A method based on genetic algorithm to improve the efficiency of automatic history matching of fracture-cavity reservoirs

Abstract

The invention provides a genetic algorithm-based auxiliary automatic oil-reservoir history matching method, belonging to the field of numerical simulation of oil reservoirs. The method comprises the steps of: by taking a mean value of matched evaluation of estimates of oil, gas and water as the degree of adaptability, performing operations by using a single-objective genetic algorithm; and setting a file to record Pareto solutions of the matched evaluation estimates of oil, gas and water generated during the whole process based on the genetic algorithm. By adopting the single-objective genetic algorithm, results superior to those of a multi-objective genetic algorithm can be obtained, and meanwhile, a group of solutions rather than a solution can be provided for users, so that the automatic oil-reservoir history matching efficiency is improved. In addition, the invention provides a novel oil-reservoir history matching evaluation function, which can effectively improve the automatic oil-reservoir history matching efficiency.

Description

technical field [0001] The invention belongs to the field of geophysical prospecting and development in geophysics, and in particular relates to an auxiliary automatic history matching method for oil reservoirs based on a genetic algorithm. Background technique [0002] History matching is a very important link in reservoir numerical simulation research, the purpose is to find a set of reservoir parameters to match the output of the simulator with the actual production of the reservoir, usually the difference between the actual measured value and the calculated value of the simulator The square of is minimized. The process of using computer technology to achieve history fitting is called automatic history fitting. [0003] History matching is a very important link in reservoir numerical simulation research, and it is the basis for predicting oilfield development performance. The so-called history matching is to use the recorded formation static parameters to calculate the ...

AI Technical Summary

Problems solved by technology

[0030] 1. For many parameters, how to quickly and accurately calculate the gradient of each parameter;

[0031] 2. The computer-aided automatic history fitting tool does not know which parameters to adjust and requires manual intervention;

[0032] 3. In actual use, the network scale is often very large, and the algorithm needs to have efficient parallel capabilities;

[0034] The main problem of using modern optimization algorithms to realize automatic history fitting is: how to effectively reduce the number of times the algorithm calls the simulator, improve efficiency, and complete the fitting operation within an acceptable time

Obviously, this fitting evaluation method cannot reasonably reflect the difference between the two fitting degrees.

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