Derivation of rock's geomechanical parameters while drilling by combining surface drilling data, gamma ray data, and machine learning techniques in carbonate formations

Abstract

This work presents a field application of an effective and reliable methodology for geomechanical parameters evaluation, using surface logging drilling data (rate of penetration, torque, weight on bit, stand pipe pressure, rotation per minute, flow rates) and well log data (sonic log, bulk density log, gamma ray log) to feed a model characterised by the combination of different machine learning algorithms (multiple linear regression, support vector regression, random forest, artificial neural network, and XGBoost). To expand the range of application to those cases where the downhole tools are not technically feasible or economically viable, a flexible workflow has been developed to derive a Synthetic Gamma Ray (SGR) from X-ray Fluorescence (XRF) analysis performed on cuttings. The methodology, applied to a dataset of 11 wells drilled in the same geological units but in different fields in Kuwait, proves its successful use to derive several geomechanical parameters, among which the results associated with Young’s Modulus, density, and Poisson’s Ratios are here presented.