This study establishes a basin-scale, formation-wise petrophysical characterization of Paleogene sandstone reservoirs in the Upper Assam Basin, India, and develops a quantitative comparative framework for reservoir ranking across the Nurpuh, Lakadong–Therria (Lk + Th), and Langpar formations. Gamma ray, spontaneous potential, neutron porosity, bulk density, and deep resistivity logs from eighteen wells are analysed using a standardized workflow. Reservoir sands are delineated through integrated lithology discrimination. Shale-corrected effective porosity (<InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(\:{\phi\:}_{e}\)</EquationSource> </InlineEquation>) is estimated using neutron–density cross-consistency. Water saturation (<InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(\:{S}_{w}\)</EquationSource> </InlineEquation>) is computed using Archie’s equation with representative parameters (<InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(\:{R}_{w}\)</EquationSource> </InlineEquation> = 0.10 Ω·m; m = 2.0; n = 2.0), followed by structured sensitivity analysis (<InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(\:{R}_{w}\)</EquationSource> </InlineEquation>= 0.08–0.12 Ω·m; m, n = 1.8–2.2). Permeability (k) is estimated using four empirical models (Timur, Coates–Denoo, Wyllie–Rose, and modified Carman–Kozeny) to constrain model dependence.Lakadong–Therria shows the largest cumulative net clean sand thickness (622.7 m from 162 sand zones) and stable reservoir-grade porosity (median <InlineEquation ID="IEq12"> <EquationSource Format="TEX">\(\:{\phi\:}_{e}\)</EquationSource> </InlineEquation> = 0.168; mean 0.167). It shows the lowest thickness-weighted mean <InlineEquation ID="IEq13"> <EquationSource Format="TEX">\(\:{S}_{w}\)</EquationSource> </InlineEquation> (0.397) and consistently ranks as the dominant flow unit across all permeability models. Nurpuh displays heterogeneous porosity (median <InlineEquation ID="IEq14"> <EquationSource Format="TEX">\(\:{\phi\:}_{e}\)</EquationSource> </InlineEquation> = 0.136; range 0.017–0.350) and higher thickness-weighted mean <InlineEquation ID="IEq15"> <EquationSource Format="TEX">\(\:{S}_{w}\)</EquationSource> </InlineEquation> (0.475), with 61.35% of net sands having <InlineEquation ID="IEq16"> <EquationSource Format="TEX">\(\:{S}_{w}\:\)</EquationSource> </InlineEquation>> 0.60. Langpar contains localized high-porosity, low-<InlineEquation ID="IEq17"> <EquationSource Format="TEX">\(\:{S}_{w}\)</EquationSource> </InlineEquation> intervals (median <InlineEquation ID="IEq18"> <EquationSource Format="TEX">\(\:{\phi\:}_{e}\)</EquationSource> </InlineEquation> = 0.217; thickness-weighted mean <InlineEquation ID="IEq19"> <EquationSource Format="TEX">\(\:{S}_{w}\)</EquationSource> </InlineEquation> = 0.147) but limited cumulative net sand development (21.85 m). Despite numerical divergence among permeability models, formation-wise ranking remains consistent, identifying Lakadong–Therria as the principal Paleogene reservoir unit at basin scale. Sensitivity analysis demonstrates that <InlineEquation ID="IEq20"> <EquationSource Format="TEX">\(\:{S}_{w}\)</EquationSource> </InlineEquation> is most influenced by the cementation exponent (m), producing deviations up to ± 18% in formation-scale mean values; however, stratigraphic reservoir ranking remains stable within realistic parameter bounds.The study introduces an uncertainty-aware, basin-scale, formation-wise comparative petrophysical framework integrating standardized lithology screening, multi-model permeability envelopes, and quantitative Archie parameter sensitivity analysis across multiple wells. The results provide quantitative stratigraphic prioritization for exploration targeting and reservoir development planning in the Upper Assam Basin. This approach provides a reproducible methodology for reservoir prioritization in heterogeneous clastic basins and strengthens interpretational transparency in log-based reservoir evaluation.

Formation-wise petrophysical characterization of Paleogene sandstone reservoirs: a case study for the Upper Assam Basin, India

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发表日期：

2026年7月14日

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Monmohan Gogoi,
Diganta Bhuyan,
Dilip Majumdar

摘要

This study establishes a basin-scale, formation-wise petrophysical characterization of Paleogene sandstone reservoirs in the Upper Assam Basin, India, and develops a quantitative comparative framework for reservoir ranking across the Nurpuh, Lakadong–Therria (Lk + Th), and Langpar formations. Gamma ray, spontaneous potential, neutron porosity, bulk density, and deep resistivity logs from eighteen wells are analysed using a standardized workflow. Reservoir sands are delineated through integrated lithology discrimination. Shale-corrected effective porosity ( \(\:{\phi\:}_{e}\) ) is estimated using neutron–density cross-consistency. Water saturation ( \(\:{S}_{w}\) ) is computed using Archie’s equation with representative parameters ( \(\:{R}_{w}\) = 0.10 Ω·m; m = 2.0; n = 2.0), followed by structured sensitivity analysis ( \(\:{R}_{w}\) = 0.08–0.12 Ω·m; m, n = 1.8–2.2). Permeability (k) is estimated using four empirical models (Timur, Coates–Denoo, Wyllie–Rose, and modified Carman–Kozeny) to constrain model dependence.

Lakadong–Therria shows the largest cumulative net clean sand thickness (622.7 m from 162 sand zones) and stable reservoir-grade porosity (median \(\:{\phi\:}_{e}\) = 0.168; mean 0.167). It shows the lowest thickness-weighted mean \(\:{S}_{w}\) (0.397) and consistently ranks as the dominant flow unit across all permeability models. Nurpuh displays heterogeneous porosity (median \(\:{\phi\:}_{e}\) = 0.136; range 0.017–0.350) and higher thickness-weighted mean \(\:{S}_{w}\) (0.475), with 61.35% of net sands having \(\:{S}_{w}\:\) > 0.60. Langpar contains localized high-porosity, low- \(\:{S}_{w}\) intervals (median \(\:{\phi\:}_{e}\) = 0.217; thickness-weighted mean \(\:{S}_{w}\) = 0.147) but limited cumulative net sand development (21.85 m). Despite numerical divergence among permeability models, formation-wise ranking remains consistent, identifying Lakadong–Therria as the principal Paleogene reservoir unit at basin scale. Sensitivity analysis demonstrates that \(\:{S}_{w}\) is most influenced by the cementation exponent (m), producing deviations up to ± 18% in formation-scale mean values; however, stratigraphic reservoir ranking remains stable within realistic parameter bounds.

The study introduces an uncertainty-aware, basin-scale, formation-wise comparative petrophysical framework integrating standardized lithology screening, multi-model permeability envelopes, and quantitative Archie parameter sensitivity analysis across multiple wells. The results provide quantitative stratigraphic prioritization for exploration targeting and reservoir development planning in the Upper Assam Basin. This approach provides a reproducible methodology for reservoir prioritization in heterogeneous clastic basins and strengthens interpretational transparency in log-based reservoir evaluation.