Method and device for determining glutenite reservoir

Abstract

An embodiment of the present application provides a method and device for determining a glutenite reservoir. The method includes: determining the ratio of the permeability to the porosity of the glutenite reservoir; determining the oil saturation of the glutenite reservoir; determining the shale content of the glutenite reservoir; determining the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir; determining a quality factor simulation data volume of the glutenite reservoir according to the quality factor and the original seismic data; performing data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area. The present application can accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration, and particularly to a method and device for determining glutenite reservoirs. Background Art

[0002] Glutenite oil and gas reservoirs refer to oil and gas reservoirs with conglomerate and glutenite as the main reservoirs, which are widely distributed around the world. For example, there are rich glutenite oil and gas reservoirs in the Los Angeles Basin in the United States, the Cuyo Basin in Argentina, the Western Canada Basin, and the Sergipe - Alagoas Basin in Brazil. In China, the Karamay Oilfield in the Junggar Basin is a typical representative, with rich oil and gas resources and great exploration potential.

[0003] Compared with sandstone reservoirs, glutenite reservoirs are significantly different in terms of sedimentary response, reservoir physical properties, and microscopic pore structures. Glutenite reservoirs are mainly developed in sedimentary systems close to the provenance, such as alluvial fans, fan deltas in steep slope zones, and subaqueous fans near the shore, which results in the characteristics of complex gravel composition, rapid lithofacies change, and strong heterogeneity in glutenite reservoirs. The response characteristics of glutenite reservoirs and non - reservoirs on conventional logging curves are not obvious, and the logging characterization and evaluation accuracy are not high; moreover, the impedance difference between the reservoir and the surrounding rock is small, and the longitudinal wave impedance can only roughly distinguish mudstone and glutenite, making it difficult to evaluate and predict effective reservoirs. Summary of the Invention

[0004] Aiming at the problems in the prior art, the present application provides a method and device for determining glutenite reservoirs, which can accurately and conveniently determine the spatial distribution characteristics of glutenite reservoirs in the target work area.

[0005] To solve at least one of the above problems, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a method for determining a glutenite reservoir, including:

[0007] Determining the ratio of permeability to porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area;

[0008] Determining the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water data;

[0009] Determining the shale content of the glutenite reservoir according to the compensated density logging curve data, the compensated neutron logging curve data of the target work area, and the shale content in the core data;

[0010] Determining the quality factor of the glutenite reservoir according to the ratio of permeability to porosity, the oil saturation, and the shale content of the glutenite reservoir;

[0011] Determine the simulated data volume of the quality factor of the glutenite reservoir according to the quality factor and the original seismic data;

[0012] Conduct data analysis on the simulated data volume of the quality factor to determine the distribution characteristics of the glutenite reservoir in the target work area.

[0013] Further, the determining the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area includes:

[0014] Obtain a dual-parameter logging interpretation model according to the core data of different strata and different grain sizes in the target work area and the density data and acoustic wave data in the logging data;

[0015] Determine the permeability and porosity data curves and the ratio of permeability to porosity of the entire well section in the target work area according to the dual-parameter logging interpretation model.

[0016] Further, the determining the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water data includes:

[0017] Determine the Archie oil saturation calculation model according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water resistivity in the formation water data;

[0018] Determine the oil saturation of the glutenite reservoir according to the Archie oil saturation calculation model.

[0019] Further, the determining the shale content of the glutenite reservoir according to the compensated density logging curve data, the compensated neutron logging curve data of the target work area, and the shale content in the core data includes:

[0020] Normalize the difference between the compensated density logging curve data and the compensated neutron logging curve data of the target work area;

[0021] Conduct data fitting on the shale content in the core data and the compensated density logging curve data and the compensated neutron logging curve data after the normalization process to obtain the shale content of the glutenite reservoir.

[0022] Further, the determining the simulated data volume of the quality factor of the glutenite reservoir according to the quality factor and the original seismic data includes:

[0023] Obtain the simulated data volume of the quality factor of the glutenite reservoir according to the characteristic curve data of the quality factor, the seismic attributes and the longitudinal wave impedance in the original seismic data, and a preset multi-attribute neural network simulation model.

[0024] Further, the data analysis of the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area includes:

[0025] Performing a well-connected cross-section analysis on the quality factor simulation data volume to determine the longitudinal and lateral variation characteristics of the glutenite reservoir in the target work area.

[0026] Further, the data analysis of the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area includes:

[0027] Performing an isochronous stratigraphic framework analysis on the quality factor simulation data volume to determine the planar distribution characteristics of the glutenite reservoir in the target work area.

[0028] In a second aspect, the present application provides a glutenite reservoir determination device, including:

[0029] A pore-permeability ratio determination module, configured to determine the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area;

[0030] An oil saturation determination module, configured to determine the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water data;

[0031] A shale content determination module, configured to determine the shale content of the glutenite reservoir according to the compensated density log curve data, the compensated neutron log curve data in the target work area, and the shale content in the core data;

[0032] A quality factor determination module, configured to determine the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir;

[0033] A quality factor simulation data volume determination module, configured to determine the quality factor simulation data volume of the glutenite reservoir according to the quality factor and the original seismic data;

[0034] A glutenite reservoir distribution characteristic analysis module, configured to perform data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area.

[0035] Further, the pore-permeability ratio determination module includes:

[0036] A two-parameter logging interpretation model construction unit, configured to obtain a two-parameter logging interpretation model according to the core data of different formations and different grain sizes in the target work area and the density data and acoustic data in the logging data;

[0037] A seepage pore ratio determination unit, configured to determine the permeability and porosity data curves of the entire well section in the target work area and the ratio of permeability to porosity according to the dual-parameter logging interpretation model.

[0038] Further, the oil saturation determination module includes:

[0039] An Archie oil saturation calculation model construction unit, configured to determine an Archie oil saturation calculation model according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water resistivity in the formation water data;

[0040] An oil saturation determination unit, configured to determine the oil saturation of the glutenite reservoir according to the Archie oil saturation calculation model.

[0041] Further, the shale content determination module includes:

[0042] A logging curve data normalization processing unit, configured to perform normalization processing on the difference between the compensated density logging curve data and the compensated neutron logging curve data in the target work area;

[0043] A data fitting unit, configured to perform data fitting processing on the shale content in the core data and the compensated density logging curve data and the compensated neutron logging curve data after the normalization processing, to obtain the shale content of the glutenite reservoir.

[0044] Further, the quality factor simulation data volume determination module includes:

[0045] A multi-attribute neural network simulation model processing unit, configured to obtain the quality factor simulation data volume of the glutenite reservoir according to the characteristic curve data of the quality factor, the seismic attributes and the longitudinal wave impedance in the original seismic data, and a preset multi-attribute neural network simulation model.

[0046] Further, the glutenite reservoir distribution characteristic analysis module includes:

[0047] A cross-well profile analysis unit, configured to perform cross-well profile analysis on the quality factor simulation data volume to determine the longitudinal and lateral variation characteristics of the glutenite reservoir in the target work area.

[0048] Further, the glutenite reservoir distribution characteristic analysis module includes:

[0049] An isochronous stratigraphic framework analysis unit, configured to perform isochronous stratigraphic framework analysis on the quality factor simulation data volume to determine the planar distribution characteristics of the glutenite reservoir in the target work area.

[0050] In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method for determining a glutenite reservoir are implemented.

[0051] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for determining a glutenite reservoir are implemented.

[0052] As can be seen from the above technical solutions, the present application provides a method and device for determining a glutenite reservoir. By clarifying the main controlling factors of the physical properties of the glutenite reservoir, and calculating the glutenite reservoir parameters that can effectively indicate the quality of the reservoir through conventional logging curves, and preferably selecting the ratio of permeability to porosity, oil saturation, and shale content of the reservoir parameters, the glutenite reservoir quality factor is obtained. Then, a seismic multi-attribute neural network simulation model is adopted to obtain the simulation data volume of the glutenite reservoir quality factor. Furthermore, through data analysis of the data volume, the spatial distribution of the effective glutenite reservoir is predicted, solving the technical problems in the prior art such as low accuracy of conventional logging characterization and classification of glutenite reservoirs, lack of pre-stack gather data and large computational amount, and great difficulty in predicting post-stack P-wave impedance. It can accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0054] Figure 1 It is a schematic flowchart of the method for determining a glutenite reservoir in an embodiment of the present application;

[0055] Figure 2 It is a schematic flowchart of the method for determining a glutenite reservoir in an embodiment of the present application;

[0056] Figure 3 It is a schematic flowchart of the method for determining a glutenite reservoir in an embodiment of the present application;

[0057] Figure 4 It is a schematic flowchart of the method for determining a glutenite reservoir in an embodiment of the present application;

[0058] Figure 5 It is a structural diagram of the device for determining a glutenite reservoir in an embodiment of the present application;

[0059] Figure 6The second structural diagram of the glutenite reservoir determination device in the embodiments of the present application;

[0060] Figure 7 The third structural diagram of the glutenite reservoir determination device in the embodiments of the present application;

[0061] Figure 8 The fourth structural diagram of the glutenite reservoir determination device in the embodiments of the present application;

[0062] Figure 9 The fifth structural diagram of the glutenite reservoir determination device in the embodiments of the present application;

[0063] Figure 10 The sixth structural diagram of the glutenite reservoir determination device in the embodiments of the present application;

[0064] Figure 11 The seventh structural diagram of the glutenite reservoir determination device in the embodiments of the present application;

[0065] Figure 12 The relationship diagram between the shale content and porosity of the reservoir in a specific embodiment of the present application;

[0066] Figure 13 The relationship diagram between the shale content and permeability of the reservoir in a specific embodiment of the present application;

[0067] Figure 14 The crossplot of K / φ and the average pore throat radius r in a specific embodiment of the present application;

[0068] Figure 15 The reservoir classification and evaluation result diagram based on RQ in a specific embodiment of the present application;

[0069] Figure 16 The seismic simulation profile result diagram of the glutenite reservoir quality factor in a specific embodiment of the present application;

[0070] Figure 17 The seismic simulation plane result diagram of the glutenite reservoir quality factor in a specific embodiment of the present application;

[0071] Figure 18 The structural schematic diagram of the electronic device in the embodiments of the present application. Specific embodiments

[0072] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0073] Considering that glutenite reservoirs have characteristics such as complex gravel composition, rapid lithofacies changes, and strong heterogeneity. The response characteristics of glutenite reservoirs and non-reservoirs on conventional logging curves are not obvious, the logging characterization and evaluation accuracy are not high; and the impedance difference between the reservoir and the surrounding rock is small, and the longitudinal wave impedance can only roughly distinguish mudstone and glutenite, making it difficult to evaluate and predict effective reservoirs. This application provides a method and device for determining glutenite reservoirs. By clarifying the main controlling factors of glutenite reservoir physical properties, glutenite reservoir parameters that can effectively indicate the quality of the reservoir are calculated through conventional logging curves. The ratio of permeability to porosity, oil saturation, and shale content of reservoir parameters are optimized to obtain the glutenite reservoir quality factor. Then, a seismic multi-attribute neural network simulation model is used to obtain the simulated data volume of the glutenite reservoir quality factor. Furthermore, through data analysis of the data volume, the spatial distribution of effective glutenite reservoirs is predicted, solving the technical problems in the prior art such as low accuracy of conventional logging characterization and classification of glutenite reservoirs, lack of pre-stack gather data and large calculation amount, and difficulty in predicting post-stack longitudinal wave impedance, and can accurately and conveniently determine the spatial distribution characteristics of glutenite reservoirs in the target work area.

[0074] It can be understood that this application analyzes the main controlling factors of glutenite reservoir physical properties through actual drilling logging, well logging, coring, experimental analysis and testing data for the characteristics of glutenite reservoirs in the target work area. Glutenite reservoirs are usually developed in sedimentary environments with rapid facies changes near the provenance. The internal factors of complex physical properties of glutenite reservoirs are mostly caused by diverse gravel compositions, extremely poor rounding and sorting, complex pore structures, and high shale matrix content. Therefore, this application determines through comprehensive comparative analysis research that the physical properties of glutenite reservoirs are greatly affected by pore structure and shale content.

[0075] To accurately and conveniently determine the spatial distribution characteristics of glutenite reservoirs in the target work area, this application provides an embodiment of a method for determining glutenite reservoirs. See Figure 1 The method for determining glutenite reservoirs specifically includes the following content:

[0076] Step S101: Determine the ratio of permeability to porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area.

[0077] Optionally, the present application determines that the reservoir pore structure is one of the main controlling factors determining the physical properties of glutenite reservoirs. Therefore, the present application uses the ratio of permeability to porosity (K / φ, hereinafter referred to as the permeability-porosity ratio) to reflect the reservoir pore structure characteristics. It is directly proportional to the throat radius r, and the change in its numerical value is sensitive to the change in the quality of the pore structure. The larger the permeability-porosity ratio, the better the pore structure characteristics and the better the reservoir physical properties.

[0078] Specifically, refer to Figure 12 the relationship diagram between the shale content and porosity of the reservoir. The Figure 12 can show that the shale content is inversely proportional to the porosity. At the same time, refer to Figure 13 the relationship diagram between the shale content and permeability of the reservoir. The Figure 13 can show that the shale content of the reservoir is inversely proportional to the permeability, and compared with Figure 12 the shale content has a greater impact on permeability than on porosity.

[0079] Optionally, to ensure the calculation accuracy of the permeability-porosity ratio (K / φ), based on the actual situation of the target work area, the present application adopts the method of calibrating logs with cores on the basis of carrying out the standardization processing of logging curves to establish porosity (φ) and permeability (K) models.

[0080] Specifically, the present application conducts correlation analysis on the core porosity of each small layer and particle size with logging density and acoustic waves, and establishes a dual-parameter porosity and permeability logging interpretation model based on density-acoustic waves through multiple regression. Finally, the porosity and permeability curves of the entire well section are calculated to ensure the accuracy of the calculation of the reservoir permeability-porosity ratio (K / φ).

[0081] Step S102: Determine the oil saturation of the glutenite reservoir according to the porosity, the rock-electric parameters of the glutenite reservoir, and the formation water data.

[0082] It can be understood that one of the influencing factors for the change in the oil saturation (So) of the reservoir is the capillary pressure, and the magnitude of the capillary force depends on the pore throat radius of the reservoir. Therefore, the magnitude of the oil saturation is directly proportional to the quality of the pore structure, that is, the higher the oil saturation, the better the pore structure characteristics and the better the reservoir physical properties.

[0083] Optionally, the present application calculates the oil saturation (So) of the glutenite reservoir according to Archie's formula. Among them, the rock-electric parameters in Archie's formula can be obtained by establishing a rock-electric parameter chart using the rock-electric samples in the actual work area, and the formation water resistivity can be obtained from the actual analysis data of the formation water.

[0084] Step S103: Determine the shale content of the glutenite reservoir according to the compensated density logging curve data, the compensated neutron logging curve data of the target work area, and the shale content in the core data.

[0085] It is understandable that the shale content (V sh ) is one of the key controlling factors for controlling the physical properties of glutenite reservoirs. The larger the shale content (V sh ), the poorer the physical properties of the glutenite reservoir. On the contrary, the physical properties of the glutenite reservoir are better.

[0086] Optionally, the present application can establish a calculation model for the shale content (V sh ) of glutenite reservoirs based on DEN (compensated density logging curve data) and CNL (compensated neutron logging curve data) of conventional logging curves. The specific formula can be:

[0087] V sh = X × Δ(DEN - CNL) + Y (1)

[0088] Wherein, V sh is the shale content of the glutenite reservoir, X and Y are constants, which are obtained by cross-fitting the normalized density and neutron difference (Δ(DEN - CNL)) with the shale content identified by core thin sections in the target interval. Among them, the value of X can be 28.0899, and the value of Y can be 6.7752.

[0089] Step S104: Determine the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir.

[0090] It is understandable that the present application can construct a quality factor (RQ) model for glutenite reservoirs through parameters such as the permeability-porosity ratio (K / φ), oil saturation (So), and shale content (V sh ), so as to calculate the characteristic curve of the quality factor (RQ) of the glutenite reservoir in the target interval of all wells in the work area.

[0091] Specifically, the quality factor (RQ) of the glutenite reservoir is calculated using formula (2):

[0092]

[0093] It is understandable that the larger the quality factor (RQ) of the glutenite reservoir, the more favorable the various reservoir conditions of the glutenite reservoir, and the better the physical properties of the reservoir. Refer to Figure 14 the cross-plot of K / φ and the average pore throat radius r. It can be seen from this Figure 14 that K / φ and r show a functional relationship, and the larger the r value, the larger the K / φ.

[0094] Step S105: Determine the quality factor simulation data volume of the glutenite reservoir according to the quality factor and the original seismic data.

[0095] Optionally, this application can adopt a multi-attribute neural network simulation model to establish a statistical relationship between the calculated characteristic curve of the reservoir quality factor (RQ) of multiple wells in the glutenite reservoir and seismic attributes, longitudinal wave impedance, etc. in the original seismic data. Starting from the well points and following the original seismic data between wells, a seismic simulation data volume of the glutenite reservoir quality factor is finally obtained.

[0096] In another embodiment of this application, before performing the above step S105, this application can also perform a quantitative classification of the reservoir according to the numerical comparison relationship between the quality factor and a preset threshold.

[0097] Optionally, cross-plot analysis is carried out on the calculated reservoir quality factor (RQ) of the glutenite reservoir and the oil-bearing property (fully oil-bearing, oil-bearing, oil-immersed, oil-stained, oil-traced, fluorescent, non-displayed, etc.) of the reservoir core samples to establish a quantitative classification and evaluation standard for effective reservoirs, so as to realize the classification and evaluation of the entire well section of the glutenite reservoir. For example, according to the analysis results, the glutenite body is divided into four categories: class I reservoir (RQ≥3.8), class II reservoir (3.8>RQ≥1.5), class III reservoir (1.5>RQ≥0.2), and non-reservoir (RQ<0.2).

[0098] It can be understood that Figure 15 is the reservoir classification and evaluation result map based on RQ. The higher the RQ value, the better the reservoir. Figure 15 In it, RQ≥1.5 is the effective reservoir section of the target layer of this well. Figure 16 is the seismic simulation profile result map of the glutenite reservoir quality factor. It can Figure 16 be seen the distribution characteristics of the first and second class effective glutenite reservoirs with RQ≥1.5 above the well and between wells. Figure 17 is the seismic simulation plane result map of this glutenite reservoir quality factor. It can Figure 17 be seen the distribution characteristics of the first and second class effective glutenite reservoirs with RQ≥1.5 on the plane of the study area.

[0099] Step S106: Perform data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area.

[0100] It can be understood that this application can adopt methods such as profiles, layer slices, and volume carving to perform a detailed analysis on the seismic simulation data volume of the glutenite reservoir quality factor. According to the above-mentioned glutenite reservoir classification and evaluation standard, the prediction research on the vertical, horizontal, and spatial distribution characteristics of the effective glutenite reservoir is realized, providing a solid basis for well location deployment and reserve calculation in oil and gas exploration work.

[0101] In an alternative embodiment of the present application, the present application can adopt the cross-well profile method to analyze the vertical and horizontal variations of various reservoirs in the vertical and horizontal directions, which is very beneficial for describing the spatial variations of glutenite reservoirs. At the same time, it is also very intuitive for verifying the consistency of the prediction results of glutenite reservoirs in wells and between wells.

[0102] In another alternative embodiment of the present application, the present application can perform isochronous stratigraphic framework-based stratigraphic slicing processing on the seismic simulation data volume of the quality factor of the glutenite reservoir in the target interval. The planar distribution pattern of the effective glutenite reservoir can be clearly seen from a single stratigraphic slice, and the vertical structure and evolution law of the effective glutenite reservoir can also be observed from multiple representative stratigraphic slices of each system tract.

[0103] As can be seen from the above description, the method for determining glutenite reservoirs provided by the embodiments of the present application can, by clarifying the main controlling factors of the physical properties of glutenite reservoirs, calculate the glutenite reservoir parameters that can effectively indicate the quality of reservoirs through conventional logging curves, optimize the ratio of permeability to porosity, oil saturation, and shale content of reservoir parameters, obtain the quality factor of the glutenite reservoir, and then adopt a seismic multi-attribute neural network simulation model to obtain the simulation data volume of the quality factor of the glutenite reservoir. Furthermore, by analyzing the data volume, the spatial distribution of the effective glutenite reservoir is predicted, solving the technical problems in the prior art such as the low accuracy of conventional logging characterization and classification of glutenite reservoirs, the lack of pre-stack gather data and large computational amount, and the difficulty in predicting post-stack P-wave impedance. It can accurately and conveniently determine the spatial distribution characteristics of glutenite reservoirs in the target work area.

[0104] In order to accurately determine the ratio of permeability to porosity of the glutenite reservoir, in an embodiment of the method for determining glutenite reservoirs of the present application, refer to Figure 2 , it may specifically include the following content:

[0105] Step S201: Obtain a dual-parameter logging interpretation model based on core data of different strata and different grain sizes in the target work area and density data and acoustic data in logging data.

[0106] Step S202: Determine the permeability and porosity data curves and the ratio of permeability to porosity for the entire well section in the target work area according to the dual-parameter logging interpretation model.

[0107] Optionally, to ensure the calculation accuracy of the permeability-porosity ratio (K / φ), the present application, according to the actual situation of the target work area, on the basis of carrying out logging curve standardization processing, adopts the method of calibrating logging with cores to establish porosity (φ) and permeability (K) models.

[0108] Specifically, in this application, relevant analysis is carried out on the core porosity in small layers and small particle sizes, as well as the logging density and acoustic wave. A dual-parameter porosity and permeability logging interpretation model based on density-acoustic wave is established through multiple regression. Finally, the porosity and permeability curves of the entire well section are calculated to ensure the accuracy of the calculation of the reservoir permeability-porosity ratio (K / φ).

[0109] In order to accurately determine the oil saturation of the glutenite reservoir, in an embodiment of the glutenite reservoir determination method of this application, refer to Figure 3 , and it may specifically include the following content:

[0110] Step S301: Determine the Archie oil saturation calculation model according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water resistivity in the formation water data.

[0111] Step S302: Determine the oil saturation of the glutenite reservoir according to the Archie oil saturation calculation model.

[0112] Optionally, this application calculates the oil saturation (So) of the glutenite reservoir according to the Archie formula. Among them, the petrophysical parameters in the Archie formula can be obtained by establishing a petrophysical parameter chart using the petrophysical samples in the actual work area, and the formation water resistivity can be obtained from the actual analysis data of the formation water.

[0113] In order to accurately determine the shale content of the glutenite reservoir, in an embodiment of the glutenite reservoir determination method of this application, refer to Figure 4 , and it may specifically include the following content:

[0114] Step S401: Normalize the difference between the compensated density logging curve data and the compensated neutron logging curve data of the target work area.

[0115] Step S402: Perform data fitting on the shale content in the core data and the compensated density logging curve data and the compensated neutron logging curve data after the normalization process to obtain the shale content of the glutenite reservoir.

[0116] Optionally, this application can establish a shale content (V sh ) calculation model of the glutenite reservoir based on the DEN (compensated density logging curve data) and CNL (compensated neutron logging curve data) of the conventional logging curve. The specific formula can be:

[0117] V sh = X × Δ(DEN - CNL) + Y (1)

[0118] Among them, V shis the shale content of the glutenite reservoir. X and Y are constants. In this embodiment, it is obtained by cross-fitting the normalized density, neutron difference (Δ(DEN-CNL)) and the shale content identified by core thin sections in the target interval. The value of X can be 28.0899, and the value of Y can be 6.7752.

[0119] In one embodiment of the method for determining a glutenite reservoir in the present application, in order to accurately determine the quality factor simulation data volume of the glutenite reservoir, the following content may also be specifically included:

[0120] According to the characteristic curve data of the quality factor, the seismic attributes and the longitudinal wave impedance in the original seismic data, and a preset multi-attribute neural network simulation model, the quality factor simulation data volume of the glutenite reservoir is obtained.

[0121] Optionally, the present application may adopt a multi-attribute neural network simulation model to establish a statistical relationship between the calculated characteristic curves of the quality factors (RQ) of the glutenite reservoirs in multiple wells and the seismic attributes, longitudinal wave impedance, etc. in the original seismic data. Starting from the well points and following the original seismic data between the wells, the quality factor seismic simulation data volume of the glutenite reservoir is finally obtained.

[0122] In one embodiment of the method for determining a glutenite reservoir in the present application, in order to accurately determine the distribution characteristics of the glutenite reservoir in the target work area, the following content may also be specifically included:

[0123] Perform a cross-well profile analysis on the quality factor simulation data volume to determine the vertical and horizontal variation characteristics of the glutenite reservoir in the target work area.

[0124] In an alternative embodiment of the present application, the present application may adopt the cross-well profile method to analyze the vertical and horizontal variations of various reservoirs in the vertical and horizontal directions, which is very beneficial for describing the spatial variations of the glutenite reservoir, and is also very intuitive for verifying the consistency of the prediction results of the glutenite reservoir in the wells and between the wells.

[0125] In one embodiment of the method for determining a glutenite reservoir in the present application, in order to accurately determine the distribution characteristics of the glutenite reservoir in the target work area, the following content may also be specifically included:

[0126] Perform an isochronous stratigraphic framework analysis on the quality factor simulation data volume to determine the planar distribution characteristics of the glutenite reservoir in the target work area.

[0127] In another alternative embodiment of the present application, the present application can perform isochronous stratigraphic framework-based stratigraphic slicing processing on the seismic simulation data volume of the glutenite reservoir quality factor in the target interval. The planar distribution pattern of the effective glutenite reservoir can be clearly seen from a single stratigraphic slice, and the vertical structure and evolution law of the effective glutenite reservoir can also be observed from multiple representative stratigraphic slices of each system tract.

[0128] In order to accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area, the present application provides an embodiment of a glutenite reservoir determination device for implementing all or part of the content of the glutenite reservoir determination method. Refer to Figure 5 , the glutenite reservoir determination device specifically includes the following contents:

[0129] The pore permeability ratio determination module 10 is used to determine the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area.

[0130] The oil saturation determination module 20 is used to determine the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir and the formation water data.

[0131] The shale content determination module 30 is used to determine the shale content of the glutenite reservoir according to the compensated density log curve data, the compensated neutron log curve data in the target work area and the shale content in the core data.

[0132] The quality factor determination module 40 is used to determine the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation and the shale content of the glutenite reservoir.

[0133] The quality factor simulation data volume determination module 50 is used to determine the quality factor simulation data volume of the glutenite reservoir according to the quality factor and the original seismic data.

[0134] The glutenite reservoir distribution characteristic analysis module 60 is used to perform data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area.

[0135] As can be seen from the above description, the glutenite reservoir determination device provided by the embodiments of the present application can, by clarifying the main controlling factors of the physical properties of the glutenite reservoir, calculate the glutenite reservoir parameters that can effectively indicate the quality of the reservoir through conventional logging curves, and optimize the ratio of permeability to porosity, oil saturation, and shale content of the reservoir parameters to obtain the glutenite reservoir quality factor. Then, a seismic multi-attribute neural network simulation model is used to obtain the simulation data volume of the glutenite reservoir quality factor, and then through data analysis of the data volume, the spatial distribution of the effective glutenite reservoir is predicted, solving the technical problems in the prior art such as low accuracy of conventional logging characterization and classification of glutenite reservoirs, lack of pre-stack gather data and large computational amount, and great difficulty in predicting post-stack P-wave impedance, and being able to accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area.

[0136] In order to accurately determine the ratio of permeability to porosity of the glutenite reservoir, in an embodiment of the glutenite reservoir determination device of the present application, refer to Figure 6 , the pore-permeability ratio determination module 10 includes:

[0137] A dual-parameter logging interpretation model construction unit 11, configured to obtain a dual-parameter logging interpretation model according to core data of different strata and different grain sizes in the target work area and density data and acoustic data in logging data.

[0138] A pore-permeability ratio determination unit 12, configured to determine the permeability and porosity data curves and the ratio of permeability to porosity of the entire well section in the target work area according to the dual-parameter logging interpretation model.

[0139] In order to accurately determine the oil saturation of the glutenite reservoir, in an embodiment of the glutenite reservoir determination device of the present application, refer to Figure 7 , the oil saturation determination module 20 includes:

[0140] An Archie oil saturation calculation model construction unit 21, configured to determine an Archie oil saturation calculation model according to the porosity, petrophysical parameters of the glutenite reservoir, and formation water resistivity in formation water data.

[0141] An oil saturation determination unit 22, configured to determine the oil saturation of the glutenite reservoir according to the Archie oil saturation calculation model.

[0142] In order to accurately determine the shale content of the glutenite reservoir, in an embodiment of the glutenite reservoir determination device of the present application, refer to Figure 8 , the shale content determination module 30 includes:

[0143] A logging curve data normalization processing unit 31, configured to perform normalization processing on the difference between the compensated density logging curve data and the compensated neutron logging curve data in the target work area.

[0144] A data fitting unit 32 is configured to perform data fitting processing on the shale content in the core data, the compensated density logging curve data and the compensated neutron logging curve data after the normalization processing, so as to obtain the shale content of the glutenite reservoir.

[0145] In order to accurately determine the quality factor simulation data volume of the glutenite reservoir, in an embodiment of the glutenite reservoir determination device of the present application, refer to Figure 9 , the quality factor simulation data volume determination module 50 includes:

[0146] A multi-attribute neural network simulation model processing unit 51 is configured to obtain the quality factor simulation data volume of the glutenite reservoir according to the characteristic curve data of the quality factor, the seismic attributes and the longitudinal wave impedance in the original seismic data, and a preset multi-attribute neural network simulation model.

[0147] In order to accurately determine the distribution characteristics of the glutenite reservoir in the target work area, in an embodiment of the glutenite reservoir determination device of the present application, refer to Figure 10 , the glutenite reservoir distribution characteristic analysis module 60 includes:

[0148] A cross-well profile analysis unit 61 is configured to perform cross-well profile analysis on the quality factor simulation data volume to determine the longitudinal and lateral variation characteristics of the glutenite reservoir in the target work area.

[0149] In order to accurately determine the distribution characteristics of the glutenite reservoir in the target work area, in an embodiment of the glutenite reservoir determination device of the present application, refer to Figure 11 , the glutenite reservoir distribution characteristic analysis module 60 includes:

[0150] An isochronous stratigraphic framework analysis unit 62 is configured to perform isochronous stratigraphic framework analysis on the quality factor simulation data volume to determine the planar distribution characteristics of the glutenite reservoir in the target work area.

[0151] From the hardware level, in order to accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area, the present application provides an embodiment of an electronic device for implementing all or part of the content in the glutenite reservoir determination method. The electronic device specifically includes the following content:

[0152] A processor, a memory, a communications interface, and a bus; wherein, the processor, the memory, and the communications interface complete communication with each other through the bus; the communications interface is used to implement information transmission between the glutenite reservoir determination device and related devices such as a core business system, a user terminal, and a related database, etc.; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., and this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the glutenite reservoir determination method and the embodiments of the glutenite reservoir determination device in the embodiments, the content of which is incorporated herein, and the repeated parts will not be elaborated.

[0153] It can be understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0154] In practical applications, part of the glutenite reservoir determination method can be executed on the electronic device side as described above, or all operations can be completed in the client device. Specifically, it can be selected according to the processing capacity of the client device and the limitations of the user usage scenario, etc. This application does not make any limitation in this regard. If all operations are completed in the client device, the client device may further include a processor.

[0155] The above-mentioned client device may have a communication module (i.e., a communication unit), and can be communicatively connected to a remote server to achieve data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster composed of multiple servers, or a server structure of a distributed device.

[0156] Figure 18 This is a schematic block diagram of the system composition of the electronic device 9600 according to an embodiment of the present application. As Figure 18 shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It should be noted that this Figure 18 is exemplary; other types of structures can also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0157] In one embodiment, the function of the glutenite reservoir determination method can be integrated into the central processing unit 9100. Among them, the central processing unit 9100 can be configured to perform the following controls:

[0158] Step S101: Determine the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area.

[0159] Step S102: Determine the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water data.

[0160] Step S103: Determine the shale content of the glutenite reservoir according to the compensated density log curve data, the compensated neutron log curve data of the target work area, and the shale content in the core data.

[0161] Step S104: Determine the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir.

[0162] Step S105: Determine the simulated data volume of the quality factor of the glutenite reservoir according to the quality factor and the original seismic data.

[0163] Step S106: Perform data analysis on the simulated data volume of the quality factor to determine the distribution characteristics of the glutenite reservoir in the target work area.

[0164] As can be seen from the above description, the electronic device provided in the embodiment of the present application determines the main control factors of the physical properties of the glutenite reservoir, calculates the parameters of the glutenite reservoir that can effectively indicate the quality of the reservoir through conventional log curves, and optimizes the ratio of the permeability to the porosity, the oil saturation, and the shale content of the reservoir parameters to obtain the quality factor of the glutenite reservoir. Then, a seismic multi-attribute neural network simulation model is used to obtain the simulated data volume of the quality factor of the glutenite reservoir. Furthermore, by performing data analysis on the data volume, the spatial distribution of the effective glutenite reservoir is predicted, solving the technical problems in the prior art such as the low accuracy of conventional log characterization and classification of glutenite reservoirs, the lack of pre-stack gather data and large calculation amount, and the difficulty in predicting the post-stack longitudinal wave impedance. It can accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area.

[0165] In another embodiment, the glutenite reservoir determination device can be separately configured from the central processing unit 9100. For example, the glutenite reservoir determination device can be configured as a chip connected to the central processing unit 9100, and the function of the glutenite reservoir determination method can be realized through the control of the central processing unit.

[0166] Such as Figure 18As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It should be noted that the electronic device 9600 does not necessarily have to include Figure 18 all the components shown in Figure 18 ; in addition, the electronic device 9600 may further include

[0167] As Figure 18 shown, the central processing unit 9100, sometimes also referred to as a controller or an operation control, may include a microprocessor or other processor devices and / or logic devices. The central processing unit 9100 receives inputs and controls the operations of the various components of the electronic device 9600.

[0168] Among them, the memory 9140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices. It can store the above information related to failures, and can also store programs for executing relevant information. And the central processing unit 9100 can execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0169] The input unit 9120 provides inputs to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to supply power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0170] The memory 9140 may be a solid-state memory. For example, it may be a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be such a memory that stores information even when powered off, can be selectively erased and has more data. Examples of such a memory are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or the processes for operating the electronic device 9600 through the central processing unit 9100.

[0171] The memory 9140 may further include a data storage unit 9143 for storing data such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the communication functions of the electronic device and / or for performing other functions of the electronic device (such as a messaging application, an address book application, etc.).

[0172] The communication module 9110 is a transmitter / receiver 9110 that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

[0173] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module, etc. The communication module (transmitter / receiver) 9110 is also coupled to the speaker 9131 and the microphone 9132 via the audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby implementing normal telecommunication functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers, etc. Additionally, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0174] Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the sandstone reservoir determination method with the execution subject being a server or a client in the above embodiments. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, all steps of the sandstone reservoir determination method with the execution subject being a server or a client in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0175] Step S101: Determine the ratio of the permeability to the porosity of the sandstone reservoir according to the core data of the sandstone reservoir in the target work area.

[0176] Step S102: Determine the oil saturation of the sandstone reservoir according to the porosity, the petrophysical parameters of the sandstone reservoir, and the formation water data.

[0177] Step S103: Determine the shale content of the sandstone reservoir according to the compensated density log curve data, the compensated neutron log curve data of the target work area, and the shale content in the core data.

[0178] Step S104: Determine the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir.

[0179] Step S105: Determine the simulated data volume of the quality factor of the glutenite reservoir according to the quality factor and the original seismic data.

[0180] Step S106: Conduct data analysis on the simulated data volume of the quality factor to determine the distribution characteristics of the glutenite reservoir in the target work area.

[0181] As can be seen from the above description, the computer-readable storage medium provided by the embodiments of the present application, by clarifying the main controlling factors of the physical properties of the glutenite reservoir, calculates the glutenite reservoir parameters that can effectively indicate the quality of the reservoir through conventional logging curves, and preferably selects the ratio of the permeability to the porosity, the oil saturation, and the shale content of the reservoir parameters to obtain the quality factor of the glutenite reservoir. Then, using the seismic multi-attribute neural network simulation model, the simulated data volume of the quality factor of the glutenite reservoir is obtained. Furthermore, by conducting data analysis on the data volume, the spatial distribution of the effective glutenite reservoir is predicted, solving the technical problems in the prior art such as the low accuracy of conventional logging characterization and classification of glutenite reservoirs, the lack of pre-stack gather data and large computational amount, and the difficulty in predicting the post-stack longitudinal wave impedance. It can accurately and conveniently determine the spatial distribution characteristics of the glutenite reservoir in the target work area.

[0182] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a device, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0183] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0184] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the processes and / or blocks Figure 1 in one or more of the processes and / or blocks Figure 1 specified in the block or blocks.

[0186] Specific embodiments are used in the present invention to illustrate the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for determining a glutenite reservoir, characterized in that The method includes: Determining the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area; Determining the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water data; Determining the shale content of the glutenite reservoir according to the compensated density log curve data, the compensated neutron log curve data of the target work area, and the shale content in the core data; Determining the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir; Determining a quality factor simulation data volume of the glutenite reservoir according to the quality factor and the original seismic data; Performing data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area; The formula for calculating the quality factor of the glutenite reservoir is: Among them, K / φ represents the ratio of permeability to porosity, So represents the oil saturation, and V sh represents the shale content, and RQ represents the quality factor of glutenite reservoir.

2. The method for determining a glutenite reservoir according to claim 1, characterized in that, The step of determining the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area includes: Obtaining a dual-parameter log interpretation model according to the core data of different strata and different grain sizes in the target work area and the density data and acoustic data in the log data; Determining the permeability and porosity data curves and the ratio of the permeability to the porosity of the entire well section in the target work area according to the dual-parameter log interpretation model.

3. The method for determining a glutenite reservoir according to claim 1, wherein The step of determining the oil saturation of the glutenite reservoir according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water data includes: Determining the Archie oil saturation calculation model according to the porosity, the petrophysical parameters of the glutenite reservoir, and the formation water resistivity in the formation water data; Determining the oil saturation of the glutenite reservoir according to the Archie oil saturation calculation model.

4. The method for determining a glutenite reservoir according to claim 1, wherein The step of determining the shale content of the glutenite reservoir according to the compensated density log curve data, the compensated neutron log curve data of the target work area, and the shale content in the core data includes: Normalizing the difference between the compensated density log curve data and the compensated neutron log curve data of the target work area; Performing data fitting on the shale content in the core data and the compensated density log curve data and the compensated neutron log curve data after the normalization process to obtain the shale content of the glutenite reservoir.

5. The method for determining a glutenite reservoir according to claim 1, wherein The step of determining a quality factor simulation data volume of the glutenite reservoir according to the quality factor and the original seismic data includes: Obtaining a quality factor simulation data volume of the glutenite reservoir according to the characteristic curve data of the quality factor, the seismic attributes and the longitudinal wave impedance in the original seismic data, and a preset multi-attribute neural network simulation model.

6. The method for determining a glutenite reservoir according to claim 1, wherein The step of performing data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area includes: Performing a cross-well profile analysis on the quality factor simulation data volume to determine the longitudinal and lateral variation characteristics of the glutenite reservoir in the target work area.

7. The method for determining a glutenite reservoir according to claim 1, wherein The step of performing data analysis on the quality factor simulation data volume to determine the distribution characteristics of the glutenite reservoir in the target work area includes: Perform isochronous stratigraphic framework analysis on the simulated data volume of the quality factor to determine the planar distribution characteristics of the glutenite reservoir in the target work area.

8. A device for determining a glutenite reservoir, characterized in that Including: A pore permeability ratio determination module for determining the ratio of the permeability to the porosity of the glutenite reservoir according to the core data of the glutenite reservoir in the target work area; An oil saturation determination module for determining the oil saturation of the glutenite reservoir according to the porosity, the rock electrical parameters of the glutenite reservoir, and the formation water data; A shale content determination module for determining the shale content of the glutenite reservoir according to the compensated density logging curve data, the compensated neutron logging curve data in the target work area, and the shale content in the core data; A quality factor determination module for determining the quality factor of the glutenite reservoir according to the ratio of the permeability to the porosity, the oil saturation, and the shale content of the glutenite reservoir; A simulated data volume determination module for the quality factor for determining the simulated data volume of the quality factor of the glutenite reservoir according to the quality factor and the original seismic data; A distribution characteristic analysis module for the glutenite reservoir for performing data analysis on the simulated data volume of the quality factor to determine the distribution characteristics of the glutenite reservoir in the target work area; The formula for calculating the quality factor of the glutenite reservoir is: Among them, K / φ represents the ratio of permeability to porosity, So represents the oil saturation, and V sh represents the shale content, and RQ represents the quality factor of glutenite reservoir.

9. The device for determining a glutenite reservoir according to claim 8, wherein The pore permeability ratio determination module includes: A dual-parameter logging interpretation model construction unit for obtaining a dual-parameter logging interpretation model according to the core data of different strata and different grain sizes in the target work area and the density data and acoustic data in the logging data; A pore permeability ratio determination unit for determining the permeability and porosity data curves of the entire well section in the target work area and the ratio of the permeability to the porosity according to the dual-parameter logging interpretation model.

10. The device for determining a glutenite reservoir according to claim 8, characterized in that, The oil saturation determination module includes: An Archie oil saturation calculation model construction unit for determining the Archie oil saturation calculation model according to the porosity, the rock electrical parameters of the glutenite reservoir, and the formation water resistivity in the formation water data; An oil saturation determination unit for determining the oil saturation of the glutenite reservoir according to the Archie oil saturation calculation model.

11. The apparatus for determining a glutenite reservoir according to claim 8, wherein The shale content determination module includes: A logging curve data normalization processing unit for performing normalization processing on the difference between the compensated density logging curve data and the compensated neutron logging curve data in the target work area; A data fitting unit for performing data fitting processing on the shale content in the core data and the compensated density logging curve data and the compensated neutron logging curve data after the normalization processing to obtain the shale content of the glutenite reservoir.

12. The device for determining a glutenite reservoir according to claim 8, wherein The simulated data volume determination module for the quality factor includes: A multi-attribute neural network simulation model processing unit for obtaining the simulated data volume of the quality factor of the glutenite reservoir according to the characteristic curve data of the quality factor, the seismic attributes and the longitudinal wave impedance in the original seismic data, and a preset multi-attribute neural network simulation model.

13. The device for determining a glutenite reservoir according to claim 8, wherein The distribution characteristic analysis module for the glutenite reservoir includes: A cross-well section analysis unit for performing cross-well section analysis on the simulated data volume of the quality factor to determine the longitudinal and lateral variation characteristics of the glutenite reservoir in the target work area.

14. The device for determining a glutenite reservoir according to claim 8, wherein The module for analyzing the distribution characteristics of the glutenite reservoir includes: An isochronous stratigraphic framework analysis unit, which is used to perform isochronous stratigraphic framework analysis on the quality factor simulation data volume to determine the planar distribution characteristics of the glutenite reservoir in the target work area.

15. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the glutenite reservoir determination method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the glutenite reservoir determination method according to any one of claims 1 to 7 are implemented.

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