Markov chain Monte Carlo automatic history fitting method and system based on t distribution

Abstract

The invention discloses a Markov Chain Monte Carlo automatic history matching method and system based on t-distribution that gets initial static parameters of oil reservoirs by t-distribution, applies history matching method based on Markov Chain Monte Carlo and calls oil reservoir value simulation software to static parameters of oil reservoirs to make iterative optimization to make predicted production dynamics approximate true values as much as possible and get optimized static parameters of oil reservoirs and oil reservoir value models. The Markov Chain Monte Carlo automatic history matching method and system based on t-distribution, first of all, gets initial static parameters of oil reservoirs by t-distribution before applying Monte Carlo method based on Markov Chain to constantly optimize model permeability and other static parameters of oil reservoirs to match actual dynamics in production, get oil reservoir value models as approximate to true models as possible, reduce matching time, enhance history matching efficiency and precision, and enable predicted results of dynamic forecast of oil field development by Monte based on Markov Chain to get closer to actual production.

Description

technical field [0001] The invention relates to the technical field of geophysical exploration and development in geophysics, in particular to a Markov chain Monte Carlo automatic history fitting method and system based on t distribution. Background technique [0002] In reservoir numerical simulation, in order to make the dynamic prediction as close as possible to the actual situation, it is usually necessary to perform historical fitting on the reservoir data, and adjust the reservoir model parameters according to the observed actual reservoir dynamics, so that the predicted value of the model is consistent with the actual situation. The error of the observation value is within the allowable range, which serves for the subsequent oil reservoir exploitation. The traditional history fitting method manually adjusts the model parameters, which is heavy workload, cumbersome and inefficient. The automatic history fitting method adopts the optimization algorithm to automatically...

AI Technical Summary

Problems solved by technology

In 2004, Tokuda and Takahashi applied the genetic algorithm to the history matching of core displacement. The experimental results showed that although the genetic algorithm can effectively solve the history matching problem, it has the problem of low computational efficiency, and may be stuck in the history fitting process. local convergence

Although the genetic algorithm can search for a better solution during the calculation process, the calculation efficiency is low when the reservoir model is large

In 2009, Yasin Hajizadeh introduced the ACO algorithm into the solution of the history fitting problem. Experimental results show that the algorithm is more efficient than the traditional genetic algorithm. In the same year, Yasin Hajizad introduced the DE algorithm into the solution of the history fitting problem. The algorithm only needs a small number of parameters to achieve automatic reservoir history fitting, but the above two algorithms are difficult to implement in large-scale reservoir models, and there are problems such as the genetic algorithm is prone to premature convergence and the calculation rate is slow, and the simulated annealing calculation is large, etc. question

[0006] In addition, the traditional method often uses Gaussian distribution to obtain the initial value of model parameters such as permeability, but due to the strong heterogeneity of the reservoir, especially after repeated water injection and multiple oil recovery after chemical agent injection, the physical properties of the reservoir will vary. Uncertainty is strong, and the model parameter characteristics generally present non-dynamic characteristics with sharp peaks and thick tails, which do not satisfy the Gaussian distribution

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