Abstract

Optimal field development and control aim to maximize the economic profit of oil and gas production. This, however, results in a complex optimization problem with a large number of correlated control variables at different levels (e.g. well locations, completions and controls) and a computationally expensive objective function (i.e. a simulated reservoir model). The typical limitations of the existing optimization frameworks are: (1) single-level optimization at a time (i.e. ignoring correlations among control variables at different levels); and (2) providing a single solution only whereas operational problems often add unexpected constraints likely to reduce the ‘optimal’, inflexible solution to a sub-optimal scenario.

The developed framework in this paper is based on sequential iterative optimization of control variables at different levels. An ensemble of close-to-optimum solutions is selected from each level (e.g. for well location) and transferred to the next level of optimization (e.g. to control settings), and this loop continues until no significant improvement is observed in the objective value. Fit-for-purpose clustering techniques are developed to systematically select an ensemble of solutions, with maximum differences in control variables but close-to-optimum objective values, at each level of optimization. The framework also considers pre-defined constraints such as the minimum well spacing, irregular reservoir boundaries, and production/injection rate limits.

The proposed framework has been tested on a benchmark case study, known as the Brugge field, to find the optimal well placement and control in two development scenarios: with conventional (surface control only) and intelligent wells (with additional zonal control using Interval Control Valves). Multiple solutions are obtained in both development scenarios, with different well locations and control settings but close-to-optimum objective values. We also show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the higher-level optimization, highlighting the value of the here-developed multi-solution framework in exploring the search space as compared to the traditional single-solution approaches. The development scenario with intelligent completion installed at the optimal well location and optimally controlled during the production period achieved the maximum added value. Our results demonstrate the advantage of the developed multi-solution optimization framework in providing the much-needed operational flexibility to field operators.

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