Real-Time Decision Making for ESP Management and Optimization

Abstract

Abstract It is estimated that 95% of oil wells globally use Artificial Lift and 2014 statistics show that Electrical Submersible Pumps (ESPs) represents 43% of the global annual artificial lift spend[0]. Occidental Petroleum is a large user of ESPs, with over 2,200 in West Texas alone, and while ESPs can produce at high rate, they are also high cost. In today's low oil price environment, it is imperative that ESPs are operated efficiently to produce at the lowest cost per barrel. When applying ESPs to unconventional wells, efficient production is even more important with the added operational complications of producing frac sand, high gas/oil ratio (GOR) reservoirs, and rapidly changing flow rates. These three factors can dramatically shorten ESP run lives if not properly managed. Providing surveillance, monitoring, and optimization to Occidental's more than 400 unconventional wells required improved field data delivery systems, surveillance practices, and optimization techniques to meet this challenge. To achieve this, Occidental internally-developed a system for real-time ESP decision making that combines real-time data transmission, a data analytics tool, and an ESP analysis engine, all supported by trained staff and engineering workflows. By pulling all of these components together in one platform called "OxyLIFT," production engineers have a tool to enable rapid problem detection and diagnosis. The OxyLIFT system represents a major step forward in real-time decision making. By performing an automated analysis process on thousands of ESPs, Occidental can take proactive action on production losses at any point in the ESP's operational phase, as well as enabling semi-predictive failure analysis. Most importantly, the system highlights proactive ESP replacement opportunities which minimizes both production downtime and equipment losses. This system can be applied to any type of ESP well however the challenges associated with an unconventional well will be specifically investigated and explained with multiple case studies.