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An artificial neural network based tool-box for screening and designing improved oil recovery methods.

机译：基于人工神经网络的工具箱，用于筛选和设计改进的采油方法。

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Typically, improved oil recovery (IOR) methods are applied to oil reservoirs that have been depleted by natural drive mechanism. Descriptive screening criteria for IOR methods are used to select the appropriate recovery technique according to the fluid and rock properties. The existing screening guidelines neither provide information about the expected reservoir performance nor suggest a set of project design parameters that can be used towards the optimization of the process.;In this study, artificial neural networks are used to build two neuro-simulation tools for screening and designing miscible injection, waterflooding and steam injection processes. The tools are intended to narrow the ranges of possible scenarios to be modeled using conventional simulation, reducing the potentially extensive time and energy spent in modeling studies and analysis.;A commercial reservoir simulator is used to generate the data supplied to train and validate the artificial neural networks. The proxy models are built considering four different well patterns with different well operating conditions as the design parameters. Different expert systems are developed for each well pattern. The screening networks, or forward application, predict oil production rate and cumulative oil production profiles for a given set of rock and fluid properties, and design parameters. The inverse application provides the necessary design parameters for a given set of reservoir characteristics and for the specified (desired) process performance indicators.;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 performance for different reservoir types undergoing diverse recovery processes. The inverse proxy models are able to predict the operation conditions at the same time that accurately provide the complete oil production profiles. Both neuro-simulation applications are built within a graphical user interface to facilitate the display of the results.;The project design tool-box helps in the quantitative project assessment if proper combinations of expected project abandonment time and total oil recovery are provided for the same reservoir. Its use, when combined with the screening network application, becomes a powerful tool that facilitates the evaluation and validation of the proposed production scenarios.;The tools proposed in this study have the potential of providing a new means to design a variety of efficient and feasible IOR processes by using artificial intelligence. Appropriate guidelines are provided to the reservoir engineer, which decrease the number of possible scenarios to be studied and reduce the time spent with conventional reservoir simulation methodology.
机译：通常，将改进的石油采收率（IOR）方法应用于已被自然驱动机制耗尽的油藏。用于IOR方法的描述性筛选标准用于根据流体和岩石特性选择适当的采收技术。现有的筛选指南既未提供有关预期储层性能的信息，也未提出可用于工艺优化的一组项目设计参数。在本研究中，人工神经网络用于构建两个用于筛选的神经模拟工具并设计混溶注入，注水和蒸汽注入工艺。该工具旨在缩小使用常规模拟方法进行建模的可能场景的范围，从而减少了在建模研究和分析中花费的大量潜在时间和精力。商业商用油藏模拟器用于生成用于训练和验证人工模型的数据神经网络。代理模型的建立将四种不同的井眼模式与不同的井眼操作条件作为设计参数。针对每种井模式开发了不同的专家系统。筛选网络或正向应用程序可预测给定的一组岩石和流体属性以及设计参数的产油率和累积产油剖面。逆向应用程序为给定的储层特征和指定的（所需的）过程性能指标提供了必要的设计参数。这项研究的结果表明，该网络能够识别出位移机制与储层之间的强相关性的特征，因为它们有效地预测了经历了不同采收过程的不同类型油藏的油气表现。逆代理模型能够同时预测可精确提供完整采油剖面的运行条件。两种神经模拟应用程序都内置在图形用户界面中，以方便显示结果。如果为同一项目提供了预期项目放弃时间和总采油量的适当组合，则项目设计工具箱可帮助进行定量项目评估。水库。当与筛选网络应用程序结合使用时，它的使用将成为一个强大的工具，有助于评估和验证所提议的生产方案。;本研究中提出的工具有可能为设计各种高效可行的方法提供一种新手段。通过使用人工智能进行IOR流程。向油藏工程师提供了适当的指导，从而减少了要研究的可能方案的数量，并减少了传统油藏模拟方法所花费的时间。

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