Seismic facies analysis using machine learning techniques: a review and case study

Owusu, Bernard Asare; Boateng, Cyril Dziedzorm; Asare, Van-Dycke Sarpong; Danuor, Sylvester Kojo; Adenutsi, Caspar Daniel; Quaye, Jonathan Atuquaye

doi:10.1007/s12145-024-01395-3

Earth Sci Inform

2024

DOI: 10.1007/s12145-024-01395-3

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Seismic facies analysis using machine learning techniques: a review and case study

Bernard Asare Owusu,

Cyril Dziedzorm Boateng,

Van-Dycke Sarpong Asare

et al.

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2024

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Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method

Zhang,

Chen,

Zhu

et al. 2024

Front. Earth Sci.

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Accurate characterization of carbonate reservoirs remains a significant challenge due to complex facies variations and the substantial effects of wave propagation. We propose a facies-constrained reflectivity inversion strategy. The method establishes a relationship between logging data and seismic waveforms, applies clustering analysis using the Self-Organizing Map (SOM) technique, and utilizes the clustering results to constrain the construction of an initial model with realistic lateral variations. Based on this initial model, a Bayesian-based reflectivity inversion is performed, incorporating a modified Cauchy prior distribution to enhance inversion accuracy and stability. The reflectivity method offers a one-dimensional analytical solution to the wave equation, tacking thin layer thicknesses and wave propagation effects into consideration, thereby significantly alleviating inversion problems encountered in marl reservoirs. Compared to traditional inversion methods based on the Zoeppritz equation, the facies-constrained reflectivity inversion delivers higher accuracy and resolution. The application of this technique to identify marl reservoirs in the Lei32 sub-member of the Sichuan Basin has yielded promising results, effectively delineating favorable reservoir areas of approximately 210 km2 and offering strong support for future exploration and development.

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Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method

Zhang,

Chen,

Zhu

et al. 2024

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

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Seismic facies analysis using machine learning techniques: a review and case study

Cited by 1 publication

References 122 publications

Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method

Prediction of marl reservoir distribution based on facies-constrained reflectivity inversion method

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