Reservoir heterogeneity parameter determination method, device and equipment
By acquiring mineral composition, indentation experiments, and formation data from core samples, and combining entropy values and probability distribution models, the heterogeneity parameters of the reservoir are calculated. This solves the problem of the inability to accurately quantify reservoir heterogeneity in existing technologies and achieves more efficient quantification of reservoir characteristics.
Patent Information
- Application Number
- CN202511000330.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-21
- Publication Date
- 2025-11-21
AI Technical Summary
Existing technologies cannot accurately and effectively quantify reservoir heterogeneity, are time-consuming and labor-intensive, and cannot fully reflect reservoir characteristics in complex sedimentary environments.
By acquiring mineral composition data, indentation test data, and formation data from core samples, mineral composition information values, microscopic shape parameters, and heterogeneity parameters are determined. Combined with entropy values and probability distribution models, the heterogeneity parameters of the target reservoir are calculated.
It enables accurate and effective quantification of reservoir heterogeneity, simplifies data processing, and reduces operational complexity and time costs.
Smart Images
Figure CN120992894A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of petroleum engineering, in particular to a method, device and equipment for determining reservoir heterogeneity parameters. BACKGROUND
[0002] The study of the heterogeneity of layered formations is of great significance for the accurate prediction of fracture propagation behavior and the optimization of engineering design in the development of oil and gas reservoirs. In actual reservoir exploitation, the heterogeneity of the formation will significantly affect the formation and propagation path of the fractures during hydraulic fracturing, and thus affect the reservoir permeability and the ultimate recovery.
[0003] Currently, the heterogeneity of the formation is determined mainly in the following ways: first, the mineral composition and sedimentary microfacies are determined through rock thin section analysis, and a relative sea level change curve is constructed to divide the third-order sequence, and finally the lithologic layer thickness distribution is counted and the fracture reservoir control characteristics are analyzed. Although this method provides an explanation for the heterogeneity from the sedimentary origin, it is not suitable for complex sedimentary environments (such as strong scour-filling structures, bioturbated zones, etc.) due to its reliance on the quantitative means of single layer thickness statistics, and the simplification of the reconstruction of sea level changes in the above process may mask the heterogeneity anomalies caused by local sedimentary events, which cannot fully reflect the complex reservoir characteristics, especially in sedimentary environments with significant irregularities. Second, the physical parameters (porosity, permeability), mechanical parameters (Young's modulus, Poisson's ratio) and mineral composition are obtained through core experiments, the horizontal / vertical heterogeneity factor and the mineral dispersion factor are calculated, and then a comprehensive evaluation factor is constructed. This method relies on simplified mathematical models in the evaluation factor construction process, which is difficult to represent the nonlinear coupling relationship between parameters, and cannot cover the spatial variability of the reservoir globally, and is strongly dependent on experiments and time-consuming.
[0004] In view of the above problems that the reservoir heterogeneity cannot be accurately and effectively quantified and time and effort are wasted, no effective solution has been proposed so far. SUMMARY
[0005] The purpose of the embodiments of the present application is to provide a method, device and equipment for determining reservoir heterogeneity parameters to solve the problem that the reservoir heterogeneity cannot be accurately and effectively quantified and time and effort are wasted.
[0006] To solve the above technical problems, the first aspect of the present specification provides a method for determining reservoir heterogeneity parameters, comprising:
[0007] obtaining mineral composition data, indentation experiment data and formation data of a core sample of a target reservoir;
[0008] determining a mineral composition information value of the target reservoir based on the mineral composition data;
[0009] determine a micro-shape parameter of the target reservoir based on the indentation experiment data;
[0010] determine a first heterogeneity parameter of the target reservoir along a first direction and a second heterogeneity parameter of the target reservoir along a second direction based on the formation data, wherein the first direction and the second direction are different directions along the target reservoir;
[0011] determine a target heterogeneity parameter of the target reservoir based on the mineral component information value, the micro-shape parameter, the first heterogeneity parameter and the second heterogeneity parameter.
[0012] In some embodiments of the present specification, the mineral component information value of the target reservoir is determined based on the mineral component data, comprising:
[0013] determine the mass proportion of each mineral in the core sample based on the mineral component data;
[0014] calculate the entropy value of the mineral component based on the mass proportion of each mineral, and take the entropy values of the plurality of minerals as the mineral component information value.
[0015] In some embodiments of the present specification, the entropy value of the mineral component is calculated based on the mass proportion of each mineral, and then further comprising:
[0016] standardize the calculated entropy value of the mineral component to obtain the relative entropy of the mineral component, and take the relative entropy of the mineral component as the mineral component information value.
[0017] In some embodiments of the present specification, the indentation experiment data is obtained by the following way:
[0018] obtain a core slice of the core sample;
[0019] polish the core slice and make the upper and lower surfaces of the core slice parallel;
[0020] perform indentation test on the core slice with a target loading speed and a target load by using an indentation tester;
[0021] obtain the mechanical property data collected during the indentation test as the indentation experiment data.
[0022] In some embodiments of the present specification, the micro-shape parameter of the target reservoir is determined based on the indentation experiment data, comprising:
[0023] fit the indentation experiment data based on a preset probability distribution model to obtain the micro-shape parameter.
[0024] In some embodiments of the present specification, based on the formation data, determining a first heterogeneity parameter of the target reservoir along a first direction and a second heterogeneity parameter of the target reservoir along a second direction comprises:
[0025] determining a lateral extension length of the beddings based on the bedding images in the formation data;
[0026] determining the first heterogeneity parameter based on the lateral extension length of the beddings;
[0027] determining longitudinal thickness variation data of the target reservoir based on reservoir interval thickness data in the formation data;
[0028] determining the second heterogeneity parameter based on the longitudinal thickness variation data.
[0029] In some embodiments of the present specification, the first heterogeneity parameter is an inverse of the lateral extension length, and the second heterogeneity parameter is an inverse of the longitudinal thickness variation data.
[0030] In some embodiments of the present specification, the target heterogeneity parameter is determined by the following formula:
[0031]
[0032] wherein n represents the target heterogeneity parameter, MCI represents the mineral component information value, m represents the microscopic shape parameter, X represents the first heterogeneity parameter, and Y represents the second heterogeneity parameter.
[0033] The second aspect of the present specification provides a device for determining a reservoir heterogeneity parameter, comprising:
[0034] an acquisition module configured to acquire mineral component data, indentation test data, and formation data of a core sample of a target reservoir;
[0035] a first calculation module configured to determine a mineral component information value of the target reservoir based on the mineral component data;
[0036] a second calculation module configured to determine a microscopic shape parameter of the target reservoir based on the indentation test data;
[0037] a third calculation module configured to determine a first heterogeneity parameter of the target reservoir along a first direction and a second heterogeneity parameter of the target reservoir along a second direction based on the formation data;
[0038] a determination module configured to determine a target heterogeneity parameter of the target reservoir based on the mineral component information value, the microscopic shape parameter, the first heterogeneity parameter, and the second heterogeneity parameter.
[0039] The third aspect of the present specification provides an electronic device, comprising a memory and a processor, which are in communication connection with each other, and the memory stores computer instructions, and the processor implements the steps of the method of any one of the first aspect by executing the computer instructions.
[0040] The fourth aspect of the present specification provides a computer storage medium, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the steps of the method of any one of the first aspect.
[0041] The fifth aspect of the present specification provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the method of the first aspect.
[0042] Based on the reservoir heterogeneity parameter determination method, device and equipment provided in the embodiments of the present specification, the mineral component data, indentation experiment data and formation data of the core sample of the target reservoir are obtained; the mineral component information value of the target reservoir is determined based on the mineral component data; the micro shape parameter of the target reservoir is determined based on the indentation experiment data; the first heterogeneity parameter along a first direction and the second heterogeneity parameter along a second direction of the target reservoir are determined based on the formation data, wherein the first direction and the second direction are different directions along the target reservoir; and the target heterogeneity parameter of the target reservoir is determined based on the mineral component information value, the micro shape parameter, the first heterogeneity parameter and the second heterogeneity parameter. Through the above method, the reservoir heterogeneity is quantified from multiple dimensions of the target reservoir, including the mineral component dimension, the micro shape dimension, and the two heterogeneity dimensions along different directions, to obtain the mineral component information value representing the disorder degree and complexity of the mineral component, the micro shape parameter representing the dispersion degree of the micro feature parameter, and the first heterogeneity parameter and the second heterogeneity parameter representing the spatial heterogeneity of the target reservoir. Then, the target heterogeneity obtained based thereon can more accurately and effectively quantify the reservoir heterogeneity, and the data processing process does not depend on complex calculation and is simple to operate, saving time and effort. BRIEF DESCRIPTION OF DRAWINGS
[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.
[0044] Figure 1A schematic diagram of a method for determining a reservoir heterogeneity parameter is shown.
[0045] Figure 2 A schematic diagram of a method for determining a first heterogeneity parameter and a second heterogeneity parameter is shown.
[0046] Figure 3 A schematic diagram of a method for determining a reservoir heterogeneity parameter is shown.
[0047] Figure 4 A schematic diagram of a device for determining a reservoir heterogeneity parameter is shown.
[0048] Figure 5 A schematic diagram of an electronic device is shown. DETAILED DESCRIPTION
[0049] In order to enable persons skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.
[0050] It should be noted that the information and data related to the user in the embodiments of the present application are information and data authorized by the user or sufficiently authorized by the relevant parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for the user or the relevant parties to choose authorization or refusal.
[0051] It should also be noted that in the embodiments of the present application, some industry existing schemes such as software, components, models, etc. may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present application, but does not mean that the applicant has or will necessarily use the scheme.
[0052] The method for determining a reservoir heterogeneity parameter provided by the embodiments of the present application will be described below with reference to the drawings.
[0053] Figure 1A schematic diagram of a method for determining reservoir heterogeneity parameters is shown. Although the present specification provides method operation steps or device structures as shown in the following examples or drawings, more or some of the operation steps or module units can be included in the method or device based on conventional or non-creative labor. The execution order of the steps or the module structure of the device is not limited to the execution order or module structure shown in the examples or drawings of the present specification in the absence of necessary causal relationship in logic. When the method or module structure is applied in actual device, server or terminal product, it can be sequentially executed or executed in parallel (for example, parallel processor or multi-thread processing environment, even including distributed processing, server cluster implementation environment) according to the method or module structure shown in the examples or drawings. For example, Figure 1 As shown, it can include:
[0054] S101: Obtain mineral composition data, indentation test data and formation data of the core sample of the target reservoir.
[0055] It can be understood that the core sample can be a representative sample in the layer of the target reservoir.
[0056] It can be understood that the mineral composition data can include the types of minerals and the contents of each type of mineral. The types of minerals can include main mineral types, secondary mineral types, clay mineral compositions, etc. The main mineral types can be, for example, quartz, feldspar, calcite, dolomite, etc. The mineral composition data can determine the physical and mechanical property data of the core sample. The information amount of the mineral composition in the core sample can be quantified by analyzing the mineral composition data. The mineral composition data can be obtained by XRD (X-ray diffraction), thin section identification, SEM-EDS (scanning electron microscope-energy spectrum), infrared spectrum, etc. on the core sample. For example, the core sample can be cut into a suitable size (usually in powder or small particle form, etc.) for X-ray diffraction (XRD) analysis. The mineral composition is tested using an XRD instrument, and the mass of each mineral in the sample is obtained as the mineral composition data. During the test, the mineral composition of the core sample in the bedding surface and the non-bedding area needs to be analyzed separately to distinguish the difference in mineral composition and obtain more accurate test results.
[0057] It can be understood that the indentation experiment data can be data collected by performing an indentation experiment on the core sample using an indentation tester (e.g., a micro indentation tester, a nano indentation tester), and the indentation experiment data can be used to characterize the mechanical properties of the core sample. The mechanical property parameters of the core sample can include hardness, which reflects the ability of the core sample to resist local plastic deformation, elastic modulus, which reflects the stiffness of the core sample, load-displacement curves, which contain information about the plastic deformation energy, elastic recovery energy, creep, and the like of the core sample, and indentation morphology, which reflects the crack, accumulation / sinking plastic behavior of the core sample. It can be understood that the indentation experiment data reflects the local mechanical properties of mineral particles, cement or micro regions in the core sample at a microscopic scale, and the microscopic mechanical properties of the core sample can be determined based on the indentation experiment data.
[0058] It can be understood that the formation data can include data related to the stratigraphic layering of the core sample, such as the stratigraphic information of the core sample, the thickness data of the reservoir section, and the like. The formation data can be obtained by collecting the stratigraphic images of the core sample, performing core experiments and / or downhole measurements, and the like.
[0059] In some embodiments of the present disclosure, the indentation experiment data can be obtained by: obtaining a core slice of the core sample; polishing the core slice and making the upper and lower surfaces of the core slice parallel; performing an indentation test on the core slice using an indentation tester at a target loading speed and a target load; and obtaining the mechanical property data collected during the indentation test as the indentation experiment data.
[0060] For example, before the experiment, a core slice for the micro indentation experiment is prepared. A thick rock slice can be prepared from the original core sample, and the core slice needs to contain as much stratigraphy as possible and be polished on the surface while ensuring that the upper and lower surfaces of the rock sample slice are parallel. The thickness of the core slice can be 5 mm, and the sample surface is polished to ensure that the upper and lower surfaces of the slice are parallel and smooth. During the indentation experiment, the sample is fixed on the sample table to ensure that the lower surface of the core slice is in full contact with the sample table. The position of the sample table is adjusted to select an appropriate measurement point position. The indentation tester is adjusted to the position where the indenter just contacts the core slice. The position of the indenter is fine-tuned by clicking the descent button on the interface of the indentation tester software until the indenter is in full contact. The indentation tester is loaded to the target load at a predetermined loading speed, for example, the maximum load of the indentation test is set to 5 N, the loading speed is 2-5 N / min, and the sample is kept for 5 seconds. After the sample is kept, the load is unloaded, and the mechanical property data of the core slice during the loading and unloading process is recorded. Each core slice can be tested at 50 selected points.
[0061] S102: Determine the mineral component information value of the target reservoir based on the mineral component data.
[0062] It can be understood that the mineral component information value is used to characterize the disorder degree and complexity of the mineral components of the target reservoir, and the complexity of the mineral components of the core sample can be quantified by the mineral component information value.
[0063] In some embodiments of the present specification, the mineral component information value can be characterized as an entropy value, and the complexity of the mineral components of the core sample can be quantified by calculating the entropy values of various minerals in the core sample. Specifically, based on the mineral component data, determining the mineral component information value of the target reservoir can include: based on the mineral component data, determining the mass proportion of various minerals in the core sample; calculating the entropy value of the mineral components based on the mass proportion of various minerals, and taking the entropy values of multiple minerals as the mineral component information value.
[0064] It can be understood that the mass proportion of various minerals can be calculated based on the mass of various minerals in the mineral component data obtained by testing the mineral components of the core sample and the mass of the core sample. Further, the entropy value of the mineral components can be calculated based on the related formula for calculating the entropy value and the calculated mass proportion of various minerals, and the calculated entropy value can be used as the mineral component information.
[0065] In some embodiments of the present specification, the entropy value of the mineral components can be determined by the following formula:
[0066]
[0067] Wherein, MCI can represent the entropy value of the mineral components, p i may represent the mass proportion of the i-th mineral, and N can represent the number of mineral types in the core sample.
[0068] Further, in order to facilitate comparison between different samples, the calculated entropy value of the mineral components can be standardized to more intuitively reflect the complexity of the mineral components of the core sample based on the entropy value. Specifically, based on the mass proportion of various minerals to calculate the entropy value of the mineral components, and then the method can further include: standardizing the calculated entropy value of the mineral components to obtain the relative entropy of the mineral components, and taking the relative entropy of the mineral components as the mineral component information value.
[0069] In some embodiments of the present specification, the relative entropy of the mineral components can be calculated by the following formula:
[0070]
[0071] Wherein, MCI' can represent the relative entropy of the mineral components.
[0072] S103: Based on the indentation test data, determine the micro shape parameter of the target reservoir.
[0073] It can be understood that the micro shape parameter can reflect the dispersion degree of the micro mechanical characteristic parameter of the core sample. The mechanical characteristic parameter can include hardness, elastic model, etc.
[0074] In some embodiments of the present specification, determining the micro shape parameter of the target reservoir based on the indentation experiment data can include: fitting the indentation experiment data based on a preset probability distribution model to obtain the micro shape parameter.
[0075] The preset probability distribution model can describe the probability distribution of the mechanical property parameter. Fitting the indentation experiment data based on the model can obtain a statistical quantity describing the dispersion degree of the mechanical property, i.e. the micro shape parameter, which can reflect the homogeneity of the core sample.
[0076] In some embodiments of the present specification, the preset probability distribution model can be represented by the following formula:
[0077]
[0078] Wherein, F(E) can represent the elastic mechanical property parameter, E can represent the cumulative distribution function not exceeding a certain value, η can represent the scale parameter, representing the characteristic modulus of the core sample, and m can represent the micro shape parameter. The micro shape parameter can reflect the dispersion degree of the mechanical property parameter. The smaller the value of m is, the higher the dispersion degree of the data is, and the stronger the heterogeneity of the core sample is.
[0079] In some embodiments of the present specification, the core sample can be partitioned based on the mineral components. The micro shape parameter of each partition can be determined by the above method, and then the micro shape parameters of multiple partitions can be combined, for example, by weighted sum, average value, etc., to determine the micro shape parameter of the core sample.
[0080] S104: determining a first heterogeneity parameter along a first direction and a second heterogeneity parameter along a second direction of the target reservoir based on the stratum data; wherein the first direction and the second direction are different directions along the target reservoir.
[0081] It can be understood that the first heterogeneity parameter and the second heterogeneity parameter can represent the heterogeneity of the core sample in space. Specifically, the first heterogeneity parameter can be a lateral heterogeneity parameter, and the second heterogeneity parameter can be a longitudinal heterogeneity parameter, i.e. the first direction is lateral and the second direction is longitudinal. It can be understood that in other embodiments, the first direction and the second direction can also be other non-overlapping directions, which are not limited in the present specification.
[0082] In some embodiments of the present disclosure, the first heterogeneity parameter can represent a variation of the bedding extension in the first direction, and the second heterogeneity parameter can represent a variation of the reservoir interval thickness in the second direction. Further, the first heterogeneity parameter can be calculated based on the bedding extension of the core sample in the first direction, and the second heterogeneity parameter can be calculated based on the reservoir interval thickness variation of the core sample in the second direction.
[0083] Referring to Figure 2 As shown, specifically, determining the first heterogeneity parameter of the target reservoir in the first direction and the second heterogeneity parameter in the second direction based on the formation data can include:
[0084] S201: determining the lateral extension length of the bedding based on the bedding image in the formation data;
[0085] S202: determining the first heterogeneity parameter based on the lateral extension length of the bedding;
[0086] S203: determining the longitudinal thickness variation data of the target reservoir based on the reservoir interval thickness data in the formation data;
[0087] S204: determining the second heterogeneity parameter based on the longitudinal thickness variation data.
[0088] It can be understood that the first heterogeneity parameter can be a lateral heterogeneity parameter, and the second heterogeneity parameter can be a longitudinal heterogeneity parameter.
[0089] Specifically, the bedding image can be obtained by a high-resolution camera to obtain the bedding image of the core sample, ensuring that the shooting angle and lighting conditions are consistent, so that the image reflects the true bedding structure of the rock. Further, the bedding image can be converted into a gray image, and an edge detection algorithm can be used to extract the bedding boundary in the bedding image, and based on the extracted bedding boundary, the starting point and the ending point of each bedding are identified to obtain the lateral extension length of the bedding. Further, for a core sample containing multiple beddings, the final lateral extension length can be obtained by integrating the lateral extension lengths.
[0090] Specifically, the variation thickness data of the reservoir interval can be obtained through core experiments or downhole measurements, and the thickness of different reservoir intervals can be recorded. Then, according to the variation thickness data of the reservoir interval, a longitudinal thickness variation graph can be drawn to analyze the periodic variation between the reservoir intervals. By calculating the thickness variation of each reservoir interval, the longitudinal period can be obtained. Further, the longitudinal thickness variation data can be determined based on the longitudinal period and the thickness of the reservoir interval in each period. Further, for a core sample containing multiple periodic reservoir intervals, the final longitudinal thickness variation data can be obtained by integrating the longitudinal thickness variations of multiple periods.
[0091] In some embodiments of the present disclosure, the first heterogeneity parameter can be the reciprocal of the lateral extension length, and the second heterogeneity parameter can be the reciprocal of the longitudinal thickness variation data. Specifically, the first heterogeneity parameter and the second heterogeneity parameter can be calculated by the following formula:
[0092]
[0093] wherein X represents the first heterogeneity parameter, and Y represents the second heterogeneity parameter.
[0094] In some embodiments of the present disclosure, the first heterogeneity parameter and the second heterogeneity parameter can be other formation parameters representing the spatial content of the target reservoir, such as porosity, permeability, seismic attribute, etc., and the present disclosure is not limited thereto. Further, for other formation parameters, the corresponding first heterogeneity parameter and the second heterogeneity parameter can be obtained in a similar manner as described above for determining the first heterogeneity parameter and the second heterogeneity parameter based on the lateral extension length and the longitudinal thickness variation.
[0095] S105: determining a target heterogeneity parameter of the target reservoir based on the mineral composition information value, the microscopic shape parameter, the first heterogeneity parameter and the second heterogeneity parameter.
[0096] In some embodiments of the present disclosure, the target heterogeneity parameter can be determined by the following formula:
[0097]
[0098] wherein n represents the target heterogeneity parameter, MCI represents the mineral composition information value, m represents the microscopic shape parameter, X represents the first heterogeneity parameter, and Y represents the second heterogeneity parameter.
[0099] It can be understood that, in the embodiments of the present disclosure, the target heterogeneity parameter is calculated by calculating the product of the mineral composition information value, the reciprocal of the microscopic shape parameter, the first heterogeneity parameter and the second heterogeneity parameter. The comprehensive heterogeneity parameter of the target reservoir is obtained by calculating the product, which can amplify the sensitivity to extreme values in each dimension parameter, such as low value parameters, peak value parameters, etc., and can reflect the influence of the coupling relationship between multiple dimension parameters on the target heterogeneity parameter, so as to realize more accurate and effective quantification of the heterogeneity of the target reservoir.
[0100] In the embodiments of this specification, the heterogeneity of the target reservoir is quantified from multiple dimensions, including the mineral composition dimension, the microscopic shape dimension, and two heterogeneity dimensions along different directions, to obtain mineral composition information values that characterize the disorder and complexity of mineral components, microscopic shape parameters that characterize the dispersion of microscopic feature parameters, and first and second heterogeneity parameters that characterize the spatial heterogeneity of the target reservoir. Therefore, the target heterogeneity obtained based on this can be quantified more accurately and effectively, and the data processing does not rely on complex calculations, making the operation simple and saving time and effort.
[0101] It is understood that the methods described in the embodiments of this specification can be applied to electronic devices, which can refer to electronic devices with data computing, processing, and storage capabilities. These electronic devices can be terminals such as PCs (Personal Computers), tablets, smartphones, wearable devices, and intelligent robots; they can also be servers. A server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.
[0102] This specification also provides a method for quantitatively characterizing reservoir heterogeneity, referencing... Figure 3 As shown, the method may include:
[0103] S301: Perform entropy state analysis on mineral components.
[0104] Specifically, representative bedding plane samples should be selected to ensure that the samples represent the mineral composition of the entire bedding structure. The rock samples are cut to an appropriate size (usually powder or small particles) for X-ray diffraction (XRD) analysis. The mineral composition is tested using an XRD instrument to obtain the relative mass percentage data (p) of each mineral in the sample. i During testing, ensure that mineral components in bedding planes and non-bedding regions are analyzed separately to distinguish differences in mineral composition. Use the Shannon entropy formula to calculate the entropy values of mineral components, quantifying their disorder and complexity. The formula is as follows: Where, p i is the mass percentage of the i-th mineral (normalized value), and N is the number of mineral types. To facilitate comparisons between different samples, the calculated MCI values need to be standardized using the following formula: After standardization, the MCI value ranges from 0 to 1. The larger the value, the stronger the heterogeneity.
[0105] S302: Calculate micromechanical shape parameters.
[0106] Specifically, before the experiment, the core slices for micron indentation experiment are prepared, thick rock slices are taken from the original rock sample, it is required to contain as many bedding as possible, and surface polishing treatment is carried out, while ensuring that the upper and lower surfaces of the rock sample slice are parallel to the thickness of the rock sample slice, the sample surface is polished to ensure that the upper and lower two bottom surfaces of the slice are parallel and smooth. During the indentation experiment, the sample is fixed on the sample table to ensure that the lower surface of the rock slice is in full contact with the sample table, and the position of the sample table is adjusted to select the appropriate measurement point position. By adjusting the right knob of the instrument, the experimental instrument indenter is brought to the position of just contacting the core sample, and the experimental instrument supporting software interface is clicked to fine-tune the position of the experimental instrument indenter until the indenter is fully contacted. The loading speed is loaded to the target load, the maximum load of the indentation test is set to 5N, the loading speed is 2-5N / min, and the sample is stopped for 5S. After stopping, unloading is started, the loading and unloading force and displacement data are recorded, and the elastic modulus and hardness of the sample are obtained. 50 selected points of single sample are tested. The elastic modulus measured in the test is used to calculate the micro shape parameter by using the preset probability distribution model, and the specific formula is as follows: F(E): elastic modulus, E: cumulative distribution function not exceeding a certain value. η: scale parameter, representing the characteristic modulus of the material. m: shape parameter, reflecting the dispersion degree of the elastic modulus data. The smaller the value of m, the higher the dispersion degree of the data, and the stronger the heterogeneity of the material.
[0107] S303: calculate the lateral bedding heterogeneity and the longitudinal bedding heterogeneity.
[0108] Specifically, the lateral heterogeneity can be quantified by the periodic change of the bedding. First, the bedding image of the rock sample is obtained by a high-resolution camera, ensuring that the shooting angle and lighting conditions are consistent, so that the image reflects the true bedding structure of the rock. Then, the image is converted to a grayscale image using Imagej, and an edge detection algorithm is used to extract the bedding boundaries in the image. Then, the starting point and the ending point of each bedding are identified, and the lateral extension length of the bedding is measured. That is, the extension length of the 2m bedding, and the bedding heterogeneity coefficient X is X is calculated by the lateral extension length of the bedding, that is:
[0109] Specifically, the longitudinal heterogeneity coefficient Y can be used to measure the change of the reservoir section in the vertical direction. First, the thickness data of the reservoir section can be obtained by core experiment or downhole measurement, and the thickness of different reservoir sections is recorded. Then, according to the thickness data of the reservoir section, the longitudinal thickness change graph is drawn, and the periodic change between the reservoir sections is analyzed. By calculating the thickness change of each reservoir section, the longitudinal period can be obtained. That is, the thickness change of the 0.2m reservoir, and the longitudinal heterogeneity coefficient Y is 5. Y is calculated by the thickness change of the reservoir section, that is:
[0110] S304: Calculate the comprehensive heterogeneity coefficient.
[0111] Specifically, the final comprehensive heterogeneity coefficient can be calculated by combining the obtained mineral composition data (MCI value) and the data obtained by micro-mechanical test (lateral and longitudinal heterogeneity coefficients). The formula is as follows: n: comprehensive heterogeneity coefficient, the stronger the heterogeneity, the larger n; MCI: mineral composition heterogeneity; m: micro-morphology parameter, reflecting the dispersion of characteristic parameters; X: lateral heterogeneity parameter Y: longitudinal heterogeneity parameter.
[0112] The method provided in the embodiments of the present specification for quantitatively calculating reservoir heterogeneity is simple and easy to operate, and can effectively analyze the heterogeneity of the reservoir. Moreover, the mineral composition, micro-mechanical properties and gray value are combined in the calculation process, thereby providing a high-precision quantitative index for the development of sandstone reservoirs. The whole process does not depend on complex calculation, is simple to operate, and is suitable for the heterogeneity evaluation of actual oilfield sites.
[0113] Based on the method for determining the reservoir heterogeneity parameter described above, one or more embodiments of the present specification also provide a device for determining the reservoir heterogeneity parameter. The device can include a device (including a distributed system), software (application), module, plug-in, server, client, etc. using the method described in the embodiments of the present specification, and a device combined with necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided by the embodiments of the present specification is described below. Since the implementation scheme of the device for solving the problem is similar to the method, the implementation of the specific device in the embodiments of the present specification can refer to the implementation of the foregoing method, and the repeated parts will not be described herein. The term "unit" or "module" used below can be a combination of software and / or hardware that can implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware or a combination of software and hardware is also possible and is conceived. Figure 4 As shown in the schematic diagram of the device for determining the reservoir heterogeneity parameter provided by the embodiments of the present specification. Figure 4 As shown, the device 400 for determining the reservoir heterogeneity parameter can include:
[0114] The acquisition module 401 is configured to acquire mineral composition data, indentation test data and formation data of a core sample of a target reservoir.
[0115] The first calculation module 402 is configured to determine a mineral composition information value of the target reservoir based on the mineral composition data.
[0116] The second calculation module 403 is configured to determine a micro-morphology parameter of the target reservoir based on the indentation test data.
[0117] The third calculation module 404 is configured to determine a first heterogeneity parameter of the target reservoir along a first direction and a second heterogeneity parameter of the target reservoir along a second direction based on the formation data, wherein the first direction and the second direction are different directions along the target reservoir.
[0118] The determination module 405 is configured to determine a target heterogeneity parameter of the target reservoir based on the mineral component information value, the microscopic shape parameter, the first heterogeneity parameter, and the second heterogeneity parameter.
[0119] In some embodiments of the present disclosure, the first calculation module 402 can be specifically configured to determine a mass proportion of each mineral in the core sample based on the mineral component data, and calculate an entropy value of the mineral component based on the mass proportions of the minerals, and take the entropy values of the minerals as the mineral component information value.
[0120] In some embodiments of the present disclosure, after calculating the entropy value of the mineral component based on the mass proportions of the minerals, the first calculation module 402 can be further configured to perform standardization processing on the calculated entropy value of the mineral component to obtain a relative entropy of the mineral component, and take the relative entropy of the mineral component as the mineral component information value.
[0121] In some embodiments of the present disclosure, the indentation experiment data can be obtained by: obtaining a core slice of the core sample; performing polishing processing on the core slice, and making the upper and lower surfaces of the core slice parallel; performing indentation testing on the core slice by using an indentation tester at a target loading speed and a target load; and obtaining mechanical property data collected in the indentation testing process as the indentation experiment data.
[0122] In some embodiments of the present disclosure, the second calculation module 403 can be specifically configured to fit the indentation experiment data based on a preset probability distribution model to obtain the microscopic shape parameter.
[0123] In some embodiments of the present disclosure, the third calculation module 404 can be specifically configured to determine a lateral extension length of the bedding based on a bedding image in the formation data, determine the first heterogeneity parameter based on the lateral extension length of the bedding, determine a longitudinal thickness variation data of the target reservoir based on a reservoir section thickness data in the formation data, and determine the second heterogeneity parameter based on the longitudinal thickness variation data.
[0124] In some embodiments of the present disclosure, the first heterogeneity parameter can be a reciprocal of the lateral extension length, and the second heterogeneity parameter can be a reciprocal of the longitudinal thickness variation data.
[0125] In some embodiments of this specification, the target heterogeneity parameter can be determined by the following formula:
[0126]
[0127] Wherein, n represents the target heterogeneity parameter, MCI represents the mineral composition information value, m represents the microstructure parameter, X represents the first heterogeneity parameter, and Y represents the second heterogeneity parameter.
[0128] The descriptions and functions of the above modules can be understood by referring to the section on methods for determining reservoir heterogeneity parameters, and will not be repeated here.
[0129] This application also provides an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 501 and a memory 502, wherein the processor 501 and the memory 502 may be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.
[0130] Processor 501 can be a central processing unit (CPU). Processor 501 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.
[0131] Memory 502, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the method for determining reservoir heterogeneity parameters in this embodiment of the invention (e.g., Figure 4 The acquisition module 401, the first calculation module 402, the second calculation module 403, the third calculation module 404, and the determination module 405 are shown. The processor 501 executes various functional applications and data processing by running non-transient software programs, instructions, and modules stored in the memory 502, thereby realizing the method for determining reservoir heterogeneity parameters in the above method embodiments.
[0132] The memory 502 can include a program storage area and a data storage area, where the program storage area can store an operating system, at least one application required by a function, and the data storage area can store data created by the processor 501 and the like. In addition, the memory 502 can include a high-speed random access memory, and can also include a non-transitory memory such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 502 can optionally include a memory disposed remotely with respect to the processor 501, which can be connected to the processor 501 through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.
[0133] The one or more modules are stored in the memory 502 and, when executed by the processor 501, perform the following method for determining a reservoir heterogeneity parameter:
[0134] Obtaining mineral composition data, indentation experiment data, and formation data of a core sample of a target reservoir; determining a mineral composition information value of the target reservoir based on the mineral composition data; determining a micro shape parameter of the target reservoir based on the indentation experiment data; determining a first heterogeneity parameter of the target reservoir along a first direction and a second heterogeneity parameter of the target reservoir along a second direction based on the formation data; wherein the first direction and the second direction are different directions along the target reservoir; and determining a target heterogeneity parameter of the target reservoir based on the mineral composition information value, the micro shape parameter, the first heterogeneity parameter, and the second heterogeneity parameter.
[0135] The above electronic device specific details can be understood with reference to the corresponding related descriptions and effects in the above method embodiments, which will not be repeated here.
[0136] The embodiments of the present specification also provide a computer storage medium, which stores computer program instructions, and the computer program instructions are executed to implement the steps of the above method for determining a reservoir heterogeneity parameter.
[0137] The embodiments of the present specification also provide a computer program product, which contains a computer program, and the computer program is executed to implement the steps of the above method for determining a reservoir heterogeneity parameter.
[0138] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.
[0139] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.
[0140] The system, device, module or unit described in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions.
[0141] For the convenience of description, the above device is described as various units respectively described by functions. Of course, the functions of each unit can be implemented in the same or more software and / or hardware in the implementation of the present application.
[0142] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and the necessary general hardware platform. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disc, an optical disc, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of some parts of the embodiments of the present application.
[0143] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, small computers, large computers, distributed computing environments including any of the above systems or devices, etc.
[0144] The application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In this case, program modules can be located in both local and remote computer storage media including memory storage devices.
[0145] While this application has been depicted, described, and is to be understood in connection with specific example embodiments, it will be appreciated that it is in no way limited to these embodiments but rather can admit to any number of modifications and variations. It is therefore desired that the present application be deemed limited only by the scope of the appended claims, which follow and the equivalents thereof.
Claims
1. A method of determining reservoir heterogeneity parameters, characterized in that, The method comprises the following steps: obtaining mineral composition data, indentation experiment data and formation data of a core sample of a target reservoir; determining a mineral composition information value of the target reservoir based on the mineral composition data; determining a micro shape parameter of the target reservoir based on the indentation experiment data; determining a first heterogeneity parameter of the target reservoir along a first direction and a second heterogeneity parameter of the target reservoir along a second direction based on the formation data; wherein the first direction and the second direction are different directions along the target reservoir; determining a target heterogeneity parameter of the target reservoir based on the mineral composition information value, the micro shape parameter, the first heterogeneity parameter and the second heterogeneity parameter.
2. The method of determining reservoir heterogeneity parameters according to claim 1, wherein, Determining the mineral composition information value of the target reservoir based on the mineral composition data comprises: determining the mass proportion of each mineral in the core sample based on the mineral composition data; calculating the entropy value of the mineral composition based on the mass proportion of each mineral, and taking the entropy values of the multiple minerals as the mineral composition information value.
3. The method of determining reservoir heterogeneity parameters according to claim 2, wherein, After calculating the entropy value of the mineral composition based on the mass proportion of each mineral, the following steps are further included: standardizing the calculated entropy value of the mineral composition to obtain the relative entropy of the mineral composition, and taking the relative entropy of the mineral composition as the mineral composition information value.
4. The method for determining reservoir heterogeneity parameters according to claim 1, characterized in that, The indentation experiment data is obtained by the following method: obtaining a core slice of the core sample; polishing the core slice and making the upper and lower surfaces of the core slice parallel; performing indentation test on the core slice at a target loading speed and a target load by using an indenter; obtaining the mechanical property data collected during the indentation test as the indentation experiment data.
5. The method for determining reservoir heterogeneity parameters according to claim 1, wherein, Determining the micro shape parameter of the target reservoir based on the indentation experiment data comprises: fitting the indentation experiment data based on a preset probability distribution model to obtain the micro shape parameter.
6. The method for determining reservoir heterogeneity parameters according to claim 1, wherein, Determining the first heterogeneity parameter of the target reservoir along a first direction and the second heterogeneity parameter of the target reservoir along a second direction based on the formation data comprises: determining the lateral extension length of the bedding based on the bedding image in the formation data; determining the first heterogeneity parameter based on the lateral extension length of the bedding; determining the longitudinal thickness variation data of the target reservoir based on the reservoir section thickness data in the formation data; determining the second heterogeneity parameter based on the longitudinal thickness variation data.
7. The method of determining reservoir heterogeneity parameters according to claim 6, wherein, The first heterogeneity parameter is the reciprocal of the lateral extension length, and the second heterogeneity parameter is the reciprocal of the longitudinal thickness variation data.
8. The method for determining reservoir heterogeneity parameters according to claim 1, wherein, The target heterogeneity parameter is determined by the following formula: wherein n represents the target heterogeneity parameter, MCI represents the mineral composition information value, m represents the micro shape parameter, X represents the first heterogeneity parameter, and Y represents the second heterogeneity parameter.
9. An apparatus for determining reservoir heterogeneity parameters, the apparatus comprising: The method comprises the following steps: an obtaining module, configured to obtain mineral composition data, indentation experiment data and formation data of a core sample of a target reservoir; a first calculating module, configured to determine a mineral composition information value of the target reservoir based on the mineral composition data; a second calculation module, configured to determine a micro shape parameter of the target reservoir based on the indentation experiment data; a third calculation module, configured to determine a first heterogeneity parameter along a first direction and a second heterogeneity parameter along a second direction of the target reservoir based on the formation data; the first direction and the second direction are different directions along the target reservoir; a determination module, configured to determine a target heterogeneity parameter of the target reservoir based on the mineral component information value, the micro shape parameter, the first heterogeneity parameter and the second heterogeneity parameter.
10. An electronic device, comprising: comprising: a memory and a processor, which are in communication connection with each other, and the memory stores computer instructions; the processor executes the computer instructions to realize the steps of the method in any one of claims 1 to 8.