The objective of gas lift optimization is maximizing the current output from a well by addressing the relationship between oil production rate and gas injection rate. The key parameter describing this relationship is the marginal increment in oil production rate per unit change in the gas lift injection rate. Under-injection of gas can lead to production rate loss due to insufficient reduction in gravitational head of production fluids. In contrast, over-injection of gas leads to high frictional head and wellhead pressure creating additional back-pressure on the formation, resulting in production loss.
The major factors unsettling this fine balance include reservoir pressure or static bottom hole pressure (SBHP), productivity index or in ow performance, wellhead pressure, gas injection depth, produced Gas-Oil ratio, Water Cut, oil API gravity, tubing diameter, and tubing roughness (i.e. friction factor). Some of these factors are not continuously measurable, and in many cases, estimates are provided based on old measurements, proximal well properties or through engineering supposition. The variability and transition in the well behavior further complicates the equation.
Additionally, variability in production rates, Gas-Oil ratios, and Water Cuts in a short span of evaluation may be due to the individual well’s daily production rates not being physically measured but mathematically allocated. The trending or transition in production rates and associated parameters can be due to the natural decline of the well or due to other interventions such as a frac hit, workover or restimulation. Considering all this, an injection rate which is optimum at a given point in time may not be so at a later stage. This challenge leads production engineers to attempt to understand the underlying state and represent the well using physics-based models.
At OspreyData, we have an excess of petroleum expertise in gas lift optimization. If you are interested in more of our thoughts on gas lift optimization, then request our white paper entitled “A Machine Learning Approach to Automate Gas Lift Optimization” written by our Chief Data Scientist, Mike Pennell, and two key contributors and Data Scientists, both Petroleum Engineers, Venkat Putcha, PhD and Nhan C. Le. Feel free to comment or ask questions about our white paper below in our comments section. We would love to hear your thoughts and begin a conversation.