Streamlining Petrophysical Workflows With Machine Learning

Abstract

The oil and gas industry is not short of data, in the form of wells, seismic and other geophysical information. However, often because of the complexity of workflows and the time taken to execute them, only a fraction of this information is utilized. Making better use of information, using modern data analytics techniques, and presenting this information in a way that is immediately useful to geologists and decision makers has the potential to dramatically reduce time to decision and the quality of the decision that is made. Here we concentrate on using machine learning approaches to streamline petrophysical workflows. However, to do this requires a rich and diverse training dataset of wells that have been consistently processed for geophysical analysis. The work discussed in this paper has focused on the estimation of clay volume, determination of mineral volumes and determination of porosity and water saturation. A variety of machine learning techniques and algorithms have been tested to find the one most suited to this application. Initial analysis is regionally focused, but we plan to investigate whether the approaches and models developed can be generalized across regions, basins and geological settings.