Enabling Autonomous Pump-by-Exception For Stripper Wells Using IOT-Enabled Edge Controllers

Optimization set point changes are repetitive and time consuming, let the machine do it.

When:  August, 28th 2018 9:00 AM CST - 9:45 AM CST

Speaker: Brian Arnst, Director of Optimization


Tens of thousands of low producing legacy or stripper wells operate throughout oil producing basins across North America. The overwhelming artificial lift type for this well set is sucker rod or beam lift due to its superior bottomhole pressure drawdown capability. While sucker rod lift can economically produce stripper wells even in terminal well decline conditions, a significant challenge lies in the daily operation and optimization of large numbers of wells.

The economics of a well producing less than 5-10 barrels of oil per day are lean as is. Further eroding those economics is the cost of conventional instrumentation and automation needed to enable pump by exception. The result is operators generally deploy a percent timer to control well runtimes and use daily field operator wellsite checks. The outcomes of a lack of real time visibility, no remote control or callout system, and incomplete data to optimize wells are higher than desired downtime, a wide spread in timer settings, and fewer wells dialed in across the field. This comes with the added high opportunity cost of daily wellsite checks where manpower could be used for higher value activities.

A North Amercian operator in the Permian Basin recognized these pain points and turned to Ambyint for support. An IoT-enabled edge controller was deployed and assumed control of the wells' on/off cycles from the percent timer previously in place. Since the edge controller is a true IOT implementation, it includes embedded communications that directly connect to the optimization platform via the cloud. This enabled real time visibility, remote control and a callout system to alert when wells are shutdown outside of timer settings. Furthermore, Ambyint's controller includes the capability to autonomously optimize on/off cycles to dynamically move wells from overpumping or underpumping states to an optimized or dialed in state.

In the end, the results included:

  • 142 timer recommendations sent with 100% acceptance rate; deep customer engagement helped create significant results
  • Production was maintained with no negative impacts
  • 54% increase in pump efficiency 
  • Power consumption reduced by 58%
  • Cycling reduction by 300%
  • Stroke count reduced by 65%
  • Run life increased by 139%
  • 6 month payback on ~2 BOPD wells

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