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ORIGINAL RESEARCH article

Front. Earth Sci.
Sec. Economic Geology
Volume 12 - 2024 | doi: 10.3389/feart.2024.1516420

Correlation of Artificial Neural Network and Multi-Attribute Transformation: A Test Case to Predict the Effective Porosity of Cretaceous Sandstones of the Sembar Formation, Southeast Pakistan

Provisionally accepted
Furqan Aftab Furqan Aftab 1,2,3,4*Mohammad Zafar Mohammad Zafar 1Muhammad Iqbal Hajana Muhammad Iqbal Hajana 1John H. Shaw John H. Shaw 2
  • 1 Department of Earth and Environmental Sciences, Bahria University, Islamabad, Pakistan, Islamabad, Islamabad, Pakistan
  • 2 Department of Earth and Planetary Sciences, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts, United States
  • 3 Institute of Geology, Faculty of Earth Sciences, Geography and Astronomy, University of Vienna, Vienna, Vienna, Austria
  • 4 Department of Earth and Ocean Sciences, School of Arts and Sciences, Tufts University, Medford, Massachusetts, United States

The final, formatted version of the article will be published soon.

    Accurate prediction of reservoir properties through linear transformation and regression methods are successful in limited cases but are geologically unrealistic and have no concrete theoretical foundation. Artificial Neural Network (ANN) emerges as an accurate tool for deriving nonlinear mathematical relationships between seismic attributes and well logs that are theoretically plausible and geologically realistic. In this paper, we devised a methodology to integrate rock physics analysis, seismic inversion, multi-attribute transformation, and Feedforward Neural Network (FNN) modeling for accurate inter-well reservoir property predictions. We test this methodology on well logs and seismic data via the application on Cretaceous sandstones of the Sembar Formation, Southern Indus Basin, Pakistan. Viable productive gas zones are identified through rock physics and Model Based Inversion (MBI) analyses. Five volume-based seismic attributes are sequentially calculated through forward stepwise regression and cross-validated for inter-well porosity prediction.When a Probabilistic Neural Network (PNN) is trained in a non-linear mode integrated with multi-attribute transformation, correlation (r 2 ) is improved from 72% to 88% between seismic attributes and observed porosity log. The PNN-derived porosity distribution is geologically more realistic and in excellent match with the actual porosity log, confirming our model's validity. We suggest that it is theoretically possible for the ANN to make predictions about any attribute of the reservoir via bridging target logs and seismic data within a short computation time.

    Keywords: artificial neural network, Feedforward neural network, Probabilistic neural network, Multi-attribute transformation, Volume-based seismic attributes, Sembar Formation, Sandstones

    Received: 24 Oct 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Aftab, Zafar, Hajana and Shaw. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Furqan Aftab, Department of Earth and Environmental Sciences, Bahria University, Islamabad, Pakistan, Islamabad, Islamabad, Pakistan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.