钻采工艺 ›› 2022, Vol. 45 ›› Issue (3): 73-78.DOI: 10.3969/J. ISSN.1006-768X.2022.03.13

• 开采工艺 • 上一篇    下一篇

基于大数据的页岩油区块产量差异分析方法研究

肖阳1,2,王家豪1,李志刚1,杨金元1,刘守昱1   

  1. 1成都理工大学能源学院 2成都理工阳光能源科技有限公司
  • 出版日期:2022-05-25 发布日期:2022-06-28
  • 作者简介:肖阳(1980-),博士,副教授,2009年毕业于西南石油大学油气田开发工程专业,现在成都理工大学能源学院主要从事复杂储 层地质力学及储层改造方面研究。
  • 基金资助:
    国家自然科学基金青年科学基金“多尺度多场应力耦合致密砂岩体积改造裂缝评价模型研究”(编号:51504042);国家科技重 大专项“鄂南致密低渗油藏水力压裂裂缝起裂及扩展规律”(编号:2016ZX05048-001-04-LH)和“水平井压裂设计优化系统”(编号:2016ZX05023-001 );四川省教育厅基金重点项目“自流注水关键节点仿真及流动耦合计算模型研究”(编号:18ZA0063)。

Study on Production Difference Analysis Method of Shale Oil Play Based on Big Data

XIAO Yang1,2,WANG Jiahao1,LI Zhigang1,YANG Jinyuan1,LIU Shouyu1   

  1. 1.College of Energy, Chengdu University of Technology, Chengdu,Sichuan 610059,China;2. Sun-Energy Technology Co. LTD. of Chengdu University of Technology Chengdu, Sichuan 610059, China
  • Online:2022-05-25 Published:2022-06-28

摘要:

长庆油田页岩油A83区块和X233区块均为典型的低孔、低渗—特低渗储层,A83区块和X233区块采用 相同的体积改造方式进行生产,但是 X233区块产量明显优于A83区块。针对这个现象,在产量影响因素分析的基 础上,综合了以测井解释数据为基础的地质参数、以岩石力学实验为基础的地质力学参数、钻完井和压裂施工的工程参数、以含水率为主的生产特征数据以及高压物性参数,创新运用了皮尔逊和斯皮尔曼大数据相关性分析方法, 明确了两个区块的产量主控因素,并在此基础上完成了区块产量差异性研究。结果表明,A83区块和X233区块地质参数基本形同,但地质力学参数、含水率、溶解气油比有明显的区别,是区块产量差异的主要原因。该方法可用于长庆页岩油区块的产量差异性及产量主控因素分析,可推广应用于非常规油气的产量影响因素分析,从而优化施工设计,指导现场作业生产,具有一定的借鉴意义。 

关键词: 页岩油, 产量, 大数据分析, 地质参数, 地质力学参数, 高压物性参数

Abstract:

The A83 block and the X233 block are both shale oil plays in Changqing oilfield, both of which are typical low porosity, low permeability and ultra-low permeability reservoirs. The A83 block and the X233 block adopt the same volume stimulation method for production, but the output of the X233 block is significantly better than that of the A83 block. In response to this phenomenon, on the basis of analysis of production influencing factors, integrated such parameters as geological parameters based on logging interpretation data, geomechanical parameters based on rock mechanics experiments, engineering parameters for drilling and completion and fracturing operations, production characteristic data such as water content, and high pressure physical property parameters, Pearson's and Spearman's big data correlation analysis method was innovatively used to clarify the main controlling factors of production, and on this basis completes the research on the production difference of the two blocks. The results show that the geological parameters of the A83 block and the X233 block are basically the same, but there are obvious differences in the geomechanical parameters, water content, and dissolved gas-oil ratio, which are the main reasons for the difference in production. This method can be used to analyze the production difference and main control factors of Changqing shale oil plays, and can be popularized and applied to analyze the influencing factors of unconventional oil and gas production, so as to optimize the construction design and guide the field operation and production, which has certain reference significance.

Key words: shale oil, production, big data analysis, geological parameters, geomechanical parameters, high pressure physical property parameters