Multi-well consistency correction method and system

Abstract

The invention relates to a multi-well consistency correction method and system, and the method comprises the steps: obtaining the logging curve data of a plurality of wells; performing logging curve quality evaluation on the logging curve data to select a standard well according to a logging curve quality result; wherein the logging curve data of the standard well is first logging data; the rest logging data are second logging data; substituting the first logging data into a preset rock physical model for fitting to obtain a fitted first model; and performing correction processing based on the first logging data, the second logging data and the first model to obtain second logging correction data. Consistency correction of logging data among multiple wells is achieved, 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 usually refer to sedimentary environments with complex sedimentary geological characteristics and sediments composed of two or more different mineral components. The strata deposited under such sedimentary environments usually do not have stable marker layers. Multi-well consistency correction is a common research content in the process of oil and gas exploration and development, also known as well-logging curve standardization. Under normal circumstances, the range of well logging curves of different wells in the same formation in the same area is very similar. However, in the actual oil and gas exploration and development process, different logging construction times, different logging companies, different logging instruments, different logging technologies, and different logging personnel may cause the range of well logging curves of different wells in the same formation to differ greatly. This situation where the range of well logging curves differs greatly due to engineering factors and human factors will cause deviations in the understanding of oil and gas geology, and thus affect the direction of oil and gas exploration. The multi-well consistency correction of well logging curves is to solve the above problems. 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: Obtain 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 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 to achieve 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 well logging first corrected data;

[0012] Substituting the second well logging data into the first model to obtain the second well logging first corrected 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 comprises:

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

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

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

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

[0022] In one embodiment, the 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 the wells that meet the preset logging data quality standards and formation characteristic 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 the distorted data segment of the 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 the 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 relation 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 logging curve data, solving to obtain the first logging interpretation parameters;

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

[0037] 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.

[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 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 logging curve data, solving to obtain the second 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 parameter, and using the optimization processing method to solve and obtain a second adjustment coefficient;

[0042] Step 4: Substituting 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;

[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, the first to fourth steps are re-executed until the error between the second model logging curve data and the first corrected 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 comprises:

[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 according to the first well logging data and the preset skeleton parameters comprises:

[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] In the formula, 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 multi-well consistency correction methods described above 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 curve data of multiple wells; performing logging curve quality assessment on the logging curve data, so as 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; the remaining logging data is the second logging data; substituting the first logging data into a preset rock physics model for fitting, so as to obtain a well-fitted first model; and based on the first logging data, the second logging data and the first model, correcting the second logging data, so as to obtain the second logging correction data. The consistency correction of the logging data between multiple wells is realized, 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 accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

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

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

[0057] Figure 3 It is the distribution diagram of the longitudinal wave acoustic wave time difference curve before the multi-well consistency correction of the present application;

[0058] Figure 4 It 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 be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0060] Aiming at the technical problems existing in the background technology: the problem of oil and gas geological understanding deviation, the logging curve data of multiple wells are obtained; the logging curve quality is evaluated on the logging curve data, and the standard well is selected according to 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; the first logging data is substituted into the preset rock physics model for fitting to obtain the fitted first model; based on the first logging data, the second logging data and the first model, correction processing is performed to obtain the second logging correction data. The consistency correction of the logging data between multiple wells is achieved, 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: Obtain logging curve data of multiple wells;

[0063] It should be noted that the logging curve data of multiple wells come from natural gamma, resistivity, acoustic time difference and other types of logging measurements of different wells in the study area. These data can be obtained in various ways, such as real-time measurement during drilling by professional logging instruments, or extracted from existing logging databases.

[0064] S2: Perform logging curve quality assessment on the logging curve data to select standard wells 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 embodiment, a total of 14 well logging data were obtained, and the XJ24-2-2 well was determined as the standard well through the well logging curve quality assessment, where the standard well is the first well logging data, and the other 13 wells are used as the second well logging data. These second well logging curve data, due to the lack of quality, need to be further improved through multi-well consistency correction technology to ensure the accuracy and reliability of subsequent geological interpretation and reservoir evaluation.

[0066] S3: Substituting the first 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 an in-depth understanding and analysis of the geological characteristics of the study area. For example, in the present invention, the remaining logging data conducted a detailed geological study on the first and second groups of strata in the study area, and concluded that these strata are mainly clastic rocks, mixed with a small amount of other types of sediments. Based on this geological feature, the remaining logging data selected the Xu-wh ite rock physics model as the preset model. The Xu-white model is widely used in logging data interpretation and reservoir evaluation under similar geological conditions because it can better reflect the physical properties of clastic rock strata and their interlayers.

[0068] The remaining logging data, including the key information such as the P-wave acoustic time difference curve, density curve, and natural gamma curve, from the standard well XJ24-2-2, were substituted into the pre-selected Xu-white rock physics model. By using the optimization processing method, the remaining logging data continuously adjusted the model parameters and adjustment coefficients, striving to minimize the difference between the model output and the measured data. The remaining logging data finally successfully constructed a precisely fitted curve-corrected rock physics model specifically suitable for the target layer in the study area. This model can not only accurately reflect the logging data characteristics of the standard well XJ24-2-2, but also provide a solid and reliable reference for the subsequent multi-well consistency correction.

[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 realizing multi-well consistency correction.

[0070] In one embodiment, correction processing is 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 the previously constructed accurate fitting first model (i.e., the curve-corrected rock physics model suitable for the target layer in the study area). The purpose of this step is to adjust and optimize the logging data of other non-standard wells by using the logging data of the standard well and the established model to obtain more accurate and consistent second logging correction data. Such correction processing is crucial to improving the consistency and comparability of data between multiple wells, and provides a more reliable data basis 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] 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 corrected data includes:

[0073] Substituting the first well logging data into the first model to obtain the 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 logging data is calculated based on the first difference to obtain the second logging first correction data.

[0077] In one embodiment, based on the first well logging data (from the standard well XJ24-2-2) and the second well logging data (from the other 13 wells to be corrected in the study area), the previously constructed first model (i.e., the curve-corrected rock physics model suitable for the target layer in the study area) is used for correction processing. First, the first well logging data is substituted into the model to obtain the first corrected data of the first well logging; then, the second well logging data is substituted into the model respectively to obtain the first corrected data of the second well logging of each well to be corrected. Next, based on the first corrected data of the first well logging and the first corrected data of the second well logging of each well to be corrected, a difference operation is performed at the same depth position to obtain the first difference. This difference is used to further adjust the second well logging, thereby obtaining the first corrected data of the second well logging.

[0078] In the rock physics model curve calculation stage, according to the rock physics model, logging interpretation parameters and adjustment coefficients obtained in the previous steps, the logging curve data of the standard well XJ24-2-2 and multiple wells to be corrected are substituted into the original logging curve parameters corresponding to the rock physics model, and their rock physics model curves are calculated respectively. The difference between the rock physics model curve of each well to be corrected and the rock physics model curve of the standard well at the same depth position is calculated, and this difference is the correction amount of the target curve of each well to be corrected. By performing such difference calculation and correction on the target curve of each well to be corrected, a single correction of the P-wave acoustic time difference curves of the 13 wells to be corrected in the study area is achieved. In the whole process, based on the constructed curve correction rock physics model, the P-wave acoustic time difference logging curves of the rock physics model of each well are calculated, and the consistency and accuracy of the corrected logging data in the key parameter of P-wave acoustic time difference are ensured.

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

[0080] 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 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 corrected data of the first well logging into the second model to obtain the second corrected data of the first well logging;

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

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

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

[0086] In one embodiment, in the further correction process, not only the first correction data of the second well logging is obtained, but also the second correction data of the second well logging is derived. This deepening step first involves substituting the first correction data of the first well logging into a preset rock physics model for fine fitting, thereby obtaining an optimized second model. Subsequently, the first correction data of the first well logging is again substituted into this second model to obtain the second correction data of the first well logging. At the same time, the first correction data of the second well logging is also substituted into the second model to generate the second correction data of the second well logging. On this basis, the second correction data of the first well logging and the second correction data of the second well logging are compared, and the second difference is obtained by difference calculation. Using this difference, the first correction data of the second well logging is further adjusted, and finally the second correction data of the second well logging is obtained.

[0087] In order to evaluate the correction effect, the data comprehensive distribution diagram of all drilling target curves before and after correction was compared and analyzed. Specifically, the changes in the value range distribution of the target curve were observed: if the value range of the target curves of each well was dispersed before correction, but became relatively concentrated after correction, it shows that the multi-well consistency correction has achieved good results, and the corrected target curve can be reliably used in subsequent oil and gas exploration research. On the contrary, if the correction effect is not ideal, it is necessary to return to S3 and rebuild the rock physics model.

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

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

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

[0091] The logging curve principle analysis and logging lithology analysis are carried out on the candidate standard wells to determine the wells that meet the preset logging data quality standards and the requirements for the accuracy of formation characteristics reflection as standard wells.

[0092] In one embodiment, in the early stage of logging curve data processing, a crucial link is to comprehensively evaluate the quality of logging curves in order to accurately select standard wells. This process first covers the borehole environment analysis of logging curve data. The stability of the borehole environment directly affects the accuracy of logging data. Therefore, the borehole condition is carefully analyzed using the borehole diameter curve. Specifically, when the actual borehole diameter curve exceeds the drill bit diameter, it is determined to be a borehole expansion; conversely, if the actual borehole diameter curve is less than the drill bit diameter, it is considered to be a borehole contraction. These analyses help screen out candidate standard wells with stable borehole conditions and less affected by expansion or contraction. In-depth logging curve principle analysis and logging lithology analysis are conducted on these candidate standard wells. The purpose of this step is to verify whether the logging curve can truly and accurately reflect the characteristics of the underground rock formation. Combining the physical principles of the logging curve, logging lithology data and other relevant information, a comprehensive evaluation is made on which logging curves can most accurately reveal the true condition of the underground rock formation. After completing the logging curve quality evaluation, the stage of determining the standard well and target curve is entered. According to the previous analysis results, the standard well was selected according to the following principles: first, the borehole environment of the standard well must be good, and the value of the borehole logging curve is stable and consistent with the drill bit diameter, indicating that its logging data is less affected by the borehole conditions; second, the logging curve of the standard well can accurately reflect the real information of the underground rock formation; finally, the value range of different logging curves of the standard well should have no obvious abnormalities, and its value range is located in the central area of ​​the comprehensive distribution map of all drilling data, which further proves the representativeness and accuracy of its data. After determining the standard well, the other wells are classified as wells to be corrected. Next, the target curves that need to be corrected for multi-well consistency are clarified. By comparing the value range distribution map of different logging curves in all wells, it is found that the value range distribution of some logging curves is relatively concentrated, indicating that their consistency is good and no correction is required; while the value range distribution of some logging curves is very scattered, which are the target curves that need to be corrected. The completion of this step has laid a solid foundation for the subsequent multi-well consistency correction work.

[0093] Furthermore, by evaluating the quality of the well logging curve data, the following steps are performed to select a standard well according to the quality results of the well logging curve:

[0094] For the first well logging data and the second well logging data: determining the distorted data segment of the 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 the 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 relation 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 the replaced logging curve.

[0098] In one embodiment, in the well logging data processing process, following the well logging curve quality assessment and standard well selection, a series of detailed correction measures are performed to ensure the accuracy and reliability of the data. This process mainly covers the correction of the wellbore environment of a single well. First, the first well logging data and the second well logging data are carefully reviewed. By comparing with the preset threshold, the distorted parts in the well logging data can be accurately identified, that is, the data segments corresponding to the maximum or minimum values ​​that deviate from the normal value range. These distorted data segments identified as distorted well logging curves constitute potential interference to the analysis. In order to repair these distorted data, a fitting method is used. Specifically, the normal data segment of the distorted well logging curve is selected and compared and fitted with the corresponding data segment of the normal well logging curve. This step aims to establish a mathematical relationship between the two, so as to derive the fitting coefficient and fitting relationship of the normal data segment. Based on this fitting relationship, the new data value that the distorted well logging curve should have in the distorted data segment can be calculated. This calculation process makes full use of the fitting coefficients and fitting relationships of the normal data segment to ensure the logical and numerical consistency of the new data segment. Finally, the newly calculated data segment replaces the original distorted data segment, thereby obtaining a corrected and more accurate logging curve. This process not only repairs the distorted data, but also improves the quality and credibility of the entire logging data set.

[0099] Further, 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 logging curve data are combined to obtain the first logging interpretation parameters;

[0103] Step 3: Using the first target logging curve data as a reference, substituting the first original logging curve into the preset rock physics model, combining the first logging interpretation parameter, using the optimization processing method, and solving to obtain the first adjustment coefficient;

[0104] Step 4: Substitute the first original well logging curve into the first model initially fitted to obtain first model well logging curve data of the first target well logging curve; wherein the first model initially fitted 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 well-fitted first model mainly includes four key steps: optimization of rock physics model, acquisition of optimized logging interpretation parameters, determination of rock physics model adjustment coefficient, and establishment of curve correction rock physics model. First, according to the geological background data of the study area and the 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. Then, using multiple original logging curves and preset skeleton parameters (usually constants), logging interpretation parameters including mineral content parameters, porosity parameters, and water saturation parameters are calculated through optimization processing methods, and these parameters will be used as input values ​​of the rock physics model. Then, the measured data of the target curve of the standard well is used as the model curve, and the other curve data of the standard well is substituted into the rock physics model, and combined with the previously obtained logging interpretation parameters, a suitable primary adjustment coefficient is obtained. This adjustment coefficient varies for different study areas and different target layers, and it can help to better adjust the rock physics model so that it can more accurately reflect the real situation of the underground rock formation.

[0108] Finally, the other logging curve data of the standard well are substituted into the rock physics model, and the model curve data of the target curve of the standard well is calculated by combining the previously obtained logging interpretation parameters and adjustment coefficients. The accuracy of the model can be evaluated by comparing with the measured curve data of the target curve of the standard well. If the error between the two is small, it means that the previously obtained logging interpretation parameters and adjustment coefficients are reasonable; if the error is large, these parameters need to be readjusted until the error is small enough. At this time, the obtained logging interpretation parameters, adjustment coefficients and corresponding rock physics model are the curve correction rock physics model. During the whole process, it is also necessary to continuously calculate the error between the model logging curve data and the target logging curve, and continuously adjust the model parameters according to the size of the error until the error is within the preset range, so as to obtain the first fitted model.

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

[0110] Substituting the first corrected data of the first well logging into the preset rock physics model for fitting, the second model is obtained including:

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

[0112] Step 3: Taking the first corrected target logging curve as a reference, substituting the first corrected logging curve into the preset rock physics model, combining the second logging interpretation parameters, and using the optimization processing 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 corrected 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 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, and the corresponding secondary well logging interpretation parameters are re-obtained through the optimization processing method. 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, the target curve was corrected twice. The secondary logging interpretation parameters and the secondary adjustment coefficients were substituted into the rock physics model, and the model curve data was calculated. By comparing with the measured corrected target logging curve, the accuracy of the model can be evaluated, 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 is within the second preset range, at which point the fitted second model is obtained, namely the secondary curve corrected rock physics model.

[0118] During the whole process, it is necessary to continuously calculate the error between the model logging curve data and the corrected target logging curve, and continuously adjust the model parameters (including logging interpretation parameters and adjustment coefficients) according to the size of the error until the error meets the preset conditions. In this way, it can be ensured that the final second model can accurately reflect the actual situation of the underground rock formation and provide a reliable basis for subsequent geological interpretation and reservoir evaluation.

[0119] Further, based on the first well logging data, the second well logging data and the first model, correction processing is performed to obtain second well logging correction data, which includes:

[0120] 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;

[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, then 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 curves 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 curves before and after correction, the correction effect can be intuitively seen. Figure 2 As shown in the figure, the rock physics model curve (pink) and the multi-well consistency correction curve (blue) show good consistency, which fully proves 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), that is, the correction amount, also provides specific quantitative information about the correction effect.

[0123] Furthermore, if Figure 3 and Figure 4 As shown in the figure, we can clearly see the changes in the P-wave acoustic time difference curve before and after correction. Before correction, the P-wave acoustic time difference curve was relatively scattered, and the data differences between different wells were large; after correction, the P-wave acoustic time difference curve became more concentrated, and the data consistency between different wells was significantly improved. This result shows that the multi-well consistency correction method has achieved good results and provided more reliable data support for subsequent geological interpretation and reservoir evaluation. Therefore, it can be determined that the second well logging correction data currently obtained is the final correction data.

[0124] Further, the second step: obtaining the first well logging interpretation parameter according to the first well logging data and the preset skeleton parameter 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] In the formula, 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, wherein 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 curve data from multiple wells to ensure data diversity and comprehensiveness, then conducts logging curve quality assessment, selects the best quality well as the standard well, and builds an accurate preset rock physics model based on its data. Then, the standard well data and model are used to calibrate the logging data of other non-standard wells to eliminate the differences in logging curve ranges caused by engineering, human factors, etc., thereby improving the consistency and comparability of data between multiple wells.

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

Claims

1. A multi-well consistency correction method, characterized in that: The method comprises: S1: Obtain logging curve data of multiple wells; 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; S3: Substituting the first logging data into a preset rock physics model for fitting to obtain a fitted first model; 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 to achieve multi-well consistency correction.

2. The multi-well consistency correction method according to claim 1, characterized in that: 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 well logging first corrected data; Substituting the second well logging data into the first model to obtain the second well logging first corrected 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 logging data is calculated based on the first difference to obtain the second logging first correction data.

3. The multi-well consistency correction method according to claim 2, characterized in that: The second well logging correction data also includes second well logging second correction data; The step S4 further comprises: 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 corrected data of the first well logging into the second model to obtain the second corrected data of the first well logging; Substituting the first correction data of the second well logging into the second model to obtain second corrected data of the second well logging; Performing difference calculation based on the second corrected data of the first well logging and the second corrected data of the second well logging to obtain a second difference; The first correction data of the second well logging is based on the second difference to obtain the second correction data of the second well logging.

4. 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 the wells that meet the preset logging data quality standards and formation characteristic reflection accuracy requirements as standard wells.

5. 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 the distorted data segment of the 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 the 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 relation 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.

6. The multi-well consistency correction method according to claim 1, characterized in that: 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 logging curve data, solving to obtain the first logging interpretation parameters; Step 3: using the first target logging curve data as a reference, substituting the first original logging curve into a preset rock physics model, combining the first logging interpretation parameter, and using the optimization processing method to solve and obtain 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 logging curve data and the first target logging curve; 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.

7. The multi-well consistency correction method according to claim 6, characterized in that: The first well logging first revised 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 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 logging curve data, solving to obtain the second 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 parameter, and using the optimization processing method to solve and obtain a second adjustment coefficient; Step 4: Substituting 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; 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, the first to fourth steps are re-executed until the error between the second model logging curve data and the first corrected target logging curve is within the second preset range, thereby obtaining a fitted second model.

8. The multi-well consistency correction method according to claim 3, characterized in that: The step of performing correction processing on 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 correction data comprises: 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.

9. The multi-well consistency correction method according to claim 6, characterized in that: The second step of obtaining the first well logging interpretation parameter according to the first well logging data and the preset skeleton parameter comprises: 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: In the formula, x m is the first logging interpretation parameter; a nm is the preset skeleton parameter; n The first logging data.

10. 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 9.

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