Enhancing Subsurface Geological Model Resolution in Challenging Seismic Conditions by Using Model-Based Deterministic Inversion

Abi Mawalid; Abdul Haris; Edy Wijanarko

doi:10.29017/scog.v49i1.1976

Authors

Abi Mawalid Universitas Indonesia
Abdul Haris Universitas Indonesia
Edy Wijanarko Testing Center for Oil and Gas LEMIGAS

DOI:

https://doi.org/10.29017/scog.v49i1.1976

Keywords:

deterministic inversion, acoustic impedance, low-resolution seismic data

Abstract

The limited resolution of 2D seismic data often limits the accuracy of subsurface interpretation. This study explores how deterministic inversion enhances the elastic representation of low-resolution intervals in Field X and contributes to more precise reservoir interpretation. By applying deterministic inversion, this study aims to improve the mapping of lithological variations throughout the interval. Petrophysical data show that the target zone contains porosity values of 11–22%, Gamma Ray readings of 10–120 API, and P-impedance values of 11022–15343. These parameters support well–seismic tying and model calibration. The inversion generates an acoustic impedance model that closely aligns with the log trends and shows a coherence error of only 6.23% within the target interval. Domains with increased permeability and reduced GR readings appear as subtle impedance irregularities, whereas more consolidated phases show higher impedance. The resulting impedance response captures geologically meaningful mid-range lithological variations, although limitations in seismic resolution still reduce the precision of stratigraphic delineation. Overall, the findings demonstrate that careful calibration with petrophysical datasets provides a consistent and quantifiable impedance framework, even in areas with limited seismic fidelity, thereby supporting more reliable reservoir interpretation.

References

Asquith, G. & Krygowski, D., 2004, Basic Well Log Analysis, American Association of Petroleum Geologists (AAPG), Tulsa.

Avseth, P., Mukerji, T., & Mavko, G., 2005, 'Quantitative Seismic Interpretation: Applying Rock Physics Tools to Reduce Interpretation Risk', Cambridge University Press, Cambridge.

Bosch, M., Mukerji, T., & Gonzalez,E. F., 2010, Seismic inversion for reservoir properties combining statistical rock physics and geostatistics, Geophysics, Society of Exploration Geophysicists.

Dewan, J.T., 2001, Essentials of Modern Open-Hole Log Interpretation, PennWell Publishing, Tulsa.

Dubois, G., & Brossier, R., 2018. Uncertainty quantification in seismic inversion. Geophysics, 83(4), R413–R426. https://doi.org/10.1190/geo2017-0552.1

Grana, D., 2017, 'Bayesian inversion of seismic data for reservoir characterization', Geophysics, Society of Exploration Geophysicists.

Grana, D., & Della Rossa, E., 2010, 'Seismic inversion using statistical rock physics models', Geophysics, Society of Exploration Geophysicists. HRS, 2011, 'Hampson Russel Guide', pp.1–62.

H. Wu, 2015, 'Deterministic and stochastic inversion techniques used to predict porosity: A case study from F3-Block' Michigan Technological University, USA.

K. J. Marfurt, 2006, 'Robust spectral decomposition for seismic attribute analysis', Geophysics, Society of Exploration Geophysicists.

Butar, M. H. P. B., Juventa, & Marlinda,L. (2023). Identifikasi prospek reservoir hidrokarbon menggunakan inversi impedansi akustik pada Blok Kampar. Lembaran Publikasi Minyak dan Gas Bumi (LPMGB), 57(1), 43–59. https://doi.org/10.29017/LPMGB.57.1.1324

Mavko, G., Mukerji, T., & Dvorkin, J., 2009, The Rock Physics Handbook: Tools for Seismic Analysis of Porous Media, Cambridge University Press, Cambridge.

Haris, A., Haryono, H., & Riyanto, A., (2018). Spectral decomposition technique based on STFT and CWT for identifying the hydrocarbon reservoir. Scientific Contributions Oil and Gas (SCOG), 40(3), 125–131.https://doi.org/10.29017/SCOG.40.3.50

R. Simm, & Bacon, M, 2012, Seismic amplitude: An interpreter’s handbook,'Cambridge University Press, New York.

Robinson, E. A., & Treitel, S., 2000. Geophysical Signal Analysis, Society of Exploration Geophysicists.

Russell, B. H. 1988. Introduction to Seismic Inversion Methods. Society of Exploration Geophysicists (SEG), Tulsa.

Sancevero, S.S., Remacre, A.Z., de Souza Portugal, R., & Mundim, E.C., 2005, ‘Comparing deterministic and stochastic seismic inversion for thin-bed reservoir characterization in a turbidite synthetic reference model of Campos Basin, Brazil’, The Leading Edge, 24, 1168–1172.

Sheriff, R. E., & Geldart, L. P. 1995. Exploration seismology (2nd ed.). Cambridge University Press.

Rose Firdiany Nur Sukma, Mohammad Syamsu Rosid, & Edy Wijanarko (2025). Telisa Formation Characterization Using Seismic Acoustic Impedance Inversion in the Akasia Area of the Central Sumatra Basin. Scientific Contributions Oil and Gas (SCOG), 48(2), 29–40.

https://doi.org/10.29017/scog.v48i2.1774

Triyoso, W., Sinaga, E. I., & Oktariena,M. 2024. Seismic data processing and seismic inversion in the ray parameter domain: Common reflection point (CRP) stack and ray impedance. Scientific Contributions Oil and Gas, 47(2), 129–139. https://doi.org/10.29017/SCOG.47.2.1621

Yilmaz, Ö., 2001, 'Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data, Society of Exploration Geophysicists (SEG)', Tulsa.