Depth alignment method, device, electronic device and storage medium for logging data

Abstract

The present application provides a method, a device, an electronic device and a storage medium for depth alignment of logging data, which can be used in the technical field of oil and gas data processing. The method includes: collecting logging data and well logging data of a target site, extracting a logging gas logging curve from the logging data, and extracting a neutron well logging curve and a density well logging curve from the well logging data; obtaining a well logging porosity curve based on the neutron well logging curve and the density well logging curve; searching for characteristic peaks of the logging gas logging curve to obtain characteristic peak data of the logging gas logging curve; determining depth correction data based on the characteristic peak data and the well logging porosity curve; wherein the depth correction data includes a depth correction amount and a depth correction direction; and performing depth correction on the logging data based on the depth correction data. The method of the present application improves the reliability and accuracy of the processing of depth alignment of logging data, and further improves the accuracy of subsequent prediction of oil and gas reservoir parameters and calculation of oil and gas reservoir production.

Description

Technical Field

[0001] This application relates to the technical field of oil and gas data processing, and particularly to a method, device, electronic device, and storage medium for depth alignment of logging data. Background Art

[0002] Logging data is the basic data in the process of oil and gas exploration and development, which covers information on wellbore returns such as solids, liquids, and gases during the drilling process. A large amount of multi-source data is included in various types of logging data, covering numerical data such as gas logging, drilling time, and drilling fluid properties, as well as text data such as cuttings and lithology. By observing, collecting, gathering, recording, and analyzing logging data, it can be used to establish a logging geological profile, discover oil and gas shows, and evaluate oil and gas layers, etc.

[0003] In order to ensure the reliability and accuracy of subsequent reservoir search and oil and gas interpretation and evaluation, logging data must be processed and applied on a unified logging curve depth profile, and it is necessary to align the logging data with the logging data in depth, that is, it is necessary to perform depth alignment on the logging data. Currently, the depth alignment of logging data usually relies on manual correction. Due to the influence of human factors such as personnel experience, technical level, and resolution ability, the existing methods have problems of strong subjectivity, low processing efficiency, and lack of clear processing standards. Moreover, in the correction process of the depth alignment of logging data, the selection of corresponding logging data lacks a clear basis and a unified processing process, resulting in low reliability and accuracy of the depth alignment results, affecting the accuracy of oil and gas interpretation and evaluation. Summary of the Invention

[0004] This application provides a method, device, electronic device, and storage medium for depth alignment of logging data to solve the technical problem that the existing method for depth alignment of logging data has low reliability and accuracy.

[0005] According to the first aspect disclosed in this application, this application provides a method for depth alignment of logging data, including:

[0006] Collect logging data and logging data of the target site, extract the logging gas logging curve from the logging data, and extract the neutron logging curve and density logging curve from the logging data;

[0007] Based on the neutron logging curve and the density logging curve, obtain the logging porosity curve;

[0008] Search for characteristic peaks of the logging gas logging curve to obtain the characteristic peak data of the logging gas logging curve;

[0009] Based on the characteristic peak data and the logging porosity curve, determine the depth correction data; wherein, the depth correction data includes a depth correction amount and a depth correction direction.

[0010] Based on the depth correction data, perform depth correction on the logging data.

[0011] In a feasible implementation manner, the obtaining of the logging porosity curve based on the neutron logging curve and the density logging curve includes:

[0012] Based on the neutron logging curve, obtain the first apparent neutron porosity under the target on-site formation and the second apparent neutron porosity under the target on-site shale layer;

[0013] Based on the density logging curve, obtain the first apparent density porosity under the target on-site formation and the second apparent density porosity under the target on-site shale layer;

[0014] Based on the neutron logging curve and the density logging curve, construct a neutron-density crossplot, and determine the formation coordinates and shale point coordinates based on the neutron-density crossplot; wherein, the formation coordinates include the first apparent neutron porosity and the first apparent density porosity, and the shale layer coordinates include the second apparent neutron porosity and the second apparent density porosity;

[0015] Based on the formation coordinates and the shale point coordinates, obtain the logging porosity, and construct the logging porosity curve based on the logging porosity.

[0016] In a feasible implementation manner, before searching for characteristic peaks in the logging gas measurement curve, the method further includes:

[0017] Obtain the first sampling interval of the logging gas measurement curve and the second sampling interval of the logging porosity curve;

[0018] If the first sampling interval is inconsistent with the second sampling interval, use the first sampling interval as the sampling reference to resample the logging porosity curve; wherein, the resampling process is used to unify the sampling intervals of the logging gas measurement curve and the logging porosity curve.

[0019] In a feasible implementation manner, the using the first sampling interval as the sampling reference to resample the logging porosity curve includes:

[0020] Use the first sampling interval as a time window to perform data sliding window on the logging porosity curve, and obtain the central value of the logging porosity curve within the time window;

[0021] Update the logging porosity curve within the time window based on the central value.

[0022] In a feasible implementation manner, the searching for characteristic peaks of the logging gas logging curve and obtaining the characteristic peak data of the logging gas logging curve includes:

[0023] Obtaining each gas logging data point of the logging gas logging curve;

[0024] For each gas logging data point, comparing the gas logging data point with adjacent gas logging data points. If the gas logging data point is greater than the adjacent gas logging data point and meets the preset peak condition, then determine the gas logging data point as a characteristic peak and add it to the characteristic peak data.

[0025] In a feasible implementation manner, before searching for characteristic peaks of the logging gas logging curve, the method further includes:

[0026] Performing a data cleaning operation on the logging gas logging curve and extracting the logging gas logging curve of the effective interval; wherein, the effective interval is a formation interval containing at least one hydrocarbon gas.

[0027] In a feasible implementation manner, the determining the depth correction data based on the characteristic peak data and the logging porosity curve includes:

[0028] Obtaining each characteristic peak of the characteristic peak data;

[0029] For each characteristic peak, obtaining the depth difference between the characteristic peak and the adjacent logging porosity peak on the logging porosity curve;

[0030] Selecting the depth difference corresponding to the characteristic peak at the highest value as the depth correction amount and determining the depth correction direction.

[0031] According to the second aspect disclosed in the present application, the present application provides a depth alignment device for logging data, including:

[0032] A data acquisition module, configured to acquire logging data and logging data of a target site, extract a logging gas logging curve from the logging data, and extract a neutron logging curve and a density logging curve from the logging data;

[0033] A porosity acquisition module, configured to acquire a logging porosity curve based on the neutron logging curve and the density logging curve;

[0034] A characteristic peak search module, configured to search for characteristic peaks of the logging gas logging curve and obtain the characteristic peak data of the logging gas logging curve;

[0035] A correction data acquisition module, configured to determine depth correction data based on the characteristic peak data and the logging porosity curve; wherein, the depth correction data includes a depth correction amount and a depth correction direction;

[0036] A data correction module, configured to perform depth correction on the logging data based on the depth correction data.

[0037] According to a third aspect disclosed in the present application, there is provided an electronic device, including a processor and a memory communicatively connected to the processor;

[0038] The memory stores computer-executable instructions;

[0039] The processor executes the computer-executable instructions stored in the memory to implement the method described in any one of the first aspects.

[0040] According to a fourth aspect disclosed in the present application, there is provided a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described in any one of the first aspects when executed by a processor.

[0041] According to a fifth aspect disclosed in the present application, there is provided a computer program product, including a computer program, which is used to implement the method described in any one of the first aspects when executed by a processor.

[0042] Compared with the prior art, the present application has the following beneficial effects:

[0043] A method, device, electronic device and storage medium for depth alignment of logging data provided by the present application use the logging gas logging curve in the logging data as the benchmark for depth correction of the logging data, and use the logging porosity curve jointly calculated by neutron logging and density logging as the physical property index. The corresponding relationship between the characteristic peaks of the gas logging abnormal high value response of the logging gas logging curve is used as the correction basis to determine the depth correction data for depth alignment of the logging data, so as to realize the depth alignment of various logging data, solve the problems of strong subjectivity and low efficiency existing in the prior art when relying on manual depth alignment of logging data, effectively improve the reliability and accuracy of the depth alignment of logging data, and further improve the accuracy of subsequent prediction of oil and gas reservoir parameters and calculation of oil and gas reservoir production. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0045] Figure 1 It is a schematic flowchart of a method for depth alignment of logging data provided by an embodiment of the present application;

[0046] Figure 2 It is a schematic flowchart of another method for depth alignment of logging data provided by an embodiment of the present application;

[0047] Figure 3 This embodiment of the present application provides a schematic diagram of the calculation effect of logging porosity based on a neutron-density crossplot;

[0048] Figure 4 This embodiment of the present application provides a schematic diagram of the resampling result of a logging porosity curve;

[0049] Figure 5 This embodiment of the present application provides a schematic diagram of characteristic peak search;

[0050] Figure 6 This embodiment of the present application provides a schematic diagram of the data cleaning operation of logging gas logging curves;

[0051] Figure 7 This embodiment of the present application provides a schematic diagram of the calculation of the depth difference between characteristic peaks and a logging porosity curve;

[0052] Figure 8 This embodiment of the present application provides a schematic diagram of the depth alignment result of logging data;

[0053] Figure 9 This embodiment of the present application provides a schematic diagram of the structure of a depth alignment device for logging data;

[0054] Figure 10 This embodiment of the present application provides a schematic diagram of the structure of an electronic device.

[0055] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0056] Here, exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0057] Logging data is the basic data in the process of oil and gas exploration and development. It covers information on wellbore returns such as solids, liquids, and gases during the drilling process. A large amount of multi-source data is included in various types of logging data, covering numerical data such as gas logging, drilling time, and drilling fluid performance, as well as text data such as cuttings and lithology. By observing, collecting, gathering, recording, and analyzing logging data, it can be used to establish a logging geological profile, discover hydrocarbon shows, and evaluate hydrocarbon layers, etc.

[0058] To ensure the reliability and accuracy of subsequent reservoir search and oil-gas interpretation and evaluation, logging data must be processed and applied on a unified logging curve depth profile, requiring the logging data to be aligned with the logging data in depth, that is, the depth alignment of the logging data is required. Currently, the depth alignment of logging data usually relies on manual correction. Due to the influence of human factors such as personnel experience, technical level, and resolution ability, the existing methods have problems of strong subjectivity, low processing efficiency, and lack of clear processing standards. Moreover, in the correction process of the depth alignment of logging data, the selection of corresponding logging data lacks a clear basis and a unified processing process, resulting in low reliability and accuracy of the depth alignment results, affecting the accuracy of oil-gas interpretation and evaluation.

[0059] In view of the above technical problems, the present application proposes a method for depth alignment of logging data. By using the logging gas logging curve and the logging porosity curve as the correction benchmark to perform depth alignment on the logging data, the reliability and accuracy of the depth alignment processing of the logging data are improved, and further the accuracy of subsequent oil-gas reservoir parameter prediction and oil-gas reservoir production calculation is enhanced.

[0060] The following specifically describes the technical solution of the method for depth alignment of logging data provided by the present application through specific embodiments. It should be noted that the following embodiments can exist independently or be combined with each other. For the same or similar content, it may not be repeated in different embodiments.

[0061] Figure 1 For the flow chart of a method for depth alignment of logging data provided by an embodiment of the present application, refer to Figure 1 , in some embodiments, the flow of the method for depth alignment of logging data includes the following steps:

[0062] S101, collect the logging data and logging data of the target site, extract the logging gas logging curve from the logging data, and extract the neutron logging curve and density logging curve from the logging data.

[0063] Specifically, logging data is the basic data in the process of oil and gas exploration and development. It covers information on wellbore returns such as solids, liquids, and gases during the drilling process. By observing, collecting, gathering, recording, and analyzing the logging data, a logging geological profile can be established, oil and gas shows can be discovered, oil and gas layers can be evaluated, and drilling information services can be provided for oil and gas exploration projects. Specifically, it includes data such as logging core data, logging cuttings data, logging drilling time data, logging mud data, and logging gas logging data.

[0064] Among them, the mud logging gas logging curve refers to the relevant data obtained by detecting the gas components and contents returned during the drilling process. Through the mud logging gas logging curve, information such as the gas-bearing property of the formation and the type of oil and gas reservoir can be understood. The mud logging gas logging curve is mainly used to reflect the oil and gas-bearing property, but at the same time, it can also play a certain guiding role in the physical properties of the reservoir. Moreover, the abnormal response values of the mud logging gas logging curve often appear in reservoirs or coal seams with better physical properties. Therefore, the mud logging gas logging curve can be used as a benchmark for correcting other various mud logging data.

[0065] Specifically, logging data refers to the data obtained by measuring in the well with logging instruments during the petroleum drilling process. These data reflect the physical properties of the underground formation, such as electrochemistry, conductivity, acoustics, radioactivity, etc., thereby helping petroleum geology and engineering technicians understand the physical parameters of formation rocks, such as density, acoustic velocity, resistivity, etc. Logging data is of great significance for determining and evaluating oil and gas layers and solving a series of geological problems. Specifically, it includes gamma logging data, acoustic logging data, resistivity logging data, neutron logging data, density logging data, etc.

[0066] Among them, neutron logging uses the fast neutrons emitted by the neutron source to interact with the atomic nuclei in the formation, and reflects the attenuation rate of neutron density in the formation with the source distance by measuring the ratio of thermal neutron counting rates. It is mainly used to detect the formation structure and mineral content around the wellbore, and is particularly suitable for identifying oil and gas layers and water layers.

[0067] Among them, density logging uses the gamma rays emitted by the gamma ray source to pass through the formation and interact with the formation material. The attenuation of the ray intensity follows an exponential law. By measuring the attenuation degree of the gamma ray intensity, the formation density is calculated. It is mainly used to determine lithology and rock density and is an important part of the lithology porosity logging series.

[0068] S102, based on the neutron logging curve and the density logging curve, obtain the logging porosity curve.

[0069] Among them, by synthesizing the neutron logging curve and the density logging curve, the logging porosity curve can be obtained more accurately.

[0070] S103, perform a characteristic peak search on the mud logging gas logging curve to obtain the characteristic peak data of the mud logging gas logging curve.

[0071] Among them, by searching for the characteristic peaks of the mud logging gas logging curve and using the characteristic peaks as the reference data for aligning the mud logging gas logging data with the logging porosity, it is convenient to obtain the depth correction data subsequently.

[0072] S104, based on the characteristic peak data and the logging porosity curve, determine the depth correction data; among them, the depth correction data includes the depth correction amount and the depth correction direction.

[0073] Among them, in well logging data, since neutron logging and density logging have good responses to the physical properties of reservoirs, the porosity curve of well logging calculated by combining neutron logging and density logging is used as a physical property index, and the corresponding relationship with the characteristic peak of the gas logging anomaly high-value response is used as the calibration basis to determine the depth correction data for depth migration.

[0074] S105, based on the depth correction data, perform depth correction on the logging data.

[0075] Among them, through the depth correction data, other logging data such as logging core data, logging cuttings data, logging drilling time data, logging mud data, etc. are also subjected to depth correction processing.

[0076] In this embodiment, the gas logging curve in the logging data is used as the benchmark for depth correction of the logging data, and the porosity curve of well logging calculated by combining neutron logging and density logging is used as a physical property index. The corresponding relationship with the characteristic peak of the gas logging anomaly high-value response of the gas logging curve is used as the calibration basis to determine the depth correction data for depth migration of the logging data, so as to realize the depth migration of various logging data, solve the problems of strong subjectivity and low efficiency existing in the existing method when relying on manual depth migration of logging data, effectively improve the reliability and accuracy of the depth migration of logging data, and further improve the accuracy of subsequent oil and gas reservoir parameter prediction and oil and gas reservoir production calculation.

[0077] In Figure 1 On the basis of the shown embodiment, the technical solution of the above-mentioned depth migration method of logging data will be further introduced below in combination with Figure 2 ...

[0078] Figure 2 FIG. [Reference number not provided] is a schematic flow chart of another depth migration method of logging data provided by an embodiment of the present application. Referring to Figure 2 ...

[0079] S201, collect the logging data and well logging data of the target site, extract the gas logging curve from the logging data, and extract the neutron logging curve and density logging curve from the well logging data.

[0080] S202, based on the neutron logging curve and density logging curve, obtain the porosity curve of well logging.

[0081] Preferably, the method for calculating the porosity curve of well logging by combining the neutron logging curve and density logging curve includes:

[0082] Step 1, based on the neutron logging curve, obtain the first apparent neutron porosity under the formation of the target site and the second apparent neutron porosity under the shale layer of the target site.

[0083] Specifically, the first apparent neutron porosity satisfies the following formula:

[0084]

[0085] Wherein, represents the first apparent neutron porosity of the formation, represents the response value of the neutron logging of the formation.

[0086] Specifically, the second apparent neutron porosity satisfies the following formula:

[0087]

[0088] Wherein, represents the second apparent neutron porosity of the shale layer, represents the response value of the neutron logging of the shale layer.

[0089] Step 2: Based on the density logging curve, obtain the first apparent density porosity under the target field formation and the second apparent density porosity under the target field shale layer.

[0090] Specifically, the first apparent density porosity satisfies the following formula:

[0091]

[0092] Wherein, represents the first apparent density porosity of the formation, represents the density of the sandstone matrix, represents the response value of the density logging of the formation, represents the density of the formation fluid.

[0093] Specifically, the second apparent density porosity satisfies the following formula:

[0094]

[0095] Wherein, represents the second apparent density porosity of the shale layer, represents the response value of the density logging of the shale layer.

[0096] Step 3: Based on the neutron logging curve and the density logging curve, construct a neutron-density crossplot, and determine the formation coordinates and shale point coordinates based on the neutron-density crossplot; wherein, the formation coordinates include the first apparent neutron porosity and the first apparent density porosity, and the shale layer coordinates include the second apparent neutron porosity and the second apparent density porosity.

[0097] Among them, after constructing a neutron-density crossplot using neutron logging curves and density logging curves, the formation coordinates in the neutron-density crossplot are determined. and the shale point coordinates .

[0098] Specifically, for the calculation effect of logging porosity based on the neutron-density crossplot, refer to Figure 3 as shown.

[0099] Step 4: Based on the formation coordinates and shale point coordinates, obtain the logging porosity, and construct a logging porosity curve based on the logging porosity.

[0100] Specifically, the logging porosity satisfies the following formula:

[0101]

[0102] where , represent the logging porosity.

[0103] S203: Obtain the first sampling interval of the mud logging gas logging curve and the second sampling interval of the logging porosity curve;

[0104] S204: Determine whether the first sampling interval and the second sampling interval are consistent.

[0105] S205: If the first sampling interval is not consistent with the second sampling interval, use the first sampling interval as the sampling benchmark to resample the logging porosity curve; among them, the resampling process is used to unify the sampling intervals of the mud logging gas logging curve and the logging porosity curve.

[0106] Among them, by making the sampling intervals of the mud logging gas logging curve and the logging porosity curve consistent, the correlation between the mud logging data and the logging data is established. This processing process can ensure the corresponding relationship between the mud logging gas logging curve and the logging porosity curve in depth, providing a consistent basis for subsequent data analysis and processing.

[0107] Preferably, using the first sampling interval as the sampling benchmark to resample the logging porosity curve includes:

[0108] Step 1: Use the first sampling interval as a time window to perform data sliding window on the logging porosity curve, and obtain the central value of the logging porosity curve within the time window.

[0109] Among them, a data sliding window can be regarded as a sub-interval or subset with a finite length on a data stream. It is used to capture data characteristics within a period of time or on a sequence. This sub-interval or subset can be dynamically moved as needed to achieve continuous analysis and processing of data. In this application, a section of logging porosity data is sequentially captured on the logging porosity curve, and the central value of this section of porosity data is calculated, thereby converting a section of logging porosity data into a logging porosity data point, achieving the purpose of unifying the sampling intervals of the logging porosity curve and the mud logging gas logging curve.

[0110] Step 2, update the logging porosity curve within the time window based on the central value.

[0111] Exemplarily, assume that the sampling interval of the mud logging data is 1m, while the sampling interval of the logging data is 0.125m. Therefore, it is necessary to resample the logging data and adjust its interval to 1m. Specifically, by thinning the logging porosity, the thinning process can be achieved by merging adjacent logging porosity data points, ensuring that the sampling interval of the logging porosity is resampled to 1m, thereby unifying the sampling intervals of the mud logging gas logging data and the logging porosity data. The data sliding window slides the logging porosity curve at the sampling interval of the mud logging gas logging curve, and the window length is set to 1m. By using the method of average value, the central value of the logging porosity within the window is obtained, and the information of the logging porosity within the 1m window length is resampled into a new data point to maintain the integrity and consistency of the front and back information.

[0112] Specifically, refer to Figure 4 shown

[0113] S206, obtain each gas logging data point on the mud logging gas logging curve.

[0114] S207, for each gas logging data point, compare the gas logging data point with the adjacent gas logging data points. If the gas logging data point is greater than the adjacent gas logging data points and meets the preset peak conditions, then determine the gas logging data point as a characteristic peak and add it to the characteristic peak data.

[0115] Among them, traverse and search each gas logging data point on the mud logging gas logging curve to determine whether each gas logging data point belongs to a characteristic peak. If the gas logging data point is a characteristic peak, then add it to the characteristic peak data.

[0116] Specifically, the peak conditions include characteristic parameters such as width, frequency, and morphology. The gas logging data point of the characteristic peak, in addition to being greater than the adjacent gas logging data points, also needs to meet the preset peak conditions, that is, its width, frequency, and morphology also need to meet the preset conditions.

[0117] Specifically, refer to Figure 5 shown.

[0118] Preferably, before searching for characteristic peaks in the logging gas measurement curve, it further includes: performing data cleaning operations on the logging gas measurement curve to extract the logging gas measurement curve of the effective interval; wherein, the effective interval is a formation interval containing at least one hydrocarbon gas.

[0119] Among them, by cleaning the logging gas measurement curve, screening the logging gas measurement curves of the effective intervals with better physical properties and oil-bearing properties, these logging gas measurement curves of the effective intervals have more obvious characteristics, can screen out invalid background values, improve data accuracy, and at the same time can narrow the search range of characteristic peak search and improve the search efficiency of characteristic peak search.

[0120] Specifically, the effective interval is the key formation for oil and gas interpretation and evaluation, and often contains curve information of hydrocarbon gases such as C1 (methane), C2 (ethane), C3 (propane), IC4 (isobutane), NC4 (n-butane), IC5 (isopentane), NC5 (n-pentane), etc. The algorithm quickly searches in the logging gas measurement curve of the whole well section and extracts the effective intervals containing these five gas measurement data, narrowing the range for the subsequent search of gas measurement characteristic peaks. If the logging gas measurement data of the effective interval cannot be screened out in the whole well section, the standard of screening conditions is relaxed, and the requirement for the types of gas measurement curves in the effective interval is reduced to ensure that at least some effective interval information can be obtained.

[0121] Specifically, the data cleaning operation of the logging gas measurement curve refers to Figure 6 as shown.

[0122] S208, obtaining each characteristic peak of the characteristic peak data.

[0123] S209, for each characteristic peak, obtaining the depth difference between the characteristic peak and the adjacent logging porosity peak on the logging porosity curve.

[0124] Among them, if the logging gas measurement curve is to be aligned with the logging porosity curve, the characteristic peaks on the logging gas measurement curve will be aligned with the adjacent logging porosity peaks on the logging porosity curve. Therefore, the depth difference between the characteristic peak and the adjacent logging porosity peak on the logging porosity curve is the depth correction amount required for the depth alignment of the logging gas measurement curve and the logging porosity curve.

[0125] Specifically, the calculation of the depth difference between the characteristic peak and the adjacent logging porosity peak on the logging porosity curve refers to Figure 7 as shown.

[0126] S210, selecting the depth difference corresponding to the characteristic peak at the highest value as the depth correction amount and determining the depth correction direction.

[0127] Among them, all the calculation results of the depth difference obtained according to the above steps will be retained, but the depth difference at the highest value of the gas logging characteristic peak will be defaulted as the final depth correction amount. This process aims to correct the depth offset caused by the differences between measurement tools and improve the accuracy and reliability of the gas logging data in logging. Here, the standard for default selection of the depth correction amount is set, and the depth difference at the highest response value in the gas logging characteristic peaks of logging will be defaulted as the final depth correction amount. Subsequently, the default selection standard can be modified according to the actual situation.

[0128] In addition, for the depth correction direction, considering that the cable stretching amount of the logging tool during downhole measurement is usually greater than the drill string stretching amount of the logging instrument, the correction direction is defaulted as downward.

[0129] Preferably, to ensure the rationality of the correction, a maximum correction threshold range is set, that is, the maximum depth correction amount of gas logging data per 1000 meters is 1 meter.

[0130] S211, based on the depth correction data, perform depth correction on the logging data.

[0131] Specifically, the depth alignment result of the logging data is referred to Figure 8 as shown.

[0132] In this embodiment, by using the gas logging curve of logging and the porosity curve of logging as the correction reference to perform depth alignment on the logging data, the reliability and accuracy of the depth alignment process of the logging data are improved, and further the accuracy of subsequent prediction of oil and gas reservoir parameters and calculation of oil and gas reservoir production is enhanced.

[0133] Figure 9 is a schematic structural diagram of a depth alignment device for logging data provided by an embodiment of the present application. Refer to Figure 9 , the depth alignment device for logging data includes various functional modules for implementing the foregoing depth alignment method of logging data, and any functional module can be implemented in software and / or hardware.

[0134] In some embodiments, the depth alignment device 900 for logging data includes a data acquisition module 901, a porosity acquisition module 902, a characteristic peak search module 903, a correction data acquisition module 904, and a data correction module 905. Among them:

[0135] The data acquisition module 901 is used to acquire the logging data and logging data of the target site, extract the gas logging curve from the logging data, and extract the neutron logging curve and density logging curve from the logging data;

[0136] The porosity acquisition module 902 is used to acquire the logging porosity curve based on the neutron logging curve and density logging curve;

[0137] The characteristic peak search module 903 is used to search for characteristic peaks in the mud logging gas logging curve and obtain the characteristic peak data of the mud logging gas logging curve;

[0138] The calibration data acquisition module 904 is used to determine depth calibration data based on the characteristic peak data and the logging porosity curve; wherein, the depth calibration data includes a depth calibration amount and a depth calibration direction;

[0139] The data calibration module 905 is used to perform depth calibration on the mud logging data based on the depth calibration data.

[0140] In some embodiments, the porosity acquisition module 902 is specifically configured to:

[0141] Based on the neutron logging curve, obtain the first apparent neutron porosity under the formation of the target site and the second apparent neutron porosity under the shale layer of the target site;

[0142] Based on the density logging curve, obtain the first apparent density porosity under the formation of the target site and the second apparent density porosity under the shale layer of the target site;

[0143] Based on the neutron logging curve and the density logging curve, construct a neutron-density crossplot, and determine the formation coordinates and shale point coordinates based on the neutron-density crossplot; wherein, the formation coordinates include the first apparent neutron porosity and the first apparent density porosity, and the shale layer coordinates include the second apparent neutron porosity and the second apparent density porosity;

[0144] Based on the formation coordinates and the shale point coordinates, obtain the logging porosity, and construct a logging porosity curve based on the logging porosity.

[0145] In some embodiments, the apparatus 900 further includes a data resampling module 906. Before searching for characteristic peaks in the mud logging gas logging curve, the data resampling module 906 is specifically configured to:

[0146] Obtain the first sampling interval of the mud logging gas logging curve and the second sampling interval of the logging porosity curve;

[0147] If the first sampling interval is inconsistent with the second sampling interval, then use the first sampling interval as the sampling reference to perform resampling processing on the logging porosity curve; wherein, the resampling processing is used to unify the sampling intervals of the mud logging gas logging curve and the logging porosity curve.

[0148] In some embodiments, the data resampling module 906 is further specifically configured to:

[0149] Use the first sampling interval as a time window to perform data sliding window on the logging porosity curve and obtain the central value of the logging porosity curve within the time window;

[0150] Update the logging porosity curve within the time window based on the central value.

[0151] In some embodiments, the feature peak search module 903 is specifically configured to:

[0152] Obtain each gas logging data point in the gas logging curve of well logging;

[0153] For each gas logging data point, compare the gas logging data point with the adjacent gas logging data points. If the gas logging data point is greater than the adjacent gas logging data points and meets the preset peak condition, determine the gas logging data point as a feature peak and add it to the feature peak data.

[0154] In some embodiments, the device 900 further includes a data cleaning module 907. Before performing feature peak search on the gas logging curve of well logging, the data cleaning module 907 is specifically configured to:

[0155] Perform data cleaning operations on the gas logging curve of well logging, and extract the gas logging curve of the effective interval; wherein, the effective interval is a formation interval containing at least one hydrocarbon gas.

[0156] In some embodiments, the calibration data acquisition module 904 is specifically configured to:

[0157] Obtain each feature peak of the feature peak data;

[0158] For each feature peak, obtain the depth difference between the feature peak and the adjacent logging porosity peak on the logging porosity curve;

[0159] Select the depth difference corresponding to the feature peak at the highest value as the depth correction amount, and determine the depth correction direction.

[0160] The depth alignment device 900 for well logging data provided by the embodiments of the present application is used to execute the technical solutions provided by the embodiments of the foregoing depth alignment method for well logging data. Its implementation principle and technical effects are similar to those in the embodiments of the foregoing method, and will not be described in detail here.

[0161] It should be noted that the division of each module of the above device is only a division of logical functions. In actual implementation, it can be fully or partially integrated into a physical entity, or physically separated. And these modules can all be implemented in the form of software called by a processing element, or all in the form of hardware, or some modules can be implemented in the form of software called by a processing element and some modules in the form of hardware. For example, the data acquisition module 901 can be a separately established processing element, or can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code and called and executed by a certain processing element of the above device to perform the functions of the above data acquisition module 901. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together or can be independently implemented. Here, the processing element can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the integrated logic circuit in the hardware of the processor element or the instructions in the form of software.

[0162] Figure 10 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Refer to Figure 10 FIG. The electronic device 1000 includes: a processor 1001 and a memory 1002 communicatively connected to the processor 1001;

[0163] The memory 1002 stores computer-executable instructions;

[0164] The processor 1001 executes the computer-executable instructions stored in the memory 1002 to implement the technical solution of the aforementioned method for depth alignment of logging data.

[0165] In the above electronic device 1000, the memory 1002 and the processor 1001 are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines, such as through a bus connection. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus. The memory 1002 stores computer execution instructions for implementing the depth alignment method of the foregoing logging data, including at least one software function module that can be stored in the memory 1002 in the form of software or firmware. The processor 1001 executes various functional applications and data processing by running the software programs and modules stored in the memory 02.

[0166] The memory 1002 includes at least one type of readable storage medium, not limited to Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), etc. Among them, the memory 1002 is used to store programs, and the processor 1001 executes the programs after receiving the execution instructions. Further, the software programs and modules in the memory 1002 may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide a running environment for other software components.

[0167] The processor 1001 may be an integrated circuit chip with the ability to process signals. The aforementioned processor 1001 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor, or the processor 1001 may also be any conventional processor, etc.

[0168] The electronic device 1000 is used to execute the technical solution provided by the foregoing embodiment of the method for depth alignment of logging data. Its implementation principle and technical effects are similar to those in the foregoing method embodiment, and will not be elaborated here.

[0169] The embodiments of the present application also provide a computer-readable storage medium. Computer-executable instructions are stored in the computer-readable storage medium. When the processor executes the computer-executable instructions, the technical solution of the method for depth alignment of logging data as described above is implemented.

[0170] The aforementioned computer-readable storage medium may 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 disk. The computer-readable storage medium may be any available medium accessible by a general-purpose or special-purpose computer.

[0171] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium may also be a component of the processor. The processor and the readable storage medium may be located in an Application Specific Integrated Circuit (ASIC). Of course, the processor and the readable storage medium may also exist as discrete components in the control device of the device for depth alignment of logging data.

[0172] The embodiments of the present application also provide a computer program product, including a computer program. When the computer program is executed by the processor, it is used to implement the technical solution of the method for depth alignment of logging data as described above.

[0173] In the above embodiments, those skilled in the art can understand that implementing the above method embodiments can be achieved in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, it can be achieved in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center via wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless network, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk (SSD)).

[0174] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0175] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the appended claims.

[0176] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A depth alignment method for mud logging data, characterized in that Including: Collecting logging data and well logging data of the target site, extracting a logging gas logging curve from the logging data, and extracting a neutron well logging curve and a density well logging curve from the well logging data; Based on the neutron well logging curve and the density well logging curve, obtaining a well logging porosity curve; Performing a characteristic peak search on the logging gas logging curve to obtain characteristic peak data of the logging gas logging curve; Based on the characteristic peak data and the well logging porosity curve, determining depth correction data; wherein, the depth correction data includes a depth correction amount and a depth correction direction; Based on the depth correction data, performing depth correction on the logging data; Wherein, the obtaining the well logging porosity curve based on the neutron well logging curve and the density well logging curve includes: Based on the neutron well logging curve, obtaining a first apparent neutron porosity under the formation of the target site and a second apparent neutron porosity under the mudstone layer of the target site; Based on the density well logging curve, obtaining a first apparent density porosity under the formation of the target site and a second apparent density porosity under the mudstone layer of the target site; Based on the neutron well logging curve and the density well logging curve, constructing a neutron-density crossplot, and determining formation coordinates and mudstone point coordinates based on the neutron-density crossplot; wherein, the formation coordinates include the first apparent neutron porosity and the first apparent density porosity, and the mudstone point coordinates include the second apparent neutron porosity and the second apparent density porosity; Based on the formation coordinates and the mudstone point coordinates, obtaining the well logging porosity, and constructing the well logging porosity curve based on the well logging porosity; Wherein, the determining the depth correction data based on the characteristic peak data and the well logging porosity curve includes: Obtaining each characteristic peak of the characteristic peak data; For each characteristic peak, obtaining a depth difference between the characteristic peak and an adjacent well logging porosity peak on the well logging porosity curve; Selecting the depth difference corresponding to the characteristic peak at the highest value as the depth correction amount, and determining the depth correction direction.

2. The method according to claim 1, wherein Before performing the characteristic peak search on the logging gas logging curve, the method further includes: Obtaining a first sampling interval of the logging gas logging curve and a second sampling interval of the well logging porosity curve; If the first sampling interval is inconsistent with the second sampling interval, using the first sampling interval as a sampling reference to perform resampling processing on the well logging porosity curve; wherein, the resampling processing is used to unify the sampling intervals of the logging gas logging curve and the well logging porosity curve.

3. The method according to claim 2, wherein The using the first sampling interval as a sampling reference to perform resampling processing on the well logging porosity curve includes: Using the first sampling interval as a time window, performing data sliding window on the well logging porosity curve, and obtaining a central value of the well logging porosity curve within the time window; Updating the well logging porosity curve within the time window based on the central value.

4. The method according to claim 1, characterized in that, The performing a characteristic peak search on the logging gas logging curve to obtain characteristic peak data of the logging gas logging curve includes: Obtaining each gas logging data point of the logging gas logging curve; For each gas logging data point, compare the gas logging data point with adjacent gas logging data points. If the gas logging data point is greater than the adjacent gas logging data point and meets the preset peak condition, determine the gas logging data point as a characteristic peak and add it to the characteristic peak data.

5. The method according to claim 4, wherein Before searching for characteristic peaks in the gas logging curve of the mud logging, the method further includes: Perform data cleaning operations on the gas logging curve of the mud logging to extract the gas logging curve of the effective interval; wherein, the effective interval is a formation interval containing at least one hydrocarbon gas.

6. A depth alignment device for mud logging data, characterized in that, It includes: A data acquisition module, configured to acquire mud logging data and logging data of a target site, extract the gas logging curve from the mud logging data, and extract the neutron logging curve and density logging curve from the logging data; A porosity acquisition module, configured to obtain a logging porosity curve based on the neutron logging curve and the density logging curve; A characteristic peak search module, configured to search for characteristic peaks in the gas logging curve of the mud logging to obtain the characteristic peak data of the gas logging curve of the mud logging; A correction data acquisition module, configured to determine depth correction data based on the characteristic peak data and the logging porosity curve; wherein, the depth correction data includes a depth correction amount and a depth correction direction; A data correction module, configured to perform depth correction on the mud logging data based on the depth correction data; Wherein, the obtaining of the logging porosity curve based on the neutron logging curve and the density logging curve includes: Based on the neutron logging curve, obtain a first apparent neutron porosity under the formation of the target site and a second apparent neutron porosity under the shale layer of the target site; Based on the density logging curve, obtain a first apparent density porosity under the formation of the target site and a second apparent density porosity under the shale layer of the target site; Based on the neutron logging curve and the density logging curve, construct a neutron-density crossplot, and determine formation coordinates and shale point coordinates based on the neutron-density crossplot; wherein, the formation coordinates include the first apparent neutron porosity and the first apparent density porosity, and the shale point coordinates include the second apparent neutron porosity and the second apparent density porosity; Based on the formation coordinates and the shale point coordinates, obtain the logging porosity, and construct the logging porosity curve based on the logging porosity; Wherein, the determining of the depth correction data based on the characteristic peak data and the logging porosity curve includes: Obtain each characteristic peak of the characteristic peak data; For each characteristic peak, obtain the depth difference between the characteristic peak and the adjacent logging porosity peak on the logging porosity curve; Select the depth difference corresponding to the characteristic peak at the highest value as the depth correction amount, and determine the depth correction direction.

7. An electronic device, characterized in that, It includes: A processor, and a memory communicatively connected to the processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory to implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when being executed by a processor.

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