Inverse
Modelling of Stratigraphy: A Tool to Predict Facies Distribution and Assess
Uncertainties for Reservoir Characterisation
Charvin, Karl1, Kerry L.
Gallagher1, Gary J. Hampson2, Jo Ann Hegre3,
Richard Labourdette4 (1) Imperial College London, London, United
Kingdom (2) T.H. Huxley School, Imperial College, London, United Kingdom (3)
Total E&P UK plc, Aberdeen, United Kingdom (4) Total S.A, Pau, France
The prediction of sub-seismic facies
architecture and associated rock-property distributions from well data is
challenging in all reservoirs. A common approach to this problem is to predict
reservoir architecture from well data via stratigraphic interpretations that
infer environmental parameters (e.g. relative sea-level, sediment supply). In
this context, reservoir geoscientists often have to deal with questions of
uniqueness, uncertainty, non-linear interaction between processes, and model
sensitivity. Moreover, the increasing demand for precision in reservoir
management requires reservoir geoscientists to assess the accuracy of, and
assess uncertainties on, reservoir architecture models, which pushes classical
geostatistical tools to their limits.
In this contribution, we address these
issues using a recently developed numerical method. The new method combines a
“process-response” forward model of shallow-marine stratigraphy (BARSIM)
simulating wave-dominated sedimentary processes, with non-linear stochastic
inverse techniques (Markov chain Monte Carlo) that describe how information on
a parameterised physical system can be derived from observed data and prior
information.
This new methodology has been validated
on synthetic case studies and has been applied to outcrop data (Aberdeen
Member, Blackhawk Formation, Book Cliffs,
AAPG Search and Discover Article #90063©2007 AAPG Annual Convention, Long Beach, California