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 "The Prediction of Reservoir Production Based Proxy Model Considering Spatial Data and Vector Data".
Innovation: The establishment of a surrogate model plays an important role in assisting history matching. Most of the existing surrogate models consider spatial data such as permeability that can be represented by images, but cannot consider vector data such as relative permeability. Based on this, this paper constructs a surrogate model based on a deep convolutional encoding-decoding network to predict the remaining oil distribution and pressure field distribution, and then calculate the reservoir production. The main innovation is that a variety of reservoir modeling data can be considered, such as spatial data (permeability, etc.) and vector data (fluid properties, ie relative permeability, etc.). In addition, this paper proposes a correction method, that is, adding constraints to the well-grid, which can improve the accuracy of the model to predict the production of oil and water wells.
Abstract: Reservoir modeling data could be divided into two categories: spatial data (i.e. permeability, effective grids, crack, irregular boundaries) and vector data (fluid properties i.e. relative permeability, density, viscosity). This paper is mainly interested in considering the permeability and relative permeability, which are the representatives of the two data types, to construct a proxy model for forecasting saturation and pressure maps in heterogeneous reservoirs during water flooding. The proxy model is built on the dense encoder-decoder network, to learn the reservoir dynamic states at different time steps. Results indicate that the trained proxy model could predict fluid saturation and pressure fields accurately. This paper presents a calibration method, which is adding a constraint to well-blocks. After calibration, the trained proxy model is utilized to calculate reservoir production. The comparison results illustrate that the proxy model can forecast well rates with relatively high accuracy. Compared with traditional reservoir numerical simulators, the proxy model could predict fluid saturation, pressure and well rates with similar accuracy and less time-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.2021.109694.
Citation:
Kai Zhang*, Xiaoya Wang, Xiaopeng Ma, Jian Wang, Yongfei Yang, Liming Zhang, Jun Yao, Jian Wang. The prediction of reservoir production based proxy model considering spatial data and vector data[J]. Journal of Petroleum Science and Engineering, 2022, 208:109694.
