Automated Offsets for Drill Bit Performance Evaluation, Analysis, and Monitoring At-Scale

Abstract

Utilizing big drilling data requires an innovative approach. The service company’s drill bits business is largely based upon an in-house drilling record system (DRS) that captures global bit record performance data. The DRS contains over 1.8 million wells drilled worldwide since 1980 with nearly 5.4 million total BHA runs from over 100 countries. In the last 10 years alone, over 1.4 million bit runs drilling over 2.8 billion ft of formation have been recorded. To utilize this vast amount of data for drill bit performance evaluation, analysis, and monitoring, the innovative approach described in this paper was developed and implemented. Traditionally, the performance of a drill bit run–often measured in terms of drilled footage and ROP–has been evaluated versus similar offset runs. Offset runs are chosen in various ways, but are typically done manually by bit engineers, meaning that offset run selection is subjective based on personal experience and bias. Furthermore, people often only evaluate the performance of test bit designs. Instead, we wanted to analyze and monitor the performance of all drill bit runs. To alleviate these biases and enable a wider breadth of considered runs, an objective offset run selection workflow was developed and implemented within DRS. Offset runs are selected based on a sophisticated filtering and scoring routine that considers many characteristics such as geographic location, time, wellbore and drilling system design, along with lithology. As new data enters DRS continuously, this workflow runs on a regular basis using an automated pipeline. The performance evaluation results of the automated offset selection workflow are available to all data analysts (engineers and salespeople) both inside DRS and extensible applications to aid in performance monitoring and new product development target-setting. Product performance is now objectively evaluated at-scale across geographies and always utilizing apples-to-apples comparisons. The workflow has proven itself quite useful and delivered business value already but also exemplifies the need for both enhanced data quality and improved bit record data capture rate. These are ongoing efforts to further enhance and improve this workflow. Automated workflows like this one can help our industry by eliminating repetitive biased tasks and allowing people to focus on more creative processes leveraging objective data. Developing new drill bit designs, material selections, or component selections to overcome new challenges are creative processes which contribute to increased drilling performance and lower costs for the industry.