西南石油大学学报(自然科学版) ›› 1991, Vol. 13 ›› Issue (2): 31-37.DOI: 10.3863/j.issn.1000-2634.1991.02.04

• 论文 • 上一篇    下一篇

人工智能在试井解释中的应用研究

王卫星 成绥民 李汝勇 王浩
  

  1. (油井完井技术中心)
  • 收稿日期:1991-01-18 修回日期:1900-01-01 出版日期:1991-04-20 发布日期:1991-04-20

USE OF ARTIFICIAL INTELLIGENCE IN WELL TEST INTERPRETATION

Wang Wei-xing Cheng Sui-min li Ru-yong Wang Hao
  

  1. (Well Completion Technology Center)
  • Received:1991-01-18 Revised:1900-01-01 Online:1991-04-20 Published:1991-04-20

摘要:

人工智能在试井中的应用是一个新的课题,国内外都刚起步。本文描述了试井油藏模型识别专家系统(RMIESWT)原型的研制成果。以压力导数为基拙,用计算机模拟人类专家的视觉判断过程,用符号表达曲线,实现推理,是一种很好的智能模拟技术。除了压力恢复数据以外,还利用辅助资料来识别油藏模型,这对于那些实测数据不完整的情况,特别有效。选用基于规则的通用型开发工具CM.1作为专家系统环境。采用分层式结构构造知识序,逻样关系清晰易懂,非常有利于规则的增添、删除和修改。终端用户同RMIESWT的对话全部是菜单式中文显示,易于被现场使用单位所接受。

关键词: 人工智能, 专家系统, 试井应用, 模型识别

Abstract:

The application of artificial intelligence to well test interpretation is a new subject, The study of it is at the initial stage both at home and abroad. This paper describes the development of an Expert System prototype for the reservoir model identification in well test interpretation (RMIESWT), The model chosen by computer is based on the pressure derivative curve, and simulates the visual diagnosis performed by a human expert. A part of the reasoning involved in such a diagnosis uses a symbolic representation of the derivative curve,which,in the case of a human expert,is built almost unconsciously, Therefore,it is a very good simulation technique of artificial intelligence. Besides test data, the system seeks additional information available from other sources, such as drilling,production, well logging,coring,fluid sampling,enhanced recovery treatment and geology,to identify a reservoir model, which is particularly available to the incomplete test data. The expert system tool CM.1 is chosen as an expert system environment, which is a current tool,based on rules. The knowledge base is organized in a hierarchy of sub-problems whose logical relations are clear and easy to understand. Any rule and the certainty factor of the rule can feasibly deleted, inserted and changed in the knowledge base. During the consultation session between a user and the expert system, all the displays are in a way of the menu and in Chinese, The system is easily acceptable for the users in oilfield.

Key words: Artificial intelligence, Expert system, Application of well test, Model identification

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