Marlstone reservoir identification method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a marlstone reservoir identification method, device and equipment and a storage medium, and belongs to the technical field of oil-gas exploration. The method comprises the steps that a coal seam with the maximum depth in a target well is obtained, and a target coal seam is obtained; and determining a target data extraction range according to the bottom depth of the target coal seam and the well bottom depth of the target well. A target density data set and a target interval transit time data set in the target data extraction range are obtained, and each depth in the target well corresponds to one piece of target density data and one piece of target interval transit time data. And drawing a target scatter projection drawing according to the target density data set and the target interval transit time data set, and obtaining a data center point and a data distribution radius on the target scatter projection drawing. The data point range is determined according to the data center point and the data distribution radius, the marlstone reservoir is recognized according to the data point range and the target scatter projection drawing, and the accuracy and efficiency of marlstone reservoir recognition are improved.

Description

Technical Field

[0001] The present application relates to the field of oil and gas exploration technology, and in particular to a method, device, equipment and storage medium for identifying a marl reservoir. Background Art

[0002] Marl is a transitional type of rock between carbonate rock and clay rock. As a transitional type of rock, marl may contain hydrocarbon resources. By accurately identifying marl reservoirs, its ability to store hydrocarbon resources can be evaluated, thereby discovering new oil and gas resources. Therefore, marl reservoir identification is an important link in oil and gas exploration. In related technologies, marl reservoirs are identified by utilizing the reflection characteristic attributes of parameters such as amplitude, phase, frequency, and energy in seismic data. However, the quality of seismic data is low, and the seismic reflection characteristics are not obvious. Multiple solutions are prone to occur when using seismic data for identification, which affects the identification results and has low identification accuracy. Therefore, how to improve the accuracy of marl reservoir identification has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a method, device, equipment and storage medium for identifying a marl reservoir, aiming to improve the accuracy of identifying a marl reservoir.

[0004] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for identifying a marl reservoir, the method comprising:

[0005] Obtain the coal seam with the greatest depth in the target well to obtain the target coal seam;

[0006] Determining a target data extraction range according to the bottom depth of the target coal seam and the bottom depth of the target well;

[0007] Acquire a target density data set and a target acoustic wave time difference data set within the target data extraction range; wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set;

[0008] Draw a target scatter point projection diagram according to the target density data set and the target acoustic wave time difference data set;

[0009] Obtaining a data center point and a data distribution radius on the target scatter projection graph;

[0010] Determine a data point range according to the data center point and the data distribution radius;

[0011] A marl reservoir is identified based on the data point range and the target scatter projection map.

[0012] In some embodiments, the target well and the candidate well are in the same target area, and after the marl reservoir is identified according to the data point range and the scatter projection map, the method further includes:

[0013] Obtaining the coal seam with the greatest depth in the candidate well to obtain a candidate coal seam;

[0014] Determining a candidate data extraction range according to the bottom depth of the candidate coal seam and the bottom depth of the candidate well;

[0015] Acquire a candidate density data set and a candidate acoustic wave time difference data set within the candidate data extraction range; wherein each depth in the candidate well corresponds to a candidate density data set and a candidate acoustic wave time difference data set;

[0016] Filtering out candidate density data and candidate acoustic wave time difference data within the data point range from the candidate density data set and the candidate acoustic wave time difference data set to obtain a selected density data set and a selected acoustic wave time difference data set;

[0017] The marl reservoir is identified based on the selected density data set and the selected sonic transit time data set.

[0018] In some embodiments, obtaining a data center point and a data distribution radius on the target scatter projection graph includes:

[0019] Obtaining the number of clusters; wherein the number of clusters is K;

[0020] Clustering the data points on the target scatter projection graph according to the K-Means algorithm, initializing K centroids;

[0021] Calculate the distance between each data point and each of the centroids, assign the data point to the cluster to which the nearest centroid belongs, and recalculate the centroid of each cluster until convergence;

[0022] The centroids of all clusters are averaged to obtain the data center point, and the maximum distances of all clusters are averaged to obtain the data distribution radius.

[0023] In some embodiments, before obtaining the number of clusters, the method further includes:

[0024] Identify outliers on the target scatter projection graph by using the interquartile range, and remove the outliers to obtain an updated target scatter projection graph;

[0025] The updated data points on the target scatter projection graph are standardized or normalized.

[0026] In some embodiments, the identifying the marl reservoir according to the data point range and the target scatter projection map includes:

[0027] Acquire data points within the data point range on the target scatter projection graph to obtain a target data point set;

[0028] Acquire a first data point with the smallest depth and a second data point with the largest depth in the target data point set;

[0029] The depth range corresponding to the first data point and the second data point is identified as the marl reservoir.

[0030] In some embodiments, obtaining the coal seam with the greatest depth in the target well to obtain the target coal seam includes:

[0031] Acquiring logging data of the target well;

[0032] Identifying the coal seam information of the target well according to the well logging data; wherein the coal seam information includes the depth of each coal seam;

[0033] The target coal seam is obtained according to the coal seam information.

[0034] In some embodiments, identifying the coal seam information of the target well according to the well logging data includes:

[0035] Inputting the logging data into a preset reservoir classification model to obtain layer information;

[0036] Coal seam information is screened according to the stratigraphic information to obtain the coal seam information.

[0037] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a marl reservoir identification device, the device comprising:

[0038] The first acquisition module is used to acquire the coal seam with the largest depth in the target well to obtain the target coal seam;

[0039] A first determination module is used to determine a target data extraction range according to the bottom depth of the target coal seam and the bottom depth of the target well;

[0040] An extraction module, used to obtain a target density data set and a target acoustic wave time difference data set within the target data extraction range; wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set;

[0041] A drawing module, used for drawing a target scatter point projection diagram according to the target density data set and the target acoustic wave time difference data set;

[0042] A second acquisition module is used to obtain a data center point and a data distribution radius on the target scatter projection graph;

[0043] A second determination module, configured to determine a data point range according to the data center point and the data distribution radius;

[0044] An identification module is used to identify the marl reservoir according to the data point range and the target scatter projection map.

[0045] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0046] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0047] The present application proposes a method, device, equipment and storage medium for identifying a marl reservoir, which obtains a target coal seam by obtaining the deepest coal seam in the target well. The target data extraction range is determined according to the bottom depth of the target coal seam and the bottom depth of the target well. The target density data set and the target acoustic wave time difference data set within the target data extraction range are obtained, wherein each depth in the target well corresponds to a target density data and a target acoustic wave time difference data. The target scatter projection map is drawn according to the target density data set and the target acoustic wave time difference data set, and the data center point and data distribution radius on the target scatter projection map are obtained. The data point range is determined according to the data center point and the data distribution radius, and the marl reservoir is identified according to the data point range and the target scatter projection map, thereby improving the accuracy and efficiency of the marl reservoir identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is an optional flow chart of the marl reservoir identification method provided in the embodiment of the present application;

[0049] Figure 2 is a flow chart of a marl reservoir identification method provided by another embodiment of the present application;

[0050] Figure 3 yes Figure 1 Flow chart of step S106 in FIG.

[0051] Figure 4 is a flow chart of a marl reservoir identification method provided by a third embodiment of the present application;

[0052] Figure 5 yes Figure 1 Flow chart of step S107 in FIG.

[0053] Figure 6 yes Figure 1 Flow chart of step S101 in FIG.

[0054] Figure 7 yes Figure 6 Flowchart of step S602 in FIG.

[0055] Figure 8 is a schematic structural diagram of a marl reservoir identification device provided in an embodiment of the present application;

[0056] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0060] In related technologies, the marlstone reservoir is identified by utilizing the reflection characteristic attributes of parameters such as amplitude, phase, frequency, and energy in seismic data. However, the quality of seismic data is low, and the seismic reflection characteristics are not obvious. When using seismic data for identification, multiple solutions are prone to occur, which affects the identification results and has a low identification accuracy.

[0061] Based on this, the embodiments of the present application provide a method, device, equipment and storage medium for identifying a marl reservoir, which is intended to determine the target data extraction range based on the target coal seam with the largest depth and the bottom hole depth. The target density data set and the target acoustic wave time difference data set within the target data extraction range are extracted for drawing a scatter projection map. The data point range is determined based on the data center point and data distribution radius on the scatter projection map. The depth corresponding to the data point within the data point range on the scatter projection map is the marl reservoir, which improves the accuracy and efficiency of marl reservoir identification.

[0062] The marl reservoir identification method, device, equipment and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the marl reservoir identification method in the embodiments of the present application is described.

[0063] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0064] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0065] The method for identifying a marl reservoir provided in the embodiment of the present application can be applied to a terminal, can be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the method for identifying a marl reservoir, etc., but is not limited to the above forms.

[0066] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0067] Please refer to Figure 1 , Figure 1 is an optional flow chart of the marl reservoir identification method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S107.

[0068] Step S101, obtaining the coal seam with the greatest depth in the target well to obtain the target coal seam;

[0069] Step S102, determining a target data extraction range according to the bottom depth of the target coal seam and the bottom depth of the target well;

[0070] Step S103, obtaining a target density data set and a target acoustic wave time difference data set within the target data extraction range; wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set;

[0071] Step S104, drawing a target scatter point projection diagram according to the target density data set and the target acoustic wave time difference data set;

[0072] Step S105, obtaining the data center point and data distribution radius on the target scatter projection diagram;

[0073] Step S106, determining the data point range according to the data center point and the data distribution radius;

[0074] Step S107, identifying the marl reservoir according to the data point range and the target scatter point projection map.

[0075] In step S101 of some embodiments, the target well is a well for identifying a marl reservoir, and a coal seam refers to a stratum containing exploitable coal resources. Under some special geological structures, the coal seam may be distributed at a higher or lower position. Since the marl reservoir can only be located below the coal seam, it is necessary to first obtain the deepest coal seam in the target well to obtain the target coal seam.

[0076] In step S102 of some embodiments, since the marl reservoir is located below the coal seam, the marl reservoir can only be located between the target coal seam and the bottom of the target well, and only the logging data between the target coal seam and the bottom of the well is needed to identify the marl reservoir. Therefore, the target data extraction range is determined according to the bottom depth of the target coal seam and the bottom depth of the well. For example, if the bottom depth of the target coal seam is X1 meters and the bottom depth of the well is Y1 meters, the target data extraction range is (X1, Y1], and the data with a depth within the range of (X1, Y1] is filtered out from the logging data.

[0077] It should be noted that well logging is to study the strata that constitute the geological profile of the well and measure it by physical methods to solve the problems related to oil and gas reservoirs. During well logging, the cable will lift or lower the downhole instrument at a uniform speed, and the physical parameters of the strata will be converted into electrical signals, which will be transmitted to the ground recorder through the cable for recording, thereby obtaining the well logging data. The well logging data includes the natural gamma (GR), resistivity (Rt), acoustic time difference (DT24), density (ZDEN) and other data corresponding to each depth in the target well.

[0078] In step S103 of some embodiments, it is verified experimentally that the density data and acoustic wave time difference data in the logging data have a greater impact on the identification of the marl reservoir. Therefore, only the target density data and target acoustic wave time difference data within the target data extraction range in the logging data are obtained to obtain a target density data set and a target acoustic wave time difference data set. Among them, each depth in the target well corresponds to a target density data and a target acoustic wave time difference data. For example, when the depth is X1+1 meters, the corresponding target density data is 2.6t / m 3 , the target acoustic wave time difference data is 56μs / m.

[0079] In step S104 of some embodiments, a target scatter projection diagram is drawn based on the data in the target density data set and the target acoustic wave time difference data set. For example, the target scatter projection diagram is drawn with the target density data as the x-axis and the target acoustic wave time difference data as the y-axis, and each data point on the target scatter projection diagram is the target density data and the target acoustic wave time difference data at the same depth.

[0080] In some embodiments, in steps S105 to S106, the data center point of the data set on the target scatter projection diagram can be determined by a normal distribution method, and the data distribution radius can be determined using a K-Means clustering algorithm. A range can be determined based on the data center point and the data distribution radius, which is called the data point range.

[0081] In step S107 of some embodiments, the depth corresponding to the target density data and the target acoustic wave time difference data within the data point range on the target scatter projection diagram is the marl reservoir.

[0082] Steps S101 to S107 shown in the embodiment of the present application are to obtain the target coal seam by obtaining the deepest coal seam in the target well. The target data extraction range is determined according to the bottom depth of the target coal seam and the bottom depth of the target well. A target density data set and a target acoustic wave time difference data set within the target data extraction range are obtained, wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set. A target scatter projection map is drawn according to the target density data set and the target acoustic wave time difference data set, and a data center point and a data distribution radius on the target scatter projection map are obtained. The data point range is determined according to the data center point and the data distribution radius, and the marl reservoir is identified according to the data point range and the target scatter projection map, thereby improving the accuracy and efficiency of marl reservoir identification.

[0083] See also Figure 2 In some embodiments, the target well and the candidate well are in the same target area. After step S107, the marl reservoir identification method may also include but is not limited to steps S201 to S205:

[0084] Step S201, obtaining the coal seam with the largest depth in the candidate well to obtain the candidate coal seam;

[0085] Step S202, determining a candidate data extraction range according to the bottom depth of the candidate coal seam and the bottom depth of the candidate well;

[0086] Step S203, obtaining a candidate density data set and a candidate acoustic wave time difference data set within the candidate data extraction range; wherein each depth in the candidate well corresponds to a candidate density data set and a candidate acoustic wave time difference data set;

[0087] Step S204, screening out candidate density data and candidate acoustic wave time difference data within the data point range from the candidate density data set and the candidate acoustic wave time difference data set to obtain a selected density data set and a selected acoustic wave time difference data set;

[0088] Step S205: identifying the marl reservoir according to the selected density data set and the selected acoustic wave time difference data set.

[0089] In step S201 of some embodiments, when the target well and the candidate well are in the same target area, when the candidate well is identified for the marl reservoir, the data point range obtained based on the target well can be directly used for identification, without drawing a scatter projection map and determining the data point range based on the scatter projection map. Since the marl reservoir in the candidate well is also located below the coal seam, it is also necessary to obtain the coal seam with the largest depth in the candidate well to obtain the candidate coal seam.

[0090] In one example, if the candidate well and the target well are not in the same target area, the marl reservoir of the candidate well is identified in the manner of steps S101 to S107.

[0091] In step S202 of some embodiments, the marl reservoir can only be located between the candidate coal seam and the bottom of the candidate well, and only the logging data between the candidate coal seam and the bottom of the well is needed to identify the marl reservoir. Therefore, the candidate data extraction range is determined according to the bottom depth of the candidate coal seam and the bottom depth of the well. For example, if the bottom depth of the candidate coal seam is X2 meters and the bottom depth of the well is Y2 meters, the candidate data extraction range is (X2, Y2], and the data with a depth within the range of (X1, Y2] is screened out from the logging data.

[0092] In step S203 of some embodiments, candidate density data and candidate acoustic wave time difference data within the candidate data extraction range are obtained from the well logging data of the candidate well to obtain a candidate density data set and a candidate acoustic wave time difference data set. Each depth in the candidate well corresponds to a candidate density data and a candidate acoustic wave time difference data. For example, when the depth is X2+1 meters, the corresponding candidate density data is 2.8t / m 3 , the candidate acoustic wave time difference data is 57μs / m.

[0093] In step S204 of some embodiments, after a screening is performed once through the above steps to screen out the data that may belong to the marl reservoir, a secondary screening is required to screen out the data that belongs to the marl reservoir. Therefore, the candidate density data and candidate acoustic wave time difference data within the data point range in the candidate density data set and the candidate acoustic wave time difference data set are screened out to obtain the selected density data set and the selected acoustic wave time difference data set. That is, the data in the selected density data set and the selected acoustic wave time difference data set are all data belonging to the marl reservoir.

[0094] In step S205 of some embodiments, the depth corresponding to the data in the selected density data set and the selected acoustic wave time difference data set is the marl reservoir. For example, the selected density data is 3.1t / m 3 , the selected acoustic time difference data is 60μs / m and the corresponding depth is M meters, then the reservoir section corresponding to M meters is identified as a marl reservoir. The selected density data is 3.2t / m 3, the selected acoustic wave time difference data is 61μs / m and the corresponding depth is N meters. Then the reservoir section corresponding to N meters is also identified as a marlstone reservoir.

[0095] It should be noted that the range between the minimum depth and the maximum depth corresponding to the data in the selected density data set can be directly identified as a marl reservoir, or the range between the minimum depth and the maximum depth corresponding to the data in the selected acoustic wave time difference data set can be directly identified as a marl reservoir.

[0096] In steps S201 to S205 shown in this embodiment, when the candidate well and the target well are in the same target area, the marl reservoir can be directly identified using the data point range obtained based on the target well, which can further improve the identification efficiency.

[0097] See also Figure 3 In some embodiments, step S106 may include but is not limited to steps S301 to S304:

[0098] Step S301, obtaining the number of clusters; wherein the number of clusters is K;

[0099] Step S302, clustering the data points on the target scatter projection graph according to the K-Means algorithm, and initializing K centroids;

[0100] Step S303, calculating the distance between each data point and each centroid, assigning the data point to the cluster to which the nearest centroid belongs, and recalculating the centroid of each cluster until convergence;

[0101] Step S304, calculating the mean of the centroids of all clusters to obtain the data center point, and calculating the mean of the maximum distances of all clusters to obtain the data distribution radius.

[0102] In step S301 of some embodiments, the appropriate number of clusters K may be determined by the elbow rule, silhouette coefficient or other methods.

[0103] In step S302 of some embodiments, the K-Means algorithm is a widely used clustering algorithm for dividing a data set into K clusters. The data points on the target scatter projection graph are clustered according to the K-Means algorithm, and K initial centroids are selected. These centroids can be randomly selected data points or data points selected by a certain strategy.

[0104] In step S303 of some embodiments, for each data point, the distance between the data point and each centroid is calculated (commonly used as the Euclidean distance), and the data point is assigned to the cluster to which the centroid closest to it belongs. The centroid of each cluster is recalculated, and the steps of assigning clusters and updating centroids are repeated until convergence, that is, the position of the centroid no longer changes significantly, or a preset number of iterations is reached.

[0105] In step S304 of some embodiments, the centroids of all clusters are averaged to obtain the data center point, and the maximum distances of all clusters are averaged to obtain the data distribution radius. The data distribution radius can also be the median or maximum value of the maximum distances of all clusters.

[0106] In steps S301 to S304 shown in this embodiment, a data center point and a data distribution radius of a target scatter projection graph are determined by a K-Means algorithm, so as to determine a data point range based on the data center point and the data distribution radius.

[0107] See also Figure 4 In some embodiments, before step S301, the method for identifying a marl reservoir may include but is not limited to steps S401 to S402:

[0108] Step S401, identifying outliers on the target scatter projection graph by using the interquartile range, and removing the outliers to obtain an updated target scatter projection graph;

[0109] Step S402, performing standardization or normalization processing on the data points on the updated target scatter projection graph.

[0110] In step S401 of some embodiments, before clustering the data points on the target scatter projection map by the K-Means algorithm, it is necessary to identify and remove the outliers on the target scatter projection map to improve the clustering accuracy. Interquartile Range (IQR) is a method in descriptive statistics. The interquartile range refers to the difference between the third quartile (Q3) and the first quartile (Q1), that is, IQR = Q3-Q1. By comparing the relationship between a single data point and the interquartile range, it can be determined whether the data point is an outlier. In general, data points exceeding Q3+1.5IQR or below Q1-1.5IQR can be regarded as outliers. After identifying the outliers, remove the outliers to obtain an updated target scatter projection map.

[0111] In step S402 of some embodiments, since the variables represented by the x-axis and y-axis on the target scatter projection diagram have different scales and ranges, this difference will cause the K-Means algorithm to be biased towards features with larger scales when calculating distances, thereby affecting the clustering results. Standardization or normalization can eliminate the scale differences between features, so that all features have the same importance in the clustering process, and can improve the performance and accuracy of the K-Means algorithm. Therefore, the data points on the updated target scatter projection diagram are standardized or normalized. After standardization or normalization, steps S301 to S304 are executed again.

[0112] In steps S401 to S402 shown in this embodiment, before clustering the data points on the target scatter projection map using the K-Means algorithm, outliers on the target scatter projection map are identified and eliminated, and standardized or normalized processing is performed, thereby improving the accuracy of the clustering results.

[0113] See also Figure 5 In some embodiments, step S107 includes but is not limited to steps S501 to S503:

[0114] Step S501, obtaining data points within a data point range on a target scatter projection graph to obtain a target data point set;

[0115] Step S502, obtaining a first data point with the smallest depth and a second data point with the largest depth in the target data point set;

[0116] Step S503: Identify the depth range corresponding to the first data point and the second data point as a marl reservoir.

[0117] In step S501 of some embodiments, the data points within the data point range on the target scatter projection map are the data points corresponding to the marl reservoir, so the data points within the data point range on the target scatter projection map are obtained to obtain a target data point set, so as to identify the marl reservoir based on the target data point set.

[0118] In some embodiments, in steps S502 to S503, the depths corresponding to all data points in the target data point set are the marl reservoirs. Therefore, the depth range corresponding to the first data point with the smallest depth and the second data point with the largest depth in the target data point set is all the marl reservoirs in the target well.

[0119] In steps S501 to S503 shown in this embodiment, the depth range corresponding to the first data point with the smallest depth and the second data point with the largest depth in the target data point set is identified as a marl reservoir, which can quickly perform identification and improve identification efficiency.

[0120] See also Figure 6 In some embodiments, step S101 may include but is not limited to steps S601 to S603:

[0121] Step S601, obtaining logging data of a target well;

[0122] Step S602, identifying the coal seam information of the target well according to the well logging data; wherein the coal seam information includes the depth of each coal seam;

[0123] Step S603, obtaining the target coal seam according to the coal seam information.

[0124] In step S601 of some embodiments, logging data of the target well is obtained by logging equipment, and the logging data includes natural gamma (GR), resistivity (Rt), acoustic time difference (DT24), density (ZDEN) and other data corresponding to each depth in the target well.

[0125] See also Figure 7 In some embodiments, step S602 may include but is not limited to steps S701 to S702:

[0126] Step S701: input the logging data into a preset reservoir classification model to obtain layer information;

[0127] Step S702: Screen the coal seam information according to the stratigraphic information to obtain the coal seam information.

[0128] In step S701 of some embodiments, a reservoir classification model is pre-trained, and after the well logging data is input into the reservoir classification model, information of all layers is output. The layer information includes each layer and the corresponding depth, and the layers include coal layers, oil layers or gas layers, etc.

[0129] In step S702 of some embodiments, coal seam information is found from the layer information. The coal seam information includes the depth corresponding to each coal seam.

[0130] In step S701 to step S702 shown in this embodiment, the stratigraphic information is obtained through the reservoir classification model, so as to obtain the coal seam information based on the stratigraphic information.

[0131] In step S603 of some embodiments, the coal seams are arranged from shallow to deep according to the coal seam information, and the deepest coal seam is taken as the target coal seam.

[0132] In steps S601 to S603 shown in this embodiment, coal seam information is obtained through a reservoir classification model to determine a target coal seam based on the coal seam information.

[0133] See also Figure 8 The present application also provides a marl reservoir identification device, which can implement the above-mentioned marl reservoir identification method, and the device includes:

[0134] The first acquisition module 801 is used to acquire the coal seam with the largest depth in the target well to obtain the target coal seam;

[0135] The first determination module 802 is used to determine the target data extraction range according to the bottom depth of the target coal seam and the bottom depth of the target well;

[0136] Extraction module 803, used to obtain a target density data set and a target acoustic wave time difference data set within the target data extraction range; wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set;

[0137] A drawing module 804 is used to draw a target scatter point projection diagram according to the target density data set and the target acoustic wave time difference data set;

[0138] The second acquisition module 805 is used to obtain the data center point and data distribution radius on the target scatter projection diagram;

[0139] A second determination module 806 is used to determine a data point range according to a data center point and a data distribution radius;

[0140] The identification module 807 is used to identify the marl reservoir according to the data point range and the target scatter point projection map.

[0141] The specific implementation of the marl reservoir identification device is basically the same as the specific implementation of the above-mentioned marl reservoir identification method, and will not be repeated here.

[0142] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned marl reservoir identification method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0143] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0144] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0145] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the marl reservoir identification method of the embodiment of this application;

[0146] Input / output interface 903, used to implement information input and output;

[0147] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0148] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0149] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0150] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned marl reservoir identification method is implemented.

[0151] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0152] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0153] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0154] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0156] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0157] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0158] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated unit is implemented in the form of 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 the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0162] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for identifying a marl reservoir, characterized in that: The method comprises: Obtain the coal seam with the greatest depth in the target well to obtain the target coal seam; Determining a target data extraction range according to the bottom depth of the target coal seam and the bottom depth of the target well; Acquire a target density data set and a target acoustic wave time difference data set within the target data extraction range; wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set; Draw a target scatter point projection diagram according to the target density data set and the target acoustic wave time difference data set; Obtaining a data center point and a data distribution radius on the target scatter projection graph; Determine a data point range according to the data center point and the data distribution radius; A marl reservoir is identified based on the data point range and the target scatter projection map.

2. The method according to claim 1, characterized in that The target well and the candidate well are in the same target area, and after the marl reservoir is identified according to the data point range and the scatter projection map, the method further includes: Obtaining the coal seam with the greatest depth in the candidate well to obtain a candidate coal seam; Determining a candidate data extraction range according to the bottom depth of the candidate coal seam and the bottom depth of the candidate well; Acquire a candidate density data set and a candidate acoustic wave time difference data set within the candidate data extraction range; wherein each depth in the candidate well corresponds to a candidate density data set and a candidate acoustic wave time difference data set; Filtering out candidate density data and candidate acoustic wave time difference data within the data point range from the candidate density data set and the candidate acoustic wave time difference data set to obtain a selected density data set and a selected acoustic wave time difference data set; The marl reservoir is identified based on the selected density data set and the selected sonic transit time data set.

3. The method according to claim 1, characterized in that The obtaining of the data center point and the data distribution radius on the target scatter projection graph includes: Obtaining the number of clusters; wherein the number of clusters is K; Clustering the data points on the target scatter projection graph according to the K-Means algorithm, initializing K centroids; Calculate the distance between each data point and each of the centroids, assign the data point to the cluster to which the nearest centroid belongs, and recalculate the centroid of each cluster until convergence; The centroids of all clusters are averaged to obtain the data center point, and the maximum distances of all clusters are averaged to obtain the data distribution radius.

4. The method according to claim 3, characterized in that Before obtaining the number of clusters, the method further includes: Identify outliers on the target scatter projection graph by using the interquartile range, and remove the outliers to obtain an updated target scatter projection graph; The updated data points on the target scatter projection graph are standardized or normalized.

5. The method according to claim 1, characterized in that: The step of identifying a marl reservoir according to the data point range and the target scatter projection map comprises: Acquire data points within the data point range on the target scatter projection graph to obtain a target data point set; Acquire a first data point with the smallest depth and a second data point with the largest depth in the target data point set; The depth range corresponding to the first data point and the second data point is identified as the marl reservoir.

6. The method according to claim 1, characterized in that The step of obtaining the coal seam with the greatest depth in the target well to obtain the target coal seam includes: Acquiring logging data of the target well; Identifying the coal seam information of the target well according to the well logging data; wherein the coal seam information includes the depth of each coal seam; The target coal seam is obtained according to the coal seam information.

7. The method according to claim 6, characterized in that The step of identifying the coal seam information of the target well according to the well logging data includes: Inputting the logging data into a preset reservoir classification model to obtain layer information; Coal seam information is screened according to the stratigraphic information to obtain the coal seam information.

8. A marl reservoir identification device, characterized in that: The device comprises: The first acquisition module is used to acquire the coal seam with the largest depth in the target well to obtain the target coal seam; A first determination module is used to determine a target data extraction range according to the bottom depth of the target coal seam and the bottom depth of the target well; An extraction module, used to obtain a target density data set and a target acoustic wave time difference data set within the target data extraction range; wherein each depth in the target well corresponds to a target density data set and a target acoustic wave time difference data set; A drawing module, used for drawing a target scatter point projection diagram according to the target density data set and the target acoustic wave time difference data set; A second acquisition module is used to obtain a data center point and a data distribution radius on the target scatter projection graph; A second determination module, configured to determine a data point range according to the data center point and the data distribution radius; An identification module is used to identify the marl reservoir according to the data point range and the target scatter projection map.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the marl reservoir identification method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying a marl reservoir according to any one of claims 1 to 7 is implemented.

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