Publications

Advanced analytics of rig parameter data using rock reduction model constraints for improved drilling performance

Raymond, David W.; Foris, Adam J.; Norton, Jaiden; McLennan, John

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. A method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improved decision-making in the field and to further discern technology performance during post-drilling evaluations. Drill rig parameter data were acquired by drilling contractor Frontier Drilling and evaluated for two wells drilled at the DOE-sponsored site, Utah Frontier Observatory for Research in Geothermal Energy (FORGE). The subject wells include: 1) FORGE 16A(78)-32, a directional well with vertical depth to a kick-off point at 5892 ft and a 65 degree tangent to a measured depth of 10987 ft and, 2) FORGE 56-32, a vertical monitoring well to a measured depth of 9145 ft. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, develop rock strength approximations, determine bit aggressiveness, characterize frictional energy losses, evaluate bit wear rates, and detect the presence of drillstring vibrations contributing to bit failure; comparisons are made to observations of bit wear and damage. Analyses are also presented to correlate performance to bit run cost drivers to provide guidance on the relative tradeoff between bit penetration rate and life. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs.