LAUSR

With the increase in the energy demand, the magnitude of energy production operation increased in scale and complexity and went too far in remote areas. To manage such a big fleet, sensors were installed to send real-time data to operation centers, where subject matter experts monitor the operations and provide live support. With the expansion of installed sensors and the number of monitored operations, the operation centers were flooded with a massive amount of data beyond human capability to handle. As a result, it became essential to capitalize on the artificial intelligence (AI) capability. Unfortunately, due to the nature of operations, the data quality is an issue limiting the impact of AI in such operations. Multiple approaches were proposed, but they require lot of time and cannot be upscaled to support active real-time data streaming. This paper presents a method to improve the quality of energy-related (drilling) real-time data, such as hook load (HL), rate of penetration (ROP), revolution per minute (RPM), and others. The method is based on a game-theoretic approach, and when applied on the HL—one of the most challenging drilling parameters—it achieved a root mean square error (RMSE) of 3.3 accuracy level compared to the drilling data quality improvement subject matter expert’s (SME) level. This method took few minutes to improve the drilling data quality compared to weeks in the traditional manual/semiautomated methods. This paper addresses the energy data quality issue, which is one of the biggest bottlenecks toward upscaling AI technology into active operations. To the authors’ knowledge, this paper is the first attempt to employ the game-theoretic approach in the drilling data improvement process, which facilitates greater integration between AI models and the energy live data streaming, also setting the stage for more research in this challenging AI-data domain.