挪威国家石油公司历史

Carbon dioxide flooding delivers dual benefits by increasing oil recovery and storing greenhouse gases, and shows strong application potential in unconventional reservoirs. These reservoirs contain abundant nanopores where strong wall fluid interactions and confinement effects substantially modify fluid thermodynamic properties. As a result, the conventional Peng Robinson (PR)-equation of state (EOS) exhibits noticeable deviations when predicting phase equilibrium at micro and nanoscale conditions. This study reviews CO2-crude oil behavior in nanopores and synthesizes current understanding of critical property shifts, phase envelope deformation, and reduction of minimum miscibility pressure. Existing approaches most often treat a single mechanism and lack integrated predictive models that couple multiple confinement effects. In this work, a dimensionless correlation between critical properties and pore size was developed on the basis of experimental observations and molecular simulations. This correlation was combined with adsorption-layer thickness adjustment, capillary pressure evaluation, and volume translation to construct a modified equation of state. The model was applied in multicomponent flash calculations together with the multiple mixing cell method to estimate minimum miscibility pressure under confinement. Validation indicates high accuracy and numerical stability across wide ranges of pore size, composition, and temperature, and the model successfully reproduces confinement-induced changes in saturation pressure and overall phase behavior. Results demonstrate that both critical temperature and critical pressure decrease nonlinearly with decreasing pore size, with the strongest variations occurring below about 10 nm. Phase behavior shifts toward lower pressure and temperature, and the two-phase region becomes narrower. Minimum miscibility pressure also decreases markedly as pore size is reduced. The study reveals the coupled multi-mechanism nature of CO2 flooding in unconventional reservoirs and provides a theoretical basis and technical guidance for optimizing injection strategies and evaluating storage potential.

Large-scale geological CO2 storage (GCS) is essential for achieving net zero targets: its scalability is constrained less by pore volume occupancy than by pressure space-the finite capacity of connected formations to dissipate pressure increases without causing undesirable consequences (such as brine expulsion, induced seismicity, and lower injection rates for a given surface pressure). Current regulatory and commercial frameworks focus on project/site scale containment of CO2, but as multiple projects start to inject in the same storage formation, the cumulative pressure buildup will limit injectivity long before pore space is filled with CO2. Here we synthesize the physics of pressure propagation, the geomechanical limits, and interference across multiuser aquifers, and review monitoring and modelling strategies from analytical screening to full field simulations. Drawing on analogues from groundwater management and three regional illustrative examples (Horda Platform, Paris Basin, Captain Fairway), we show that pressure footprints can exceed plume extents by two orders of magnitude and propagate across tens to hundreds of kilometers. We argue that commons-based governance, underpinned by effective monitoring, open data, regional models, and adaptive allocation of pressure budgets, is essential for safe, efficient, and equitable storage. Treating pressure as a shared resource is not optional: it is the foundation for gigatonne scale CO2 storage and sustainable multiuser use of the subsurface.

This paper aims to identify the gaps on the path to achieving sustainable development of floating offshore wind in Japan. Japan has a strong desire for floating offshore wind development, motivated by energy security, climate change, and industry promotion. The key challenges are described with an emphasis on the unique environmental conditions of Japan, such as earthquakes and tropical cyclones. In addition, the absence of oil and gas development in Japan has led to social challenges, such as a lack of supply chain, infrastructure, and human resources in offshore wind. A review of state-of-the-art technologies is provided in each technology domain for four research domains: site selection and characterization, technology and engineering, project execution and operation, and industry and economic enabling. The gaps identified in the paper suggest the need for specific research topics, such as the assessment of unique environmental conditions; the design of robust and cost-effective structures considering fabrication, transportation, installation, and operation; and the creation of a data sharing strategy for efficient and rapid learning. In addition, many challenges show technology gaps across domains, indicating the need for interdisciplinary research collaboration and system integration of complex systems with digital engineering approaches. Finally, the need for the development of an industry roadmap to address these challenges is discussed.

Microfossil analysis is important in subsurface mapping, for example to match strata between wells. This analysis is currently conducted by specialist geoscientists who manually investigate large numbers of physical samples with the aim of identifying informative microfossil species and genera. The current digitalization of large volumes of microfossil samples that is being conducted by the Norwegian Offshore Directorate, paired with AI development, opens up new opportunities for automating parts of the analysis to help the geologist in the analysis. Unsupervised representation learning is a research area in Artificial Intelligence (AI) that lies at the core of this challenge, as this way of learning can create useful image representations by utilizing large volumes of data without requiring labels. Previous work has presented good results for the classification of a limited number of classes, but there are still challenges related to classification in realistic settings where additional unknown species are present. In this paper, we connect unsupervised representation learning and uncertainty estimation and create a comprehensive tool to automate microfossil analysis. We present our methodology and results in three parts. In the first part, we train several AI models from scratch using state-of-the-art self-supervised learning methods, obtaining excellent results compared against state-of-the-art foundation models for image classification and content-based image retrieval. In the second part, we develop a method based on conformal prediction which enables our classifier to handle a large pool of images containing both in-distribution and out-of-distribution data, while at the same time allowing us to create error estimates to control the uncertainty of the prediction sets. In the third part, we use our method to create distribution charts of fossils for a range of genera in multiple wells.

Lithium (Li) concentrations in oilfield brines can exceed those expected from evaporative concentration of precursor seawater, yet the processes responsible for this enrichment remain poorly constrained. Brines from the Late Devonian Bakken Formation (Williston Basin, USA) have a median Li concentration of 50.1 mg/L, which is approximately 250 times greater than that of unmodified seawater (similar to 0.2 mg/L). Integrated brine major- and trace-element geochemistry, shale lithogeochemistry, and mass-balance modelling demonstrate that lithium is sourced from the Bakken shales and released during burial. Strontium isotope ratios preclude a significant contribution from external fluids. Lithium concentrations in the Lower Bakken shales decrease from similar to 90 ppm along the basin margins to similar to 70 ppm in the thermally mature interior, corresponding to a loss of approximately 20% of the bulk lithium. Monte Carlo mass-balance calculations using formation thickness, porosity, water saturation and shale Li content show that diagenetic lithium loss from the shales can reproduce Bakken brine Li concentrations, but cannot account for the Three Forks Formation brine, which require additional sources. Similar geochemical relationships are found in the Duvernay-Leduc system (Alberta), indicating that thin, Li-bearing shales can act as regional sources that charge neighboring reservoirs. This source-sink framework links shale diagenesis to lithium-rich basinal brines and improves the prediction of Li distribution in sedimentary basins.