The oil and gas sector is responsible for approximately 5.1 Gt CO2eq of direct operational emissions annually, representing roughly 15% of global energy-related greenhouse gas emissions, yet faces an accelerating mandate to decarbonize while sustaining global energy supply. This paper presents a systematic review of 218 peer-reviewed and institutional sources published between 2013 and 2024 to evaluate the technical readiness, economic viability, and integration potential of three principal decarbonization pathways: carbon capture and storage (CCS), hydrogen technologies, and digital optimization. CCS sub-technologies are assessed across the full value chain, from post-combustion amine scrubbing (TRL 9, $40–80/tonne CO2) to direct air capture (TRL 5, $250–600/tonne CO2), with global project capacity estimated at 439 Mtpa across 628 active projects as of 2024. Hydrogen production routes are evaluated from grey steam methane reforming ($0.8–2.0/kgH2) through blue hydrogen with CCS ($1.2–3.0/kgH2) to green electrolytic hydrogen ($2.0–7.0/kgH2), with cost trajectories suggesting grid parity in high-irradiance regions by the mid-2030s. Digital optimization, encompassing AI-driven production management, digital twins, and satellite-based methane monitoring, is assessed as the most immediately deployable pathway, with IEA estimates suggesting 15–20% upstream emissions reductions achievable by 2030 at payback periods of one to three years. Integrated strategies combining all three pathways within industrial cluster frameworks are shown to achieve 40–55% sectoral emissions reductions at 30–40% lower cost than single-pathway approaches. Key barriers to deployment include high capital costs and long payback periods for CCS and hydrogen projects, fragmented regulatory frameworks across jurisdictions, IT-OT integration challenges in legacy operational environments, and the disproportionately limited access to decarbonization capital in developing economies. This review concludes that no single pathway is sufficient and that coordinated action across technology development, carbon pricing policy, multilateral financing, and shared infrastructure investment is required to place the oil and gas sector on a credible net-zero trajectory.
Z. Iyiola, O. Ejehu, M. Ezeh et al.· SPE Nigeria Annual Internati...· 0 citations
Accurate prediction of CO2 solubility in formation brines is central to carbon storage design because dissolution trapping reduces CO2 mobility and supports long term containment. Yet, solubility data and correlations are often limited in coverage, uncertain at high salinity and pressure, and can be unreliable when extrapolated beyond the calibration range. This work develops a physics-informed benchmarking framework that evaluates when machine learning (ML) models provide reliable CO2 solubility predictions under reservoir relevant conditions, and when established physics-based correlations remain the safer choice. A physics-informed CO2 brine dataset was generated over geologically realistic ranges that represent deep saline reservoirs at approximately 4,900 to 13,000 ft depth, spanning 35 to 347 bar, 285 to 430 K, and 0 to 259 g/L salinity. Ground-truth solubilities were produced using Henry's law with van't Hoff temperature dependence and a Setchenov salting-out correction, then supplemented with fugacity and activity-coefficient adjustments to address non-ideal behavior at higher pressure and salinity. A calibrated non-ideality term was included to preserve physically consistent monotonic trends across the full Pressure-Temperature-Salinity(P–T–S) space. To emulate laboratory uncertainty without changing the sampled inputs, controlled zero-mean Gaussian noise of 1%, 3%, 5%, and 10% relative standard deviation was applied to solubility targets. Three ML models, Linear Regression, Random Forest, and a three-layer MLP (64-32-16, ReLU), were trained using identical feature sets (P, T, S), standardized preprocessing, and consistent train, validation, and test splits. Model performance was evaluated using R2, MAE, RMSE, k-fold cross-validation, parity and residual diagnostics, calibration curves, and bootstrap uncertainty estimates. Out-of-distribution (OOD) robustness was quantified by withholding a high-salinity band (S > 200 g/L) and a low-temperature band (T < 300 K) from training to test generalization in regimes relevant to storage screening. The resulting workflow provides a practical, physics-consistent basis for selecting solubility predictors and defining reliable application envelopes for ML in CCUS studies.
O. Ejehu, A. J. Whitcomb, M. Hunter et al.· SPE Nigeria Annual Internati...· 0 citations
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