Case Studies in Deriving Holistic Insights from an Automated Drilling Optimization Platform

Abstract

The last decade has seen a tremendous increase in the use of oilfield data to understand problems of well construction, evaluate drilling performance and derive guidance on future drilling. Despite the tremendous increase in investment and application of various data mining techniques, the outcomes in the well construction domain have been subpar when compared to that of other industries. Yet analysis of drilling data has resulted in useful predictions on the small scale–comparing a few wells where the huge number of variables that influence drilling (formation, rig, bottomhole assemblies [BHA], etc.) have little fluctuation. However, making actionable predictions on a much greater scale, and in the presence of significant variability, is substantially more challenging. The authors introduce a new platform for automated drilling analysis and optimization. The system starts with an enhanced in-bit measurement device that enables an improved view of downhole conditions at a higher density. This data is then tied to surface measurements through robust data validation and streamlined capture of variables. Finally, the platform ties state-of-the-art, physics-based models, which capture the physics of drilling from cutter-rock interface, to power delivery from the rig through the BHA to quantify the influence of each variable. This coupling of physics-based modeling and models based on drilling data is used to deliver key holistic insights–not just on speed of drilling, but also balance it against borehole quality, efficiency and health of the tools. Results of this new optimization platform, which was tested in the SCOOP and STACK plays of Central Oklahoma, are presented in this paper. The platform shows the power of combining physics and measured data to detect hidden insights, derive actions and enhance drilling performance.