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首页> 外文会议>SPE annual technical conference and exhibition;ATCE 2005 >Integration of Artificial Intelligence and Lean Sigma for Large-Field ProductionOptimization: Application to Kern River Field
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Integration of Artificial Intelligence and Lean Sigma for Large-Field ProductionOptimization: Application to Kern River Field

机译：人工智能和精益Sigma集成用于大田生产优化：在克恩河油田的应用

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摘要

This paper represents an integration of artificial intelligenceand lean sigma techniques to achieve large field productionoptimization. The first part of the methodology (detailed inSPE 90266 'Zonal Allocation and Increased Production Op-portunitiesUsing Data Mining in Kern River') involves datamanagement and predictive data mining for increased produc-tionopportunity identification. It utilizes a set of data miningtools including clustering techniques and neural networks toidentify new candidates for clean-outs, perforating, sidetracks,deepening, and other types of workovers. Furthermore, theexpert system was used to predict the estimated production in-creasefor these candidates. The second part of the methodol-ogyoptimizes the implementation and post-workover followup of the opportunities identified in part one. It involves theuse of lean sigma tools such as value stream mapping, levelloading, continuous flow production, standard operating pro-cedures,and kanbans which optimize execution cycle time,peak oil production, decision making process, cost, andsafety 2 . This approach was successfully applied and executedin the Kern River field.
机译：本文介绍了人工智能和精益西格玛技术的集成，以实现大批量生产的优化。该方法的第一部分（在SPE 90266“区域分配和增加的生产机会，在克恩河中使用数据挖掘”中进行了详细介绍）涉及数据管理和预测性数据挖掘，以提高生产机会的识别能力。它利用包括聚类技术和神经网络在内的一组数据挖掘工具来识别清理，打孔，侧移，深化和其他修井作业的新候选人。此外，专家系统用于预测这些候选产品的估计产量增加。方法的第二部分-优化了第一部分中确定的机会的实施和修整后的跟进工作。它涉及精益sigma工具的使用，例如价值流图，水平加载，连续流生产，标准操作程序和看板，以优化执行周期，峰值石油产量，决策过程，成本和安全性2。该方法已在克恩河油田成功应用并执行。

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