Method, device, storage medium and equipment for constructing shale gas reservoir sweet spot model

Abstract

The present invention discloses a shale gas reservoir sweet spot model construction method, device, storage medium and equipment, which obtains attribute data corresponding to shale gas reservoir attribute parameters based on a pre-constructed three-dimensional stratigraphic grid model, obtains variogram data of shale gas reservoir attribute parameters by performing geological statistical analysis on the attribute data; performs spatial heterogeneity analysis to obtain a spatial trend model of shale gas reservoir attribute parameters; constructs a source-reservoir coupling constraint framework of shale gas reservoir attribute parameters under different lithofacies types; and establishes attribute parameter models of each shale gas reservoir attribute parameter based on the acquired variogram data, spatial trend model and source-reservoir coupling constraint framework. A shale gas reservoir sweet spot model is constructed based on the attribute parameter models of each shale gas reservoir attribute parameter and the contribution ratio of the shale gas reservoir attribute parameter. By effectively associating the source-reservoir coupling constraint relationship between each shale gas reservoir attribute parameter, the accuracy and reliability of the shale gas reservoir attribute model are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas exploration and development, and in particular to a method, device, storage medium and equipment for constructing a shale gas reservoir sweet spot model. Background Art

[0002] Shale gas is the successor energy source to conventional oil and gas in my country. By conducting in-depth analysis of the geological characteristics of this type of gas reservoir, forming a shale gas reservoir geological modeling method, and establishing a high-precision geological model, it can serve as a true "bridge" between shale gas exploration and development.

[0003] Current technology still mostly adopts the modeling ideas and technologies of conventional oil and gas reservoirs for modeling related shale gas reservoir properties. However, shale gas reservoirs are both source rocks and reservoirs. The source and reservoir parameters cooperate with each other in space to form shale gas geological sweet spots. Current technology still lacks consideration in establishing shale reservoir property models: (1) Shale gas reservoirs are mostly developed by horizontal drilling. The way the horizontal section passes through the model grid is different from that of vertical wells. Since the plane grid step size is usually much larger than the vertical grid, its sampling method is different from that of vertical wells. Existing methods rarely effectively sample horizontal drilling data into the grid; (2) The distribution of shale reservoir parameters is not only related to the primary sedimentary environment, but also controlled by the later diagenetic environment (such as total organic carbon is related to the degree of organic matter evolution). The spatial geological statistical laws of these parameters are far less obvious than those of clastic rocks or carbonate rocks; (3) Shale reservoir parameters have certain correlations. For example, the porosity in the shale matrix is ​​affected by the organic pores and total organic carbon (TOC). The content of brittle minerals is related to the biological content during shale deposition, and thus has a certain relationship with the abundance of organic matter, and at the same time, it also affects the distribution of rock physical parameters.

[0004] Based on the above analysis, the existing shale gas reservoir attribute correlation modeling method does not take into account the impact of the environment on the shale gas reservoir attribute parameters, nor does it take into account the correlation between the shale gas reservoir attribute parameters. In order to solve the problem of low reliability of shale gas production capacity prediction, this application provides a shale gas reservoir sweet spot model construction method. Summary of the invention

[0005] The technical problem to be solved by the present invention is: how to improve the reliability of the shale gas reservoir sweet spot model and effectively predict the production capacity of shale.

[0006] In order to solve the above technical problems, the present invention provides a method, device, storage medium and equipment for constructing a shale gas reservoir sweet spot model.

[0007] A first aspect of the present invention provides a method for constructing a shale gas reservoir sweet spot model, comprising:

[0008] Based on the pre-built three-dimensional stratigraphic grid model, the attribute data corresponding to the attribute parameters of the shale gas reservoir are obtained;

[0009] Performing geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determining a variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain variogram data of the shale gas reservoir attribute parameters;

[0010] Performing spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters;

[0011] According to the source-reservoir coupling relationship between the shale gas reservoir attribute parameters under different lithofacies types, a source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters is constructed;

[0012] Establishing an attribute parameter model of each of the shale gas reservoir attribute parameters based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework;

[0013] Obtaining the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type;

[0014] Based on the attribute parameter models of the shale gas reservoir attribute parameters and the contribution ratios of the shale gas reservoir attribute parameters, a shale gas reservoir sweet spot model is constructed.

[0015] Preferably, based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework, an attribute parameter model of each of the shale gas reservoir attribute parameters is established, including:

[0016] Determining the hierarchical constraint order of each of the shale gas reservoir attribute parameters and the upper level attribute parameter corresponding to each of the shale gas reservoir attribute parameters based on the source-reservoir coupling constraint framework;

[0017] Obtaining an initial attribute parameter model of a previous level attribute parameter corresponding to the shale gas reservoir attribute parameter;

[0018] Based on the hierarchical constraint order, the variogram data and the spatial trend model, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is used as a collaborative constraint variable to sequentially establish the attribute parameter model of each shale gas reservoir attribute parameter.

[0019] Preferably, the method for constructing a shale gas reservoir sweet spot model further comprises:

[0020] Establishing a well logging interpretation model for the shale gas reservoir attribute parameters;

[0021] Based on the well logging interpretation model, acquiring well logging data of the shale gas reservoir attribute parameters;

[0022] The logging data is coarsened into the three-dimensional stratigraphic grid model to obtain attribute data corresponding to the shale gas reservoir attribute parameters.

[0023] Preferably, coarsening the logging data into the three-dimensional stratigraphic grid model comprises:

[0024] For the horizontal section of the horizontal well, the logging data is coarsened into the three-dimensional stratigraphic grid model using a neighbor likelihood method;

[0025] For the vertical section of the horizontal well, the arithmetic average method is used to coarsen the logging data into the three-dimensional stratigraphic grid model.

[0026] Preferably, the method for constructing a shale gas reservoir sweet spot model further comprises: based on factors of shale gas productivity, screening out main controlling factor parameters of shale gas sweet spots as the shale gas reservoir attribute parameters.

[0027] Preferably, the method for constructing a shale gas reservoir sweet spot model further comprises: determining the lithofacies type of the shale in the study area based on the sedimentary facies and the logging data corresponding to the main controlling factor parameters.

[0028] Preferably, the main controlling factor parameters include organic matter content and mineral content.

[0029] The determining of the lithofacies type of shale in the study area based on the well logging data corresponding to the sedimentary facies and the main controlling factor parameters includes:

[0030] Based on the sedimentary facies differences, determining the genetic type of shale in the study area;

[0031] Forming a mineral content triangle diagram of the mineral components in the shale in the study area according to the mineral content, and determining the major lithofacies based on the genetic type of the shale in the study area;

[0032] The lithofacies type of the shale in the study area is determined based on the major lithofacies categories and the organic matter content and the mineral content of the shale in the study area.

[0033] Preferably, after determining the lithofacies type of the shale in the study area, the method further comprises: identifying the gas production capacity of the lithofacies type based on the gas content of the lithofacies type, and screening out high-quality lithofacies types based on the gas production capacity of the lithofacies type.

[0034] Preferably, the step of obtaining the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type includes:

[0035] Obtaining the correlation coefficient between the main controlling factor parameter and the gas content under the high-quality lithofacies type;

[0036] The correlation coefficient corresponding to the main controlling factor parameter is normalized to obtain the contribution ratio of the main controlling factor parameter to the gas content.

[0037] A second aspect of the present invention provides a shale gas reservoir sweet spot model construction device, comprising:

[0038] An attribute data acquisition module, which is used to acquire attribute data corresponding to shale gas reservoir attribute parameters based on a pre-built three-dimensional stratigraphic grid model;

[0039] A geostatistical analysis module, which is used to perform geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determine the variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain the variogram data of the shale gas reservoir attribute parameters;

[0040] A spatial heterogeneity analysis module, which is used to perform spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters;

[0041] A source-reservoir coupling constraint framework construction module, which is used to construct a source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters according to the source-reservoir coupling relationship between the shale gas reservoir attribute parameters under different lithofacies types;

[0042] An attribute parameter model building module, which is used to establish an attribute parameter model of each of the shale gas reservoir attribute parameters based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework;

[0043] A gas content contribution ratio analysis module is used to obtain the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type;

[0044] A shale gas reservoir sweet spot model construction module is used to construct a shale gas reservoir sweet spot model based on the attribute parameter model of each of the shale gas reservoir attribute parameters and the contribution ratio of the shale gas reservoir attribute parameters.

[0045] According to a third aspect of the present invention, a storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the method for constructing a shale gas reservoir sweet spot model as described above can be implemented.

[0046] A fourth aspect of the present invention provides a device, comprising: a processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the method for constructing a shale gas reservoir sweet spot model as described above can be implemented.

[0047] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0048] The shale gas reservoir sweet spot model construction method, device, storage medium and equipment of the present invention are applied to obtain attribute data corresponding to shale gas reservoir attribute parameters based on a pre-constructed three-dimensional stratigraphic grid model, perform geological statistical analysis on the attribute data and determine the variogram model of shale gas reservoir attribute parameters based on the analysis, and use the determined variogram model to obtain the variogram data of shale gas reservoir attribute parameters; perform spatial heterogeneity analysis on the attribute data of shale gas reservoir attribute parameters to obtain the spatial trend model of shale gas reservoir attribute parameters; construct the source-reservoir coupling constraint framework of shale gas reservoir attribute parameters through the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types; and establish the attribute parameter model of each shale gas reservoir attribute parameter based on the obtained variogram data, spatial trend model and source-reservoir coupling constraint framework. Finally, by obtaining the contribution ratio of shale gas reservoir attribute parameters to gas content under each lithofacies type, a shale gas reservoir sweet spot model is constructed based on the attribute parameter model of each shale gas reservoir attribute parameter and the contribution ratio of shale gas reservoir attribute parameters. By effectively associating the source-reservoir coupling constraint relationship between the attribute parameters of each shale gas reservoir, the accuracy and reliability of the shale gas reservoir attribute model are effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings. The drawings included are:

[0050] Figure 1 A schematic diagram of a process for constructing a shale gas reservoir sweet spot model provided by an embodiment of the present invention is shown;

[0051] Figure 2 A schematic diagram showing a process of constructing a shale gas reservoir sweet spot model provided by another embodiment of the present invention;

[0052] Figure 3 A schematic diagram showing the longitudinal heterogeneity characteristics of shale gas reservoir attribute parameters provided by an embodiment of the present invention is shown;

[0053] Figure 4 A schematic diagram of a total organic carbon spatial distribution trend model provided by an embodiment of the present invention is shown;

[0054] Figure 5 A schematic diagram showing the correlation between the organic carbon content and the brittle mineral content provided by an embodiment of the present invention is shown;

[0055] Figure 6 A schematic diagram of a source-reservoir coupling constraint framework for shale gas reservoir attribute parameters provided by an embodiment of the present invention is shown;

[0056] Figure 7 A schematic diagram of a parameter model of TOC, brittle minerals and porosity provided in an embodiment of the present invention is shown;

[0057] Figure 8 A schematic diagram of a shale gas reservoir sweet spot model provided by an embodiment of the present invention is shown;

[0058] Fig. 9 A schematic diagram of the structure of a shale gas reservoir sweet spot model construction device provided by an embodiment of the present invention is shown;

[0059] Table 1 shows a shale lithofacies classification table provided by an embodiment of the present invention;

[0060] Table 2 shows a variogram parameter table of preferred shale gas reservoir property parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the objectives, technical solutions and advantages of the present invention clearer, the implementation method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0062] Current technology still mostly uses the modeling ideas and techniques of conventional oil and gas reservoirs for modeling shale gas reservoir properties. However, shale gas reservoirs are both source rocks and reservoirs. The source and reservoir parameters cooperate with each other in space to form shale gas geological sweet spots. Current technology still lacks consideration in establishing shale reservoir property models: (1) Shale gas reservoirs are mostly developed by horizontal drilling. The way the horizontal section passes through the grid is different from that of vertical wells. Since the plane grid step size is usually much larger than the vertical grid, its sampling method is different from that of vertical wells. It is difficult to effectively apply horizontal drilling data in existing methods; (2) The distribution of shale reservoir parameters is not only related to the primary sedimentary environment, but also controlled by the later diagenetic environment (such as total organic carbon is related to the degree of organic matter evolution). The spatial geological statistical laws of these parameters are far less obvious than those of clastic rocks or carbonate rocks; (3) Shale reservoir parameters have certain correlations. For example, the porosity in the shale matrix is ​​affected by the relationship between organic pores and total organic carbon (TOC). The content of brittle minerals is related to the biological content during shale deposition, and thus has a certain relationship with the abundance of organic matter, and at the same time, it also affects the distribution of rock physical parameters.

[0063] Based on the above analysis, the existing shale gas reservoir attribute related modeling methods do not take into account the impact of the environment on the shale gas reservoir attribute parameters, nor the correlation between the attribute parameters. For the problem of low reliability of shale gas production capacity prediction, the present application provides a shale gas reservoir sweet spot model construction method. The method obtains attribute data corresponding to the shale gas reservoir attribute parameters based on a pre-constructed three-dimensional stratigraphic grid model, performs geological statistical analysis on the attribute data and determines the variogram model of the shale gas reservoir attribute parameters based on the analysis, and uses the determined variogram model to obtain the variogram data of the shale gas reservoir attribute parameters; obtains the spatial trend model of the shale gas reservoir attribute parameters by performing spatial heterogeneity analysis on the attribute data of the shale gas reservoir attribute parameters; constructs the source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters through the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types; and establishes the attribute parameter model of each shale gas reservoir attribute parameter based on the acquired variogram data, the spatial trend model and the source-reservoir coupling constraint framework. Finally, by obtaining the contribution ratio of shale gas reservoir attribute parameters to gas content under each lithofacies type, a shale gas reservoir sweet spot model is constructed based on the attribute parameter model of each shale gas reservoir attribute parameter and the contribution ratio of shale gas reservoir attribute parameters. By effectively associating the source-reservoir coupling constraint relationship between each shale gas reservoir attribute parameter, the accuracy and reliability of the shale gas reservoir attribute model are effectively improved.

[0064] Embodiment 1

[0065] See also Figure 1 As shown, Figure 1 A schematic flow chart of a method for constructing a shale gas reservoir sweet spot model provided by an embodiment of the present invention is shown, comprising:

[0066] Step S101: Based on the pre-constructed three-dimensional stratigraphic grid model, attribute data corresponding to shale gas reservoir attribute parameters are obtained.

[0067] Step S102: performing geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determining the variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain the variogram data of the shale gas reservoir attribute parameters.

[0068] Step S103: Perform spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters.

[0069] Step S104: constructing a source-reservoir coupling constraint framework for shale gas reservoir attribute parameters based on the source-reservoir coupling relationship between attribute parameters of shale gas reservoirs under different lithofacies types.

[0070] Step S105: Establishing an attribute parameter model of each shale gas reservoir attribute parameter based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework.

[0071] Step S106: Obtain the contribution ratio of shale gas reservoir attribute parameters to gas content under each lithofacies type.

[0072] Step S107: constructing a shale gas reservoir sweet spot model based on the attribute parameter models of the shale gas reservoir attribute parameters and the contribution ratios of the shale gas reservoir attribute parameters.

[0073] Among them, shale gas reservoir attribute parameters can be divided into organic geochemical, mineral, physical and rock physical types. Organic geochemical parameters are source parameters of shale gas reservoirs and may include total organic carbon (TOC); mineral, physical and rock physical parameters are storage parameters of shale gas reservoirs. Mineral parameters may include brittle mineral content, physical property parameters may include porosity, gas content or saturation, and rock physical parameters may include Poisson's ratio, Young's modulus or compressibility index.

[0074] In the embodiment of the present invention, by selecting a variety of shale gas reservoir attribute parameters and effectively combining the relevant information of the various shale gas reservoir attribute parameters, a shale gas reservoir sweet spot attribute model with richer content and better accuracy can be constructed.

[0075] In step S101, the attribute data corresponding to the shale gas reservoir attribute parameters may be obtained based on a pre-built three-dimensional stratigraphic grid model to obtain the continuously distributed attribute data corresponding to the shale gas reservoir attribute parameters.

[0076] Step S102 may specifically include controlling the trend and distribution form of various shale gas reservoir attribute parameters by layer and lithofacies and converting the attribute data of each shale gas reservoir attribute parameter into normal distribution data; determining the spatial variation characteristics of the attribute data of each shale gas reservoir attribute parameter based on the normal distribution data of each shale gas reservoir attribute parameter; selecting a suitable variogram model based on the spatial variation characteristics of the attribute data of each shale gas reservoir attribute parameter to obtain the variogram data of the shale gas reservoir attribute parameters.

[0077] The variation function data may include at least one of a main range direction, a main range, a secondary range direction, a secondary range and a vertical range.

[0078] In an embodiment of the present invention, step S103 may be specifically as follows: according to the spatial heterogeneity of each shale gas reservoir attribute parameter and the change of the lithofacies layer within the shale section, the vertical distribution ratio of the shale gas reservoir attribute parameters is adjusted; according to the research results of the sedimentary phase or seismic attributes, the spatial distribution change of the shale gas reservoir attribute parameters is determined, and then a spatial trend model of the shale gas reservoir attribute parameters is established.

[0079] In an embodiment of the present invention, the correlation between the attribute parameters of each shale gas reservoir can be determined according to the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir. As a specific example, the main parameters of lithofacies classification are shale organic matter and mineral content, and the shale organic matter and mineral content are both controlled by the sedimentary environment. Therefore, by analyzing that the organic matter comes from plankton during deposition, and the shell of the plankton is mainly brittle minerals such as silicon and calcium, it can be determined that there is a positive correlation between the abundance of organic matter and the content of brittle minerals. For the source parameter TOC related to the abundance of organic matter and the reservoir parameter brittle mineral content, based on the source-reservoir coupling relationship, a source-reservoir coupling constraint framework can be constructed, and TOC is higher than the level of brittle mineral content in the source-reservoir coupling framework, and TOC is at the highest level in the source-reservoir coupling constraint framework.

[0080] As an example, step S105 may specifically be:

[0081] Based on the source-reservoir coupling constraint framework, the hierarchical constraint order of each shale gas reservoir attribute parameter and the upper level attribute parameter corresponding to each shale gas reservoir attribute parameter are determined;

[0082] Obtaining an initial attribute parameter model of a previous level attribute parameter corresponding to a shale gas reservoir attribute parameter;

[0083] Based on the hierarchical constraint order, variogram data and spatial trend model, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is used as the collaborative constraint variable, and the attribute parameter model of each shale gas reservoir attribute parameter is established in turn.

[0084] Among them, when the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is the parameter corresponding to the highest level in the source-reservoir coupling constraint framework, the initial attribute parameter model corresponding to the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter, that is, the highest-level parameter, is established based on the variogram data and the spatial trend model. The initial attribute parameter models corresponding to the shale gas reservoir attribute parameters except the highest-level parameters are all initial attribute parameter models constructed based on the variogram data, the spatial trend model and the attribute parameter model corresponding to the higher-level shale gas reservoir attribute parameter as constraints.

[0085] Step S106 and step S107 may be specifically as follows: Under a specific lithofacies type, the correlation between the attribute parameters of each shale gas reservoir in the lithofacies type and the gas content is statistically analyzed to obtain the correlation coefficient between the attribute parameters of each shale gas reservoir and the gas content; the correlation coefficient between the attribute parameters of each shale gas reservoir and the gas content is normalized to obtain the contribution ratio of each shale gas reservoir attribute parameter to the gas content. Based on the attribute parameter model of each shale gas reservoir attribute parameter, the parameter value of each shale gas reservoir attribute parameter is obtained, and the contribution volume distribution model of the shale gas reservoir attribute parameter to the shale gas reservoir sweet spot is obtained by calculating the parameter value of each shale gas reservoir attribute parameter and its corresponding contribution ratio; by integrating the contribution volume distribution model corresponding to each shale gas reservoir attribute parameter, a shale gas reservoir sweet spot model is constructed.

[0086] In the embodiment of the present invention, by combining the source-reservoir coupling constraint relationship, the problem of poor correlation between the attribute parameters of shale gas reservoirs in traditional shale geological modeling is solved, and the attribute parameters of each shale gas reservoir can be effectively associated, so that the constructed shale gas reservoir sweet spot model is more accurate, thereby enabling more reliable analysis and prediction of the shale gas reservoir sweet spot, providing a basis for subsequent natural fracture and artificial fracturing modeling, and providing a reliable model guarantee for the effective development of shale gas.

[0087] The above is a method for constructing a shale gas reservoir sweet spot model provided by an embodiment of the present invention. The attribute data corresponding to the shale gas reservoir attribute parameters are obtained based on a pre-constructed three-dimensional stratigraphic grid model. The variogram model of the shale gas reservoir attribute parameters is determined by performing geological statistical analysis on the attribute data and based on the analysis, and the variogram data of the shale gas reservoir attribute parameters are obtained using the determined variogram model; the spatial trend model of the shale gas reservoir attribute parameters is obtained by performing spatial heterogeneity analysis on the attribute data of the shale gas reservoir attribute parameters; the source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters is constructed through the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types; the attribute parameter model of each shale gas reservoir attribute parameter is established based on the obtained variogram data, spatial trend model and source-reservoir coupling constraint framework. Finally, the shale gas reservoir sweet spot model is constructed by obtaining the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type, based on the attribute parameter model of each shale gas reservoir attribute parameter and the contribution ratio of the shale gas reservoir attribute parameter. By effectively associating the source-reservoir coupling constraint relationship between the attribute parameters of each shale gas reservoir, the accuracy and reliability of the shale gas reservoir attribute model are effectively improved.

[0088] Embodiment 2

[0089] See also Figure 2 As shown, Figure 2 A schematic flow chart of a method for constructing a shale gas reservoir sweet spot model provided by another embodiment of the present invention is shown, which includes:

[0090] Step S201: Based on the factors of shale gas productivity, the main controlling factor parameters of shale gas sweet spots are screened out as the preferred shale gas reservoir attribute parameters.

[0091] The attribute parameters of shale gas reservoirs can be divided into organic geochemical, mineral, physical and rock physical parameters. The organic geochemical parameters are the source parameters of shale gas reservoirs and may include total organic carbon (TOC); the mineral, physical and rock physical parameters are the storage parameters of shale gas reservoirs. The mineral parameters may include the content of brittle minerals, the physical property parameters may include porosity, gas content or saturation, and the rock physical parameters may include Poisson's ratio, Young's modulus or compressibility index.

[0092] Screening out the main controlling factor parameters of shale gas sweet spots can be to select shale gas reservoir attribute parameters that are highly correlated with shale gas content and closely related to the sedimentary environment as the main controlling factor parameters of shale gas sweet spots.

[0093] Step S202: Establishing a well logging interpretation model for each shale gas reservoir attribute parameter.

[0094] Step S203: Based on the well logging interpretation model, well logging data of shale gas reservoir attribute parameters are obtained.

[0095] Step S204: storing the logging data into the three-dimensional stratigraphic grid model to obtain attribute data corresponding to the shale gas reservoir attribute parameters.

[0096] In the embodiment of the present invention, step S204 may specifically be:

[0097] For the horizontal section of the horizontal well, the neighbor likelihood method is used to coarsen the logging data into a three-dimensional stratigraphic network model; for the vertical section of the horizontal well, the arithmetic average method is used to coarsen the logging data into a three-dimensional stratigraphic network model.

[0098] By distinguishing the horizontal and vertical sections of horizontal wells and using different methods to coarsen the sections, the logging data of the horizontal wells can be effectively sampled into the three-dimensional stratigraphic grid model.

[0099] Step S205: Based on the pre-constructed three-dimensional stratigraphic grid model, attribute data corresponding to the shale gas reservoir attribute parameters are obtained.

[0100] The attribute data corresponding to the shale gas reservoir attribute parameters may be continuity attribute data corresponding to the shale gas reservoir attribute parameters obtained after coarsening the well logging interpretation data corresponding to the shale gas reservoir attribute parameters into the three-dimensional stratigraphic grid model.

[0101] Step S206: Performing geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determining a variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain variogram data of the shale gas reservoir attribute parameters.

[0102] Step S206 may specifically include controlling the trend and distribution form of various shale gas reservoir attribute parameters by layer and lithofacies and converting the attribute data of each shale gas reservoir attribute parameter into normal distribution data; determining the spatial variation characteristics of the attribute data of each shale gas reservoir attribute parameter based on the normal distribution data of each shale gas reservoir attribute parameter; selecting a suitable variogram model based on the spatial variation characteristics of the attribute data of each shale gas reservoir attribute parameter to obtain the variogram data of the shale gas reservoir attribute parameters.

[0103] The variation function data may include at least one of a main range direction, a main range, a secondary range direction, a secondary range and a vertical range.

[0104] Step S207: Perform spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters.

[0105] According to the spatial heterogeneity of the attribute parameters of each shale gas reservoir and the changes of the lithofacies layers within the shale section, the vertical distribution ratio of the attribute parameters of the shale gas reservoir is adjusted. According to the research results of sedimentary facies or seismic attributes, the spatial distribution changes of the attribute parameters of the shale gas reservoir are determined, and then the spatial trend model of the attribute parameters of the shale gas reservoir is established.

[0106] Step S208: constructing a source-reservoir coupling constraint framework for shale gas reservoir attribute parameters based on the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types.

[0107] In an embodiment of the present invention, the correlation between the attribute parameters of each shale gas reservoir can be determined according to the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir. As a specific example, the main parameters of lithofacies classification are shale organic matter and mineral content. The organic matter and mineral content of shale are both controlled by the sedimentary environment. Therefore, by analyzing the organic matter, it is derived from plankton during deposition, and the shell of plankton is mainly brittle minerals such as silicon and calcium. Therefore, it can be determined that there is a positive correlation between the abundance of organic matter and the content of brittle minerals. For the source parameter TOC related to the abundance of organic matter and the reservoir parameter brittle mineral content, based on the source-reservoir coupling relationship, a source-reservoir coupling constraint framework can be constructed. TOC is higher than the level of brittle mineral content in the source-reservoir coupling framework, and TOC is at the highest level in the source-reservoir coupling constraint framework.

[0108] Step S209: Establishing an attribute parameter model of each shale gas reservoir attribute parameter based on the variogram data, the spatial trend model, and the source-reservoir coupling constraint framework.

[0109] As an example, step S209 may be specifically as follows:

[0110] Based on the source-reservoir coupling constraint framework, the hierarchical constraint order of each shale gas reservoir attribute parameter and the upper level attribute parameter corresponding to each shale gas reservoir attribute parameter are determined;

[0111] Obtaining an initial attribute parameter model of a previous level attribute parameter corresponding to a shale gas reservoir attribute parameter;

[0112] Based on the hierarchical constraint order, variogram data and spatial trend model, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is used as the collaborative constraint variable, and the attribute parameter model of each shale gas reservoir attribute parameter is established in turn.

[0113] Among them, when the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is the parameter corresponding to the highest level in the source-reservoir coupling constraint framework, the initial attribute parameter model corresponding to the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter, that is, the highest-level parameter, is established based on the variogram data and the spatial trend model. The initial attribute parameter models corresponding to the shale gas reservoir attribute parameters except the highest-level parameters are all initial attribute parameter models constructed based on the variogram data, the spatial trend model and the attribute parameter model corresponding to the higher-level shale gas reservoir attribute parameter as constraints.

[0114] Step S210: Determine the lithofacies type of shale in the study area based on the well logging data corresponding to the sedimentary facies and the main controlling factor parameters.

[0115] Among them, the main controlling factor parameters include organic matter content and mineral content. Based on the logging data corresponding to the sedimentary phase and the main controlling factor parameters, the lithofacies type of shale in the study area is determined, including:

[0116] Based on the differences in sedimentary facies, the genetic type of shale in the study area is determined;

[0117] A triangular diagram of the mineral content of the mineral components in the shale in the study area is formed based on the mineral content, and the major lithofacies categories are determined based on the genetic type of the shale in the study area;

[0118] The lithofacies type of shale in the study area is determined based on the major lithofacies categories and the organic matter content and mineral content of the shale in the study area.

[0119] Step S211: Identify the gas production capacity of the lithofacies type based on the gas-bearing property of the lithofacies type, and select high-quality lithofacies types based on the gas production capacity of the lithofacies type.

[0120] Step S212: Obtain the contribution ratio of shale gas reservoir attribute parameters to gas content under each high-quality lithofacies type.

[0121] In an embodiment of the present invention, this step may specifically include: obtaining the correlation coefficient between the main controlling factor parameter and the gas content under the high-quality lithofacies type; normalizing the correlation coefficient corresponding to the main controlling factor parameter to obtain the contribution ratio of the main controlling factor parameter to the gas content. It should be noted that the main controlling factor parameter is the preferred shale gas reservoir attribute parameter. By selecting the main controlling factor parameter to determine the correlation coefficient with the gas content, the accuracy of analyzing the correlation between the shale gas reservoir attribute parameter and the shale gas production capacity can be effectively improved.

[0122] Step S213: constructing a shale gas reservoir sweet spot model based on the attribute parameter models of the shale gas reservoir attribute parameters and the contribution ratios of the shale gas reservoir attribute parameters.

[0123] Based on the attribute parameter model of each shale gas reservoir attribute parameter, the parameter value of each shale gas reservoir attribute parameter is obtained, and the contribution volume distribution model of the shale gas reservoir attribute parameter to the shale gas reservoir sweet spot is obtained by calculating the parameter value of each shale gas reservoir attribute parameter and its corresponding contribution ratio; and the shale gas reservoir sweet spot model is constructed by integrating the contribution volume distribution models corresponding to the attribute parameters of each shale gas reservoir.

[0124] In an embodiment of the present invention, a shale gas reservoir attribute parameter model is established based on the regional shale lithofacies distribution. By selecting high-quality lithofacies types and screening out the main controlling factor parameters, and then combining the source-reservoir coupling constraint relationship, each shale gas reservoir attribute parameter model is constructed, thereby improving the accuracy of each shale gas reservoir attribute parameter model. Based on each shale gas reservoir attribute parameter model, the spatial distribution of shale reservoir sweet spots is further characterized. By combining the source-reservoir coupling constraint relationship, the problem of poor correlation between the attribute parameters of each shale gas reservoir in traditional shale address modeling is solved, and the attribute parameters of each shale gas reservoir can be effectively associated, so that the constructed shale gas reservoir sweet spot model is more accurate, thereby enabling more reliable analysis and prediction of the shale gas reservoir sweet spots, providing a basis for subsequent natural fracture and artificial fracturing modeling, and providing a reliable model guarantee for the effective development of shale gas.

[0125] The above is a method for constructing a shale gas reservoir sweet spot model provided by another embodiment of the present invention. By screening out the main controlling factor parameters of the shale gas sweet spot based on the factors of shale gas productivity as the preferred shale gas reservoir attribute parameters. On the one hand, a logging interpretation model of each shale gas reservoir attribute parameter is established, the logging data of the shale gas reservoir attribute parameter is obtained, and the acquired logging data is stored in the three-dimensional stratigraphic grid model, so as to obtain the attribute data corresponding to the shale gas reservoir attribute parameter based on the pre-constructed three-dimensional stratigraphic grid model. The attribute data of the shale gas reservoir attribute parameter are respectively subjected to geological statistical analysis and the variogram model of the shale gas reservoir attribute parameter is determined based on the analysis, so as to obtain the variogram data of the shale gas reservoir attribute parameter by using the variogram model; spatial heterogeneity analysis is performed to obtain the spatial trend model of the shale gas reservoir attribute parameter; according to the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types, the source-reservoir coupling constraint framework of the shale gas reservoir attribute parameter is constructed, and then the attribute parameter model of each shale gas reservoir attribute parameter is established based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework. On the other hand, based on the logging data corresponding to the sedimentary phase and the main controlling factor parameters, the lithofacies type of the shale in the study area is determined, and the gas production capacity of the lithofacies type is identified based on the gas content of the lithofacies type, and then the high-quality lithofacies type is screened out, and the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each high-quality lithofacies type is obtained. Finally, based on the attribute parameter model of each shale gas reservoir attribute parameter and the contribution ratio of the shale gas reservoir attribute parameters, a shale gas reservoir sweet spot model is constructed. The reliability of the shale gas reservoir sweet spot model is effectively improved, and the production capacity of shale can be predicted more accurately.

[0126] Embodiment 3

[0127] The following is a specific example provided by Example 3 of the present invention. The test area of ​​this example is the Jiaoshiba block of the Fuling shale gas field in the Sichuan Basin, which includes a certain area of ​​structural gentle areas and structural slope areas, and the shale gas content and production capacity of these two areas are very different. The study area has three-dimensional seismic data, including dozens of well platforms, a total of 26 wells (3 pilot wells and 23 horizontal wells), and an average well spacing of about 600m. At present, the seismic information has been processed and interpreted to interpret the bottom and top surfaces of the shale gas layer. The drillings all contain data such as logging curves, logging data, and test well production. The logging interpretation can provide information such as TOC and mineral content for the establishment of a shale gas reservoir attribute model.

[0128] The shale sections in the study area are developed in the Wufeng Formation of the Ordovician System and the lower section of the Longmaxi Formation of the Silurian System. They are deep-water shelf deposits, and shale is mainly produced in siliceous shelf phases and sandy shelf phases. Drilling reveals that the TOC, gas content and other parameters of the shale sections in the study area have obvious differences in the vertical direction, with the lower shale section being the best, and the overall organic matter abundance tends to decrease from bottom to top. The brittle minerals of shale include quartz, potassium feldspar and carbonate minerals. The brittle mineral content measured in the core is above 50%, which is highly brittle and easy to fracture. The brittle mineral content gradually increases from top to bottom. TOC, brittle minerals and porosity in shale are spatially heterogeneous, which affects the distribution of the original gas content.

[0129] The method provided in Example 2 of the present invention is used to establish a sweet spot model for the shale gas reservoir.

[0130] (1) According to the sedimentary characteristics of shale in the study area, representative siliceous, calcareous and argillaceous minerals were selected to form a mineral content triangle diagram. According to the three main controlling factors of sedimentary phase, mineral content and TOC content, the shale layer was divided into 8 different lithofacies (see Table 1). Among them, the lower shale lithofacies has the characteristics of rich carbon, high silicon and high gas content, and is the main shale gas producing layer in the test area. A logging interpretation model for shale gas reservoir attribute parameters was established, in which the total organic carbon content (TOC) logging calculation model can be calculated using the natural gamma ray spectroscopy method, and the mineral content can be calculated using the full component volume model prediction method. Finally, continuous data of each type of attribute parameter of the shale gas reservoir at the drilling site were obtained. According to the lithofacies classification scheme, the lithofacies type distribution of a single well was calculated.

[0131] Table 1 Lithofacies classification of shale in the study area

[0132]

[0133] (2) The continuity attribute data of the shale gas reservoir attribute parameters obtained by logging interpretation are coarsened into the three-dimensional stratigraphic grid model. If it is a horizontal well, the vertical section and the horizontal section are coarsened separately, where the vertical section is coarsened using the same arithmetic mean method as the vertical well, and the horizontal section is coarsened using the critical likelihood method.

[0134] (3) Data processing is performed on the attribute data of each type of shale gas reservoir attribute parameter after coarsening, including data input by layer and lithofacies, output truncation, and trend and distribution morphology control, etc., to convert the attribute data of each type of attribute parameter into normal distribution data. Based on the attribute data after data conversion, according to the spatial variation characteristics of the attribute data, an appropriate theoretical variogram model is selected, and the variogram analysis of the attribute data is performed by layer and direction to obtain the variogram parameters of each shale gas reservoir attribute parameter (see Table 2).

[0135] Table 2 Variation function parameters of shale gas reservoir attribute parameters for shale optimization in the study area

[0136]

[0137] (4) The attribute parameters of shale gas reservoirs in the study area have obvious differences in the vertical direction. According to the changes of lithofacies layers in the shale layer, the distribution ratio of attribute parameters in the vertical direction is adjusted. Figure 3 As shown, Figure 3 A schematic diagram of the longitudinal heterogeneity characteristics of the shale gas reservoir attribute parameters provided by an embodiment of the present invention is shown. For example, the longitudinal distribution characteristics of the TOC data are statistically analyzed, and the data decreases significantly from bottom to top; brittle minerals also show a trend of gradually decreasing upward; and the porosity is higher in the upper and lower sections and relatively lower in the middle section in the longitudinal distribution. On the plane, based on the results of sedimentary phase research, the distribution changes of the attribute parameters on the plane are clarified. For example, the average porosity of the main area is significantly higher than that of the east. Within the main area, the north is better than the south, and the gas content has a similar distribution pattern. According to the distribution patterns of these parameters in space, a spatial distribution trend model of the attribute parameters of each shale gas reservoir is established, see Figure 4 As shown, Figure 4 A schematic diagram of the total organic carbon spatial distribution trend model provided by an embodiment of the present invention is shown.

[0138] (5) Based on the source-reservoir coupling constraint relationship of shale gas reservoir attribute parameters, the correlation between the attribute parameters of various shale gas reservoirs was analyzed, and the statistical results showed that: ① the organic matter (source) and lithological mineral content of shale are controlled by the sedimentary environment and are also the main parameters of lithofacies classification. Therefore, the lithofacies model needs to be established on the basis of the TOC model and the mineral content model; ② organic matter comes from plankton during sedimentation, and the biological shells are mainly siliceous and calcareous (both are brittle minerals). Therefore, there is a certain positive correlation between the abundance of organic matter and the content of brittle minerals, see Figure 5 As shown, Figure 5 The schematic diagram of the correlation between the organic carbon content and the brittle mineral content provided by the embodiment of the present invention is shown; ③ Organic matter will form pores during the evolution process, and the porosity (reservoir) in the shale reservoir has a certain positive correlation with TOC because it includes organic pores; ④ Both porosity and TOC are positively correlated with gas content; ⑤ Siliceous minerals contain quartz components, and the brittle mineral content has a certain correlation with rock physical parameters. By analyzing the correlation between attribute data and their degree of correlation, the hierarchical constraint order between various continuous attribute parameters under the control of different lithofacies types is clarified, and a source-reservoir coupling constraint framework based on shale gas reservoir attribute parameters is established, see Figure 6 As shown, Figure 6 A schematic diagram of a source-reservoir coupling constraint framework for shale gas reservoir property parameters provided by an embodiment of the present invention is shown.

[0139] (6) According to the modeling order of each shale gas reservoir attribute parameter in the source-reservoir coupling constraint framework of the shale gas reservoir attribute parameter created by this invention, the variogram parameters and spatial trend model of the corresponding shale gas reservoir attribute parameter are used to control the data distribution state of its attribute parameter in various places in space, and at the same time, the upper-level attribute parameter model in the constraint framework is used as the second variable for collaborative constraint, and the parameter model of each shale gas reservoir attribute parameter is established in turn, see Figure 7 As shown, Figure 7 A schematic diagram of the parameter model of TOC, brittle minerals and porosity provided by an embodiment of the present invention is shown, wherein: ① the TOC attribute is established based on the constraints of the variogram model and the spatial trend model; ② the brittle mineral attribute is established based on the constraints of the variogram model and the spatial trend model, and the TOC model is used as the second constraint variable; ③ according to the lithofacies classification, a lithofacies distribution model is established based on the TOC model and the brittle mineral model; ④ the porosity attribute is established based on the constraints of the variogram model and the trend model, and the lithofacies attribute is used as the constraint; ⑤ the compressible index attribute is established based on the constraints of the ground stress trend model, and the brittle mineral content model is used as the second constraint variable.

[0140] (7) Statistics show that the high-quality lithofacies with high shale gas content in the study area are mainly carbon-rich, calcium-containing, mud-containing siliceous shale and carbon-rich mixed shale. The correlation between shale gas reservoir attribute parameters and shale gas content in the high-quality lithofacies is statistically analyzed. In this embodiment, TOC, porosity and brittle mineral content have the best correlation with gas content, which are 0.831, 0.756 and 0.689, respectively. That is, the contribution of organic matter abundance, physical properties and lithology in shale to shale gas sweet spots is approximately 36.5%, 33.2% and 30.3%, respectively.

[0141] (8) The established TOC model, porosity model, and brittle mineral content model are multiplied by the corresponding sweet spot contribution ratios and fused to obtain the shale gas sweet spot distribution model, see Figure 8 As shown, Figure 8 The schematic diagram of the shale gas reservoir sweet spot model provided by the embodiment of the present invention is shown. Through the analysis of the model, it is found that the shale gas sweet spots in the northeastern part of the study area are distributed in a continuous area, the scale in the northwest area is reduced, and the shale gas sweet spots in the southern structural slope area are scattered, which matches the actual shale gas production capacity in the study area.

[0142] As the main body of shale gas formation and enrichment, shale plays the dual roles of source rock and reservoir. Traditional shale gas layer attribute models are mostly established based on a certain attribute feature, without considering the characteristics of shale gas reservoir source-reservoir symbiosis and source-reservoir integration. As a result, only the spatial distribution of a certain attribute is reproduced, and the distribution relationship between various attributes is difficult to conform to the actual geological laws; on the other hand, traditional shale gas layer attribute modeling is more based on the lithofacies model to judge the gas content and gas production capacity of shale gas, and has not effectively utilized the spatial distribution law of shale gas source and reservoir parameters, resulting in an inaccurate understanding of the sweet spots of shale gas layers.

[0143] By comparison, it is found that the shale gas layer attribute model established by the method of this invention well describes the spatial distribution of various attribute parameters inside the shale, ensuring that the distribution relationship of different shale gas reservoir attribute parameters matches the geological law of shale gas evolution, and at the same time can more accurately reflect the spatial distribution of shale gas geology and engineering sweet spots. The attribute model of shale gas reservoir attribute parameters established by this method is more accurate and reliable, and can also ensure the spatial distribution of source and reservoir attributes in the shale layer. Based on this attribute model, a reliable shale gas reservoir sweet spot model can be further obtained, and the quality and distribution of sweet spots can be more targeted for high-quality shale lithofacies layers, providing guarantees for subsequent optimization of well locations and numerical simulation models.

[0144] Embodiment 4

[0145] Another aspect of the present invention also provides a shale gas reservoir sweet spot model construction device, see Fig. 9 As shown, Fig. 9 The structure diagram of the shale gas reservoir sweet spot model construction device provided by an embodiment of the present invention is shown, which includes:

[0146] The attribute data acquisition module 01 is used to acquire attribute data corresponding to shale gas reservoir attribute parameters based on a pre-built three-dimensional stratigraphic grid model;

[0147] A geostatistical analysis module 02, which is used to perform geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determine the variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain the variogram data of the shale gas reservoir attribute parameters;

[0148] A spatial heterogeneity analysis module 03, which is used to perform spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters;

[0149] Source-reservoir coupling constraint framework construction module 04, which is used to construct a source-reservoir coupling constraint framework of shale gas reservoir attribute parameters according to the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types;

[0150] The attribute parameter model building module 05 is used to establish the attribute parameter model of each shale gas reservoir attribute parameter based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework;

[0151] Gas-bearing property contribution ratio analysis module 06, which is used to obtain the contribution ratio of shale gas reservoir attribute parameters to gas-bearing property under each lithofacies type;

[0152] The shale gas reservoir sweet spot model construction module 07 is used to construct a shale gas reservoir sweet spot model based on the attribute parameter model of each shale gas reservoir attribute parameter and the contribution ratio of the shale gas reservoir attribute parameter.

[0153] Among them, shale gas reservoir attribute parameters can be divided into organic geochemical, mineral, physical and rock physical types. Organic geochemical parameters are source parameters of shale gas reservoirs and may include total organic carbon (TOC); mineral, physical and rock physical parameters are storage parameters of shale gas reservoirs. Mineral parameters may include brittle mineral content, physical property parameters may include porosity, gas content or saturation, and rock physical parameters may include Poisson's ratio, Young's modulus or compressibility index.

[0154] In the embodiment of the present invention, by selecting a variety of shale gas reservoir attribute parameters and effectively combining the relevant information of the various shale gas reservoir attribute parameters, a shale gas reservoir sweet spot attribute model with richer content and better accuracy can be constructed.

[0155] The attribute data acquisition module 01 can acquire continuously distributed attribute data corresponding to the shale gas reservoir attribute parameters based on a pre-built three-dimensional stratigraphic grid model.

[0156] The geological statistics analysis module 02 can be used to process the attribute data, control the trend and distribution form of various shale gas reservoir attribute parameters by layer and lithofacies, and convert the attribute data of each shale gas reservoir attribute parameter into normal distribution data; determine the spatial variation characteristics of the attribute data of each shale gas reservoir attribute parameter based on the normal distribution data of each shale gas reservoir attribute parameter; select a suitable variogram model based on the spatial variation characteristics of the attribute data of each shale gas reservoir attribute parameter to obtain the variogram data of the shale gas reservoir attribute parameters.

[0157] The variation function data may include at least one of a main range direction, a main range, a secondary range direction, a secondary range and a vertical range.

[0158] The spatial heterogeneity analysis module 03 can be used to adjust the vertical distribution ratio of shale gas reservoir attribute parameters according to the spatial heterogeneity of each shale gas reservoir attribute parameter and the change of lithofacies layers within the shale section, determine the spatial distribution change of shale gas reservoir attribute parameters according to the research results of sedimentary phase or seismic attributes, and then establish a spatial trend model of shale gas reservoir attribute parameters.

[0159] In an embodiment of the present invention, the correlation between the attribute parameters of each shale gas reservoir can be determined according to the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir. As a specific example, the main parameters of lithofacies classification are shale organic matter and lithological mineral content, and the shale organic matter and lithological mineral content are both controlled by the sedimentary environment. Therefore, by analyzing that the organic matter comes from plankton during deposition, and the shell of the plankton is mainly brittle minerals such as silicon and calcium, it can be determined that there is a positive correlation between the abundance of organic matter and the content of brittle minerals. For the source parameter TOC related to the abundance of organic matter and the reservoir parameter brittle mineral content, based on the source-reservoir coupling relationship, a source-reservoir coupling constraint framework can be constructed, and TOC is higher than the level of brittle mineral content in the source-reservoir coupling framework, and TOC is at the highest level in the source-reservoir coupling constraint framework.

[0160] The attribute parameter model building module 05 can be used for:

[0161] Based on the source-reservoir coupling constraint framework, the hierarchical constraint order of each shale gas reservoir attribute parameter and the upper level attribute parameter corresponding to each shale gas reservoir attribute parameter are determined;

[0162] Obtaining an initial attribute parameter model of a previous level attribute parameter corresponding to a shale gas reservoir attribute parameter;

[0163] Based on the hierarchical constraint order, variogram data and spatial trend model, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is used as the collaborative constraint variable, and the attribute parameter model of each shale gas reservoir attribute parameter is established in turn.

[0164] Among them, when the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is the parameter corresponding to the highest level in the source-reservoir coupling constraint framework, the initial attribute parameter model corresponding to the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter, that is, the highest-level parameter, is established based on the variogram data and the spatial trend model. The initial attribute parameter models corresponding to the shale gas reservoir attribute parameters except the highest-level parameters are all initial attribute parameter models constructed based on the variogram data, the spatial trend model and the attribute parameter model corresponding to the higher-level shale gas reservoir attribute parameter as constraints.

[0165] The gas content contribution ratio analysis module 06 can be used to statistically analyze the correlation between the attribute parameters of each shale gas reservoir in the lithofacies type and the gas content under a specific lithofacies type, and obtain the correlation coefficient between the attribute parameters of each shale gas reservoir and the gas content; normalize the correlation coefficient between the attribute parameters of each shale reservoir and the gas content, and obtain the contribution ratio of each shale gas reservoir attribute parameter to the gas content.

[0166] The shale gas reservoir sweet spot model construction module 07 can be used to obtain the parameter value of each shale gas reservoir attribute parameter based on the attribute parameter model of each shale gas reservoir attribute parameter, and obtain the contribution volume distribution model of the shale gas reservoir attribute parameter to the shale gas reservoir sweet spot by calculating the parameter value of each shale gas reservoir attribute parameter and its corresponding contribution ratio; and construct a shale gas reservoir sweet spot model by integrating the contribution volume distribution models corresponding to each shale gas reservoir attribute parameter.

[0167] By combining the source-reservoir coupling constraint relationship, the problem of poor correlation between the attribute parameters of shale gas reservoirs in traditional shale geological modeling is solved. The attribute parameters of each shale gas reservoir can be effectively associated, making the constructed shale gas reservoir sweet spot model more accurate, thereby enabling more reliable analysis and prediction of shale gas reservoir sweet spots, providing a basis for subsequent natural fracture and artificial fracturing modeling, and providing a reliable model guarantee for the effective development of shale gas.

[0168] The above is a shale gas reservoir sweet spot model construction device provided by an embodiment of the present invention. The attribute data corresponding to the shale gas reservoir attribute parameters are obtained based on a pre-constructed three-dimensional stratigraphic grid model. The variogram model of the shale gas reservoir attribute parameters is determined by performing geological statistical analysis on the attribute data and based on the analysis, and the variogram data of the shale gas reservoir attribute parameters are obtained using the determined variogram model; the spatial trend model of the shale gas reservoir attribute parameters is obtained by performing spatial heterogeneity analysis on the attribute data of the shale gas reservoir attribute parameters; the source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters is constructed through the source-reservoir coupling relationship between the attribute parameters of each shale gas reservoir under different lithofacies types; the attribute parameter model of each shale gas reservoir attribute parameter is established based on the obtained variogram data, spatial trend model and source-reservoir coupling constraint framework. Finally, by obtaining the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type, the shale gas reservoir sweet spot model is constructed based on the attribute parameter model of each shale gas reservoir attribute parameter and the contribution ratio of the shale gas reservoir attribute parameter. By effectively associating the source-reservoir coupling constraint relationship between the attribute parameters of each shale gas reservoir, the accuracy and reliability of the shale gas reservoir attribute model are effectively improved.

[0169] Embodiment 5

[0170] Another aspect of the present invention provides a storage medium storing a computer program, which, when executed by a processor, can implement the method for constructing a shale gas reservoir sweet spot model as described in the first or second embodiment above.

[0171] Another aspect of the present invention provides a device, which includes a processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the method for constructing a shale gas reservoir sweet spot model as described in the first or second embodiment above can be implemented.

[0172] The processes, functions, methods and / or software described above may be recorded, stored or fixed in one or more computer-readable storage media, which include program instructions, which will be implemented by the computer so that the processor executes the program instructions. The medium may also include program instructions, data files, data structures, etc. alone, or a combination thereof. The medium or program instructions may be specifically designed and understood by a person skilled in the art of computer software, or the medium or instructions may be known and available to a person skilled in the art of computer software. Examples of computer-readable media include: magnetic media, such as hard disks, floppy disks, and tapes; optical media, such as CDROM disks and DVDs; magneto-optical media, such as optical disks; and hardware devices, specifically configured to store and execute program instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, etc. Examples of program instructions include machine code (e.g., code generated by a compiler) and files containing high-level code, which can be executed by a computer using an interpreter. The described hardware devices may be configured to be used as one or more software modules to perform the operations and methods described above, and vice versa. In addition, the computer-readable storage medium may be distributed in networked computer systems, and computer-readable codes or program instructions may be stored and executed in a decentralized manner.

[0173] It should be noted that the device may include one or more processors and memory, and the processor and memory may be connected via a bus or other means. The memory, as a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer executable programs and modules. The processor executes various functional applications and data processing of the device by running the non-volatile software programs, instructions and modules stored in the memory, that is, realizing the method for constructing a shale gas reservoir sweet spot model as described above.

[0174] Although the embodiments disclosed in the present invention are as above, the contents described are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for constructing a shale gas reservoir sweet spot model, characterized in that: include: Based on the pre-built three-dimensional stratigraphic grid model, the attribute data corresponding to the attribute parameters of the shale gas reservoir are obtained; Performing geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determining a variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain variogram data of the shale gas reservoir attribute parameters; Performing spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters; According to the source-reservoir coupling relationship between the shale gas reservoir attribute parameters under different lithofacies types, a source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters is constructed; Establishing an attribute parameter model of each of the shale gas reservoir attribute parameters based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework; Based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework, an attribute parameter model of each of the shale gas reservoir attribute parameters is established, including: Determining the hierarchical constraint order of each of the shale gas reservoir attribute parameters and the upper level attribute parameter corresponding to each of the shale gas reservoir attribute parameters based on the source-reservoir coupling constraint framework; Obtaining an initial attribute parameter model of a previous level attribute parameter corresponding to the shale gas reservoir attribute parameter; Based on the hierarchical constraint order, the variogram data and the spatial trend model, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is used as a collaborative constraint variable to sequentially establish the attribute parameter model of each shale gas reservoir attribute parameter; Wherein, when the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is the parameter corresponding to the highest level in the source-reservoir coupling constraint framework, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is established based on the variogram data and the spatial trend model; Obtaining the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type; Based on the attribute parameter models of the shale gas reservoir attribute parameters and the contribution ratios of the shale gas reservoir attribute parameters, a shale gas reservoir sweet spot model is constructed.

2. The method according to claim 1, characterized in that The method further comprises: Establishing a well logging interpretation model for the shale gas reservoir attribute parameters; Based on the well logging interpretation model, acquiring well logging data of the shale gas reservoir attribute parameters; The logging data is coarsened into the three-dimensional stratigraphic grid model to obtain attribute data corresponding to the shale gas reservoir attribute parameters.

3. The method according to claim 2, characterized in that Coarsening the logging data into the three-dimensional stratigraphic grid model includes: For the horizontal section of the horizontal well, the logging data is coarsened into the three-dimensional stratigraphic grid model using a neighbor likelihood method; For the vertical section of the horizontal well, the arithmetic average method is used to coarsen the logging data into the three-dimensional stratigraphic grid model.

4. The method according to any one of claims 1 to 3, characterized in that The method further includes: based on the factors of shale gas productivity, selecting the main controlling factor parameters of shale gas sweet spots as the shale gas reservoir attribute parameters.

5. The method according to claim 4, characterized in that The method further comprises: determining the lithofacies type of the shale in the study area based on the sedimentary facies and the logging data corresponding to the main controlling factor parameters.

6. The method according to claim 5, characterized in that The main controlling factor parameters include organic matter content and mineral content. The determining of the lithofacies type of shale in the study area based on the well logging data corresponding to the sedimentary facies and the main controlling factor parameters includes: Based on the sedimentary facies differences, determining the genetic type of shale in the study area; Forming a mineral content triangle diagram of the mineral components in the shale in the study area according to the mineral content, and determining the major lithofacies based on the genetic type of the shale in the study area; The lithofacies type of the shale in the study area is determined based on the major lithofacies categories and the organic matter content and the mineral content of the shale in the study area.

7. The method according to claim 6, characterized in that After determining the lithofacies type of the shale in the study area, the method further includes: identifying the gas production capacity of the lithofacies type based on the gas content of the lithofacies type, and screening out high-quality lithofacies types based on the gas production capacity of the lithofacies type.

8. The method according to claim 7, characterized in that The obtaining of the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type includes: Obtaining the correlation coefficient between the main controlling factor parameter and the gas content under the high-quality lithofacies type; The correlation coefficient corresponding to the main controlling factor parameter is normalized to obtain the contribution ratio of the main controlling factor parameter to the gas content.

9. A shale gas reservoir sweet spot model construction device, characterized in that: include: An attribute data acquisition module, which is used to acquire attribute data corresponding to shale gas reservoir attribute parameters based on a pre-built three-dimensional stratigraphic grid model; A geostatistical analysis module, which is used to perform geostatistical analysis on the attribute data of the shale gas reservoir attribute parameters and determine the variogram model of the shale gas reservoir attribute parameters based on the geostatistical analysis to obtain the variogram data of the shale gas reservoir attribute parameters; A spatial heterogeneity analysis module, which is used to perform spatial heterogeneity analysis based on the attribute data of the shale gas reservoir attribute parameters to obtain a spatial trend model of the shale gas reservoir attribute parameters; A source-reservoir coupling constraint framework construction module, which is used to construct a source-reservoir coupling constraint framework of the shale gas reservoir attribute parameters according to the source-reservoir coupling relationship between the shale gas reservoir attribute parameters under different lithofacies types; An attribute parameter model building module, which is used to establish an attribute parameter model of each of the shale gas reservoir attribute parameters based on the variogram data, the spatial trend model and the source-reservoir coupling constraint framework; The attribute parameter building module is also used to: Determining the hierarchical constraint order of each of the shale gas reservoir attribute parameters and the upper level attribute parameter corresponding to each of the shale gas reservoir attribute parameters based on the source-reservoir coupling constraint framework; Obtaining an initial attribute parameter model of a previous level attribute parameter corresponding to the shale gas reservoir attribute parameter; Based on the hierarchical constraint order, the variogram data and the spatial trend model, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is used as a collaborative constraint variable to sequentially establish the attribute parameter model of each shale gas reservoir attribute parameter; A gas content contribution ratio analysis module is used to obtain the contribution ratio of the shale gas reservoir attribute parameters to the gas content under each lithofacies type; A shale gas reservoir sweet spot model construction module, which is used to construct a shale gas reservoir sweet spot model based on the attribute parameter model of each of the shale gas reservoir attribute parameters and the contribution ratio of the shale gas reservoir attribute parameters; Among them, when the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is the parameter corresponding to the highest level in the source-reservoir coupling constraint framework, the initial attribute parameter model of the upper-level attribute parameter corresponding to the shale gas reservoir attribute parameter is established based on the variation function data and the spatial trend model.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for constructing a shale gas reservoir sweet spot model according to any one of claims 1 to 8 can be implemented.

11. A device, characterized in that: include: A processor and a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by the processor, the method for constructing a shale gas reservoir sweet spot model as described in any one of claims 1 to 8 can be implemented.

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