A Case Study for Evaluating Effective Geomechanical Parameters and the Influence of the Biot Coefficient on the Stability of a Wellbore Wall in the Asmari Formation using Machine Learning

In oil extraction projects, knowledge of reservoir geomechanics is essential for estimating the stability of wellbore walls drilled at great depths. In this regard, mechanics of porous media according to the definition of Biot’s coefficient instead of Terzaghi effective stress can provide a more accurate estimate compared to other analyses. Additionally, using artificial intelligence and machine learning algorithms such as XGBoost and optimizing it with algorithms like Bayesian, along with using SHAP algorithm as an interpretable AI model, can provide us with deeper insights into available data. In this research, 1st geomechanical data of a well in Asmari formation in southwest Iran was obtained through well logs and operational reports and then analyzed by machine learning. Also, a 1-way coupled reservoir rock-fluid model was built to investigate the volume of fractured rock around the well. The interpretation of machine learning results helps us better understand the parameters affecting instability in this well’s wall. Moreover, finite element model results indicate that assuming a value equal to 1 for Biot coefficient (or Terzaghi effective stress) leads to incorrect results and overestimates the volume of fractured rock around this well up to 13 times more than actual values. Therefore, any proper analysis regarding wellbore wall stability and evaluating effective stresses requires accurate knowledge about real and existing values of this coefficient due to simultaneous behavior of stress and pore pressure. HIGHLIGHTS One of the major challenges in the oil industry is the factors affecting the stability of the walls of oil wells during the extraction process, which identifying and addressing these factors can greatly assist in reducing extraction costs Nowadays, the collection of precise and extensive data through well-logging is a reliable source for assessing reservoir geomechanics, which, alongside machine learning can help us better understand reservoir geomechanics Given the conditions of hydrocarbon reservoirs, the mechanics of porous media, along with the definition of the Biot coefficient, can provide us with a better understanding of how shear failure occurs in well walls The XGBoost algorithm is useful for data analysis through regression and classification, as it can easily be optimized using the Bayesian algorithm and interpreted using algorithms such as SHAP Assuming a value equal to 1 for the Biot coefficient, in comparison to its true and in-situ value can cause many errors in estimating the stress-pore pressure interaction and the sanding rate of the well due to shear failure in it GRAPHICAL ABSTRACT