Data mining and well logging interpretation: application to a conglomerate reservoir

数据挖掘与测井解释:应用于砾岩储层（英文）

Data mining is the process of extracting implicit but potentially useful information from incomplete, noisy, and fuzzy data. Data mining offers excellent nonlinear modeling and self-organized learning, and it can play a vital role in the interpretation of well logging data of complex reservoirs. We used data mining to identify the lithologies in a complex reservoir. The reservoir lithologies served as the classification task target and were identified using feature extraction, feature selection, and modeling of data streams. We used independent component analysis to extract information from well curves. We then used the branch-and- bound algorithm to look for the optimal feature subsets and eliminate redundant information. Finally, we used the C5.0 decision-tree algorithm to set up disaggregated models of the well logging curves. The modeling and actual logging data were in good agreement, showing the usefulness of data mining methods in complex reservoirs.

数据挖掘是从大量的、不完全的、有噪声的、模糊的数据中提取隐含的、但又是潜在有用的信息和知识的过程,由于数据挖掘具有出色的非线性建模能力和自组织学习能力,因此可以在复杂储层的测井解释中发挥作用。本文用数据挖掘方法识别复杂储层的岩性。将岩性识别作为一种分类任务建立数据挖掘流程,包括特征提取、特征选择和建立模型等步骤。本文用独立成分分析法从测井曲线中提取信息;然后使用分支定界算法寻找最佳的特征子集,并消除冗余信息;最后采用C5.0决策树算法建立分类模型的测井曲线。模型和实际测井数据吻合较好,表明在复杂油藏的研究中数据挖掘方法是有效的。