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Collaborating Authors
Western Australia
SUMMARY The ability of the marine controlled source electromagnetic method to resolve anisotropy in the sediment conductivity is not very well understood. In this study, we address the resolvability of anisotropy using a Bayesian approach. Two markedly different methods, slice sampling and reversible jump Markov Chain Monte Carlo have been used for the Bayesian inversion of a synthetic model of a resistive oil reservoir trapped beneath the seabed. We use this to identify which components of data can provide the strongest constraints on anisotropy in the overburden, reservoir and underlying sediments.
anisotropy, Artificial Intelligence, Bayesian Inference, CSEM data, history matching, interface, inversion, machine learning, main menu bayesian inversion, Markov chain Monte Carlo, posterior distribution, Reservoir Characterization, reservoir simulation, resistivity, seg las vegas 2012, structural geology, the overburden, tiv anisotropic csem data, Upstream Oil & Gas, vertical resistivity
Oilfield Places:
- Oceania > Australia > Western Australia > North West Shelf > Carnarvon Basin > Exmouth Plateau > WA-1-R > Scarborough Field (0.99)
- Africa > South Africa > Western Cape Province > Indian Ocean > Bredasdorp Basin > Block 9 > EM Field (0.99)
SPE Disciplines:
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (0.72)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (0.72)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (0.56)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.49)
Technology:
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.55)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (0.34)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.34)