Automatic Optimization of Oilfield-Scale-Inhibitor Squeeze Treatments Delivered by Diving-Support Vessel
- Oscar Vazquez (Heriot Watt University) | Gill Ross (Chrysaor) | Myles Martin Jordan (Nalco Champion) | Dionysius Angga Adhi Baskoro (Heriot-Watt University) | Eric Mackay (Heriot-Watt University) | Clare Johnston (Nalco Champion) | Alistair Strachan (Nalco Champion)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- February 2019
- Document Type
- Journal Paper
- 60 - 70
- 2019.Society of Petroleum Engineers
- automatic, squeeze treatment, DSV, optimization
- 1 in the last 30 days
- 200 since 2007
- Show more detail
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Oilfield-scale deposition is one of the important flow-assurance challenges facing the oil industry. There are a number of methods to mitigate oilfield scale, such as reducing sulfates in the injected brine, reducing water flow, removing damage by using dissolvers or physically by milling or reperforating, and inhibition, which is particularly recommended if a severe risk of sulfate-scale deposition is present. Inhibition consists of injecting a chemical that prevents the deposition of scale, either by stopping nucleation or by retarding crystal growth. The inhibiting chemicals are either injected in a dedicated continuous line or bullheaded as a batch treatment into the formation, commonly known as a scale-squeeze treatment. In general, scale-squeeze treatments consist of the following stages: preflush to condition the formation or act as a buffer to displace tubing fluids; the main treatment, where the main pill of chemical is injected; overflush to displace the chemical deep into the reservoir; a shut-in stage to allow further chemical retention; and placing the well back in production. The well will be protected as long as the concentration of the chemical in the produced brine is greater than a certain threshold, commonly known as minimum inhibitor concentration (MIC). This value is usually between 1 and 20 ppm. The most important factor in a squeeze-treatment design is the squeeze lifetime, which is determined by the volume of water or days of production where the chemical-return concentration is greater than the MIC.
The main purpose of this paper is to describe the automatic optimization of squeeze-treatment designs using an optimization algorithm, in particular particle-swarm optimization (PSO). The algorithm provides a number of optimal designs, which result in squeeze lifetimes close to the target. To determine the most efficient design of the optimal designs identified by the algorithm, the following objectives were considered: operational-deployment costs, chemical cost, total-injected-water volume, and squeeze-treatment lifetime. Operational-deployment costs include the support vessel, pump, and tank hire. There might not be a single design optimizing all objectives, and thus the problem becomes a multiobjective optimization. Therefore, a number of Pareto optimal solutions exist. These designs are not dominated by any other design and cannot be bettered. Calculating the Pareto is essential to identify the most efficient design (i.e., the most cost-effective design).
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Crabtree, M., Eslinger, D., Fletcher, P. et al. 1999. Fighting Scale: Removal and Prevention. Oilfield Rev. 11 (3): 30–45.
Jordan, M. M. 2009. The Modelling, Application, and Monitoring of Scale Squeeze Treatments in Heterogeneous Reservoirs, North Sea. Presented at the SPE International Symposium on Oilfield Chemistry, The Woodlands, Texas, 20–22 April. SPE-121142-MS. https://doi.org/10.2118/ 121142-MS.
Jordan, M., Mackay, E., and Vazquez, O., 2008. The Influence of Overflush Fluid on Scale Squeeze Life Time—Field Examples and Placement Simulation Examples. In Proceedings of the NACE International Corrosion Conference, New Orleans, Louisiana, 16–20 March. NACE-08356.
Kahrwad, M., Sorbie, K. S., and Boak, L. S. 2009. Coupled Adsorption/Precipitation of Scale Inhibitors: Experimental Results and Modeling. SPE Prod & Oper 24 (3): 481–491. SPE-114108-PA. https://doi.org/10.2118/114108-PA.
Kan, A. T., Fu, G. M., Tomson, M. B. et al. 2004. Factors Affecting Scale Inhibitor Retention in Carbonate-Rich Formation During Squeeze Treatment. SPE J. 9 (3): 280–289. SPE-80230-PA. https://doi.org/10.2118/80230-PA.
Kennedy, J. and Eberhart, R. 1995. Particle Swarm Optimization. Proc., IEEE International Conference on Neural Networks, Perth, Australia, 27 November–1 December. https://doi.org/10.1109/ICNN.1995.488968.
Mackay, E. J. and Jordan, M. M. 2003. SQUEEZE Modelling: Treatment Design and Case Histories. Presented at the SPE European Formation Damage Conference, The Hague, 13–14 May. SPE-82227-MS. https://doi.org/10.2118/82227-MS.
Mohamed, L., Christie, M. A., and Demyanov, V. 2010a. Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification. SPE J. 15 (1): 31–38. SPE-119139-PA. https://doi.org/10.2118/119139-PA.
Mohamed, L., Christie, M. A., and Demyanov, V. 2010b. Reservoir Model History Matching With Particle Swarms: Variants Study. Presented at the SPE Oil and Gas India Conference and Exhibition, Mumbai, 20–22 January. SPE-129152-MS. https://doi.org/10.2118/129152-MS.
Reyes-Sierra, M. and Coello Coello, C. A. 2006. Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. Int. J. Computat. Intell. Res. 2 (3): 287–308. https://doi.org/10.1.1.138.1829.
Selle, O. M., Wat, R. M. S., Vikane, O. et al. 2003. A Way Beyond Scale Inhibitors—Extending Scale Inhibitor Squeeze Life Through Bridging. Presented at the International Symposium on Oilfield Scale, Aberdeen, 29–30 January. SPE-80377-MS. https://doi.org/10.2118/80377-MS.
Sorbie, K. S. and Gdanski, R. D. 2005. A Complete Theory of Scale-Inhibitor Transport and Adsorption/Desorption in Squeeze Treatments. Presented at the SPE International Symposium on Oilfield Scale, Aberdeen, United Kingdom, 11–12 May. SPE-95088-MS. https://doi.org/10.2118/95088-MS.
Tomson, M. B., Kan, A. T., Fu, G. et al. 2008. Mechanistic Understanding of Rock/Phosphonate Interactions and Effect of Metal Ions on Inhibitor Retention. SPE J. 13 (3): 325–336. https://doi.org/10.2118/100494-PA.
Vazquez, O., Corne, D., Mackay, E. et al. 2013. Automatic Isotherm Derivation From Field Data for Oilfield Scale-Inhibitor Squeeze Treatments. SPE J. 18 (3): 563–574. SPE-154954-PA. https://doi.org/10.2118/154954-PA.
Vazquez, O., Mackay, E. J., and Jordan, M. M. 2008. Modeling the Impact of Diesel vs. Water Overflush Fluids on Scale-Squeeze-Treatment Lives Using a Two-Phase Near-Wellbore Simulator. SPE Prod & Oper 24 (3): 473–480. SPE-114105-PA. https://doi.org/10.2118/114105-PA.
Vazquez, O., Mackay, E. J., and Sorbie, K. S. 2006. Development of a Non-Aqueous Scale Inhibitor Squeeze Simulator. Presented at the SPE International Oilfield Scale Symposium, Aberdeen, 31 May–1 June. SPE-100521-MS. https://doi.org/10.2118/100521-MS.
Vazquez, O., Mackay, E., Tjomsland, T. et al. 2014. Use of Tracers To Evaluate and Optimize Scale-Squeeze-Treatment Design in the Norne Field. SPE Prod & Oper 29 (1): 5–13. SPE-164114-PA. https://doi.org/10.2118/164114-PA.
Vazquez, O., Van Ommen, T., Chen, P. et al. 2011. Modeling a Series of Nonaqueous Field-Scale Inhibitor Squeeze Treatments in the Heidrun Field. SPE Prod & Oper 26 (1): 98–110. SPE-131496-PA. https://doi.org/10.2118/131496-PA.
Wang, L. K., Hung, Y.-T., and Shammas, N. K. eds. 2006. Advanced Physicochemical Treatment Processes. New York City: Humana Press.
Yan, F., Zhang, F., Bhandari, N. et al. 2015. Adsorption and Precipitation of Scale Inhibitors on Shale Formations. J. Pet. Sci. Eng. 136 (December): 32–40. https://doi.org/10.1016/j.petrol.2015.11.001.
Zhang, H. and Sorbie, K. 1997. Squeeze V, User’s Manual, Department of Petroleum Engineering, Heriot-Watt University, Edinburgh, Scotland.