Unsung Hero: Artificial Lift Boosts Production Without Breaking the Bank
Authors: Pallanich Jennifer 
Published: October 2024

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In the quest to cost-effectively boost production, operators are experimenting with artificial lift methods and how digital tech can boost its performance.
Artificial lift experts are increasingly adding artificial intelligence (AI), machine learning (ML), and autonomous operations into their workflows because these data-driven technologies are demonstrating they can generate uplift when they are thoughtfully developed and deployed. Operators are also looking at the performance of high-pressure gas lift (HPGL) wells compared to wells using electrical submersible pumps (ESPs) for artificial lift. Finally, operators sometimes have to decide whether to risk replacing equipment that seems to be functioning, even if at subpar levels, in a bid to increase production.

Digital Boost
Optimized gas lift workflows and autonomously implemented ESP set points are helping ExxonMobil and Vital Energy produce more oil.
Gas lift is a popular artificial lift method partly because the system is simple, robust, and inexpensive to operate, but optimizing it has historically been time-consuming. SPE 219553 outlines ExxonMobil’s approach to automating gas lift optimization for wells in the Permian Basin. The resulting workflow, which relies on AI and ML, generated a 2.2% uplift in production at the more than 1,300 wells where it was deployed, without requiring changes to surface or downhole equipment or generating additional work for personnel.
Derek Burmaster, production optimization engineer for ExxonMobil Upstream Integrated Solutions, said during SPE’s Artificial Lift Conference and Exhibition (ALCE) in August that one of the big benefits of applying the closed-loop iterative well-by-well gas lift optimization workflow is that it removes the need for an engineer or specialist to have to go into the field repeatedly to change injection rates and analyze the well’s data.
“For each well, that might take a few hours, at least,” he said. “We’ve done this on hundreds and hundreds of wells, and it happens every month without anybody, any manpower, or anything. So that’s one of the huge benefits of doing this in a data-driven manner.”
Gas lift is so robust that it can be challenging to diagnose if the system is operating in a suboptimal state, the paper noted. Changes in a well’s water cut or productivity can cause a previously optimized system to lose efficiency, which may not otherwise be known for years.
ExxonMobil was seeking a method to optimize gas lift that would continuously monitor the state of the system and respond immediately. The resulting closed-loop software iteratively performs multirate tests, analyzes the results, and remotely implements set-point changes to optimize the wells.
“The optimization process is most effective in an iterative form, in which each multirate test produces a model that suggests a given change,” the paper stated. While each model produces its own optimal injection rate, subsequent models may point to the same injection rate, and thus changes are not always made, Burmaster noted.
Even if an initially suggested set point is not optimal, it will “push the well in the right direction,” which improves the starting point for the next test and helps the well reach an optimal set point over the course of a few iterations, according to the paper.
The workflow was tested in two different stages, with the first 3-month stage requiring manual approval of the recommended set points.