Pore pressure estimation in the Asmari formation of the Kupal oil field using 3D seismic inversion

Document Type : Research Article

Authors

Dept. of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

10.22034/anm.2025.23186.1682

Abstract

This study presents a workflow for pore pressure prediction in the carbonate Asmari Formation of the Kupal oil field, southwestern Iran, by integrating 3D seismic inversion with machine learning. The methodology combines acoustic impedance models derived from post-stack seismic inversion with well-log data (RFT pressure measurements, sonic logs, density logs) to train predictive models. Three approaches—multivariate regression, probabilistic neural networks (PNN), and multilayer feedforward neural networks (MLFN)—were developed and validated using a blind-test well. Results demonstrate that MLFN outperformed other methods, achieving a validation correlation of 87% and a mean relative error of 2.8%. Overpressure zones, attributed to shale layers and structural compartmentalization, were identified through deviations from hydrostatic trends. The study highlights the value of 3D seismic data for spatially continuous pressure modeling in heterogeneous reservoirs, offering practical solutions to reduce drilling risks and optimize reservoir management.

Keywords

Main Subjects

References

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History

  • Receive Date: 29 May 2025
  • Revise Date: 12 October 2025
  • Accept Date: 08 November 2025

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