Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Abstract

A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

Untitled

“…Review Summary. Optimization Algorithm Reference Data driven proxies Self-Optimizing Control [33], [146], [151], [153] - [155] Correlation based models [82], [112] - [126] Reduced order models [100], [127] - [130] Model Predictive control [132] - [139] Machine learning [157] Mean Variance optimization [18], [27] Robust optimization, Sequential Quadratic programming (SQP) [14] - [32], [35] - [39] Optimal Control theory [10], [85]- [93], [94] - [111] Ensemble Kalman Filter [14], [38], [71] - [82]…”

Optimization Control of Oilfield Waterflooding Systems Based on Different Zone and Pressure

“…In order to meet the pressure requirements of high-pressure wells, when the system is in a high-pressure state it leads to an increase in energy consumption and complicates the optimal operation of waterflooding pipe networks. In this case it is necessary to analyze the characteristics and rules of the pressure distribution of wells, to analyze the feasibility of different zones and pressure waterflooding according to production requirements, and to study the application of new intelligent optimization methods and machine learning methods to solve the problems of topology optimization and pumping scheme optimization in high-low pressure areas [1][2][3].…”