The Energy Paradox: Artificial Intelligence for Seismic Computational Load Reduction in Oil and Gas — A Technical Survey and Position Analysis.

Abstract — The oil and gas (O (ii) Introducing data-driven components within a classical optimization framework — hybrid architectures that replace computationally expensive sub-steps with learned proxies; (iii) Full end-to-end inversion — the neural network directly maps seismic observations to velocity models. Processing and inference times for deep learning models trained on seismic inversion tasks have been reported to be an order of magnitude lower than the optimization time of classical FWI approaches 6. This reduction is particularly pronounced at inference time, where — after a computationally intensive offline training phase — the network produces velocity model predictions in seconds rather than hours. The dimensionality-reduction and representation-learning capabilities of deep neural networks are particularly well-suited to seismic inversion, where the relationship between wavefield observations and subsurface parameters is high-dimensional and non-linear 6. Convolutional architectures have shown strong performance on velocity model reconstruction tasks, exploiting the spatial coherence of both seismic data and subsurface geology. B. Physics-Informed Neural Networks (PINNs) A limitation of purely data-driven surrogates is their dependence on large, labeled training datasets — a constraint that is challenging in the O&G context due to the commercial sensitivity of subsurface data and the high cost of generating synthetic training data at industrial resolution. Physics-Informed Neural Networks address this limitation by embedding the gover