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The reservoir target for this research is located in Talang Akar formation which dominated with sand and shale lithology. This study area has a complex geology structure, reservoir thickness below the tuning thickness. Crossplot sensitivity analysis describes that acoustic impedance is not sensitive...
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id-itb.:200892017-10-09T10:24:09Z#TITLE_ALTERNATIVE# (NIM : 22310012); Pembimbing : Ign. Sonny Winardhi, M. Sc. Ph. D, FADILLAH Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/20089 The reservoir target for this research is located in Talang Akar formation which dominated with sand and shale lithology. This study area has a complex geology structure, reservoir thickness below the tuning thickness. Crossplot sensitivity analysis describes that acoustic impedance is not sensitive to distinguish between sand and shale. With several weakness, integration of Extended Elastic Impedance method and Bayesian Classification to delineate sandstone reservoir is needed. Sensitive parameter for distinguishing lithology sensitivity analysis is based on lambda-rho and vp/vs- ratio with EEI (23) and EEI (40). Bayesian classification is applied in both EEI inversion result and well data to quantify uncertainty on predicting lithology and fluid distribution. The classification results show that integration of EEI method with Bayesian Classification can help to delineate thin layers with a limited well data. text |
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The reservoir target for this research is located in Talang Akar formation which dominated with sand and shale lithology. This study area has a complex geology structure, reservoir thickness below the tuning thickness. Crossplot sensitivity analysis describes that acoustic impedance is not sensitive to distinguish between sand and shale. With several weakness, integration of Extended Elastic Impedance method and Bayesian Classification to delineate sandstone reservoir is needed. Sensitive parameter for distinguishing lithology sensitivity analysis is based on lambda-rho and vp/vs- ratio with EEI (23) and EEI (40). Bayesian classification is applied in both EEI inversion result and well data to quantify uncertainty on predicting lithology and fluid distribution. The classification results show that integration of EEI method with Bayesian Classification can help to delineate thin layers with a limited well data. |
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Theses |
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(NIM : 22310012); Pembimbing : Ign. Sonny Winardhi, M. Sc. Ph. D, FADILLAH |
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(NIM : 22310012); Pembimbing : Ign. Sonny Winardhi, M. Sc. Ph. D, FADILLAH #TITLE_ALTERNATIVE# |
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(NIM : 22310012); Pembimbing : Ign. Sonny Winardhi, M. Sc. Ph. D, FADILLAH |
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(NIM : 22310012); Pembimbing : Ign. Sonny Winardhi, M. Sc. Ph. D, FADILLAH |
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https://digilib.itb.ac.id/gdl/view/20089 |
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