Plume Evolution Research Articles

Development on Surrogate Models for Predicting Plume Evolution Features of Groundwater Contamination with Natural Attenuation

Predicting the key plume evolution features of groundwater contamination are crucial for assessing uncertainty in contamination control and remediation, while implementing detailed complex numerical models for a large number of scenario simulations is time-consuming and sometimes even impossible. This work develops surrogate models with an effective and practicable pathway for predicting the key plume evolution features, such as the distance of maximum plume spreading, of groundwater contamination with natural attenuation. The representative various scenarios of the input parameter combinations were effectively generated by the orthogonal experiment method and the corresponding numerical simulations were performed by the reliable Groundwater Modeling System. The PSO-SVM surrogate models were first developed, and the accuracy was gradually enhanced from 0.5 to 0.9 under a multi-objective condition by effectively increasing the sample data size from 54 sets to 78 sets and decreasing the input variables from 25 of all the considered parameters to a smaller number of the key controlling factors. The statistical surrogate models were also constructed with the fitting degree all above 0.85. The achieved findings provide effective generic surrogate models along with a scientific basis and investigation approach reference for the environmental risk management and remediation of groundwater contamination, particularly with limited data.

Assessing the value of combined use of distributed acoustic sensing (DAS) and multicomponent vertical seismic profiling (VSP) data with network-based full waveform inversion and uncertainty quantification: A case study in Alberta, Canada

Distributed acoustic sensing (DAS) technology, deployed in a vertical seismic profiling (VSP) experimental configuration, has emerged as a candidate for non-disruptive and low-cost seismic monitoring of CO2 geostorage and plume evolution. Full waveform inversion (FWI) has likewise received significant attention because it uses relatively complete physical models of wave propagation and because of its sample-by-sample incorporation of data information. Recent artificial neural network-based FWI algorithms (built with, for instance, recursive neural networks, or RNNs) have added to FWI a range of flexible and efficient tools for gradient computation and options for uncertainty assessment and initial model proxies. An important current research area for the use of DAS data is to better understand how they change our confidence levels in models selected through FWI. In particular, we seek to understand whether either DAS data or conventional geophone data alone are optimal for FWI model selection in the CO2 problem, and if not, to what degree they complement each other. The Snowflake 4D VSP dataset, which includes multi-offset and multi-azimuth broadband sources illuminating both fiberoptic cable and densely sampled accelerometer phones in the borehole, was acquired by our group to directly address these questions. In this study, we quantify uncertainty (UQ) by sampling the posterior model covariance matrix from the inverse Hessian matrix at the end of RNN-FWI runs on the Snowflake baseline data, invoking a velocity-density parameterization, and involving mixtures of accelerometer and DAS data. In this UQ context, the complementary effect of combining accelerometer and DAS data is evident in the Vp and Vs models. Our confidence in the approach is bolstered by its independent prediction of a region of known high uncertainty. In the overall pursuit of reliable and low-cost monitoring tools, this supports continued consideration of a multicomponent sensors supported by DAS approach.

Self-similar solution for laminar bubbly flow evolving from a vertical plate

The development of a bubble plume from a vertical gas-evolving electrode is driven by buoyancy and hydrodynamic bubble dispersion. This canonical fluid mechanics problem is relevant for both thermal and electrochemical processes. We adopt a mixture model formulation for the two-phase flow, considering variable density (beyond Boussinesq), viscosity and hydrodynamic bubble dispersion. Introducing a new change of coordinates, inspired by the Lees–Dorodnitsyn transformation, we obtain a new self-similar solution for the laminar boundary layer equations. The results predict a wall gas fraction and gas plume thickness that increase with height to the power of 1/5 before asymptotically reaching unity and scaling with height to the power 2/5, respectively. The vertical velocity scales with height to the power of 3/5. Our analysis shows that self-similarity is only possible if gas conservation is entirely formulated in terms of the gas specific volume instead of the gas fraction.

Spatio-temporal neural networks for monitoring and prediction of CO[formula omitted] plume migration from measurable field data

Carbon capture, utilization, and storage (CCUS) technologies are crucial for mitigating greenhouse gas emissions. The success of CCUS projects hinges on accurate prediction and monitoring of the CO2 plume migration during and after injection. To address the computational burden of traditional numerical simulation methods, previous studies have successfully used neural networks as proxy models to expedite the prediction of the CO2 plume migration. However, these models rely on uncertain inputs, such as the distribution of heterogeneous permeability and porosity maps, which can lead to erroneous predictions and pose a significant hurdle for their adoption in real-world applications. To address this issue, this study introduces a framework for reconstruction and short-term prediction of CO2 plume migration based solely on field measurements, which can directly provide information on CO2 plumes, thus eliminating the dependency on uncertain geological information as model input. The framework trains a spatio-temporal neural network model using simulated data under various geologic scenarios to capture the plume evolution dynamics without constraining it to specific geological scenarios. Once trained, the model integrates global and local field measurements from multiple sources to reconstruct the CO2 plume and predict its spatio-temporal evolution. The effectiveness of the proposed framework is tested using two case studies: one using a synthetic dataset and another with data generated from a model of a real field in the Southern San Joaquin Basin. The results show that the proposed framework can accurately reconstruct and perform short-term predictions of the CO2 plume migration by integrating various forms of field measurements.

Development and observation of a three-dimensional scanning coaxial Mie lidar for dynamic monitoring of near-surface aerosol plumes

Accurate three-dimensional spatiotemporal distribution information on near-surface aerosols is of great significance for environmental research. In this study, a 3D scanning coaxial Mie lidar (3D-STML) was developed to achieve a fast three-dimensional scanning observation of aerosol diffusion processes in near-surface areas. 3D-STML generates high-spatiotemporal resolution images of aerosol extinction coefficient in real-time and captures the dynamic changes of aerosols in near real-time. By optimizing the design of the light guide mirror and the telescope sub-mirror, the system has a small overlap. Based on this, a highly stable and high-speed mechanical rotation mechanism was developed to enable three-dimensional observations. The integration of a solid-state high-repetition-rate pulsed laser and a coaxial, optical system for the transmitter and receiver ensures rapid tracking of aerosol plumes. To meet the observation requirements of near-surface aerosols, an aerosol inversion algorithm combining the Fernald and Klett methods was designed and developed. For aerosol plume monitoring needs, an aerosol plume-tracking algorithm based on Kalman filtering was developed to track the spatiotemporal evolution of aerosols automatically. Experimental results demonstrated that 3D-STML is capable of detecting aerosols in a range from 15 m to 4 km, with a distance resolution of 1.5 m and a time resolution of 0.083 s. It can effectively track and capture aerosol plumes. It can be used for large-scale, long-term observation of near-surface aerosols and for monitoring the spatiotemporal evolution of aerosol plumes.

Effect of RF acetylene plasma on the composition and dynamics of a titanium plasma plume in a plasma enhanced pulsed laser deposition system

We report the effect of radio frequency (RF) acetylene plasma on the dynamics and composition of titanium (Ti) plasma plume in a plasma-enhanced pulsed laser deposition (PEPLD) system. The titanium target, mounted inside a capacitively coupled RF discharge, was ablated by using a nanosecond Nd:YAG pulsed laser at 1064 nm with a power density of 2.65 GW/cm2. The experiments were performed at different operating pressures of acetylene. Fast imaging and optical emission spectroscopy were employed to study the physics behind the pulsed laser deposition in both (PLD) and PEPLD systems. A nonlinear dependence of the plasma plume evolution was observed over a range of pressure. Different expansion regimes correspond to the pressure of the experiments. The plume expansion velocity ranges between 6 × 103 m/s and 30 × 103 m/s. Emission spectra reveal the presence of C II and Ti II lines depending on the experimental conditions. The presence of background RF plasma leads to substantial enhancement of the emission intensity of the C II spectral lines. In addition, with increasing RF power and background pressure, the intensities of the C II spectral lines increase; whereas the intensities of the Ti II spectral lines decrease with the increase in RF power. Plasma temperature was estimated from the Ti II lines using the Boltzmann plot method, whereas the electron density was estimated from the Stark-broadened Ti II line at 454.9 nm. The calculated densities and temperatures lie between 1017–1018 cm−3 and 0.8–2.0 eV, respectively. These results show the effects of the different backgrounds (either neutral or RF plasma) on the propagation of the laser-produced plasma (LPP), which we propose to be useful in the thin film deposition process using PLD.

Characterizing Flow Dynamics in a Two-dimensional Flow Cell: Developing Methodologies for Enhanced Subsurface Contaminant Remediation Research

This study used a two-dimensional (2-D) flow cell apparatus (25 cm × 25 cm × 1 cm) to characterize water flow and contaminant transport in porous media. The primary objective was to enhance the understanding of flow dynamics and contaminant behavior in controlled 2-D environments, which is crucial for optimizing the application of remediation technologies to treat contaminants in saturated and unsaturated soil conditions. The 2-D cell was designed with transparent walls to simulate subsurface conditions using medium grained sand, allowing for precise control and observation of fluid movement under saturated and quasi-saturated (trapped air) conditions. Visualization techniques and flow measurement tools were employed to capture detailed data on flow patterns, velocities, and dispersion characteristics. Analysis of breakthrough curves for salt tracer tests using electrical conductivity measurements, alongside varying flow rates, flow fractions, and port configurations, provided significant insights into fluid behavior and contaminant transport. The study also explored the effects of varying salt concentrations (and solution densities) on flow dynamics, contributing to a better understanding of the role of contaminant concentration in contaminant plume development and remediation processes. Visual analysis using dyed contaminant complemented the quantitative data, offering real-time observations of flow patterns and contaminant distribution. A custom-built apparatus was used to control the hydraulic head of multiple outlet ports to sample water from different depths. The experiments demonstrated that gravity plays a crucial role in solute (salt) breakthrough, with lower ports showing earlier breakthrough. Flow fields formed vertically, from bottom-to-top, and horizontally, from inlet-to-outlet, with lower portions advancing sooner. However, multi-port configurations, especially involving five ports, disrupted the sequential breakthrough order, possibly due to convection in the clear well that formed the outlet boundary of the flow cell. These findings will support research students in conducting similar experiments with flow and transport that varies with depth, with future insights for professionals in developing and optimizing remediation strategies related to subsurface contaminant flow. Special thanks are extended to Liam Price, Julia-Barnes James, and Brooke Belfall who were helpful during the research and the experimentation process.

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Modelling the maximum ceiling temperature with bifurcated plume in tunnel fires

Evaluation of the temperature profile induced by tunnel fire is of great significance for fire detection and structural damage assessment. In this work, a special plume model, namely, the “plume bifurcation” is introduced, and its impact on the temperature distribution is analysed theoretically. Full-scale tunnel fire experiments and numerical simulations are conducted to investigate the plume evolution under various heat release rates and ventilation conditions. Results show that the plume will sperate into two sub-streams under strong ventilation condition, resulting in the multi-dimensional plume movement pattern when rising. The sub-plume impingement point location, quantified by the longitudinal vertical deflection angle θv and horizontal bifurcation angle θh, is correlated with the fire heat release rate and ventilation condition. The plume bifurcation angle is 0 when the dimensionless ventilation velocity V′≤0.33, marking a single-stream plume; and rapidly increase and then slowly decrease with V′ when V′>0.33, representing the sudden occurrence and decay trend of plume bifurcation with the ventilation velocity. Finally, a novel bifurcated plume-based model to estimate the maximum ceiling temperature is proposed. This study reveals the role of multi-dimensional smoke movement in reshaping the temperature field, providing new perspectives for a deeper understanding of the smoke transport behaviour in longitudinally ventilated tunnel fires.

Seasonal, Interannual and Long‐Term Variability of Sea Surface Temperature in the NW Iberian Upwelling, 1982–2020

AbstractHere the seasonal, inter‐annual and long‐term variability of satellite‐derived sea surface temperature (SST) was analyzed in the NW Iberian margin from 1982 to 2020 to explore spatial differences at high (5‐km) resolution. In‐situ temperature measurements from five coastal buoys were used to validate the satellite‐derived SST data, discarding pixels from areas close to the shoreline. Regional SST increased significantly from 0.07 to 0.25°C per decade, with the lowest rates in shelf waters directly affected by seasonal coastal upwelling. Upwelling filaments also contributed to dampen the temperature increase in the adjacent slope and ocean waters. The spatial variability observed in the amplitude and timing of the seasonal cycle of SST is directly related to hydrography and circulation patterns of the region such as upwelling events during summer and the development of the river's buoyant plume and the warm Iberian Poleward Current during winter.

On-site monitoring and numerical simulation on groundwater flow and pollution plume evolution in a hexavalent-chromium contaminated site

Accurately ascertaining spatiotemporal distribution of pollution plume is critical for evaluating the effectiveness of remediation technologies and environmental risks associated with contaminated sites. This study concentrated on a typical Cr(VI) contaminated smelter being currently remediated using pump-and-treat (PAT) technology. Long-term on-site monitoring data revealed that two highly polluted regions with Cr(VI) concentrations of 162.9 mg/L and 234.5 mg/L existed within the contaminated site, corresponding to previous chromium slag yard and sewage treatment plant, respectively. The PAT technology showed significant removal performance in these highly polluted areas (>160 mg/L) after six months of pumping, ultimately achieving complete removal of the pollutants in these high-pollution areas. Numerical simulation results showed that although the current remediation scheme significantly reduced the Cr(VI) pollution degree, it did not effectively prevent the incursion of the pollution plume into the downstream residential area after 20 years. Additionally, an improved measure involving supplementary pumping wells was proposed, and its remediation effects were quantitatively evaluated. Results indicated that the environmental pollution risk of groundwater downstream could be effectively mitigated by adding pumping wells, resulting in a reduction of the pollution area by 20 % in the case of adding an internal well and 41 % with the addition of external wells after 20 years. The findings obtained in this study will provide an important reference and theoretical guidance for the reliability analysis and design improvement of the PAT remediation project.

A coupling numerical model for simulating groundwater uranium removal in coupling device of electric field and PRB

The remediation of groundwater uranium pollution has been successfully researched and implemented. However, the cost-effectiveness remains challenging to evaluate. Numerical modeling presents a potential solution, while the coupling of multi-physics in uranium pollution remediation has been a global challenge. This study developed a multi-physics coupled model to simulate the evolution of groundwater uranium plumes in composite remediation system, which was composed of permeable reactive barrier (PRB) and electrokinetic remediation (EKR). Meanwhile, pH changes are also taken into account. The research revolved around sandbox dynamic migration experiments, where key parameters were measured for model building. Based on this, a mathematical model for achieving multi field coupling has been proposed. Substitute equations were used to unify the calculation of flow field and electric field into the same dimension. The results showed that the pH variations induced by electrolysis propagate towards the center of the electric field. Medium adsorption of uranium reached its peak when the pH was between 4 and 5. The driving force of groundwater flow in the coupled field was about 3.5 times that of electroosmosis, occupying a dominant position. Electric fields can serve as auxiliary means to delay the migration of uranium. Under ensuring the effective operation of the system for 360 days, the system can save 25 % of PRB thickness with the EKR assistance. The model can not only predict evolution of uranium plume in groundwater, but also calculate the performance and lifespan of composite remediation systems, providing guidance and recommendations for the design of real-site remediation systems.

Dynamic modeling studies of basin-scale pressure interference and CO2 plume evolution in multi-well geologic CO2 storage

Large-scale decarbonization through carbon capture and storage (CCS) may likely involve many commercial-scale CO2 storage projects located in proximity. Large volumes of CO2 injected will elevate reservoir pressures and may lead to rapid convergence of fracture pressure thresholds. This analysis employs numerical modeling to analyze how CO2 plumes and pressure fronts evolve when CO2 is injected into a single storage formation from multiple projects located in proximity. This analysis also evaluates the extent to which injection well spacing alleviates pressure buildup in the absence of active pressure management tactics. The simulation approach was based on a single, homogenous saline aquifer in which CO2 injection occurs under a one-injector baseline case and several multi-well cases where well spacing varies. Analysis results show that the extent of pressure buildup is in the range of tens or a few hundreds of kilometers and contingent upon the defining pressure buildup demarcating the front edge. For the geological setting evaluated in this paper, our analysis suggests that without active basin pressure management strategies, commercial-scale projects would likely need to be sited far apart to avoid pressure interference from one another. Analysis results show the radius of CO2 plume varies approximately from 2 to 3 km from injection wells (each injecting 1 Mt/year for 30 years) depending on cases and modeling parameters assumed. Given the pressure interference, this paper thus draws attention to the importance of greater coordination among storage operators and regulatory stakeholders. Because this analysis assumes a very specific geologic situation, this exploratory analysis bears further investigations across other geologic situations.

Wildfires impact on PM2.5 concentration in galicia Spain

Wildfire intensity and severity have been increasing in the Iberian Peninsula in recent years, particularly in the Galicia region, due to rising temperatures and accumulating drier combustible vegetation in unmanaged lands. This leads to substantial emissions of air pollutants, notably fine particles (PM2.5), posing a risk to public health. This study aims to assess the impact of local and regional wildfires on PM2.5 levels in Galicia's main cities and their implications for air quality and public health. Over a decade (2013–2022), PM2.5 data during wildfire seasons were analyzed using statistical methods and Lagrangian tracking to monitor smoke plume evolution. The results reveal a notable increase in PM2.5 concentration during the wildfire season (June–November) in Galicia, surpassing health guidelines during extreme events and posing a significant health risk to the population. Regional wildfire analyses indicate that smoke plumes from Northern Portugal contribute to pollution in Galician cities, influencing the seasonality of heightened PM2.5 levels. During extensive wildfires, elevated PM2.5 concentration values persisted for several days, potentially exacerbating health concerns in Galicia. These findings underscore the urgency of implementing air pollution prevention and management measures in the region, including developing effective alerts for large-scale events and improved wildfire management strategies to mitigate their impact on air quality in Galician cities.

SeisRAFT: A recurrent deep learning network for 4D seismic registration and CO2 storage monitoring

The alignment or registration of seismic images is an important and extensively researched aspect in seismic data processing. This process plays a pivotal role in evaluating the evolution of CO2 plumes and assessing the potential for CO2 leakage from its reservoir storage when using 4D or time-lapse seismic methodology. The computer vision community has demonstrated considerable advantages in employing deep learning for such tasks, particularly when confronted with substantial shifts, mismatches, and noises in complex images. In this study, we leverage a cutting-edge recurrent deep learning network known as recurrent all-pairs field transforms (RAFT), incorporating tailored modifications to address seismic matching challenges. We evaluate the network's performance on both synthetic and field 4D CO2 seismic data sets, showcasing its superior accuracy and effectiveness compared to a widely used traditional method. Particularly, in some intricate and ambiguous instances, where even human vision may struggle to identify corresponding events between images, our deep learning method exhibits strong and reliable performance. Validated results confirm that this lightweight multiscale network, trained through self-supervised learning, offers a rapid, parameter-free, and end-to-end solution for 4D image matching in subsurface CO2 storage monitoring with potential applicability to many other seismic registration tasks.

Numerical simulation and analysis of initial plume discharge of deep-sea mining

Deep-sea mining involved environmental issues that urgently need to be addressed. The fine particle plume released from the surface vessel during deep-sea mining can impact the sea ecosystem, so investigating the characteristics of initial plume discharge is of significant importance for deep-sea mining systems. By numerical simulating the plume discharge process based on the two-fluid Eulerian model, this study explores the influence of initial discharge velocity, initial particle concentration, pipe diameter, and particle size on the surface plume discharge. The evolution mechanism of the plume is investigated through classic plume models involving the interaction of volume flux, momentum flux, buoyancy flux and the numerical simulation results exhibit a consistent trend with theoretical expectations. The results indicate that fine particle discharge velocity, discharge concentration, and pipe diameter are key influencing factors in plume evolution. The discharge velocity and pipe diameter primarily affect the evolution morphology of the plume through momentum flux and buoyancy flux. The discharge concentration mainly affects the plume's evolution through buoyancy flux.

Stable isotopes of landfast sea ice as a record of La Grande River under-ice plume dispersal

Vertical profiles of salinity, and isotopic abundance ratios of hydrogen (δ2H) and oxygen (δ18O) of 18 landfast ice cores, collected along the northeast coast of James Bay in March 2019, and one ice core collected in Belcher Islands, were used to obtain the winter timeseries of the spatiotemporal evolution of the under-ice plume of La Grande River (LGR), the dominant river in the area. Variability in the isotopic composition and salinity of the ice cores indicated changes to the water source composition at the ice–water interface when the ice layers formed. The increased presence of river water beneath the ice during January–March was marked by more negative isotopic ratios in the lower portion of the ice as river discharge was increased for hydroelectric production. River water was the source of ∼43% of the ice in our ice core samples ( n = 320) with the interquartile range from 16% to 71%. The river water fractions incorporated into the ice indicate that LGR under-ice plume extended more than 75 km north and at least 30 km south of the river mouth for ∼3 months. These findings correspond well with more challenging to obtain hydrographic observations. End-of-winter ice core sampling and analysis for isotopic abundance has potential as a tool to monitor dispersal of LGR discharge into the ice-covered coastal environment.

Experimental study of the temperature profile induced by the tunnel wall fire plume at various source-ceiling heights

The smoke temperature profile beneath the ceiling of tunnel is one of the most important parameters to evaluate the tunnel fire risk and guide the early fire detection. This paper investigates experimentally the temperature profile induced by the tunnel wall fire plume at various source-ceiling heights. A series of experiments were carried out in a reduced-scale tunnel and a burner combustion inside. The burner elevation above the tunnel floor was changed at the center of the tunnel, and it attaches the side-wall of the tunnel to simulate the wall fire plume evolution with fire growth inside the tunnel. The temperatures beneath the ceiling were measured by the thermocouple array at the corner between the ceiling and side-wall of the tunnel. The maximum temperature beneath the ceiling could be correlated by the heat release rate and the source-ceiling height. A new characteristic parameter model considering the virtual point source at the side of the tunnel was proposed firstly considering the tunnel fire scenario to describe the tunnel plume temperature profile. The obtained experimental data and proposed temperature profile model in this work provide a basic understanding of the heat/thermal impact on the tunnel wall fire plume.

Basin modelling workflow applied to the screening of deep aquifers for potential CO 2 storage

The temporal and spatial scale of interest of CO 2 storage studies lies in between reservoir and basin models. While reservoir modelling software is best fitted to address some of the multiphysics issues related to the behaviour of CO 2 once injected into the subsurface (adsorption, dissolution, near injection wellbore mechanics and temperature, and, in some cases, fluid rock interaction) within a human timescale, basin modelling tools handle better the full basin volume and time-scale heterogeneities that impact the storage potential and risk associated with CO 2 injection. This study takes a basin modelling approach to provide an assessment of the influence of geological evolution on CO 2 storage capacity, both at the reservoir level, by helping to estimate the amount of CO 2 that can be stored in its connected porosity, and at the cap-rock level, by assessing the seal integrity. Our basin model also captures the evolution of the pressure plume induced by the CO 2 injection, taking into account the pressure and temperature fields, aquifer connectivity and permeability, and seal integrity, on a much shorter timescale than is usually considered by such a model. The results show the impact of basin evolution on aquifer properties and consequently on the dissipation of the induced pressure plume. They also highlight the large-scale influence of the CO 2 on the pressure field both vertically along the stratigraphic column, when the pressure plume reaches shallower aquifers through unconformities, and horizontally, when good aquifer injectivity and connectivity allows the pressure plume to dissipate widely.

Sleipner 26 years: how well-established subsurface monitoring work processes have contributed to successful offshore CO 2 injection

In August 2022, the world's longest running offshore industrial CO 2 injection project celebrated its 26-year anniversary. During these years, the Sleipner CO 2 injection project has been invaluable in demonstrating that offshore CO 2 storage is feasible, safe, and efficient. We will here show how time-lapse seismic monitoring of the CO 2 plume development has revealed depositional architecture in the Utsira Formation, and how thin mudstone layers have contributed to distributing the CO 2 in a larger rock volume, promoting trapping by dissolution. The relatively shallow depth (800–1000 m) of Utsira Formation in the Sleipner area also makes the Sleipner CO 2 injection site a good proxy for understanding the effects of overburden stratigraphy for deeper injection sites, giving important knowledge of detectability of thin, shallow CO 2 accumulations. Finally, we will show how the experience from Sleipner CO 2 injection has built confidence when planning monitoring programmes for future CO 2 injection sites.