recently, the team of Zhang Kai has made new progress in the field of reservoir history matching, and the related research has been published in the Journal of Petroleum Science and Engineering. The paper is titled "A novel hybrid recurrent convolutional network for surrogate modeling of history matching and uncertainty quantification".
Innovation: Recently, deep-learning-based surrogate modeling methods have been widely studied, but most of them are based on an image-to-image regression framework. In this study, based on the image-to-sequence modeling method, we proposed a novel surrogate model which integrats the residual convolution and multi-layer recurrent neural network. And we introduce a multimodal distributed estimation algorithm to the solving of automatic history matching, which can effectively find multiple solutions.
Abstract: Automatic history matching (AHM) has been widely studied in petroleum engineering due to it can provide reliable numerical models for reservoir development and management. However, AHM is still a challenging problem because it usually involves running a great deal of time-consuming numerical simulations during the solving process. To address this issue, this article studies a hybrid recurrent convolutional network (HRCN) model for surrogate modeling of numerical simulation used in AHM. The HRCN model is end-to-end trainable for predicting the well production data of high-dimensional parameter fields. In HRCN, a convolutional neural network (CNN) is first developed to learn the high-level spatial feature representations of the input parameter fields. Following that, a recurrent neural network (RNN) is constructed with the purpose of modeling complex temporal dynamics and predicting the production data. In addition, given that the fluctuations of production data are influenced by well control measures, the well control parameters are used as auxiliary inputs of RNN. Moreover, the proposed surrogate model is incorporated into a multimodal estimation of distribution algorithm (MEDA) to formulate a novel surrogate-based AHM workflow. The numerical studies performed on a 2D and a 3D reservoir model illustrate the performance of the proposed surrogate model and history matching workflow. Compared with the MEDA using only numerical simulations, the surrogate-based AHM workflow significantly reduces the computational cost.
The Journal of Petroleum Science and Engineering covers the fields of petroleum and natural gas exploration, production and flow in its broadest possible sense. Topics include: reservoir engineering; reservoir simulation; rock mechanics; petrophysics; pore-level phenomena; well logging, testing and evaluation; mathematical modeling; enhanced oil and gas recovery; petroleum geology; compaction/diagenesis; petroleum economics; drilling and drilling fluids; thermodynamics and phase behavior; fluid mechanics; multi-phase flow in porous media; production engineering; formation evaluation; exploration methods; CO2 Sequestration in geological formations/sub-surface, etc. 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 2.
Paper link:
https://doi.org/10.1016/j.petrol.2022.110109
Citation:
Xiaopeng Ma, Kai Zhang, Jinding Zhang, et al. A novel hybrid recurrent convolutional network for surrogate modeling of history matching and uncertainty quantification[J]. Journal of Petroleum Science and Engineering, 2022, 210: 110109.
