Abstract:

In the traditional data-driven data mining process, there are huge gaps between the efficient algorithms and intelligent tools as well as the invalidity of knowledge which is obtained by traditional data-driven data mining. Meanwhile, each data in the earth science field contains a solid physical meaning. If there is no corresponding domain knowledge involved in the mining process, the information explored by data-driven data mining will be lack of practicability and not able to effectively solve problems in the earth science area. Therefore, the task-driven data mining is proposed. Additionally, task-driven data mining concepts and principles are elaborated with the help of data mining concepts and techniques. It is divided into seven elements such as data warehousing, data preprocessing, feature subset selection, model formation, model evaluation, model modification and model published. Those constitute a cyclic and iterative process until a predictive model which is capable of effectively achieving the objectives. The task-driven data mining is applied to recognizing the low resistivity reservoirs, and the whole analysis process is elaborated. The white-box model of decision tree and the black-box model of support vector machine are introduced to identify the low resistivity reservoirs, and the accuracy is more than 90%.

Key words: task-driven data mining, low resistivity oil reservoir, classification algorithms, decision tree, support vector machine, predictive model, reservoirs