Application of Data Analytics to Improve Drilling Performance and Manage Drill Stem Vibrations

Abstract

Abstract Data mining and Artificial Intelligence (AI) methodologies are underdeveloped in the oil and gas industry, despite the need to improve drilling performance and remain globally competitive in all capital-intensive projects. Drilling companies allocate significant resources to improve well planning, drilling schedules and rig management. Well planning comprises of two main elements; drilling performance and the reduction of drill stem vibrations. Therefore, modeling methodologies such as drill string statics, dynamic tools and rate of penetration modeling are applied to determine the optimum bottom hole assembly (BHA) components and drill bit design. However, more attention is required on drill stem fatigue, non-productive time (NPT) and their impacts on drilling operations. In this paper, Data Analytics (DA) is applied to drilling logs taken from three wells that recorded vibration readings from different geological stratification. In turn, the work in this paper establishes a relationship between drill stem vibrations and various measurement and logging data while drilling. Statistical regression and multivariate analysis were used to examine correlations of drilling parameters, including BHA assembly, to vibration data. Therefore, the results include a composite vibration model that describes the drilling stem vibration behavior as a function of drilling parameters, and geological formations. Results of the vibration models built in this study indicate that the drill stem lateral vibration behaves parabolically as a function of the drill pipe length, length of drill collar, gamma ray (GR) response, and weight on bit (WOB). The analysis of drill stem vibration effect on the mechanical specific energy (MSE) was inconclusive for depths below 1350 meters. However, for depths above 1350 meters a strong correlation was observed to ROP.