数据挖掘在石油勘探数据库中的应用前景

Prospect of the Application of Data Mining in Petroleum Exploration Databases

通过世界现状分析,指出数据挖掘(Data Mining,以下简称DM)在石油勘探数据库中的应用尚处于起步阶段。在前人的工作基础上,简述了数据库DM技术的结构、功能、算法及关键技术。以一个测井解释实例具体介绍DM的操作过程、技术方法及应用效果,验证该技术的可行性、实用性。实例中,采用多元回归分析,实现数据降维;分别采用人工神经网络和支持向量机两个挖掘算法,进行知识发现。基于国内大批石油勘探数据库(包括数据银行、数据仓库等)的陆续建成,认为DM技术的研发已提到议事日程,必将成为石油地质研究和勘探决策的有力手段。

This paper indicates through worldwide analysis that the application of data mining (DM) in petroleum exploration databases is in the initial stage. It briefly describes the structures, functions, algorithms and key techniques of data mining based on the work of pioneer contributors. A case study of well-logging interpretation introduces the processes, methods and application results of DM in concrete terms, proving that the DM used is feasible and practical. In the case study, multiple regression analysis is adopted as a dimension-reduction algorithm, while artificial neural network and support vector machine are employed as mining algorithms for knowledge discovery. Since a great number of petroleum exploration databases (including databanks and datastores) have been or will be built in China, it is time to further develop the techniques of DM that will become powerful tools for petroleum geology research and exploration decision.