Isometric well logging data processing method and device

Abstract

The application discloses a heterogeneous well logging data processing method and device, and the method comprises the following steps: collecting well logging data of a target well hole; determining data screening conditions of different well logging data types according to depth index sources corresponding to the different well logging data types; the data screening conditions comprise a reference depth and a curve value mode; determining candidate well logging data according to the data screening conditions of the different well logging data types comprising the reference depth and the curve value mode; and determining target well logging data according to the candidate well logging data. By determining the data screening conditions of the different well logging data types, such as the reference depth and the curve value mode, and then determining the target well logging data from the candidate well logging data determined based on the data screening conditions, the efficiency and accuracy of well logging data screening can be improved.

Description

Technical Field

[0001] This invention relates to the field of oil well logging technology, and in particular to a method and apparatus for processing heterogeneous well logging data. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Due to the wide variety of logging instruments, the types of data recorded in logging data are also diverse. Based on storage methods, all logging data and related parameters can be categorized into two types: structured data and unstructured data. Unstructured data mainly includes plotting templates, parameter card files, interpretation reports, etc. This data is generally generated during logging data processing and is processed by specialized software; it only needs to be saved in its raw form. Structured data is a collection of indices and values. The index is typically depth, and the values ​​may be single or multiple. Based on organization, it is generally divided into two main categories: curves and tables. Curves are further divided into discrete curves and continuous curves. Continuous curves are used to store various continuous curves from conventional logging and imaging logging, including one-dimensional, two-dimensional, and three-dimensional curves. Discrete curves cover the storage of various types of data from continuous, segmented, and fixed-point measurements in production logging. Tables are used to store all data with two-dimensional characteristics, such as interpretation conclusions, stratification, and logging profiles.

[0004] In the process of well logging processing and interpretation, it is often necessary to set specific filtering conditions to define a baseline depth index, so as to uniformly organize and manage relevant curve and table data. Therefore, it is of great significance to know how to conveniently construct a baseline depth to filter curve and table data.

[0005] Some logging software provides export functions for individual data such as curves and tables, allowing users to filter specific data by setting conditions in an Excel spreadsheet. However, this method is time-consuming, labor-intensive, and inefficient. Other software offers data filtering based on index depth defined by table segments: custom depth segments or depth segments obtained from a table. This method cannot add control conditions to the segments, resulting in data that does not meet expectations and leading to poor logging data processing. Summary of the Invention

[0006] This invention provides a method for processing heterogeneous logging data to improve the efficiency and accuracy of logging data screening. The method includes:

[0007] Collect logging data from the target wellbore;

[0008] Based on the depth index source corresponding to different well logging data types, determine the data filtering conditions for different well logging data types; the data filtering conditions include the benchmark depth and the curve value selection method;

[0009] The selection of logging data is determined based on data filtering criteria that include the baseline depth and curve value retrieval method for different types of logging data.

[0010] The target logging data is determined based on the logging data to be selected.

[0011] This invention also provides a heterogeneous logging data processing device to improve the efficiency and accuracy of logging data screening. The heterogeneous logging data processing device includes:

[0012] The well logging data acquisition module is used to acquire logging data from the target well.

[0013] The data filtering condition determination module is used to determine the data filtering conditions for different well logging data types based on the depth index source corresponding to different well logging data types; the data filtering conditions include the benchmark depth and the curve value selection method;

[0014] The candidate logging data determination module is used to determine candidate logging data based on data filtering conditions including benchmark depth and curve value retrieval method for different logging data types.

[0015] The target logging data determination module is used to determine the target logging data based on the candidate logging data.

[0016] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described heterogeneous logging data processing method.

[0017] This invention also provides a computer-readable storage medium storing a computer program that performs the above-described heterogeneous logging data processing method.

[0018] In this embodiment of the invention, logging data of the target wellbore is collected; based on the depth index source corresponding to different logging data types, data filtering conditions for different logging data types are determined; the data filtering conditions include a reference depth and curve value selection method; candidate logging data is determined based on the data filtering conditions including reference depth and curve value selection method for different logging data types; and target logging data is determined based on the candidate logging data. This embodiment of the invention improves the efficiency and accuracy of logging data filtering by determining data filtering conditions for different logging data types, such as reference depth and curve value selection method, and then determining target logging data from the candidate logging data determined based on the data filtering conditions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0020] Figure 1 The flowchart illustrates the implementation of the heterogeneous logging data processing method provided in the first embodiment of the present invention.

[0021] Figure 2 This is a flowchart illustrating the implementation of step 102 in the heterogeneous logging data processing method provided in the second embodiment of the present invention.

[0022] Figure 3 This is a flowchart illustrating the implementation of step 102 in the heterogeneous logging data processing method provided in the third embodiment of the present invention.

[0023] Figure 4 This is a flowchart illustrating the implementation of step 102 in the heterogeneous logging data processing method provided in the fourth embodiment of the present invention.

[0024] Figure 5 This is a flowchart illustrating the implementation of step 102 in the heterogeneous logging data processing method provided in the fifth embodiment of the present invention.

[0025] Figure 6 This is a flowchart illustrating the implementation of step 102 in the heterogeneous logging data processing method provided in the sixth embodiment of the present invention.

[0026] Figure 7 This is a functional block diagram of the heterogeneous logging data processing device provided in the seventh embodiment of the present invention;

[0027] Figure 8 This is a structural block diagram of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the eighth embodiment of the present invention;

[0028] Figure 9 This is a structural block diagram of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the ninth embodiment of the present invention;

[0029] Figure 10 This is a structural block diagram of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the tenth embodiment of the present invention;

[0030] Figure 11 This is a structural block diagram of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the eleventh embodiment of the present invention;

[0031] Figure 12 This is a structural block diagram of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the twelfth embodiment of the present invention;

[0032] Figure 13 This is a schematic diagram of the reference depth for screening discrete curves of a certain wellbore provided in the thirteenth embodiment of the present invention;

[0033] Figure 14 This is a schematic diagram of the reference depth for screening a general table for a certain wellbore, provided in the fourteenth embodiment of the present invention;

[0034] Figure 15 A schematic diagram illustrating the reference depth for selecting a custom layer segment in a wellbore, provided in the fifteenth embodiment of the present invention;

[0035] Figure 16 This is a schematic diagram illustrating the filtering conditions for a specific value in a depth column of a wellbore, provided in the sixteenth embodiment of the present invention.

[0036] Figure 17 This is a schematic diagram of a borehole curve value retrieval method provided in the seventeenth embodiment of the present invention;

[0037] Figure 18 This is a schematic representation of the target data of a wellbore that is finally generated according to the eighteenth embodiment of the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0039] Figure 1 The implementation flow of the heterogeneous logging data processing method provided in the first embodiment of the present invention is shown. For ease of description, only the parts related to the embodiment of the present invention are shown, and are detailed below:

[0040] like Figure 1 As shown, the heterogeneous logging data processing method includes:

[0041] Step 101: Collect logging data for the target wellbore;

[0042] Step 102: Determine the data filtering conditions for different well logging data types based on the depth index source corresponding to different well logging data types; the data filtering conditions include the benchmark depth and the curve value selection method.

[0043] Step 103: Determine the candidate logging data based on the data filtering conditions of different logging data types, including the reference depth and curve value selection method;

[0044] Step 104: Determine the target logging data based on the selected logging data.

[0045] When processing heterogeneous logging data, the (full) logging data of the target wellbore is first acquired. In a preferred embodiment, the logging data of the target wellbore includes one or more of the following logging data: discrete logging curves, general logging tables, and one-dimensional logging curves.

[0046] The characteristics of discrete logging curves are two-dimensional table data with discontinuous depth, and the first column is the depth column by default; the characteristics of general logging tables are that there are several different sets of values, each column represents a different parameter, and each row represents a set of parameter values; the characteristics of one-dimensional logging curves are that each depth index point corresponds to a value output, and the depth is continuous.

[0047] Different logging data types correspond to different depth index sources. Discrete logging curves, general logging tables, and one-dimensional logging curves represent three different logging data types. There is a one-to-one correspondence between logging data types and depth index sources. The depth index source is determined based on the different logging data types; for example, based on the received depth index source instruction, the depth index source corresponding to the logging data type is determined. Furthermore, data filtering conditions for different logging data types are determined based on the depth index source. These data filtering conditions for different logging data types include at least the baseline depth and curve value retrieval method for each logging data type.

[0048] After determining the data filtering criteria for different logging data types, the candidate logging data is determined based on these criteria. This candidate data range may include some unwanted logging data and some necessary logging data. Finally, the required raw logging data is selected as the target logging data from the candidate data. For example, the target logging data is determined from the candidate data based on a received target logging data selection instruction. This target logging data may include one or more of the following: target logging discrete curves, target logging general tables, and target logging one-dimensional curves. For example, a target data table is generated from the target logging data and saved as a local file or clipboard.

[0049] In this embodiment of the invention, logging data of the target wellbore is collected; based on the depth index source corresponding to different logging data types, data filtering conditions for different logging data types are determined; the data filtering conditions include a reference depth and curve value selection method; candidate logging data is determined based on the data filtering conditions including reference depth and curve value selection method for different logging data types; and target logging data is determined based on the candidate logging data. This embodiment of the invention improves the efficiency and accuracy of logging data filtering by determining data filtering conditions for different logging data types, such as reference depth and curve value selection method, and then determining target logging data from the candidate logging data determined based on the data filtering conditions.

[0050] Figure 2 The implementation flow of step 102 in the heterogeneous logging data processing method provided in the second embodiment of the present invention is shown. For ease of description, only the parts related to the embodiment of the present invention are shown, and are detailed below:

[0051] In one embodiment of the present invention, in order to improve the accuracy of determining the screening conditions for well logging discrete curve data, such as... Figure 2 As shown, step 102 involves determining the data filtering conditions for different well logging data types based on the depth index source corresponding to those data types, including:

[0052] Step 201: When the depth index source is a well logging discrete curve, the first column of the well logging discrete curve is used as the reference depth of the well logging discrete curve.

[0053] Step 202: When taking values ​​at a single depth point, determine whether the curve taking method of the well logging discrete curve is nearest neighbor point or linear interpolation;

[0054] Step 203: When taking values ​​within a layer, determine the curve taking method for the well logging discrete curve as the average value of all curves within the layer or the curve sampling interval within the layer.

[0055] When the depth index source is a well logging discrete curve, the data filtering conditions for the well logging discrete curve are determined. In this case, when determining the reference depth in the well logging discrete curve data filtering conditions, the first column of the well logging discrete curve is used as the reference depth. When determining the curve value selection method in the well logging discrete curve data filtering conditions, it is necessary to distinguish between single-depth-point values ​​and values ​​within a layer segment.

[0056] When taking values ​​at a single depth point, the method for determining the discrete logging curve can be either from nearby points or through linear interpolation. Nearby points refer to measurement depths where there is no corresponding curve; in this case, the measurement depths of the curve are traversed to find the measurement depth point most adjacent to the current depth. Linear interpolation, on the other hand, involves finding the two most adjacent measured depths above and below the current depth to determine the curve value using a linear interpolation algorithm.

[0057] When taking values ​​within a layer, the method for determining the discrete logging curve can be either the average value of all curves within the layer or the sampling interval of curves within the layer. The sampling interval refers to the time interval between two samples taken by the logging instrument.

[0058] In this embodiment of the invention, when the depth index source is a well logging discrete curve, the first column of the well logging discrete curve is used as the reference depth of the well logging discrete curve; when taking values ​​at a single depth point, the curve value method of the well logging discrete curve is determined from nearby points or linear interpolation; when taking values ​​within a layer, the curve value method of the well logging discrete curve is determined from the average value of all curves within the layer or the sampling interval of curves within the layer, which can improve the accuracy of determining the screening conditions for well logging discrete curve data.

[0059] Figure 3 The implementation flow of step 102 in the heterogeneous logging data processing method provided in the third embodiment of the present invention is shown. For ease of description, only the parts related to the embodiment of the present invention are shown, and are detailed below:

[0060] In one embodiment of the present invention, in order to improve the accuracy of determining the data filtering conditions for the general logging table when taking single depth point values, such as... Figure 3 As shown, step 102 involves determining the data filtering conditions for different well logging data types based on the depth index source corresponding to those data types, including:

[0061] Step 301: Determine the number columns of the general logging table based on the header attributes of the general logging table;

[0062] Step 302: When taking values ​​at a single depth point, use one column of the numerical column in the general logging table as the reference depth of the general logging table;

[0063] Step 303: When taking values ​​at a single depth point, determine whether the curve value taking method of the general logging table is nearest point or linear interpolation.

[0064] Obtain the table data object using the table name of the general logging table, find all numeric columns through the data type attribute defined in the table header template of the table data object, and use these numeric columns as the depth column options of the general logging table.

[0065] When taking values ​​at a single depth point, only one column needs to be selected from the numerical columns of the general logging table as the reference depth for the general logging table; at the same time, when taking values ​​at a single depth point, the curve value taking method of the general logging table is determined from either the nearest point or the linear interpolation method.

[0066] In this embodiment of the invention, the numerical columns of the general logging table are determined according to the header attributes of the general logging table; when taking values ​​at a single depth point, one column of the numerical columns of the general logging table is used as the reference depth of the general logging table; when taking values ​​at a single depth point, the curve taking method of the general logging table is determined to be nearest point or linear interpolation, which can improve the accuracy of determining the data screening conditions of the general logging table when taking values ​​at a single depth point.

[0067] Figure 4 The implementation flow of step 102 in the heterogeneous logging data processing method provided in the fourth embodiment of the present invention is shown. For ease of description, only the parts related to the embodiments of the present invention are shown, and are detailed below:

[0068] In one embodiment of the present invention, in order to improve the accuracy of determining the screening conditions for general logging table data when taking values ​​within a layer, such as... Figure 4 As shown, step 102 involves determining the data filtering conditions for different well logging data types based on the depth index source corresponding to those data types, including:

[0069] Step 301: Determine the number columns of the general logging table based on the header attributes of the general logging table;

[0070] Step 401: When taking values ​​within a layer, use two columns from the numerical column of the general logging table as the start and end depths in the reference depth column of the general logging table, respectively.

[0071] Step 402: When taking values ​​within a layer, determine the curve value taking method of the general logging table as the average value of all curves within the layer or the curve sampling interval within the layer.

[0072] Obtain the table data object using the table name of the general logging table, find all numeric columns through the data type attribute defined in the table header template of the table data object, and use these numeric columns as the depth column options of the general logging table.

[0073] When taking values ​​within a layer, select two columns from the numerical columns of the general logging table, which will serve as the start and end depths in the reference depth column of the general logging table, respectively; the depth between the start and end depths is the reference depth. Simultaneously, when taking values ​​within a layer, determine the curve value selection method for the general logging table from either the average value of all curves within the layer or the curve sampling interval within the layer.

[0074] In this embodiment of the invention, the numerical columns of the general logging table are determined according to the header attributes of the general logging table; when taking values ​​within a layer, two columns in the numerical columns of the general logging table are respectively used as the start depth and end depth in the reference depth column of the general logging table; when taking values ​​within a layer, the curve value taking method of the general logging table is determined from the average value of all curves within the layer or the curve sampling interval within the layer, which can improve the accuracy of determining the data screening conditions of the general logging table when taking values ​​within a layer.

[0075] Figure 5 The implementation flow of step 102 in the heterogeneous logging data processing method provided in the fifth embodiment of the present invention is shown. For ease of description, only the parts related to the embodiments of the present invention are shown, and are detailed below:

[0076] In one embodiment of the present invention, when the logging data is a general logging table, the data filtering conditions also include specific value filtering conditions. To further improve the accuracy and flexibility of determining the data filtering conditions for the general logging table, such as... Figure 5 As shown, step 102, determining the data filtering conditions for different well logging data types based on the depth index source corresponding to different well logging data types, also includes:

[0077] Step 501: Determine the specific value filtering conditions for the target depth determined based on the reference depth in the general logging table;

[0078] Step 502: Determine the candidate general logging tables based on the data filtering conditions of the general logging tables, including the reference depth, curve value selection method, and specific value filtering conditions.

[0079] For the baseline depth in a general well logging table, a specific value for the target depth (column) within the baseline depth can be set as a filter condition for the target depth. That is, the target depth is determined based on the baseline depth; for example, a portion of the baseline depth (column) is selected as the target depth (column), and then a specific value filter condition for that portion of the target depth in the general well logging table is determined. Then, the general well logging tables to be selected are determined through data filtering conditions such as including the baseline depth, curve value selection method, and specific value filter conditions. The specific value filter conditions include at least three categories: including, equal to, and not equal to.

[0080] In this embodiment of the invention, specific value filtering conditions for the target depth determined based on the reference depth are determined in the general logging table; the candidate general logging table is determined according to the data screening conditions of the general logging table, which include the reference depth, curve value selection method and specific value filtering conditions, which can further improve the accuracy and flexibility of determining the data screening conditions of the general logging table.

[0081] Figure 6The implementation flow of step 102 in the heterogeneous logging data processing method provided in the sixth embodiment of the present invention is shown. For ease of description, only the parts related to the embodiment of the present invention are shown, and are detailed below:

[0082] In one embodiment of the present invention, in order to improve the accuracy of determining the screening conditions for one-dimensional logging curve data, such as... Figure 6 As shown, step 102 involves determining the data filtering conditions for different well logging data types based on the depth index source corresponding to those data types, including:

[0083] Step 601: Determine the reference depth of the one-dimensional logging curve using a custom layer list;

[0084] Step 602: Determine whether the curve value of the one-dimensional logging curve is taken as a nearest neighbor or linear interpolation.

[0085] When determining the baseline depth in the screening criteria for one-dimensional logging curve data, a custom segment list is used to determine the baseline depth of the one-dimensional logging curve. The custom segment list supports three definition methods: 1) entering segments in a segment table; 2) selecting segment data from a general logging table; and 3) copying segment data from an Excel spreadsheet or text file.

[0086] When determining the curve value selection method in the screening conditions for one-dimensional logging curve data, the curve value selection method for the one-dimensional logging curve is determined from two methods: nearest neighbor point or linear interpolation.

[0087] In this embodiment of the invention, the reference depth of the well logging one-dimensional curve is determined by a custom layer list; the curve value selection method of the well logging one-dimensional curve is determined to be nearest neighbor or linear interpolation, which can improve the accuracy of determining the data screening conditions for the well logging one-dimensional curve.

[0088] This invention also provides a heterogeneous logging data processing device, as described in the following embodiments. Since the principles underlying these devices are similar to those of the heterogeneous logging data processing method, the implementation of these devices can be referred to the implementation of the method, and repeated details will not be elaborated further.

[0089] Figure 7 The functional modules of the heterogeneous logging data processing device provided in the seventh embodiment of the present invention are shown. For ease of explanation, only the parts related to the embodiments of the present invention are shown, and are described in detail below:

[0090] refer to Figure 7 The heterogeneous logging data processing device includes various modules for performing... Figure 1 For details of each step in the corresponding embodiment, please refer to [link / reference]. Figure 1 as well as Figure 1The relevant descriptions in the corresponding embodiments will not be repeated here. In this embodiment of the invention, the heterogeneous logging data processing device includes a wellbore logging data acquisition module 701, a data filtering condition determination module 702, a candidate logging data determination module 703, and a target logging data determination module 704.

[0091] The well logging data acquisition module 701 is used to acquire logging data of the target well.

[0092] The data filtering condition determination module 702 is used to determine the data filtering conditions for different well logging data types based on the depth index source corresponding to different well logging data types; the data filtering conditions include the benchmark depth and the curve value selection method.

[0093] The candidate logging data determination module 703 is used to determine candidate logging data based on data filtering conditions including benchmark depth and curve value selection method for different logging data types.

[0094] The target logging data determination module 704 is used to determine the target logging data based on the candidate logging data.

[0095] In a preferred embodiment, the logging data of the target wellbore includes one or more of the following logging data: logging discrete curves, logging general tables, and logging one-dimensional curves.

[0096] In this embodiment of the invention, the well logging data acquisition module 701 acquires logging data from the target well; the data filtering condition determination module 702 determines data filtering conditions for different logging data types based on the depth index source corresponding to different logging data types; the data filtering conditions include a reference depth and curve value selection method; the candidate logging data determination module 703 determines candidate logging data based on the data filtering conditions including reference depth and curve value selection method for different logging data types; and the target logging data determination module 704 determines the target logging data based on the candidate logging data. By determining data filtering conditions for different logging data types, such as reference depth and curve value selection method, the data filtering condition determination module 702 of this embodiment of the invention enables the target logging data determination module 704 to determine the target logging data from the candidate logging data determined based on the data filtering conditions, thereby improving the efficiency and accuracy of logging data filtering.

[0097] Figure 8 The diagram shows the structure of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the eighth embodiment of the present invention. For ease of explanation, only the parts related to the embodiments of the present invention are shown, and are described in detail below:

[0098] In one embodiment of the present invention, in order to improve the accuracy of determining the screening conditions for well logging discrete curve data, reference is made to... Figure 8The data filtering condition determination module 702 includes various units for performing... Figure 2 For details of each step in the corresponding embodiment, please refer to [link / reference]. Figure 2 as well as Figure 2 The relevant descriptions in the corresponding embodiments will not be repeated here. In this embodiment of the invention, the data filtering condition determination module 702 includes a discrete curve baseline depth determination unit 801, a discrete curve first curve value acquisition unit 802, and a discrete curve second curve value acquisition unit 803.

[0099] The discrete curve reference depth determination unit 801 is used to take the first column of the well logging discrete curve as the reference depth of the well logging discrete curve when the depth index source is a well logging discrete curve.

[0100] The discrete curve first curve value unit 802 is used to determine whether the curve value method of the well logging discrete curve is nearest point or linear interpolation when taking values ​​at a single depth point.

[0101] The discrete curve second curve value unit 803 is used to determine the curve value method of the well logging discrete curve when taking values ​​within a layer segment, which is either the average value of all curves within the layer segment or the curve sampling interval within the layer segment.

[0102] In this embodiment of the invention, when the depth index source is a well logging discrete curve, the discrete curve reference depth determination unit 801 uses the first column of the well logging discrete curve as the reference depth of the well logging discrete curve; the discrete curve first curve value taking unit 802 determines the curve value taking method of the well logging discrete curve from nearby points or linear interpolation when taking values ​​at a single depth point; the discrete curve second curve value taking unit 803 determines the curve value taking method of the well logging discrete curve from the average value of all curves in the layer or the curve sampling interval in the layer when taking values ​​within the layer, which can improve the accuracy of determining the well logging discrete curve data screening conditions.

[0103] Figure 9 The diagram shows the structure of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the ninth embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0104] In one embodiment of the present invention, in order to improve the accuracy of determining the data filtering conditions of the general logging table when taking single depth point values, reference is made to... Figure 9 The data filtering condition determination module 702 includes various units for performing... Figure 3 For details of each step in the corresponding embodiment, please refer to [link / reference]. Figure 3 as well as Figure 3The relevant descriptions in the corresponding embodiments will not be repeated here. In this embodiment of the invention, the data filtering condition determination module 702 includes a number column determination unit 901, a general table first curve value taking unit 902, and a general table second curve value taking unit 903.

[0105] The number column determination unit 901 is used to determine the number columns of the general logging table based on the header attributes of the general logging table.

[0106] The first curve value unit 902 of the general table is used to take a column of the numerical column of the general logging table as the reference depth of the general logging table when taking values ​​at a single depth point.

[0107] The second curve value unit 903 of the general table is used to determine whether the curve value method of the general logging table is proximity point or linear interpolation when taking values ​​at a single depth point.

[0108] In this embodiment of the invention, the number column determination unit 901 determines the number column of the general logging table according to the header attributes of the general logging table; the general table first curve value taking unit 902 takes one column of the number column of the general logging table as the reference depth of the general logging table when taking values ​​at a single depth point; the general table second curve value taking unit 903 determines the curve value taking method of the general logging table as nearest point or linear interpolation when taking values ​​at a single depth point, which can improve the accuracy of determining the data screening conditions of the general logging table when taking values ​​at a single depth point.

[0109] Figure 10 The diagram shows the structure of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the tenth embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0110] In one embodiment of the present invention, in order to improve the accuracy of determining the data screening conditions of the general logging table when taking values ​​within a layer, reference is made to... Figure 10 The data filtering condition determination module 702 includes various units for performing... Figure 4 For details of each step in the corresponding embodiment, please refer to [link / reference]. Figure 4 as well as Figure 4 The relevant descriptions in the corresponding embodiments will not be repeated here. In this embodiment of the invention, the data filtering condition determination module 702 includes a number column determination unit 901, a general table third curve value taking unit 1001, and a general table fourth curve value taking unit 1002.

[0111] The number column determination unit 901 is used to determine the number columns of the general logging table based on the header attributes of the general logging table.

[0112] The third curve value unit 1001 of the general table is used to take two columns in the numerical column of the general logging table as the start depth and end depth in the reference depth column of the general logging table when taking values ​​within the layer.

[0113] The fourth curve value unit 1002 of the general table is used to determine the curve value method of the general logging table when taking values ​​within a layer, which is either the average value of all curves within the layer or the curve sampling interval within the layer.

[0114] In this embodiment of the invention, the number column determination unit 901 determines the number column of the general logging table according to the header attributes of the general logging table; when the general table third curve value taking unit 1001 takes values ​​within the layer, it uses two columns from the number column of the general logging table as the start depth and end depth in the reference depth column of the general logging table, respectively; when the general table fourth curve value taking unit 1002 takes values ​​within the layer, it determines the curve value taking method of the general logging table from the average value of all curves within the layer or the curve sampling interval within the layer, which can improve the accuracy of determining the data screening conditions of the general logging table when taking values ​​within the layer.

[0115] Figure 11 The diagram illustrates the structure of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the eleventh embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0116] In one embodiment of the present invention, when the logging data is a general logging table, the data filtering conditions also include specific value filtering conditions. To further improve the accuracy and flexibility of determining the data filtering conditions for the general logging table, refer to... Figure 11 The data filtering condition determination module 702 includes various units for performing... Figure 5 For details of each step in the corresponding embodiment, please refer to [link / reference]. Figure 5 as well as Figure 5 The relevant descriptions in the corresponding embodiments will not be repeated here. In this embodiment of the invention, the data filtering condition determination module 702 includes a specific value filtering condition determination unit 1101 and a candidate well logging general table determination unit 1102.

[0117] The specific value filtering condition determination unit 1101 is used to determine the specific value filtering condition of the target depth determined based on the reference depth in the general logging table.

[0118] The candidate well logging general form determination unit 1102 is used to determine the candidate well logging general form based on the data screening conditions of the well logging general form, including the reference depth, curve value selection method and specific value filtering conditions.

[0119] In this embodiment of the invention, the specific value filtering condition determination unit 1101 determines the specific value filtering condition of the target depth determined based on the reference depth in the general logging table; the candidate general logging table determination unit 1102 determines the candidate general logging table according to the data screening conditions of the general logging table including the reference depth, curve value method and specific value filtering condition, which can further improve the accuracy and flexibility of determining the data screening conditions of the general logging table.

[0120] Figure 12 The diagram illustrates the structure of the data filtering condition determination module 702 in the heterogeneous logging data processing device provided in the twelfth embodiment of the present invention. For ease of explanation, only the parts related to the embodiments of the present invention are shown, and are described in detail below:

[0121] In one embodiment of the present invention, in order to improve the accuracy of determining the screening conditions for one-dimensional logging curve data, reference is made to... Figure 12 The data filtering condition determination module 702 includes various units for performing... Figure 6 For details of each step in the corresponding embodiment, please refer to [link / reference]. Figure 6 as well as Figure 6 The relevant descriptions in the corresponding embodiments will not be repeated here. In this embodiment of the invention, the data filtering condition determination module 702 includes a one-dimensional curve reference depth determination unit 1201 and a one-dimensional curve value determination unit 1202.

[0122] The one-dimensional curve reference depth determination unit 1201 is used to determine the reference depth of the well logging one-dimensional curve through a custom layer list.

[0123] The one-dimensional curve value determination unit 1202 is used to determine whether the curve value determination method of the one-dimensional logging curve is nearest point or linear interpolation.

[0124] In this embodiment of the invention, the one-dimensional curve reference depth determination unit 1201 determines the reference depth of the well logging one-dimensional curve through a custom layer list; the one-dimensional curve value determination unit 1202 determines that the curve value determination method of the well logging one-dimensional curve is nearest neighbor or linear interpolation, which can improve the accuracy of determining the well logging one-dimensional curve data screening conditions.

[0125] The basic principles and process of this invention are briefly explained below, using the logging data processing of a specific wellbore as an example:

[0126] Step 1: Collect logging data from the target wellbore. Taking a specific well as an example, iterate through and find all discrete curves, general tables, and one-dimensional curves in the well.

[0127] Step 2: Filter the baseline depth column.

[0128] Select the depth index source (in this example, select the general logging table: interpretation conclusion, using the intra-layer value retrieval method). This includes three logging data types:

[0129] ① Discrete curve, the first column is the reference depth column by default. Figure 13 This illustration shows a reference depth for screening discrete curves of a wellbore, provided in the thirteenth embodiment of the present invention.

[0130] ② General Table: Retrieve the table data object from a well using the table name. Locate all numeric columns using the data type attribute defined in the table object's header template, and use these columns as depth column options. When using a single depth point, only one column needs to be selected as the baseline depth column; when using a segment-based depth point, two columns must be selected as the baseline depth columns. Figure 14 The diagram illustrates the reference depth for selecting a general table for a wellbore, as provided in the fourteenth embodiment of the present invention.

[0131] ③ Customize the segment list, supporting three definition methods: one is to enter the segment in the segment table, the other is to select the segment data from the general table, and the third is to copy the segment data from the Excel sheet or text file. Figure 15 This illustration shows a reference depth for selecting a custom layer segment in a wellbore, as provided in the fifteenth embodiment of the present invention.

[0132] Step 3: Set specific value filtering conditions for the target depth column

[0133] For depth lists in a general table, specific values ​​in the table columns can be set as additional control conditions for the target depth. Figure 16 This illustration shows a specific value filtering condition for a wellbore depth column provided in the sixteenth embodiment of the present invention. Figure 16 As can be seen, the filtering condition in this example is: the value in the fourth column of the interpretation conclusion is equal to the oil layer.

[0134] Step 4: Set the curve value selection method

[0135] When using single-depth-point sampling, curve data can be obtained by selecting nearby points or linear interpolation; when using intra-segment sampling, two methods can be selected: the average value of all curve data within the segment or sampling data according to the curve sampling interval. Figure 17 This illustration shows a method for determining the value of a wellbore curve according to the seventeenth embodiment of the present invention. Figure 17 As can be seen, the curve values ​​in this example are average values.

[0136] Step 5: Select the curve or table column data to be processed to generate the target data table.

[0137] from Figure 17 Select the one-dimensional curve and table column data to be processed from the selected well logging data. Figure 17(The logging data checked in the small box on the right) is used to filter the baseline depth list according to the multiple value control conditions set above. The curve or table column data is extracted from a well in sequence using a specific depth value, and finally the target data table is generated. Figure 18 This illustration shows the target data representation of a wellbore ultimately generated according to the eighteenth embodiment of the present invention. Figure 18 As can be seen, this example saves the target data table as a local file, for example, named regular_data.csv.

[0138] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described heterogeneous logging data processing method.

[0139] This invention also provides a computer-readable storage medium storing a computer program that performs the above-described heterogeneous logging data processing method.

[0140] The heterogeneous logging data processing method proposed in this invention can be easily implemented in logging software. It uses multiple filtering conditions to determine the baseline depth, and then accurately filters out different types of data such as tables and curves in the logging. The implementation method is flexible, convenient, efficient and accurate. Moreover, it is the first time that interactive heterogeneous data can be uniformly organized in logging software.

[0141] In summary, this embodiment of the invention involves: acquiring logging data from the target wellbore; determining data filtering conditions for different logging data types based on the depth index source corresponding to different logging data types; the data filtering conditions including baseline depth and curve value selection method; determining candidate logging data based on the data filtering conditions including baseline depth and curve value selection method for different logging data types; and determining target logging data based on the candidate logging data. This embodiment of the invention improves the efficiency and accuracy of logging data filtering by determining data filtering conditions for different logging data types, such as baseline depth and curve value selection method, and then determining target logging data from the candidate logging data determined based on the data filtering conditions.

[0142] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0144] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0146] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of processing heterogeneous well log data, the method comprising: The method comprises the following steps: collecting well logging data of a target wellbore; determining data screening conditions of different well logging data types according to depth index sources corresponding to the different well logging data types; the data screening conditions comprise a reference depth and a curve value mode; determining candidate well logging data according to the data screening conditions of the different well logging data types comprising the reference depth and the curve value mode; determining target well logging data according to the candidate well logging data; wherein, the determining of the data screening conditions of the different well logging data types according to the depth index sources corresponding to the different well logging data types comprises: if the well logging data of the target wellbore is well logging discrete curves, then: taking the first column of the well logging discrete curves as the reference depth of the well logging discrete curves; when a single depth point is taken as a value, determining that the curve value mode of the well logging discrete curves is a nearby point or linear interpolation; when a layer section is taken as a value, determining that the curve value mode of the well logging discrete curves is an average value of all curves in the layer section or a curve sampling interval in the layer section; if the well logging data of the target wellbore is well logging general tables, then: determining a digital column of the well logging general tables according to a table header attribute of the well logging general tables; when a single depth point is taken as a value, taking one column in the digital column of the well logging general tables as the reference depth of the well logging general tables; when a single depth point is taken as a value, determining that the curve value mode of the well logging general tables is a nearby point or linear interpolation; when a layer section is taken as a value, taking two columns in the digital column of the well logging general tables as a start depth and an end depth in the reference depth column of the well logging general tables respectively; when a layer section is taken as a value, determining that the curve value mode of the well logging general tables is an average value of all curves in the layer section or a curve sampling interval in the layer section; if the well logging data of the target wellbore is well logging one-dimensional curves, then: determining the reference depth of the well logging one-dimensional curves through a self-defined layer section list; determining that the curve value mode of the well logging one-dimensional curves is a nearby point or linear interpolation.

2. The heterogeneous well log data processing method of claim 1, wherein, When the well logging data is well logging general tables, the data screening conditions further comprise a specific value filtering condition, and the determining of the data screening conditions of the different well logging data types according to the depth index sources corresponding to the different well logging data types further comprises: determining a specific value filtering condition of a target depth in the well logging general tables based on the reference depth; determining the candidate well logging general tables according to the data screening conditions of the well logging general tables comprising the reference depth, the curve value mode and the specific value filtering condition.

3. A heterogeneous logging data processing apparatus, characterized by, The method comprises the following steps: a wellbore well logging data collecting module is configured to collect well logging data of a target wellbore; a data screening condition determining module is configured to determine data screening conditions of different well logging data types according to depth index sources corresponding to the different well logging data types; the data screening conditions comprise a reference depth and a curve value mode; a candidate well logging data determining module is configured to determine candidate well logging data according to the data screening conditions of the different well logging data types comprising the reference depth and the curve value mode; a target well logging data determining module is configured to determine target well logging data according to the candidate well logging data; the data screening condition determining module comprises: a discrete curve reference depth determining unit is configured to, if the well logging data of the target wellbore is well logging discrete curves, take the first column of the well logging discrete curves as the reference depth of the well logging discrete curves; The discrete curve first curve value unit is configured to, if the logging data of the target wellbore is a logging discrete curve, determine a curve value mode of the logging discrete curve as a nearby point or linear interpolation when a single depth point is valued. The discrete curve second curve value unit is configured to, if the logging data of the target wellbore is a logging discrete curve, determine a curve value mode of the logging discrete curve as an average value of all curves in a layer section or a curve sampling interval in the layer section when the layer section is valued. The data screening condition determination module further comprises: The digital column determination unit is configured to, if the logging data of the target wellbore is a logging general table, determine a digital column of the logging general table according to a table header attribute of the logging general table. The general table first curve value unit is configured to, if the logging data of the target wellbore is a logging general table, take one column in the digital column of the logging general table as a reference depth of the logging general table when a single depth point is valued. The general table second curve value unit is configured to, if the logging data of the target wellbore is a logging general table, determine a curve value mode of the logging general table as a nearby point or linear interpolation when a single depth point is valued. The general table third curve value unit is configured to, if the logging data of the target wellbore is a logging general table, take two columns in the digital column of the logging general table as a start depth and an end depth in the reference depth column of the logging general table when a layer section is valued. The general table fourth curve value unit is configured to, if the logging data of the target wellbore is a logging general table, determine a curve value mode of the logging general table as an average value of all curves in a layer section or a curve sampling interval in the layer section when the layer section is valued. The one-dimensional curve reference depth determination unit is configured to, if the logging data of the target wellbore is a logging one-dimensional curve, determine a reference depth of the logging one-dimensional curve through a self-defined layer section list. The one-dimensional curve value mode determination unit is configured to, if the logging data of the target wellbore is a logging one-dimensional curve, determine a curve value mode of the logging one-dimensional curve as a nearby point or linear interpolation.

4. The heterogeneous well log data processing apparatus of claim 3, wherein, When the logging data is a logging general table, the data screening condition further comprises a specific value filtering condition, and the data screening condition determination module further comprises: The specific value filtering condition determination unit is configured to determine a specific value filtering condition of a target depth determined based on the reference depth in the logging general table. The candidate logging general table determination unit is configured to determine a candidate logging general table according to the data screening condition of the logging general table, which comprises the reference depth, the curve value mode and the specific value filtering condition.

5. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the heterogeneous logging data processing method in any of claims 1 to 2.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the heterogeneous logging data processing method in any of claims 1 to 2.

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