AI-Integrated Framework for Cost Reduction in Petroleum Extraction and Refining: A Bayesian-Enhanced Model with Advanced Global Sensitivity Analysis, GPU-Accelerated Neural Operators, Economic Feasibility Assessment, Real-World Case Studies, Environmental Impact Assessment, and Ethical Considerations

Vollständiger Abstract

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This manuscript elucidates a sophisticated AI-integrated conceptual framework engineered to realize substantial operational cost reductions in petroleum extraction and refining through optimized enhanced oil recovery (EOR) strategies. Utilizing physics-informed neural operators (PINOs) as high-fidelity surrogates for multiphase flow, simulations evince up to 30% cost savings in benchmarks, consonant with 2025-2026 industry metrics of 10-25% efficiencies via AI [15, 29, 103]. The framework amalgamates deep reinforcement learning (DRL), Bayesian inference for uncertainty quantification (UQ), and advanced variance-based global sensitivity analysis (GSA) incorporating interaction effects, Morris screening, FAST methods, and uncertainty propagation. It assimilates real-time telemetry with predictive analytics to ameliorate inefficiencies, modeling physics-informed linkages in CO 2-EOR impacting crude quality (API uplift 7-12%, viscosity reduction 75%) [74, 10]. Mathematical foundations include stochastic partial differential equations (SPDEs), hybrid variational inference-Hamiltonian Monte Carlo (VI-HMC) for scalable UQ, adjointoptimized non-convex problems with advanced derivatives (Jacobians, Hessians), and rigorous proofs of convergence. GPU-accelerated Python codes, employing PyTorch for PINOs and custom HMC, corroborate mitigations across heterogeneous reservoirs, calibrated to OPEX benchmarks (55-65 USD/bbl) [33]. Falsifiability is affirmed via SPE benchmarks (SPE10, CSP11) and posterior predictive checks, with posteriors delineating sensitivities (ϕ > 0.20 for viability). Economic feasibility is rigorously assessed through NPV, ROI, and payback analyses, projecting 30-70% profit increments for stakeholders [15]. Real-world case studies from Permian Basin, H59 block, Denver Unit, Bell Creek, and Indonesian fields validate efficacy, while environmental impacts of CO 2-EOR are scrutinized, balancing sequestration benefits (0.3-0.5 tCO 2 /bbl stored) against risks (net emissions increase, water contamination, induced seismicity) [74, 57, 25, 27, 26, 28, 30, 4, 89, 66, 3, 38, 82, 52, 10]. Ethical considerations address data privacy, algorithmic bias, cybersecurity, job displacement, and accountability in AI deployment [79, 34, 94, 69, 60, 53, 106, 101, 55, 23, 65]. This paradigm propels petroleum engineering with a verifiable, scalable architecture, surmounting bottlenecks via tensor parallelism and hybrid VI-HMC (O(dlogd)).