Machine learning based decline curve analysis for short-term oil production forecast
Machine learning based decline curve analysis for short-term oil production forecast
复制标题
DOI：
10.1177/01445987211011784
复制
发表时间：
2021-05
影响因子：
2.7
通讯作者：
Amine Tadjer;Aojie Hong;R. Bratvold
中科院分区：
文献类型：
--
作者：
Amine Tadjer;Aojie Hong;R. Bratvold
传统的递减曲线分析 (DCA)，无论是确定性的还是概率性的，都使用特定模型来拟合生产数据以进行生产预测。非常规井应用了多种递减曲线模型，包括Arps模型、拉伸指数模型、Duong模型、容阻组合模型等。然而，确定应使用哪个模型并不简单，因为多个模型可能同样适合数据集，但提供不同的预测，并且仓促选择概率 DCA 模型可能会低估生产预测的不确定性。数据科学、机器学习和人工智能通过更有效和高效地利用计算能力正在彻底改变石油和天然气行业。我们在本文中提出了一种数据驱动的方法来对非常规石油产量进行短期预测。已经测试了两种最先进的模型：DeepAR 和使用 Prophet 对石油生产数据进行时间序列分析。与使用递减曲线模型的传统方法相比，机器学习方法可以被视为“无模型”（非参数），因为不需要预先确定递减曲线模型。这项工作的主要目标是开发神经网络和时间序列技术并将其应用于油井数据，而无需了解有关提取过程或地质和动态参数之间的物理关系的大量知识。出于评估和验证的目的，所提出的方法被应用于美国米德兰油田的选定井。通过比较我们的结果，我们可以推断 DeepAR 和 Prophet 分析都有助于更好地了解油井的行为，并且可以减轻因使用单一递减曲线模型进行预测而导致的高估/低估。此外，所提出的方法在将模型不确定性扩展到生产预测的不确定性方面表现良好；也就是说，我们最终得到的预测优于标准 DCA 方法。
Traditional decline curve analyses (DCAs), both deterministic and probabilistic, use specific models to fit production data for production forecasting. Various decline curve models have been applied for unconventional wells, including the Arps model, stretched exponential model, Duong model, and combined capacitance-resistance model. However, it is not straightforward to determine which model should be used, as multiple models may fit a dataset equally well but provide different forecasts, and hastily selecting a model for probabilistic DCA can underestimate the uncertainty in a production forecast. Data science, machine learning, and artificial intelligence are revolutionizing the oil and gas industry by utilizing computing power more effectively and efficiently. We propose a data-driven approach in this paper to performing short term predictions for unconventional oil production. Two states of the art level models have tested: DeepAR and used Prophet time series analysis on petroleum production data. Compared with the traditional approach using decline curve models, the machine learning approach can be regarded as” model-free” (non-parametric) because the pre-determination of decline curve models is not required. The main goal of this work is to develop and apply neural networks and time series techniques to oil well data without having substantial knowledge regarding the extraction process or physical relationship between the geological and dynamic parameters. For evaluation and verification purpose, The proposed method is applied to a selected well of Midland fields from the USA. By comparing our results, we can infer that both DeepAR and Prophet analysis are useful for gaining a better understanding of the behavior of oil wells, and can mitigate over/underestimates resulting from using a single decline curve model for forecasting. In addition, the proposed approach performs well in spreading model uncertainty to uncertainty in production forecasting; that is, we end up with a forecast which outperforms the standard DCA methods.