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Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review

Author

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  • Marek Stadtműller

    (Oil and Gas Institute-National Research Institute, ul. Lubicz 25A, 31-503 Krakow, Poland)

  • Jadwiga A. Jarzyna

    (Faculty of Geology Geophysics and Environmental Protection, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

The purpose of this review paper is to show the possibilities of carbonate reservoir characterization using well logging and laboratory measurements. Attention was focused on standard and new methods of well logging acquisition and interpretation including laboratory experiments to show a part of the history of carbonate rock investigations as hydrocarbon or water reservoirs. Brief information on the geology, mineralogy and petrography of carbonate rocks was delivered. Reservoir properties, i.e., porosity (including fracturing), permeability, and saturation, were defined to emphasize the specific features of carbonates, such as fractures, and vugs. Examples of methodologies were selected from the commonly used laboratory techniques (thin sections examination, mercury and helium porosimetry, X-ray diffraction—XRD) combined with the standard well logs (bulk density—RHOB, neutron porosity—NPHI, sonic slowness—DT, and deep resistivity—Rd) to show the methods that have been used since the very beginning of the scientific and engineering studies of carbonates. Novelty in well logging, i.e., resistivity and acoustic imaging, nuclear magnetic resonance–NMR, dipole shear sonic imaging–DSI, and a spectral neutron-gamma log-geochemical device–GLT combined with modern laboratory investigations (NMR laboratory experiments, scanning electron microscopy SEM), showed how continuous information on mineral composition, porosity and saturation could be obtained and juxtaposed with very detailed laboratory data. Computed X-ray tomography (CT) enabling the 2D and 3D analyses of pores and fractures was presented as a quantitative methodology, effective in pore space characterization, revealing rock filtration abilities. Deep learning and artificial intelligence were used for joining various types of data. It was shown that thanks to new computational technologies original data from very small samples (micro scale), extensively describing the flow ability of the reservoir, could be extended to mezzo scale (core samples) and macro scale (well log images). Selected examples from the published papers illustrated the review. References cited in the text, together with the issues included in them, were the rich source of the practical knowledge processed These were checked by the authors and could be used in other projects.

Suggested Citation

  • Marek Stadtműller & Jadwiga A. Jarzyna, 2023. "Estimation of Petrophysical Parameters of Carbonates Based on Well Logs and Laboratory Measurements, a Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4215-:d:1151655
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    References listed on IDEAS

    as
    1. Marek Stadtműller & Paulina I. Krakowska-Madejska & Grzegorz Leśniak & Jadwiga A. Jarzyna, 2021. "Characterization of the Carbonate Formation Fracture System Based on Well Logging Data and Results of Laboratory Measurements," Energies, MDPI, vol. 14(19), pages 1-24, September.
    2. Jadwiga A. Jarzyna & Stanisław Baudzis & Mirosław Janowski & Edyta Puskarczyk, 2021. "Geothermal Resources Recognition and Characterization on the Basis of Well Logging and Petrophysical Laboratory Data, Polish Case Studies," Energies, MDPI, vol. 14(4), pages 1-26, February.
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    Cited by:

    1. Weronika Kaczmarczyk-Kuszpit & Krzysztof Sowiżdżał, 2024. "Integration of Well Logging and Seismic Data for the Prognosis of Reservoir Properties of Carbonates," Energies, MDPI, vol. 17(2), pages 1-15, January.
    2. Nafees Ali & Xiaodong Fu & Jian Chen & Javid Hussain & Wakeel Hussain & Nosheen Rahman & Sayed Muhammad Iqbal & Ali Altalbe, 2024. "Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves," Energies, MDPI, vol. 17(15), pages 1-22, July.

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