Affiliation:
1. University of Calgary, Calgary, Canada. (corresponding author)
2. Geological Survey of Brazil, São Paulo, Brazil.
3. University of Calgary, Calgary, Canada.
Abstract
Despite significant developments in the past few years in the application of machine learning algorithms for the lithologic classification of rock samples, publicly available labeled data sets are very scarce. We open source a fully labeled data set containing more than 16,000 scanning electron microscopy (SEM) images of drill cutting samples—mounted on thin sections—from a low-permeability reservoir in western Canada. We develop a simplified image processing workflow to segment and isolate the rock chips into individual SEM images, which in turn are used to identify, classify, and quantify rock types based on textural characteristics. In addition, using this data set, we explore the use of convolutional neural networks (CNNs) as a baseline tool for acceleration and automatization of rock-type classification. Without significant modifications to popular CNN models, we obtain an accuracy of approximately 90% for the test set. Results demonstrate the potential of CNN as a fast approach for lithologic classification in low-permeability siltstone reservoirs. In addition to making the data set publicly available, we believe our workflow to segment and isolate drill cutting samples in individual images of rock chips will facilitate future research of drill cuttings properties (e.g., lithology, porosity, and particle size) using machine learning algorithms.
Funder
Sponsors of the Tight Oil Consortium
Publisher
Society of Exploration Geophysicists
Reference69 articles.
1. Use of Drill Cuttings for Improved Design of Hydraulic Fracturing Jobs in Horizontal Wells
2. Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize wheathered subarkose sandstone
3. Ayling, B., P. Rose, and S. Petty, 2011, Using QEMSCAN® to characterize fracture mineralization at the Newberry Volcano EGS Project, Oregon: A pilot study: Geothermal Resources Council Annual Meeting.
4. Ayling, B., P. Rose, S. Petty, E. Zemach, and P. Drakos, 2012, QEMSCAN® (Quantitative Evaluation of Minerals by Scanning Electron Microscopy): Capability and application to fracture characterization in geothermal systems: 37th Workshop on Geothermal Reservoir Engineering Stanford University.
5. Deep convolutions for in-depth automated rock typing
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