This study provides the proof-of-concept for identifying the most suitable machine-learning (ML) model that predicts minimum miscibility pressure (MMP) based on temperature, crude oil, and injected fluid composition. MMP defined as the lowest pressure injected gas developing miscibility with reservoir oil is crucial for gas-enhanced oil recovery. Slimtube experiments considered the most reliable for MMP predictions are time-consuming. Although researchers have considered ML to expedite MMP predictions, validation of the optimal model that integrates the main controlling factors remains outstanding. We tested eight ML models of different complexities to determine the most suitable for predicting MMP. The models were trained and tested using 75 and 25% of 142 publicly available slim-tube experiments and validated using six in-house slim-tube MMP experiments. The injected gas compositions varied and included H₂S, CO₂, N₂, CH₄, and C₂⁺. We assessed model suitability using mean absolute error (MAE). Models with MAEs <7% estimated the MMP. The highest-performing model after testing and validation was the neural network. This work identifies the most suitable machine-learning technique for MMP prediction validated using recent experiments. The optimal model provides an instantaneous MMP chart for gas injections typical to a Permian field. Also, we demonstrate a workflow for recommending optimal injection gas compositions with low MMP and reduced emissions associated with gas EOR. The procedure ultimately reduces the cost of performing the slim-tube experiment.

中文翻译：

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使用机器学习优化最小混相压力预测：综合评估和验证


这项研究为确定最合适的机器学习 (ML) 模型提供了概念验证，该模型可根据温度、原油和注入流体成分预测最小混相压力 (MMP)。 MMP 定义为最低压力注入气体，与储层油形成混溶性，对于气体强化采收率至关重要。 Slimtube 实验被认为是最可靠的 MMP 预测，但非常耗时。尽管研究人员认为机器学习可以加快 MMP 预测，但整合主要控制因素的最佳模型的验证仍然悬而未决。我们测试了八个不同复杂度的 ML 模型，以确定最适合预测 MMP 的模型。这些模型使用 142 个公开的细管实验中的 75% 和 25% 进行训练和测试，并使用 6 个内部细管 MMP 实验进行验证。注入的气体成分各不相同，包括 H ₂ S、CO ₂ 、N ₂ 、CH ₄ 和 C ₂ 。我们使用平均绝对误差 (MAE) 评估模型的适用性。 MAE <7% 的模型估计了 MMP。经过测试和验证后性能最高的模型是神经网络。这项工作确定了最适合 MMP 预测的机器学习技术，并使用最近的实验进行了验证。最佳模型提供了二叠纪油田典型气体注入的瞬时 MMP 图。此外，我们还展示了推荐最佳注入气体成分的工作流程，该组合物具有低 MMP 和减少与气体 EOR 相关的排放。该程序最终降低了进行细管实验的成本。