A Multisolution Optimization Framework for Well Placement and Control

Abstract

Summary Field development and control optimization aim to maximize the economic profit of oil and gas production. Mathematically, this results in a complex optimization problem with a large number of correlated decision (also known as control) variables of various types at different levels (e.g., at the level of well location variables or at the level of well production/injection control also know as well control or control settings variables) and a computationally expensive objective function (i.e., a reservoir simulation model). Current multilevel optimization frameworks provide only a single optimal scenario for a field development and control problem. However, unexpected problems that commonly arise during field development and operations can impose extra constraints, resulting in operators having to eventually select an adjusted, and most-likely, nonoptimal scenario. This work proposes a novel multisolution optimization framework, based on sequential optimization of control variables at multiple levels, providing the flexibility for operators to make optimal decisions while considering operational constraints. An ensemble of close-to-optimum solutions is selected from each level (e.g., from the well location optimization level) and transferred to the next level of optimization (e.g., to the control settings optimization), and this loop continues until no significant improvement is observed in the objective value. Fit-for-purpose clustering techniques are also developed to systematically select an ensemble of solutions, with maximum differences in well locations and control settings but small variation in the objective values, at each level of the optimization. The developed framework has been tested on two benchmark case studies. The results demonstrate high economic and operational efficiency of the developed multisolution framework as compared to the traditional approaches that rely on single-solution optimization. It is shown that suboptimal solutions from an early optimization level could approach the optimal solution at the next level(s), highlighting the value of the developed multisolution framework to deliver operational flexibility by a more efficient exploration of the search space.

Unconventional hydrocarbon resources: geological statistics, petrophysical characterization, and field development strategies

“…In addition, clustering has been usually adopted for determining the representative set of samples for realization. Also, the Net Present Value (NPV) is usually taken as an objective function (Jesmani et al 2020;Salehian et al 2021aSalehian et al , 2021bSalehian et al , 2020Chen et al 2012;Shirangi and Durlofsky 2016;Wang et al 2012;Sefat et al 2016;Moraes et al 2019;Mohsin Siraj et al 2017;Brito and Durlofsky 2021;Lu and Reynolds 2020;Fonseca et al 2020).…”

Robust integrated optimization of well placement and control under field production constraints

“…Initial sensitivity analysis in this work showed that more stable search process and faster convergence is achieved when 𝛼 0 = 0.5 and 𝑐 𝑚𝑖𝑛 = 0.08 for both well location and control optimization. Detailed information about SPSA formulation for well placement and control optimization can be found in Salehian et al (2020a). The following constraints are applied to control variables during each optimization level:…”

“…• Well locations are maintained within the actual, irregular reservoir boundary limits, represented by a binary matrix with 0 and 1 elements indicating null and active reservoir grids, respectively. Following Salehian et al (2020a), each well is moved to the nearest active grid if it appears outside the reservoir boundaries during location optimization.…”

“…Tavallali and Karimi (2016) used the joint method to simultaneously optimize well placement and control settings in a synthetic reservoir model using a heuristic search optimization method, which increased the economic profit of the optimum case. As for the sequential approach: so far it has been used only in subsurface optimization problems without considering the production network (Li et al, 2013;Forouzanfar et al, 2016;Lu et al, 2017a;Salehian et al, 2020a).…”

“…The iterative sequential optimization method (Li and Jafarpour, 2012) is employed to optimize well placement and control settings iteratively, and this loop continues until no significant improvement in the expected objective values is observed. SPSA is used as the optimizer based on its superior performance compared to alternative derivative-free algorithms such as PSO/GA (Spall et al, 2006) as well as following its recent successful applications in large-scale problems (Li et al, 2013;Pouladi et al, 2020;Salehian et al, 2020a). Commercial simulator ECLIPSE (Schlumberger, 2017) has been used to model multiphase flow through the integrated system consisting of both the reservoir and the production network.…”