Artificial Neural Network- (ANN-) Based Proxy Model for Fast Performances’ Forecast and Inverse Schedule Design of Steam-Flooding Reservoirs

Abstract

Steam flooding is one of the most effective and mature technology in heavy oil development. In this paper, a numerical simulation technology of steam flooding reservoir based on the finite volume method is firstly established. Combined with the phase change of steam phase, the fully implicit solution for steam flooding is carried out by using adaptive-time-step Newton iteration method. The Kriging method is used for stochastically to generate 4250 geological model samples by considering reservoir heterogeneity, and corresponding production schedule parameters are randomly given; then, these reservoir model samples are handled by the numerical simulation technology to obtain corresponding dynamic production data, which constitute the data for artificial neural network (ANN) training. By using the highly nonlinear global effect of artificial neural network and its powerful self-adaptive and self-learning functions, the forward-looking and inverse design ANN models of steam-flooding reservoirs are established, which provides a new method for rapid prediction of steam-flooding production performance and production schedule parameter design. In 4250 samples, the error of the forward-looking model is basically less than 0.1%, and the error of the inverse design model is generally less than 15%. It fully shows that the ANN models developed in this paper can quickly and effectively predict oil production and design production parameters and have an important guiding role in the implementation of the steam flooding technology. Finally, the forward-looking ANN model is applied to efficiently analyze the influencing factors of steam flooding process, and uncertainty analysis of the inverse design ANN model is conducted by Monte Carlo Simulation to illustrate its robustness. Besides, this paper may provide a reference for the application of neural network models to underground oil and gas reservoir, which is a typical invisible black box.

Numerical Simulation of Ultra-Low Permeability Reservoirs: Progress and Challenges

“…In recent years, due to the rapid development of computers, reservoir numerical simulation technology has made great progress, especially in terms of calculation methods, program design, and image processing of calculation results. Numerous scholars have enhanced the current conventional reservoir numerical simulation techniques to investigate the nonlinear flow characteristics of fluids in ultra-low permeability reservoirs (Rao et al, 2021a;Zhou et al, 2021). Currently, fluid flow in ultra-low permeability porous media is a nonlinear flow with a minimum starting pressure gradient.…”

Numerical Simulation of a Two-Phase Flow with Low Permeability and a Start-Up Pressure Gradient

“…Although the variable permeability numerical simulation method can accurately describe the nonlinear flow law of ultra-low permeability porous media. However, it cannot yet reflect the continuity and smoothness of the equation of state, so this numerical simulation method is still in the research stage [17].…”

Forecasting of oil production driven by reservoir spatial–temporal data based on normalized mutual information and Seq2Seq-LSTM

“…The applicability of ML in various domains of petroleum engineering has attracted extensive attention and interest. Vector autoregression (Zhang and Jia, 2021), support vector regression (Huang et al, 2021;Masoud et al, 2020), random forest (Bhattacharya et al, 2019;Xue et al, 2021) and artificial neural network (Liu et al, 2021b;Negash and Yaw, 2020;Zhou et al, 2021b) are used to predict oil and gas production. However, these traditional ML methods do not take into account the trend of production over time and the correlation between the data before and after.…”