Quantitative assessment of rock lithology from gamma-ray and mud logging data

Abstract

The data on vertical variations of the rock lithological content along the geological profiles of boreholes are essential for interwell correlation, geological or hydrodynamic modeling, optimization of drilling process, etc. There are direct (investigation of core samples) and indirect (processing and interpretation of well-logging data) methods applied in petroleum engineering to determine rock lithology. Both approaches have several limitations (such as low quality of logging data in case of washouts, low frequency of sampling of drill cuttings, limited number of wells drilled with coring, etc.) calling for alternative approaches to determine rock lithological content along the borehole. One of the alternative solutions could be application of mud logging (drilling and mud gas data) due to the strong physical background of interrelations between drilling and mud gas data on one side and rock lithology on the other. Literature review shows that many of existing machine learning-based solutions assume application of mud logging data mainly for rock typing (qualitative assessment of rock lithology). Concurrently, many applied problems require quantitative data on rock lithology (volumetric concentrations of rock lithological components). To fill this gap, we assessed the capabilities of mud logging data for quantitative characterization of rock lithology. We proposed a new algorithm for the task at hand relying on special data processing and machine learning algorithms. As an input data we used drilling, mud gas, and gamma-ray data. The results of the developed algorithm application for three industrial wells confirmed the effectiveness of the developed algorithm. During our research we compared the performance of XGBoost algorithm and 1-D convolutional neural network. Obtained results allow concluding that the obtained regression models and developed algorithm exhibit good generalization ability providing the average total prediction uncertainty on unseen well of less than 0.15 c.u.