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<p>Determination of reservoir rock types is an important and necessary task for reservoir development and production. Fluid production from complex carbonate reservoirs that have undergone diagenetic processes, necessitates a thorough and reliable assessment of the reservoir characteristics. Since core samples from hydrocarbon reservoirs are not always available, nuclear magnetic resonance (NMR) and conventional well logs, or a combination of other methods are usually used as alternatives to estimate petrophysical properties. This study examines a giant carbonate reservoir of Asmari Formation (depth interval of 2513.5–2674.8&#xa0;m) in the Dezful Embayment, southwest Iran. NMR and conventional well-log data together with multi-resolution graph-based clustering (MRGC) methods were used to determine the rock types based on electrofacies and flow zonation indicator (FZI). Porosity (φ) and permeability (K) were derived from the NMR data using the Schlumberger-Doll-Research (SDR) and Timur-Coats methods. The estimated core-derived porosity and permeability data were used to classify six distinct FZI groups and associated rock types. The conventional well logs were exposed to MRGC algorithm to distinguish five electrofacies-based clusters. The results of the FZI and MRGC methods were then compared and reconciled to determine consistent rock types. The analysis reveals that rock type R1 determined by the FZI method displays the highest quality reservoir with FZI &gt; 4.896 and reservoir quality index (RQI) &gt; 0.852. Applying the MRGC method using just two NMR-derived variables resulted in six rock-type clusters. Rock type R1 displays a total combinable magnetic resonance porosity (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{T}_{cmr}\)</EquationSource> </InlineEquation>) of 0.13 and a logarithmic mean of T2 distribution (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:{T}_{2lm})\)</EquationSource> </InlineEquation> of 440.33, identifying it as the rock type with the best reservoir quality. Evaluating NMR and conventional well-log data with MRGC results provides a comprehensive and reliable classification of the reservoir rock types and assessing their reservoir quality, providing an alternative scheme based on NMR-FZI rock-type analysis.</p>

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Rock type determination using integration of core data, nuclear magnetic resonance and conventional well logs: a case study from the Asmari carbonate reservoirs, southwestern Iran

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Farnoush Ghadiri,
  • Bahram Habibnia,
  • Ezatallah Kazemzadeh,
  • Ali Kadkhodaie,
  • David A. Wood

摘要

Determination of reservoir rock types is an important and necessary task for reservoir development and production. Fluid production from complex carbonate reservoirs that have undergone diagenetic processes, necessitates a thorough and reliable assessment of the reservoir characteristics. Since core samples from hydrocarbon reservoirs are not always available, nuclear magnetic resonance (NMR) and conventional well logs, or a combination of other methods are usually used as alternatives to estimate petrophysical properties. This study examines a giant carbonate reservoir of Asmari Formation (depth interval of 2513.5–2674.8 m) in the Dezful Embayment, southwest Iran. NMR and conventional well-log data together with multi-resolution graph-based clustering (MRGC) methods were used to determine the rock types based on electrofacies and flow zonation indicator (FZI). Porosity (φ) and permeability (K) were derived from the NMR data using the Schlumberger-Doll-Research (SDR) and Timur-Coats methods. The estimated core-derived porosity and permeability data were used to classify six distinct FZI groups and associated rock types. The conventional well logs were exposed to MRGC algorithm to distinguish five electrofacies-based clusters. The results of the FZI and MRGC methods were then compared and reconciled to determine consistent rock types. The analysis reveals that rock type R1 determined by the FZI method displays the highest quality reservoir with FZI > 4.896 and reservoir quality index (RQI) > 0.852. Applying the MRGC method using just two NMR-derived variables resulted in six rock-type clusters. Rock type R1 displays a total combinable magnetic resonance porosity ( \(\:{T}_{cmr}\) ) of 0.13 and a logarithmic mean of T2 distribution ( \(\:{T}_{2lm})\) of 440.33, identifying it as the rock type with the best reservoir quality. Evaluating NMR and conventional well-log data with MRGC results provides a comprehensive and reliable classification of the reservoir rock types and assessing their reservoir quality, providing an alternative scheme based on NMR-FZI rock-type analysis.