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Sequential model aggregation for production forecasting

Abstract : Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservoir. However, identifying such models can be very challenging as they need to be constrained to all available data. In particular, they should reproduce past production data, which requires to solve a complex non-linear inverse problem. In this paper, we thus propose to investigate the potential of machine learning algorithms to predict the future production of a reservoir based on past production data without model calibration. We focus more specifically on robust online aggregation, a deterministic approach that provides a robust framework to make forecasts on a regular basis. This method does not rely on any specific assumption or need for stochastic modeling. Forecasts are first simulated for a set of base reservoir models representing the prior uncertainty, and then combined to predict production at the next time step. The weight associated to each forecast is related to its past performance. Three different algorithms are considered for weight computations: the exponentially weighted average algorithm, ridge regression and the Lasso regression. They are applied on a synthetic reservoir case study, the Brugge case, for sequential predictions. To estimate the potential of development scenarios, production forecasts are needed on long periods of time without intermediary data acquisition. An extension of the deterministic aggregation approach is thus proposed in this paper to provide such multi-step-ahead forecasts.
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https://hal.archives-ouvertes.fr/hal-01939813
Contributor : Gilles Stoltz <>
Submitted on : Sunday, July 14, 2019 - 10:08:58 PM
Last modification on : Wednesday, October 14, 2020 - 4:08:49 AM

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Raphaël Deswarte, Véronique Gervais, Gilles Stoltz, Sébastien da Veiga. Sequential model aggregation for production forecasting. Computational Geosciences, Springer Verlag, 2019, 23 (5), pp.1107-1124. ⟨10.1007/s10596-019-09872-1⟩. ⟨hal-01939813v2⟩

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