Efficiency and optimization in well drilling: an approach based on real-time analysis of mechanical parameters and access to natural energy resources

Abstract

Nowadays, the exploration of different deep subsurface natural energy resources, such as oil, gas, geothermal energy, and natural hydrogen, is becoming more important. However, the exploration of these deep subsurface resources can present several challenges, including the complex relationships among the drilling parameters, such as the weight-on-bit (WOB), drill-bit rotation speed (RPM), torque-on-bit (TOB), standpipe pressure (SPP), flow rate (FLOW), as well as the depth of the reservoirs, ultra-deep water, and lithology. In this context, new approaches focused on enhancing the drilling process must be developed and implemented. Thus, this report presents several important publications on the drilling process, focusing on optimization and efficiency analyses in well drilling activities based on rate of penetration (ROP) and mechanical specific energy (MSE). The publication's results demonstrated that, traditionally, the drilling industry uses two-dimensional (2D) analysis of drilling parameters as a function of depth to identify the parameters that most influence the process. An alternative approach to the 2D analysis is the response surface method (RSM), which offers a more comprehensive evaluation, integrating input and response drilling parameters in a three-dimensional space that better reflects the drilling operations. Multi-objective analyses go even further by simultaneously optimizing multiple response variables. The simultaneous combination of maximum ROP and minimum MSE through multi-objective optimization can enhance drilling efficiency by balancing penetration speed with energy efficiency and equipment reliability. The application of pre-operational tests, such as the drill-rate test (DRT) and drill-off test (DOT), allows simulation and estimates the drillability of the formations. This approach enables a better understanding of the possible parameters and design combinations to optimize drilling performance and enhance efficiency. Additionally, machine learning algorithms offer significant advantages for predictive modeling, exceeding the capabilities of traditional physics-based and statistical approaches in drilling performance prediction. These algorithms can acquire knowledge from extensive datasets and capture non-linear interactions, enabling them to identify patterns and provide predictions in the drilling process. In conclusion, these results demonstrate the importance of developing and implementing new methods and processes applied to the drilling engineering discipline. These advanced approaches enable more precise decision-making, operational cost reduction, and greater efficiency in resource exploration, making them indispensable in today’s technologically and economically challenging scenarios.