Modeling with Confidence: Leveraging Conformal Prediction for Calibrated Machine Learning Based Mechanical and Petrophysical Models

Machine learning (ML) is revolutionizing reservoir characterization practices by directly using well log and drilling data. However, deterministic predictions of ML models can be misleading and result in expensive mistakes. Hence, uncertainties in ML-predicted mechanical and/or petrophysical properties need to be well quantified. By leveraging a distribution-free and computationally efficient uncertainty quantification method called Conformal Prediction (CP), we can derive calibrated ML models and quantify the uncertainties in their predictions. Several ML models are developed for permeability and elastic modulus prediction in a geothermal site in south Saskatchewan. The Catboost model outperforms other models achieving an R2 of 0.91 and 0.92 for permeability and elastic modulus, respectively. A Conformal Prediction is then built on the selected ML models to complement the predictions with valid measures of prediction intervals with 95% coverage. For a test well, where two different lab-measured permeabilities exist, more than 90% of measured permeabilities fall within the 95% prediction interval. Triaxial geomechanical test results are also comfortably within the bounds of the 95% interval. This suggests that these models provide reliable predictions with limited uncertainties. This paper underscores the crucial role of uncertainty quantification of ML-based prediction models. The study demonstrates how quantifying uncertainty can enhance our confidence in ML-predicted reservoir properties for rigorous subsurface reservoir characterization.