Support Vector Machine Algorithm for Automatically Identifying Depositional Microfacies Using Well Logs

Dahai Wang, Jun Peng, Qian Yu, Yuanyuan Chen and Hanghang Yu
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Dahai Wang: College of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China
Jun Peng: College of Earth Science and Technology, Southwest Petroleum University, Chengdu 610500, China
Qian Yu: Chengdu Center of China Geological Survey, Chengdu 610081, China
Yuanyuan Chen: Southwest Geophysical Exploration Branch of Oriental Geophysical Company, Chengdu 610213, China
Hanghang Yu: Southwest Oil & Gas Field Company Exploration Division, Petro China, Chengdu 610041, China

Sustainability, 2019, vol. 11, issue 7, 1-15

Abstract: Depositional microfacies identification plays a key role in the exploration and development of oil and gas reservoirs. Conventionally, depositional microfacies are manually identified by geologists based on the observation of core samples. This conventional method for identifying depositional microfacies is time-consuming, and only the depositional microfacies in a few wells can be identified due to the limited core samples in these wells. In this study, the support vector machine (SVM) algorithm is proposed to identify depositional microfacies automatically using well logs. The application of SVM includes the following steps: First, the depositional microfacies are determined manually in several wells with core samples. Then, the training sets used in the SVM algorithm are extracted from the well logs. Finally, a quantitative discrimination model based on the SVM algorithm is established to realize the classification of depositional microfacies. Field application shows that this innovative and constructive solution can be effectively used in uncored wells to identify depositional microfacies with a rate of accuracy approaching 84%. It overcomes the limitation of the conventional manual method which greatly contributes to the cost-saving of core analysis and improves the sustainable profitability of oil and gas exploration.

Keywords: support vector machine; automatic identification; depositional microfacies; data mining; machine learning algorithm; support vector (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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