Closed-loop Feedback Control in Intelligent Wells: Application to a Heterogeneous, Thin Oil-Rim Reservoir in the North Sea

Abstract

Important challenges remain in the development of optimized control strategies for intelligent wells, particularly with respect to incorporating the impact of reservoir uncertainty. Most optimization methods are model-based and are effective only if the model or ensemble of models used in the optimization capture all possible reservoir behaviors at the individual well and completion level. This is rarely the case. Moreover, reservoir models are rarely predictive at the spatial and temporal scales required to identify control actions. We evaluate the benefit of using closed-loop control strategies, based on direct feedback between reservoir monitoring and inflow valve settings, within a geologically heterogeneous, thin oil-rim reservoir. This approach does not omit model predictions completely; rather, model predictions are used to optimise a number of adjustable parameters within a general direct feedback relationship between measured data and inflow control settings. A high-resolution sector model is used to capture reservoir heterogeneity, which incorporates a locally refined horizontal grid in the oil zone, to accurately represent the horizontal well geometry and fluid contacts, and capture water and gas flow. Two inflow control strategies are tested. The first is an open-loop approach, using fixed inflow control devices to balance the pressure drawdown along the well, sized prior to installation. The second is a closed-loop, feedback control strategy, employing variable inflow control valves that can be controlled from the surface in response to multiphase flow data obtained downhole. The closed-loop strategy is optimized using a base case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model but not re-optimising. We find that closed-loop feedback control yields positive gains in NPV for the majority of reservoir behaviours investigated, and higher gains than the open-loop strategy. Closed-loop control can also yield positive gains in NPV even when the reservoir does not behave as expected. However, inflow control can be risky, because unpredicted reservoir behavior also leads to negative returns. Moreover, assessing the benefits of inflow control over an arbitrarily fixed well life can be misleading, as observed gains depend on when the calculation is made.

Rapid screening of oil-rim reservoirs for development and management

“…To keep the problem tractable, we employ a simple box reservoir model, characterized by 50 × 50 × 167 simulation cells. Each cell has an approximate dimension of 20 m × 20 m × 3 m. The choice of cell area dimension of 20 m × 20 m was arbitrary, but finer than the cell dimensions of 50 m × 50 m and 100 m × 100 m typically used to simulate realistic reservoirs in practice (Lawal et al 2016(Lawal et al , 2017Dilib et al 2015;Uwaga and Lawal 2006;Kabir et al 2004). However, prior grid sensitivity tests suggested that a 3 m layer thickness was adequate to achieve computational efficiency without significant numerical artefacts for this specific model.…”

“…The early and sustained production of these unwanted associated fluids increases the operating expenses related to handling gas and water produced per unit volume of oil recovered, hence eroding project value. Because coning rates are usually below economic thresholds, the strategy of limiting oil offtake below gas and water coning rates has not recorded significant success in practice (Balogun et al 2015;Dilib et al 2015;Yeoh 2014;Lawal et al 2010;Kromah and Dawe 2008;Uwaga and Lawal 2006).…”

“…In searching for an optimum solution, the rigorous evaluation of the various development options often requires extensive reservoir simulation studies. This optimization problem entails multiple variables, which include geologic, engineering, economic, regulatory and operational factors (Ziablitckaia et al 2019;Elharith et al 2019;Ogolo et al 2018;Dilib et al 2015;Thomas and Bratvold 2015;Chan et al 2014;Uwaga and Lawal 2006). These variables usually exhibit varied dependencies, thereby complicating the problem of selecting the most appropriate combination of well type, development concepts and production constraints.…”

Application of simulated annealing optimization algorithm to optimal operation of intelligent well completions in an offshore oil reservoir

“…Doublet et al (2009) formulated a constrained optimization problem as an augmented Lagrangian saddle point problem. Dilib et al (2012) optimized a closed-loop strategy using a base case model, and then tested against unexpected reservoir behavior by adjusting a number of uncertain parameters in the model. They found that closedloop feedback control yields positive gains in NPV for the majority of reservoir behaviors investigated, and higher gains than the open-loop strategy since closed-loop control can also yield positive gains in NPV even when the reservoir does not behave as expected.…”

Multi-Asset Closed-Loop Reservoir Management Using Deep Reinforcement Learning

“…The use of control policies [1,[17][18][19] has proven to be an effective technique to address the conservative nature of robust optimization solutions in reservoir management problems. These control policies are designed to map observed data to optimal well settings.…”