Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Gas lift is one of the most widely used artificial lift methods, and the use of nodal analysis to generate the gas lift performance curve is well established. However, the optimal gas injection rate is often selected as the point with maximum liquid production, which neglects the cost of incremental injection gas volume. This paper investigates the determination of the optimal operational point using a multiobjective optimization technique by considering the trade-off between gas consumption and oil production. The indicator-based evolutionary algorithm transforms the multiobjective problem into a single objective one using the hypervolume metric computed in the objective space. For the gas lift problem, which is a bi-objective problem aimed at maximizing oil production while minimizing gas injection rate, the hypervolume metrics are identically equivalent to geometric hyperareas under the trade-off curve. The optimization is only applied to the monotonically increasing portion of the gas lift performance curve; thus, all trivial sub-optimal conditions are excluded. The optimal operational point of gas injection rate is determined by finding the maximum rectangular hyperarea under the performance curve. The proper determination of the optimal injection gas rate could not only improve the efficiency of the gas lift itself, but also reduce the burden on the maintenance of surface facilities. The method is also applied to the multi-well scenario where a novel multi-well gas lift performance curve is generated using multiobjective Genetic Algorithm, which could help determine the optimal gas allocation/distribution scenario. The described process is incorporated in an integrated workflow which further leads to fast delivery of analysis/results that enable production engineers to make smarter decisions faster in a repeatable way.
Gas lift is one of the most widely used artificial lift methods, and the use of nodal analysis to generate the gas lift performance curve is well established. However, the optimal gas injection rate is often selected as the point with maximum liquid production, which neglects the cost of incremental injection gas volume. This paper investigates the determination of the optimal operational point using a multiobjective optimization technique by considering the trade-off between gas consumption and oil production. The indicator-based evolutionary algorithm transforms the multiobjective problem into a single objective one using the hypervolume metric computed in the objective space. For the gas lift problem, which is a bi-objective problem aimed at maximizing oil production while minimizing gas injection rate, the hypervolume metrics are identically equivalent to geometric hyperareas under the trade-off curve. The optimization is only applied to the monotonically increasing portion of the gas lift performance curve; thus, all trivial sub-optimal conditions are excluded. The optimal operational point of gas injection rate is determined by finding the maximum rectangular hyperarea under the performance curve. The proper determination of the optimal injection gas rate could not only improve the efficiency of the gas lift itself, but also reduce the burden on the maintenance of surface facilities. The method is also applied to the multi-well scenario where a novel multi-well gas lift performance curve is generated using multiobjective Genetic Algorithm, which could help determine the optimal gas allocation/distribution scenario. The described process is incorporated in an integrated workflow which further leads to fast delivery of analysis/results that enable production engineers to make smarter decisions faster in a repeatable way.
Approximately 40% of unconventional wells in the United States with artificial lift are using gas lift. One of the blessings of gas lift is that it is very forgiving and rarely fails. This has led some operators to the conclusion that gas lift doesn’t require a proper control and optimization scheme. The objective of this study is to use field data and show the importance and benefit of a controlled and optimized gas lift compressor to aid production. The intelligent gas lift compressor utilizes a heavy computational process of taking a composite Vogel reservoir inflow model coupled with a Hagedorn & Brown outflow model that informs the Guo model to solve for a minimum critical rate to lift liquid droplets out of the wellbore. First, an empirical range of injection rates is used to find the rate with maximum unloading as indicated by a drop in casing injection pressure. Once this rate is found, reservoir inflow performance relationship (IPR) parameters are estimated to meet that condition. These parameters are held constant for a time period and injection rate needed is calculated based on production rate and pressure coming into the programmable logic controller (PLC) via digital connections. This approach is tested on a well and the production is monitored over a 3-month period. The collected data are used to analyze the benefits of an intelligent gas lift compressor control. The data from a well with this intelligent gas lift system is monitored over a 3-month period. This includes a 20-day period of the compressor searching for an injection rate that shows to be the most efficient. Then the compressor control is set up with a critical rate control mode. This calculation is performed with an edge server that runs the intense calculations every minute to instruct the compressor what volume of injection it needs to achieve manipulation of an automated suction control valve and speed control of the engine driver. The surface casing pressure data shows a very stable unloading behavior profile, which is confirmed with very stable oil and gas production data over the observation period. An estimate of gained production during the periods of high line pressure along with stable unloading is given as the justification for outfitting the well site with the intelligent compressor controls. Despite gas lift’s importance as a lift technique, its control and optimization are still not properly addressed within the industry. This work’s proposed intelligent gas lift scheme can be a potentially valuable solution to unstable unloading of liquids, and benefit the operators significantly in production and revenues.
The purpose of this paper is to highlight the results of a comprehensive investigative study that quantifies the multiphase flow-related differences in multiphase hydrostatic pressure gradient, oil holdup and gas velocities as the gas injection depth is lowered from vertical to higher angles along the heel and into the lateral sections of horizontal wells. The results of this work enable a deeper understanding of gas slippage under gas lift operation at high angle sections of horizontal wells. When used for optimizing horizontal well liquids unloading, gas lift valves are placed as low in the well as operationally allowable. But what happens if gas lift is applied along the bend or lateral? To help address this important question, we first leverage the vast knowledge gained from the inclined multiphase flow literature. The scientific knowledge base for up/down inclined multiphase flows reveals why such behaviors in laterals are so complex, namely, the extreme slip effects that exist. In this work, we start with selecting published lab experiments in this area, and then simulate their flow behaviors using an advanced, cutting-edge analytical multiphase flow simulator. Next, we extend our validation to the field-scale using actual horizontal well gas lift field datasets sourced from different unconventional shale oil plays. With this detailed flow modeling substantiated, we then conduct the principal investigation of this work by quantifying the horizontal well gas lift performance at various representative inclinations (0, 30, 60, 88, 90 and 92 degrees from vertical) to better understand how changes in four major sensitivity variables, namely, diameter, gas injection rate, total liquids rate and water cut, impact the effectiveness of the gas lift process. Then, for each of these sensitivities and at each inclination, we analyze and compare the difference in value (value before gas lift - value after gas lift) of the multiphase hydrostatic pressure gradient, oil holdup, wellbore gas velocity and critical gas velocity. A new learning from this work is that the prior vertical well experience and basis for gas lift being more effective at deeper depths does not translate to horizontal wells. The experience-driven industry viewpoint that gas lift is unaffected by inclination is not supported by both controlled inclined flow loop lab data and horizontal well field data. From the multiphase view, gas lift optimization is governed by the slip behaviors and it is demonstrated in this work that the multiphase hydrostatic pressure gradient reduction will be much lower at horizontal well inclinations of greater than 45 degrees from vertical, meaning the gas lift technique becomes less effective at these higher inclinations deep in the heel and lateral regions. Our results show that in this latter scenario, most of the gas will slip past the liquids, and increasingly so at higher angles (the pipe acts as a separator at these higher angles) and the effectiveness of the gas lift significantly lowers as the flow starts to undergo slugging and other high-slip transitional flow patterns. This has a significant practical impact to operators trying to optimize end-of-tubing (EOT) placement in conjunction with the gas lift lowest valve placement. Summarily, the results from our detailed modeling are used to demonstrate what is and what is not possible in terms of liquids evacuation from horizontal wellbores using gas-assisted lift at up/down inclined angles - and specifically - how gas injection rates affect hydrostatic pressure gradients, oil holdups, wellbore gas velocities and critical unloading gas velocities along the bend and lateral.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.