The digital transformation of the upstream energy industry has spawned exponential growth in the amounts and types of data generated during the process of drilling oil & gas wells. 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 market 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, have been awarded a $192,763 Phase I grant to begin development and commercialization of a machine learning-based drilling optimization system.
The objective of the grant is to develop a commercial 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 incorporate transformational digital technologies including 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. The system will identify incipient bit dysfunctions using a near-bit embedded computer to process critical down-hole sensor data which will be passed through a high-speed EM short-hop telemetry tool to be transmitted to the surface using E-Spectrum’s popular Drill Dog™ MWD telemetry platform. At the surface, a PC-based dynamic advisory application using machine learning and data mining algorithms will integrate this incipient failure information with numerous other data streams to provide drillers with critical real-time advice on how to set drilling parameters in order to optimize ROP and avoid damaging drill string components.
While the Phase I research is targeted at ROP optimization, the advisory system will be designed to be modular and scalable to allow future incorporation of expanded data inputs and data driven algorithms to address other drilling problems that are currently difficult or impossible to solve using physics-based models.