Receding horizon control for oil reservoir waterflooding process

Abstract

Waterflooding is a recovery technique where water is pumped into an oil reservoir for increase in production. Changing reservoir states will require different injection and production settings for optimal operation which can be formulated as a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is sensitive to uncertainties which is inevitable to reservoirs. Direct feedback control has been proposed recently for optimal waterflooding operations with the aim to counteract the effects of reservoir uncertainties. In this work, a feedback approach based on the principle of receding horizon control (RHC) was developed for waterflooding process optimization. Application of RHC strategy to counteract the effect of uncertainties has yielded gains that vary from 0.14% to 19.22% over the traditional open-loop approach. The gain increases with introduction of more uncertainties into the configuration. The losses incurred as a result of the effect of feedback is in the range of 0.25%-15.21% in comparison to 0.39%-31.51% for the case of traditional open-loop control approach. ARTICLE HISTORY

Design of optimal control strategies for a supply chain with competing manufacturers under consignment contract

“…Future research topics include the extension of the proposed strategies to a dynamic supply chain model with endogenous revenue share and make the decision-making with hope to improve the performance of channel when consumer return affects the demand. In addition, we aim to consider the related applications of proposed method based on the background as in Grema & Cao (2017) and Tavakol & Binazadeh (2017).…”

Untitled

“…Of the many control-based optimization, one of the commonly used techniques is the Model Predictive Control (MPC). MPC is a numerical optimization technique that corresponds to a finite horizon optimal control [128], a new optimization strategy called Receding Horizon approach since the system's state is updated at every given sampling period [129]. The complexity of MPC solely depends on the complexity of the model.…”

“…Considering the complexities and nonlinearities of reservoir model, a Nonlinear MPC is better suited. Several approaches in waterflood optimization using NMPC has being studied for conventional wells [129][130][131][132] and nonconventional well production [133], including the Receding Horizon approach [129], [134], [135]. Based on the data reviewed in this work, data driven algorithms are the second most used optimization techniques in the oil and gas fields for more than two decades, with an average implementation of about 33% after gradient-based techniques which has a record of about 40% usage (Fig.…”

Robust H∞ control for delayed systems with randomly varying nonlinearities under uncertain occurrence probability via sliding mode method

“…Over the past decades, as a useful robust control method, the sliding mode control (SMC) has been extensively investigated and applied in engineering systems because of its attractive invariant properties (Choi, 2007;Davila, Fridman, & Levant, 2005;Foo & Rahman, 2010;Gao & Hung, 1993;Grema & Cao, 2017;Lee, Kim, & Sastry, 2009;Li, Yu, Hilton, & Liu, 2013), such as the insensitiveness to time-varying parameter perturbations and disturbances on prescribed sliding surface. According to the structural characteristic between various systems, a great number of methods have been proposed to copy with the robust control problems via SMC method, see e.g.…”