Propagating Image-Based Rock Classes from Cored to Non-Cored Depth Intervals Using Supervised Machine Learning

High-resolution image data is instrumental in quantifying the variation of rock fabric in formation evaluation that conventional well logs fail to capture. However, the acquisition of image data for all wells in a reservoir is restricted due to technology limitations and operational constraints and the high cost involved. The main objective of this paper is to propose a workflow to extrapolate rock fabric information from imaged wells to nearby un-imaged wells for enhanced formation evaluation in un-imaged wells. To propagate rock fabric information, we trained a supervised learning algorithm in a well with core photos, CT-scan images, and conventional well logs. Subsequently, the trained model is used to identify fabric-influenced well-log-based rock classes using only conventional well logs in un-imaged depth intervals/well (referred to as fabric-based rock classes). We applied the proposed workflow to two wells in a siliciclastic formation with spatial variation in rock fabric. Comparison of the detected fabric-based rock classes in the nearby depth intervals/well using the trained model with image-based rock classes resulted in an average accuracy of 94%. The outcomes of this paper contribute to accelerated identification of rock types honoring rock fabric while minimizing extensive imaging and coring efforts. Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows