Enhancing Drilling Efficiency for Reduced CO2 Emissions Through Optimization of Mud Motor Power Section

Abstract

An approach that combines data-driven analysis and modeling was used to select the optimal mud motor power section tailored to specific drilling requirements. Additionally, a novel methodology was developed for estimating the CO2 footprint of drilling operations based on operational data. Practical demonstrations verified that the strategic utilization of the optimal power section enhances drilling efficiency and mitigates the CO2 impact associated with drilling processes. Our focus revolves around choosing the optimal power section for the drilling, considering both drilling conditions and limits. Additionally, we estimatethe CO2 footprint for the entire drilling process after the job to underscore the positive impact of selecting the right power section. To achieve these goals, we integrate an advanced physical model of the power section with the capabilities of machine learning and data science. This power section model empowers us to predict its performance and durability in advance, facilitating an optimal choice based on expected drilling conditions. The established workflow for CO2 footprint estimation utilizes surface drilling data, ensuring precise results. To optimize the power section for drilling, we employ a modeling method coupled with a machine-learning approach. This aids in selecting a suitable power section type and determining drilling parameters based on specific requirements and equipment specifications, contributing to heightened drilling efficiency. Leveraging digital capabilities enables strategic implementation to minimize greenhouse gas emissions. Enhancing drilling efficiency via optimal mud motor power section selection improves rate of penetration, reduces nonproductive time, and substantially cuts field failures. This cumulatively shortens total drilling time, leading to both direct and indirect CO2 emissions reductions. To gauge the carbon footprint during drilling operations, we construct a physical model. This model predicts rig power consumption by considering surface drilling parameters (as pressure, flow rate, etc.). It estimates power consumption by components like mud pumps, top drive, and draw works, using system efficiency data. Additionally, a model encompassing generator sets and diesel combustion is employed to estimate CO2 emissions. Application of the proposed methodology to a real field example demonstrated the impact of enhanced drilling performance with optimal power sections versus conventional ones, along with effective greenhouse gas emissions reduction. The entirety of the results and conclusions highlights the substantial value of digital technologies and a smart approach in selecting drilling equipment for the energy transition.