Multi-well consistency correction method and system

Abstract

The present application relates to a kind of multi-well consistency correction method and system, wherein the steps of the method include, obtaining the well logging curve data of multiple wells;Well logging curve quality evaluation is carried out on well logging curve data, to select standard well according to well logging curve quality result;Wherein the well logging curve data of standard well is first logging data;The remaining logging data is second logging data;First logging data is substituted into the preset rock physics model to carry out fitting, to obtain the first model that is well fitted;Based on first logging data, second logging data and first model, correction processing is carried out, to obtain second logging correction data.The consistency correction of logging data between multiple wells is realized, and then the accuracy and reliability of oil and gas exploration and development are improved.

Description

Technical Field

[0001] The present invention relates to the field of petroleum exploration and development, and in particular to a multi-well consistency correction method and system. Background Art

[0002] Mixed sedimentary areas typically refer to sedimentary environments with complex geological characteristics, where sediments are composed of two or more different mineral components. Strata deposited under such sedimentary environments typically lack stable marker layers. Multi-well consistency correction, also known as well-logging curve standardization, is a common research topic in oil and gas exploration and development. Normally, the ranges of well-logging curves from different wells in the same region and in the same formation are very similar. However, in actual oil and gas exploration and development, different logging construction times, different logging companies, different logging instruments, different logging technologies, and different logging personnel may result in significant differences in the ranges of well-logging curves from different wells in the same formation. These large differences in well-logging curve ranges due to engineering and human factors can lead to deviations in understanding oil and gas geology, which in turn affects the direction of oil and gas exploration. Multi-well consistency correction of well-logging curves is designed to address this issue. Summary of the Invention

[0003] The present invention provides a multi-well consistency correction method and system, which can solve the problem of oil and gas geological recognition deviation.

[0004] In order to solve the above technical problems, the steps of the multi-well consistency correction method include:

[0005] S1: Acquire logging curve data of multiple wells;

[0006] S2: performing a logging curve quality assessment on the logging curve data to select a standard well according to the logging curve quality result; wherein the logging curve data of the standard well is the first logging data, and the remaining logging data is the second logging data;

[0007] S3: Substituting the first well logging data into a preset rock physics model for fitting to obtain a fitted first model;

[0008] S4: Based on the first well logging data, the second well logging data and the first model, the second well logging data is corrected to obtain second well logging correction data, thereby achieving multi-well consistency correction.

[0009] In one embodiment, the second well logging correction data includes second well logging first correction data;

[0010] The step S4 comprises:

[0011] Substituting the first well logging data into the first model to obtain first corrected first well logging data;

[0012] Substituting the second well logging data into the first model to obtain the second well logging first revised data;

[0013] performing a difference operation based on the first corrected data of the first well logging and the first corrected data of the second well logging to obtain a first difference;

[0014] The second logging data is calculated based on the first difference to obtain the second logging first correction data.

[0015] In one embodiment, the second well logging correction data further includes second well logging second correction data;

[0016] The step S4 further includes:

[0017] Substituting the first corrected data of the first well logging into the preset rock physics model for fitting to obtain a second model;

[0018] Substituting the first revised data of the first well logging into the second model to obtain the second revised data of the first well logging;

[0019] Substituting the first correction data of the second well logging into the second model to obtain second revised data of the second well logging;

[0020] performing a difference calculation based on the second revised data of the first well logging and the second revised data of the second well logging to obtain a second difference;

[0021] The first corrected data of the second well logging is based on the second difference to obtain second corrected data of the second well logging.

[0022] In one embodiment, step S1 includes:

[0023] Performing wellbore environment analysis based on the well logging curve data to screen out candidate standard wells that meet preset wellbore conditions;

[0024] The candidate standard wells are subjected to logging curve principle analysis and logging lithology analysis to determine wells that meet preset logging data quality standards and formation feature reflection accuracy requirements as standard wells.

[0025] In one embodiment, the step S1 includes:

[0026] For the first well logging data and the second well logging data: determining a distorted data segment of a distorted well logging curve in the well logging data by comparing with a preset threshold;

[0027] Fitting the normal data segment of the distorted logging curve to the data segment of the normal logging curve to obtain a fitting coefficient of the normal data segment;

[0028] The new data segment of the distorted logging curve is obtained by calculating the fitting coefficient and fitting relationship of the normal data segment;

[0029] The distorted data segment of the distorted logging curve is replaced by the new data segment of the distorted logging curve to obtain a replaced logging curve.

[0030] In one embodiment, the first well logging data includes a first original well logging curve and a first target well logging curve;

[0031] The step S3 comprises:

[0032] Step 1: Get the preset skeleton parameters;

[0033] Step 2: Based on the optimization processing method, combining the preset skeleton parameters and the first original well logging curve data, solving to obtain the first well logging interpretation parameters;

[0034] Step 3: Using the first target well logging curve data as a reference, substituting the first original well logging curve into a preset rock physics model, combining the first well logging interpretation parameters, and using the optimization processing method to solve for a first adjustment coefficient;

[0035] Step 4: Substituting the first original well logging curve into the initially fitted first model to obtain first model well logging curve data of the first target well logging curve; wherein the initially fitted first model includes the first well logging interpretation parameter and the first adjustment coefficient;

[0036] Calculating a first error between the first model well logging curve data and the first target well logging curve;

[0037] If the first error is not within a first preset error range, re-execute steps 2 to 5 until the error between the first model logging curve data and the first target logging curve is within a preset range, thereby obtaining a fitted first model.

[0038] In one embodiment, the first well logging first revised data includes a first revised well logging curve and a first revised target well logging curve;

[0039] Substituting the first corrected data of the first well logging data into the preset rock physics model for fitting to obtain the second model comprises:

[0040] Step 2: Based on the optimization processing method, combining the preset skeleton parameters and the first original well logging curve data, solving to obtain second well logging interpretation parameters;

[0041] Step 3: Using the first revised target logging curve as a reference, substituting the first revised logging curve into a preset rock physics model, combining the second logging interpretation parameters, and using the optimization processing method to solve for a second adjustment coefficient;

[0042] Step 4: Substituting the first revised well log curve into the initially fitted second model to obtain second model well log curve data of the first revised target well log curve; wherein the initially fitted second model includes the second well log interpretation parameter and the second adjustment coefficient;

[0043] calculating a second error between the second model well log data and the first revised target well log;

[0044] If the second error is not within the second preset error range, re-execute steps one to four until the error between the second model logging curve data and the first revised target logging curve is within the second preset range, thereby obtaining a fitted second model.

[0045] In one embodiment, the correcting the second well logging data based on the first well logging data, the second well logging data, and the first model to obtain second well logging corrected data includes:

[0046] Performing an evaluation based on the distribution of the P-wave and S-wave time difference curves in the second well logging data and the second well logging correction data;

[0047] If the second well logging data meets the preset effect condition, and the second well logging correction data meets the preset correction effect condition, the current second well logging correction data is determined to be the final second well logging correction data.

[0048] In one embodiment, the second step of obtaining first well logging interpretation parameters based on the first well logging data and the preset skeleton parameters includes:

[0049] Substituting the first well logging data into a preset formula to obtain a first well logging interpretation parameter, wherein the preset formula includes a preset skeleton parameter, and the preset formula is:

[0050]

[0051] Where x m is the first logging interpretation parameter; a nm is the preset skeleton parameter; n The first logging data.

[0052] The present application also provides a multi-well consistency correction system, comprising a processor and a memory storing a computer program, wherein the processor implements the steps of any of the above-mentioned multi-well consistency correction methods when executing the computer program.

[0053] The present invention relates to a multi-well consistency correction method and system, wherein the method comprises the following steps: obtaining logging data from multiple wells; performing a logging quality assessment on the logging data to select a standard well based on the logging quality results; wherein the logging data of the standard well is first logging data; and the remaining logging data is second logging data; substituting the first logging data into a preset petrophysical model for fitting to obtain a fitted first model; and correcting the second logging data based on the first logging data, the second logging data, and the first model to obtain second logging correction data. This method achieves consistency correction of logging data across multiple wells, thereby improving the accuracy and reliability of oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 This is a flow chart of the multi-well consistency correction method of the present application;

[0056] Figure 2 This is a comparison chart of multiple wells in the study area of ​​this application before and after consistency correction;

[0057] Figure 3 This is the distribution diagram of the P-wave acoustic time difference curve before multi-well consistency correction in this application;

[0058] Figure 4 This is the distribution diagram of the longitudinal wave acoustic wave time difference curve after multi-well consistency correction in this application. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0060] To address the technical issues of prior art, including the problem of misconceptions about oil and gas geology, this system acquires logging data from multiple wells; performs a logging quality assessment on the logging data to select a standard well based on the logging quality results; the logging data from the standard well is the first logging data, and the remaining logging data is the second logging data; the first logging data is fitted into a preset rock physics model to obtain a fitted first model; and correction processing is performed based on the first logging data, the second logging data, and the first model to obtain the second logging correction data. This system achieves consistency correction of logging data across multiple wells, thereby improving the accuracy and reliability of oil and gas exploration and development.

[0061] like Figure 1 As shown, the steps of the multi-well consistency correction method include:

[0062] S1: Acquire logging curve data of multiple wells;

[0063] It should be noted that the well logging data for multiple wells is derived from various types of logging measurements, including natural gamma, resistivity, and sonic transit time, from different wells within the study area. This data can be obtained in a variety of ways, including real-time measurements during drilling using specialized logging instruments or extracted from existing logging databases.

[0064] S2: Perform a logging curve quality assessment on the logging curve data to select a standard well based on the logging curve quality results; the logging curve data of the standard well is the first logging data; the remaining logging data is the second logging data; it should be noted that the quality assessment includes the integrity, continuity, frequency of abnormal values ​​of the logging curve, and the correlation between these curves and other logging curves.

[0065] In one example, logging data from 14 wells was acquired. Through log quality assessment, well XJ24-2-2 was selected as the standard well. The standard well served as the primary logging data, while the other 13 wells served as secondary logging data. Due to quality deficiencies in these secondary logging data, multi-well consistency correction technology was used to further improve their accuracy and consistency, ensuring the accuracy and reliability of subsequent geological interpretation and reservoir evaluation.

[0066] S3: Substituting the first well logging data into a preset rock physics model for fitting to obtain a fitted first model;

[0067] In one embodiment, the selection of the preset rock physics model is based on a thorough understanding and analysis of the geological characteristics of the study area. For example, in the present invention, the remaining well logging data was used to conduct a detailed geological study of the first and second groups of strata in the study area, concluding that these strata are primarily composed of clastic rocks, interbedded with small amounts of other types of sediments. Based on this geological characteristic, the Xu-white rock physics model was selected as the preset model for the remaining well logging data. The Xu-white model is widely used in well logging data interpretation and reservoir evaluation under similar geological conditions because it can effectively reflect the physical properties of clastic strata and their interlayers.

[0068] The remaining logging data, including key information such as compressional wave acoustic transit time curves, density curves, and natural gamma ray curves, were substituted into a pre-selected Xu-White rock physics model from the standard well XJ24-2-2. Using optimization methods, the remaining logging data were used to continuously adjust the model parameters and adjustment coefficients, striving to minimize the discrepancy between the model output and the measured data. This data ultimately led to the successful construction of a precisely fitted, curve-calibrated rock physics model specifically adapted for the target formation in the study area. This model not only accurately reflects the logging data characteristics of the standard well XJ24-2-2 but also provides a solid and reliable reference for subsequent multi-well consistency calibration.

[0069] S4: Based on the first well logging data, the second well logging data and the first model, the second well logging data is corrected to obtain second well logging correction data, thereby achieving multi-well consistency correction.

[0070] In one embodiment, a calibration process was performed based on the first logging data (i.e., logging data from the standard well XJ24-2-2), the second logging data (logging data from other non-standard wells), and a previously constructed, accurately fitted first model (i.e., a curve-calibrated rock physics model applicable to the target formation in the study area). The purpose of this step is to adjust and optimize the logging data from the other non-standard wells by utilizing the logging data from the standard well and the established model, thereby obtaining more accurate and consistent second-calibrated logging data. This calibration process is crucial for improving the consistency and comparability of data across multiple wells, providing a more reliable data foundation for subsequent geological interpretation, reservoir evaluation, and resource assessment.

[0071] Further, the second well logging correction data includes the second well logging first correction data;

[0072] Correcting the second well logging data based on the first well logging data, the second well logging data, and the first model to obtain second well logging corrected data includes:

[0073] Substituting the first well logging data into the first model to obtain first corrected data of the first well logging;

[0074] Substituting the second well logging data into the first model to obtain the first corrected data of the second well logging;

[0075] Performing a difference operation based on the first corrected data of the first well logging and the first corrected data of the second well logging to obtain a first difference;

[0076] The second well logging data is calculated based on the first difference to obtain the second well logging first correction data.

[0077] In one embodiment, a calibration process was performed using a previously constructed first model (i.e., a curve-calibrated rock physics model applicable to the target formation in the study area) based on first well logging data (from standard well XJ24-2-2) and second well logging data (from 13 other wells in the study area to be calibrated). First, the first well logging data was substituted into the model to obtain the first-corrected first well logging data. Subsequently, the second well logging data was substituted into the model to obtain the first-corrected second well logging data for each well to be calibrated. Next, a difference calculation was performed at the same depth based on the first-corrected first well logging data and the first-corrected second well logging data for each well to be calibrated, resulting in a first difference. This difference was used to further adjust the second well logging data, thereby obtaining the first-corrected second well logging data.

[0078] During the rock physics model curve calculation phase, based on the rock physics model, logging interpretation parameters, and adjustment coefficients obtained in the previous steps, the logging data from the standard well XJ24-2-2 and several wells to be calibrated were substituted into the original logging curve parameters corresponding to the rock physics model to calculate their respective rock physics model curves. The difference between the rock physics model curve of each well to be calibrated and the rock physics model curve of the standard well at the same depth was calculated. This difference served as the correction for the target curve of each well to be calibrated. By performing this difference calculation and correction on the target curve of each well to be calibrated, a single correction of the P-wave acoustic transit time curves of the 13 wells to be calibrated in the study area was achieved. Throughout this process, the P-wave acoustic transit time log curves of the rock physics model were calculated for each well based on the constructed curve-calibrated rock physics model, ensuring the consistency and accuracy of the corrected logging data with respect to the key parameter of P-wave acoustic transit time.

[0079] Furthermore, the second well logging correction data also includes second well logging second correction data;

[0080] Correcting the second well logging data based on the first well logging data, the second well logging data, and the first model to obtain second well logging corrected data further includes:

[0081] Substituting the first corrected data of the first well logging into a preset rock physics model for fitting to obtain a second model;

[0082] Substituting the first revised data of the first well logging into the second model to obtain the second revised data of the first well logging;

[0083] Substituting the first correction data of the second well logging into the second model to obtain second revised data of the second well logging;

[0084] performing a difference calculation based on the second revised data of the first well logging and the second revised data of the second well logging to obtain a second difference;

[0085] The first corrected data of the second well logging is based on the second difference to obtain second corrected data of the second well logging.

[0086] In one embodiment, during the further correction process, not only the first-corrected data for the second well log are obtained, but also the second-corrected data for the second well log are derived. This deepening step first involves substituting the first-corrected data for the first well log into a preset rock physics model for fine-tuning, thereby obtaining an optimized second model. Subsequently, the first-corrected data for the first well log are again substituted into this second model to obtain the second-corrected data for the first well log. Simultaneously, the first-corrected data for the second well log are also substituted into the second model to generate the second-corrected data for the second well log. On this basis, the second-corrected data for the first well log are compared with the second-corrected data for the second well log, and a second difference is calculated through difference calculation. Using this difference, the first-corrected data for the second well log are further adjusted, ultimately obtaining the second-corrected data for the second well log.

[0087] To evaluate the effectiveness of the correction, we compared and analyzed the comprehensive distribution of target curve data for all wells before and after correction. Specifically, we observed changes in the target curve range distribution. If the target curve ranges for each well were dispersed before correction but became relatively concentrated after correction, this indicates that the multi-well consistency correction has been effective and that the corrected target curves can be reliably used in subsequent oil and gas exploration research. Conversely, if the correction is unsatisfactory, we need to return to S3 and rebuild the rock physics model.

[0088] Furthermore, further measures were taken to optimize the calibration process: the single-corrected P-wave acoustic transit time curves were re-substituted into the initial rock model, and the secondary logging interpretation parameters and adjustment coefficients were recalculated to construct a quadratic curve-corrected rock physics model. Based on this new model, the P-wave acoustic transit time curves were again calibrated to ensure the accuracy and reliability of the calibration results.

[0089] Furthermore, by evaluating the quality of the well logging curve data, standard wells are selected based on the well logging curve quality results, including:

[0090] Conduct wellbore environment analysis based on well logging data to select candidate standard wells that meet preset wellbore conditions;

[0091] Conduct logging curve principle analysis and logging lithology analysis on candidate standard wells to determine wells that meet the preset logging data quality standards and formation feature reflection accuracy requirements as standard wells.

[0092] In one embodiment, a crucial step in the initial stages of well log data processing is a comprehensive assessment of log quality to accurately select reference wells. This process begins with a borehole environment analysis of the log data. The stability of the borehole environment directly impacts the accuracy of the log data, so caliper curves are used to carefully analyze the borehole conditions. Specifically, when the actual caliper curve exceeds the drill bit diameter, it is considered a borehole expansion; conversely, when the actual caliper curve is less than the drill bit diameter, it is considered a borehole contraction. This analysis helps select candidate reference wells with stable borehole conditions and minimal impact from borehole expansion or contraction. These candidate reference wells undergo in-depth analysis of the log principles and mud logging lithology. This step aims to verify that the logs truly and accurately reflect the characteristics of the subsurface formation. By combining the physical principles of the logs, mud logging lithology data, and other relevant information, a comprehensive assessment is made of the logs that most accurately reveal the true conditions of the subsurface formation. After completing the log quality evaluation, the reference well and target curve selection phase begins. Based on the previous analysis, the following principles were followed to select a standard well: First, the wellbore environment must be good, with stable caliper log values ​​consistent with the drill bit diameter, indicating that the logging data is minimally affected by wellbore conditions. Second, the logs of the standard wells must accurately reflect the true information of the subsurface formations. Finally, the ranges of the different log values ​​of the standard wells must be free of significant anomalies and located centrally within the comprehensive distribution of all drilling data, further demonstrating the representativeness and accuracy of the data. After determining the standard well, the remaining wells were classified as wells for calibration. Next, the target curves for multi-well consistency calibration were identified. By comparing the range distributions of the different log values ​​across all wells, it was found that some log values ​​were concentrated, indicating good consistency and no need for calibration; whereas some log values ​​were highly dispersed; these were the target curves requiring calibration. This step laid a solid foundation for subsequent multi-well consistency calibration.

[0093] Furthermore, after performing a logging curve quality assessment on the logging curve data and selecting a standard well based on the logging curve quality results, the following steps are included:

[0094] For the first well logging data and the second well logging data: determining a distorted data segment of a distorted well logging curve in the well logging data by comparing with a preset threshold;

[0095] Fitting the normal data segment of the distorted logging curve to the data segment of the normal logging curve to obtain a fitting coefficient of the normal data segment;

[0096] The new data segment of the distorted logging curve is obtained by calculating the fitting coefficient and fitting relationship of the normal data segment;

[0097] The distorted data segment of the distorted logging curve is replaced by the new data segment of the distorted logging curve to obtain a replaced logging curve.

[0098] In one embodiment, during the well logging data processing process, following the log quality assessment and reference well selection, a series of meticulous correction measures are implemented to ensure data accuracy and reliability. This process primarily involves correcting the wellbore environment of a single well. First, the first and second log data are carefully reviewed. By comparing the data against a preset threshold, distorted portions of the log data can be accurately identified—data segments corresponding to maximum or minimum values ​​that deviate from the normal range. These distorted data segments, identified as distorted logs, pose a potential threat to the analysis. To correct these distorted data, a fitting method is employed. Specifically, normal data segments of the distorted logs are selected and compared and fitted with corresponding data segments of the normal logs. This step aims to establish a mathematical relationship between the two, thereby deriving the fitting coefficients and fitting equations for the normal data segments. Based on this fitting equation, the new data values ​​that the distorted logs should have in the distorted data segments are calculated. This calculation process fully utilizes the fitting coefficients and fitting relationships of the normal data segment, ensuring the logical and numerical consistency of the new data segment. Finally, this newly calculated data segment replaces the original distorted data segment, resulting in a corrected, more accurate well logging curve. This process not only repairs the distorted data but also improves the quality and credibility of the entire logging dataset.

[0099] Furthermore, the first well logging data includes a first original well logging curve and a first target well logging curve;

[0100] Substituting the first logging data into a preset rock physics model for fitting to obtain a fitted first model includes:

[0101] Step 1: Get the preset skeleton parameters;

[0102] Step 2: Based on the optimization processing method, the preset skeleton parameters and the first original well logging curve data are combined to obtain the first well logging interpretation parameters;

[0103] Step 3: Using the first target well logging curve data as a reference, the first original well logging curve is substituted into the preset rock physics model, and the first well logging interpretation parameters are combined with an optimization processing method to obtain the first adjustment coefficient;

[0104] Step 4: Substituting the first original well logging curve into the initially fitted first model to obtain first model well logging curve data of the first target well logging curve; wherein the initially fitted first model includes the first well logging interpretation parameter and the first adjustment coefficient;

[0105] Calculating a first error between the first model well logging curve data and the first target well logging curve;

[0106] If the first error is not within the first preset error range, the second to fifth steps are re-executed until the error between the first model logging curve data and the first target logging curve is within the preset range, thereby obtaining a fitted first model.

[0107] In one embodiment, the process of constructing a fitted first model primarily includes four key steps: optimizing the rock physics model, obtaining optimized logging interpretation parameters, determining the rock physics model adjustment coefficient, and establishing a curve-calibrated rock physics model. First, based on the geological background data of the study area and basic information such as the stratigraphic lithology of the exploration target layer, a rock physics model suitable for the study area is selected from a number of mature rock physics initial models. Next, using multiple original logging curves and preset skeleton parameters (usually constants), an optimization process is used to calculate logging interpretation parameters, including mineral content parameters, porosity parameters, and water saturation parameters. These parameters serve as input values ​​for the rock physics model. Then, using the measured data of the target curve of a standard well as the model curve, the other curve data of the standard well is substituted into the rock physics model. Combined with the previously obtained logging interpretation parameters, an appropriate primary adjustment coefficient is obtained. This adjustment coefficient varies for different study areas and target layers, and it can help better adjust the rock physics model to more accurately reflect the actual conditions of the underground rock formations.

[0108] Finally, the remaining logging data from the standard well is substituted into the rock physics model. Combined with the previously calculated logging interpretation parameters and adjustment coefficients, the model curve data for the standard well's target curve is calculated. The accuracy of the model can be assessed by comparing this data with the measured curve data for the standard well's target curve. If the error between the two is small, the previously calculated logging interpretation parameters and adjustment coefficients are reasonable. If the error is large, these parameters need to be readjusted until the error is sufficiently small. The resulting logging interpretation parameters, adjustment coefficients, and corresponding rock physics model are then referred to as the curve-calibrated rock physics model. Throughout this process, the error between the model logging data and the target logging curve is continuously calculated, and the model parameters are adjusted accordingly until the error falls within the preset range, resulting in a well-fitted first model.

[0109] Furthermore, the first revised well logging data includes a first revised well logging curve and a first revised target well logging curve;

[0110] Substitute the first corrected data of the first well logging into the preset rock physics model for fitting, and obtain the second model including:

[0111] Step 2: Based on the optimization processing method, the preset skeleton parameters and the first original well logging curve data are combined to obtain the second well logging interpretation parameters;

[0112] Step 3: Using the first revised target logging curve as a reference, substitute the first revised logging curve into the preset rock physics model, combine it with the second logging interpretation parameters, and use the optimization method to solve and obtain the second adjustment coefficient;

[0113] Step 4: Substitute the first revised well logging curve into the initially fitted second model to obtain the second model well logging curve data of the first revised target well logging curve; wherein the initially fitted second model includes the second well logging interpretation parameter and the second adjustment coefficient;

[0114] calculating a second error between the second model well log data and the first revised target well log;

[0115] If the second error is not within the second preset error range, the first to fourth steps are re-executed until the error between the second model logging curve data and the first revised target logging curve is within the second preset range, thereby obtaining a fitted second model.

[0116] In one embodiment, a fitting process is performed on the first corrected data of the first well logging data to obtain a second model. This process mainly includes three key steps: adjustment of the well logging interpretation parameters and adjustment coefficients after a single correction, secondary correction of the target curve, and multi-well consistency correction evaluation. First, the well logging interpretation parameters and adjustment coefficients are adjusted. The target curve after a single correction and other original well logging curves that have not been corrected are substituted into the step of obtaining the first well logging curve before, and the target curve after a single correction and other original well logging curves that have not been corrected are substituted into the original well logging interpretation parameters obtained before. Through the optimization processing method, the corresponding secondary well logging interpretation parameters are re-obtained. Subsequently, these secondary well logging interpretation parameters are used to replace the previously obtained primary well logging interpretation parameters, and the secondary adjustment coefficient is further obtained.

[0117] Next, a secondary correction of the target curve is performed. The secondary logging interpretation parameters and secondary adjustment coefficients are substituted into the rock physics model, and the model curve data is calculated. By comparing this with the measured, corrected target logging curve, the model accuracy can be assessed and further adjustments can be made if necessary. This process is repeated until the error between the model curve data and the corrected target logging curve falls within a second preset range. At this point, a well-fitted second model, the quadratic curve-corrected rock physics model, is obtained.

[0118] Throughout the entire process, the error between the model log data and the revised target log is continuously calculated. Model parameters (including log interpretation parameters and adjustment coefficients) are then adjusted based on the error until the error meets the preset conditions. This ensures that the resulting secondary model accurately reflects the true state of the subsurface rock formation, providing a reliable foundation for subsequent geological interpretation and reservoir evaluation.

[0119] Furthermore, performing correction processing based on the first well logging data, the second well logging data, and the first model to obtain second well logging correction data includes:

[0120] evaluating based on the distribution of the P-wave acoustic time difference curves in the second well logging data and the second well logging correction data;

[0121] If the second well logging data meets the preset effect condition, and the second well logging correction data meets the preset correction effect condition, the current second well logging correction data is determined to be the final second well logging correction data.

[0122] In one embodiment, after obtaining the first well logging data, the second well logging data and the first model obtained by fitting, correction processing is performed to obtain the second well logging correction data. Subsequently, a comprehensive evaluation is performed based on the distribution of the longitudinal wave acoustic wave time difference curve in the second well logging data and the second well logging correction data. Specifically, it is first checked whether the second well logging data meets the preset effect conditions, which usually involves considerations such as the integrity, accuracy and representativeness of the data. At the same time, the second well logging correction data is also strictly verified to ensure that it meets the preset correction effect conditions, which usually includes whether the corrected data is more accurate, consistent and reliable. During the evaluation process, special attention was paid to the longitudinal wave acoustic wave time difference curve after the second correction. By comparing and analyzing the distribution of the longitudinal wave acoustic wave time difference curve before and after correction, the correction effect can be intuitively seen. As Figure 2 As shown in the figure, the rock physics model curve (pink) shows good agreement with the curve after multi-well consistency correction (blue), which fully demonstrates the rationality of the constructed rock physics model. At the same time, the difference between the original curve (red) and the multi-well consistency correction curve (blue), i.e., the correction amount, also provides specific quantitative information on the correction effect.

[0123] Furthermore, if Figure 3 and Figure 4 As shown in the figure, the changes in the P-wave acoustic transit time curves before and after correction are clearly visible. Before correction, the P-wave acoustic transit time curves were relatively scattered, with significant data discrepancies between wells. After correction, however, the P-wave acoustic transit time curves became more concentrated, significantly improving data consistency across wells. This result demonstrates the effectiveness of the multi-well consistency correction method, providing more reliable data support for subsequent geological interpretation and reservoir evaluation. Therefore, the current second well logging correction data can be determined to be the final correction data.

[0124] Furthermore, the second step of obtaining first well logging interpretation parameters based on the first well logging data and the preset skeleton parameters includes:

[0125] Substitute the first well logging data into a preset formula to obtain a first well logging interpretation parameter, wherein the preset formula includes a preset skeleton parameter, and the preset formula is:

[0126]

[0127] Where x m is the first logging interpretation parameter; a nm is the preset skeleton parameter; n The first logging data.

[0128] The present application also provides a multi-well consistency correction system, comprising a processor and a memory storing a computer program. The processor implements the steps of any of the above-mentioned multi-well consistency correction methods when executing the computer program.

[0129] This application obtains logging data from multiple wells to ensure data diversity and comprehensiveness. The application then conducts a logging quality assessment, selects the best-quality well as a reference well, and constructs a precise, pre-defined rock physics model based on its data. The reference well data and model are then used to calibrate the logging data from other non-standard wells to eliminate differences in logging ranges caused by engineering and human factors, thereby improving the consistency and comparability of data across multiple wells.

[0130] It is understandable that the above embodiments only express the preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the patent scope of the present invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present invention, the above technical features can be freely combined, and several deformations and improvements can be made, all of which fall within the scope of protection of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should fall within the scope of coverage of the claims of the present invention.

Claims

1. A multi-well consistency correction method, characterized in that: The method comprises: Step S1: Acquire well logging curve data of multiple wells; Step S2: performing a logging curve quality assessment on the logging curve data to select a standard well according to the logging curve quality results; wherein the logging curve data of the standard well is the first logging data, and the remaining logging data is the second logging data; Step S3: Substituting the first logging data into a preset rock physics model for fitting to obtain a fitted first model; Step S4: Based on the first well logging data, the second well logging data and the first model, correcting the second well logging data to obtain second well logging correction data, thereby achieving multi-well consistency correction; Wherein, the first well logging data includes a first original well logging curve and a first target well logging curve; The step S3 comprises: Step 1: Get the preset skeleton parameters; Step 2: Based on the optimization processing method, combining the preset skeleton parameters and the first original well logging curve data, solving to obtain the first well logging interpretation parameters; Step 3: Using the first target well logging curve data as a reference, substituting the first original well logging curve into a preset rock physics model, combining the first well logging interpretation parameters, and using the optimization processing method to solve for a first adjustment coefficient; Step 4: Substituting the first original well logging curve into the initially fitted first model to obtain first model well logging curve data of the first target well logging curve; wherein the initially fitted first model includes the first well logging interpretation parameter and the first adjustment coefficient; Calculating a first error between the first model well logging curve data and the first target well logging curve; If the first error is not within a first preset error range, re-execute steps 2 to 5 until the error between the first model logging curve data and the first target logging curve is within a preset range, thereby obtaining a fitted first model; Wherein, the second well logging correction data includes the second well logging first correction data; The step S4 comprises: Substituting the first well logging data into the first model to obtain first corrected first well logging data; Substituting the second well logging data into the first model to obtain the second well logging first revised data; performing a difference operation based on the first corrected data of the first well logging and the first corrected data of the second well logging to obtain a first difference; The second well logging data is calculated based on the first difference to obtain the second well logging first correction data; Wherein, the second well logging correction data also includes second well logging second correction data; The step S4 further includes: Substituting the first corrected data of the first well logging into the preset rock physics model for fitting to obtain a second model; Substituting the first revised data of the first well logging into the second model to obtain the second revised data of the first well logging; Substituting the first correction data of the second well logging into the second model to obtain second revised data of the second well logging; performing a difference calculation based on the second revised data of the first well logging and the second revised data of the second well logging to obtain a second difference; The first corrected data of the second well logging is based on the second difference to obtain second corrected data of the second well logging.

2. The multi-well consistency correction method according to claim 1, characterized in that: The step S1 comprises: Performing wellbore environment analysis based on the well logging curve data to screen out candidate standard wells that meet preset wellbore conditions; The candidate standard wells are subjected to logging curve principle analysis and logging lithology analysis to determine wells that meet preset logging data quality standards and formation feature reflection accuracy requirements as standard wells.

3. The multi-well consistency correction method according to claim 1, characterized in that: The step S1 then includes: For the first well logging data and the second well logging data: determining a distorted data segment of a distorted well logging curve in the well logging data by comparing with a preset threshold; Fitting the normal data segment of the distorted logging curve to the data segment of the normal logging curve to obtain a fitting coefficient of the normal data segment; The new data segment of the distorted logging curve is obtained by calculating the fitting coefficient and fitting relationship of the normal data segment; The distorted data segment of the distorted logging curve is replaced by the new data segment of the distorted logging curve to obtain a replaced logging curve.

4. The multi-well consistency correction method according to claim 1, characterized in that: The first revised well logging data includes a first revised well logging curve and a first revised target well logging curve; Substituting the first corrected data of the first well logging data into the preset rock physics model for fitting to obtain the second model comprises: Step 2: Based on the optimization processing method, combining the preset skeleton parameters and the first original well logging curve data, solving to obtain second well logging interpretation parameters; Step 3: Using the first revised target logging curve as a reference, substituting the first revised logging curve into a preset rock physics model, combining the second logging interpretation parameters, and using the optimization processing method to solve for a second adjustment coefficient; Step 4: Substituting the first revised well log curve into the initially fitted second model to obtain second model well log curve data of the first revised target well log curve; wherein the initially fitted second model includes the second well log interpretation parameter and the second adjustment coefficient; calculating a second error between the second model well log data and the first revised target well log; If the second error is not within the second preset error range, re-execute steps one to four until the error between the second model logging curve data and the first revised target logging curve is within the second preset range, thereby obtaining a fitted second model.

5. The multi-well consistency correction method according to claim 1, characterized in that: The step of correcting the second well logging data based on the first well logging data, the second well logging data, and the first model to obtain second well logging corrected data includes: Performing an evaluation based on the distribution of the P-wave and S-wave time difference curves in the second well logging data and the second well logging correction data; If the second well logging data meets the preset effect condition, and the second well logging correction data meets the preset correction effect condition, the current second well logging correction data is determined to be the final second well logging correction data.

6. The multi-well consistency correction method according to claim 1, characterized in that: The second step: based on the optimization processing method, combining the preset skeleton parameters and the first original well logging curve data, solving to obtain the first well logging interpretation parameters includes: Substituting the first original well logging curve data into a preset formula to obtain a first well logging interpretation parameter, wherein the preset formula includes a preset skeleton parameter, and the preset formula is: Where, x1 to x m is the first logging interpretation parameter; a 11 to a nm are preset skeleton parameters; y1 to y n The first logging data.

7. A multi-well consistency correction system comprising a processor and a memory storing a computer program, characterized in that: When executing the computer program, the processor implements the steps of the multi-well consistency correction method according to any one of claims 1 to 6.

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