Well control optimization using a two-step surrogate treatment

Abstract

Large numbers of flow simulations are typically required for the determination of optimal well settings. These simulations are often computationally demanding, which poses challenges for the optimizations. In this paper we present a new two-step surrogate treatment (ST) that reduces the computational expense associated with well control optimization. The method is applicable for oil production via waterflood, with well rates optimized at a single control period. The two-step ST entails two separate optimizations, which can both be performed very efficiently. In the first optimization, optimal well-rate ratios (i.e., the fraction of total injection or production associated with each well) are determined such that a measure of velocity variability over the field is minimized, leading to more uniform sweep. In the second step, overall injection and production rates are determined. The flow physics in the first step is highly simplified, while the actual physical system is simulated in the second step. Near-globally-optimal results can be determined in both cases, as the first optimization is posed as a QP problem, and the second step entails just a single optimization variable. Under full parallelization, the overall elapsed time for the ST corresponds to the runtime for 1-2 full-order simulations. Results are presented for multiple well configurations, for 2D and 3D channelized models, and comparisons with formal optimization procedures (mesh adaptive direct search or MADS, and an adjoint-gradient method) are conducted. Three different fluid mobility ratios (M = 1, 3 and 5) are considered. Optimization results demonstrate that the two-step ST provides results in reasonable agreement with those from MADS and adjoint-gradient methods, with speedups of 5× or more. We also show that the ST is applicable in the inner-loop in field development optimization, where it will be especially useful since many different well configurations must be evaluated.

Multi-solution well placement optimization using ensemble learning of surrogate models

“…Physics-based approaches can approximate the original reservoir behavior with lower-order equations to reduce the computational cost. However, they have been so far tested on synthetic, box-shaped models only (de Brito and Durlofsky, 2020a;de Brito and Durlofsky, 2020b) and can become unrepresentative in real fields with often complex structures. ML techniques are widely applied within the context of well control optimization (Ahmadi and Bahadori, 2015;Golzari et al, 2015;Chugh et al, 2016;Guo and Reynolds, 2018;Chen et al, 2020;Zhao et al, 2020) and are shown to provide a reasonably accurate, data-driven SM while considering the reservoir simulator as a black box.…”

Fast Optimization of Injector Selection for Waterflood, CO2-EOR and Storage Using an Innovative Machine Learning Framework

“…Ohers [3] have proposed an iterative or nested scheme where the well placement and configuration is optimized in an outer loop and the well controls are optimized in the inner loop, given the well types and locations. Recent improvement to the nested approach [4] apply a two-step process, optimizing well placement assuming certain constraints, and then optimizing well rates. However, this approach assumes simplified physics of 1-D flow.…”