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Advantages of Automating Gas Lift Optimization

There are many advantages to automating gas lift optimization, but we’ll discuss just a few here. When considering machine learning approaches, there are too many wells and too few engineers to constantly monitor, analyze, update and evaluate the gas injection rate to establish optimal lift performance. A typical simulation model development followed by parametric analysis to decide a gas injection rate may potentially require close to half a day of work for a qualified engineer. This is due to the efforts in the collection of sensor data, completion and lift design data, reservoir, and fluid properties, decision making
to select the range, distribution, and granularity
of modeling parameters, history matching or validation, and deciding the gas injection set-point value.

In the case of unconventional wells, the static bottom hole pressure (SBHP) and productivity index are transitioning and difficult to measure. Hence, extreme discretion is required to perform deterministic nodal analysis. We contend that a probabilistic approach that learns from the history of the well using inverse modeling offers a more reliable solution.

Updating the operating gas lift set-point is required several times over the life of the well. This can be due to natural effects such as decline, or due to intervention such as restimulation
or workover. Accounting for these factors, the work hours invested can be about 7-10 days per engineer per well annually for human-driven simulation analysis. For a 100 well field, this translates to approximately 2-3 engineering
years. With the high volume of wells, it becomes unlikely for production engineers and well managers to undergo this intensive process on a proactive and regular basis.

This leads to a state where the optimization process is performed infrequently, losing out on production, or injection gas, or both. Popularly, rules of thumb based
 on the experience of personnel can dominate 
the decision-making. Such an approach can
 be subjective to the level of diligence of the individuals managing the wells, who need to astutely evaluate if the conditions under which the rules of thumb were formulated continue to hold. In order to provide a solution that scales up to the challenges of such a problem, automation is necessary.

If these benefits resonate with you, then we would love for you to request our white paper entitled “A Machine Learning Approach to Automating Gas Lift Optimization” written with two key contributors from OspreyData – both Data Scientists and Petroleum Engineers, Venkat Putcha, Ph.D. and Nhan C. Le. By clicking the link, you can request our paper to learn more about our human augmented approach to machine learning.