Irregular spatial sampling, acquisition gaps, and operational constraints compromise the resolution and fidelity of seismic imaging, often causing aliasing, amplitude distortions, and migration artifacts that hinder structural interpretation and reservoir characterization. This research evaluates five-dimensional (5D) regularization and interpolation for reconstructing incomplete seismic datasets. The study applies 3D seismic data from the Bazuzi Field, Sirte Basin, North Libya—a region characterized by sparse shot-receiver spacing and irregular offset–azimuth coverage. By integrating 5D interpolation into the pre-stack domain (inline, crossline, offset, azimuth, and time), a continuous, uniformly sampled wavefield is reconstructed, providing optimized input for imaging. The workflow incorporates data conditioning, Fourier-based interpolation, Radon transforms, and least-squares optimization for trace reconstruction. Comparative assessment between the nominal grid (250 × 250 m) and an upsampled interpolated grid (125 × 125 m) demonstrates significant improvements. Nominal fold coverage increases from 120 to 480 traces per 25 × 25 m bin, offset–azimuth sampling is enhanced, and acquisition footprints are suppressed. 5D interpolation restores missing traces while preserving amplitude fidelity, enabling more reliable AVO/AVA analysis. Upsampled interpolation improves structural continuity, reduces migration smiles and edge effects, and increases signal-to-noise ratio across in-line and cross-line sections. Quality control on time slices, offset classes, and CDP gathers confirm improved geological plausibility and kinematic consistency. These results underscore the ability of 5D techniques to mitigate acquisition limitations and enhance seismic imaging. With growing computational power, 5D regularization is poised to become increasingly central to high-resolution subsurface interpretation and hydrocarbon exploration.
Mohannad O. AboBakr, Muhammad A. El Hameedy, W. Mabrouk et al.· Scientific Reports· 0 citations
Accurate permeability prediction is essential for reliable reservoir characterization and simulation, yet remains challenging due to complex nonlinear relationships and subsurface heterogeneity. Conventional hydraulic flow unit (HFU) methods rely on discrete rock typing and fixed porosity–permeability relationships, limiting their ability to capture continuous variations. Physics-informed neural networks (PINNs) offer a data-driven alternative with embedded physical constraints, but their effectiveness is often limited by weak enforcement of physics during inference. In this study, a physics-guided multi-task neural network (MT-PINN) is proposed to simultaneously predict permeability and hydraulic flow units within a unified framework. The model integrates data-driven learning with physics-based relationships and probabilistic rock typing, enabling permeability to be estimated as a weighted combination of multiple flow units and allowing smoother transitions between facies. The proposed approach was evaluated using core and well log data and compared against conventional HFU and standard PINN methods. Within the studied dataset, the MT-PINN demonstrated improved predictive performance, with a higher correlation coefficient (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:R\:=\:0.936$$\end{document}) compared to HFU (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:R\:=\:0.89$$\end{document}) and PINN (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:R\:=\:0.90$$\end{document}), along with a reduction in log-scale error. The model also provides more continuous and stable permeability predictions along depth. In addition to permeability estimation, the MT-PINN outputs both discrete HFU classifications and associated class probabilities, which can be incorporated into 3D stochastic reservoir modeling workflows for uncertainty-aware multi-realization analysis. The proposed framework demonstrates the potential to integrate traditional petrophysical methods with modern machine learning techniques, providing a promising workflow for permeability prediction and reservoir characterization within the studied reservoir.
Khaled Saleh, W. Mabrouk, A. Metwally· Scientific Reports· 0 citations
