Comparative analysis of machine learning techniques for predicting drilling rate of penetration (ROP) in geothermal wells: A case study of FORGE site

Enhanced Geothermal Systems (EGS) present a compelling means to unlock the considerable yet largely untapped thermal energy within the earth's crust globally. The EGS drilling cost constitutes a substantial portion, up to 60 %, of the overall expenses. Consequently, streamlining and optimizing the drilling processes for these systems hold immense economic significance, with the potential to substantially lower costs, advance the utilization of geothermal energy, and contribute significantly to the reduction of carbon emissions. This research aims to enhance EGS drilling operations by seeking ways of reducing drilling cost. In this research, we harnessed the predictive power of 10 state-of-the-art machine learning (ML) algorithms to anticipate a crucial drilling parameter: ROP. Using the FORGE dataset, we developed a code tailored for the intensive preprocessing of drilling data. This code offers many options, including various noise-removing techniques and scaling approaches. However, the primary focus of our work extends to quantifying uncertainties intrinsic to the predictions of the 10 algorithms employed. To achieve this, a comprehensive approach involved subjecting each algorithm to 40 runs, utilizing the best model from the tuning process. The results show that unaddressed uncertainties may lead to unstable model behavior, where small changes in the input data or random initialization result in significant prediction variations. Thus, models that do not account for uncertainty may be overfit to the noise in the training data, leading to poor generalization to new, unseen data. Remarkably, the results highlight the superiority of the Extreme Tree, Light Gradient Boost, Random Forest, and Gradient Boosting algorithms, showcasing mean absolute error (MAE) and R2 values within the ranges of 3.5-4.5 and 0.9-0.95, respectively. Conversely, artificial neural networks and support vector machine algorithms demonstrate comparatively lower performance, with MAE ranging from 4.7 to 6.3 and R2 from 0.85 to 0.91. Although the results presented in this work are only based on the drilling parameters from one well of the FORGE site and do not include rock properties or geological parameters, however, the proposed nuanced understanding of algorithmic performance is valuable for refining predictive models in geothermal drilling applications, ensuring robustness and reliability in the face of diverse operational scenarios. Navigating a successful workflow, adeptly processing data, and meticulously quantifying uncertainties linked to each predictive algorithm emerge as formidable yet indispensable tasks. Our research not only sheds light on these complexities but also paves the way for a strategic optimization approach based on data-driven ROP models. Therefore, the potential for substantial savings in real-time drilling operations becomes apparent, unlocking a new realm of possibilities for the geothermal energy sector.