Enhanced production surveillance using probabilistic dynamic models



Published Nov 19, 2020
Ashutosh Tewari Stijn de Waele Niranjan Subrahmanya


Production surveillance is the task of monitoring oil and gas production from every well in a hydrocarbon field. A key opportunity in this domain is to improve the accuracy of flow measurements per phase (oil, water, gas) from a multi-phase flow. Multi-phase flow sensors are costly and therefore not instrumented for every production well. Instead, several low fidelity surrogate measurements are performed that capture different aspects of the flow. These measurements are then reconciled to obtain per-phase rate estimates. Current practices
may not appropriately account for the production dynamics and the sensor issues, thus, fall far short in terms of achieving a desired surveillance accuracy. To improve surveillance accuracy, we pose rate reconciliation as a state estimation problem. We begin with hypothesizing a model that describes the dynamics of production rates and their relationship with the
field measurements. The model appropriately accounts for the uncertainties in field conditions and measurements. We then develop robust probabilistic estimators for reconciliation
to yield the production estimates and the uncertainties therein. We highlight recent advancements in the area of probabilistic programming that can go a long way in improving the performance and the portability of such estimators. The exposition of our methods is accompanied by experiments in a simulation environment to illustrate improved surveillance accuracy achieved in different production scenarios.

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production surveillance, probabilistic rate reconciliation, probabilistic programming

Alspach, D. L., & Sorenson, H. W. (1972, August). Nonlin-ear Bayesian estimation using Gaussian sum approximations. Automatic Control, IEEE Transactions on, 17(4), 439–448. doi: 10.1109/tac.1972.1100034
Arulampalam, S. M., Maskell, S., Gordon, N., & Clapp, T. (2002, February). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Trans. Sig. Proc., 50(2), 174–188. doi: 10.1109/78.978374
Bilmes, J. (1997). A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models (Tech. Rep. No. TR-97-021). International Computer Science Institute.
Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., . . . Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, Articles, 76(1), 1–32.
Chib, S., & Greenburg, E. (1995). Understanding the metropolis-hastings algorithm. The American Statistician, 49(4), 327-335.
Goh, K.-C., Moncur, C. E., Van Overschee, P., Briers, J., et al. (2007). Production surveillance and optimization with data driven models. In International petroleum technology conference.
Gordon, A. D., Henzinger, T. A., Nori, A. V., & Rajamani, S. K. (2014). Probabilistic programming. In International conference on software engineering (icse, fose track).
Hoffman, M. D., & Gelman, A. (2014). The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research, 15(1), 1593–1623.
H¨o¨ok, M., Davidsson, S., Johansson, S., & Tang, X. (2013). Decline and depletion rates of oil production: a comprehensive investigation. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 372(2006). doi: 10.1098/rsta.2012.0448
Kucukelbir, A., Ranganath, R., Gelman, A., & Blei, D. M. (2015). Automatic variational inference in stan. In Neural information processing systems.
Liu, J., & West, M. (2001). Combined parameter and state estimation in simulation-based filtering. In A. Doucet, N. de Freitas, & N. Gordon (Eds.), Sequential monte carlo methods in practice (pp. 197–223). New York, NY: Springer New York.
Neal, R. M. (2003). Slice sampling. The Annals of Statistics, 31(3), 705-741.
Poulisse, H., Van Overschee, P., Briers, J., Moncur, C. E., Goh, K.-C., et al. (2006). Continuous well production flow monitoring and surveillance. In Intelligent energy conference and exhibition.
Simon, D. (2010, August). Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory Applications, 4(8), 1303-1318. doi: 10.1049/iet-cta.2009.0032
Sondergaard, T., & Lermusiaux, P. F. J. (2013). Data assimilation with gaussian mixture models using the dynamically orthogonal field equations. part i: Theory and scheme. Monthly Weather Review, 141(6), 1737-1760. doi: 10.1175/MWR-D-11-00295.1
Sorenson, H. W., & Alspach, D. L. (1971, July). Recursive bayesian estimation using gaussian sums. Automatica, 7(4), 465–479. doi: 10.1016/0005-1098(71)90097-5
Srkk, S. (2013). Bayesian filtering and smoothing. New York, NY, USA: Cambridge University Press.
Tokdar, S. T., & Kass, R. E. (2010). Importance sampling: a review. Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 54–60. doi: 10.1002/wics.56
Vo, B.-n., & Ma, W.-k. (2006). The gaussian mixture probability hypothesis density filter. IEEE Trans. SP, 4091–4104.
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