首页|Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification

Stochastic seismic inversion and Bayesian facies classification applied to porosity modeling and igneous rock identification

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We apply stochastic seismic inversion and Bayesian facies classification for porosity modeling and igneous rock identification in the presalt interval of the Santos Basin.This integration of seismic and well-derived information enhances reservoir characterization.Stochastic inversion and Bayesian classi-fication are powerful tools because they permit addressing the uncertainties in the model.We used the ES-MDA algorithm to achieve the realizations equivalent to the percentiles P10,P50,and P90 of acoustic impedance,a novel method for acoustic inversion in presalt.The facies were divided into five:reservoir 1,reservoir 2,tight carbonates,clayey rocks,and igneous rocks.To deal with the overlaps in acoustic impedance values of facies,we included geological information using a priori probability,indicating that structural highs are reservoir-dominated.To illustrate our approach,we conducted porosity modeling using facies-related rock-physics models for rock-physics inversion in an area with a well drilled in a coquina bank and evaluated the thickness and extension of an igneous intrusion near the carbonate-salt interface.The modeled porosity and the classified seismic facies are in good agreement with the ones observed in the wells.Notably,the coquinas bank presents an improvement in the porosity towards the top.The a priori probability model was crucial for limiting the clayey rocks to the structural lows.In Well B,the hit rate of the igneous rock in the three scenarios is higher than 60%,showing an excellent thickness-prediction capability.

Stochastic inversionBayesian classificationPorosity modelingCarbonate reservoirsIgneous rocks

Fábio Júnior Damasceno Fernandes、Leonardo Teixeira、Antonio Fernando Menezes Freire、Wagner Moreira Lupinacci

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Exploratory Interpretation and Reservoir Characterization Group(GIECAR),Department of Geology and Geophysics,Federal Fluminense University,Niterói,RJ,24210-346,Brazil

Petrobras,Rio de Janeiro,RJ,20231-030,Brazil

National Institute of Science and Technology of Petroleum Geophysics(INCT-GP/CNPQ),Niterói,RJ,24210-346,Brazil

Equinor for financing the Research and Development project and the Agência Nacional do Petróleo(ANP)Institute of Science and Technology of Petroleum Geophysics of Brazil

2024

石油科学(英文版)
中国石油大学(北京)

石油科学(英文版)

EI
影响因子:0.88
ISSN:1672-5107
年,卷(期):2024.21(2)