Global oil demand is rapidly increasing and is expected to reach 106.3 million barrels per day by 2040. Thus, it is vital for hydrocarbon extraction industries to forecast their production to optimize their operations and avoid losses. Big companies have realized that exploiting the power of deep learning (DL) and the massive amount of data from various oil wells for this purpose can save a lot of operational costs and reduce unwanted environmental impacts. In this direction, researchers have proposed models using conventional machine learning (ML) techniques for oil production forecasting. However, these techniques are inappropriate for this problem as they can not capture historical patterns found in time series data, resulting in inaccurate predictions. This research aims to overcome these issues by developing advanced data-driven regression models using sequential convolutions and long short-term memory (LSTM) units. Exhaustive analyses are conducted to select the optimal sequence length, model hyperparameters, and cross-well dataset formation to build highly generalized robust models. A comprehensive experimental study on Volve oilfield data validates the proposed models. It reveals that the LSTM-based sequence learning model can predict oil production better than the 1-D convolutional neural network (CNN) with mean absolute error (MAE) and R2 score of 111.16 and 0.98, respectively. It is also found that the LSTM-based model performs better than all the existing state-of-the-art solutions and achieves a 37% improvement compared to a standard linear regression, which is considered the baseline model in this work.

翻译：全球石油需求正在迅速增长，预计到2040年将达到每天1.063亿桶。因此，碳氢化合物开采行业必须对其产量进行预测，以优化运营并避免损失。大型企业已经意识到，利用深度学习（DL）的强大能力以及来自不同油井的大量数据进行预测，可以节省大量运营成本并减少不良环境影响。为此，研究人员提出了使用传统机器学习（ML）技术的油产量预测模型。然而，这些技术不适用于此问题，因为它们无法捕捉时间序列数据中的历史模式，导致预测不准确。本研究旨在通过开发基于序列卷积和长短期记忆（LSTM）单元的高级数据驱动回归模型来克服这些问题。通过详尽的分析来选择最佳序列长度、模型超参数以及跨井数据集构建方法，从而建立高度泛化的稳健模型。在Volve油田数据上进行的全面实验研究验证了所提出的模型。结果表明，基于LSTM的序列学习模型在油产量预测方面优于一维卷积神经网络（CNN），其平均绝对误差（MAE）和R²分数分别为111.16和0.98。研究还发现，基于LSTM的模型性能优于所有现有的最先进解决方案，相较于作为本工作基线模型的标准线性回归，实现了37%的提升。