基于机器学习的不确定条件下油田开发方案改进方法  

Machine learning inspired workflow to revise field development plan under uncertainty

作　　者：LOOMBA Ashish Kumar BOTECHIA Vinicius Eduardo SCHIOZER Denis José LOOMBA Ashish Kumar;BOTECHIA Vinicius Eduardo;SCHIOZER Denis José(Universidade Estadual de Campinas,SP 13084778,Brazil)

机构地区：[1]坎皮纳斯州立大学,圣保罗13084778,巴西

出　　处：《石油勘探与开发》2023年第6期1268-1277,共10页Petroleum Exploration and Development

摘　　要：提出了一种高效的风险导向型闭环油田开发(CLFD)工作流,使用油田开发中获取的数据信息不断改进油田开发方案。该方法整合了机器学习、智能选择和增加代表性油藏模型等手段,将这些手段与基于聚类的学习与进化算法结合,有效发掘决策变量的搜索空间。与以往研究不同,该方法考虑了闭环油田开发工作流的实施时间,使用实际时间线验证闭环油田开发工作流的实用性。为了认识数据同化和新测井数据在闭环油田开发工作流中的重要性,在不确定属性保持不变的苛刻油田条件下开展了研究;利用极为耗时的模拟模型,将闭环油田开发工作流用于特大型油田基准案例分析。研究表明,仅采用100种情景即可对一个强非均质性特大型油田的地质不确定性进行量化评价。与以往方法相比,该方法可以提效85%以上。提出了对闭环油田开发工作流的一些新认识,指出了闭环油田开发工作流在实际应用中存在的与数据同化有关的问题。We present an efficient and risk-informed closed-loop field development(CLFD)workflow for recurrently revising the field development plan(FDP)using the acquired information.To make the process practical,we integrated multiple concepts of machine learning,an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models.These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables.Unlike previous studies,we also added the execution time of the closed-loop field development(CLFD)workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow.To appreciate the importance of data assimilation and new well-logs in a CLFD workflow,we carried out researches at rigorous conditions without a reduction in uncertainty attributes.The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models.The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty,despite working with a giant field with highly heterogeneous characteristics.It is demonstrated that the CLFD workflow can improve the efficiency by over 85%compared to the previously validated workflow.Finally,we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

关 键 词：油田开发方案 闭环油田开发工作流 油藏模型 机器学习 油藏不确定性 

分 类 号：TE34[石油与天然气工程—油气田开发工程]