Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies

Authors

  • Ikenna Tobechukwu Okorocha Department of Industrial/Production Engineering, Nnamdi Azikiwe University, Awka
  • Chuka Emmanuel Chinwuko Department of Industrial/Production Engineering, Nnamdi Azikiwe University, Awka
  • Chika Edith Mgbemena Department of Industrial/Production Engineering, Nnamdi Azikiwe University, Awka
  • Chinedum Ogonna Mgbemena Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun

DOI:

https://doi.org/10.12928/ijio.v1i2.2470

Keywords:

Gas Lift, Optimization, Oil, Production Strategies

Abstract

Gas Lift operation involves the injection of compressed gas into a low producing or non-performing well to maximize oil production. The oil produced from a gas lift well is a function of the gas injection rate. The optimal gas injection rate is achieved by optimization. However, the gas lift, which is an artificial lift process, has some drawbacks such as the deterioration of the oil well, incorrect production metering, instability of the gas compressor, and over injection of gas. This paper discusses the various optimization techniques for the gas lift in the Oil and Gas production process. A systematic literature search was conducted on four databases, namely Google Scholar, Scopus, IEE Explore and DOAJ, to identify papers that focused on Gas lift optimizations. The materials for this review were collected primarily via database searches. The major challenges associated with gas lift were identified, and the different optimization strategies available in the literature reviewed. The strategies reviewed were found to be based on artificial intelligence (AI) and machine learning (ML). The implementation of any of the optimization strategies for the gas lift will enhance profitability, reduce operational cost, and extend the life of the wells.

Author Biographies

Chuka Emmanuel Chinwuko, Department of Industrial/Production Engineering, Nnamdi Azikiwe University, Awka

Reader, Industrial/Production Engineering Department

Chika Edith Mgbemena, Department of Industrial/Production Engineering, Nnamdi Azikiwe University, Awka

Senior Lecturer, Industrial/Production Engineering Department

Chinedum Ogonna Mgbemena, Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun

Chinedum Ogonna Mgbemena is a Senior Lecturer in the Department of Mechanical Engineering, Federal University of Petroleum Resources. His main research areas are Manufacturing and Engineering Design.

References

Abdalsadig, M. ., Nourian, A., Nasr, G. ., & Babaie, M. (2016). Gas lift optimization to improve well performance. Salford. Retrieved from http://usir.salford.ac.uk/38144/

Bordbar, M., Neshat, A., Javadi, S., Pradhan, B., & Aghamohammadi, H. (2020). Meta-heuristic algorithms in optimizing GALDIT framework: A comparative study for coastal aquifer vulnerability assessment. Journal of Hydrology, 585, 124768. https://doi.org/10.1016/j.jhydrol.2020.124768

Chen, L., Liu, Z., & Ma, N. (2018). Time-Delayed Polynomial Grey System Model with the Fractional Order Accumulation. Mathematical Problems in Engineering, 2018. https://doi.org/10.1155/2018/3640625

Chen, L., Liu, Z., & Ma, N. (2019). Optimize production allocation for the oil-gas field basing on a novel grey model. Journal of Natural Gas Geoscience, 4(2), 121–128. https://doi.org/10.1016/j.jnggs.2019.03.003

Christiansen, L. H., Hørsholt, S., & Jørgensen, J. B. (2018). A Least Squares Method for Ensemble-based Multi-objective Oil Production Optimization. IFAC-PapersOnLine, 51(8), 7–12. https://doi.org/10.1016/j.ifacol.2018.06.347

Codas, A., Jahanshahi, E., & Foss, B. (2016). A two-layer structure for stabilization and optimization of an oil gathering network. IFAC-PapersOnLine, 49(7), 931–936. https://doi.org/10.1016/j.ifacol.2016.07.317

Duran Toksarı, M. (2007). Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy, 35(8), 3984–3990. https://doi.org/10.1016/j.enpol.2007.01.028

François, G., & Bonvin, D. (2014). Use of transient measurements for the optimization of steady-state performance via modifier adaptation. Industrial and Engineering Chemistry Research, 53(13), 5148–5159. https://doi.org/10.1021/ie401392s

Ghaedi, M., Ghotbi, C., & Aminshahidy, B. (2014). The optimization of gas allocation to a group of wells in a gas lift using an efficient Ant Colony Algorithm (ACO). Energy Sources, Part A: Recovery, Utilization and Environmental Effects, 36(11), 1234–1248. https://doi.org/10.1080/15567036.2010.536829

Hamedi, H., Rashidi, F., & Khamehchi, E. (2011). A novel approach to the gas-lift allocation optimization problem. Petroleum Science and Technology, 29(4), 418–427. https://doi.org/10.1080/10916460903394110

Hansen, H. H. (2012). A Comparative Study of Control Structures Applied in Gas Lift Systems to Prevent Casing Heading. Norwegian University of Science and Technology. Retrieved from http://folk.ntnu.no/skoge/diplom/diplom12/hansen/Diplom_Henrik_Hansen_DAIM.pdf

Hsu, C.-C., & Chen, C.-Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management, 44(14), 2241–2249. https://doi.org/10.1016/S0196-8904(02)00248-0

Hsu, C., & Wen, Y. (1998). Improved grey prediction models for the trans‐pacific air passenger market. Transportation Planning and Technology, 22(2), 87–107. https://doi.org/10.1080/03081069808717622

Ismail, W. R. B., & Trjangganung, K. (2014). Mature field gas lift optimisation: Challenges & strategies, case study of D-field, Malaysia. Society of Petroleum Engineers - International Petroleum Technology Conference 2014, IPTC 2014 - Innovation and Collaboration: Keys to Affordable Energy, 2, 1703–1714. https://doi.org/10.2523/iptc-17896-ms

Kadkhodaie-Ilkhchi, A. (2015). A systematic approach for estimation of reservoir rock properties using Ant Colony Optimization. Geopersia, 5(1), 7–17.

Kesavan, V., Kamalakannan, R., Sudhakarapandian, R., & Sivakumar, P. (2020). Heuristic and meta-heuristic algorithms for solving medium and large scale sized cellular manufacturing system NP-hard problems: A comprehensive review. Materials Today: Proceedings, 21, 66–72. https://doi.org/10.1016/j.matpr.2019.05.363

Khishvand, M., & Khamehchi, E. (2012). Nonlinear risk optimization approach to gas lift allocation optimization. Industrial and Engineering Chemistry Research, 51(6), 2637–2643. https://doi.org/10.1021/ie201336a

Krishnamoorthy, D., Fjalestad, K., & Skogestad, S. (2019). Optimal operation of oil and gas production using simple feedback control structures. Control Engineering Practice, 91, 104107. https://doi.org/10.1016/j.conengprac.2019.104107

Krishnamoorthy, D., Jahanshahi, E., & Skogestad, S. (2018). Gas-lift Optimization by Controlling Marginal Gas-Oil Ratio using Transient Measurements⁎. IFAC-PapersOnLine, 51(8), 19–24. https://doi.org/10.1016/j.ifacol.2018.06.349

Kumar, A., Tarun Soota, B., & Jitendra Kumar, B. (n.d.). Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology. Journal of Industrial Engineering International. https://doi.org/10.1007/s40092-018-0264-8(

López, S., Koç, U., Bakker, E., & Rahmani, J. (2019). Optimization of Lift Gas Allocation using Evolutionary Algorithms. International Journal of Computer Applications Technology and Research, 8(9), 353–357. https://doi.org/10.7753/ijcatr0809.1003

Ma, H., & Wu, Y. (2010). A Grey Forecasting Model Based on BP Neural Network for Crude Oil Production and Consumption in China. In 2010 Third International Symposium on Information Processing (pp. 105–109). IEEE. https://doi.org/10.1109/ISIP.2010.12

Mahdiani, M. R., & Khamehchi, E. (2015). Preventing Instability Phenomenon in Gas-lift Optimization. Iranian Journal of Oil & Gas Science and Technology (Vol. 4). Retrieved from http://ijogst.put.ac.ir

Namdar, H. (2019). Developing an improved approach to solving a new gas lift optimization problem. Journal of Petroleum Exploration and Production Technology. https://doi.org/10.1007/s13202-019-0697-7

Namdar, H., & Shahmohammadi, M. A. (2019, November 2). Optimization of production and lift-gas allocation to producing wells by a new developed GLPC correlation and a simple optimization method. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. https://doi.org/10.1080/15567036.2019.1568635

Nishikiori, N., Redner, R. A., Doty, D. R., & Schmidt, Z. (1989). An Improved Method for Gas Lift Allocation Optimization. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/19711-MS

Oloro, J., & Ogbolu, E. (2013). Gas Lift Optimization: Using Least Square Method and Solver. Greener Journal of Petroleum and Gas Exploration, 1(1), 1–006. Retrieved from www.gjournals.org

Rashid, K. (2010). Optimal allocation procedure for gas-lift optimization. Industrial and Engineering Chemistry Research, 49(5), 2286–2294. https://doi.org/10.1021/ie900867r

Rashid, K., Bailey, W., & Couët, B. B. (2012). A Survey of Methods for Gas-Lift Optimization. Modelling and Simulation in Engineering, 2012, 16. https://doi.org/10.1155/2012/516807

Saepudin, D., Soewono, E., Sidarto, K. A., Gunawan, A. Y., Siregar, S., & Sukarno, P. (2007). An investigation on gas lift performance curve in an oil-producing well. International Journal of Mathematics and Mathematical Sciences, 2007. https://doi.org/10.1155/2007/81519

Sharma, R., Fjalestad, K., & Glemmestad, B. (2012). Optimization of lift gas allocation in a gas lifted oil field as non-linear optimization problem. Modeling, Identification and Control, 33(1), 13–25. https://doi.org/10.4173/mic.2012.1.2

Sukarno, P., Saepudin, D., Dewi, S., Soewono, E., Sidarto, K. A., & Gunawan, A. Y. (2009). Optimization of gas injection allocation in a dual gas lift well system. Journal of Energy Resources Technology, Transactions of the ASME, 131(3), 0331011–0331017. https://doi.org/10.1115/1.3185345

Sylvester, O. (2015). Gas Lift Technique a Tool to Production Optimization. International Journal of Oil, Gas and Coal Engineering, 3(3), 41. https://doi.org/10.11648/j.ogce.20150303.12

Tavakoli, R., Daryasafar, A., Mohammad, K., & Milad, B. (2017). Optimization of Gas Lift Allocation Using Different Models. Recent Advances in Petrochemical Science, 1(2). https://doi.org/10.19080/rapsci.2017.01.555559

Truong, D. Q., & Ahn, K. K. (2012). An accurate signal estimator using a novel smart adaptive grey model SAGM(1,1). Expert Systems with Applications, 39(9), 7611–7620. https://doi.org/10.1016/j.eswa.2012.01.002

Vijayalakshmi, P., Francis, S. A. J., & Dinakaran, J. A. (2015). A robust energy efficient ant colony optimization routing algorithm for multi-hop ad hoc networks in MANETs. Wireless Networks 2015 22:6, 22(6), 2081–2100. https://doi.org/10.1007/S11276-015-1082-1

Yakoot, M. S., Shedid, S. A., & Arafa, M. I. (2014). A simulation approach for optimization of gas lift performance and multi-well networking in an Egyptian oil field. In Proceedings of the Annual Offshore Technology Conference. https://doi.org/10.4043/24703-ms

Zerafat, M. M., Ayatollahi, S., & Roosta, A. A. (2009). Genetic Algorithms and Ant Colony Approach for Gas-lift Allocation Optimization. Journal of the Japan Petroleum Institute, 52(3), 102–107. https://doi.org/10.1627/jpi.52.102

Downloads

Published

2020-08-21

How to Cite

Okorocha, I. T., Chinwuko, C. E., Mgbemena, C. E., & Mgbemena, C. O. (2020). Gas lift optimization in the oil and gas production process: a review of production challenges and optimization strategies. International Journal of Industrial Optimization, 1(2), 61–70. https://doi.org/10.12928/ijio.v1i2.2470

Issue

Vol. 1 No. 2 (2020)

Section

Articles

License

License and Copyright Agreement

In submitting the manuscript to the journal, the authors certify that:

  • They are authorized by their co-authors to enter into these arrangements.
  • The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read the International Journal of Industrial Optimization (IJIO) Author Guidelines at http://journal2.uad.ac.id/index.php/ijio/about/submissions#onlineSubmissions
  • That it is not under consideration for publication elsewhere,
  • That its publication has been approved by all the author(s) and by the responsible authorities tacitly or explicitly of the institutes where the work has been carried out.
  • They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
  • They agree to the following license and copyright agreement.

Copyright

Authors who publish with the International Journal of Industrial Optimization (IJIO) agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.