Predict Geomechanical Parameters with Machine Learning Combining Drilling and Gamma Ray

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 has been applied to different geological contexts, both in conventional – carbonates and siliciclastic – and in unconventional, and it proves its successful use to derive several geomechanical parameters, among which the results associated with Young’s Modulus, density, UCS and Poisson’s Ratios are here presented.

BIOGRAPHY:

Eliana Russo has overall 9 years of experience in the Oil&Gas Industry. She earned an MSc Degree in Chemical Engineering at the University of Naples, Federico II. She started in Geolog with Fast Track training program and worked as a field Data Engineer/Unit Manager in Europe and Latin America. She was recruited to the HQ in Milan working in the R&D Department and as GeoFracture Specialist supporting Drilling Dept. In 2019 she joined Geolog Technologies as R&D Engineer/Project Manager to develop new technologies for multiphase drilling fluid flow rate and rheological measurements, fractured reservoir modeling, and geomechanical parameters prediction. She is currently Global Account Manager and Business Development Manager for Italy in Geolog.

Eliana has several studies published in different journals and conferences.