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Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

Author

Listed:
  • Abdulrauf R. Adebayo

    (Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Lamidi Babalola

    (Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Syed R. Hussaini

    (Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Abdullah Alqubalee

    (Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Rahul S. Babu

    (Center for Integrative Petroleum Research, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

The petrophysical characterization of tight gas sands can be affected by clay minerals, gas adsorption, microfractures, and the presence of high-density minerals. In this study, we conducted various petrophysical, petrographic, and high-resolution image analyses on Saudi Arabian tight sand in order to understand how a complex pore system responds to measurement tools. About 140 plug samples extracted from six wells were subjected to routine core analyses including cleaning, drying, and porosity–permeability measurements. The porosity–permeability data was used to identify hydraulic flow units (HFU). In order to probe the factors contributing to the heterogeneity of this tight sand, 12 subsamples representing the different HFUs were selected for petrographic study and high-resolution image analysis using SEM, quantitative evaluation of minerals by scanning electron microscope (QEMSCAN), and micro-computed tomography (µCT). Nuclear magnetic resonance (NMR) and electrical resistivity measurements were also conducted on 56 subsamples representing various lithofacies. NMR porosity showed good agreement with other porosity measurements. The agreement was remarkable in specific lithofacies with porosity ranging from 0.1% to 7%. Above this range, significant scatters were seen between the porosity methods. QEMSCAN results revealed that samples with <7% porosity contain a higher proportion of clay than those with porosity >7%, which are either microfractured or contain partially dissolved labile minerals. The NMR T2 profiles also showed that samples with porosity <7% are dominated by micropores while samples with porosity >7% are dominated by macropores. Analysis of the µCT images revealed that pore throat sizes may be responsible for the poor correlation between NMR porosity and other porosity methods. NMR permeability values estimated using the Shlumberger Doll Research (SDR) method are fairly correlated with helium permeability (with an R2 of 0.6). Electrical resistivity measurements showed that the different rock types fall on the same slope of the formation factors versus porosity, with a cementation factor of 1.5.

Suggested Citation

  • Abdulrauf R. Adebayo & Lamidi Babalola & Syed R. Hussaini & Abdullah Alqubalee & Rahul S. Babu, 2019. "Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir," Energies, MDPI, vol. 12(22), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4302-:d:285915
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    Citations

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    Cited by:

    1. Pinghe Sun & Meng Han & Han Cao & Weisheng Liu & Shaohe Zhang & Junyi Zhu, 2020. "Development and Performance Evaluation of Solid-Free Drilling Fluid for CBM Reservoir Drilling in Central Hunan," Energies, MDPI, vol. 13(18), pages 1-16, September.
    2. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.
    3. Aliya Mukhametdinova & Andrey Kazak & Tagir Karamov & Natalia Bogdanovich & Maksim Serkin & Sergey Melekhin & Alexey Cheremisin, 2020. "Reservoir Properties of Low-Permeable Carbonate Rocks: Experimental Features," Energies, MDPI, vol. 13(9), pages 1-25, May.
    4. Zhang Qiang & Qamar Yasin & Naser Golsanami & Qizhen Du, 2020. "Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan," Energies, MDPI, vol. 13(2), pages 1-19, January.

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