CN111983722A - Reservoir logging characteristic parameter extraction method based on target preference coding - Google Patents
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Abstract
The invention discloses a reservoir logging characteristic parameter extraction method based on target preference coding, and belongs to the technical field of petroleum logging. The method mainly comprises the following steps: step 1, screening a logging curve capable of reflecting reservoir characteristics according to the evaluation characteristics of a target reservoir, determining a priority level, and sorting and arranging the logging curve for data coding; step 2, eliminating the top and bottom section data, and performing statistical analysis on the distribution characteristics of the residual data; step 3, coding the logging data based on the preference of the reservoir evaluation target; step 4, merging the feature codes, making a feature code analysis rule, and finding out a feature value sampling depth point; and 5, resampling the sampling depth points, and determining characteristic values of logging curves of the target reservoir for reservoir parameter modeling and fluid identification plate modeling. Compared with the manual extraction of the characteristic value, the method has the advantages of high speed and high accuracy through the characteristic depth point and the characteristic value extraction based on the target preference coding, and greatly improves the efficiency of reservoir logging data interpretation and evaluation.
Description
Technical Field
The invention belongs to the technical field of petroleum logging, and particularly relates to a reservoir logging characteristic parameter extraction method based on target preference coding.
Background
Reservoir logging evaluation is important content of reservoir evaluation, and reservoir logging characteristic values are core parameters for reservoir physical property parameter model research and fluid identification chart establishment, so that the reservoir logging characteristic values are particularly important to selection. The traditional extraction method mainly depends on experts with abundant experience to adopt manual extraction, the method has high requirements on regional well logging interpretation experience, the phenomenon of 'thousands of people and thousands of faces' is often easy to occur, and in order to reduce artificial extraction errors and improve the stability of characteristic value extraction, a reservoir characteristic value extraction method is urgently needed to be provided, so that the efficiency and the stability can be greatly improved, and the reservoir rapid evaluation and the fluid property rapid identification can be realized.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a reservoir logging characteristic parameter extraction method based on target preference coding, which has the advantages of high data processing speed and high efficiency and can meet the actual engineering requirements.
The invention is realized by the following technical scheme:
a reservoir logging characteristic parameter extraction method based on target preference coding comprises the following steps:
step 1: according to the evaluation characteristics of a target reservoir, screening logging curves capable of reflecting reservoir characteristics, determining priority levels according to importance degrees, and sorting;
step 2: removing the top and bottom section data of the target reservoir, and performing statistical analysis on the distribution characteristics of the residual data;
and step 3: coding the residual logging data obtained in the step 2 based on the preference of the reservoir evaluation target;
and 4, step 4: merging the feature codes to find out a feature value sampling depth point;
and 5: and (4) extracting characteristic parameters of all logging curves of the whole section of the target reservoir according to the characteristic value sampling depth points obtained in the step (4).
Preferably, the specific steps of step 1 are:
step 1.1: analyzing the geology and oil reservoir characteristics of the research area;
step 1.2: analyzing the weight of the logging curve in data interpretation;
step 1.3: and determining the arrangement sequence of the logging curves according to the analysis result of the step 1.2.
Preferably, the specific steps of step 2 are:
step 2.1: respectively removing 5-8 data points in a reservoir with the top and the bottom of 0.6-1 meter of a target reservoir, and taking the residual data set as a final data analysis sample;
step 2.2: sequentially carrying out statistics on data of each logging curve to obtain the maximum value and the minimum value of each curve in the reservoir section;
step 2.3: the final data analysis samples were used to plot a frequency distribution histogram.
Further preferably, in step 2.3, statistics are performed in 3 sections for reservoir thicknesses less than 2.5 meters, and in 5 sections for reservoir thicknesses greater than 2.5 meters.
Preferably, the specific steps of step 3 are:
step 3.1: according to the statistical result of the step 2, counting the number of depth points in each interval;
step 3.2: and coding each interval in sequence from more to less according to the number of the depth points.
Preferably, the specific steps of step 4 are:
step 4.1: and carrying out synthetic coding on each depth point according to the logging curve, wherein the synthetic code value at the jth depth is Cj，Cj＝c1jc2j*…*cmjM is the number of well logging curves;
step 4.2: and taking the point with the minimum code value and the nearest distance to the center of the reservoir as a characteristic value sampling depth point of the target reservoir.
Further preferably, step 4.2 is specifically: firstly, selecting a code value corresponding to the middle position of a reservoir as an initial layer characteristic value, and recording the position; then, moving a sampling point upwards and downwards respectively, taking out the position code value, comparing the position code value with the current characteristic value, selecting a smaller code value as a reservoir code value, modifying the position information of the code value, and keeping the current code value unchanged if the code values are the same; and repeating the steps until the whole reservoir interval is searched, and obtaining the depth position of the characteristic point of the reservoir.
Further preferably, the depth of the sampling point is 0.125 meters.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention discloses a reservoir logging characteristic parameter extraction method based on target preference coding, and mainly aims to improve the reservoir parameter rapid modeling efficiency and the fluid identification plate rapid establishing efficiency. The traditional reservoir logging curve characteristic value depends on expert experience, the time consumption is long, the automation degree is low, the traditional reservoir logging curve characteristic value depends on human-computer interaction, and the short, even and fast scientific research and attack requirements cannot be met. The automatic and rapid extraction method provided by the invention is equivalent to expert experience in precision and accuracy, is based on algorithm programming, simulates a manual extraction process by introducing an automatic characteristic value extraction method simulating human value-taking thought, achieves the same effect, is automatically processed in data processing, is high in speed and efficiency, and can meet the actual production requirements.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram illustrating the effect of extracting reservoir characteristic values in the example;
FIG. 3 is a diagram showing the processing results of reservoir characteristic values in the example;
FIG. 4 is a graph of the comparative effect of extracting the time difference characteristic value of the logging sound wave in the embodiment.
Detailed Description
The invention will now be described in further detail with reference to the drawings and specific examples, which are given by way of illustration and not by way of limitation.
In order to quickly extract the logging characteristic parameters of the reservoir in the research area, the lithology of the reservoir needs to be analyzed firstly. The invention utilizes a machine learning method to construct a model for predicting the reservoir lithology by utilizing the reservoir characteristic parameters, and realizes the prediction of the reservoir lithology in the development area. In order to generate training samples required by modeling, the characteristic parameters of reservoir logging and the lithology of coring are extracted from a reservoir coring interval to form the training samples. The invention relates to a reservoir characteristic parameter extraction method based on target preference, and the implementation flow of the method is shown in figure 1.
The method for extracting the characteristic parameters of reservoir logging based on the target preference code comprises the following steps:
step 1: analyzing the geology and oil reservoir characteristics of the research area, wherein the analysis is mainly carried out on the basis of characteristics such as reservoir lithology, oil reservoir type and the like; the weighted analysis of the logging curve in data interpretation, for example, in sand shale reservoir evaluation, natural gamma is used as a lithology identification standard, the priority is highest, the density curve reflecting porosity is second, deep lateral resistivity reflecting oil-gas-containing property is arranged in the third, a sound wave time difference curve reflecting total porosity is arranged in the fourth, and a natural potential curve reflecting invasion characteristic is arranged in the last; determining the arrangement sequence of the curves, arranging according to the sequence, and logging the curves L1，L2，L3，L4，L5And a corresponding depth profile D.
Step 2: cutting 5-8 data points of the top and bottom data points of the target layer, wherein the storage layer is about 0.6-1 meter, and the residual data set is used as a final data analysis sample. Sequentially carrying out statistics on data of each logging curve to obtain the maximum value and the minimum value of each curve in the reservoir section; first, the maximum value of the curve segment is searchedMiAnd minimum value miMaking a statistical histogram of the current logging curve data;
and drawing a frequency distribution histogram for the current data set, generally, counting in 3 sections when the thickness of the reservoir is less than 2.5 meters, and counting in 5 sections when the thickness of the reservoir is more than 2.5 meters. The length of the equal division interval is recorded as DeltaLi：
ΔLi＝(Mi-mi) K, K ═ 3, or 5.
In this embodiment, the level data points are greater than 20 points, the histogram is divided into 5 levels, and the number ranges of the levels are:
(-∞,mi+ΔLi]，(mi+ΔLi,mi+2ΔLi]，(mi+2ΔLi,mi+3ΔLi]，
(mi+3ΔLi,mi+4ΔLi]，((mi+4ΔLi,+∞)。
and step 3: encoding the logging data based on the target preference; counting the number of depth points in different intervals; the encoding is performed in a number size from 1 to 5. Counting the number of data points falling into each interval, and recording the number of data points in each interval as: n is a radical of1，N2，N3，N4，N5。
And 4, step 4: synthesizing and coding each depth point according to the logging curve; and sequencing the data points from large to small, wherein the interval with the most data number is coded as 1, the code with the data number positioned at the 2 nd bit is coded as 2, and so on, so as to finish the coding of each logging curve. Thus, at each depth point, each curve has a code value at that point, denoted as cijAnd represents the code value of the ith curve at the jth depth. And carrying out synthetic coding on each depth point according to the logging curve, wherein the synthetic code value at the jth depth is CjI.e. Cj＝c1jc2jc3jc4jc5jThis code value is an integer. As shown in fig. 2, after the GR log of the reservoir is coded, the corresponding code is 01122. DEN, RT, AC and SP are encoded in sequence according to the method.
And selecting the characteristic points of the reservoir, and taking the point with the minimum code value and the nearest distance from the center of the reservoir as the characteristic points of the target reservoir. And selecting the point with the minimum code value and the nearest distance from the center of the reservoir as the characteristic point of the target reservoir. The realization method comprises the following steps: firstly, selecting a code value corresponding to the middle position of a reservoir as an initial layer characteristic value, and recording the position; and then, moving the code value upwards and downwards one respectively, taking out the position code value, comparing the position code value with the current characteristic value, selecting a smaller code value as a reservoir code value, modifying the position information of the code value, and keeping the current code value unchanged if the code values are the same. And repeating the steps until the whole reservoir interval is searched, and obtaining the depth position of the characteristic point of the reservoir
And 5: and extracting a logging curve characteristic value of the target reservoir section. And (4) extracting characteristic values of all logging curves of the whole section of the target reservoir according to the depth points obtained in the step (4). The natural gamma, acoustic wave time difference, density, deep lateral direction and natural potential values of the depth point are extracted, and the values are used as characteristic values of a target reservoir section and participate in reservoir parameter modeling and fluid identification plate modeling.
FIG. 3 is a comparison graph of the depth positions of characteristic values representing a target reservoir interval searched by the algorithm of the present invention and the depth positions of the characteristic values extracted by an experienced engineer. Therefore, the reservoir logging characteristic parameters rapidly extracted through the target preference codes meet the actual production requirements.
Fig. 4 shows that the AC curve feature values extracted by the algorithm of the present invention have high correlation consistency with the curve feature values manually extracted by experienced engineers.
It should be noted that the above description is only a part of the embodiments of the present invention, and equivalent changes made to the system described in the present invention are included in the protection scope of the present invention. Persons skilled in the art to which this invention pertains may substitute similar alternatives for the specific embodiments described, all without departing from the scope of the invention as defined by the claims.
Claims (8)
1. A reservoir logging characteristic parameter extraction method based on target preference coding is characterized by comprising the following steps:
step 1: according to the evaluation characteristics of a target reservoir, screening logging curves capable of reflecting reservoir characteristics, determining priority levels according to importance degrees, and sorting;
step 2: removing the top and bottom section data of the target reservoir, and performing statistical analysis on the distribution characteristics of the residual data;
and step 3: coding the residual logging data obtained in the step 2 based on the preference of the reservoir evaluation target;
and 4, step 4: merging the feature codes to find out a feature value sampling depth point;
and 5: and (4) extracting characteristic parameters of all logging curves of the whole section of the target reservoir according to the characteristic value sampling depth points obtained in the step (4).
2. The method for extracting the characteristic parameters of the reservoir logging based on the target preference code as claimed in claim 1, wherein the specific steps of the step 1 are as follows:
step 1.1: analyzing the geology and oil reservoir characteristics of the research area;
step 1.2: analyzing the weight of the logging curve in data interpretation;
step 1.3: and determining the arrangement sequence of the logging curves according to the analysis result of the step 1.2.
3. The method for extracting the characteristic parameters of the reservoir logging based on the target preference code as claimed in claim 1, wherein the specific steps of the step 2 are as follows:
step 2.1: respectively removing 5-8 data points in a reservoir with the top and the bottom of 0.6-1 meter of a target reservoir, and taking the residual data set as a final data analysis sample;
step 2.2: sequentially carrying out statistics on data of each logging curve to obtain the maximum value and the minimum value of each curve in the reservoir section;
step 2.3: the final data analysis samples were used to plot a frequency distribution histogram.
4. The method for extracting the characteristic parameters of the reservoir logging based on the target preference code as claimed in claim 3, wherein in the step 2.3, statistics are carried out in 3 sections for reservoir thickness less than 2.5 m, and statistics are carried out in 5 sections for reservoir thickness greater than 2.5 m.
5. The method for extracting the characteristic parameters of the reservoir logging based on the target preference code as claimed in claim 1, wherein the specific steps of the step 3 are as follows:
step 3.1: according to the statistical result of the step 2, counting the number of depth points in each interval;
step 3.2: and coding each interval in sequence from more to less according to the number of the depth points.
6. The method for extracting the characteristic parameters of the reservoir logging based on the target preference code as claimed in claim 1, wherein the specific steps of the step 4 are as follows:
step 4.1: and carrying out synthetic coding on each depth point according to the logging curve, wherein the synthetic code value at the jth depth is Cj，Cj＝c1jc2j*…*cmjM is the number of well logging curves;
step 4.2: and taking the point with the minimum code value and the nearest distance to the center of the reservoir as a characteristic value sampling depth point of the target reservoir.
7. The method for extracting the characteristic parameters of the reservoir logging based on the target preference code as claimed in claim 6, wherein the step 4.2 is specifically as follows: firstly, selecting a code value corresponding to the middle position of a reservoir as an initial layer characteristic value, and recording the position; then, moving a sampling point upwards and downwards respectively, taking out the position code value, comparing the position code value with the current characteristic value, selecting a smaller code value as a reservoir code value, modifying the position information of the code value, and keeping the current code value unchanged if the code values are the same; and repeating the steps until the whole reservoir interval is searched, and obtaining the depth position of the characteristic point of the reservoir.
8. The method of claim 7, wherein the depth of the sampling point is 0.125 m.
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