A method and system for automatic standardization of well logging curves

Abstract

The present invention discloses an automatic standardization processing method for logging curves, including: determining a reference well in an oil field area, and combining the geological data of all wells to be processed and the reference well in the area, selecting a standard layer for representing all wells to be processed for logging curve standardization processing; resampling the logging data of the standard layer of each well to be processed; constructing an objective function of the correction coefficient required for linear correction of the well to be processed; solving the objective function using a genetic algorithm based on the logging data of the standard layer of the reference well and the logging data of the standard layer of each well to be processed after resampling; using the solution result, obtaining the corresponding corrected full-well logging curve from the original full-well logging curve of each well to be processed. The present invention improves the efficiency of initial standardization of logging curves, eliminates the systematic error of logging curves, and lays a foundation for improving the efficiency and accuracy of logging interpretation.

Description

Technical Field

[0001] The invention relates to the field of oil and gas development and exploration, and in particular to a method and system for automatically standardizing a well logging curve. Background Art

[0002] Since the porosity, permeability and other more important rock physical parameters of the formation are derived from the interpretation of logging data. However, logging data are often subject to large systematic errors during the acquisition process due to the influence of environmental factors, instrument types, operator proficiency, etc., which may lead to deviations between the acquired logging data and the actual formation properties of the well section at the same depth, affecting the judgment of the reservoir properties. Therefore, standardization of logging data of multiple wells in the same oil field is an indispensable process.

[0003] Well logging curve standardization is based on the principle that lithologic formations in well sections of the same depth generally have similar logging response characteristics under the same sedimentary environment. At present, the curve standardization methods in the existing technology are roughly divided into single-well comparison method and multi-well evaluation method. The single-well comparison method mainly relies on manual analysis and judgment of information such as intersection diagrams. This method is very inefficient and seriously delays the implementation time of subsequent well logging interpretation; while the multi-well comparison method also relies on manual methods to compare one by one with standard wells to obtain the linear correction amount of each well. When there are many wells to be corrected, the efficiency will be very low, and it is difficult to ensure a unified correction scale standard, which has major problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method for automatic standardization of logging curves, the method comprising: determining a reference well in an oil field area, and combining the geological data of all the wells to be processed and the full-well section of the reference well in the area, selecting a standard layer for representing all the wells to be processed for standardization of logging curves; resampling the logging data of the standard layer of each well to be processed; constructing an objective function of the correction coefficient required for linear correction of the wells to be processed; solving the objective function using a genetic algorithm based on the logging data of the standard layer of the reference well and the logging data of the standard layer of each well to be processed after resampling; and using the solution result, obtaining the corresponding corrected full-well logging curve from the original full-well logging curve of each well to be processed.

[0005] Preferably, the objective function is expressed by the following expression:

[0006]

[0007] Wherein, obj represents the objective function, Log sta Represents the logging data of the standard layer of the reference well, Log corrrepresents the corrected well logging data of the standard layer of the well to be processed, n represents the total number of sampling points in the well logging data of the standard layer, and m represents the number of standard layers. The corrected well logging data of the standard layer of the well to be processed is represented by the following expression:

[0008]

[0009] Among them, Log raw represents the original logging curve of the standard layer of the well to be processed, and a and b represent the first correction coefficient and the second correction coefficient required for the current linear correction respectively.

[0010] Preferably, the step of solving the objective function using a genetic algorithm based on the logging data of the standard layer of the reference well and the logging data of the standard layer of each well to be processed after resampling includes: setting the variation range of each quantity to be solved respectively; randomly generating a group consisting of multiple individuals for each quantity to be solved; calculating the objective function of each group, and evaluating the fitness of the individuals in the population based on the logging data of the standard layer of the reference well and the logging data of the standard layer of the corresponding well to be processed after resampling; performing selection, crossover and mutation operations in sequence to evolve each group, and judging whether the optimal solution of the new generation group meets the stopping condition; outputting the optimal individual as the first correction coefficient and the second correction coefficient for inversion.

[0011] Preferably, the step of resampling the well logging data of the standard layer of each well to be processed includes: using the adjacent point interpolation method to resample the well logging data of the standard layer of each well to be processed, so that the number of sampling points contained in the well logging data of the resampled standard layer is the same as the number of sampling points contained in the well logging data of the standard layer of the reference well.

[0012] Preferably, in the process of selecting the standard layer, further, referring to the formation permeability data of the entire well section of each well to be treated and the reference well, a standard formation depth well section with stable lithology and impermeability is selected, wherein the standard formation depth well section is a well section included in all wells in the area.

[0013] Preferably, further, the upper and lower boundaries of the first correction coefficient are set to [1.3, 1], and the upper and lower boundaries of the second correction coefficient are set to [0.7, -0.5]; the number of initial groups is preferably 100; the crossover probability is preferably 0.7, and the mutation probability is preferably 0.01.

[0014] On the other hand, the present invention also proposes a system for automatic standardization of logging curves, the method system comprising: a reference well and standard layer selection module, which determines the reference well in the oil field area, and combines the geological data of all the wells to be processed and the full-well section of the reference well in the area to select the standard layer used to represent all the wells to be processed for standardization of logging curves; a resampling module, which resamples the logging data of the standard layer of each well to be processed; an objective function construction module, which constructs an objective function of the correction coefficient required for linear correction of the wells to be processed; a correction coefficient generation module, which uses a genetic algorithm to solve the objective function based on the logging data of the standard layer of the reference well and the logging data of the standard layer of each well to be processed after resampling; a full-well curve correction module, which uses the solution result to obtain the corresponding corrected full-well logging curve from the original full-well logging curve of each well to be processed.

[0015] Preferably, the objective function is expressed by the following expression:

[0016]

[0017] Wherein, obj represents the objective function, Log sta Represents the logging data of the standard layer of the reference well, Log corr represents the corrected well logging data of the standard layer of the well to be processed, n represents the total number of sampling points in the well logging data of the standard layer, and m represents the number of standard layers. The corrected well logging data of the standard layer of the well to be processed is represented by the following expression:

[0018]

[0019] Among them, Log raw represents the original logging curve of the standard layer of the well to be processed, and a and b represent the first correction coefficient and the second correction coefficient required for the current linear correction respectively.

[0020] Preferably, the correction coefficient generation module includes: a variation range determination unit, which sets the variation range of each quantity to be solved respectively; a population initialization unit, which randomly generates a population consisting of multiple individuals for each quantity to be solved; a fitness evaluation unit, which calculates the objective function of each population, and evaluates the fitness of the individuals in the population based on the logging data of the standard layer of the reference well and the logging data of the standard layer of the corresponding well to be processed after resampling; an optimal solution solving unit, which sequentially performs selection, crossover, and mutation operations to evolve each population, and determines whether the optimal solution of the new generation population meets the stopping condition; a correction coefficient output unit, which outputs the first correction coefficient and the second correction coefficient for inversion of the optimal individual.

[0021] Preferably, the resampling module further adopts a neighboring point interpolation method to resample the logging data of the standard layer of each well to be processed, so that the logging data of the resampled standard layer has the same number of sampling points as the logging data of the standard layer of the reference well.

[0022] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0023] The present invention proposes a method and system for automatic standardization of logging curves. The method and system first need to perform pre-correction pre-processing work including selecting reference wells and standard layers in the same oil field area, and resampling the logging data of the standard layers of each well to be processed; then, a genetic intelligence algorithm is used to determine the two linear correction coefficients required for each well to be processed during correction; finally, based on the full-well section logging curves of each well to be processed, and according to these linear correction coefficients, the corresponding corrected full-well section logging curves are obtained. The present invention greatly improves the efficiency of initial standardization of logging curves, eliminates the systematic errors of logging curves, thereby improving the quality of logging data, and lays a foundation for further improving the efficiency and accuracy of logging interpretation. The computer software can quickly complete the batch automatic standardization of logging curves within 10 seconds, solving the problem that the traditional logging curve method often has large errors in the correction processing results due to systematic errors and low efficiency.

[0024] Other advantages, objectives, and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the following description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0026] Figure 1 This is a step diagram of the automatic normalization processing method for well logging curves according to an embodiment of the present application.

[0027] Figure 2 This is a schematic diagram of well logging curves of a reference well and a standard layer of a well to be processed in a certain area in the method for automatic standardization of well logging curves in an embodiment of the present application.

[0028] Figure 3 This is a flow chart of the objective function solving steps in the automatic normalization processing method for well logging curves according to an embodiment of the present application.

[0029] Figure 4 This is a schematic diagram of the effect of correcting the full-well logging curve of a certain well to be processed in the automatic logging curve standardization processing method of an embodiment of the present application.

[0030] Figure 5 This is a system block diagram of the automatic normalization processing system for well logging curves according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that as long as there is no conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other, and the technical solutions formed are all within the protection scope of the present invention.

[0032] In addition, the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. Also, although a logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a sequence different from that here.

[0033] Since the porosity, permeability and other more important rock physical parameters of the formation are derived from the interpretation of logging data. However, logging data are often subject to large systematic errors during the acquisition process due to the influence of environmental factors, instrument types, operator proficiency, etc., which may lead to deviations between the acquired logging data and the actual formation properties of the well section at the same depth, affecting the judgment of the reservoir properties. Therefore, standardization of logging data of multiple wells in the same oil field is an indispensable process.

[0034] Well logging curve standardization is based on the principle that lithologic formations in well sections of the same depth generally have similar logging response characteristics under the same sedimentary environment. At present, the curve standardization methods in the existing technology are roughly divided into single-well comparison method and multi-well evaluation method. The single-well comparison method mainly relies on manual analysis and judgment of information such as intersection diagrams. This method is very inefficient and seriously delays the implementation time of subsequent well logging interpretation; while the multi-well comparison method also relies on manual methods to compare one by one with standard wells to obtain the linear correction amount of each well. When there are many wells to be corrected, the efficiency will be very low, and it is difficult to ensure a unified correction scale standard, which has major problems.

[0035] The genetic intelligence algorithm is based on Darwin's theory of evolution, simulating the natural selection and survival of the fittest. It obtains the optimal solution through N generations of inheritance, mutation, crossover, replication and other steps. It is particularly suitable for solving the objective function in the batch automatic standardization of logging curves. It can adaptively adjust the solution parameters according to different formation characteristics to eliminate the systematic errors of logging curves of different formation types.

[0036] In order to solve the above technical problems, the present invention proposes a method and system for automatic standardization of logging curves. The method and system first determine the reference wells in the oil field area and the standard layer that can represent all the wells to be processed (wells to be corrected) for standardization of logging curves; based on the logging data of the standard layer of the reference well, the logging data of the standard layer of each well to be processed is resampled; an objective function for the correction coefficient required for linear correction of the wells to be processed is constructed; then, according to the logging data of the standard layer of the reference well and the logging data of the standard layer of each well to be processed after resampling, the above objective function is solved by using a genetic algorithm, and the first correction coefficient and the second correction coefficient required for linear correction of each well to be processed are output; finally, based on the original full-well logging curve of each well to be processed, the full-well logging curve of each well to be processed is corrected by using the solved correction coefficient to obtain the corrected full-well logging curve of each well to be processed.

[0037] In this way, the present invention is implemented through a logging software platform, and can use a genetic intelligence algorithm to quickly invert the two correction coefficients required for linear correction for each well to be processed (well to be corrected), and quickly and automatically standardize the original full-well logging curves in batches, thereby effectively improving the efficiency of initial standardization of logging curves, eliminating systematic errors in logging curves, and improving the quality of logging data, laying a foundation for further improving the efficiency and accuracy of logging interpretation, and achieving the effect of being able to complete batch automatic standardization of logging curves within 10 seconds.

[0038] Embodiment 1

[0039] Figure 1 This is a step diagram of the automatic normalization processing method of the well logging curve in the embodiment of the present application. Figure 1 , the automatic standardization processing method for logging curves described in the present invention is described in detail below.

[0040] First, step S110 determines the reference wells in the oilfield area, and combines the full-well section geological data of all the wells to be processed and the reference wells in the current oilfield area, and selects a standard layer from the full-well section depths of all the wells to be processed and the reference wells to represent all the wells to be processed for logging curve standardization. In an embodiment of the present invention, the same oilfield area refers to an area where the formation rock lithology has similar logging response characteristics under the same formation deposition environment. In step S110, it is first necessary to select a reference well from all the wells in the same oilfield area. The criterion for selecting a reference well is to compare the drilling construction quality and drilling instrument quality of all wells in the current oilfield area, and select the well with the best instrument quality and construction quality in the area as the reference well. At this time, in the current oil field area, historical wells other than the reference wells are used as wells to be processed, which need to use the logging data of the reference wells as the standard for logging curve standardization, also called wells to be corrected. It is necessary to perform targeted logging curve standardization on each well to be processed to solve the systematic error caused by factors such as instrument quality, construction quality, and environmental conditions (systematic error is the error between logging data and actual formation characteristics caused by factors other than formation and reservoir characteristics).

[0041] Further, after completing the selection of the reference well, it is necessary to continue to screen the standard layer. In the process of standard layer, it is necessary to refer to the full-well section formation permeability data of each well to be processed and the reference well in the current oil field area, and select the standard formation depth section with stable lithology and impermeability from the full-well section depth range of each well. Among them, the standard formation depth section is the well section depth range included in all the wells to be processed and the reference wells in the current oil field area. More specifically, when selecting the standard layer involved in the curve correction, the standard layer is generally lithologically stable and has a certain thickness. At the same time, the standard layer is generally impermeable, and the formation is not affected by the invasion of oil, gas, water and logging mud; more importantly, all wells in the current oil field area must include this standard layer. In an embodiment of the present invention, 1 or 2 standard layers are selected in the same oil field area. In this way, the depth range of the standard layer representing all the wells to be processed for logging curve standardization (logging curve correction) is determined, thereby entering step S120.

[0042] Figure 2 Schematic diagram of the well logging curves of the reference well and the standard layer of the well to be processed in a certain area in the method for automatically standardizing the well logging curves in the embodiment of the present application. Figure 2 As shown, the solid line is the density logging curve of the standard layer of the reference well, and the dotted line is the density logging curve of the standard layer of a well to be calibrated. The depth range and thickness of the standard layers of the two wells are similar.

[0043] Step S120 resamples the well logging data in the standard layer of each well to be processed in the current oil field area. That is, in step S120, it is necessary to filter out the actual well logging data in the standard layer depth range based on the original well logging data of each well to be processed (the original well logging data refers to the actual well logging data before standardization that is actually obtained when drilling the current well to be processed); then, based on the actual well logging data of each well to be processed in the standard layer depth range, resample the well logging data of each well to be processed in the standard layer.

[0044] Since the resampling process of each well to be processed is the same, the present invention only takes the resampling process of one well to be processed as an example for explanation. Specifically, in the embodiment of the present invention, the neighboring point interpolation method is used to resample the logging data of the current well to be processed in the standard layer, so that the number of sampling points of the logging data of the standard layer of the well to be corrected after resampling is guaranteed to be the same as the number of sampling points of the reference well in the standard layer. In this way, the resampling process of the logging data for all wells to be processed (preprocessing before correction) is completed through the above step S120, thereby completing the preprocessing process before the logging curve correction process (logging curve standardization process), and then entering step S130.

[0045] Step S130 constructs an objective function for the correction coefficient required for linear correction of the wells to be treated. In the embodiment of the present invention, a linear correction method is used when correcting each well to be treated, and thus, it is particularly critical to determine the correction coefficient required for linear correction of each well to be treated. Furthermore, it is necessary to adaptively determine the correction coefficient for each well according to the different logging data of each well in the standard layer, so as to achieve the purpose of targeted full-well logging curve correction processing for the well.

[0046] Among them, the objective function of the correction coefficient required for each well to be treated during linear correction is expressed by the following expression:

[0047]

[0048] Among them, obj represents the objective function, Log sta Indicates the logging data of the reference well in the standard layer (each sampling point of the logging curve within the depth range of the standard layer), Log corr represents the well logging data of the well to be processed in the standard layer after correction (each sampling point of the well logging curve within the depth range of the standard layer), n represents the total number of well logging data sampling points within the depth range of the standard layer, and m represents the number of standard layers. Further, the well logging data in the standard layer of the well to be processed after correction is expressed by the following expression:

[0049]

[0050] Among them, Log raw represents the original logging curve of the well to be processed in the standard layer, and a and b respectively represent the first correction coefficient and the second correction coefficient required for the linear correction of the well to be processed.

[0051] After the construction of the objective function is completed, the process proceeds to step S140 to solve the objective function. Step S140 solves the objective function constructed in step S130 using a genetic algorithm based on the actual logging data of the reference well in the standard layer and the actual logging data of each well to be processed in the standard layer after resampling, so that the first correction coefficient and the second correction coefficient that are suitable for each well to be processed can be output.

[0052] Since the principles, steps and methods involved in the process of solving the objective function of each well to be treated are the same, the present invention will take the process of solving the objective function of a well to be treated as an example to illustrate the specific process involved in step S140. Figure 3 This is a flow chart of the objective function solving steps in the automatic normalization processing method for well logging curves according to an embodiment of the present application.

[0053] like Figure 3 As described above, step S301 needs to set the variation range of each quantity to be solved respectively. That is to say, it is necessary to set the variation range of the first correction coefficient and the variation range of the second correction coefficient. Then, step S302 randomly generates a group consisting of multiple individuals for the variation range of each quantity to be solved to initialize the group processing, and then enters step S303. Step S303 calculates the objective function of each group, and evaluates the fitness of the population individuals based on the logging data in the standard layer of the reference well and the logging data in the standard layer of the current well to be processed after resampling. Step S304 is based on the fitness of the various population individuals in step S303, and sequentially performs selection, crossover, and mutation operations to evolve each population, and determines whether the optimal solution of the new generation population meets the stop condition, until the stop condition is met and enters step S305. Step S305 outputs the first correction coefficient and the second correction coefficient of the inversion of the optimal individual, thereby obtaining the first correction coefficient and the second correction coefficient required for the current well to be processed when performing linear correction.

[0054] In one embodiment, step (1) sets the range of the content to be solved, for example, the upper and lower boundaries of the first correction coefficient of the independent variable are set to [1.3, 1], and the upper and lower boundaries of the second correction coefficient of the independent variable are set to [0.7, -0.5]; step (2) randomly generates a group consisting of multiple individuals, and the number of the initial group is preferably 100; step (3) calculates the fitness function (objective function) of each group based on the logging data of the standard layer of the reference well and the logging data of the standard layer of the current well to be processed after resampling; step (4) based on the fitness result of step (3), sequentially performs selection, crossover, and mutation operations to evolve the group, the crossover probability is preferably 0.7, and the mutation probability is preferably 0.01, and determines whether the optimal solution of the new generation group meets the stopping condition. After the termination condition is met, enter step (5); step (5) outputs the optimal individual as the correction coefficients a and b of the inversion. At this time, the correction coefficient a solved is 1.06 and b is 1.68.

[0055] In this way, the correction coefficient solution process for all the wells to be processed is completed through the above step S140, so that the first correction coefficient and the second correction coefficient adapted to the standard well logging data are obtained for each well to be calibrated, and then the process proceeds to step S150.

[0056] Step S150 is based on the original full-well logging curve of each well to be processed, and according to the first correction coefficient and the second correction coefficient required for linear correction for each well to be corrected obtained in step S140, the following full-well correction formula is used to obtain the corrected full-well logging curve of each well to be processed, thereby completing the correction processing (standardization processing) of the full-well logging curve of each well to be processed. Among them, the original full-well logging curve refers to the actual logging curve based on the full-well depth range drawn according to the actual collected logging data when drilling the current well to be processed. Among them, the full-well correction formula is expressed by the following expression:

[0057] Log final =aLog+b (3)

[0058] Among them, Log final It represents the full-well (section) logging curve of the current well to be corrected after correction, and Log represents the original full-well (section) logging curve of the well to be corrected before correction. Figure 4 This is a schematic diagram of the effect of correcting the full-well logging curve of a certain well to be processed in the automatic logging curve standardization processing method of an embodiment of the present application.

[0059] In this way, the present invention only needs to determine the reference well, the standard layer depth range, and the preset parameters involved in the genetic algorithm, and can directly use computer software to automatically batch calibrate each well to be calibrated in the current oil field area, thereby effectively improving the efficiency of the initial standardization of the logging curve, eliminating the systematic error of the logging curve, thereby improving the quality of the logging data, and laying the foundation for further improving the efficiency and accuracy of the logging interpretation.

[0060] Embodiment 2

[0061] On the other hand, the present invention also proposes an automatic standardization processing system for well logging curves based on an automatic standardization processing method for well logging curves. Figure 5 1 is a system block diagram of the automatic standardization processing system for logging curves in the embodiment of the present application. Figure 5 As shown, the system includes: a reference well and standard layer selection module 51, a resampling module 52, an objective function construction module 53, a correction coefficient generation module 54 and a full well curve correction module 55. Among them, the reference well and standard layer selection module 51 is implemented according to the method described in step S110 above, and is configured to determine the reference well in the oil field area, and select the standard layer for representing all the wells to be processed for logging curve standardization processing in combination with the full well section geological data of all the wells to be processed and the reference wells in the current area. The resampling module 52 is implemented according to the method described in step S120 above, and is configured to resample the logging data of the standard layer of each well to be processed. The objective function construction module 53 is implemented according to the method described in step S130 above, and is configured to construct an objective function for the correction coefficient required for linear correction of the well to be processed. The correction coefficient generation module 54 is implemented according to the method described in step S140 above, and is configured to solve the objective function using a genetic algorithm based on the logging data of the standard layer of the reference well and the logging data of the standard layer of each well to be processed after resampling. The full-well curve correction module 55 is implemented according to the method described in the above step S150, and is configured to use the solution results for each well to be processed obtained by the correction coefficient generation module 54, and obtain the corrected full-well logging curve of each well to be processed from the original full-well logging curve of each well to be processed.

[0062] Furthermore, the resampling module 52 is further configured to resample the logging data of the standard layer of each well to be processed by using a neighboring point interpolation method, so that the logging data of the resampled standard layer has the same number of sampling points as the logging data of the standard layer of the reference well.

[0063] Further, in the objective function construction module 53, the objective function is expressed by the following expression:

[0064]

[0065] Where obj represents the objective function, Log sta Represents the logging data of the standard layer of the reference well, Log corr represents the well logging data of the standard layer of the well to be processed after correction, n represents the total number of sampling points in the well logging data of the standard layer, and m represents the number of standard layers. Among them, the well logging data of the standard layer of the well to be processed after correction is expressed by the following expression:

[0066]

[0067] Among them, Log raw represents the original logging curve of the standard layer of the well to be processed, and a and b represent the first correction coefficient and the second correction coefficient required for the current linear correction respectively.

[0068] Further, the correction coefficient generation module 54 includes: a variation range determination unit 541, a population initialization unit 542, a fitness evaluation unit 543, an optimal solution solving unit 544 and a correction coefficient output unit 545. Among them, the variation range determination unit 541 is configured to set the variation range of each quantity to be solved respectively. The population initialization unit 542 is configured to randomly generate a population composed of multiple individuals for each quantity to be solved. The fitness evaluation unit 543 is configured to calculate the objective function of each population, and evaluate the fitness of the population individuals according to the logging data of the standard layer of the reference well and the logging data of the standard layer of the corresponding well to be processed after resampling. The optimal solution solving unit 544 is configured to perform selection, crossover and mutation operations in sequence to evolve each population based on the fitness calculation result of the fitness evaluation unit 543, and judge whether the optimal solution of the new generation population meets the stop condition. When the stop condition is met, it enters the correction coefficient output unit 545. The correction coefficient output unit 545 is configured to output the first correction coefficient and the second correction coefficient of the inversion of the optimal individual.

[0069] The present invention proposes a method and system for automatic standardization of logging curves. The method and system first need to perform pre-correction pre-processing work including selecting reference wells and standard layers in the same oil field area, and resampling the logging data of the standard layers of each well to be processed; then, a genetic intelligence algorithm is used to determine the two linear correction coefficients required for each well to be processed during correction; finally, based on the full-well section logging curves of each well to be processed, and according to these linear correction coefficients, the corresponding corrected full-well section logging curves are obtained. In this way, the present invention greatly improves the efficiency of initial standardization of logging curves, eliminates the systematic errors of logging curves, thereby improving the quality of logging data, and lays a foundation for further improving the efficiency and accuracy of logging interpretation. The computer software can quickly complete the batch automatic standardization of logging curves within 10 seconds, solving the problem that the traditional logging curve method often has large errors in the correction processing results due to systematic errors and low efficiency.

[0070] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any person familiar with the technology within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0071] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should be extended to equivalent substitutions of these features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0072] The "one embodiment" or "embodiment" mentioned in the specification means that the specific features, structures or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, the phrases "one embodiment" or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0073] Although the embodiments disclosed in the present invention are as above, the above contents are only embodiments adopted for facilitating the understanding of the present invention and are not intended to limit the present invention. Any technician in the technical field to which the present invention belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present invention, but the patent protection scope of the present invention shall still be subject to the scope defined in the attached claims.

Claims

1. A method for automatic standardization of well logging curves, characterized in that: The automatic standardization processing method for logging curves comprises: Determine a reference well in the oilfield area, and select a standard layer for representing all the wells to be processed for logging curve standardization processing by combining the geological data of all the wells to be processed and the reference well in the area; Resample the well logging data of the standard layer of each well to be processed; constructing an objective function for correction coefficients required for linear correction of the treated wells; Solving the objective function using a genetic algorithm based on the well logging data of the standard layer of the reference well and the well logging data of the standard layer of each well to be processed after resampling; By using the solution results, the corresponding corrected full-well logging curve is obtained from the original full-well logging curve of each well to be processed, wherein the objective function is expressed by the following expression: Wherein, obj represents the objective function, Log sta Represents the logging data of the standard layer of the reference well, Log corr represents the corrected well logging data of the standard layer of the well to be processed, n represents the total number of sampling points in the well logging data of the standard layer, and m represents the number of standard layers. The corrected well logging data of the standard layer of the well to be processed is represented by the following expression: Among them, Log raw represents the original logging curve of the standard layer of the well to be processed, and a and b represent the first correction coefficient and the second correction coefficient required for the current linear correction respectively.

2. The automatic standardization processing method for well logging curves according to claim 1 is characterized in that: The step of solving the objective function by using a genetic algorithm according to the well logging data of the standard layer of the reference well and the well logging data of the standard layer of each well to be processed after resampling includes: Set the variation range of each quantity to be solved respectively; For each of the quantities to be solved, a group consisting of multiple individuals is randomly generated; Calculating the objective function of each population, and evaluating the fitness of the individuals in the population based on the well logging data of the standard layer of the reference well and the well logging data of the standard layer of the corresponding well to be processed after resampling; Performing selection, crossover, and mutation operations in sequence to evolve each population, and determining whether the optimal solution of the new generation population satisfies the stopping condition; The optimal individual is output as the first correction coefficient and the second correction coefficient for inversion.

3. The automatic standardization processing method for logging curves according to claim 1 or 2, characterized in that: The step of resampling the well logging data of the standard layer of each well to be processed includes: The logging data of the standard layer of each well to be processed are resampled by using the adjacent point interpolation method, so that the number of sampling points contained in the logging data of the standard layer after resampling is the same as that in the logging data of the standard layer of the reference well.

4. The automatic standardization processing method for logging curves according to claim 1 or 2, characterized in that: In the process of selecting the standard layer, With reference to the formation permeability data of the entire well section of each well to be treated and the reference well, a standard formation depth well section with stable lithology and impermeability is selected, wherein the standard formation depth well section is a well section included in all wells in the area.

5. The automatic standardization processing method for logging curves according to claim 2 is characterized in that: Set the upper and lower bounds of the first correction coefficient to [1.3, 1], and the upper and lower bounds of the second correction coefficient to [0.7, -0.5]; The number of initial groups is 100; The crossover probability is 0.7 and the mutation probability is 0.

01.

6. A well logging curve automatic standardization processing system, characterized in that: The automatic standardization processing system for logging curves comprises: A reference well and standard layer selection module determines the reference well in the oil field area, and selects a standard layer for representing all the wells to be processed for logging curve standardization processing by combining the geological data of all the wells to be processed and the reference well in the area; A resampling module, which resamples the well logging data of the standard layer of each well to be processed; An objective function construction module that constructs an objective function for correction coefficients required for linear correction of the treated wells; A correction coefficient generation module, which uses a genetic algorithm to solve the objective function according to the well logging data of the standard layer of the reference well and the well logging data of the standard layer of each well to be processed after resampling; The full-well curve correction module uses the solution result to obtain the corresponding corrected full-well logging curve from the original full-well logging curve of each well to be processed, wherein the objective function is expressed by the following expression: Wherein, obj represents the objective function, Log sta Represents the logging data of the standard layer of the reference well, Log corr represents the corrected well logging data of the standard layer of the well to be processed, n represents the total number of sampling points in the well logging data of the standard layer, and m represents the number of standard layers. The corrected well logging data of the standard layer of the well to be processed is represented by the following expression: Among them, Log raw represents the original logging curve of the standard layer of the well to be processed, and a and b represent the first correction coefficient and the second correction coefficient required for the current linear correction respectively.

7. The automatic standardization processing system for logging curves according to claim 6 is characterized in that: The correction coefficient generation module comprises: A variation range determination unit, which sets the variation range of each quantity to be solved; A group initialization unit, which randomly generates a group consisting of multiple individuals for each quantity to be solved; A fitness evaluation unit, which calculates the objective function of each population and evaluates the fitness of the individuals in the population according to the well logging data of the standard layer of the reference well and the well logging data of the standard layer of the corresponding well to be processed after resampling; An optimal solution solving unit, which sequentially performs selection, crossover, and mutation operations to evolve each of the populations, and determines whether the optimal solution of the new generation population meets the stopping condition; A correction coefficient output unit is configured to output the first correction coefficient and the second correction coefficient of the inversion of the optimal individual.

8. The automatic standardization processing system for logging curves according to claim 6 or 7, characterized in that: The resampling module also uses a neighboring point interpolation method to resample the logging data of the standard layer of each well to be processed, so that the number of sampling points contained in the logging data of the resampled standard layer is the same as that of the logging data of the standard layer of the reference well.

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