Automatic history matching of reservoirs based on dual input-output convolutional neural network agent model

Abstract

The conventional reservoir automatic history matching method requires multiple computationally time-consuming reservoir numerical simulations. Deep learning agent models can perform alternative reservoir numerical simulation calculations with approximate accuracy and greater computational efficiency. In the reservoir automatic history matching method based on the deep learning agent model， the reservoir uncertainty parameters adjusted by the reservoir automatic history matching method are usually used as the input parameters of the deep learning agent model. Existing deep learning agent models are often single input-output neural network model architectures. They do not consider that reservoir automatic history matching methods require the adjustment of multiple reservoir uncertainty parameters. Multiple deep learning agent models need to be trained to predict water saturation field distribution and pressure field distribution in reservoirs. For solving this problem， a reservoir automatic history matching method based on a dual input-output convolutional neural network agent model was proposed to simultaneously predict the water saturation field distribution and pressure field distribution in a reservoir by using a dual input-output convolutional neural network， with the reservoir permeability field distribution and phase permeability parameters as input. The production was calculated with the help of the Peaceman equation. It was coupled to the ensemble smoother with multiple data assimilation （ES-MDA） methods to invert the reservoir permeability field distribution and phase permeability parameters to achieve a more efficient reservoir automatic history matching solution. The results of the study show that the prediction accuracy of the reservoir water saturation field distribution and pressure field distribution is above 93% at the specified time step based on the dual input-output convolutional neural network agent model. Compared with the traditional reservoir automatic history matching method， the proposed reservoir automatic history matching method based on a dual input-output convolutional neural network agent model avoids the time-consuming computation of multiple calls to the reservoir numerical simulator and improves the efficiency of the matching.

Keywords

• reservoir automatic history matching
• reservoir numerical simulation
• deep learning
• agent model
• dual input-output convolutional neural network