Applying Artificial Intelligence to Optimize Oil and Gas Production

Abstract

The Internet of Things (IoT) — combined with advances in sensor technology, data analytics, and artificial intelligence (AI) — has paved the way for significant efficiency and productivity gains in the oil and gas industry. One application, in particular, has been proven to benefit from these technologies: electrical submersible pumps (ESPs). It's well understood across the E&P industry that nearly all wells must eventually incorporate some form of artificial lift to continue production, and ESPs drive about half of that. Although ESPs are designed to operate in harsh conditions, such as corrosive liquids, extreme temperatures, and under intense pressures, they can fail. Costs for repair or replacement are high but are usually dwarfed by the cost of lost production. In some cases, especially offshore, that cost can run into millions of dollars per day, including idle operational resources and output losses. This paper explores a unique AI-based application that enables operators to preempt costly ESP failures, while optimizing production at the same time. To illustrate, a use case will be shared. As a proof-of-concept and later a pilot project in an onshore oilfield, 30 ESPs driven by pumps ranging in power from as low as 200 kW to as high as 500 kW were deployed and monitored using an AI-supported predictive maintenance model. The positive results are applicable to offshore applications. In one case, the probability of an ESP failure was determined 12 days before an actual failure of the ESP occurred.