Lithology classification of volcanic rocks based on conventional logging data of machine learning: A case study of the eastern depression of Liaohe oil field

Abstract

The reservoirs in the eastern depression of Liaohe basin are formed by multistage igneous eruption. The lithofacies and lithology are complex, and the lithology is mainly intermediate and basic igneous rocks. Based on the integration of debris data of igneous rocks and logging data, this article selected 6,462 continuous logging data with complete cuttings data and five conventional logging curves (RLLD, AC, DEN, GR, and CNL) from four wells in the eastern depression of Liaohe basin as the training set. A variety of lithologic identification schemes based on support vector machine and random forest are established to classify the pure igneous strata and actual strata. By comparing the classification results with the identification data of core slice and debris slice, a practical lithologic classification scheme for igneous rocks in the eastern depression of Liaohe basin is obtained, and the classification accuracy reaches 97.46%.

Keywords

• igneous rock
• machine learning
• lithology classification