Oil and gas extraction from subsea reservoirs is a complex process that is highly reliant on accurate mathematical modeling for optimization. A key challenge is identifying control settings to maximize oil production while accounting for the nonlinear behavior and constraints of the underlying physical systems. This paper introduces a new method using Rectified Linear Units (ReLU) neural networks to model these complexities. We focus on a subsea system connecting multiple oil wells to a manifold with multiple headers, explicitly modeling flow splitting to optimize oil yield. Our approach employs mixed-integer reformulations of ReLU neural networks to approximate pressure-drop functions in risers, allowing efficient optimization of production platforms considering routing possibility and flow-splitting operations. Computational analysis employing a branch-and-cut approach and p-split partitioning (for the ReLU model) shows that our ReLU-based methodology converges faster than standard piecewise-linear (PWL) approaches while delivering comparably effective results. Notably, ReLU found solutions in scenarios where PWL failed to find an incumbent solution within the time limit. These results underscore a significant advancement in the optimization of offshore production platforms by using ReLU-based models.

中文翻译：

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用于最大化海底平台石油产量的基于 ReLU 的线性化方法：分流应用


从海底储层中提取石油和天然气是一个复杂的过程，高度依赖于精确的数学模型进行优化。一个关键的挑战是确定控制设置以最大限度地提高石油产量，同时考虑非线性行为和底层物理系统的约束。本文介绍了一种使用修正线性单元 (ReLU) 神经网络来对这些复杂性进行建模的新方法。我们专注于将多个油井连接到具有多个集管的管汇的海底系统，对分流进行显式建模以优化石油产量。我们的方法采用 ReLU 神经网络的混合整数重构来近似立管中的压降函数，从而可以考虑路由可能性和分流操作来有效优化生产平台。采用分支剪切方法和 p-split 分区（针对 ReLU 模型）的计算分析表明，我们基于 ReLU 的方法比标准分段线性 (PWL) 方法收敛得更快，同时提供了相当有效的结果。值得注意的是，ReLU 在 PWL 未能在时限内找到现有解决方案的情况下找到了解决方案。这些结果强调了使用基于 ReLU 的模型在优化海上生产平台方面取得的重大进步。