168.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-29
- Publication Date
- 2025-11-11
AI Technical Summary
Existing reservoir classification and evaluation methods rely on a single parameter, are highly subjective, and are difficult to comprehensively characterize reservoirs with multi-scale and multi-type heterogeneous characteristics, resulting in biased production capacity predictions and unsatisfactory development effects. Furthermore, they lack unified standards and procedures.
A reservoir classification and evaluation method based on multiple geological elements is proposed. By constructing planar maps of fault distance, fracture density, matrix reservoir and mobility parameters, and combining weighting coefficients for image registration and overlay, a coupling strength index map is generated, enabling reservoir identification and classification under the collaborative control of multiple media.
It significantly improves the accuracy and efficiency of complex reservoir evaluation, realizes reservoir identification and classification under the coordinated control of multiple media, provides scientific processes and standards, and enhances the overall benefits of oil and gas field development.
Smart Images
Figure CN120925855A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of geological technology for oil and gas field development, and in particular to reservoir classification and evaluation methods, devices, equipment and media based on multiple geological elements. Background Technology
[0002] As a key target for oil and gas exploration and development, the internal structure and seepage characteristics of reservoirs directly determine the occurrence and development effectiveness of oil and gas. In actual geological environments, reservoirs are not ideal homogeneous media, but rather exhibit significant heterogeneity, especially in tight sandstone, carbonate, and shale oil and gas reservoirs, where the heterogeneity is particularly prominent, severely restricting the accuracy of reservoir evaluation and the overall efficiency of oil and gas field development.
[0003] Conventional reservoir classification and evaluation methods are usually based on single-scale lithological or physical property parameters, such as porosity and permeability. However, when faced with multi-scale and multi-type heterogeneous characteristics, these methods often fail to fully characterize the distribution of effective reservoir space and seepage channels within the reservoir, which can easily lead to problems such as biased production capacity prediction, low recovery rate, and unsatisfactory development results. Secondly, reservoir evaluation and classification lack unified standards and procedures, relying more on the experience and judgment of geologists, which is highly subjective and difficult to promote and apply. Summary of the Invention
[0004] In view of this, the purpose of this invention is to provide a reservoir classification and evaluation method, apparatus, equipment, and medium based on multiple geological elements. This effectively solves the technical bottlenecks of traditional reservoir evaluation methods, such as strong subjectivity, single parameters, and inaccurate classification. Furthermore, it enables reservoir identification and classification under the coordinated control of multiple media, significantly improving the accuracy and efficiency of complex reservoir evaluation. The specific solution is as follows:
[0005] Firstly, this application discloses a reservoir classification and evaluation method based on multiple geological elements, including:
[0006] Based on the fault analysis data of the target study area, a fault distance attribute plan map is constructed. Based on the core fracture analysis data and imaging logging fracture interpretation data, a fracture density distribution map is constructed. Furthermore, a matrix reservoir parameter plan map and a flowability parameter plan map are constructed using matrix core analysis data.
[0007] The parameters corresponding to the target map set are standardized to obtain the processed map set. The weight coefficients of each geological element are determined according to the control effect of various geological elements in the reservoir. The target map set includes the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the mobility parameter plane map. The geological elements include faults, fractures, and matrix.
[0008] The processed atlases are overlaid based on the weighting coefficients to obtain a coupling strength index map. The target study area is then classified according to the index in the coupling strength index map and the spatial distribution of the coupling region corresponding to the index, so as to obtain the corresponding reservoir classification evaluation results.
[0009] Optionally, the fault analysis data includes the distance from the well to the fault; the fracture analysis data includes the number of fractures and their orientation; the matrix core analysis data includes matrix reservoir parameters and flowability parameters; the matrix reservoir parameters include any one or a combination of porosity, pore structure parameters, and oil saturation; and the flowability parameters include permeability and / or flow index.
[0010] Optionally, the construction of matrix reservoir parameter planar maps and flowability parameter planar maps using matrix core analysis data includes:
[0011] The matrix core analysis data were regionally expanded using spatial interpolation methods to obtain a planar map of matrix reservoir parameters and a planar map of flowability parameters.
[0012] Optionally, the step of overlaying the processed atlases based on the weighting coefficients to obtain a coupling strength index map includes:
[0013] The processed image set is placed in the same coordinate system, and the images in the processed image set are registered.
[0014] The target study area is divided into several grids of target size. The fault distance attribute value, fracture density value, matrix reservoir parameter value and fluidity parameter value corresponding to each grid are determined according to the fault distance attribute map, the fracture density distribution map, the matrix reservoir parameter map and the fluidity parameter map, respectively.
[0015] Based on the weighting coefficients and the fault distance attribute value, fracture density value, matrix reservoir parameter value and fluidity parameter value corresponding to each grid, the coupling strength index corresponding to each grid is determined;
[0016] The coupling strength index diagram is determined by the coupling strength index corresponding to each of the grids.
[0017] Optionally, determining the coupling strength index corresponding to each grid based on the weighting coefficient and the fault distance attribute value, fracture density value, matrix reservoir parameter value, and mobility parameter value corresponding to each grid includes:
[0018] Determine the first product between the weight coefficient corresponding to the fault and the fault distance attribute value;
[0019] Determine the second product between the weighting coefficient corresponding to the crack and the crack density value;
[0020] Determine the third product between the weighting coefficient corresponding to the matrix storage parameter and the value of the matrix storage parameter;
[0021] Determine the fourth product between the weighting coefficient corresponding to the liquidity parameter and the value of the liquidity parameter;
[0022] The coupling strength index corresponding to each grid is determined based on the sum of the first product, the second product, the third product, and the fourth product.
[0023] Optionally, classifying the target study area based on the indices in the coupling strength index map and the spatial distribution of the coupling regions corresponding to the indices to obtain corresponding reservoir classification evaluation results includes:
[0024] The first coupling region and the second coupling region are determined according to the index in the coupling strength index diagram; wherein, the first coupling region is the region where the coupling strength index meets the preset high coupling condition, and the second coupling region is the region where the coupling strength index meets the preset low coupling condition.
[0025] The boundaries of different reservoirs in the target study area are determined based on the isolinear values of coupling strength and steep transition zones.
[0026] Based on the reservoir determination results and the boundaries of the reservoirs, the classification and evaluation results of each reservoir in the target study area are determined;
[0027] The reservoir determination results include:
[0028] If the spatial distribution of the first coupling zone is strip-shaped and / or linear, and the fault orientation of the first coupling zone coincides with the orientation of a preset large fault, then the reservoir of the first coupling zone corresponding to the target study area is determined to be a fault-fracture controlled reservoir.
[0029] If the spatial distribution of the first coupling region is a blocky and / or sheet-like distribution, and there are no large faults inside the coupling region, then the reservoir in the first coupling region corresponding to the target study area is determined to be a matrix-dominated reservoir.
[0030] If the spatial distribution of the first coupling zone is strip-shaped and there are block-shaped high-value areas of the target area on both sides of the fault, then the reservoir of the first coupling zone corresponding to the target study area is determined to be a complex coupling type reservoir.
[0031] If the regional index of the second coupling region is less than the preset index threshold and is widely distributed, then the reservoir in the second coupling region corresponding to the target study area is determined to be a matrix reservoir or a non-reservoir.
[0032] Optionally, the reservoir classification and evaluation method based on multiple geological elements further includes:
[0033] The reservoir classification and evaluation results of the target study area are verified based on actual development data; the actual development data includes fracturing response, well test connectivity, and production dynamics.
[0034] If the verification fails, the range of coupling strength index corresponding to different types of reservoirs will be adjusted to correct the boundaries of different reservoirs in the target study area.
[0035] Secondly, this application discloses a reservoir classification and evaluation device based on multiple geological elements, comprising:
[0036] The map construction module is used to construct a fault distance attribute planar map based on fault analysis data of the target study area, construct a fracture density distribution map based on core fracture analysis data and imaging logging fracture interpretation data, and construct a matrix reservoir parameter planar map and a fluidity parameter planar map using matrix core analysis data.
[0037] The weighting coefficient determination module is used to standardize the parameters corresponding to the target map set to obtain the processed map set, and to determine the weighting coefficients corresponding to each geological element according to the control effect of various geological elements in the reservoir; the target map set includes the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the mobility parameter plane map; the geological elements include faults, fractures, and matrix.
[0038] The classification module is used to overlay the processed map set based on the weight coefficients to obtain a coupling strength index map. The target study area is classified according to the index in the coupling strength index map and the spatial distribution pattern of the coupling region corresponding to the index to obtain the corresponding reservoir classification evaluation results.
[0039] Thirdly, this application discloses an electronic device, including:
[0040] Memory, used to store computer programs;
[0041] A processor is used to execute computer programs to implement reservoir classification and evaluation methods based on multiple geological elements, as described above.
[0042] Fourthly, this application discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned reservoir classification and evaluation method based on multiple geological elements.
[0043] In this invention, when classifying and evaluating reservoirs, a fault distance attribute planar map is first constructed based on fault analysis data of the target study area. A fracture density distribution map is constructed based on core fracture analysis data and imaging logging fracture interpretation data. Furthermore, matrix reservoir parameter planar maps and fluidity parameter planar maps are constructed using matrix core analysis data. The parameters corresponding to the target map set are standardized to obtain a processed map set. Weight coefficients for each geological element are determined based on their controlling role in the reservoir. The target map set includes the fault distance attribute planar map, the fracture density distribution map, the matrix reservoir parameter planar map, and the fluidity parameter planar map. The geological elements include faults, fractures, and matrix. Based on the weight coefficients, the processed map set is overlaid to obtain a coupling strength index map. The target study area is classified according to the index in the coupling strength index map and the spatial distribution of the corresponding coupling regions to obtain the corresponding reservoir classification and evaluation results. Therefore, this application, based on the distribution maps of various triple-medium properties constructed by overlaying on a plane and combined with production data, delineates the favorable distribution range of multi-medium reservoirs, thus achieving reservoir characterization. By systematically analyzing the fracture-fault-matrix characteristics, this approach not only effectively addresses the technical bottlenecks of traditional reservoir evaluation methods, such as strong subjectivity, single parameters, and inaccurate classification, but also enables the identification and classification of reservoirs under the synergistic control of multiple media. This significantly improves the accuracy and efficiency of complex reservoir evaluation and provides support for the evaluation of oil and gas field reservoir-permeability coupling bodies. Attached Figure Description
[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0045] Figure 1 This is a flowchart of a reservoir classification and evaluation method based on multiple geological elements disclosed in this application;
[0046] Figure 2 This is a schematic diagram illustrating the classification of a reservoir-permeability coupling body disclosed in this application;
[0047] Figure 3 This is a schematic diagram of a reservoir classification and evaluation device based on multiple geological elements disclosed in this application;
[0048] Figure 4 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation
[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0050] Significant progress has been made in reservoir evaluation, but for reservoir systems with complex multi-media synergistic effects, existing technologies still have the following major technical shortcomings:
[0051] (1) The reservoir evaluation and classification methods are too simplistic: they mainly rely on physical property parameters such as porosity and permeability. The lack of spatial coupling analysis of multiple media parameters leads to the failure of the classified reservoir evaluation to take into account the synergistic characteristics of reservoir space and seepage channels.
[0052] (2) Evaluation and classification methods are highly subjective and lack uniform standards: Reservoir evaluation schemes rely heavily on human experience and expert judgment in the selection of indicators, determination of parameter weights and classification. They lack scientific processes and standards, are highly subjective, and are difficult to promote and apply across blocks and oil and gas reservoir types.
[0053] (3) The reservoir evaluation and development effect are not closely linked: the evaluation process is fragmented and the parameter system is singular. Existing methods are difficult to closely integrate with the actual oil and gas field development capacity evaluation and dynamic monitoring, which restricts the refined management and adjustment of reservoir development and affects the overall development benefits and recovery rate of oil and gas reservoirs.
[0054] To address the aforementioned technical problems, this invention discloses a reservoir classification and evaluation method, apparatus, equipment, and medium based on multiple geological elements. This method effectively solves the technical bottlenecks of traditional reservoir evaluation methods, such as strong subjectivity, single parameters, and inaccurate classification. Furthermore, it enables the identification and classification of reservoirs under the coordinated control of multiple media, significantly improving the accuracy and efficiency of complex reservoir evaluation.
[0055] See Figure 1 As shown in the figure, this invention discloses a reservoir classification and evaluation method based on multiple geological elements, including:
[0056] Step S11: Construct a fault distance attribute planar map based on the fault analysis data of the target study area, construct a fracture density distribution map based on the core fracture analysis data and imaging logging fracture interpretation data, and construct a matrix reservoir parameter planar map and a fluidity parameter planar map using the matrix core analysis data.
[0057] In this embodiment, matrix core analysis data (matrix reservoir parameters such as porosity, flowability parameters such as permeability), fault analysis data (distance from well to fault, etc.), and fracture analysis data (number of fractures, fracture orientation, etc.) are first collected from the study area. Specifically, fault analysis data includes the distance from well to fault; fracture analysis data includes the number of fractures and their orientation; matrix core analysis data includes reservoir parameters and flowability parameters; the matrix reservoir parameters include, but are not limited to, porosity, pore structure parameters, and oil saturation; and the flowability parameters include, but are not limited to, permeability and / or flow index. Then, based on the fault interpretation results and combined with the well-to-fault distance information, a fault distance attribute map of the study area is constructed to reflect the degree of influence of the fault on the well. Next, based on core fracture analysis data and imaging logging fracture interpretation data, a fracture density distribution map is established to reflect the development intensity and distribution characteristics of fractures in the study area. Finally, for matrix reservoir parameters and fluidity parameters, spatial interpolation methods such as Kriging interpolation are used to regionally expand the single-well analysis results, forming high-resolution matrix reservoir parameter and fluidity parameter mapes, achieving spatial visualization of matrix reservoir capacity. In other words, the matrix core analysis data is regionally expanded using spatial interpolation methods to obtain matrix reservoir parameter and fluidity parameter mapes.
[0058] Step S12: Standardize the parameters corresponding to the target map set to obtain the processed map set. Determine the weight coefficients of each geological element according to the control effect of various geological elements in the reservoir. The target map set includes the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the mobility parameter plane map. The geological elements include faults, fractures, and matrix.
[0059] In this embodiment, the fault distance attribute planar map displays the location, orientation, and extent of subsurface faults (fracture surfaces of rocks). Faults can serve as channels for fluid (such as oil and gas) migration or as barriers to fluid flow. The fracture density distribution map shows the degree of development of fractures (smaller fractures than faults) in the rock. High-density fracture zones mean that the rock is more easily penetrated by fluids and has better permeability. The matrix reservoir parameter and flowability parameter planar distribution maps include porosity maps (showing how much space the rock itself has to store fluids) and permeability maps (showing the rock matrix's ability to allow fluids to pass through). This represents the most basic reservoir properties of the rock. Then, based on the construction of the fault distance attribute planar map, fracture density distribution map, and matrix reservoir parameter and flowability parameter planar distribution maps, a multi-level, multi-scale geological element fusion layer system is established using a multi-source geological information fusion overlay analysis strategy. This system uses the main controlling factors of geological processes as a guide, with fault activity, fracture development, and matrix reservoir performance as core control indicators. Through spatial registration and grid-based representation, it integrates various element maps point-by-point within a unified spatial framework. Specifically, it integrates geological information from different sources (faults, fractures, matrix) and of different types. Overlay analysis strategy: This is a core function of the geographic information system, referring to the overlay calculation of spatial information from multiple layers. Multi-level, multi-scale system: This indicates that the fusion is not a simple, crude overlay. It may be performed in layers (e.g., first fusing structural information, then fusing with physical property information), and considers the different scales of different data (e.g., faults are at a regional scale, fractures at a local scale) to ensure the scientific validity of the fusion. Furthermore, it needs to ensure that all maps are in the same coordinate system, and that the same point on the map corresponds to the same location underground. Because the units and numerical ranges of different parameters vary greatly (e.g., porosity is a percentage of 0-30%, fracture density is a few fractures per square meter, and fault influence is a distance or range), direct comparison and calculation are not possible. Therefore, the parameters corresponding to the target atlas need to be standardized to obtain the processed atlas. Standardization involves transforming all these data into the same dimensionless range (e.g., between 0 and 1) to make them comparable.
[0060] During the superposition process, the relative weight coefficients of various geological elements are first set according to their controlling effects in the reservoir. For example, fault control zones affect reservoir connectivity and the degree of isolation, and are assigned a higher weight to the structural factor; fracture density reflects the degree of development of natural flow channels; while matrix reservoir parameters and flowability parameters represent the original reservoir capacity of the matrix. Because the importance of faults, fractures, and matrix is different, they cannot be directly added together. Therefore, the weight coefficients corresponding to each geological element are determined according to their controlling effects in the reservoir.
[0061] Step S13: Based on the weighting coefficients, the processed atlas is overlaid to obtain a coupling strength index map. The target study area is classified according to the index in the coupling strength index map and the spatial distribution of the coupling region corresponding to the index, so as to obtain the corresponding reservoir classification evaluation results.
[0062] In this embodiment, the processed atlas is placed in the same coordinate system, and image registration is performed on each image in the processed atlas. The target study area is divided into several grids of target size. Based on the fault distance attribute planar map, the fracture density distribution map, the matrix reservoir parameter planar map, and the mobility parameter planar map, the fault distance attribute value, fracture density value, matrix reservoir parameter value, and mobility parameter value corresponding to each grid are determined. Based on the weighting coefficients and the corresponding fault distance attribute value, fracture density value, matrix reservoir parameter value, and mobility parameter value of each grid, the coupling strength index corresponding to each grid is determined. The coupling strength index map is determined using the coupling strength index corresponding to each grid. This application first performs spatial registration: ensuring that all images are in the same coordinate system, and that the same point on the image corresponds to the same location underground. Then, the data is represented grid-wise (rasterization): the entire study area is divided into countless small, regular grids (e.g., checkerboard or pixel grids). Each grid contains the attribute values of all geological elements (such as the fault influence value, fracture density value, matrix reservoir parameter value, and fluidity parameter value for that grid; for example, porosity and permeability values—the specific two values are not fixed and depend on which two data points are selected from the matrix reservoir parameter and fluidity parameter, and whether porosity, permeability, or other oil saturation values, flow index values, etc., are used). This forms the basis for computer overlay calculations. Afterward, each small grid is weighted and accumulated point-by-point: Coupling strength index = (fault weight × fault distance attribute value for that grid) + (fracture weight × fracture density value for that grid) + (matrix reservoir parameter weight × matrix reservoir parameter value for that grid) + (fluidity parameter weight × fluidity parameter value for that grid). In one specific embodiment, the coupling strength index = (fault weight × fault distance attribute value of the grid) + (fracture weight × fracture density value of the grid) + (porosity weight × porosity value of the grid) + (permeability weight × permeability value of the grid). That is, this application determines the coupling strength index corresponding to each grid by: determining a first product between the weight coefficient corresponding to the fault and the fault distance attribute value; determining a second product between the weight coefficient corresponding to the fracture and the fracture density value; determining a third product between the weight coefficient corresponding to the matrix reservoir parameter and the matrix reservoir parameter value; determining a fourth product between the weight coefficient corresponding to the fluidity parameter and the fluidity parameter value; and finally determining the coupling strength index corresponding to each grid based on the sum of the first, second, third, and fourth products.
[0063] In the fused layer, the coupling strength distribution reveals the structural control characteristics, reservoir connectivity, and heterogeneity development of different regions. For example, high coupling zones often correspond to composite advantageous areas with fracture intersections, dense fractures, and excellent matrix reservoir properties, possessing good seepage channels and storage capacity; while low coupling zones are mostly dense, isolated, or fracture-poor areas with poor seepage and low development value. Specifically, the coupling strength index map is a new map. High-value areas (high coupling zones) in the map: here, faults, fractures, and matrix porosity and permeability are all good and well-configured. This means that there is both good storage space (porosity) and efficient flow channels (faults and fractures), making them the preferred target for drilling. Low-value areas (low coupling zones): "Poor areas." These may have dense rock (porosity and permeability differences), lack fracture connectivity, or be isolated by faults, resulting in low development value.
[0064] The reservoir in this application can be defined as a reservoir-permeability coupled body. A reservoir-permeability coupled body refers to a lithological or lithological assemblage that, within the same sedimentary cycle or tectonic evolution stage, is jointly controlled by three types of media: faults, fractures, and matrix. It possesses a certain degree of spatial continuity and consistency in physical properties (such as porosity and permeability) in both the lateral and vertical directions, and is a fundamental geological unit in oil and gas reservoirs where reservoir characteristics and permeability characteristics are highly coupled. Among these, fault media, as the main carrier of tectonic deformation, have a decisive influence on the spatial pattern and permeability channels of the reservoir. Faults typically act as reservoir boundaries or barriers or channels for fluid migration, controlling the boundary morphology and connectivity of the reservoir-permeability coupled body. Fracture media mainly refer to the natural fracture network in the rock, which is an important permeability channel connecting matrix pores and faults. The occurrence (strike, dip, density) and development degree of fractures directly affect the permeability capacity and complexity of the permeability path of the reservoir. The matrix media is the foundation of the reservoir, determining its basic physical properties such as pore type, porosity, and permeability. The lithological composition and sedimentary characteristics of the matrix medium affect the reservoir's storage capacity and fluid distribution. These three elements have complex spatial superposition and interaction relationships: (1) Faults, as the skeleton structure of the reservoir-permeability coupling body, limit the distribution range and orientation of the fracture network due to their location and development status; (2) Fracture distribution is regulated by tectonic stress field and fault activity, while connecting matrix pores to realize the construction of a multi-scale seepage network; (3) The matrix provides basic storage space and physical environment, providing a stable carrier for fractures and faults. Thus, it is proposed that the reservoir-permeability coupling body is a basic geological unit jointly controlled by three types of media: faults, fractures, and matrix, within the same sedimentary cycle or tectonic evolution stage. This unit has a certain spatial continuity and physical property consistency in the lateral and vertical directions, especially manifested in the high coupling of storage and seepage characteristics such as porosity and permeability. The study clarifies the synergistic control role of faults, fractures, and matrix in reservoir spatial patterns and seepage paths, breaking through the limitations of traditional single physical property parameters. Based on this, a scientific and reasonable classification system of three-dimensional media matching relationships is constructed, enabling precise classification of different reservoir-permeability coupling body types and comprehensively reflecting the diversity and complexity of reservoir seepage behavior.
[0065] The three-media matching relationship classification aims to scientifically classify different reservoir-permeability coupling bodies based on the interaction characteristics of faults, fractures, and matrix. By clarifying the combination patterns between different media and their impact on reservoir permeability, a reasonable classification system is constructed to provide guidance for the classification and evaluation of reservoir-permeability coupling bodies. Based on faults, fractures, and matrix, the classification is as follows: Figure 2 The four types of reservoir-permeability coupling structures shown have the following specific characteristics:
[0066] Type I reservoir-permeability coupling: This type of reservoir-permeability coupling belongs to fault-controlled reservoirs. It features large faults and a high linear density fracture network, with a matrix exhibiting high porosity and high permeability. High-permeability channels develop within the reservoir, resulting in rapid fluid migration and good flowability.
[0067] II. Reservoir-permeability Coupled Formations: These formations belong to the matrix-dominated reservoir type. They feature small to medium-sized faults and moderate linear density fractures, with the matrix exhibiting moderate porosity and permeability. Reservoir flow is relatively uniform, and pore characteristics significantly control reservoir capacity.
[0068] III. Reservoir-permeability coupling bodies: These types of reservoir-permeability coupling bodies are typically matrix reservoirs or non-reservoir structures, with low or absent fault and fracture development. The matrix is characterized by low porosity and low permeability, resulting in restricted reservoir flow, difficult fluid flow, and poor permeability performance.
[0069] IV. Reservoir-permeability coupling bodies: These bodies typically consist of complex coupled reservoirs with intricate fault networks, multiple fracture formations, and significant spatial differentiation in matrix properties. They exhibit multi-scale flow paths, marked reservoir heterogeneity, complex flow characteristics, diverse fluid flow paths, and are greatly influenced by reservoir property differentiation.
[0070] In this way, given the significant differences in seepage characteristics and production performance among different reservoir-permeability coupling bodies, a scientifically sound classification system based on three-dimensional media matching relationships is constructed, using fault characteristics, fracture development degree, and matrix properties as core parameters. This system can systematically classify reservoir-permeability coupling body types, reveal the seepage behavior characteristics of each type of reservoir, and provide important basis for reservoir evaluation.
[0071] This invention comprehensively evaluates the control effects of faults on matrix reservoirs, fractures on seepage channels, and matrix porosity-permeability properties on effective reservoir space based on coupling strength criteria and distribution pattern characteristics. The study area is divided into fault-fracture controlled reservoirs, matrix-dominated reservoirs, matrix- or non-reservoir reservoirs, and complex coupled reservoirs, enabling precise identification of reservoir seepage units under different structural control mechanisms. Specifically, this application determines a first coupling zone and a second coupling zone based on the index in the coupling strength index diagram; wherein, the first coupling zone is a region where the coupling strength index meets a preset high coupling condition, and the second coupling zone is a region where the coupling strength index meets a preset low coupling condition; the boundaries of different reservoirs in the target study area are determined based on the isolinear values and steep transition zones of the coupling strength; the classification and evaluation results of each reservoir in the target study area are determined based on the reservoir determination results and the boundaries of the reservoirs; wherein, the reservoir determination results include: if the spatial distribution of the first coupling zone is strip-shaped and / or linear, and the fault strike of the first coupling zone coincides with the strike of a preset large fault, then the reservoir in the first coupling zone corresponding to the target study area is determined to be a fault-fracture controlled reservoir; this type of reservoir has developed large faults and a high-density fracture network, the matrix has high porosity and high permeability characteristics, high-permeability channels are developed in the reservoir, the fluid migration speed is fast, and the fluidity is good. If the spatial distribution of the first coupling zone is a clump-like and / or sheet-like distribution, and there are no large faults within the coupling region, then the reservoir in the first coupling zone corresponding to the target study area is determined to be a matrix-dominated reservoir. This type of reservoir has medium and small faults and medium-density fractures, moderate matrix porosity and permeability, uniform reservoir flow, and pore characteristics that significantly control storage capacity. If the spatial distribution of the first coupling zone is a strip-like distribution, and there are clump-like high-value areas of the target area on both sides of the fault, then the reservoir in the first coupling zone corresponding to the target study area is determined to be a complex coupling reservoir. This type of reservoir has a complex fault network and multiple sets of fractures, obvious spatial differentiation of matrix properties, multi-scale flow paths, significant reservoir heterogeneity, complex flow characteristics, diverse fluid flow paths, and is greatly affected by reservoir property differentiation. If the regional index of the second coupling zone is less than the preset index threshold and is widely distributed, then the reservoir in the second coupling zone corresponding to the target study area is determined to be a matrix reservoir or a non-reservoir. This type of reservoir has a complex fault network and multiple sets of fractures, obvious spatial differentiation of matrix properties, multi-scale seepage paths, significant reservoir heterogeneity, complex seepage characteristics, diverse fluid flow paths, and is greatly affected by reservoir property differentiation.
[0072] In the classification process, the coupling strength index is a "comprehensive score." A high score (high coupling zone) indicates that faults, fractures, and matrix are all highly developed, and they perfectly overlap and synergize spatially. This is a necessary condition for the formation of efficient reservoir-permeability units. A low score (low coupling zone) indicates that at least one (or even multiple) component performs poorly, resulting in the inability to form effective reservoir and permeability spaces. However, this application does not only consider the numerical value during classification but also combines the spatial distribution of the index with geological knowledge to infer the dominant factors causing such high or low scores, thereby classifying the zones accordingly. Identifying high coupling zones and determining their type:
[0073] If the highly coupled region exhibits a distinct strip-like or linear distribution and highly coincides with the strike of a known major fault:
[0074] Genetic inference: The high concentration here is mainly contributed by faults and associated fracture zones. The fault itself acts as a conduit; the surrounding rock is fractured and highly fractured (high fracture density), thus significantly increasing seepage capacity. Matrix porosity and permeability may be good or only average, but the alteration effect of the fault plays a dominant role.
[0075] Classification result: Fault-fracture controlled reservoir. This type of unit is a high-speed channel for fluids.
[0076] If the highly coupled region is distributed in a clump or sheet-like pattern, covers a large area, and has a very high and uniform internal index, but no large faults pass through the region:
[0077] Inference about its formation: The high score here is mainly due to the excellent porosity and permeability (matrix properties) of the rock itself. Fractures may also be present, but they are not the dominant factor.
[0078] Classification result: Matrix-dominated reservoir. This type of unit is a good fluid reservoir.
[0079] If the high coupling region is both strip-shaped (along the fault) and exhibits large-area, blocky high-value regions on both sides of the fault:
[0080] Genetic inference: This is the most ideal situation. The fault provided the passage, the fracture improved the surrounding area, and the matrix properties of the rock itself were also very good.
[0081] Classification result: Complex coupled reservoir.
[0082] Identify low-coupling regions:
[0083] If the regional index is generally low and widely distributed:
[0084] Inferences about the formation: The rock itself is dense (porosity and permeability), and the cracks are not well developed, or although there are certain pores, they are sealed by faults (the faults act as barriers).
[0085] Classification result: Non-reservoir or tight interlayer. No exploitation value.
[0086] Furthermore, after successful classification, the boundaries of different reservoir units can be preliminarily determined based on the contour lines of coupling strength and abrupt change zones (locations where the index value changes drastically). For example, the index values on both sides of a fault may differ significantly, and the fault itself constitutes a natural boundary. Within each delineated polygonal unit, based on the causal inference from the previous step, a type attribute is assigned (e.g., fault-fracture controlled, matrix-dominated, etc.). This ultimately forms a reservoir unit classification map. This map not only shows the geometric boundaries of the units but also provides a clear type label for each region.
[0087] Finally, based on the classification of reservoir-permeability coupling bodies, actual development dynamic data (such as fracturing response, well test connectivity, and production dynamics) are further introduced to verify the results and correct the boundaries, improving the engineering adaptability of the model. Ultimately, consistent reservoir-permeability unit boundaries with interpretable geological origins, spatial continuity, and dynamic response are delineated, realizing the transformation of reservoir classification from "static geometry" to "dynamic coupling structure." In other words, this application verifies the reservoir classification and evaluation results of the target study area based on actual development data; the actual development data includes fracturing response, well test connectivity, and production dynamics; if verification fails, the coupling strength index range corresponding to different types of reservoirs is adjusted to correct the boundaries of different reservoirs in the target study area.
[0088] In this invention, when classifying and evaluating reservoirs, a fault distance attribute planar map is first constructed based on fault analysis data of the target study area. A fracture density distribution map is constructed based on core fracture analysis data and imaging logging fracture interpretation data. Furthermore, matrix reservoir parameter planar maps and fluidity parameter planar maps are constructed using matrix core analysis data. The parameters corresponding to the target map set are standardized to obtain a processed map set. Weight coefficients for each geological element are determined based on their controlling role in the reservoir. The target map set includes the fault distance attribute planar map, the fracture density distribution map, the matrix reservoir parameter planar map, and the fluidity parameter planar map. The geological elements include faults, fractures, and matrix. Based on the weight coefficients, the processed map set is overlaid to obtain a coupling strength index map. The target study area is classified according to the index in the coupling strength index map and the spatial distribution of the corresponding coupling regions to obtain the corresponding reservoir classification and evaluation results. Therefore, this application, based on the distribution maps of various triple-medium properties constructed by overlaying on a plane and combined with production data, delineates the favorable distribution range of multi-medium reservoirs, thus achieving reservoir characterization. By systematically analyzing the fracture-fault-matrix characteristics, this approach not only effectively addresses the technical bottlenecks of traditional reservoir evaluation methods, such as strong subjectivity, single parameters, and inaccurate classification, but also enables the identification and classification of reservoirs under the synergistic control of multiple media. This significantly improves the accuracy and efficiency of complex reservoir evaluation and provides support for the evaluation of oil and gas field reservoir-permeability coupling bodies.
[0089] See Figure 3 As shown in the figure, an embodiment of the present invention discloses a reservoir classification and evaluation device based on multiple geological elements, comprising:
[0090] The graph construction module 11 is used to construct a fault distance attribute planar map based on the fault analysis data of the target study area, construct a fracture density distribution map based on the core fracture analysis data and imaging logging fracture interpretation data, and construct a matrix reservoir parameter planar map and a fluidity parameter planar map using the matrix core analysis data.
[0091] The weight coefficient determination module 12 is used to standardize the parameters corresponding to the target map set to obtain the processed map set, and to determine the weight coefficients corresponding to each geological element according to the control effect of various geological elements in the reservoir; the target map set includes the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the mobility parameter plane map; the geological elements include faults, fractures, and matrix.
[0092] The classification module 13 is used to overlay the processed map set based on the weight coefficient to obtain a coupling strength index map, and classify the target study area according to the index in the coupling strength index map and the spatial distribution pattern of the coupling area corresponding to the index to obtain the corresponding reservoir classification evaluation results.
[0093] In this invention, when classifying and evaluating reservoirs, a fault distance attribute planar map is first constructed based on fault analysis data of the target study area. A fracture density distribution map is constructed based on core fracture analysis data and imaging logging fracture interpretation data. Furthermore, matrix reservoir parameter planar maps and fluidity parameter planar maps are constructed using matrix core analysis data. The parameters corresponding to the target map set are standardized to obtain a processed map set. Weight coefficients for each geological element are determined based on their controlling role in the reservoir. The target map set includes the fault distance attribute planar map, the fracture density distribution map, the matrix reservoir parameter planar map, and the fluidity parameter planar map. The geological elements include faults, fractures, and matrix. Based on the weight coefficients, the processed map set is overlaid to obtain a coupling strength index map. The target study area is classified according to the index in the coupling strength index map and the spatial distribution of the corresponding coupling regions to obtain the corresponding reservoir classification and evaluation results. Therefore, this application, based on the distribution maps of various triple-medium properties constructed by overlaying on a plane and combined with production data, delineates the favorable distribution range of multi-medium reservoirs, thus achieving reservoir characterization. By systematically analyzing the fracture-fault-matrix characteristics, this approach not only effectively addresses the technical bottlenecks of traditional reservoir evaluation methods, such as strong subjectivity, single parameters, and inaccurate classification, but also enables the identification and classification of reservoirs under the synergistic control of multiple media. This significantly improves the accuracy and efficiency of complex reservoir evaluation and provides support for the evaluation of oil and gas field reservoir-permeability coupling bodies.
[0094] In some specific embodiments, the graph construction module 11 can be used to expand the matrix core analysis data regionally based on spatial interpolation methods to obtain a matrix reservoir parameter planar map and a flowability parameter planar map.
[0095] In some specific embodiments, the classification module 13 can be used to place the processed atlas in the same coordinate system and perform image registration on each image in the processed atlas; divide the target study area into several grids of target size; determine the fault distance attribute value, fracture density value, matrix reservoir parameter value, and fluidity parameter value corresponding to each grid according to the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the fluidity parameter plane map, respectively; determine the coupling strength index corresponding to each grid based on the weighting coefficient and the fault distance attribute value, fracture density value, matrix reservoir parameter value, and fluidity parameter value corresponding to each grid; and determine the coupling strength index map through the coupling strength index corresponding to each grid.
[0096] In some specific embodiments, the classification module 13 can be used to determine a first product between the weight coefficient corresponding to the fault and the fault distance attribute value; determine a second product between the weight coefficient corresponding to the fracture and the fracture density value; determine a third product between the weight coefficient corresponding to the matrix storage parameter and the matrix storage parameter value; determine a fourth product between the weight coefficient corresponding to the fluidity parameter and the fluidity parameter value; and determine the coupling strength index corresponding to each of the grids based on the sum of the first product, the second product, the third product, and the fourth product.
[0097] In some specific embodiments, the classification module 13 can be used to determine a first coupling region and a second coupling region based on the index in the coupling strength index map; wherein, the first coupling region is a region where the coupling strength index meets a preset high coupling condition, and the second coupling region is a region where the coupling strength index meets a preset low coupling condition; the boundaries of different reservoirs in the target study area are determined based on the isolinear values and steep transition zones of the coupling strength; the classification evaluation results of each reservoir in the target study area are determined based on the reservoir determination results and the boundaries of the reservoirs; wherein, the reservoir determination results include: if the spatial distribution of the first coupling region is strip-shaped and / or linear, and the fault strike of the first coupling region is aligned with a preset large fault... If the layers coincide, the reservoir in the first coupling zone corresponding to the target study area is determined to be a fault-fracture controlled reservoir. If the spatial distribution of the first coupling zone is a nodular and / or sheet-like distribution, and there are no large faults within the coupling zone, the reservoir in the first coupling zone corresponding to the target study area is determined to be a matrix-dominated reservoir. If the spatial distribution of the first coupling zone is a strip-like distribution, and there are nodular high-value areas of the target area on both sides of the fault, the reservoir in the first coupling zone corresponding to the target study area is determined to be a complex coupling reservoir. If the regional index of the second coupling zone is less than a preset index threshold and is widely distributed, the reservoir in the second coupling zone corresponding to the target study area is determined to be a matrix reservoir or a non-reservoir.
[0098] The device can also be used to verify the reservoir classification evaluation results of the target study area based on actual development data; the actual development data includes fracturing response, well test connectivity, and production dynamics; if the verification fails, the coupling strength index range corresponding to different types of reservoirs is adjusted to correct the boundaries of different reservoirs in the target study area.
[0099] Furthermore, embodiments of this application also disclose an electronic device, Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.
[0100] Figure 4 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the reservoir classification and evaluation method based on multiple geological elements disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.
[0101] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.
[0102] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.
[0103] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the reservoir classification and evaluation method based on multiple geological elements disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.
[0104] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned reservoir classification and evaluation method based on multiple geological elements. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.
[0105] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.
[0106] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0107] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.
[0108] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0109] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A reservoir classification and evaluation method based on multiple geological elements, characterized in that, include: Based on the fault analysis data of the target study area, a fault distance attribute plan map is constructed. Based on the core fracture analysis data and imaging logging fracture interpretation data, a fracture density distribution map is constructed. Furthermore, a matrix reservoir parameter plan map and a flowability parameter plan map are constructed using matrix core analysis data. The parameters corresponding to the target map set are standardized to obtain the processed map set. The weight coefficients of each geological element are determined according to the control effect of various geological elements in the reservoir. The target map set includes the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the mobility parameter plane map. The geological elements include faults, fractures, and matrix. The processed atlases are overlaid based on the weighting coefficients to obtain a coupling strength index map. The target study area is then classified according to the index in the coupling strength index map and the spatial distribution of the coupling region corresponding to the index, so as to obtain the corresponding reservoir classification evaluation results.
2. The reservoir classification and evaluation method based on multiple geological elements according to claim 1, characterized in that, The fault analysis data includes the distance from the well to the fault; the fracture analysis data includes the number of fractures and their orientation; the matrix core analysis data includes matrix reservoir parameters and flowability parameters; the matrix reservoir parameters include any one or a combination of porosity, pore structure parameters, and oil saturation; the flowability parameters include permeability and / or flow index.
3. The reservoir classification and evaluation method based on multiple geological elements according to claim 1, characterized in that, The construction of matrix reservoir parameter planar maps and flowability parameter planar maps using matrix core analysis data includes: The matrix core analysis data were regionally expanded using spatial interpolation methods to obtain a planar map of matrix reservoir parameters and a planar map of flowability parameters.
4. The reservoir classification and evaluation method based on multiple geological elements according to claim 2, characterized in that, The process of overlaying the processed atlases based on the weighting coefficients to obtain a coupling strength index map includes: The processed image set is placed in the same coordinate system, and the images in the processed image set are registered. The target study area is divided into several grids of target size. The fault distance attribute value, fracture density value, matrix reservoir parameter value and fluidity parameter value corresponding to each grid are determined according to the fault distance attribute map, the fracture density distribution map, the matrix reservoir parameter map and the fluidity parameter map, respectively. Based on the weighting coefficients and the fault distance attribute value, fracture density value, matrix reservoir parameter value and fluidity parameter value corresponding to each grid, the coupling strength index corresponding to each grid is determined; The coupling strength index diagram is determined by the coupling strength index corresponding to each of the grids.
5. The reservoir classification and evaluation method based on multiple geological elements according to claim 4, characterized in that, The determination of the coupling strength index corresponding to each grid based on the weighting coefficient and the fault distance attribute value, fracture density value, matrix reservoir parameter value, and mobility parameter value corresponding to each grid includes: Determine the first product between the weight coefficient corresponding to the fault and the fault distance attribute value; Determine the second product between the weighting coefficient corresponding to the crack and the crack density value; Determine the third product between the weighting coefficient corresponding to the matrix storage parameter and the value of the matrix storage parameter; Determine the fourth product between the weighting coefficient corresponding to the liquidity parameter and the value of the liquidity parameter; The coupling strength index corresponding to each of the grids is determined based on the sum of the first product, the second product, the third product, and the fourth product.
6. The reservoir classification and evaluation method based on multiple geological elements according to any one of claims 1 to 5, characterized in that, The classification of the target study area based on the indices in the coupling strength index map and the spatial distribution of the corresponding coupling regions to obtain corresponding reservoir classification evaluation results includes: The first coupling region and the second coupling region are determined according to the index in the coupling strength index diagram; wherein, the first coupling region is the region where the coupling strength index meets the preset high coupling condition, and the second coupling region is the region where the coupling strength index meets the preset low coupling condition. The boundaries of different reservoirs in the target study area are determined based on the isolinear values of coupling strength and steep transition zones. Based on the reservoir determination results and the boundaries of the reservoirs, the classification and evaluation results of each reservoir in the target study area are determined; The reservoir determination results include: If the spatial distribution of the first coupling zone is strip-shaped and / or linear, and the fault orientation of the first coupling zone coincides with the orientation of a preset large fault, then the reservoir of the first coupling zone corresponding to the target study area is determined to be a fault-fracture controlled reservoir. If the spatial distribution of the first coupling region is a blocky and / or sheety distribution, and there are no large faults inside the coupling region, then the reservoir of the first coupling region corresponding to the target study area is determined to be a matrix-dominated reservoir. If the spatial distribution of the first coupling zone is strip-shaped and there are block-shaped high-value areas of the target area on both sides of the fault, then the reservoir of the first coupling zone corresponding to the target study area is determined to be a complex coupling type reservoir. If the regional index of the second coupling region is less than the preset index threshold and is widely distributed, then the reservoir in the second coupling region corresponding to the target study area is determined to be a matrix reservoir or a non-reservoir.
7. The reservoir classification and evaluation method based on multiple geological elements according to claim 6, characterized in that, Also includes: The reservoir classification and evaluation results of the target study area were verified based on actual development data; The actual development data includes fracturing response, well test connectivity, and production dynamics. If the verification fails, the range of coupling strength index corresponding to different types of reservoirs will be adjusted to correct the boundaries of different reservoirs in the target study area.
8. A reservoir classification and evaluation device based on multiple geological elements, characterized in that, include: The map construction module is used to construct a fault distance attribute planar map based on fault analysis data of the target study area, construct a fracture density distribution map based on core fracture analysis data and imaging logging fracture interpretation data, and construct a matrix reservoir parameter planar map and a fluidity parameter planar map using matrix core analysis data. The weighting coefficient determination module is used to standardize the parameters corresponding to the target map set to obtain the processed map set, and to determine the weighting coefficients corresponding to each geological element according to the control effect of various geological elements in the reservoir; the target map set includes the fault distance attribute plane map, the fracture density distribution map, the matrix reservoir parameter plane map, and the mobility parameter plane map; the geological elements include faults, fractures, and matrix; The classification module is used to overlay the processed map set based on the weight coefficients to obtain a coupling strength index map. The target study area is classified according to the index in the coupling strength index map and the spatial distribution pattern of the coupling region corresponding to the index to obtain the corresponding reservoir classification evaluation results.
9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program to implement the reservoir classification and evaluation method based on multiple geological elements as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, implements the reservoir classification and evaluation method based on multiple geological elements as described in any one of claims 1 to 7.