Reducing uncertainties in hydrocarbon prediction through seismic inversion

Qazi Sohail Imran, Numair Ahmad Siddiqui, Abdul Halim Abdul Latif, Yasir Bashir, Almasgari A.A. Saeed Ali, Muhammad Jamil, Nisar Ahmad

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

Abstract

Petroleum systems in offshore are often very complex and subtle because of variety of depositional environments. Delineating a reservoir based on conventional seismic and well-log stratigraphic analysis in such complex settings often leads to uncertainties. A reliable model of reservoir can forecast the production and performance of a reservoir which can reduce the drilling risks and associated uncertainties significantly. This work is aimed to develop a new concept in reservoir modeling by integrating seismic inversion and rock physics tools. First, in order to define litho facies, rock physics modeling was carried out through well log analysis separately for each facie. Next, the available subsurface information is incorporated in a Bayesian engine which outputs the several realizations of elastic reservoir properties and their respective probabilities, which then are used for post-inversion analysis. Seismic inversion fully exploited the vast areal coverage of the seismic data and integrated it with the well logs resulted in high-resolution acoustic impedance realizations. 3D impedance models coupled with the petrophysical analysis were then used to delineate the reservoir bodies.

Original languageEnglish
Article number012002
JournalIOP Conference Series: Earth and Environmental Science
Volume1003
Issue number1
DOIs
Publication statusPublished - 22 Apr 2022
Externally publishedYes
Event2nd International Conference on Earth Resources 2020, ICER 2020 - Sarawak, Virtual, Malaysia
Duration: 13 Jul 202115 Jul 2021

Bibliographical note

Publisher Copyright:
© Published under licence by IOP Publishing Ltd.

Keywords

  • Characterization
  • Prestack/Poststack Inversion
  • Reservoir Delineation
  • Reservoir Modeling

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