MACHINE LEARNING SOLVES DRILLING PROBLEMS.

 
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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 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[degrees]C; perform downhole bit dysfunction identification using machine learning; and transmit near-bit data using high-speed short-hop EM telemetry.

The system will use machine learning algorithms hosted on a near-bit embedded computer to identify incipient bit dysfunctions and pass them to an MWD telemetry system via a high-speed EM short-hop link. This bit dysfunction information will be transmitted to the surface using E- Spectrum's popular Drill Dog MWD telemetry platform which is being...

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