生成式AI驱动的石油产量预测
Generative AI-driven forecasting of oil production
AI总结：
本研究利用生成式AI技术，采用TimeGrad和Informer模型对四个多井站点四十年的油气产量时间序列进行预测，发现Informer在效率和精度上表现更优，为油田决策提供了有效支持。
摘要：
从多井油田预测石油产量是石油和地热能开采以及储能技术中的一个重要问题。石油预测的准确性是经济预测、碳氢化合物储量估算、流体处理设施建设和能源价格波动的关键决定因素。我们利用生成式AI技术，对跨越四十年的四个多井站点的石油和水产量时间序列预测进行了建模。我们的目标是有效地对不确定性进行建模并做出精确的预测，为油田尺度的决策过程提供信息。我们使用了一种名为TimeGrad的自回归模型和一种名为Informer的transformer架构变体，专门用于预测长序列时间序列数据。TimeGrad和Informer的预测结果均与真实数据高度吻合。Informer的整体表现尤为突出，在所有站点的石油产量预测中，其效率比TimeGrad更高。
英文摘要：
Forecasting oil production from oilfields with multiple wells is an important problem in petroleum and geothermal energy extraction, as well as energy storage technologies. The accuracy of oil forecasts is a critical determinant of economic projections, hydrocarbon reserves estimation, construction of fluid processing facilities, and energy price fluctuations. Leveraging generative AI techniques, we model time series forecasting of oil and water productions across four multi-well sites spanning four decades. Our goal is to effectively model uncertainties and make precise forecasts to inform decision-making processes at the field scale. We utilize an autoregressive model known as TimeGrad and a variant of a transformer architecture named Informer, tailored specifically for forecasting long sequence time series data. Predictions from both TimeGrad and Informer closely align with the ground truth data. The overall performance of the Informer stands out, demonstrating greater efficiency compared to TimeGrad in forecasting oil production rates across all sites.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
