Abstract

This presentation shows a robust and reliable approach for assessing geomechanical parameters, either during drilling operations or as a post-mortem analysis. The methodology utilizes surface logging drilling data—such as Rate of Penetration, Rotations Per Minute, Weight on Bit, Torque, Standpipe Pressure, and Flow Rates—alongside well log data, including Sonic Log, Bulk Density Log, and Gamma Ray Log. These datasets serve as inputs for the model, which integrates various data processing techniques and machine learning algorithms, including Multiple Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Networks, and XGBoost. This comprehensive approach ensures accurate and detailed evaluation of geomechanical parameters in different geological contexts.

Biography

Ivo Colombo is GEOLOG TECHNOLOGIES R&D Team Manager at GEOLOG Milan HQ in Italy, providing support and expertise for various projects in GEOLOG involving AI & Digital Solutions.

Ivo has been working in the Energy Industry for over 10 years, and has a Ph.D. in Environmental and Infrastructure Engineering from Politecnico Milano working on a project focused on the characterization under uncertainties of mechanical and geochemical compaction processes in sedimentary basins.

Since 2021, Ivo has been coordinating cross-functional teams in GEOLOG, focusing on research projects involving digital solutions, engineering, and geothermal activities. He is responsible for defining, developing, and promoting GEOLOG’s digital strategy and leading the Digital Solution Team.

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