THE AGE OF ANALYTICS
Machine learning at scale will be the norm Engineering ranks said to embrace the emerging discipline By Kevin Parker
been a ‘ We’ve little surprised at the sheer volume of demand.
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n September, Baker Hughes Co. and C3.ai launched BHC3 Reliability, the first artificial intelligence (AI) software application developed by the BakerHughesC3.ai joint venture. More recently, Baker Hughes, C3.ai and Microsoft in November announced an alliance to make adoption of advanced analytics easier by bringing together cloud infrastructure, an AI platform and domain-specific applications. “Use of analytics, machine learning and artificial intelligence is not new to the oil & gas industries. But today, we’ve reached an inflection point, to deploy these technologies at scale,” said Dan Brennan, SVP & COO, BakerHughesC3.ai. Baker Hughes is a nearly $23 billion provider of integrated oilfield products, services and digital solutions. C3.ai is an AI software provider. The core of the C3.ai offering is a model-driven AI architecture that enhances data science and application development. In June the two companies announced a joint venture to combine Baker Hughes expertise with C3.ai’s AI software suite, for application in the oil & gas industry. BHC3 Reliability machine-learning models identify anomalous conditions that lead to equipment failure and process upsets. Application alerts enable proactive action by operators. BHC3 Reliability can scale to assets and processes across offshore and onshore platforms, compressor stations, refineries, and petrochemical plants, reducing downtime and increasing productivity. “If you look at the not-too-distant past,” Brennan said, “a lot of investment went into automating facilities to enable things like condition monitoring to reduce non-productive time. Extensive use was made of physics-based and rules-based models to better understand equipment operation. Therefore, we’re at a different point of maturity today, where more than 40% of unplanned downtime originates from non-critical equipment, which even today is not highly instrumented.“
4 • DECEMBER 2019 OIL&GAS ENGINEERING
Things and kinds of things The kinds of analytics include principles-driven and data-driven, with two kinds of each. Physics-based analytics incorporate the physical and thermodynamics laws of how things work, while rules-based analysis is a kind of principles-driven analysis based on observation and domain expertise, including failure mode effect analysis (FMEA). On the other hand, data-driven analytics include, first, statistical models based on techniques like linear and other type regression, and second, advanced analytics that include artificial intelligence and machine learning, often based on pattern recognition. Roughly put, the domain of principles-driven analytics are the pumps, heat exchangers and myriad other equipment types whose workings are well understood by the engineering community. Root-cause analyses are often part of the picture. On the other hand, the realm of machine learning and artificial intelligence is the complex problems and to-be-discovered challenges native to process, plantwide and enterprise systems, where custom configurations of complex systems lead to unknowns. Thus, while physics-based models still play a crucial role, Brennan said, that’s being augmented by a data-based approach, to better understand, for example, what a normal operating range is, or, drilling down in detail, to better understand the correlations involved. But while machine learning may uncover otherwise unrecognized correlations, it’s not necessarily able to recognize cause-and-effect relationships. One aspect of scale is the data amounts involved. A machine learning model can exist for any given valve or pump, and for every instance of it, which may involve hundreds or even thousands of instances, or it may be looking at a system of systems.
