Innovation: Automatic history matching involves a large number of reservoir numerical simulations, and a single reservoir numerical simulation often takes minutes or even hours. Therefore, in recent years, the deep-learning-based high precision surrogate modeling method has been widely studied in the field of history matching. Considering that the characteristics of the numerical simulation of automatic history fitting problem, and the corresponding production data of wells are simulated from the high-dimensional parameter fields, this study proposes a surrogate modeling framework from space to time series, which is proposed for the first time in the field of inversion modeling.

Abstract: Surrogate modeling has shown to be effective in improving the solving efficiency for history matching in the development of oil and gas, but the traditional surrogate models are difficult to directly deal with the high-dimensional spatial uncertain parameters, such as the permeability field. In this paper, we introduce a new deep-learning-based surrogate modeling framework, image-to-sequence regression, which can directly predict the production data from the high-dimensional spatial parameters. Specifically, a spatial-temporal convolution recurrent neural network surrogate model is proposed based on a densely connected convolutional neural network (CNN) model and a stacked multilayer long short-term memory (LSTM) model. And a surrogate-based history-matching workflow is then developed by combining the proposed surrogate model with an improved ensemble smoother data assimilation algorithm. Two case studies on a 2D and a 3D reservoir model demonstrate that the proposed surrogate model can effectively predict production data and improve the efficiency of history matching.

SPE Journal covers novel theories and emerging concepts (not including review articles or multi-part articles) spanning all aspects of engineering for oil and gas exploration and production, including drilling and completions, geomechanics, production and facilities, oilfield chemistry, CO2 sequestration and injection, reservoir evaluation and engineering, numerical simulation, data analytics, economics and externalities including health, safety, environment, and sustainability. The latest impact factor of the journal is 4.346, and the average impact factor IF in the past 3 years is 3.646. This journal currently appears in the JCR Q1 or the Chinese Academy of Sciences ranking 3.

Paper link:

https://doi.org/10.2118/208604-PA

Citation:

Xiaopeng Ma, Kai Zhang, Jian Wang, et al. An Efficient Spatial-Temporal Convolution Recurrent Neural Network Surrogate Model for History Matching [J]. SPE Journal, 2022, 27(02): 1160-1175.