Featured in the American Oil & Gas Reporter: Machine Learning Helps Production Engineers Optimize Artificial Lift

Artificial lift optimization is one of the core activities that production engineers and technicians are asked to perform on a daily basis. The emphasis on optimizing the performance of artificial lift systems is particularly important in today’s operating environment with operators restarting shut-in wells and focusing like never before on improving the overall economics of every producing asset.

By Far the most widely used type of artificial lift, rod lift is deployed on horizontal and vertical wells alike in all types of conventional and unconventional fields. Because of the prolific application of rod lift systems, a strong body of best practices exists for designing, operating, and maintaining beam pumps and rod strings. When operators closely adhere to these operational best practices, meaningful increases in field profitability of even the lowest-producing wells often follow.

Despite their differences in the horizontal development and vertical legacy well context, industry best practices to optimize rod lift around efficiency have a consistent logic and methodology. First, wells are diagnosed as underpumping, dialed in or overpumping. Second, based on well classifications, remediating actions increase production or lower the number of wasteful, damaging strokes into the system.

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