考虑“井工厂”学习效应的平台位置优化方法
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  • 英文篇名:Optimization of platform positioning considering the learning effect in the "well factory" mode
  • 作者:王志月 ; 高德利 ; 刁斌斌 ; 胡德高
  • 英文作者:Wang Zhiyue;Gao Deli;Diao Binbin;Hu Degao;MOE Key Laboratory of Petroleum Engineering//China University of Petroleum;Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Ltd.;
  • 关键词:页岩气 ; 井工厂 ; 学习效应 ; 定向钻井 ; 丛式水平井 ; 钻井平台 ; 遗传算法 ; 建井费用
  • 英文关键词:Shale gas;;"Well factory";;Learning effect;;Directional drilling;;Cluster horizontal wells;;Drilling platform;;Genetic algorithm;;Well construction costs
  • 中文刊名:TRQG
  • 英文刊名:Natural Gas Industry
  • 机构:中国石油大学石油工程教育部重点实验室;中石化重庆涪陵页岩气勘探开发有限公司;
  • 出版日期:2018-01-25
  • 出版单位:天然气工业
  • 年:2018
  • 期:v.38;No.291
  • 基金:国家自然科学基金创新研究群体项目“复杂油气井钻井与完井基础研究”(编号:51521063)、国家自然科学基金重点项目“页岩和致密油气田高效开发建井基础研究”(编号:U1762214);; 国家科技重大专项(编号:2017ZX05009-003);; 中石化重庆涪陵页岩气勘探开发有限公司“页岩气水平井提高钻速技术研究”(编号:31400970-17-ZC0607-0001);; 中国石油大学(北京)科研基金资助项目(编号:2462014YJRC035)
  • 语种:中文;
  • 页:TRQG201801018
  • 页数:7
  • CN:01
  • ISSN:51-1179/TE
  • 分类号:108-114
摘要
随着"井工厂"技术的不断应用,"井工厂"模式所产生的学习效应对钻井费用及平台位置的选择具有重要的影响,研究该模式下的平台位置优化问题就变得很有必要。为此,结合"井工厂"钻井学习曲线,提出了"钻井学习指数"的概念,以量化"井工厂"模式下单井平均节约费用与平台布井数量之间的关系;进而考虑"钻井学习指数"、平台最大容量及轨道复杂度对平台位置选择的影响,建立了"井工厂"模式下丛式水平井平台位置优化模型;根据遗传算法原理,并结合每口井只能对应一个平台以及每个平台所允许的最大井数等特点,对遗传算子进行了改进,给出了所建立模型的解算步骤。算例分析结果表明,所提出的优化模型能够在满足所给定约束条件的前提下,减少平台数量,增加每个平台的井数。结论认为,考虑"井工厂"学习效应的平台位置优化模型能够充分发挥"井工厂"的技术优势,是一种降低丛式水平井建井费用的有效方法。
        With the promotion and application of the "well factory" technique, the learning effect generated under the "well factory" mode plays an important role in the selection and optimization of well quantity and platform position. Combined with the learning curves of "well factory" drilling, the concept of "learning index of drilling" was proposed to quantify the reduced drilling costs versus the number of wells on the platform under the "well factory" mode. Considering the impacts of "learning index of drilling", the maximum capacity of platform and trajectory complexity on the selection of platform position, the optimized model for platform positioning for cluster wells under the "well factory" mode was built. In accordance with the genetic algorithm(GA) principles and with consideration to the correspondence of one well to one platform and the maximum allowable number of wells on one platform, the GA factors were modified. Then, the procedures for the solution of the proposed model were proposed, and calculations were made for two example cases. The results are revealed in two aspects. First, under the "well factory" mode, learning effect, platform capacity, trajectory complexity and other factors may significantly affect platform positioning of cluster horizontal wells and relevant drilling costs. Second, the optimized model can effectively minimize the number of platforms required, enhance the number of wells on one platform and cut down relevant drilling costs under all given constraints. The optimized model with consideration to the learning effect in the "well factory" mode can fully extend the advantages of "well factory", thus it is an effective technique to minimize the costs of constructing cluster horizontal wells.
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