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The AAPG/Datapages Combined Publications Database
AAPG Bulletin
Abstract
AAPG Bulletin, V.
DOI: 10.1306/100212121019
Deep convolutional neural networks for generating grain-size logs from core photographs
Thomas T. Tran,1 Tobias H. D. Payenberg,2 Feng X. Jian,3 Scott Cole,4 and Ishtar Barranco5
1Chevron North America Exploration and Production, Covington, Louisiana; [email protected]
2Chevron Australia Pty. Ltd., Perth, Western Australia, Australia; [email protected]
3Independent consultant; [email protected]
4Chevron Australia Pty. Ltd., Perth, Western Australia, Australia; [email protected]
5Chevron Australia Pty. Ltd., Perth, Western Australia, Australia; [email protected]
ABSTRACT
Primary porosity and permeability are critical inputs to reliable static and dynamic earth models. In many clastic sedimentary systems, these rock properties have a strong relationship to grain size. Having an automated and consistent core grain-size interpretation free from interpreter bias brings consistency and improved model accuracy while also potentially helping to reduce interpretation cost and time. In this paper, a method is presented to characterize grain size rapidly and consistently from core photographs using deep convolutional neural networks (CNNs). In this proof-of-concept study, several deep CNNs were trained with different network architectures to predict discrete grain size from core photographs and to achieve satisfactory accuracy on blind-test data. The training data includes 300-pixels-per-inch photographs of more than 100 m of cores from one well
and associated grain-size description. To generate training and validation data for supervised training, “postage-stamp” 512 × 512 pixel images (4.3 cm × 4.3 cm) were sampled from the core photographs and discrete grain-size labels were assigned to each image. In this way, tens of thousands of labeled examples were generated for training, validating, and testing the CNNs. A blind test on two different wells was conducted by comparing the CNN-predicted against the manually interpreted grain size. Numerical error
analysis
and visual inspection of the prediction showed the CNNs produced satisfactory accuracy of a continuous grain-size
log
and even picked up some heterogeneities that were “upscaled” by the manual core interpretation process. This was an important improvement over the manual core interpretation process as these heterogeneities are important to capture when modeling fluid flow.
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