Using Edge Computing and Autonomous Control to Manage and Optimize Well Performance in Cyclic Steam Stimulation Operations.

Abstract

The age of Production 4.0 has made possible the collection of large amounts of data. Proper analysis and eventual effective utilization of this data is still going through its "trial and error" period. This is where autonomous control systems can utilize the information being gathered continuously and assist in making real-time decisions that would optimize well production, reduce surface and sub-surface equipment wear, maintain production sustainability (reduce well downtime) and provide economic benefit, all without human intervention. The controller agnostic Edge IoT platform provided "out-of-the-box" and customized autonomous control to analyze daily average operational data and make recommendations and implement set point changes to manage well optimization and operations. A multitude of different instrumentation was also utilized to determine how additional data would assist in further optimization of well operations and well management through exception. Additional instrumentation included a different controller than the incumbent in the field along with wired and wireless load cells, inclinometers and a regenerative Variable Frequency Drive (VFD). The observation period of the pilot lasted approximately 7 months which encompassed the majority of the active production cycle of the Cyclic Steam Stimulation (CSS) operated 24 well pad. 16 wells had the Edge IoT platform installed on them whereas the remaining 8 were "control" wells which were managed as per standard operating procedures (SOP) by operations. Analysis of data from dynamometer cards, average surface pumping unit speed and average pump fillage in relation to target speed, target pump fillage and associated minimum and maximum limits, led to implementation of set point recommendations. Field results indicated that the Edge IoT platform was successful in making real-time decisions that led to increased production. Advantages and challenges were both observed in regard to different instrumentation piloted. The next generation Edge IoT platform with its system analysis methods, high frequency data access, customizable autonomous control logic and real-time alerts, allows for better data granularity and optimization of well production and operations.