EssRisk explained. Uncertainty-based history matching

SPE paper provides detailed description of Hamiltonian MCMC approach.

EssencePS CEO and chief developer Nigel Goodwin has brought to our attention a paper presented at the recent SPE Reservoir Simulation Symposium which offers a technical description of methods and algorithms used in EssRisk, Essence’s software flagship for history matching, uncertainty-based prediction and production optimization. Goodwin argues that brute force Markov chain Monte Carlo methods cannot be applied exhaustively to the complex field of fluid flow simulation. Even fast proxy models may fail to represent the full range of uncertainty. Moreover, the ‘black box’ nature of proxy models make their evaluation hard. Engineers generally prefer straightforward deterministic models.

Goodwin advocates Hamiltonian MCMC techniques, along with efficient proxy models which lead to reliable and uncertainty quantification and also generate an ensemble of deterministic reservoir models. The technique is claimed to be the foundation of a new generation of uncertainty tools and workflows.

The paper (50 pages and 178 equations ) is not for the faint hearted. But unusually, as Goodwin observes, ‘unlike most vendors, service providers and oil company research departments we are completely open about the algorithms used. Our added value lies in efficient implementation and in our user interface.’ More from EssencePS.

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