CN-121978770-A - Multi-well consistency correction method, system and equipment based on Neisserial distance

CN121978770ACN 121978770 ACN121978770 ACN 121978770ACN-121978770-A

Abstract

The application discloses a multi-well consistency correction method, system and equipment based on a gas field distance, and relates to the field of oil and gas field exploration and development; according to the equal-width histogram of the standard well and the logging curves of a plurality of wells to be corrected, based on an empirical distribution function method, taking the minimum of the Holstein distance between each well to be corrected and the standard well as a target, adopting an annealing algorithm to carry out iterative optimization on parameters of a consistency mapping function to obtain optimal parameters corresponding to each well to be corrected, inputting the consistency mapping function to obtain optimal matching correction models corresponding to each well to be corrected, respectively inputting the logging curves of each well to be corrected to the corresponding optimal matching correction models to obtain corrected logging curves of each well to be corrected, and completing multi-well consistency correction. The method can improve the accuracy of the corrected logging curve, thereby laying a solid data foundation for subsequent seismic inversion and reservoir prediction.

Inventors

ZHENG QIANG
LI JUNHUI
SUN YU
WANG HAIXUE
SU YUPING
YANG PENG

Assignees

东北石油大学三亚海洋油气研究院
东北石油大学

Dates

Publication Date: 20260505
Application Date: 20260407

Claims (10)

1. A multi-well consistency correction method based on a Neisseria distance is characterized in that, the multi-well consistency correction method based on the Neisserian distance comprises the following steps: acquiring a logging curve of a standard well and logging curves of a plurality of wells to be corrected; constructing an equal-width histogram of the standard well based on a logging curve of the standard well; according to the equal-width histogram of the standard well and the logging curves of a plurality of wells to be corrected, based on an empirical distribution function method, taking the minimum distance between each well to be corrected and the standard well as a target, and adopting an annealing algorithm to carry out iterative optimization on parameters of a consistency mapping function so as to obtain optimal parameters corresponding to each well to be corrected; inputting the optimal parameters corresponding to each well to be corrected into the consistency mapping function to obtain an optimal matching correction model corresponding to each well to be corrected; And respectively inputting the well logging curves of the wells to be corrected into corresponding optimal matching correction models to obtain corrected well logging curves of the wells to be corrected, and completing multi-well consistency correction.
2. The multi-well consistency correction method of the base Yu Wase statan distance according to claim 1, wherein for any well to be corrected, according to the equal-width histogram of the standard well and the logging curve of the well to be corrected, based on an empirical distribution function method, taking the minimum gas statan distance between the well to be corrected and the standard well as a target, adopting an annealing algorithm to carry out iterative optimization on parameters of a consistency mapping function to obtain optimal parameters corresponding to the well to be corrected, and specifically comprising the following steps: determining parameters of the consistency mapping function under the current iteration times to obtain the current parameters, and determining initial parameters of the consistency mapping function according to the equal-width histogram of the standard well if the current iteration times are initial iteration times; Inputting the current parameters into the consistency mapping function to obtain a current matching correction model; Inputting the logging curve of the well to be corrected into a current matching correction model to obtain a current correction logging curve of the well to be corrected; constructing a current equal-width histogram of the well to be corrected according to the current correction logging curve; Calculating an equal-width histogram of the standard well and a current equal-width histogram of the well to be corrected by adopting an empirical distribution function method to obtain the distance between the well to be corrected and the standard well under the current iteration times; Obtaining a first parameter according to the distance between the well to be corrected and the standard well under the current iteration number and the distance between the well to be corrected and the standard well under the previous iteration number, wherein the first parameter is the current parameter or the parameter under the previous iteration number; Judging whether the distance between the to-be-corrected well and the standard well, corresponding to the first parameter, reaches a distribution matching threshold value or not; if yes, determining the first parameter as an optimal parameter; And if not, executing an updating process, wherein the updating process comprises the steps of determining the current annealing temperature according to the annealing temperature under the previous iteration times and the current iteration times, obtaining updated parameters according to the current annealing temperature and the first parameters, taking the updated parameters as parameters of the consistency mapping function under the next iteration times, updating the iteration times, and returning to determine parameters of the consistency mapping function under the current iteration times to obtain the current parameters.
3. The method for multi-well consistency correction based on Yu Wase stent distance according to claim 2, wherein if the current iteration number is the initial iteration number, determining initial parameters of a consistency mapping function according to an equal-width histogram of a standard well, specifically comprising: Determining a standard numerical range based on the equal-width histogram of the standard well, wherein the standard numerical range is the numerical range of a logging curve of the standard well; And determining initial parameters of the consistency mapping function by taking the standard numerical value range as constraint of the numerical value range to be corrected, wherein the numerical value range to be corrected is the numerical value range of the logging curve after the well to be corrected is transformed.
4. The method for multi-well consistency correction of base Yu Wase stats distance according to claim 2, wherein obtaining the first parameter according to the distance between the to-be-corrected well and the standard well at the current iteration number and the distance between the to-be-corrected well and the standard well at the previous iteration number specifically includes: judging whether the distance between the well to be corrected and the standard well is smaller than the distance between the well to be corrected and the standard well in the previous iteration number or not in the current iteration number, and obtaining a first judgment result; if the first judgment result is yes, the current parameter is determined to be a first parameter; if the first judgment result is negative, determining whether to accept the current parameter according to a Metropolis acceptance criterion, and obtaining a first parameter.
5. The method of claim 4, wherein determining whether to accept the current parameter based on Metropolis acceptance criteria, to obtain the first parameter, comprises: determining whether to accept the current parameters according to a Metropolis acceptance criterion to obtain a second judgment result; if the second judgment result is yes, determining the current parameter as a first parameter; And if the second judgment result is negative, determining the parameter under the previous iteration number as a first parameter.
6. The method for multi-well consistency correction based on Yu Wase stent distance according to claim 1, wherein the step of obtaining a log of a standard well and a log of a plurality of wells to be corrected, specifically comprises: determining any one of a plurality of target wells as a standard well, and determining the remaining wells as wells to be corrected; acquiring initial logging curves of a plurality of target wells; And performing data cleaning on the initial logging data of the target wells to obtain logging curves of the standard well and logging curves of the wells to be corrected.
7. The multi-well consistency correction method based on Yu Wase stent distance as recited in claim 1, wherein the expression of the best match correction model is: ; Wherein, the A corrected log for the well to be corrected; Is a slope parameter; normalization; A logging curve of the well to be corrected; is an intercept parameter.
8. The multi-well consistency correction method based on Yu Wase stent distance according to claim 1, wherein the calculation formula of the gas stent distance is: ; Wherein, the The distance between the well to be corrected and the standard well is the gas; A cumulative distribution function for the well to be corrected; a cumulative distribution function for a standard well; a corrected log for the well to be corrected; a corrected log for a standard well; The integral variable represents the value range of the logging curve.
9. A multi-well consistency correction system based on a Neisserian distance is characterized in that, the multi-well consistency correction system based on the Neisserian distance comprises: The acquisition module is used for acquiring the logging curves of the standard well and the logging curves of the plurality of wells to be corrected; the construction module is used for constructing an equal-width histogram of the standard well based on the logging curve of the standard well; The optimization module is used for carrying out iterative optimization on parameters of the consistency mapping function by adopting an annealing algorithm based on an empirical distribution function method and taking the minimum distance between each well to be corrected and the standard well as a target according to the equal-width histogram of the standard well and the logging curves of the plurality of wells to be corrected, so as to obtain optimal parameters corresponding to each well to be corrected; the correction model generation module is used for inputting the optimal parameters corresponding to each well to be corrected into the consistency mapping function to obtain an optimal matching correction model corresponding to each well to be corrected; The correction module is used for respectively inputting the well logging curves of the wells to be corrected into the corresponding optimal matching correction models to obtain corrected well logging curves of the wells to be corrected, and completing multi-well consistency correction.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-well consistency correction method of base Yu Wase stent distance as in any of claims 1-8.

Description

Multi-well consistency correction method, system and equipment based on Neisserial distance Technical Field The application relates to the technical field of oil and gas field exploration and development, in particular to a multi-well consistency correction method, system and equipment based on a gas field distance. Background In oil and gas exploration and development, physical logging is an essential key technology for quantitatively evaluating lithology, physical properties and fluid properties of underground formations. However, in actual production, multi-well logging data is subject to systematic baseline inconsistencies. This inconsistency stems primarily from differences in the type of logging instrument used, the scale standard, the borehole environment, and the drilling fluid properties for different well orders. These factors result in that even with the same geologic horizon, there may be significant background shifts or amplitude scaling differences in the measured curve values in different wells. The multi-well inconsistency severely restricts the application effect of logging data in cross-well comparison, seismic attribute calibration and quantitative interpretation models, and reduces the accuracy and reliability of reservoir prediction results. In some cases, multi-well consistency correction methods commonly used in the industry, such as mean-variance methods based on statistical features, histogram matching methods, and the like, have limitations such as (1) insufficient accuracy of distribution form matching, most of which focus on aligning the statistical moment (e.g., mean, variance) of corrected data with standard wells, but consider the matching of the overall probability distribution form of the data. For example, simple histogram shifting or scaling is difficult to fine tune complex morphologies such as non-gaussian distribution, multimodal distribution, etc., resulting in substantial differences in statistical distribution structure between corrected curves and standard curves, which cannot meet the stringent requirements for data distribution consistency for high-precision reservoir characterization. (2) Correction parameters (e.g., translation, scaling factors) of most techniques are mostly set by manual experience or determined by simple linear regression, lacking an objective, adaptive optimization framework. The mode mainly comprising 'manual intervention' is low in correction efficiency, and is difficult to ensure the optimality and repeatability of results due to strong subjectivity. In general, the prior art has not effectively solved the difficult problem of high-precision and self-adaptive matching from the data distribution form level. Disclosure of Invention The application aims to provide a multi-well consistency correction method, a system and equipment based on a Neisserial distance, which can improve the accuracy of a corrected logging curve, thereby laying a solid data foundation for subsequent seismic inversion and reservoir prediction. In order to achieve the above object, the present application provides the following. In a first aspect, the present application provides a method for multi-well consistency correction based on a gas distance, comprising: acquiring a logging curve of a standard well and logging curves of a plurality of wells to be corrected; constructing an equal-width histogram of the standard well based on a logging curve of the standard well; according to the equal-width histogram of the standard well and the logging curves of a plurality of wells to be corrected, based on an empirical distribution function method, taking the minimum distance between each well to be corrected and the standard well as a target, and adopting an annealing algorithm to carry out iterative optimization on parameters of a consistency mapping function so as to obtain optimal parameters corresponding to each well to be corrected; inputting the optimal parameters corresponding to each well to be corrected into the consistency mapping function to obtain an optimal matching correction model corresponding to each well to be corrected; And respectively inputting the well logging curves of the wells to be corrected into corresponding optimal matching correction models to obtain corrected well logging curves of the wells to be corrected, and completing multi-well consistency correction. In a second aspect, the present application provides a multi-well consistency correction system based on a gas distance, comprising the following modules: The acquisition module is used for acquiring the logging curves of the standard well and the logging curves of the plurality of wells to be corrected; the construction module is used for constructing an equal-width histogram of the standard well based on the logging curve of the standard well; The optimization module is used for carrying out iterative optimization on parameters of the consistency mapping function by adopting an annealing al