Reservoir flow unit identification method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a reservoir flow unit identification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring rock core analysis data, wherein the rock core analysis data is used for indicating lithology and physical properties of a reservoir to be tested; inputting the core analysis data into a reservoir flow unit identification model to obtain an identification result, the reservoir flow unit identification model being determined based on a response curve of a detected well and corresponding historical logging data; and classifying the flow units of the reservoir to be tested according to the identification result. According to the method, the reservoir flow unit can be quickly and efficiently identified, and the identification precision of the reservoir flow unit is improved.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development technology, and in particular to a method, apparatus, electronic device and storage medium for identifying reservoir flow units. Background Technology

[0002] A reservoir flow unit refers to a reservoir rock mass with similar lithology and physical properties within the same fluid flow field. Different types of flow units have different lithology and physical properties.

[0003] The main purpose of reservoir flow unit identification is to subdivide the reservoir into units with similar fluid flow characteristics in order to more accurately describe the heterogeneity of the reservoir, predict the flow behavior of fluids in the reservoir, and provide a scientific basis for oilfield development.

[0004] In existing reservoir flow unit identification methods, the identification efficiency and accuracy are low due to the limited number of key factors considered in field applications. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for identifying reservoir flow units, in order to solve the problem of insufficient accuracy and efficiency caused by differences in the selection of key test factors in complex field environments.

[0006] In a first aspect, embodiments of this application provide a method for identifying reservoir flow units, the method comprising:

[0007] Acquire core analysis data, which is used to indicate the lithology and physical properties of the reservoir to be tested;

[0008] The core analysis data is input into the reservoir flow unit identification model to obtain the identification result. The reservoir flow unit identification model is determined based on the response curves of the detected wells and the corresponding historical logging data. The response curves of the detected wells are determined based on the historical logging data.

[0009] Based on the identification results, the flow units of the reservoir to be tested are classified.

[0010] In one possible implementation, prior to acquiring the core analysis data, the method further includes:

[0011] Historical logging data of multiple detected wells are acquired, and based on the historical logging data, a response curve for each detected well is determined, wherein the response curve is used to indicate the physical properties of the formation at different depths within the detected well.

[0012] Based on the response curves of the multiple detected wells, a reservoir flow unit identification model is constructed.

[0013] In one possible implementation, constructing the reservoir flow unit identification model based on the response curves of the plurality of detected wells includes:

[0014] For any one of the multiple monitored wells, the sensitivity intensity of the monitored well is determined based on the historical logging data and response curve of the monitored well.

[0015] A target response curve is determined from the plurality of response curves, and the detection well corresponding to the target response curve has the highest sensitivity intensity.

[0016] Based on the target response curve, a multi-parameter fitting equation is determined, and based on the multi-parameter fitting equation, the reservoir flow unit identification model is constructed.

[0017] In one possible implementation, determining the multi-parameter fitting equation based on the target response curve includes:

[0018] Based on the target response curve, determine the first flow unit index corresponding to multiple reservoir parameters;

[0019] The multi-parameter fitting equation is determined based on multiple first flow unit indices and fitting coefficients.

[0020] In one possible implementation, the core analysis data includes porosity, permeability, and clay content. The step of inputting the core analysis data into a reservoir flow unit identification model to obtain identification results includes:

[0021] The porosity, permeability, and clay content are substituted into the multi-parameter fitting equation to obtain the flow unit identification result of the reservoir to be tested. The flow unit identification result is used to indicate the flow units that match the porosity, permeability, and clay content, respectively.

[0022] In one possible implementation, classifying the flow units of the reservoir under test based on the identification result includes:

[0023] Based on the identification results and the core analysis data, cluster analysis is performed to obtain a cluster analysis spectrum.

[0024] The cluster analysis spectrum and the identification results are subjected to intersection graph analysis to obtain the flow unit classification results of the reservoir to be tested. The classification results are used to indicate the types of multiple flow units corresponding to the reservoir to be tested and the reservoir physical property characteristics of each type. The reservoir physical property characteristics corresponding to different types of flow units are different.

[0025] In one possible implementation, the method further includes:

[0026] Based on the response curves of multiple wells that have been tested, a well logging interpretation model is established;

[0027] The historical logging data of the multiple detected wells are input into the logging interpretation model to obtain the first interpretation result for each detected well. The first interpretation result is used to indicate the second flow unit index of the detected well.

[0028] When the second flow unit index of the multiple detected wells matches the corresponding historical flow unit index, the core analysis data is input into the well logging interpretation model to obtain the second interpretation result of the reservoir to be tested. The second interpretation result is used to indicate the third flow unit index of the reservoir to be tested.

[0029] The accuracy of the classification results is verified based on the third flow unit index.

[0030] Secondly, embodiments of this application provide a reservoir flow unit identification device, comprising:

[0031] The acquisition module is used to acquire core analysis data, which is used to indicate the lithology and physical properties of the reservoir to be tested.

[0032] The processing module is used to input the core analysis data into the reservoir flow unit identification model to obtain the identification result. The reservoir flow unit identification model is determined based on the response curve of the detected well and the corresponding historical logging data. The response curve of the detected well is determined based on the historical logging data.

[0033] The processing module is further configured to classify the flow units of the reservoir to be tested based on the identification results.

[0034] In one possible implementation, the acquisition module is further configured to acquire historical logging data of multiple detected wells, and determine the response curve of each detected well based on the historical logging data, wherein the response curve is used to indicate the physical properties of formations at different depths within the detected well.

[0035] The processing module is used to construct the reservoir flow unit identification model based on the response curves of the multiple detected wells.

[0036] In one possible implementation, the processing module is further configured to, for any one of the multiple monitored wells, determine the sensitivity intensity of the monitored well based on the historical logging data and response curve of the monitored well; determine a target response curve from the multiple response curves, wherein the monitored well corresponding to the target response curve has the highest sensitivity intensity; determine a multi-parameter fitting equation based on the target response curve, and construct the reservoir flow unit identification model based on the multi-parameter fitting equation.

[0037] In one possible implementation, the processing module is further configured to determine first flow unit indices corresponding to multiple reservoir parameters based on the target response curve; and to determine the multi-parameter fitting equation based on the multiple first flow unit indices and fitting coefficients.

[0038] In one possible implementation, the core analysis data includes porosity, permeability, and clay content. The processing module is further configured to input the porosity, permeability, and clay content into the multi-parameter fitting equation to obtain the flow unit identification result of the reservoir to be tested. The flow unit identification result is used to indicate the flow units that match the porosity, permeability, and clay content, respectively.

[0039] In one possible implementation, the processing module is further configured to perform cluster analysis based on the identification results and the core analysis data to obtain a cluster analysis spectrum; and to perform cross-plot analysis on the cluster analysis spectrum and the identification results to obtain the flow unit classification results of the reservoir to be tested, wherein the classification results are used to indicate the types of multiple flow units corresponding to the reservoir to be tested and the reservoir physical property characteristics of each type, wherein the reservoir physical property characteristics corresponding to different types of flow units are different.

[0040] In one possible implementation, the processing module is further configured to: establish a well logging interpretation model based on the response curves of multiple detected wells; input historical well logging data of the multiple detected wells into the well logging interpretation model to obtain a first interpretation result for each detected well, wherein the first interpretation result is used to indicate a second flow unit index of the detected well; if the second flow unit index of the multiple detected wells matches the corresponding historical flow unit index, input the core analysis data into the well logging interpretation model to obtain a second interpretation result for the reservoir to be tested, wherein the second interpretation result is used to indicate a third flow unit index of the reservoir to be tested; and verify the accuracy of the classification result based on the third flow unit index.

[0041] Thirdly, embodiments of this application provide a device for identifying reservoir flow units, the device comprising:

[0042] Memory;

[0043] processor;

[0044] The memory stores computer-executed instructions;

[0045] The processor executes computer execution instructions stored in the memory, causing the processor to perform the reservoir flow unit identification method as described in the first aspect and various possible implementations of the first aspect above.

[0046] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the reservoir flow unit identification method as described in the first aspect and various possible implementations of the first aspect.

[0047] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method for identifying reservoir flow units as described in the first aspect and various possible implementations of the first aspect.

[0048] This application provides a method, apparatus, electronic device, and storage medium for identifying reservoir flow units. The method acquires core analysis data, which indicates the lithology and physical properties of the reservoir to be tested. The core analysis data is input into a reservoir flow unit identification model to obtain identification results. The reservoir flow unit identification model is determined based on the response curves of detected wells and corresponding historical logging data. The response curves of the detected wells are determined based on the historical logging data. Based on the identification results, the flow units of the reservoir to be tested are classified. This method can quickly and efficiently identify reservoir flow units, improving the accuracy of reservoir flow unit identification. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] Figure 1 A flowchart illustrating a method for identifying reservoir flow units provided in this application. Figure 1 ;

[0051] Figure 2 A flowchart illustrating a method for identifying reservoir flow units provided in this application. Figure 2 ;

[0052] Figure 3 A flowchart illustrating a method for identifying reservoir flow units provided in this application. Figure 3 ;

[0053] Figure 4 A schematic diagram of the structure of a reservoir flow unit identification device provided in this application. Figure 1 ;

[0054] Figure 5 A schematic diagram of the structure of a reservoir flow unit identification device provided in this application. Figure 1 .

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0057] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0058] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0059] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0060] A reservoir flow unit refers to a reservoir rock mass with similar lithology and physical properties within the same fluid flow field. Different types of flow units have different lithologies and physical properties, reflecting the increasing sophistication of reservoir research and closely linking it to the oil-water movement patterns within the reservoir.

[0061] Reservoir flow unit identification is a key task in oil and gas field exploration and development, aiming to divide reservoirs into units with similar fluid flow characteristics. This identification process, based on the similarity of reservoir lithology, physical properties, and fluid flow characteristics, is crucial for gaining a deeper understanding of reservoir heterogeneity, predicting fluid flow behavior, and developing effective development strategies.

[0062] Reservoir flow identification enables the rational division and evaluation of reservoirs, improves the accuracy of permeability interpretation, and provides a stratification basis for reservoir numerical simulation. It not only helps to gain a deeper understanding of reservoir heterogeneity and fluid flow characteristics, but also provides a scientific basis for formulating effective development strategies.

[0063] In existing methods for identifying reservoir flow units, due to technical and application limitations, the selection of parameters when using reservoir parameter analysis often depends on the researcher's experience and subjective judgment. Different researchers may choose different combinations of parameters, resulting in differences in the division of flow units. Furthermore, existing technologies may face many difficulties in field applications, such as difficulty in data acquisition and complex data processing, leading to low identification efficiency and accuracy.

[0064] To address the aforementioned issues, this application provides a method for identifying reservoir flow units. This method involves inputting core analysis data into a reservoir flow unit identification model to obtain identification results, and then classifying the reservoir flow units based on these results. Since the parameter selection in the reservoir flow unit identification model does not depend on subjective choices, and by analyzing data from multiple sources, the identification efficiency and accuracy of reservoir flow units are improved.

[0065] Figure 1 The flowchart of a method for identifying reservoir flow units provided in this application embodiment Figure 1 .like Figure 1 As shown, the method for identifying reservoir flow units provided in this embodiment includes:

[0066] S101. Obtain core analysis data, which is used to indicate the lithology and physical properties of the reservoir to be tested.

[0067] Lithology refers to properties that reflect the characteristics of rocks, such as color, composition, structure, cement and cement type, and special minerals. Rock physical properties indicate the mechanical, thermal, electrical, acoustic, and radiological characteristics and physical quantities of rocks. In this step, core analysis data may include, for example, the porosity and permeability of the core.

[0068] When developing oil and gas fields, it is necessary to obtain core analysis data of the rocks within the reservoir. This data can be obtained, for example, through physical testing methods.

[0069] S102. Input the core analysis data into the reservoir flow unit identification model to obtain the identification result. The reservoir flow unit identification model is determined based on the response curve of the detected well and the corresponding historical logging data. The response curve of the detected well is determined based on the historical logging data.

[0070] Well logging data refers to data on underground geological and rock physical properties obtained through various logging tools and techniques during or after drilling. Historical well logging data includes a collection of well logging data accumulated over time. Well logging response curves are graphs of formation physical properties changing with depth, recorded during the well logging process, and are used to provide important information about the characteristics of underground rock formations and fluids.

[0071] For example, a reservoir flow unit identification model can be based on well logging response curves determined from historical detection data, a target response curve can be determined from the response curves of detected wells, a multi-parameter fitting equation can be determined using the target response curve, and a reservoir flow unit identification model can be constructed based on the multi-parameter fitting equation.

[0072] The identification results mainly include the flow unit index of the reservoir flow unit, which is used for the classification of reservoir flow units.

[0073] The flow unit index is a parameter defined based on reservoir geological characteristics (such as mineral composition, rock structure, porosity, and permeability) to describe the differences in fluid flow within the reservoir.

[0074] S103. Based on the identification results, classify the flow units of the reservoir to be tested.

[0075] Understandably, classifying the flow units in the reservoir to be tested is mainly achieved by analyzing the flow unit indices in the identification results.

[0076] In one possible implementation, the classification of flow units in the reservoir to be tested, based on the identification results, mainly includes: analyzing the flow unit indices in the identification results, visualizing the data, identifying flow units of the same category, and grouping flow units of the same category into the same group, thereby achieving the classification of flow units in the reservoir to be tested.

[0077] The reservoir flow unit identification method provided in this embodiment acquires core analysis data, which indicates the lithology and physical properties of the reservoir to be tested. The core analysis data is then input into a reservoir flow unit identification model to obtain identification results. This model is determined based on the response curves of detected wells and corresponding historical logging data. The response curves of the detected wells are based on the historical logging data. Based on the identification results, the flow units of the reservoir to be tested are classified. This method can quickly and efficiently identify reservoir flow units, improving the accuracy of reservoir flow unit identification.

[0078] Figure 2 The flowchart of a method for identifying reservoir flow units provided in this application embodiment Figure 2 This embodiment is in Figure 1 Based on the examples, a possible implementation method for constructing the reservoir flow unit identification model is further explained. For example... Figure 2 As shown, the method includes:

[0079] S201. Obtain historical logging data from multiple wells that have been inspected, and determine the response curve for each well based on the historical logging data, wherein the response curve is used to indicate the physical properties of the formation at different depths within the well.

[0080] In this step, historical logging data of the monitored wells can be obtained through specialized databases, data service industries, or other online channels, or by re-logging the old wells; there are no restrictions on this.

[0081] S202. For any one of the multiple monitored wells, determine the sensitivity intensity of the monitored well based on the historical logging data and response curve of the monitored well.

[0082] In well logging interpretation, sensitivity intensity is often used to assess the degree to which certain characteristics or parameters in the formation respond to different well logging curves. Based on the sensitivity intensity, an appropriate well logging response curve can be selected for formation analysis.

[0083] In this step, the sensitivity of the monitored well can be determined by calculation based on the logging response curve and logging parameters.

[0084] In one possible implementation, the sensitivity intensity of the detected wells can be calculated according to the following steps:

[0085] Clearly define the research objectives and geological background, determine the logging parameters to be analyzed, preprocess the data and remove outliers. Logging parameters include resistivity, sonic transit time, spontaneous potential, natural gamma, etc., which can reflect the electrical, acoustic and radioactive characteristics of the formation.

[0086] Determine the sensitivity intensity of each response curve to changes in reservoir characteristics;

[0087] The sensitivity indexes of different logging parameters can be calculated using statistical methods, geological models, or experimental data, without any restrictions.

[0088] S203. Determine the target response curve from the plurality of response curves, wherein the detected well corresponding to the target response curve has the highest sensitivity intensity.

[0089] Among these methods, determining the target curve from multiple response curves based on sensitivity intensity can improve interpretation accuracy, optimize logging costs, adapt to different reservoir types, and reduce interpretation errors.

[0090] In this step, the target response curve is determined by comparing the sensitivity intensity of different response curves based on the calculated sensitivity index, and selecting the response curve with the highest sensitivity intensity as the target response curve, that is, the curve that best reflects the changes in reservoir characteristics.

[0091] S204. Based on the target response curve, determine the first flow unit index corresponding to multiple reservoir parameters.

[0092] Among them, the first flow unit index corresponding to multiple reservoir parameters is determined based on the target response curve. Different methods can be selected depending on the type of available data and the complexity of the analysis.

[0093] In one possible implementation, determining the first flow unit index corresponding to multiple reservoir parameters based on the target response curve can be achieved in the following way:

[0094] The target logging data corresponding to the target response curve is determined from historical logging data of multiple wells that have been inspected, and feature selection is performed on the target logging data.

[0095] In one possible implementation, characterizing the target logging data can be achieved, for example, in the following ways:

[0096] The extracted target logging data is initially screened to remove obviously abnormal or irrelevant data points;

[0097] The correlation between each logging parameter and the target response is calculated, and logging parameters with a high correlation to the target response curve are selected as candidate features. This correlation can be evaluated using metrics such as correlation coefficient and information gain.

[0098] Calculate the variance of each candidate feature to evaluate its information content;

[0099] Select features with larger variance and use them as the logging data after feature selection.

[0100] Reservoir characteristic parameters are calculated using target logging data after feature selection, and the reservoir characteristic parameters are then substituted into the flow unit index calculation formula to obtain the flow unit index.

[0101] In one possible implementation, the flow unit index can be calculated using reservoir characteristic parameters, for example, by the following formula:

[0102]

[0103] Where FZI is the flow unit index, RQI is the reservoir quality index, which can be calculated using permeability, and K is the permeability. Porosity.

[0104] S205. Based on multiple first flow unit indices and fitting coefficients, determine the multi-parameter fitting equation.

[0105] In one possible implementation, the multi-parameter fitting equation can be determined based on multiple first flow unit indices and fitting coefficients in the following manner:

[0106] The first flow unit index is used as the independent variable, the target logging response curve is used as the fitting coefficient, and the intercept term is used as the error coefficient. The intercept term can be solved using the least squares method.

[0107] Using the flow unit index as the dependent variable, the flow unit index is solved using the first flow unit index, the target logging response curve, and the intercept term.

[0108] S206. Based on the multi-parameter fitting equation, construct the reservoir flow unit identification model.

[0109] The reservoir flow unit identification method provided in this embodiment acquires historical logging data from multiple monitored wells and determines the response curve of each monitored well based on the historical logging data. The response curve indicates the physical properties of formations at different depths within the monitored well. For any one monitored well, the sensitivity intensity of the monitored well is determined based on its historical logging data and response curve. A target response curve is determined from the multiple response curves, and the monitored well corresponding to the target response curve has the highest sensitivity intensity. Based on the target response curve, flow unit indices corresponding to multiple reservoir parameters are determined. A multi-parameter fitting equation is determined based on the multiple flow unit indices and fitting coefficients. Based on the multi-parameter fitting equation, a reservoir flow unit identification model is constructed. This method utilizes logging data and logging response curves to determine a multi-parameter fitting equation and constructs a reservoir flow unit identification model based on the multi-parameter fitting equation, overcoming application limitations and improving the identification efficiency of reservoir flow units.

[0110] Figure 3 The flowchart of a method for identifying reservoir flow units provided in this application embodiment Figure 3 In this embodiment, the core analysis data includes: porosity, permeability, and clay content. This embodiment... Figure 1 Based on the embodiments, a possible implementation method for determining the identification result of the reservoir to be tested and classifying the flow units of the reservoir to be tested based on the identification result is described in detail. For example... Figure 3 As shown, the method includes:

[0111] S301. Substitute the porosity, permeability, and clay content into the multi-parameter fitting equation to obtain the flow unit identification result of the reservoir to be tested. The flow unit identification result is used to indicate the flow units that match the porosity, permeability, and clay content, respectively.

[0112] S302. Based on the identification results and the core analysis data, perform cluster analysis to obtain a cluster analysis spectrum.

[0113] Cluster analysis, an unsupervised learning method, is based on information about objects and their relationships found in data. It groups data objects into multiple clusters or groups according to the characteristics or attributes of each object, ensuring that objects within a group are similar to each other, while objects in different groups are distinct. By classifying and grouping data, it achieves data compression, dimensionality reduction, and anomaly detection, improving the efficiency and accuracy of data processing.

[0114] Cluster analysis spectra are a visual representation of cluster analysis results, containing information on the number and shape of clusters, the distribution of samples within clusters, and the distance and relationships between clusters, used to understand the inherent structure and patterns of the data.

[0115] In this step, cluster analysis can be implemented using various algorithms, which can be selected based on the data type and clustering requirements; no restrictions are imposed here.

[0116] S303. Perform cross-plot analysis on the cluster analysis spectrum and the identification results to obtain the flow unit classification results of the reservoir to be tested.

[0117] The classification results are used to indicate the types of multiple flow units corresponding to the reservoir under test and the reservoir physical properties of each type. Different types of flow units correspond to different reservoir physical properties.

[0118] Cross-plot analysis is a method that projects two or more attributes or variables in pairs onto the same coordinate system to reveal the relationship between them. The appropriate type of cross-plot is selected based on the data type and application scenario. Cross-plots can intuitively display the changing patterns between different attributes.

[0119] Reservoir physical properties are a series of parameters and characteristics that describe the properties of reservoir rocks and fluids. These parameters and characteristics together determine the reservoir performance. By measuring and analyzing reservoir physical properties, we can better understand the reservoir's storage capacity, fluidity, and stability, providing a basis for energy exploration, development, and production.

[0120] In one possible implementation, Figure 3 Based on the examples, the accuracy of the determined classification results can also be verified. The specific verification process is as follows:

[0121] Based on the response curves of multiple wells that have been tested, a well logging interpretation model is established;

[0122] In this step, the well logging interpretation model is established based on the response curves of the wells that have been checked, and the corresponding flow unit index can be calculated using the well logging response curves.

[0123] Understandably, the first interpretation result was calculated using historical logging data and logging response curves from the monitored wells.

[0124] Among them, the well logging interpretation model can be constructed by methods such as those based on empirical formulas or those based on physical models.

[0125] In one possible implementation, the well logging interpretation model can be established using an empirical formula-based approach:

[0126] Using logging data from cored wells and the corresponding flow unit index, an empirical formula between the logging response curve and the flow unit index is established through statistical analysis or expert experience. This empirical formula may involve a combination of one or more logging curves, as well as possible constant terms or coefficients.

[0127] The empirical formula obtained above is applied to the logging data of uncored wells to calculate the flow unit index of the uncored wells.

[0128] Among them, a cored well refers to a well in which core samples were obtained during the drilling process using specialized coring tools; a non-cored well refers to a well in which no core samples were obtained during the drilling process.

[0129] The historical logging data of the multiple detected wells are input into the logging interpretation model to obtain the first interpretation result for each detected well. The first interpretation result is used to indicate the second flow unit index of the detected well.

[0130] When the second flow unit index of the multiple detected wells matches the corresponding historical flow unit index, the core analysis data is input into the well logging interpretation model to obtain the second interpretation result of the reservoir to be tested. The second interpretation result is used to indicate the third flow unit index of the reservoir to be tested.

[0131] Understandably, determining the matching of the second flow unit index and the corresponding historical flow unit index of multiple detected wells is to verify the accuracy and reliability of the model and to ensure that the data used as input to the model is accurate and reliable.

[0132] In one possible implementation, if there is a mismatch between the second flow unit index and the corresponding historical flow unit index of multiple detected wells, the well logging interpretation model needs to be recalibrated. This can be achieved by adjusting the well logging interpretation model through methods such as data quality checks, adding data sources, adjusting model complexity, and uncertainty analysis, so that the second flow unit index of multiple detected wells matches the corresponding historical flow unit index.

[0133] The accuracy of the classification results is verified based on the third flow unit index.

[0134] Among them, the accuracy verification of the classification results is carried out by calculating the accuracy rate of the flow unit index corresponding to the flow unit in the classification results and the third flow unit index. This can be used to evaluate the accuracy and reliability of the reservoir flow unit identification model and can be used as the reservoir identification compliance rate.

[0135] In this step, when calculating the accuracy of the flow unit index and the third flow unit index corresponding to the flow unit in the classification results, it is based on the dataset, that is, the dataset composed of the flow unit indices of all flow units in the reservoir and the dataset composed of the third flow unit index obtained from the well logging interpretation model. The accuracy can be calculated and evaluated by combining common methods and classification standards.

[0136] Understandably, the reservoir identification accuracy obtained through the above calculation can be used to represent the degree of agreement between the identified reservoir and the actual existing reservoir. The higher the accuracy, the higher the accuracy of the geological model and exploration technology, thus enabling the effective prediction and identification of the location and properties of underground reservoirs.

[0137] The reservoir flow unit identification method provided in this embodiment uses core analysis data including porosity, permeability, and clay content. The porosity, permeability, and clay content are input into a multi-parameter fitting equation to obtain the flow unit identification results for the reservoir under test. These results indicate flow units that match the porosity, permeability, and clay content, respectively. Based on the identification results and the core analysis data, cluster analysis is performed to obtain a cluster analysis spectrum. Cross-plot analysis is then performed on the cluster analysis spectrum and the identification results to obtain the flow unit classification results for the reservoir under test. This method uses cluster analysis and cross-plot processing to classify reservoir flow units and verifies the accuracy of the determined classification results, thus improving the accuracy of reservoir flow unit identification.

[0138] Figure 4 This application provides an embodiment of a reservoir flow unit identification device, which is applied to a terminal device. For example... Figure 4 As shown, an embodiment of this application provides a reservoir flow unit identification device 400 comprising:

[0139] The acquisition module 401 is used to acquire core analysis data, which is used to indicate the lithology and physical properties of the reservoir to be tested.

[0140] The processing module 402 is used to input the core analysis data into the reservoir flow unit identification model to obtain the identification result. The reservoir flow unit identification model is determined based on the response curve of the detected well and the corresponding historical logging data. The response curve of the detected well is determined based on the historical logging data.

[0141] The processing module 402 is further configured to classify the flow units of the reservoir to be tested based on the identification results.

[0142] In one possible implementation, the acquisition module 401 is further configured to acquire historical logging data of multiple detected wells, and determine the response curve of each detected well based on the historical logging data, wherein the response curve is used to indicate the physical properties corresponding to formations at different depths within the detected well.

[0143] The processing module 402 is used to construct the reservoir flow unit identification model based on the response curves of the multiple detected wells.

[0144] In one possible implementation, the processing module 402 is further configured to, for any one of the multiple monitored wells, determine the sensitivity intensity of the monitored well based on the historical logging data and response curve of the monitored well; determine a target response curve from the multiple response curves, wherein the monitored well corresponding to the target response curve has the highest sensitivity intensity; determine a multi-parameter fitting equation based on the target response curve; and construct the reservoir flow unit identification model based on the multi-parameter fitting equation.

[0145] In one possible implementation, the processing module 402 is further configured to determine the first flow unit index corresponding to multiple reservoir parameters based on the target response curve; and to determine the multi-parameter fitting equation based on the multiple first flow unit indices and fitting coefficients.

[0146] In one possible implementation, the core analysis data includes porosity, permeability, and clay content. The processing module 402 is further configured to input the porosity, permeability, and clay content into the multi-parameter fitting equation to obtain the flow unit identification result of the reservoir to be tested. The flow unit identification result is used to indicate the flow units that match the porosity, permeability, and clay content, respectively.

[0147] In one possible implementation, the processing module 402 is further configured to perform cluster analysis based on the identification results and the core analysis data to obtain a cluster analysis spectrum; and to perform cross-plot analysis on the cluster analysis spectrum and the identification results to obtain the flow unit classification results of the reservoir to be tested, wherein the classification results are used to indicate the types of multiple flow units corresponding to the reservoir to be tested and the reservoir physical property characteristics of each type, wherein the reservoir physical property characteristics corresponding to different types of flow units are different.

[0148] In one possible implementation, the processing module 402 is further configured to: establish a well logging interpretation model based on the response curves of multiple detected wells; input historical well logging data of the multiple detected wells into the well logging interpretation model to obtain a first interpretation result for each detected well, wherein the first interpretation result is used to indicate a second flow unit index of the detected well; if the second flow unit index of the multiple detected wells matches the corresponding historical flow unit index, input the core analysis data into the well logging interpretation model to obtain a second interpretation result for the reservoir to be tested, wherein the second interpretation result is used to indicate a third flow unit index of the reservoir to be tested; and perform accuracy verification on the classification result based on the third flow unit index.

[0149] This embodiment provides a reservoir flow unit identification device, which can execute the reservoir flow unit identification method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0150] Figure 5 A schematic diagram of the structure of the reservoir flow unit identification device provided in this application. (See attached diagram.) Figure 5 As shown, the reservoir flow unit identification device 500 provided in this application includes: a receiver 501, a transmitter 502, a processor 503, and a memory 504.

[0151] Transmitter 502 is used to send commands and data;

[0152] Memory 504 is used to store instructions executed by the computer;

[0153] Processor 503 is used to execute computer execution instructions stored in memory 504 to implement the various steps performed by the reservoir flow unit identification method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing embodiments of the reservoir flow unit identification method.

[0154] Alternatively, the memory 504 can be either standalone or integrated with the processor 503.

[0155] When the memory 504 is set up independently, the electronic device also includes a bus for connecting the memory 504 and the processor 503.

[0156] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the reservoir flow unit identification method performed by the aforementioned reservoir flow unit identification device.

[0157] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the various steps performed by the reservoir flow unit identification method described above. For details, please refer to the relevant descriptions in the embodiments of the aforementioned reservoir flow unit identification method.

[0158] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0159] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0160] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0161] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0162] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0163] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0164] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0165] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0168] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0169] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0170] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for identifying reservoir flow units, characterized in that, include: Acquire core analysis data, which is used to indicate the lithology and physical properties of the reservoir to be tested; The core analysis data is input into the reservoir flow unit identification model to obtain the identification results. The reservoir flow unit identification model is determined based on the response curves of the detected wells and the corresponding historical logging data. Based on the identification results, the flow units of the reservoir to be tested are classified.

2. The method according to claim 1, characterized in that, Before acquiring the core analysis data, the method further includes: Historical logging data of multiple detected wells are acquired, and based on the historical logging data, a response curve for each detected well is determined, wherein the response curve is used to indicate the physical properties of the formation at different depths within the detected well. Based on the response curves of the multiple detected wells, a reservoir flow unit identification model is constructed.

3. The method according to claim 2, characterized in that, The process of constructing the reservoir flow unit identification model based on the response curves of the multiple detected wells includes: For any one of the multiple monitored wells, the sensitivity intensity of the monitored well is determined based on the historical logging data and response curve of the monitored well. A target response curve is determined from the plurality of response curves, and the detection well corresponding to the target response curve has the highest sensitivity intensity. Based on the target response curve, a multi-parameter fitting equation is determined, and based on the multi-parameter fitting equation, the reservoir flow unit identification model is constructed.

4. The method according to claim 3, characterized in that, The step of determining the multi-parameter fitting equation based on the target response curve includes: Based on the target response curve, determine the first flow unit index corresponding to multiple reservoir parameters; The multi-parameter fitting equation is determined based on multiple first flow unit indices and fitting coefficients.

5. The method according to claim 1, characterized in that, The core analysis data includes porosity, permeability, and clay content. The core analysis data is then input into the reservoir flow unit identification model to obtain identification results, including: The porosity, permeability, and clay content are substituted into the multi-parameter fitting equation to obtain the flow unit identification result of the reservoir to be tested. The flow unit identification result is used to indicate the flow units that match the porosity, permeability, and clay content, respectively.

6. The method according to claim 5, characterized in that, The step of classifying the flow units of the reservoir to be tested based on the identification results includes: Based on the identification results and the core analysis data, cluster analysis is performed to obtain a cluster analysis spectrum. The cluster analysis spectrum and the identification results are subjected to intersection graph analysis to obtain the flow unit classification results of the reservoir to be tested. The classification results are used to indicate the types of multiple flow units corresponding to the reservoir to be tested and the reservoir physical property characteristics of each type. The reservoir physical property characteristics corresponding to different types of flow units are different.

7. The method according to claim 6, characterized in that, The method further includes: Based on the response curves of multiple wells that have been tested, a well logging interpretation model is established; The historical logging data of the multiple detected wells are input into the logging interpretation model to obtain the first interpretation result for each detected well. The first interpretation result is used to indicate the second flow unit index of the detected well. When the second flow unit index of the multiple detected wells matches the corresponding historical flow unit index, the core analysis data is input into the well logging interpretation model to obtain the second interpretation result of the reservoir to be tested. The second interpretation result is used to indicate the third flow unit index of the reservoir to be tested. The accuracy of the classification results is verified based on the third flow unit index.

8. A device for identifying reservoir flow units, characterized in that, include: The acquisition module is used to acquire core analysis data, which is used to indicate the lithology and physical properties of the reservoir to be tested. The processing module is used to input the core analysis data into the reservoir flow unit identification model to obtain the identification result. The reservoir flow unit identification model is determined based on the response curve of the detected well and the corresponding historical logging data. The response curve of the detected well is determined based on the historical logging data. The processing module is further configured to classify the flow units of the reservoir to be tested based on the identification results.

9. A device, characterized in that, include: Memory; processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for identifying reservoir flow units as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method for identifying reservoir flow units as described in any one of claims 1-7.

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