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Automatic history matching method and system based on automatic encoder and multi-objective optimization

An auto-encoder, multi-objective optimization technology, applied in the field of automatic history matching methods and systems, can solve the problems that geological data cannot achieve dimensionality reduction effect, fall into local convergence, and are not easy to understand, so as to shorten the fitting time and improve the The effect of fitting accuracy and improving accuracy

Inactive Publication Date: 2017-01-04
CHINA UNIV OF GEOSCIENCES (WUHAN)
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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
The linear dimensionality reduction algorithm has low computational complexity and is simple and efficient, but it cannot achieve a good dimensionality reduction effect in the face of geological data with strong attribute correlation or nonlinear correlation
The nonlinear dimensionality reduction algorithm, especially the dimensionality reduction algorithm based on popular learning, has a good dimensionality reduction effect when dealing with nonlinear data, but the calculation is relatively complicated and not easy to understand.

Method used

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Embodiment 1

[0077] Embodiment 1. Automatic history fitting method based on autoencoder and multi-objective optimization. Combine below Figure 1 to Figure 25 The method provided in this embodiment will be described in detail.

[0078] see figure 1 , S1. Read the original high-dimensional reservoir static parameters, and use an autoencoder to reduce the dimensionality of the high-dimensional reservoir static parameters, and obtain the reduced-dimensional reservoir static parameters.

[0079] Specifically, the autoencoder objective function is constructed, and the high-dimensional reservoir static parameters in the autoencoder input layer are compressed to the hidden layer according to the autoencoder objective function, and redundant information in the data is removed, and then in In the output layer, dimensionality reduction is performed on the data compressed into the hidden layer to obtain the static parameters of the reservoir after dimensionality reduction, wherein the high-dimensio...

Embodiment 2

[0199] Embodiment 2. Automatic history fitting system based on autoencoder and multi-objective optimization. Combine below Figure 26 The system provided in this embodiment will be described in detail.

[0200] see Figure 26 , an automatic history fitting system based on an autoencoder and multi-objective optimization provided in this embodiment, the system includes a reading dimensionality reduction module, an optimization module, a reconstruction module, a simulation calculation module, a comparison and judgment module, and an output module .

[0201] The reading dimensionality reduction module is used to read the original high-dimensional reservoir static parameters, and use an autoencoder to reduce the dimensionality of the high-dimensional reservoir static parameters to obtain dimensionally reduced reservoir static parameters.

[0202] Specifically, the reading dimensionality reduction module is used to construct an autoencoder objective function, and compress the hig...

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Abstract

The invention discloses an automatic history matching method and system based on an automatic encoder and multi-objective optimization. The automatic encoder is used for reducing the dimension of static parameters of a high-dimensional reservoir to realize the bidirectional mapping between the static parameters of the high-dimensional reservoir and a low-dimensional data space, and then the static parameters of the reservoir with the dimension reduced are optimized by using a multi-objective algorithm to realize analog automatic history matching of a reservoir numerical value to obtain a reservoir numerical model close to an actual geological model. According to the automatic history matching method and system disclosed by the invention, the automatic encoder based on depth learning and the multi-objective algorithm are applied to the history matching problem of the reservoir, thereby greatly reducing the search spaces of optimization parameters, improving the calculation efficiency and precision and making the optimized reservoir numerical value be closer to the actual geological model.

Description

technical field [0001] The invention relates to the technical field of geophysical exploration and development in geophysics, in particular to an automatic history fitting method and system based on an automatic encoder and multi-objective optimization. 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 parameters of the reservoir model according to the observed actual reservoir dynamics, so that the calculation of the model fits The error between the quantity and the actual reservoir dynamic observation value is within the allowable range, which serves for the subsequent reservoir development. The traditional history fitting method continuously manually adjusts the model parameters, which is heavy workload, tedious and inefficient. The automatic history fitting method automatically ad...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 张冬梅姜鑫维沈奥康志江陈小岛邓泽程迪汪海丁亚雷金佳琪
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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