A new research grant awarded under the United States Department of Energy's Small Business Innovation Research (SBIR) program aims to develop technologies that can harness these large data sets to help drillers make time-critical decisions. E-Spectrum Technologies, a leader in the development of technology-driven telemetry solutions for upstream energy markets, in partnership with the Harold Vance Department of Petroleum Engineering at Texas A&M University, has been awarded a Phase I grant to begin development and commercialization of a machine learning-based drilling optimization system.
The objective of the grant is to develop and commercialize a real-time computer advisory system to help drillers make more effective decisions and optimize the Rate of Penetration (ROP) achieved during drilling operations. The advisory system will use transformational digital technologies such as distributed processing and machine learning techniques to quickly identify ongoing or incipient vibration and loading patterns that can damage drill bits and slow the drilling process. Features of the drilling advisor include the ability to: operate in geothermal wells at temperatures up to 250°C; perform downhole bit dysfunction identification using machine learning; and transmit near-bit data using high-speed short-hop EM telemetry.
