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.

The purpose of this paper is to demonstrate the power and business benefits of leveraging online analytical processing (OLAP) cubes in the utilization of high-level data analytics and data dashboards from an established drilling record system (DRS). The DRS contains over 1.4 million wells, including 75,000 offshore wells drilled worldwide since 1980 with nearly 5 million total bottomhole assembly (BHA) runs from over 100 countries. Since 2009, over 1.5 million BHA runs drilling 2.6 billion feet of formation have been captured. Being able to visualize and understand the drilling data allows for increased efficiencies, reducing the days on wells for operators from deepwater to inland barge and land drilling worldwide. The development of the OLAP cubes required a multidisciplinary team consisting of software developers, business managers, domain champions, field-based engineers, and data scientists. The OLAP cubes consist of multidimensional databases built from relational and algorithmic interpretations of DRS transaction data. These algorithms are generated and developed by an iterative cycle of continuous improvement, development, and utilization of the OLAP cubes in parallel to improve the functionality and business impact for performance analysis, sales, product development, product reliability, and marketing. The data can be analyzed and visualized in the Microsoft Office suite by directly querying the DRS OLAP cubes. This also allows for dashboards to be updated in real time as data are added to DRS. OLAP cubes have been developed to analyze the performance of drill bits, motors, reamers, rotary steerable tools, and many more downhole tools. The DRS cubes assist in identifying failure causes on bits to identify high-risk intervals to better target products and parameters to reduce costly nonproductive time. Fit-for-purpose OLAP cubes have been developed to understand drilling efficiencies and strategies in multibit versus single-bit sections using variable trip speeds and field performance. Traditional business reports were made more efficient and auto-updated and dashboards were built to identify major business trends to equip business managers. This OLAP cube development has allowed for increased usage of the world's largest drilling record database and has made it easier to access and analyze the data. Ultimately, the techniques and development described in this paper help answer business questions to make better business decisions through data-driven analytics.