Abstract
Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0–6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field.
Highlights
Machine learning is a subfield of artificial intelligence that aims to learn structure in data and fit those data into models that can be utilized for automating mundane tasks and gaining intelligent insights
Based on tests comparing the performance of other popular deep convolutional neural networks (DCNNs) architectures, we found VGG-16 to yield the best performance in terms of accuracy and computational efficiency for the ichnology classification task
In order to ascertain the applicability of deep learning to ichnological analysis, we ran two main experiments
Summary
Machine learning is a subfield of artificial intelligence that aims to learn structure in data and fit those data into models that can be utilized for automating mundane tasks and gaining intelligent insights. It has shown great potential in tackling long-standing research problems across science and engineering disciplines. Most noticeable has been the contributions of deep learning, a branch of machine learning that is founded on artificial neural networks It has made remarkable breakthroughs in a variety of fields, including biology, natural language processing, and computer vision [1,2,3,4,5]. Image classification using convolution neural networks (CNNs) has recently shown remarkable levels of performance in core-based facies analysis e.g., [9], where long hours of visual observation are essential
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