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A stochastic optimization model for short-term production of offshore oil platforms with satellite wells using gas lift

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Abstract

Continuous gas lift is a popular method to enhance productivity in offshore oil platforms. We propose a steady-state two-stage stochastic programming model to maximize production, where the first-stage injection level determines the production potential, while recourse actions ensure capacity and platform constraints for each uncertainty realization. In particular, we develop a concave approximation of the performance curve that incorporates uncertainty in the water cut (WC) and gas–oil ratio (GOR). We generate WC and GOR realizations using a two-step data-driven approach: we extrapolate the trends using a \(\ell _1\)-filter, and bootstrap historical deviations to generate future realizations of WC and GOR. We present numerical results for the sample average approximation of the problem and assess the solution quality using standard techniques in the literature. Our numerical results suggest that taking uncertainty into account in the problem can lead to considerable gains.

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Fig. 1

adapted from Foss and Jensen (1981)

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Notes

  1. SOS2 stands for Special Ordered Sets of type 2, which is an ordered set of non-negative variables with the following properties: at most two can be non-zero, and if two are non-zero these must be consecutive in their ordering.

  2. Here, \(\xi\) is viewed as a particular realization of the random variable \(\xi\); we use the same symbol in order to alleviate the notation, the interpretation will be clear from the context.

References

  • Alarcón G, Torres C, Gómez L (2002) Global optimization of gas allocation to a group of wells in artificial lift using nonlinear constrained programming. J Energy Res Technol 124(4):262–268

    Article  Google Scholar 

  • Birge J, Louveaux F (2011) Introduction to stochastic programming. Springer, Berlin

    Book  Google Scholar 

  • Camponogara E, Teixeira A, Hulse E, Silva T, Sunjerga S, Miyatake L (2017) Integrated methodology for production optimization from multiple offshore reservoirs in the santos. IEEE Trans Autom Sci Eng 14(2):669–680

    Article  Google Scholar 

  • Dunning I, Huchette J, Lubin M (2017) Jump: a modeling language for mathematical optimization. SIAM Rev 59(2):295–320

    Article  Google Scholar 

  • Fang W, Lo K et al (1996) A generalized well management scheme for reservoir simulation. SPE Reserv Eng 11(02):116–120

    Article  Google Scholar 

  • Foss B, Jensen J (1981) Performance analysis for closed-loop reservoir management. SPE J 16(1):183–190

    Article  Google Scholar 

  • Foss B, Knudsen B, Grimstad B (2018) Petroleum production optimization—a static or dynamic problem? Comput Chem Eng 20:245–253

    Article  Google Scholar 

  • Gunnerud V, Conn A, Foss B (2013) Embedding structural information in simulation-based optimization. Comput Chem Eng 53:35–43

    Article  Google Scholar 

  • Hanssen K, Foss B (2015) Production optimization under uncertainty applied to petroleum production. IFAC-PaersOnline 48(8):217–222

    Article  Google Scholar 

  • Homem-de-Mello T, Bayraksan G (2014) Monte Carlo sampling-based methods for stochastic optimization. Surv Oper Res Manag Sci 19:56–85

    Google Scholar 

  • Kanu E, Mach J, Brown K et al (1981) Economic approach to oil production and gas allocation in continuous gas lift (includes associated papers 10858 and 10865). J Petrol Technol 33(10):1–887

    Article  Google Scholar 

  • Keha A, de Farias Jr I, Nemhauser G (2004) Models for representing piecewise linear cost functions. Oper Res Lett 32(1):44–48

    Article  Google Scholar 

  • Kim SJ, Koh K, Boyd S, Gorinevsky D (2009) \(\ell _1\) trend filtering. SIAM Rev 51(2):339–360

    Article  Google Scholar 

  • Kleywegt A, Shapiro A, Homem-de-Mello T (2001) The sample average approximation method for stochastic discrete optimization. SIAM J Optim 12(2):479–502

    Article  Google Scholar 

  • Mak WK, Morton D, Wood K (1999) Monte carlo bounding techniques for determining solution quality in stochastic programs. Oper Res Lett 24(1–2):47–56

    Article  Google Scholar 

  • Misener R, Gounaris C, Floudas C (2008) Global optimization of gas lifting operations: a comparative study of piecewise linear formulations. Ind Eng Chem Res 48(13):6098–6104

    Article  Google Scholar 

  • Ruszczyński A (2003) Decomposition methods. In: Ruszczyński A, Shapiro A (eds) Handbook of stochastic optimization. Elsevier Science Publishers B.V, Amsterdam

    Google Scholar 

  • Shapiro A, Dentcheva D, Ruszczyński A (2014) Lectures on stochastic programming : modeling and theory, 2nd edn. SIAM, Philadelphia

    Book  Google Scholar 

  • Teixeira AF (2013) Otimização da produção de poços de petróleo com gas lift contínuo. Master’s thesis

  • Valladão D, Torrado R, Flach B, Embid S (2013) On the stochastic response surface methodology for the determination of the development plan of an oil and gas field, pp 534–545. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899412890&partnerID=40&md5=4d0c1b8b5ad4de206bcafe60d461f8ff

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Acknowledgements

The authors acknowledge the support of Cenpes and Embrapii.

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Correspondence to Carlos Gamboa.

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Gamboa, C., Silva, T., Valladão, D. et al. A stochastic optimization model for short-term production of offshore oil platforms with satellite wells using gas lift. TOP 28, 549–574 (2020). https://doi.org/10.1007/s11750-020-00547-0

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  • DOI: https://doi.org/10.1007/s11750-020-00547-0

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