A logging data fusion method, device, storage medium and electronic device

Abstract

The present invention provides a logging data fusion method, device, storage medium and electronic device. The method includes: obtaining different types of logging data, where the different types of logging data at least include one of logging curve data, core analysis and test data, production test data and logging interpretation parameter data; regularizing each type of logging data into logging data arranged by rows in a target format; and merging the logging data corresponding to different types of logging data arranged by rows in the target format. The present invention facilitates extracting corresponding logging data according to the needs of logging evaluation, generating a logging evaluation data set, meeting the requirements of logging evaluation tasks, and is applicable to the processing and interpretation of oil and gas logging data.

Description

Technical Field

[0001] The present invention belongs to the technical field of petroleum geophysical exploration, and particularly relates to a well logging data fusion method, device, storage medium and electronic device. Background Art

[0002] Well logging evaluation tasks require various data from different sources and at different scales, such as well logging curves, core analysis data, production test data, interpretation parameters, etc. Analyzing from the characteristics, well logging curves are indexed by depth and are longitudinally continuous (usually with a sampling interval of 0.125 meters), core analysis data is indexed by depth and is longitudinally discontinuous, while production test data and interpretation parameters and other data are indexed by layer. Well logging evaluation needs to comprehensively utilize these data.

[0003] Currently, for well logging evaluation tasks, there is little research on the fusion of well logging curves and data with different characteristics such as core analysis and testing. When actually using computer well logging data processing and interpretation, especially when applying artificial intelligence methods to carry out well logging interpretation in the future, it is necessary to explore data fusion methods for well logging evaluation tasks. Summary of the Invention

[0004] Aiming at the problem that various data with different scales and from different sources in well logging evaluation tasks cannot be directly associated and extracted, the present invention provides a well logging data fusion method, device, storage medium and electronic device. Facing the requirements of well logging evaluation tasks, after effectively regularizing and fusing the required well logging data, it is convenient to extract corresponding well logging data according to the needs of well logging evaluation and generate a well logging evaluation data set.

[0005] In a first aspect, an embodiment of the present invention provides a well logging data fusion method, including:

[0006] Obtain different types of well logging data, where the different types of well logging data include at least one of well logging curve data, core analysis data, production test data, and well logging interpretation parameter data;

[0007] Regularize each type of well logging data into well logging data arranged in rows in a target format;

[0008] Merge the well logging data arranged in rows corresponding to different types of well logging data.

[0009] In some embodiments, in the above well logging data fusion method, the target format includes parameter name, depth, parameter value, and data type.

[0010] In some embodiments, in the above well logging data fusion method, the data type of the well logging curve data is continuous data indexed by depth, and the well logging curve data includes:

[0011] Well logging curve information; and

[0012] Curve value data stored in depth order;

[0013] Wherein, the well logging curve information includes curve name, starting depth, ending depth, and sampling interval;

[0014] When regularizing the well logging curve data into well logging data arranged in rows in a target format, the parameter name corresponds to the curve name, the parameter value corresponds to the curve value data stored in depth order, and the data type corresponds to the first type, and the first type is continuous data indexed by depth.

[0015] In some embodiments, in the above well logging data fusion method, the data type of the core analysis and testing data is non - continuous data indexed by depth, and the core analysis and testing data includes:

[0016] Core analysis and testing item name; and

[0017] Data values of core analysis and testing items stored in depth order;

[0018] When regularizing the core analysis and testing data into well logging data arranged in rows in a target format, the parameter name corresponds to the core analysis and testing item name, the parameter value corresponds to the data values of core analysis and testing items stored in depth order, and the data type corresponds to the second type, and the second type is non - continuous data indexed by depth.

[0019] In some embodiments, in the above well logging data fusion method, the data types of the production test data and the well logging interpretation parameter data are data indexed by layer depth, and both the production test data and the well logging interpretation parameter data include:

[0020] Starting depth;

[0021] Ending depth;

[0022] Data values stored in depth order within the depth range from the starting depth to the ending depth; and

[0023] Data name;

[0024] When regularizing the production test data or the well logging interpretation parameter data into well logging data arranged in rows in a target format, the parameter name corresponds to the data name of the production test data or the well logging interpretation parameter data, the parameter value corresponds to the data value, and the data type corresponds to the third type, and the third type is data indexed by layer depth.

[0025] In some embodiments, in the above logging data fusion method, the curve names include acoustic travel time logging curves and / or density logging curves.

[0026] In some embodiments, in the above logging data fusion method, the core analysis and testing item names include porosity data and / or permeability data.

[0027] In a second aspect, an embodiment of the present invention provides a logging data fusion device, including:

[0028] An acquisition module, configured to acquire different types of logging data, where the different types of logging data include at least one of logging curve data, core analysis and testing data, production test data, and logging interpretation parameter data;

[0029] A regularization module, configured to regularize each type of logging data into logging data arranged in rows in a target format;

[0030] A merging module, configured to merge the logging data arranged in rows in a target format corresponding to different types of logging data.

[0031] In a third aspect, an embodiment of the present invention provides a storage medium, on which a computer program is stored. When the computer program is executed by one or more processors, the logging data fusion method described in the first aspect is implemented.

[0032] In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor. A computer program is stored on the memory. When the computer program is executed by the processor, the logging data fusion method described in the first aspect is implemented.

[0033] Compared with the prior art, one or more embodiments of the present invention can at least bring the following beneficial effects:

[0034] The present invention provides a logging data fusion method, device, storage medium, and electronic device, which acquire different types of logging data, where the different types of logging data include at least one of logging curve data, core analysis and testing data, production test data, and logging interpretation parameter data; regularize each type of logging data into logging data arranged in rows in a target format; by merging the logging data arranged in rows in a target format corresponding to different types of logging data, after effectively regularizing and fusing the required logging data, it is convenient to extract corresponding logging data according to the needs of logging evaluation, generate a logging evaluation data set, meet the requirements of the logging evaluation task, and is applicable to the processing and interpretation of oil and gas logging data. Description of the Drawings

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0036] Figure 1 is a flowchart of a well logging data fusion method provided by an embodiment of the present invention;

[0037] Figure 2 is a block diagram of a well logging data fusion device provided by an embodiment of the present invention. Detailed implementation manners

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0039] In the related art, seismic records are synthesized using curves such as acoustic wave and density curves, and the deep-time relationship is obtained by using the similarity between the synthetic seismic record and the seismic trace beside the well, so as to convert the logging data in the depth domain into the time domain and realize the fusion of logging data and seismic data in the time domain. Multiple logging curves are fused into one curve by signal filtering, and activity analysis is carried out on the fused curve for geological stratification, achieving good results. According to a certain fusion model, isotopes and flow curves are fused, and on this basis, an expert decision-making tool for logging interpretation is established. The transient electromagnetic method, apparent resistivity logging, and seismic exploration result data are fused by means of principal component analysis and other means, and the geophysical exploration data from three different sources are reconstructed into a composite pseudo-apparent resistivity curve. The fusion of the wellbore model and the geological model is realized through layer matching and attribute consistency processing. The data fusion method of principal component analysis, clustering analysis, and Bayes discrimination criterion is applied to the data fusion of seven logging curves such as GR, SP, RILD, RILM, DEN, CNL, and AC, and is used for logging facies-lithofacies interpretation. The method of wavelet analysis is used to fuse multiple logging curves into a parameter curve, enhancing the common information contained in the original logging curves. The logging data fusion method based on wavelet multi-scale edge detection is applied to the quantitative division of sequence stratigraphic units, achieving obvious effects superior to single curves. However, for logging evaluation tasks, there is less research on the fusion of logging curves and data with different characteristics such as core analysis and testing. After the applicant's analysis, in the actual processing and interpretation of computer logging data, especially when applying artificial intelligence methods to carry out logging interpretation in the future, it is very necessary to fuse these data to facilitate the generation of logging-core analysis or logging-production test data sets according to requirements. Therefore, it is necessary to explore data fusion methods for logging evaluation tasks.

[0040] Logging evaluation tasks require various data from different sources and scales, such as logging curves, core analysis data, production test data, interpretation parameters, etc. Analyzing from the characteristics, logging curves are indexed by depth and are longitudinally continuous (usually with a sampling interval of 0.125 meters), core analysis data are indexed by depth and are longitudinally discontinuous, while production test data and interpretation parameters and other data are indexed by layer. Logging evaluation needs to comprehensively utilize these data. Especially when automatically processing and interpreting logging data, in order to facilitate the computer to quickly associate different types of data, some rules need to be established to fuse these data with different characteristics. The present invention provides a logging data fusion method, device, storage medium, and electronic device. Facing the requirements of logging evaluation tasks, after effectively regularizing and fusing the required logging data, it is convenient to extract corresponding logging data according to the needs of logging evaluation and generate a logging evaluation data set.

[0041] Example 1

[0042] Figure 1A flowchart of a well logging data fusion method is shown. As Figure 1 shown, this embodiment provides a well logging data fusion method, including:

[0043] Step S110: Obtain different types of well logging data.

[0044] In practical applications, different types of well logging data are various well logging data from different sources and scales required for well logging evaluation tasks, including at least one of well logging curve data, core analysis and laboratory data, production test data, and well logging interpretation parameter data.

[0045] Analyzed from the characteristics, well logging curves are indexed by depth and are longitudinally continuous (usually with a sampling interval of 0.125 meters). Therefore, the data type of well logging curve data is continuous data indexed by depth. Core analysis and laboratory data are indexed by depth and are longitudinally discontinuous. Therefore, the data type of core analysis and laboratory data is discontinuous data indexed by depth. Production test data and data such as interpretation parameters are indexed by layer. Therefore, the data types of production test data and well logging interpretation parameter data are data indexed by layer depth. Well logging evaluation needs to comprehensively utilize these data. To facilitate the computer to quickly generate a sample set by depth or by layer during automatic processing, this method is used to perform data fusion for well logging evaluation tasks.

[0046] In some embodiments, the data types of well logging data include: the first type, the second type, and the third type. Among them, the first type is continuous data indexed by depth, the second type is discontinuous data indexed by depth, and the third type is data indexed by layer depth. During the process of data fusion, the first type, the second type, and the third type can be represented by 0, 1, and 2 respectively.

[0047] Step S120: Regularize each type of well logging data into well logging data arranged in rows in a target format.

[0048] In some embodiments, the target format includes parameter name, depth, parameter value, and data type. Specifically, for each type of well logging data, the four data of parameter name, depth, parameter value, and data type are stored in each row in the order of depth, so that different types of well logging data are stored in the same format. When performing well logging evaluation tasks, well logging data of different data types can be quickly extracted to form a well logging evaluation data set without a complicated data processing process.

[0049] In some embodiments, a well logging curve data file is obtained by a computer. The well logging curve data includes:

[0050] Well logging curve information; and

[0051] Curve value data stored in the order of depth;

[0052] Among them, the logging curve information includes the curve name, starting depth, ending depth, and sampling interval.

[0053] Furthermore, when the logging curve data is regularized into logging data arranged in rows in a target format, the parameter name corresponds to the curve name, the parameter value corresponds to the curve value data stored in depth order, and the data type is 0, that is, the first type.

[0054] The depth in the target format can be determined by the starting depth, sampling interval in the logging curve information, and the sorting of the curve value data, as shown in the following formula:

[0055] Depi = sdep + (i - 1) * rlev (1)

[0056] Among them, i is the sorting number of the curve value data, starting from 1, depi is the depth corresponding to the i-th curve value data, sdep is the starting depth, rlev is the sampling interval, and the sampling interval usually used in logging is 0.125 meters, but it is not limited to this.

[0057] The arrangement format of the single-well logging curve data after regularization is as follows:

[0058] CrvName1, Dep1, CrvValue1, DataType

[0059] CrvName1, Dep2, CrvValue2, DataType

[0060] ……

[0061] CrvName1, Depi, CrvValuei, DataType

[0062] Among them, CrvName1 is the curve name of the first logging curve, Depi is the depth corresponding to the i-th curve value data, CrvValuei is the i-th curve value data, and DataType is the data type.

[0063] In some embodiments, the curve name includes but is not limited to the acoustic travel time logging curve and / or the density logging curve. That is to say, the obtained logging curve data may only contain acoustic travel time logging curve data, or only contain density logging curve data, or contain both acoustic travel time logging curve data and density logging curve data at the same time.

[0064] It should be understood that the well logging curve data is not limited to the acoustic travel time well logging curve and the density well logging curve, and may also include other well logging curves, which will not be exemplified one by one in this embodiment. When more than one well logging curve is obtained, for example, both the acoustic travel time well logging curve and the density well logging curve are included at the same time, one of the well logging curves is taken as the first well logging curve, and the other well logging curve is taken as the second well logging curve. When regularizing the well logging curve data into well logging data arranged in rows in the target format, the data of the first well logging curve is arranged first, and then the data of the second well logging curve is arranged, that is, continuing to arrange according to the curve name.

[0065] In some embodiments, the core analysis and laboratory test data includes:

[0066] The name of the core analysis and laboratory test item; and

[0067] The data values of the core analysis and laboratory test items stored in depth order.

[0068] Further, when regularizing the core analysis and laboratory test data into well logging data arranged in rows in the target format, the parameter name corresponds to the name of the core analysis and laboratory test item, the parameter value corresponds to the data value of the core analysis and laboratory test item stored in depth order, and the data type is 1, that is, the second type.

[0069] In some cases, the well logging data read from the core analysis and laboratory test data file is a two-dimensional table, which mainly includes a depth column (depth is discrete, without a fixed sampling interval) and a data value column (there may be multiple columns, and different header items represent different core analysis and laboratory test items). The core analysis and laboratory test data is arranged according to the name of the core analysis and laboratory test item, and the data values of the core analysis and laboratory test items are arranged in depth order. Each row stores four data, including the parameter name, depth, parameter value, and data type. The parameter name is the name of the core analysis and laboratory test item, the depth is determined by the read depth column, the parameter value is determined by the read data value column, and the data type is 1 (representing this type of non-continuous data indexed by depth).

[0070] The arrangement format of the data of a single core analysis and laboratory test item after regularization is as follows:

[0071] CorePhysicsName1, Dep1, CorePhysicsValue1, DataType

[0072] CorePhysicsName1, Dep2, CorePhysicsValue2, DataType

[0073] ……

[0074] CorePhysicsName1, Depi, CorePhysicsValuei, DataType

[0075] Among them, CorePhysicsName1 is the name of the core analysis and testing item, Depi is the depth corresponding to the i-th data value, CorePhysicsValuei is the i-th data value, and DataType is the data type.

[0076] In some embodiments, the names of core analysis and testing items include but are not limited to core porosity and / or core permeability. That is to say, the obtained core analysis and testing item data may only contain core porosity, or only contain core permeability, or contain both core porosity and core permeability.

[0077] It should be understood that the core analysis and testing data is not limited to core porosity and core permeability, and may also include data of other core analysis and testing items, which will not be exemplified one by one in this embodiment.

[0078] In some cases, when there is more than one type of obtained core analysis and testing data, for example, data containing both core porosity and core permeability, one of the core analysis and testing items is taken as the first core analysis and testing item, and the other core analysis and testing item is taken as the second core analysis and testing item. When regularizing the core analysis and testing item data into well logging data arranged in rows in the target format, the data of the first core analysis and testing item is arranged first, and then the data of the second core analysis and testing item is arranged, that is, continuing to arrange according to the name of the core analysis and testing item. That is, when there are multiple core analysis item data, continue to arrange according to the name of the core analysis item.

[0079] In some embodiments, the production test data and well logging interpretation parameter data both include:

[0080] Starting depth;

[0081] Ending depth;

[0082] Data values stored in depth order within the depth range from the starting depth to the ending depth; and

[0083] Data name.

[0084] When regularizing the production test data or well logging interpretation parameter data into well logging data arranged in rows in the target format, the parameter name corresponds to the data name of the production test data or well logging interpretation parameter data, the parameter value corresponds to the data value, and the data type corresponds to the third type.

[0085] Production test data and interpretation parameter data of this type are data indexed by layer depth (including starting depth and ending depth), that is, the third type. When reading this type of data in practical applications, a two-dimensional table is read, mainly including a starting depth column, an ending depth column, and a data value column (there may be multiple columns, and different table header item names represent different parameters). The production test and interpretation parameters are arranged in the order of parameter name and depth. Each row stores four data, including parameter name, depth, parameter value, and data type. When regularizing production test data or log interpretation parameter data into log data arranged in rows in the target format, the parameter name is the table header item name read from this two-dimensional table (for example, production test data includes daily oil production DAOP, daily water production DAWP, etc., and interpretation parameter data includes matrix density, matrix acoustic wave, etc.). The depth is determined by the starting depth and the ending depth. Starting from the starting depth, the next depth is obtained by adding a sampling interval to the previous depth (this sampling interval can be a fixed value, such as 0.125, but in practical applications, it is not limited to using 0.125 as the sampling interval). The depth cannot be greater than the ending depth, and the last depth is the ending depth. The parameter value is determined by the read data value column, and the data type is 2 (representing this type of data indexed by layer depth).

[0086] The data arrangement after regularizing a single production test data or log interpretation parameter data is as follows:

[0087] ParaName1, sdep, ParaValue1, DataType

[0088] ParaName1, sdep + rlev, ParaValue1, DataType

[0089] ……

[0090] ParaName1, sdep + (i - 1)*rlev, ParaValue1, DataType

[0091] ……

[0092] ParaName1, edep, ParaValue1, DataType

[0093] Among them, the starting depth is sdep, the ending depth is edep, the parameter name is ParaName1, and the parameter value is ParaValue1. It should be understood that when there are multiple parameter data, they are arranged continuously according to the parameter name.

[0094] Step S130: Merge the log data corresponding to different types of log data arranged in rows in the target format.

[0095] Through the method of this embodiment, various types of well logging data after regularization are merged. The merged data integrates data from multiple different sources and at different scales in a unified format, facilitating the subsequent extraction of corresponding data according to the requirements of well logging evaluation to generate a well logging evaluation dataset.

[0096] Example 2

[0097] The following provides an application example of the method of Embodiment 1.

[0098] Using the method of the present invention, the relevant data for reservoir well logging evaluation of Well HH1057-3 in the Chang 8 section of an oilfield in the southern Ordos Basin, China, is fused:

[0099] The computer reads the well logging curve data information and reads two well logging curve data: acoustic travel time and density;

[0100] The curve information part of the acoustic travel time includes:

[0101] Curve name: AC, starting depth: 2228, ending depth 2235, interval used: 0.125;

[0102] The curve value data is: 252.509 253.499 258.746 270.855 285.474 294.514 296.462 290.917 279.442 267.662 257.008 247.513 239.341 233.886 230.445 229.158 229.371 230.031 230.792 231.847 233.022 233.167 231.882 229.143 225.842 223.377 222.739 223.694 226.044 229.193 232.403 235.211 236.92 237.602 237.509 237.131 236.681 236.248 235.507 233.547 230.156 225.991 221.919 219.297 218.588 219.493 221.168 223.169 225.336 227.364 229.343 231.129 232.529 233.502 234.049 233.738 232.504.

[0103] The curve information part of the density includes:

[0104] Curve name: DEN, starting depth: 2228, ending depth 2235, interval used: 0.125;

[0105] The curve value data are: 2.571 2.574 2.563 2.452 2.215 2.011 2.05 2.185 2.317 2.413 2.466 2.487 2.493 2.497 2.504 2.509 2.509 2.5 2.497 2.504 2.521 2.544 2.57 2.591 2.604 2.613 2.618 2.615 2.598 2.569 2.532 2.499 2.48 2.476 2.481 2.487 2.49 2.489 2.493 2.504 2.523 2.548 2.572 2.582 2.571 2.544 2.52 2.51 2.513 2.517 2.512 2.499 2.487 2.484 2.485 2.483 2.479。

[0106] Regularize the data of these two logging curves according to the rules. The regularized data are shown in Table 1:

[0107] Table 1 Regularized Logging Curve Data

[0108]

[0109]

[0110]

[0111]

[0112] The computer reads the core analysis and test data information and reads a two-dimensional table as shown in Table 2, which includes the core sample depth (after correction according to the logging depth) and the corresponding core sample analysis porosity data (core-POR) and permeability data (core-PERM).

[0113] Table 2 Core Analysis and Test Data Table

[0114]

[0115]

[0116] Regularize the data in Table 2. The regularized data are shown in Table 3.

[0117] Table 3 Regularized Core Analysis and Test Data

[0118]

[0119]

[0120]

[0121] The computer reads the production test data information and reads a two-dimensional table (Table 4), which includes the starting depth, ending depth, daily oil production (DAOP), and daily water production (DAWP) data of the test layer.

[0122] Table 4 Test Data Table

[0123] Starting depth Ending depth DAOP DAWP 2231 2233 0.04 2.38

[0124] The data in Table 4 is regularized (the sampling interval value is the same as that of the logging curve, 0.125), and the regularized data is shown in Table 5.

[0125] Table 5 Regularized Test Data

[0126]

[0127]

[0128] Merge Table 1, Table 3, and Table 5, and the fused logging data can be obtained for logging evaluation, which includes logging curve data, core analysis and laboratory data, and production test data from different sources and at different scales. The data storage format is unified, facilitating computer reading and processing.

[0129] Example 3

[0130] Figure 2 A block diagram of a logging data fusion device is shown, as Figure 2 shown. This embodiment provides a logging data fusion device, including:

[0131] An acquisition module 210 for acquiring different types of logging data, where the different types of logging data include at least one of logging curve data, core analysis and laboratory data, production test data, and logging interpretation parameter data;

[0132] A regularization module 220 for regularizing each type of logging data into logging data arranged in rows in a target format;

[0133] A merging module 230 for merging the logging data arranged in rows in a target format corresponding to different types of logging data.

[0134] It should be understood that the acquisition module 210 can be used to execute step S110 in Embodiment 1, the regularization module 220 can be used to execute step S120 in Embodiment 1, and the merging module 230 can be used to execute step S130 in Embodiment 1. For the specific steps, please refer to Embodiment 1.

[0135] Those skilled in the art should understand that the above-mentioned modules or steps can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. Optionally, they can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0136] Example 4

[0137] This embodiment provides a storage medium on which a computer program is stored. When the computer program is executed by one or more processors, the well logging data fusion method of Embodiment 1 is implemented.

[0138] In this embodiment, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disc. For the content of the method, please refer to Embodiment 1.

[0139] Example 5

[0140] This embodiment provides an electronic device, including a memory and a processor. A computer program is stored on the memory. When the computer program is executed by the processor, the well logging data fusion method of Embodiment 1 is implemented.

[0141] In this embodiment, the processor may be implemented by an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is used to execute the method in the foregoing embodiment. The method implemented when the computer program running on the processor is executed may refer to the specific embodiments of the method provided in the foregoing embodiments of the present invention, which will not be elaborated herein.

[0142] A logging data fusion method, device, storage medium, and electronic device provided by an embodiment of the present invention perform regularization processing on data such as logging curve data, core analysis data, test production, and interpretation parameters, and can be used for computer processing and interpretation of logging data. It not only retains data information at different scales but also unifies the data arrangement format, facilitating computer reading and operation.

[0143] In several embodiments provided by the embodiments of the present invention, it should be understood that the disclosed systems and methods may also be implemented in other ways. The system and method embodiments described above are merely illustrative.

[0144] It should be noted that in this article, the terms "include", "comprise", or any other variation thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article, or device including the element.

[0145] Although the disclosed embodiments of the present invention are as above, the content described is only an embodiment adopted for the convenience of understanding the present invention and is not intended to limit the present invention. Any person skilled in the art within the technical field of the present invention may make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed by the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A logging data fusion method, characterized in that Including: Obtaining different types of logging data, where the different types of logging data include at least one of logging curve data, core analysis and test data, production test data, and logging interpretation parameter data; the data type of the logging curve data is continuous data indexed by depth, and the logging curve data includes: logging curve information; and curve value data stored in depth order, where the logging curve information includes curve name, starting depth, ending depth, and sampling interval; the data type of the core analysis and test data is discontinuous data indexed by depth, and the core analysis and test data includes: core analysis and test item name; and data values of core analysis and test items stored in depth order; the data types of the production test data and the logging interpretation parameter data are data indexed by layer depth, and the production test data and the logging interpretation parameter data both include: starting depth; ending depth; data values stored in depth order within the depth range from the starting depth to the ending depth; and data name. Regularizing each type of logging data into logging data arranged in rows in a target format, where the target format includes parameter name, depth, parameter value, and data type. Merging the logging data arranged in rows in the target format corresponding to different types of logging data. When regularizing the logging curve data into logging data arranged in rows in the target format, the parameter name corresponds to the curve name, the parameter value corresponds to the curve value data stored in depth order, the data type corresponds to the first type, and the first type is continuous data indexed by depth. When regularizing the core analysis and test data into logging data arranged in rows in the target format, the parameter name corresponds to the core analysis and test item name, the parameter value corresponds to the data values of core analysis and test items stored in depth order, the data type corresponds to the second type, and the second type is discontinuous data indexed by depth. When regularizing the production test data or the logging interpretation parameter data into logging data arranged in rows in the target format, the parameter name corresponds to the data name of the production test data or the logging interpretation parameter data, the parameter value corresponds to the data value, and the data type corresponds to the third type, and the third type is data indexed by layer depth.

2. The logging data fusion method according to claim 1, wherein The curve name includes acoustic travel time logging curve and / or density logging curve.

3. The logging data fusion method according to claim 1, characterized in that The core analysis and test item name includes porosity data and / or permeability data.

4. A logging data fusion device, characterized in that, Including: An acquisition module for acquiring different types of logging data, where the different types of logging data include at least one of logging curve data, core analysis and laboratory test data, production test data, and logging interpretation parameter data; the data type of the logging curve data is continuous data indexed by depth, and the logging curve data includes: logging curve information; and curve value data stored in depth order, where the logging curve information includes curve name, starting depth, ending depth, and sampling interval; the data type of the core analysis and laboratory test data is non - continuous data indexed by depth, and the core analysis and laboratory test data includes: core analysis and laboratory test item name; and data values of core analysis and laboratory test items stored in depth order; the data types of the production test data and the logging interpretation parameter data are data indexed by layer depth, and both the production test data and the logging interpretation parameter data include: starting depth; ending depth; data values stored in depth order within the depth range from the starting depth to the ending depth; and data name. A regularization module for regularizing each type of logging data into logging data arranged in rows in a target format, where the target format includes parameter name, depth, parameter value, and data type; when regularizing the logging curve data into logging data arranged in rows in the target format, the parameter name corresponds to the curve name, the parameter value corresponds to the curve value data stored in depth order, the data type corresponds to the first type, and the first type is continuous data indexed by depth; when regularizing the core analysis and laboratory test data into logging data arranged in rows in the target format, the parameter name corresponds to the core analysis and laboratory test item name, the parameter value corresponds to the data values of core analysis and laboratory test items stored in depth order, the data type corresponds to the second type, and the second type is non - continuous data indexed by depth; when regularizing the production test data or the logging interpretation parameter data into logging data arranged in rows in the target format, the parameter name corresponds to the data name of the production test data or the logging interpretation parameter data, the parameter value corresponds to the data value, and the data type corresponds to the third type, and the third type is data indexed by layer depth. A merging module for merging the logging data arranged in rows in the target format corresponding to different types of logging data.

5. A storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by one or more processors, the logging data fusion method according to any one of claims 1 to 3 is implemented.

6. An electronic device, characterized in that, It includes a memory and a processor, a computer program is stored on the memory, and when the computer program is executed by the processor, the logging data fusion method according to any one of claims 1 to 3 is implemented.

Images