Using Surrogates as Faster Objective Function Estimators for Oil Production Strategy Optimization in a Real-Case Reservoir

Summary Effective decision-making plays a crucial role in maximizing economic and global returns from an oil reservoir. When architecting strategies for exploration, development, and production throughout a reservoir’s life cycle, evaluating production forecasts through numerical simulations is essential. But the significant computational expense associated with simulating these strategies using reservoir models imposes time constraints and limitations. To address these challenges, researchers have explored methods to replace simulation with surrogate models (also known as proxies) that act as faster objective function estimators (FOFE), which can save time at each stage of the decision-making processes. We developed three analytical surrogates to handle multiple inputs with robustness to enhance an optimization algorithm. One surrogate used a genetic algorithm (GA) to parameterize inputs before calculating linear coefficients using the gradient descent technique. These surrogates underwent testing across 1,080 unique training conditions to assess their effectiveness in mitigating the simulation cost of a production strategy optimization. A method for comparing population samples provided by surrogates vs. those derived from optimization methods was proposed, and the relationship of these metrics with the surrogate’s prediction-to-true outcome correlation was investigated. Additionally, two methods for obtaining samples with surrogates were proposed, each with decision criteria of using surrogate with the Pearson’s correlation (r). An optimization algorithm that incorporates these surrogates was developed and applied to optimize well management in a real-case carbonated reservoir in the Brazilian presalt region. To evaluate the surrogate accuracy for different sample sizes during an optimization, three optimization population sizes were tested: 50, 30, and 20. The surrogates enabled optimization speedup of 48, 69, and 79% for the population sizes, respectively. When used to improve the net present value (NPV), the surrogate-aided optimization algorithms helped improve the base strategy’s NPV from USD 3.9 up to USD 4.3 billion compared with the benchmark.