Refining Oil and Gas Discovery with Deep Learning
Over the last two years, we have highlighted deep learning use cases in enterprise areas including genomics, large-scale business analytics, and beyond, but there are still many market areas that are still building a profile for where such approaches fit into existing workflows. Even though model training and inference might be useful, for some areas that have complex simulation-driven workflows, there are great efficiencies that could come from deep neural nets, but integrating those elements is difficult.
The oil and gas industry is one area where deep learning holds promise, at least in theory. For some steps in the resource discovery workflow, deep learning could lead to faster and more accurate results for potential discovery zones. Reservoir characterization is a critical step in this discovery process and is currently a hot area for explorations into how deep learning might be applied. Characterization uses seismic image data to analyze various features of potential resource sites. Making this process accurate is critical, hence a great deal of supercomputing might goes into resource discovery to ensure drilling investments are not wasted on sites that do not yield.
Machine learning methods have been applied to reservoir characterization for decades already, but these models are becoming more sophisticated and can benefit from algorithmic advances in deep learning. As one group of researchers notes in their analysis of existing machine learning methods for reservoir characterization, the very problem of reservoir uncertainty is better suited to machine learning and deep learning than traditional numerical approaches.
They go on to note that these approaches get these results by “establishing non-linear relations between the log measurements and the core values for prediction—techniques that have been reported to outperform the statistical regression tools.”
Existing approaches to machine learning include the support vector machines (SVM), self-organizing maps, decision trees, ensemble methods (random forests, case-based reasoning, and multivariate adaptive regression splines. As a group from the China University of Geosciences describes, other methods, including artificial neural networks also play a role but these use cases are still somewhat limited.
Of course, as this team, among others point out, there is still the perception that newer methods for reservoir characterization and other oil and gas workloads create a “black box” problem. In short, more development is needed for neural networks in particular before they enter production. There are companies working towards this, including global oil and gas giant, Chevron.
Although standard machine learning techniques have a long history in the reservoir characterization workflow, deep learning is something of a newcomer. Researchers at Chevron are among several companies considering the ways deep learning might fit into existing reservoir characterization workflows.
