CN113806998B - Reservoir permeability curve simulation method - Google Patents
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Abstract
The invention relates to a reservoir permeability curve simulation method, in particular to a novel method for realizing permeability curve simulation prediction based on a machine learning algorithm. The method comprises the following steps: preliminarily determining reservoir permeability influence factors; fusion pretreatment of original data; screening and combining major influencing factors of reservoir permeability simulation, and determining a feature set; establishing an independent training set and verification set based on the preprocessed data set; determining a prediction algorithm; and generating a reservoir stratum permeability curve prediction model under each water saturation condition, checking and summarizing to obtain the water saturation test model. The invention realizes the real-time generation of the target well section seepage curve of each well of the oil field, and makes the target well section seepage curve become a necessary path for acquiring the physical data of reservoir permeability. And the reservoir permeability change prediction is realized, and the reservoir current situation is accurately reflected. The method is popularized and applied to the research of oil reservoir engineering and oil reservoir numerical simulation, and the geological research precision and efficiency are effectively improved.
Description
Technical Field
The invention relates to a reservoir permeability curve simulation method, in particular to a novel method for realizing permeability curve simulation prediction based on a machine learning algorithm.
Background
The traditional acquisition of reservoir permeability data currently mainly comprises three methods: indoor experimental methods, empirical formulas and well logging data interpretation methods. The whole phase permeation curve can be obtained by an indoor experiment method, but the process is finer and more expensive, and time is consumed, and the data volume only accounts for sixty percent of the total well number; the empirical formula method is to perform correlation estimation under the condition of lacking experimental data, and has poor accuracy; the well logging data interpretation method only obtains the permeability value under a certain water saturation, and cannot reflect the permeability change condition of the oil reservoir. The existing permeability predicted based on the neural network is also a value in a certain state, and the permeability curve with complete physical properties cannot be predicted. In the high water content phase of the oil field development as a whole, the physical properties and the original state of the oil reservoir can be greatly changed, and a new method is needed to dynamically reflect the current change condition of the relative permeability of the reservoir.
Disclosure of Invention
The invention provides a reservoir permeability curve simulation method. The existing oilfield physical property data is fully utilized, an artificial intelligent algorithm is optimally applied, multi-attribute condition constraint is achieved, a reservoir permeability curve prediction model is established, and an oilfield well and reservoir permeability curve complete set is formed. The real-time generation of the target well section seepage curve of each well of the oil field is realized, so that the target well section seepage curve becomes a necessary path for acquiring the physical data of reservoir permeability. And the reservoir permeability change prediction is realized, and the reservoir current situation is accurately reflected. The method is popularized and applied to the research of oil reservoir engineering and oil reservoir numerical simulation, and the geological research precision and efficiency are effectively improved.
The invention is realized by adopting the following technical scheme:
the invention provides a reservoir permeability curve simulation method, which comprises the following steps:
(1) Preliminarily determining reservoir permeability influence factors;
(2) Fusion pretreatment of original data;
(3) Screening and combining major influencing factors of reservoir permeability simulation, and determining a feature set;
(4) Establishing an independent training set and verification set based on the preprocessed data set;
(5) Determining a prediction algorithm;
(6) And generating a reservoir stratum permeability curve prediction model under each water saturation condition, checking and summarizing to obtain the water saturation test model.
Preferably, in the step (1), various physical experiments surrounding the core are performed, and various experimental results are counted to form a reservoir permeability influence factor tree catalog.
Further preferably, the physical property experiment includes: pore, permeation, saturation, carbon routine experiment, oil-water permeation experiment, wettability experiment, five-sensitivity experiment, mercury-pressing experiment and rock electricity experiment.
Preferably, in step (2), a spatially and temporally correlated data fusion process is included;
Carrying out multi-type data fusion and consistency data processing on logging curve data, indoor test data and geological interpretation data according to geological scales of a well layer level in space; and transversely fusing the multi-source data according to the sample numbers of the same experimental project in time, and preprocessing the fused data to form a preprocessed data set.
Further preferably, the consistency data processing uses an experimental sample depth value to correlate with a log depth value.
Preferably, in step (3), the feature values are screened using algorithms including genetic algorithm, forward stepwise regression algorithm and reverse stepwise regression algorithm; preferably, an inverse regression algorithm is employed.
Preferably, the influencing factors used for screening comprise porosity, air permeability, median particle size, wettability, irreducible water saturation, maximum oil phase permeability, residual oil saturation, maximum water phase permeability corresponding to residual oil saturation and oil-water equal permeability points.
Preferably, in step 4, the water saturation is set up at intervals of 0.1%, and the artificial intelligence algorithm training set and the verification set under each water saturation condition are established, wherein 30% of the data strip number is used as the verification set.
Preferably, in step 5, a plurality of algorithms are selected for trial calculation, preferably including additional random trees, support vector machines, decision trees, neural networks; algorithm preference is performed according to EVS, MAE, MSE, R algorithm evaluation indexes.
Preferably, in step 6, the prediction algorithm determined in step 5 is adopted, the oilfield dynamic parameters are used as constraint conditions, a reservoir permeability curve prediction model under each water saturation condition is generated, and training is performed;
preferably, the oilfield dynamic parameters include oil accumulation and water accumulation;
Preferably, the model verification is carried out by comparing the predicted value of the reservoir permeability curve predicted model under each water saturation condition with the experimental true value, and the accuracy rate reaches 90 percent to be qualified; if the model parameters are not qualified, improving the optimized model parameters, and training and verifying again until the prediction is qualified;
preferably, the summary builds a reservoir permeability curve predictive model in the range of 0% -100% water saturation.
Compared with the prior art, the invention has the beneficial effects that:
(1) The method can generate the oil field single-well section permeability curve in real time, is not limited by test conditions, is not limited by oil development stages and the like, and can generate the well section permeability curve in real time, thereby realizing the real-time generation of the oil reservoir permeability parameters which change along with the change of the oil field development situation and fully reflecting the permeability change prediction of the reservoir.
(2) The method forms the complete set of the Shan Jingjing sections of the permeability curves of the oil field, takes the logging curve as input to predict the characteristic value of the permeability curve, and has the advantage that each well has the greatest input of the logging curve of the oil field. Therefore, the single well can be provided with a logging curve, so that the target well section permeability curve can be intelligently achieved; the method provides an phase-permeability data basis for different levels of oil fields, blocks, well groups and the like in oil reservoir engineering research, solves the problem that a target block uses one phase-permeability value in oil reservoir engineering numerical simulation, and enables simulation to be more accurate.
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 invention.
FIG. 1 is a flow chart of a reservoir permeability curve simulation method according to an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular forms also are intended to include the plural forms unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, and/or combinations thereof.
In order to enable those skilled in the art to more clearly understand the technical scheme of the present invention, the technical scheme of the present invention will be described in detail with reference to specific embodiments.
As shown in fig. 1, the reservoir permeability curve simulation method includes the following steps:
step 110, describing a service scene of a reservoir permeability curve to form a tree-shaped service catalog;
step 120, fusion preprocessing of the original data, and multi-type data fusion and consistency data processing of logging curve data, indoor test data and geological interpretation data are carried out according to the well-small layer geological scale;
130, screening characteristic values by adopting a plurality of algorithms, and selecting and determining main physical property parameter characteristics by adopting an inverse regression algorithm;
Step 140, based on the preprocessed data set, the water saturation is taken as an interval of 0.1%, and an artificial intelligence algorithm training set and a verification set under each water saturation condition are established;
step 150, optimizing an artificial neural network algorithm, adding the dynamic parameters of the oil field in step 160 as constraint conditions, generating a reservoir permeability curve prediction model under each water saturation condition, training and verifying, and successfully entering the next step without successfully repeating training;
Step 170, testing the reservoir permeability curve prediction model under each water saturation condition, and turning to step 180 to improve the optimized model parameters if the test result is unqualified, and turning back to step 150 for training and verification.
Step 190, outputting a single water saturation running beyond strategy model result after the test result is qualified;
And 200, establishing a reservoir permeability curve prediction model in the water saturation range of 0% -100%.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (8)
1. A reservoir permeability curve simulation method, the method comprising:
(1) Preliminarily determining reservoir permeability influence factors;
(2) Fusion pretreatment of original data;
(3) Screening and combining major influencing factors of reservoir permeability simulation, and determining a feature set;
(4) Establishing an independent training set and verification set based on the preprocessed data set;
(5) Determining a prediction algorithm;
(6) Generating a reservoir permeability curve prediction model under each water saturation condition, checking and summarizing to obtain the water saturation prediction model;
In step (2), including spatially and temporally correlated data fusion processing; carrying out multi-type data fusion and consistency data processing on logging curve data, indoor test data and geological interpretation data according to geological scales of a well layer level in space; transversely fusing multi-source data according to sample numbers of the same experimental project in time, and preprocessing fused data to form a preprocessed data set;
In the step (3), screening characteristic values by adopting an algorithm, wherein the algorithm comprises a genetic algorithm, a forward stepwise regression algorithm and a reverse stepwise regression algorithm;
in the step (4), the water saturation takes 0.1% as interval, and an artificial intelligent algorithm training set and a verification set under each water saturation condition are established, wherein 30% of data strips are adopted as the verification set;
In the step (6), the prediction algorithm determined in the step (5) is adopted, the dynamic parameters of the oil field are used as constraint conditions, a reservoir stratum permeability curve prediction model under each water saturation condition is generated, and training is carried out;
the oilfield dynamic parameters comprise oil accumulation and water accumulation;
Comparing the predicted value of the reservoir permeability curve predicted model under each water saturation condition with the experimental true value to perform model verification, wherein the accuracy rate reaches 90% to be qualified; if the model parameters are not qualified, improving the optimized model parameters, and training and verifying again until the prediction is qualified;
And (5) summarizing and establishing a reservoir permeability curve prediction model in the water saturation range of 0% -100%.
2. The reservoir permeability curve simulation method according to claim 1, wherein in the step (1), various physical experiments surrounding the core are performed, and various experimental results are counted to form a reservoir permeability influence factor tree catalog.
3. The reservoir permeability curve simulation method of claim 2, wherein the physical property experiment comprises: pore, permeation, saturation, carbon routine experiment, oil-water permeation experiment, wettability experiment, five-sensitivity experiment, mercury-pressing experiment and rock electricity experiment.
4. The reservoir permeability curve simulation method of claim 1, wherein the consistency data processing uses correlation of experimental sample depth values with log depth values.
5. The reservoir permeability curve simulation method according to claim 1, wherein the algorithm is an inverse regression algorithm.
6. The reservoir permeability curve simulation method according to claim 1, wherein the influencing factors for screening include porosity, air permeability, median particle size, wettability, irreducible water saturation, maximum oil phase permeability, residual oil saturation, maximum water phase permeability corresponding to residual oil saturation, and oil-water equality penetration point.
7. The reservoir permeability curve simulation method according to claim 1, wherein in step 5, a plurality of algorithms are selected for trial calculation.
8. The reservoir permeability curve simulation method according to claim 1, wherein in step 5, the algorithm comprises an additional random tree, a support vector machine, a decision tree, a neural network; algorithm preference is performed according to EVS, MAE, MSE, R algorithm evaluation indexes.
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