Hydrocarbons have powered economic growth for 150 years, but their emissions are destabilizing the earth's climate. Now that the atmospheric impact of fossil fuels is widely recognized, the sector is under increasing pressure. Policy makers, investors, and society are pressing for change, threatening operators’ license to operate.

Operators have responded with strategic convening and conspicuous investments in innovation and diversification. Yet they have barely begun to address the 4.1 GtCO2e of emissions—almost 10 percent of all anthropogenic greenhouse gas—created every year by their own operations, two-thirds of it from upstream activities.

Technologies to optimize and decarbonize the extraction and production of hydrocarbons already exist and many are economically viable, yet the sector's atmospheric emissions continue to rise.

This paper describes a comprehensive field operating concept that leverages latest technologies — edge computing, data analytics, artificial intelligence (AI) to optimize wells using artificial lifts. Our research and pilot onshore deployments have focused initially on sucker rod pumping (SRP) models because they are a predominant solution for maintaining the productivity of mature, low-pressure wells. We are confident our concept can be applied also to other artificial lift technologies like electric submersible pumps (ESPs) or progressing cavity pumps (PCPs), whether onshore or offshore.

The solution is based on an open and scalable software stack that is capable of performing advanced analytics and softsensing to calculate pump conditions, trigger alerts, and optimize production from wells using artificial lift technology. What's more, with the addition of secure connectivity capabilities, E&P operators of multiple wells, even thousands of them, can gain remote fleet management and enterprise-wide visibility of their artificial lift operations.

Advances in data analytics and sensor technology have opened the door for industrial asset management and optimization across all vertical markets, including oil and gas. Edge computing, data analytics, artificial intelligence (AI), and Internet of Things (IoT) connectivity make it possible to scale up and optimize whole fleets of equipment. The machine learning aspects covered in the pilot implementation made the effective motoring of sucker rods feasible.

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