pISSN 0705-3797 eISSN 2586-1298
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Episodes 2023; 46(3): 389-405

Published online September 1, 2023


Copyright © International Union of Geological Sciences.

Bayesian stochastic inversion with petro-elastic relation to quantify thin gas sands of Khadro Formation, Zamzama gas field

Zahid Ullah Khan1,2*, MonaLisa1, Muyyassar Hussain1,2, Syed Adnan Ahmed1,2

1Department of Earth Sciences, Quaid-I-Azam University, Islamabad 45320, Pakistan
2LMK Resources Pakistan (Private) Limited, Islamabad 44210, Pakistan

Correspondence to:*E-mail: zahidkhan680@yahoo.com

Received: September 30, 2022; Revised: October 19, 2022; Accepted: October 19, 2022

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


A challenging situation in reservoir characterization arises when reservoirs occur below the vertical resolution of seismic, such as the 6 m gas-prone sands of the heterogenous Khadro Formation. This formation is the Zamzama gas field's secondary reservoir and needs detailed evaluation for optimum production of the field as the deeper primary reservoir of Pab sandstone is facing the problem of early water encroachment in its producing wells. The characterization of thin heterogeneous gas-bearing facies is hampered by the insensitivity of well-bore recording tools in precisely capturing elastic properties. Consequently, petro-elastic models (PEMs) of identified facies are generated that produce consistent elastic responses and discriminate facies in their true elastic domains. The resolution of seismic elastic properties is enhanced to illuminate the thin gas-bearing sands by adopting a Bayesian stochastic inversion process in collaboration with PEMs modeled elastic responses on a fine-scale stratigraphic grid. In addition, the Bayesian framework is used to estimate the litho-facies probability cubes to mitigate the drilling risks by integrating highly sampled posterior elastic volumes, modeled elastic logs, and geologic information of identified litho-facies. The outcomes included prospect identification at the drilled well locations that need to be perforated, while new potential zones are present for additional wells.