Evaluating Data-Driven Techniques to Optimize Drilling on the Moon

Abstract Several companies and countries have announced plans to drill in the lunar South Pole in the next five years. The drilling process on the Moon or any other planetary body is similar to other exploration drilling by using rotary drills, for example the oil and gas drilling. However, the key performance indicators (KPIs) for this type of drilling are significantly different. This work aimed to develop the drilling optimization algorithms to optimize drilling on the Moon based on the experiences with the terrestrial drilling in related industries. A test drilling unit was designed and fabricated under a NASA Early Stage Innovation (ESI) grant; A high-frequency data acquisition system was used to record drilling responses at 1000 Hz. Parameters like weight on bit (WOB), torque, RPM, rate of penetration (ROP), mechanical specific energy (MSE), field penetration index (FPI), and the uniaxial compressive strength (UCS) were recorded for 40 boreholes in the analog formations. This work utilizes the large dataset comprising of more than 1 billion data points recorded while drilling into various lunar analogous formations and cryogenic lunar formations to optimize power consumption and bit wear during drilling operations. The dataset was processed to minimize the noise. The effect of drilling dysfunctions like auger choking and bit wear was also removed. Extensive feature engineering was performed to identify how each of the parameter affects power consumption and bit wear. The data was then used to train various regression algorithms based on the machine learning approaches like the random forest, gradient boosting, support vector machines, logistic regression, polynomial regression, and artificial neural network to evaluate the applicability of each of these approach in optimizing the power consumption using the control variables like RPM and penetration rate. The best performing algorithm based on ease of application, runtime, and accuracy of the algorithm was selected to provide recommendations for ROP and RPM which would result in minimum power consumption and bit wear for a specific bit design. Since the target location for most lunar expeditions is in permanently shadowed regions, the power available for a drilling operation is extremely limited. The bit wear will significantly affect the mission life too. Algorithms developed here would be vital in ensuring efficient and successful operations on the Moon leading to more robust exploration of the targeted lunar regions.