Machine learning and deep learning, a subset of machine learning, have become successful predictive modeling tools. Machine learning applications include classifying DNA sequences and fraud detection. Deep learning applications include language processing and facial recognition. Both methods have shown to be useful in understanding unconventional reservoirs when traditional techniques used to understand conventional reservoirs do not readily transfer. In this work, machine learning and deep learning are used to predict geological facie classifications using computed tomography (CT).

A combined approach is developed to predicted geological facies. The first aspect of this approach utilizes molecular weight, density, and porosity from CT scans on core samples to learn from existing user-defined geological facie classifications. Once the machine understands the relationship between geological facies and CT scan physics, the resulting model is used to predict subsequent geological facies using the CT scan parameters. The second aspect of this approach utilizes CT scan thin section images of core samples to learn from corresponding geological facie classifications. Once a deep learning model learns from these CT scan images, the resulting model is used to predict subsequent geological facies using CT scan images.

Results show misclassification rates at approximately less than 30% which are more favorable to error rates associated with a single person classifying facies. In addition, unsupervised machine learning results show robust clustering of geological facies. Machine learning and deep learning show the value of utilizing CT scan parameters and images in unconventional oil and gas reservoir workflows. While typically used in other areas such as cancer detection, CT scans in conjunction with machine learning and deep learning can drastically reduce the error rate and time it takes to fully categorize an unconventional play.

Current methodologies involve having a team of petrophysicists who characterize core samples at every specified CT scan resolution depth. These workflows can be cumbersome and time-consuming for a team of petrophysicists and at times result in facies classification governed by human bias. The proposed methodology in this work drastically reduces human error and the time to understand geological facies. As a result, business decisions can be made quicker. These models are expected to provide substantially more accurate and consistent geological facie predictions.

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