Linking Geostatistics with Basin and Petroleum System Modeling: Assessment of Spatial Uncertainties

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Abstract/Contents

Abstract: Basin and Petroleum System Modeling covers a large spatial and temporal interval. Many of the input parameters are highly uncertain. While probabilistic approaches based on Monte Carlo simulations have been used to address some uncertain parameters, the impact of spatial uncertainties remains unexplored. Facies map is one of the key modeling inputs since all the rock properties are wrapped into facies definition. Many techniques had been developed for facies modeling in reservoir characterization regime. These methods can be applied directly in basin modeling. In particular Multi-point Geostatistical Method had been proven to be very effective in modeling categorical variables. Another important spatial parameter is the structure model. Present day structure model is the starting point for reconstruction the deposition history. In this work we first conducted the traditional uncertainty analysis in basin modeling. Then the impact of facies distribution and structure uncertainty from time-to-depth conversion were studied. It is shown that facies distribution has great impact on the oil accumulation and different geological interpretations give quite different results. Structure uncertainty from time-to-depth conversion has less impact in this case because the target area is quite homogeneous and it is expected that structure uncertainty is still the first order uncertainty sources in basin modeling.

Type of resource	text
Date created	June 2010

Author	Jia, Bin
Primary advisor	Mukerji, Tapan
Degree granting institution	Stanford University, Department of Energy Resources Engineering

Subject	School of Earth Energy & Environmental Sciences
Genre	Thesis

Location	https://purl.stanford.edu/mv127hj3223

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Preferred Citation: Jia, Bin. (2010). Linking Geostatistics with Basin and Petroleum System Modeling: Assessment of Spatial Uncertainties. Stanford Digital Repository. Available at: https://purl.stanford.edu/mv127hj3223

Master's Theses, Doerr School of Sustainability

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