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A New Screening Tool for Improved Oil Recovery Methods Using Artificial Neural Networks

机译：一种新的筛选工具，用于利用人工神经网络改进的储油方法

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摘要

In more recent years, improved oil recovery (IOR) techniques are applied to reservoirs even before their natural energy drive is exhausted by primary depletion. Screening criteria for IOR methods are used to select the appropriate recovery technique in view of the reservoir characteristics. However, further reservoir appraisal is necessary after the applicable recovery technique is identified. The methodology proposed in this paper allows the preliminary evaluation of reservoir performance in parallel with the IOR screening process. In this study, artificial neural network (ANN) methodology is used to build a high-performance neuro-simulation tool for screening IOR methods such as waterflooding, steam injection and miscible injection of CO2 and N2. This innovative tool integrates the field development plan into the screening method. The reservoir characteristics are evaluated together with a proposed production scenario to assess the most suitable recovery process and, at the same time, the reservoir performance is forecasted by providing the estimated oil production curve. The screening toolbox consists of proxy models that implement a multilayer cascade feedforward back propagation artificial network algorithm. The proxy models work for a diverse range of reservoir fluids and rock properties. The field development plan is featured in the tool by different well patterns, well spacing and well operating conditions. The ANN screening tool predicts oil production rate, cumulative oil production and estimated production time. The tool also provides the flexibility to compare the hydrocarbon production for different sets of inputs, which facilitates comparison of various depletion strategies in the screening process as well. The results of this study show that the networks are able to recognize the strong correlation between the displacement mechanism and the reservoir characteristics as they effectively forecast hydrocarbon production for different reservoirs. The tool presents a new means to design an efficient and feasible IOR project by using artificial intelligence. The proposed tool facilitates the appraisal of diverse field development strategies for oil reservoirs and allows comparison of reservoir performance under diverse production schemes.
机译：在近年来，即使在自然能量驱动通过初级耗尽耗尽之前，还将改善的储油技术（IOR）技术应用于储层。筛选IOR方法的标准用于考虑到储存器特征来选择适当的恢复技术。但是，在确定适用的恢复技术后，还需要进一步的储层评估。本文提出的方法允许与IOR筛选过程平行初步评估储层性能。在本研究中，人工神经网络（ANN）方法用于构建高性能神经仿真工具，用于筛选IOR方法，例如水上的方法，蒸汽喷射和可混溶注射CO2和N2。此创新工具将现场开发计划集成到筛选方法中。储存器特性与建议的生产方案一起评估，以评估最合适的恢复过程，同时通过提供估计的石油生产曲线预测储层性能。筛选工具箱由代理模型组成，实现多层级联前馈回传输人工网络算法。代理模型适用于各种储层流体和岩石性能。现场开发计划通过不同的井图案，井间距以及操作条件良好的工具。 ANN筛选工具预测石油生产率，累计油生产和估计的生产时间。该工具还提供了对比较不同输入集合的碳氢化合物产生的灵活性，这有助于比较筛选过程中的各种耗尽策略。该研究的结果表明，该网络能够识别出位移机构与储层特性之间的强烈相关性，因为它们有效地预测了不同储层的烃生产。该工具通过使用人工智能来设计一种设计高效和可行的IOR项目的新方法。该拟议的工具促进了石油储层的各种现场发展战略评估，并允许在各种生产方案下进行储层性能。

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