An array induction logging abnormal signal correction method, electronic equipment and storage medium

Abstract

This invention discloses a method, electronic device, and storage medium for correcting abnormal signals in array induction logging. The method includes: acquiring the original conductivity signal obtained after array induction logging of a target well; extracting target statistical values ​​of multiple target conductivity curves at the target frequency of the target source-distance subarray within the target well section of the target well from the original conductivity signal; if the target statistical value is negative, obtaining a correction amount based on the target statistical value and a preset statistical value that is positive; correcting the uncorrected conductivity curves at each frequency of the target source-distance subarray according to the correction amount to obtain corrected conductivity curves at each frequency; wherein the corrected statistical values ​​of multiple corrected conductivity curves at the target frequency within the target well section are greater than or equal to the preset statistical value. This invention avoids the occurrence of negative value anomalies.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of well logging technology in oil exploration, and particularly to a method for correcting abnormal signals in array induction logging, an electronic device, and a storage medium. Background Technology

[0002] Carbonate reservoirs play a crucial role in the seven major oil and gas basins. Extensive actual well drilling data indicates that carbonate formations typically possess high resistivity. Tight reservoirs with poorly developed fractures and vulnerabilities and low matrix porosity often exhibit resistivity reaching thousands or even tens of thousands of ohm-meters (Ω·m), correspondingly with extremely low electrical conductivity, typically only a few millisiemens per meter (mS / m).

[0003] Currently, there are two main methods for open-hole resistivity logging: array induction logging and lateral logging. Array induction logging instruments are generally suitable for low-resistivity formations with resistivity below 100 Ω·m, measuring electrical conductivity. Lateral logging instruments, on the other hand, directly measure resistivity and can be used for resistivity measurements ranging from several thousand to tens of thousands of Ω·m, but require conductive mud in the wellbore.

[0004] In recent years, with the increase in drilling depth, the complex stress environment has necessitated the use of non-conductive oil-based mud (i.e., oil-based drilling fluid) in carbonate formation drilling. Consequently, induction logging instruments have become essential for open-hole resistivity logging. However, the high temperature and pressure at depth cause significant temperature drift in the circuitry of induction logging instruments. Coupled with the extremely low background values ​​of the measured object, this often results in negative values ​​in the raw conductivity signals acquired by induction logging instruments. Consequently, the output resistivity curve does not conform to the formation characteristics and cannot be used for formation evaluation, a problem that urgently needs to be solved. Summary of the Invention

[0005] This invention provides a method, electronic device, and storage medium for correcting abnormal signals in array induction logging, which solves the problem that the original conductivity signal often shows negative abnormal values.

[0006] According to one aspect of the present invention, a method for correcting anomaly signals in array induction logging is provided, which may include:

[0007] The raw conductivity signal obtained after array induction logging of the target well is acquired. The target conductivity curve at the target frequency of the target source distance subarray is extracted from the raw conductivity signal, and multiple target conductivity curves at the target well section of the target well are obtained.

[0008] Multiple target conductivity target statistical values ​​are obtained. When the target statistical value is negative, a correction value is obtained based on the target statistical value and the preset statistical value, where the preset statistical value is positive.

[0009] Based on the correction amount, the conductivity curves before correction at each frequency of the target source distance subarray are corrected to obtain the conductivity curves after correction at each frequency.

[0010] Among them, the corrected statistical values ​​of multiple corrected conductivity curves at the target frequency in the target well section are greater than or equal to the preset statistical values.

[0011] According to another aspect of the present invention, an electronic device is provided, which may include:

[0012] At least one processor; and

[0013] A memory that is communicatively connected to at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by at least one processor, such that when the at least one processor executes the program, it implements the method for correcting array induction logging anomaly signals provided in any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided having computer instructions stored thereon, the computer instructions being configured to cause a processor to execute and implement the method for correcting array induction logging anomaly signals provided in any embodiment of the present invention.

[0016] The technical solution of this invention involves acquiring the raw conductivity signal obtained after array induction logging of a target well, extracting the target conductivity curve at the target frequency of the target source-distance subarray from the raw conductivity signal, and obtaining multiple target conductivity values ​​of the target conductivity curve in the target well section of the target well. Then, target statistical values ​​of the multiple target conductivity values ​​are obtained. If the target statistical value is negative, i.e., there is a negative value anomaly, a correction amount is obtained based on the target statistical value and a preset statistical value that is positive. Furthermore, based on the correction amount, the pre-correction conductivity curve at each frequency of the target source-distance subarray is corrected to obtain the corrected conductivity curve for each frequency. This ensures that the corrected statistical values ​​of the multiple corrected conductivity values ​​of the corrected conductivity curve at the target frequency in the target well section are greater than or equal to the preset statistical value. This technical solution, by adaptively correcting the raw conductivity signal obtained from array induction logging, avoids negative value anomalies, thereby ensuring that the final output resistivity curve conforms to formation characteristics and can be used for formation evaluation.

[0017] It should be understood that the description in this section is not intended to identify key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a method for correcting abnormal signals in array induction logging according to an embodiment of the present invention;

[0020] Figure 2 This is a flowchart of another method for correcting abnormal signals in array induction logging according to an embodiment of the present invention;

[0021] Figure 3 This is a flowchart of another method for correcting abnormal signals in array induction logging according to an embodiment of the present invention;

[0022] Figure 4 This is a flowchart of another method for correcting abnormal signals in array induction logging according to an embodiment of the present invention;

[0023] Figure 5 This is a flowchart of an optional example of a method for correcting abnormal signals in array induction logging provided by an embodiment of the present invention;

[0024] Figure 6a This is a schematic diagram of an example of anomaly in the average conductivity of four long-source-pitch subarrays at eight frequencies in a tight layer, in another method for correcting abnormal signals in array induction logging provided by an embodiment of the present invention.

[0025] Figure 6b This is a schematic diagram of an example of calibration of four long-source-distance subarrays in a tight formation in a method for correcting abnormal signals in array induction logging provided by an embodiment of the present invention.

[0026] Figure 6c This is a schematic diagram illustrating an example of processing an array induction logging signal in a deep carbonate rock formation in another method for correcting abnormal signals in array induction logging according to an embodiment of the present invention.

[0027] Figure 7 This is a structural block diagram of a correction device for abnormal signals in array induction logging according to an embodiment of the present invention;

[0028] Figure 8 This is a schematic diagram of the structure of an electronic device for implementing the correction method for abnormal signals in array induction logging according to an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. The same applies to "target," "original," etc., and will not be repeated here. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 This is a flowchart illustrating a method for correcting abnormal signals in array induction logging provided in this embodiment of the invention. This embodiment is applicable to adaptive correction of the raw conductivity signal obtained from array induction logging. The method can be executed by the array induction logging abnormal signal correction device provided in this embodiment of the invention. This device can be implemented in software and / or hardware and can be integrated into an electronic device, which can be various user terminals or servers.

[0032] See Figure 1 The method of this invention specifically includes the following steps:

[0033] S110. Obtain the original conductivity signal obtained after array induction logging of the target well, extract the target conductivity curve at the target frequency of the target source distance subarray from the original conductivity signal, and obtain multiple target conductivity curves at the target well section of the target well.

[0034] The target well can be understood as the well for which resistivity curve measurement is to be performed. The original conductivity signal can be understood as the signal obtained after array induction logging of the target well. This signal can include the conductivity measured at all frequencies of each subarray, and each subarray corresponds to a specific source distance.

[0035] The target source distance subarray can be understood as a subarray under a target source distance, which can be understood as the source distance that is prone to negative anomalies among all source distances corresponding to the original conductivity signal. Based on this, and considering the application scenarios that may be involved in the embodiments of this invention, for example, the target source distance can be derived from the longest first preset number of source distances X among all source distances; that is, these source distances X can each be a target source distance. In the embodiments of this invention, if there are multiple target source distances, each target source distance can be processed separately.

[0036] The target frequency can be understood as the frequency corresponding to the original conductivity signal, or it can be understood as the frequency corresponding to the target source-to-subarray. Furthermore, this frequency can be one of the frequencies most prone to negative values ​​among all the corresponding frequencies. Based on this, and considering the application scenarios that may be involved in the embodiments of this invention, for example, the target frequency can originate from the largest second preset number of frequencies X among all the frequencies corresponding to the original conductivity signal; that is, these frequencies X can each be a target frequency. In the embodiments of this invention, if multiple target frequencies exist, each target frequency can be processed separately.

[0037] The original conductivity signal may include multiple conductivity curves. Based on this, the target conductivity curve can be understood as the conductivity curve of the target source distance sub-array at the target frequency.

[0038] The target well section can be understood as the section within the entire target well that is prone to negative anomalies. Based on this, the target conductivity can be understood as the conductivity of the target conductivity curve within the target well section.

[0039] The original conductivity signal is acquired, the target conductivity curve is extracted from the original conductivity signal, and multiple target conductivity values ​​are obtained from the target conductivity curve.

[0040] S120. Obtain target statistical values ​​for multiple target conductivities, and if the target statistical value is negative, obtain a correction value based on the target statistical value and the preset statistical value, wherein the preset statistical value is positive.

[0041] In this process, statistical analysis is performed on multiple target electrical conductivities to obtain target statistical values. Based on this, and considering the application scenarios that may be involved in the embodiments of this invention, the target statistical value may optionally be the average value, median value, or mode, especially the mathematical average value. This is because the mathematical average value reflects the average level trend of all sampling points in the target well section (i.e., the target electrical conductivities mentioned above). The samples come from the population, and each sampling point has the same weight. Therefore, after the translation scale correction, the correction amount of each sampling point is exactly the same, and the final result can more objectively reflect the electrical characteristics of the actual formation.

[0042] Furthermore, if the target statistical value is negative, this indicates that at least some of the target conductivity values ​​exhibit negative anomalies. In this case, a correction amount can be obtained based on the target statistical value and a preset statistical value. The target statistical value can then be recalibrated based on this correction amount, ensuring that the corrected (i.e., recalibrated) target statistical value (i.e., the corrected statistical value described below) is greater than or equal to the preset statistical value, which is positive. For example, the correction amount can be obtained by subtracting the target statistical value from the preset statistical value; or by subtracting the target statistical value from the preset statistical value by a preset multiple greater than 1; etc. The specific method of obtaining the correction amount can be set according to actual needs and is not specifically limited here.

[0043] S130. Based on the correction amount, the conductivity curves before correction at each frequency of the target source distance subarray are corrected to obtain the conductivity curves after correction at each frequency.

[0044] Among them, the corrected statistical values ​​of multiple corrected conductivity curves at the target frequency in the target well section are greater than or equal to the preset statistical values.

[0045] The target source distance subarray corresponds to multiple frequencies, and the target frequency described above is one of these multiple frequencies. The conductivity curve before correction can be understood as the conductivity curve before correction at a certain frequency, that is, each frequency corresponds to a different conductivity curve before correction.

[0046] Based on the correction amount, each conductivity curve before correction is corrected separately. Specifically, a translation scale correction can be performed separately to obtain the corrected conductivity curve for each frequency.

[0047] It should be noted that, to facilitate the distinction between the conductivity and statistical values ​​before and after correction, the conductivity before correction was referred to as the target conductivity, and the statistical value before correction was referred to as the target statistical value. Here, the conductivity of the corrected conductivity curve in the target well section (i.e., the corrected conductivity) is referred to as the corrected conductivity, and the statistical value of multiple corrected conductivity curves (i.e., the corrected statistical value) is referred to as the corrected statistical value. Furthermore, it is important to emphasize that the corrected statistical value corresponding to each corrected conductivity curve obtained through the above steps is greater than or equal to the preset statistical value.

[0048] The technical solution of this invention involves acquiring the raw conductivity signal obtained after array induction logging of a target well, extracting the target conductivity curve at the target frequency of the target source-distance subarray from the raw conductivity signal, and obtaining multiple target conductivity values ​​of the target conductivity curve in the target well section of the target well. Then, target statistical values ​​of the multiple target conductivity values ​​are obtained. If the target statistical value is negative, i.e., there is a negative value anomaly, a correction amount is obtained based on the target statistical value and a preset statistical value that is positive. Furthermore, based on the correction amount, the pre-correction conductivity curve at each frequency of the target source-distance subarray is corrected to obtain the corrected conductivity curve for each frequency. This ensures that the corrected statistical values ​​of the multiple corrected conductivity values ​​of the corrected conductivity curve at the target frequency in the target well section are greater than or equal to the preset statistical value. This technical solution, by adaptively correcting the raw conductivity signal obtained from array induction logging, avoids negative value anomalies, thereby ensuring that the final output resistivity curve conforms to formation characteristics and can be used for formation evaluation.

[0049] An optional technical solution, after obtaining the corrected conductivity curves for each frequency, the above correction method may further include: combining all corrected conductivity curves and all remaining conductivity curves for the remaining source distance subarrays in the original conductivity signal, so as to generate the resistivity curve of the target well based on the obtained combination result.

[0050] The remaining source distance subarray can be understood as the subarray under the remaining source distance, which can be understood as the source distance other than the target source distance among all the source distances corresponding to the original conductivity signal, i.e., the source distances that are less likely to have negative anomalies. The original conductivity signal includes multiple conductivity curves. In order to distinguish them from the target conductivity curve and the conductivity curve before correction described above, the conductivity curve located under the remaining source distance subarray is referred to as the remaining conductivity curve. The number of these remaining conductivity curves can be one or more, and in particular, multiple, depending on the actual situation, and no specific limitation is made here.

[0051] Based on this, all the corrected conductivity curves and all the remaining conductivity curves are combined to obtain the combined conductivity curve of the target well (i.e., the combined result). Furthermore, the resistivity curve of the target well (i.e., the array resistivity curve) can be generated based on the combined conductivity curve, thus realizing the resistivity curve measurement.

[0052] Figure 2This is a flowchart of another method for correcting abnormal signals in array induction logging provided in this embodiment of the invention. This embodiment is based on and optimized from the above-mentioned technical solutions. In this embodiment, optionally, the number of target frequencies is at least two, and each target frequency corresponds to its own target statistical value. When the target statistical value is negative, the correction amount is obtained based on the target statistical value and a preset statistical value, including: when there are negative values ​​among all target statistical values, determining a correction statistical value from all target statistical values; and obtaining the correction amount based on the correction statistical value and the preset statistical value. The explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0053] See Figure 2 The method in this embodiment may specifically include the following steps:

[0054] S210. Obtain the raw conductivity signal obtained after array induction logging of the target well.

[0055] S220. For each target frequency corresponding to the target source distance subarray in the original conductivity signal, extract the target conductivity curve at that target frequency from the target source distance subarray, and obtain the target statistical values ​​of multiple target conductivity values ​​of the target conductivity curve in the target well section of the target well.

[0056] The number of target frequencies corresponding to the target source distance subarray can be two or more. Therefore, in the steps, each target frequency is processed separately to obtain the target statistical value corresponding to each target frequency, and then it is determined whether there is a negative target statistical value among the target statistical values.

[0057] S230. In the case that there are negative values ​​among the target statistical values, determine the correction statistical value from all the target statistical values, and obtain the correction amount based on the correction statistical value and the preset statistical value, wherein the preset statistical value is a positive value.

[0058] In this context, the presence of negative values ​​among the target statistics indicates an anomaly. A correction statistic can then be determined from all target statistics, serving as the target statistic used to determine the correction amount. Based on this, and considering potential application scenarios in this embodiment, optionally, the smallest target statistic among all target statistics can be used as the correction statistic. This ensures that the determined correction amount can correct each target statistic to a positive value, particularly correcting each negative target statistic to a positive value, thus better addressing the anomaly problem. Alternatively, the correction statistic can be determined using other methods, such as using the median or mode among all target statistics. This can be set according to actual needs and is not specifically limited here.

[0059] Furthermore, a correction amount is obtained based on the correction statistic and the preset statistic. This correction amount is then used to recalibrate the correction statistic, ensuring that the corrected (i.e., recalibrated) correction statistic is greater than or equal to the preset statistic. For example, the correction amount can be obtained by subtracting the correction statistic from the preset statistic; or by subtracting the correction statistic from the preset statistic by a preset multiple greater than 1; and so on. The specific method for obtaining the correction amount from the correction statistic can be set according to actual needs and is not specifically limited here.

[0060] S240. Based on the correction amount, the conductivity curves before correction at each frequency of the target source distance subarray are corrected to obtain the conductivity curves after correction at each frequency.

[0061] Among them, the corrected statistical values ​​of multiple corrected conductivity curves at the target frequency in the target well section are greater than or equal to the preset statistical values.

[0062] The technical solution of this invention achieves accurate acquisition of the correction amount by determining the correction statistical value from all target statistical values ​​and then obtaining the correction amount based on the correction statistical value.

[0063] Figure 3 This is a flowchart of another method for correcting abnormal signals in array induction logging provided in this embodiment of the invention. This embodiment is based on and optimized from the above-mentioned technical solutions. In this embodiment, optionally, before obtaining multiple target conductivity curves of the target conductivity curve in the target well section, the above correction method further includes: identifying tight layers from all well sections of the target well; and determining the target well section from all tight layers based on the burial depth and continuous thickness corresponding to each tight layer. The explanations of terms that are the same as or corresponding to those in the above embodiments are not repeated here.

[0064] See Figure 3 The method in this embodiment may specifically include the following steps:

[0065] S310. Identify the dense layer from all the well sections of the target well, and determine the target well section from all the dense layer sections based on the burial depth and continuous thickness of each dense layer.

[0066] In this context, a tight section can be understood as a section within the target well that is prone to negative value anomalies. The number of such tight sections can be one or more, depending on the specific circumstances, and is not specifically limited here. Further, for each tight section, its burial depth and continuous thickness are obtained. Then, based on the burial depth and continuous thickness of each tight section, the target well section can be determined from all tight sections. This target well section can be understood as the tight section most prone to negative value anomalies.

[0067] S320. Obtain the original conductivity signal obtained after array induction logging of the target well, extract the target conductivity curve at the target frequency of the target source distance subarray from the original conductivity signal, and obtain multiple target conductivity curves at the target well section of the target well.

[0068] S330. Obtain target statistical values ​​for multiple target conductivities, and if the target statistical value is negative, obtain a correction value based on the target statistical value and the preset statistical value, where the preset statistical value is positive.

[0069] S340. Based on the correction amount, the conductivity curves before correction at each frequency of the target source distance subarray are corrected to obtain the conductivity curves after correction at each frequency.

[0070] Among them, the corrected statistical values ​​of multiple corrected conductivity curves at the target frequency in the target well section are greater than or equal to the preset statistical values.

[0071] The technical solution of this invention determines the tight layer section of the target well, and then determines the target well section based on the burial depth and continuous thickness of each tight layer section. This achieves accurate determination of the target well section, which is an important prerequisite for subsequent accurate correction.

[0072] An optional technical solution involves identifying tight zones from all sections of the target well, including:

[0073] Acquire conventional logging data of the target well. The conventional logging data is the conventional logging curve with the difference between the corresponding measurement depth interval and the corresponding measurement depth interval of the array induction logging within a preset range and is aligned with the depth of the array induction logging.

[0074] Based on conventional logging data, the porosity and permeability of the target well are obtained. Based on the porosity and permeability, the dense layers are identified from all sections of the target well.

[0075] The above technical solution uses conventional logging data to obtain the porosity and permeability of the target well, and then uses these two parameters to accurately identify the dense layer.

[0076] Figure 4 This is a flowchart illustrating another method for correcting abnormal signals in array induction logging provided in this embodiment of the invention. This embodiment optimizes the above-described technical solutions and provides an example of calibration correction for abnormal signals in high-resistivity array induction logging of oil-based drilling fluids. Explanations of terms that are the same as or corresponding to those in the above embodiments will not be repeated here.

[0077] See Figure 4 and Figure 5 The method in this embodiment may specifically include the following steps:

[0078] S410. Acquire the raw conductivity signal obtained after array induction logging of the target well, and acquire the conventional logging data of the target well.

[0079] For example, the raw data of array induction logging and the corresponding conventional logging data of the target well are collected. The raw data of array induction logging can be understood as logging data that includes all sub-arrays and all frequencies of conductivity measurements of each sub-array are complete, i.e., the raw conductivity signal in the above steps. The conventional logging data can be understood as conventional logging curves that are basically consistent with the measurement depth range of array induction logging (i.e., the difference is within the preset range) and are aligned with the depth of array induction logging.

[0080] S420. Based on conventional logging data, obtain the porosity and permeability of the target well.

[0081] For example, for conventional logging data, a preset model is used to calculate the porosity and permeability of the formation throughout the well section. This model can be understood as an empirical formula for calculating porosity and permeability after calibration using core experimental data from the block where the target well is located. Its calculation accuracy should meet relevant industry standards.

[0082] S430. Based on porosity and permeability, identify the dense layers in all sections of the target well.

[0083] For example, based on the calculated porosity-permeability curve, dense layers are identified according to relevant industry standards and labeled from top to bottom as S1, S2, ... S M In this embodiment of the invention, optionally, well sections with porosity and permeability below the effective reservoir lower limit standard, which do not produce fluid or have a daily production rate below the industrial standard, can be designated as tight reservoir sections. The aforementioned effective reservoir lower limit standard is a preset value.

[0084] S440. Based on the burial depth and continuous thickness of each tight layer, determine the target well section from all tight layers.

[0085] For example, among all the dense strata, the dense stratum S with the largest burial depth and a continuous thickness exceeding 5m is selected. KThe formation with the highest temperature is the one in which the original conductivity signal of the array induction logging is most likely to show negative anomalies. It is also the section with the largest amplitude of negative anomalies in the entire measurement well section, and this section is used as the target well section.

[0086] S450. For each target frequency corresponding to the target source distance subarray in the original conductivity signal, extract the target conductivity curve at that target frequency from the target source distance subarray, and obtain the mathematical average of multiple target conductivity values ​​of the target conductivity curve in the target well section of the target well.

[0087] Among them, the target source distance subarray is a subarray under the target source distance. The target source distance is derived from the longest first preset number of source distances among all source distances corresponding to the original conductivity signal; the target frequency is derived from the largest second preset number of frequencies among all frequencies corresponding to the target source distance subarray.

[0088] For example, taking a first preset quantity of 4 and a second preset quantity of N as an example, the conductivity curves of N frequencies of the four long-source-pitch subarrays (i.e., the four subarrays with the longest source pitches among all source pitches) of the array induction logging are calculated, respectively in the dense layer segment S. K The mathematical average of all sampling points (i.e., the frequency conductivity described above) is denoted as

[0089] S460. Determine whether there are negative values ​​among the mathematical averages corresponding to all target frequencies.

[0090] For example, a two-dimensional graph is drawn, with the horizontal axis representing N frequencies and the vertical axis representing the mathematical average, and each... Projected onto a two-dimensional chart to facilitate analysis of various aspects. Distribution of [something].

[0091] Next, each long-source-pitch subarray is processed separately. Specifically, for the i-th long-source-pitch subarray, it is determined whether its two highest frequencies (i.e., N and N-1) exhibit negative anomalies. In practical applications, optionally, it can also be determined whether the minimum value among the mathematical averages corresponding to the two highest frequencies is negative. This helps to directly apply the minimum value in subsequent steps if it is negative.

[0092] S470 If yes, then the correction amount = preset average value - the smallest mathematical average value among all mathematical average values, and continue to execute S480, where the preset average value is a positive value; otherwise, execute S490.

[0093] For example, if the mathematical average of either of the two highest frequencies is negative, the minimum value between the two is selected. The correction amount is determined by the preset average value. Here, we take a preset average value of 0.25 as an example. This ensures that after translational correction is completed, in the dense layer segment S... K The i-th long source distance subarray and All are no less than 0.25 mS / m. Then, S480 is executed to correct the conductivity curves of the N frequencies of the i-th long-pitch subarray.

[0094] Of course, if the mathematical average values ​​corresponding to the two highest frequencies are not negative, then there is no need to correct the conductivity curves of the N frequencies of the i-th long source-pitch subarray.

[0095] S480. Based on the correction amount, the conductivity curves before correction at each frequency of the target source distance subarray are corrected to obtain the conductivity curves after correction at each frequency.

[0096] Among them, the mathematical average of the corrected conductivity curves at the target frequency and the multiple corrected conductivity values ​​in the target well section is greater than or equal to the preset average value.

[0097] For example, for the raw array induction logging data of the target well, the conductivity curves of N frequencies of the long source-pitch subarray with negative anomalies are subjected to translational calibration correction throughout the entire well section, i.e., the same correction amount is applied to all of them.

[0098] S490. For the remaining conductivity curves of the remaining source distance subarrays in the original conductivity signal, combine all corrected conductivity curves and all remaining conductivity curves to generate the resistivity curve of the target well based on the obtained combination result. The remaining source distances corresponding to the remaining source distance subarrays are the source distances other than the target source distance among all source distances.

[0099] For example, the short source distance subarrays (i.e. the remaining source distance subarrays described above) that have not undergone translation scale correction in the raw array induction logging data are combined with the four long source distance subarrays that have undergone translation scale correction to generate the array resistivity curve of the target well based on the combination result.

[0100] Building upon this, to demonstrate that the aforementioned calibration example can generate resistivity curves consistent with formation characteristics, an illustrative explanation is provided below with specific illustrations. For an example, see [link to illustrative example]. Figures 6a-6c :

[0101] Figure 6a The tight section S of a certain well was shown. K The average conductivity at eight frequencies of the four long-pitch subarrays (i.e., the mathematical averages described above) Figure 6aAn example of anomaly in average conductivity (in the sample). In this example, the array induction logging instrument uses 8 frequencies per subarray in the selected tight formation S. K The average conductivity of the four long-pitch subarrays at eight frequencies was calculated, with the highest frequencies 7 and 8 showing negative anomalies.

[0102] Figure 6b Showing the Figure 6a The example shown is the result after calibration. It can be seen that after calibration calibration of the four long-source-pitch subarrays, in the compact layer segment S... K The average conductivity at frequencies 7 and 8 is greater than 0.25 mS / m.

[0103] Figure 6c The image shows an example of array induction logging and conventional logging curves and calibration correction in a deep carbonate formation. Tracks 3, 4, and 5 in the figure represent the conductivity curves at the three highest frequencies of several long-spacing subarrays in the array induction logging, within the tight layer S marked in the figure. K Subarrays 5 and 7 both exhibit significant negative conductivity anomalies. Channel 7 shows the array resistivity curve obtained after processing the original conductivity signal without calibration correction. The curves at different detection depths from top to bottom are flat and overlap, completely failing to reflect the true rock physical characteristics of the strata. Channel 8 shows the array resistivity curve obtained after calibration correction using this calibration example. Its accuracy is significantly improved, proving the effectiveness of the above calibration example.

[0104] In the example above, to address the negative anomaly in the raw conductivity signal of the array induction logging, a dense layer segment S with the highest resistivity, lowest conductivity, and greatest burial depth was selected. K The average conductivity of the two highest frequencies of the long-source-pitch subarray is calibrated to ensure that the average conductivity of these two highest frequencies is not lower than a certain lower limit (i.e., 0.25 mS / m in the example above). Then, this calibration is used to calibrate the conductivity curves of the long-source-pitch subarray at all frequencies throughout the entire measurement well section.

[0105] The technical solution of this invention selects a reasonable high-resistivity, tight formation and shifts the average conductivity of the "best" conductivity curves in the array induction logging instrument to 0.25 mS / m. After the shift, even in the high-resistivity, tight formation, the conductivity at each specific point may still be less than or greater than 0.25 mS / m, but the average conductivity will not be less than 0.25 mS / m, or approximately 4000 Ω·m. This adaptive calibration correction method can significantly improve the conductivity curves with the longest source spacing and relatively good quality in the array induction logging instrument, thereby making the final array resistivity curve conform to formation characteristics and usable for oil and gas reservoir interpretation and evaluation.

[0106] Figure 7 This is a structural block diagram of a device for correcting abnormal signals in array induction logging provided in an embodiment of the present invention. This device is used to execute the method for correcting abnormal signals in array induction logging provided in any of the above embodiments. This device and the method for correcting abnormal signals in array induction logging in the above embodiments belong to the same inventive concept. Details not described in detail in the embodiments of the device for correcting abnormal signals in array induction logging can be found in the embodiments of the method for correcting abnormal signals in array induction logging. See also... Figure 7 The device may specifically include: a target conductivity obtaining module 510, a correction amount obtaining module 520, and a corrected conductivity curve obtaining module 530.

[0107] Among them, the target conductivity acquisition module 510 is used to acquire the original conductivity signal obtained after array induction logging of the target well, extract the target conductivity curve at the target frequency of the target source distance subarray from the original conductivity signal, and obtain multiple target conductivity curves at the target well section of the target well.

[0108] The correction quantity acquisition module 520 is used to obtain target statistical values ​​of multiple target conductivity, and when the target statistical value is negative, to obtain a correction quantity based on the target statistical value and a preset statistical value, wherein the preset statistical value is positive.

[0109] The module 530, which obtains the corrected conductivity curve, can be used to correct the pre-correction conductivity curve of the target source distance subarray at each frequency according to the correction amount, and obtain the corrected conductivity curve at each frequency. Among them, the corrected statistical values ​​of multiple corrected conductivity curves at the target frequency in the target well section are greater than or equal to the preset statistical values.

[0110] Optionally, the number of target frequencies is at least two, and each target frequency corresponds to its own target statistical value. The correction quantity obtaining module 520 may include:

[0111] The correction statistics determination submodule is used to determine the correction statistics from all target statistics when there are negative values ​​among the target statistics.

[0112] The correction amount acquisition submodule is used to obtain the correction amount based on the correction statistics and preset statistics.

[0113] Based on this, the optional correction statistics determination submodule may include:

[0114] The correction statistic determination unit is used to take the smallest target statistic among all target statistic values ​​as the correction statistic.

[0115] Optionally, the calibration quantity obtaining module 520 may include:

[0116] The correction quantity acquisition unit is used to subtract the target statistical value from the preset statistical value to obtain the correction quantity.

[0117] Optionally, the above-mentioned calibration device may further include:

[0118] The tight zone identification module is used to identify tight zones from all sections of the target well before obtaining multiple target conductivity curves for the target well section;

[0119] The target well section determination module is used to determine the target well section from all the tight layers based on the burial depth and continuous thickness of each tight layer.

[0120] Based on this, the optional dense layer segment identification module may include:

[0121] The conventional logging data acquisition unit is used to acquire conventional logging data of the target well. The conventional logging data is a conventional logging curve whose difference between the corresponding measurement depth interval and the corresponding measurement depth interval of the array induction logging is within a preset range and is aligned with the depth of the array induction logging.

[0122] The tight section identification unit is used to obtain the porosity and permeability of the target well based on conventional logging data, and to identify the tight section from all sections of the target well based on the porosity and permeability.

[0123] Optionally, the above-mentioned calibration device may further include:

[0124] The resistivity curve generation module is used to combine all the corrected conductivity curves and all the remaining conductivity curves under the other source distance subarray in the original conductivity signal after obtaining the corrected conductivity curves at each frequency, so as to generate the resistivity curve of the target well based on the obtained combination result.

[0125] Based on any of the above devices, the following options are available:

[0126] The target source distance subarray is a subarray with a target source distance, which is derived from the longest first preset number of source distances among all source distances corresponding to the original conductivity signal; and / or,

[0127] The target frequency is derived from the largest of the second preset number of frequencies among all frequencies corresponding to the original conductivity signal; and / or,

[0128] The target statistic is the mathematical mean.

[0129] The array induction logging anomaly signal correction device provided in this embodiment of the invention acquires the original conductivity signal obtained after array induction logging of the target well through a target conductivity acquisition module, and extracts the target conductivity curve at the target frequency of the target source distance subarray from the original conductivity signal, and then obtains multiple target conductivity values ​​of the target conductivity curve in the target well section of the target well. Then, through a correction amount acquisition module, the target statistical values ​​of the multiple target conductivity values ​​are obtained, and when the target statistical value is negative, that is, there is a negative value anomaly, the correction amount is obtained based on the target statistical value and the preset statistical value that is positive. Further, through a correction conductivity curve acquisition module, the pre-correction conductivity curve at each frequency of the target source distance subarray is corrected according to the correction amount to obtain the correction conductivity curve at each frequency, thereby making the correction statistical value of the multiple correction conductivity values ​​of the correction conductivity curve at the target frequency in the target well section greater than or equal to the preset statistical value. The aforementioned device adaptively corrects the raw conductivity signal obtained from array induction logging, thereby avoiding negative value anomalies and ensuring that the final output resistivity curve conforms to formation characteristics, which can be used for formation evaluation.

[0130] The array induction logging abnormal signal correction device provided in this embodiment of the invention can execute the array induction logging abnormal signal correction method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0131] It is worth noting that in the embodiments of the above-mentioned array induction logging abnormal signal correction device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0132] Figure 8 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0133] like Figure 8As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0134] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0135] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the correction method for array induction logging anomaly signals.

[0136] In some embodiments, the method for correcting array induction logging anomaly signals can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for correcting array induction logging anomaly signals described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for correcting array induction logging anomaly signals by any other suitable means (e.g., by means of firmware).

[0137] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0141] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0142] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0143] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0144] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of correcting for array induction logging anomalies, comprising: include: The raw conductivity signal obtained after array induction logging of the target well is acquired. The target conductivity curve at the target frequency of the target source distance subarray is extracted from the raw conductivity signal, and multiple target conductivity curves at the target well section of the target well are obtained. Multiple target statistical values ​​of the target conductivity are obtained, and when the target statistical value is negative, a correction amount is obtained based on the target statistical value and a preset statistical value, wherein the preset statistical value is positive. Based on the correction amount, the pre-correction conductivity curves of the target source distance sub-array at each frequency are corrected to obtain the post-correction conductivity curves for each frequency. Wherein, the corrected statistical values ​​of the corrected conductivity curve at the target frequency at the target well section are greater than or equal to the preset statistical value.

2. The method of claim 1, wherein, The number of target frequencies is at least two, and each target frequency corresponds to its own target statistical value. Then, when the target statistical value is negative, a correction amount is obtained based on the target statistical value and a preset statistical value, including: If any of the target statistical values ​​are negative, a correction statistical value is determined from all the target statistical values. The correction amount is obtained based on the correction statistics and the preset statistics.

3. The method of claim 2, wherein, Determining the correction statistic from all the target statistic values ​​includes: The smallest of all the target statistical values ​​is taken as the correction statistical value.

4. The method of claim 1, wherein, The step of obtaining the correction amount based on the target statistical value and the preset statistical value includes: The correction amount is obtained by subtracting the target statistical value from the preset statistical value.

5. The method of claim 1, wherein, Before obtaining the target conductivity curves for multiple target conductivity values ​​in the target well section of the target well, the method further includes: Identify tight formations in all sections of the target well; The target well section is determined from all the dense layers based on the burial depth and continuous thickness corresponding to each of the dense layers.

6. The method of claim 5, wherein, The process of identifying tight formations from all sections of the target well includes: Obtain conventional logging data of the target well, wherein the conventional logging data is a conventional logging curve whose difference between the corresponding measurement depth interval and the corresponding measurement depth interval of the array induction logging is within a preset range and is depth-aligned with the array induction logging. Based on the conventional logging data, the porosity and permeability of the target well are obtained, and based on the porosity and permeability, tight layers are identified from all sections of the target well.

7. The method of claim 1, wherein, After obtaining the corrected conductivity curves for each of the aforementioned frequencies, the method further includes: For the remaining conductivity curves under the remaining source distance subarray in the original conductivity signal, all the corrected conductivity curves and all the remaining conductivity curves are combined to generate the resistivity curve of the target well based on the obtained combination result.

8. The method according to any one of claims 1 to 7, characterized in that, The target source distance subarray is a subarray under a target source distance, and the target source distance is derived from the longest first preset number of source distances among all source distances corresponding to the original conductivity signal; and / or The target frequency is derived from the largest second preset number of frequencies among all frequencies corresponding to the original conductivity signal; and / or The target statistical value is the mathematical average.

9. An electronic device, comprising: include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the correction method for array induction logging anomaly signals as described in any one of claims 1-8.

10. A computer readable storage medium characterized by, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the correction method for array induction logging anomaly signals as described in any one of claims 1-8.

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