CN113672853A - Automatic standardized processing method and system for logging curve - Google Patents
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Abstract
The invention discloses an automatic standardized processing method of a logging curve, which comprises the following steps: determining a reference well in an oil field area, and selecting a standard layer for representing all wells to be processed to carry out logging curve standardization processing by combining all wells to be processed in the area and the geological data of the whole well section of the reference well; resampling the logging data of the standard layer of each well to be processed; constructing an objective function of a correction coefficient required for linear correction of a well to be processed; solving the objective function by using a genetic algorithm 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; and obtaining a corresponding corrected full-well logging curve from the original full-well logging curve of each well to be processed by utilizing the solving result. The invention improves the efficiency of initial standardization of the logging curve, eliminates systematic errors of the logging curve, and lays a foundation for improving the efficiency and precision of well logging interpretation.
Description
Technical Field
The invention relates to the field of oil and gas development and exploration, in particular to a method and a system for automatically standardizing and processing a logging curve.
Background
The more important petrophysical parameters, such as porosity and permeability of the formation, are all derived from the interpretation of well log data. However, the logging data is affected by environmental factors, instrument types, proficiency of operators and the like in the acquisition engineering, so that a large systematic error exists, which may cause deviation between the acquired logging data and the actual formation property of the well section with the same depth, and affect the judgment of the reservoir property. Therefore, it is an indispensable procedure to standardize the logging data of a plurality of wells in the same field area.
Well log normalization is based on the principle that lithologic formations of well sections of the same depth generally have similar log response characteristics in the same depositional environment. At present, curve standardization methods in the prior art are roughly divided into a single-well comparison method and a multi-well evaluation method, the single-well comparison method mainly depends on analysis and judgment of information such as an artificial cross plot, and the like, so that the method is very low in efficiency, and the implementation time of subsequent well logging interpretation is seriously delayed; the multi-well comparison method is also based on a manual mode, and is compared with standard wells one by one to obtain the linear correction value of each well, so that the efficiency is very low under the condition of large number of wells to be corrected, the unified correction scale standard is difficult to ensure, and the problem is large.
Disclosure of Invention
In order to solve the technical problem, the invention provides an automatic well logging curve standardization processing method, which comprises the following steps: determining a reference well in an oil field area, and selecting a standard layer for representing all wells to be processed to carry out logging curve standardization processing by combining all wells to be processed in the area and the geological data of the whole well section of the reference well; resampling the logging data of the standard layer of each well to be processed; constructing an objective function of a correction coefficient required for linear correction of a well to be processed; solving the objective function by using a genetic algorithm 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; and obtaining a corresponding corrected whole well logging curve from the original whole well logging curve of each well to be processed by utilizing the solving result.
Preferably, the objective function is expressed by the following expression:
wherein obj represents the objective function, LogstaLog data, Log, representing a standard interval of the reference wellcorrThe well logging data of the standard layer of the corrected well to be processed is represented, n represents the total number of sampling points in the well logging data of the standard layer, m represents the number of the standard layers, and the well logging data of the standard layer of the corrected well to be processed is represented by the following expression:
wherein, LograwAnd a and b respectively represent a first correction coefficient and a second correction coefficient required by the current linear correction.
Preferably, the step of solving the objective function by using a genetic algorithm 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 comprises: respectively setting the variation range of each to-be-solved quantity; randomly generating a group consisting of a plurality of individuals for each solution quantity to be solved; calculating a target function of each group, and evaluating the fitness of the individual group 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; carrying out selection, crossing and mutation operations in sequence to carry out evolution on each population, and judging whether the optimal solution of the new generation population meets the stop condition; outputting the first correction coefficient and the second correction coefficient of which the optimal individuals are inverted.
Preferably, in the step of resampling the log data of the standard bed of each well to be processed, the method comprises: and resampling the logging data of each standard layer of the well to be processed by adopting a near 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 of the logging data of the standard layer of the reference well.
Preferably, in the process of selecting the standard stratum, further, referring to the stratum permeability data of the whole well section of each well to be treated and the reference well, selecting a standard stratum depth well section with stable lithology and impermeability, wherein the standard stratum depth well section is a well section contained by all wells in the area.
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 populations is preferably 100; the crossover probability is preferably 0.7 and the mutation probability is preferably 0.01.
In another aspect, the present invention further provides an automatic well log standardization processing system, where the system includes: the system comprises a reference well and standard layer selection module, a data acquisition module and a data processing module, wherein the reference well and standard layer selection module is used for determining a reference well in an oil field area, and selecting a standard layer for representing all wells to be processed to carry out logging curve standardization processing by combining all wells to be processed in the area and the geological data of the whole well section of the reference well; the resampling module is used for resampling the logging data of the standard layer of each well to be processed; the target function construction module is used for constructing a target function of a correction coefficient required by linear correction of a well to be processed; the correction coefficient generation module is used for solving the objective function by utilizing a genetic algorithm 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; and the whole well curve correction module is used for obtaining a corresponding corrected whole well logging curve from the original whole well logging curve of each well to be processed by utilizing the solving result.
Preferably, the objective function is expressed by the following expression:
wherein obj represents the objective function, LogstaLog data, Log, representing a standard interval of the reference wellcorrThe well logging data of the standard layer of the corrected well to be processed is represented, n represents the total number of sampling points in the well logging data of the standard layer, m represents the number of the standard layers, and the well logging data of the standard layer of the corrected well to be processed is represented by the following expression:
wherein, LograwAnd a and b respectively represent a first correction coefficient and a second correction coefficient required by the current linear correction.
Preferably, the correction coefficient generation module includes: a variation range determination unit that sets a variation range of each to-be-solved quantity, respectively; a population initialization unit that randomly generates a population composed of a plurality of individuals for each solution amount to be solved; the fitness evaluation unit is used for calculating the objective function of each group and evaluating the fitness of the individual group 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 is used for evolving each group by sequentially carrying out selection, crossing and mutation operations and judging whether the optimal solution of the new generation group meets the stop condition or not; a correction coefficient output unit that outputs the first correction coefficient and the second correction coefficient whose optimal individuals are inversions.
Preferably, the resampling module further uses a near point interpolation method to resample the logging data of each standard layer of the well to be processed, so that the number of sampling points included in the re-sampled logging data of the standard layer is the same as the number of sampling points included in the logging data of the standard layer of the reference well.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
the invention provides a method and a system for automatically standardizing a logging curve. The method and the system firstly need to carry out preprocessing work before correction, including selecting a reference well and a standard layer in the same oil field area and resampling logging data of the standard layer of each well to be processed; then, determining two linear correction coefficients required by each well to be processed during correction by using a genetic intelligent algorithm; and finally, obtaining a corresponding corrected full-well-section logging curve based on the full-well-section logging curves of the wells to be processed and according to the linear correction coefficients. The invention greatly improves the efficiency of initial standardization of the logging curve, eliminates systematic errors of the logging curve, thereby improving the quality of logging data, lays a foundation for further improving the logging interpretation efficiency and precision, and can quickly complete batch automatic standardization processing of the logging curve within 10 seconds by computer software, thereby solving the problems that the error of a correction processing result is larger and the efficiency is low due to the systematic errors in the traditional logging curve method.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a step diagram of an automatic well log normalization processing method according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a log of a reference well and a standard layer of a well to be processed in a certain area in the automatic well log standardization processing method according to the embodiment of the present application.
Fig. 3 is a flowchart of an objective function solving step in the automatic well log normalization processing method according to the embodiment of the present application.
Fig. 4 is a schematic diagram illustrating an effect of performing full-well log curve correction on a well to be processed in the log curve automatic standardized processing method according to the embodiment of the present application.
Fig. 5 is a system block diagram of an automatic well log normalization processing system according to an embodiment of the present disclosure.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described may be performed in an order different than here.
The more important petrophysical parameters, such as porosity and permeability of the formation, are all derived from the interpretation of well log data. However, the logging data is affected by environmental factors, instrument types, proficiency of operators and the like in the acquisition engineering, so that a large systematic error exists, which may cause deviation between the acquired logging data and the actual formation property of the well section with the same depth, and affect the judgment of the reservoir property. Therefore, it is an indispensable procedure to standardize the logging data of a plurality of wells in the same field area.
Well log normalization is based on the principle that lithologic formations of well sections of the same depth generally have similar log response characteristics in the same depositional environment. At present, curve standardization methods in the prior art are roughly divided into a single-well comparison method and a multi-well evaluation method, the single-well comparison method mainly depends on analysis and judgment of information such as an artificial cross plot, and the like, so that the method is very low in efficiency, and the implementation time of subsequent well logging interpretation is seriously delayed; the multi-well comparison method is also based on a manual mode, and is compared with standard wells one by one to obtain the linear correction value of each well, so that the efficiency is very low under the condition of large number of wells to be corrected, the unified correction scale standard is difficult to ensure, and the problem is large.
The genetic intelligent algorithm is based on Darwin evolution theory, natural competition and selection are simulated, a suitable person survives, an optimal solution is obtained through N generations of heredity and through the steps of mutation, intersection, copying and the like, the method is particularly suitable for solving a target function in the batch automatic standardization processing of logging curves, and the method can be used for adaptively adjusting solving parameters aiming at different stratum characteristics so as to eliminate systematic errors of logging curves of different stratum types.
In order to solve the technical problems, the invention provides a method and a system for automatically standardizing a logging curve. The method and the system firstly determine a reference well in an oil field area and a standard layer which can represent all wells to be processed (wells to be corrected) to carry out logging curve standardization processing; based on the logging data of the standard layer of the reference well, resampling the logging data of the standard layer of each well to be processed; constructing an objective function of a correction coefficient required for linear correction of a well to be processed; then, solving the objective function by using a genetic algorithm 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, and outputting a first correction coefficient and a second correction coefficient required by linear correction for each well to be processed; and finally, based on the original full-well logging curve of each well to be processed, respectively correcting the full-well logging curve of each well to be processed by using the solved correction coefficient to obtain the corrected full-well logging curve of each well to be processed.
Thus, the invention is realized by a logging software platform, can quickly invert two correction coefficients required by linear correction for each well to be processed (well to be corrected) by utilizing a genetic intelligent algorithm, and quickly carry out batch automatic standardized processing on each original full-well logging curve, thereby effectively improving the initial standardized efficiency of the logging curve, eliminating the systematic error of the logging curve, improving the quality of logging data, laying a foundation for further improving the logging interpretation efficiency and precision, and achieving the effect of completing the batch automatic standardized processing of the logging curve within 10 seconds.
Example one
Fig. 1 is a step diagram of an automatic well log normalization processing method according to an embodiment of the present application. Referring to fig. 1, the automatic well log standardization processing method according to the present invention will be described in detail.
Firstly, step S110 determines a reference well in the oil field area, and selects a standard layer for representing all wells to be processed to perform well logging curve standardization processing from the full well section depths of all wells to be processed and the reference well in combination with the full well section geological data of all wells to be processed and the reference well in the current oil field area. In the embodiment of the invention, the same oilfield region refers to a region with similar logging response characteristics of rock lithology of the stratum under the same stratum sedimentary environment. In step S110, a reference well is selected from all wells in the same field area. The standard of selecting the reference well is to compare the drilling construction quality and the drilling instrument quality of all wells in the current oil field 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 oilfield region, the historical wells except the reference well are used as the wells to be processed, which need to perform well logging curve standardization processing by taking the well logging data of the reference well as the standard, and are also called as wells to be corrected, and each well to be processed needs to be subjected to well logging curve standardization processing in a targeted manner, so that the problem of systematic errors (errors between the well logging data and actual formation characteristics caused by factors except the formation and reservoir characteristics) caused by the quality of instruments, construction quality, environmental conditions and the like is solved.
Further, after the selection of the reference well is completed, the standard layer needs to be continuously screened. In the standard layer process, the stratum permeability data of all the wells to be treated and the reference wells in the current oil field area need to be referred, and standard stratum depth well sections with stable lithology and impermeability are selected from the depth range of all the well sections of each well. And the standard stratum depth well section is the well section depth range contained by all wells to be treated and the reference well in the current oilfield area. More specifically, when a standard layer involved in curve correction is selected, the standard layer is generally lithologic and has a certain thickness, and meanwhile, the standard layer is generally impermeable, and the stratum is not influenced by invasion of oil, gas, water and logging mud; more importantly, all wells in the current field area need to contain this standard layer. In the embodiment of the invention, 1 or 2 standard layers are selected in the same oilfield area. In this way, the depth range of the standard layer representing all the wells to be processed for the well log normalization (well log correction) process is determined, and the process proceeds to step S120.
Fig. 2 is a schematic diagram of a log of a reference well and a standard layer of a well to be processed in a certain area in the automatic well log standardization processing method according to the embodiment of the present application. As shown in fig. 2, the solid line is the density log of the standard zone of the reference well, the dotted line is the density log of the standard zone of a certain well to be corrected, and the depth range and thickness of the standard zone of the two wells are similar.
Step S120, the logging data in the standard layer of each well to be processed in the current oil field area are re-sampled respectively. That is, in step S120, the actual logging data in the depth range of the standard layer needs to be screened out according to the original logging data of each well to be processed (the original logging data refers to the actual logging data before the standardized processing actually obtained when the current well to be processed is subjected to drilling construction); and then, according to the actual logging data of each well to be processed in the depth range of the standard layer, resampling the logging data of each well to be processed in the standard layer respectively.
Since the resampling process of each well to be treated is the same, the present invention will be described by taking the resampling process of only one well to be treated as an example. Specifically, in the embodiment of the present invention, a near point interpolation method is adopted 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 resampled well to be corrected is ensured to be the same as the number of sampling points of the reference well in the standard layer. In this way, the resampling process (pre-correction process) for the logging data of all the wells to be processed is completed in step S120, so that the pre-processing process before the logging curve correction process (logging curve normalization process) is completed, and the process proceeds to step S130.
Step S130 constructs an objective function of correction coefficients required for linear correction of the well to be processed. In the embodiment of the invention, when each well to be processed is corrected, a linear correction method is adopted, so that the determination of the correction coefficient required by each well to be processed during linear correction is particularly critical. Furthermore, a correction coefficient for each well needs to be adaptively determined according to different logging data of each well in the standard layer, so that the aim of performing targeted full-interval logging curve correction processing on the well is fulfilled.
The objective function of the correction coefficient required by each well to be treated in linear correction is expressed by the following expression:
wherein obj represents the objective function, LogstaLog data representing a reference well in a standard formation (individual sample points of a Log over the depth of the standard formation), LogcorrAnd (3) representing the corrected logging data (each sampling point of the logging curve in the depth range of the standard layer) of the well to be processed in the standard layer, wherein n represents the total number of the logging data sampling points in the depth range of the standard layer, and m represents the number of the standard layers. Further, the corrected logging data in the standard layer of the well to be processed is represented by the following expression:
wherein, LograwAnd a and b respectively represent a first correction coefficient and a second correction coefficient required by the current well to be processed when linear correction is carried out.
After the construction of the objective function is completed, the process proceeds to step S140 to solve for the objective function. Step S140 is to solve the objective function constructed in step S130 by using a genetic algorithm according to the actual logging data of the reference well in the standard layer and the actual logging data of the resampled wells in the standard layer, so that a first correction coefficient and a second correction coefficient suitable for each well to be processed can be output.
Since the solution process of the objective function of each well to be processed involves the same principles, steps and methods, the present invention takes the objective function solution process of one well to be processed as an example to describe the specific process involved in step S140. Fig. 3 is a flowchart of an objective function solving step in the automatic well log normalization processing method according to the embodiment of the present application.
As shown in fig. 3, step S301 needs to separately set the variation range of each solution amount. That is, it is necessary to set a variation range of the first correction coefficient and a variation range of the second correction coefficient. Then, step S302 randomly generates a population composed of a plurality of individuals for each variation range of the amount to be solved to perform the initial population processing, and then proceeds to step S303. Step S303 is to calculate the objective function of each group, and evaluate the fitness of the individual group according to 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 to perform the operations of selection, crossover, and mutation in turn to evolve each population based on the fitness of each population in step S303, and determine whether the optimal solution of the new generation population satisfies the stop condition, and then proceed to step S305 until the stop condition is satisfied. Step S305 outputs a first correction coefficient and a second correction coefficient, which are inverted as optimal individuals, so as to obtain a first correction coefficient and a second correction coefficient required when linear correction is performed on the current well to be treated.
In one embodiment, step (1) sets the range of variation of the content to be solved, for example: respectively setting the upper and lower boundaries of the independent variable first correction coefficient as [1.3, 1] and the upper and lower boundaries of the independent variable second correction coefficient as [0.7, -0.5 ]; randomly generating a population consisting of a plurality of individuals, wherein the number of the initial population is preferably 100; step (3) calculating a fitness function (target function) of each group according to 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 performing selection, crossing and mutation operations to evolve the population, wherein the crossing probability is preferably 0.7, the mutation probability is preferably 0.01, judging whether the optimal solution of the new generation population meets the stopping condition, and entering step (5) after meeting the stopping condition; and (5) outputting correction coefficients a and b with the optimal individuals as inversions. At this time, the solved correction coefficient a was 1.06 and b was 1.68.
In this way, the correction coefficient solving 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 corrected, and the process proceeds to step S150.
Step S150 is to obtain the corrected whole-well log curve of each well to be processed by using the following whole-well section correction formula based on the original whole-well log curve of each well to be processed and according to the first correction coefficient and the second correction coefficient required for the linear correction of each well to be corrected obtained in step S140, thereby completing the correction processing (normalization processing) of the whole-well section log curve of each well to be processed. The original full-well-section logging curve refers to an actual logging curve based on a full-well-section depth range, which is drawn according to actually acquired logging data when a current well to be processed is subjected to drilling construction. Wherein the whole-interval correction formula is represented by the following expression:
Logfinal＝aLog+b (3)
wherein, LogfinalAnd the Log represents the logging curve of the whole well (section) of the current well to be corrected after correction, and the Log represents the logging curve of the original whole well (section) of the well to be corrected before correction. Fig. 4 is a schematic diagram illustrating an effect of performing full-well log curve correction on a well to be processed in the log curve automatic standardized processing method according to the embodiment of the present application.
Therefore, the invention can directly carry out automatic batch correction on each well to be corrected in the current oil field area through computer software only by determining the reference well, the depth range of the standard layer and the related preset parameters in the genetic algorithm, thereby effectively improving the initial standardization efficiency of the logging curve, eliminating the systematic error of the logging curve, improving the quality of the logging data and laying a foundation for further improving the logging interpretation efficiency and precision.
Example two
On the other hand, the invention is based on an automatic well logging curve standardization processing method and also provides an automatic well logging curve standardization processing system. Fig. 5 is a system block diagram of an automatic well log normalization processing system according to an embodiment of the present disclosure. As shown in fig. 5, 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 whole well curve correction module 55. The reference well and standard layer selecting module 51, implemented according to the method in step S110, is configured to determine reference wells in the oilfield region, and select a standard layer representing all wells to be processed for performing well logging curve standardization processing by combining all geological data of the wells to be processed and the whole well section of the reference wells in the current region. A resampling module 52, implemented according to the method described in step S120 above, configured to resample the well log data of the standard layer of each well to be processed. An objective function construction module 53, implemented according to the method described in step S130 above, is configured to construct an objective function of correction coefficients required for linear correction of the well to be treated. A correction coefficient generation module 54, implemented according to the method described in step S140 above, configured to solve the objective function by using a genetic algorithm 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 whole-well curve correcting module 55, which is implemented according to the method described in the step S150, is configured to obtain the corrected whole-well log curve of each well to be processed from the original whole-well log curve of each well to be processed by using the solution result for each well to be processed, which is obtained by the correction coefficient generating module 54.
Further, 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 near point interpolation method, so that the number of sampling points included in the logging data of the standard layer after resampling is the same as the number of sampling points included in the logging data of the standard layer of the reference well.
Further, in the objective function construction module 53, the objective function is expressed by the following expression:
in the formula, obj represents an objective function, LogstaLog data, Log, representing a standard layer of a reference wellcorrAnd the well logging data of the standard layer of the well to be processed after correction is represented, n represents the total number of sampling points in the well logging data of the standard layer, and m represents the number of the standard layers. The corrected logging data of the standard layer of the well to be processed is represented by the following expression:
wherein, LograwAnd a and b respectively represent a first correction coefficient and a second correction coefficient required by the current linear correction.
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. Wherein the variation range determination unit 541 is configured to set a variation range of each to-be-solved amount, respectively. The population initializing unit 542 is configured to randomly generate a population composed of a plurality of individuals for each solution quantity to be solved. The fitness evaluation unit 543 is configured to calculate an objective function of each group, and evaluate the fitness of the individual group 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, based on the fitness calculation result of the fitness evaluation unit 543, selection, intersection, and mutation operations in sequence to evolve each group, and determine whether the optimal solution of the new generation group meets the stop condition, and when the stop condition is met, enter 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 for which the optimal individuals are inverted.
The invention provides a method and a system for automatically standardizing a logging curve. The method and the system firstly need to carry out preprocessing work before correction, including selecting a reference well and a standard layer in the same oil field area and resampling logging data of the standard layer of each well to be processed; then, determining two linear correction coefficients required by each well to be processed during correction by using a genetic intelligent algorithm; and finally, obtaining a corresponding corrected full-well-section logging curve based on the full-well-section logging curves of the wells to be processed and according to the linear correction coefficients. Therefore, the invention greatly improves the efficiency of initial standardization of the logging curve, eliminates systematic errors of the logging curve, thereby improving the quality of logging data, lays a foundation for further improving the logging interpretation efficiency and precision, and can quickly complete batch automatic standardization processing of the logging curve within 10 seconds by computer software, thereby solving the problems that the error of a correction processing result is larger and the efficiency is low due to systematic errors in the traditional logging curve method.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An automatic well log standardization processing method, which is characterized by comprising the following steps:
determining a reference well in an oil field area, and selecting a standard layer for representing all wells to be processed to carry out logging curve standardization processing by combining all wells to be processed in the area and the geological data of the whole well section of the reference well;
resampling the logging data of the standard layer of each well to be processed;
constructing an objective function of a correction coefficient required for linear correction of a well to be processed;
solving the objective function by using a genetic algorithm 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;
and obtaining a corresponding corrected whole well logging curve from the original whole well logging curve of each well to be processed by utilizing the solving result.
2. The method of claim 1, wherein the objective function is represented by the following expression:
wherein obj represents the objective function, LogstaLog data, Log, representing a standard interval of the reference wellcorrThe well logging data of the standard layer of the corrected well to be processed is represented, n represents the total number of sampling points in the well logging data of the standard layer, m represents the number of the standard layers, and the well logging data of the standard layer of the corrected well to be processed is represented by the following expression:
wherein, LograwAnd a and b respectively represent a first correction coefficient and a second correction coefficient required by the current linear correction.
3. The method of claim 2, wherein the step of solving the objective function using a genetic algorithm based on the well log data of the standard interval of the reference well and the well log data of the standard interval of each well to be processed after resampling comprises:
respectively setting the variation range of each to-be-solved quantity;
randomly generating a group consisting of a plurality of individuals for each solution quantity to be solved;
calculating a target function of each group, and evaluating the fitness of the individual group 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;
carrying out selection, crossing and mutation operations in sequence to carry out evolution on each population, and judging whether the optimal solution of the new generation population meets the stop condition;
outputting the first correction coefficient and the second correction coefficient of which the optimal individuals are inverted.
4. A method according to any one of claims 1 to 3, wherein in the step of resampling log data for a standard interval for each well to be treated, comprising:
and resampling the logging data of each standard layer of the well to be processed by adopting a near 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 of the logging data of the standard layer of the reference well.
5. The method according to any one of claims 1 to 4, wherein, in the step of selecting the standard layer, further,
and referring to the stratum permeability data of each well to be treated and the whole well section of the reference well, and selecting a standard stratum depth well section with stable lithology and impermeability, wherein the standard stratum depth well section is a well section contained in all wells in the area.
6. The method of claim 3, further comprising,
setting the upper and lower boundaries of the first correction coefficient to [1.3, 1] and the upper and lower boundaries of the second correction coefficient to [0.7, -0.5 ];
the number of initial populations is preferably 100;
the crossover probability is preferably 0.7 and the mutation probability is preferably 0.01.
7. An automatic well log standardization processing system, which is characterized in that the method system comprises:
the system comprises a reference well and standard layer selection module, a data acquisition module and a data processing module, wherein the reference well and standard layer selection module is used for determining a reference well in an oil field area, and selecting a standard layer for representing all wells to be processed to carry out logging curve standardization processing by combining all wells to be processed in the area and the geological data of the whole well section of the reference well;
the resampling module is used for resampling the logging data of the standard layer of each well to be processed;
the target function construction module is used for constructing a target function of a correction coefficient required by linear correction of a well to be processed;
the correction coefficient generation module is used for solving the objective function by utilizing a genetic algorithm 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;
and the whole well curve correction module is used for obtaining a corresponding corrected whole well logging curve from the original whole well logging curve of each well to be processed by utilizing the solving result.
8. The system of claim 7, wherein the objective function is represented by the expression:
wherein obj represents the objective function, LogstaLog data, Log, representing a standard interval of the reference wellcorrThe well logging data of the standard layer of the corrected well to be processed is represented, n represents the total number of sampling points in the well logging data of the standard layer, m represents the number of the standard layers, and the well logging data of the standard layer of the corrected well to be processed is represented by the following expression:
wherein, LograwRepresenting the original log of the standard bed of the well to be treated, a, b respectively representing the second required for the current linearity correctionA correction coefficient and a second correction coefficient.
9. The system of claim 8, wherein the correction coefficient generation module comprises:
a variation range determination unit that sets a variation range of each to-be-solved quantity, respectively;
a population initialization unit that randomly generates a population composed of a plurality of individuals for each solution amount to be solved;
the fitness evaluation unit is used for calculating the objective function of each group and evaluating the fitness of the individual group 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 is used for evolving each group by sequentially carrying out selection, crossing and mutation operations and judging whether the optimal solution of the new generation group meets the stop condition or not;
a correction coefficient output unit that outputs the first correction coefficient and the second correction coefficient whose optimal individuals are inversions.
10. The system according to any one of claims 7 to 9,
and the resampling module is used for resampling the logging data of the standard layer of each well to be processed by further adopting a near 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 of the logging data of the standard layer of the reference well.
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