International Journal of Greenhouse Gas Control最新文献

This study investigates the integration of machine learning (ML) and data assimilation (DA) techniques, focusing on implementing surrogate models for Geological Carbon Storage (GCS) projects while maintaining the high fidelity physical results in posterior states. Initially, we evaluate the surrogate modeling capability of two distinct machine learning models, Fourier Neural Operators (FNOs) and Transformer UNet (T-UNet), in the context of CO₂ injection simulations within channelized reservoirs. We introduce the Surrogate-based hybrid ESMDA (SH-ESMDA), an adaptation of the traditional Ensemble Smoother with Multiple Data Assimilation (ESMDA). This method uses FNOs and T-UNet as surrogate models and has the potential to make the standard ESMDA process at least 50% faster or more, depending on the number of assimilation steps. Additionally, we introduce Surrogate-based Hybrid RML (SH-RML), a variational data assimilation approach that relies on the randomized maximum likelihood (RML) where both the FNO and the T-UNet enable the computation of gradients for the optimization of the objective function, and a high-fidelity model is employed for the computation of the posterior states. Our comparative analyses show that SH-RML offers a better uncertainty quantification when compared to the conventional ESMDA for the case study.

A novel carbonate-based CO₂ capture process was developed at VTT, and proof-of-concept was tested using a bench scale device. The process has closed-loop, carbonate liquid, and the captured CO₂ is released at under-pressure. The temperature is around 65 °C, which enables, for example, utilisation of waste heat streams in process integrations. The process performance and its parameters were tested using 8 w-% sodium carbonate liquid with simulated gases, real flue gases and biogas. Also, the absorption mass transfer kinetics and suitable flow parameters of the invented microbubble generator were assessed. The device has successfully and continuously run for tens of hours, and the typical CO₂ capture rate between 72 and 84 % were performed at the liquid pH value of 9.3, using a near-atmospheric pressure process. Mass transfer rates k_La for carbonate liquid and air were between 0.14 and 0.34 s⁻¹, which indicates efficient gas absorption, enabling size reduction of the device size.

System modelling efforts have shown that carbon capture is a key technology to enable a cost-effective reduction of hard-to-abate emissions in energy-intensive industries. The CO${}_2$ that is captured can either be utilised (CCU), or stored with carbon capture and storage (CCS). This paper examines the implications of both European carbon pricing mechanisms (ETS I & ETS II) on the level-playing field between CCS and CCU investments. Our contribution is threefold. First, we develop an equilibrium model that enables us to mimic market outcomes under different regulatory conditions. With a numerical case study applied to a fuel production chain, the model confirms that the current ETS regulation can have an adverse effect on CCU uptake. Especially with zero or low ETS II prices a lock-in effect can occur on CCS, potentially prolonging conventional refinery activities. Second, we propose an alternative approach to better integrate CCUS into the EU ETS. Results show that this approach maintains the level-playing field between CCU and CCS, regardless of any carbon price differentials. That results in a closer to Pareto optimal outcome in terms of welfare and emission abatement. Third, we present an analytic analysis to express the CCUS trade-off from a theoretical point of view. This provides generalised and concrete insights into how EU ETS influences the profitability and likelihood of CCUS. Our results help policymakers to gain a better understanding of the impact of ETS regulations on decarbonisation efforts in the industry.

CO₂ Capture and Storage (CCS) is today seen as a key technology to cut carbon emissions in many hard-to-abate sectors such as energy-intensive processing industries and the waste sector. Although CO₂ capture is technically possible, key challenges for realizing CCS persist. Over the past decade, CCS has taken a new direction with more focus on application in energy-intensive industries rather than the energy sector. For CCS value chains to materialize, innovation and implementation thus needs to occur amongst an array of actors, with different innovation modes, institutions, and policy regimes, and with varying sectoral capacities for adaptation and change. There has so far been limited social science research on CCS innovation dynamics, which we suggest approaching as a socio-technical change process. To better understand this process, we draw on the sustainability transitions research field and employ the Technological Innovation System (TIS) framework to study the CCS innovation system in Norway. We find that, overall, the Norwegian CCS TIS displays systemic weaknesses for example in the form of market formation and resource mobilization, yet recent developments suggest a relatively positive momentum for this technological field which is key to meeting Norwegian and global climate mitigation targets.

Pipeline transport has emerged as the most cost-effective method for transporting CO₂ onshore. The CO₂ flow rate is a key factor driving transport costs, underscoring the need to understand the impact of flow rate variability in CO₂ pipeline networks on their design and economics. This paper presents an optimal pipeline design methodology for CO₂ pipelines operating under variable flow that considers the probability distribution of CO₂ flow rate and the pipeline length. The results imply that pipelines designed for optimal performance under variable flow rates often demand a higher level of overdesign compared to pipelines intended for steady-state conditions. Decision-makers must balance the trade-offs between pipeline oversizing and installing multiple pressure boosting stations, especially applicable to large transportation distances and projects of extended duration. The examination of different approaches to pipeline design reveals that a variable flow pipeline design based on mean flow rate is not recommended, because it is incapable of handling the maximum flow rate. This drawback is overcome by adopting the variable flow stochastic pipeline design presented in this paper.

Geological storage of carbon dioxide is a cornerstone in almost every realistic emissions reduction scenario outlined by the Intergovernmental Panel on Climate Change. Our ability to accurately forecast storage efficacy is, however, mostly unknown due to the long timescales involved (hundreds to thousands of years). To study perceived forecast accuracy, we designed a double-blind forecasting study. As ground truth, we constructed a laboratory-scale carbon storage operation, retaining the essential physical processes active on the field scale, within a time span of five days. Separately, academic groups with experience in carbon storage research were invited to forecast key carbon storage efficacy metrics. The participating groups submitted forecasts in two stages: First independently without any cross-group interaction, then finally after workshops designed to share and assimilate understanding between the forecast groups. Their confidence in reported forecasts was monitored throughout the forecasting study. Our results show that participating groups provided forecasts that appear bias-free with respect to carbon storage as a technology, yet the forecast intervals are too narrow to capture the ground truth (overconfidence bias). When asked to qualitatively self-assess their forecast uncertainty (and later when asked to provide an external assessment of other forecast groups), the assessment of the participants indicated an understanding that the forecast intervals (both their own and those of others) were too narrow. However, the participants did not display an understanding of how poorly the forecast intervals calibrated to the ground truth. The quantitative uncertainty assessments contrast the qualitative comments supplied by the participants, which indicate an acute awareness of the challenges associated with assessing the uncertainty of forecasts for complex systems such as the geological storage of carbon dioxide.

International maritime shipping contributed nearly 3 % of global CO₂eq emissions in 2018. The IMO has set an ambition to reach net zero CO₂ emissions from maritime activities by 2050 with checkpoints of 40 % reduction by 2030 and 70 % by 2040 compared to 2008 baseline levels. In addition to alternative fuels (LNG, biofuel, methanol, hydrogen, ammonia) and efficiency-based technologies, mobile carbon capture (MCC, called also ship-borne/based carbon capture SBCC) could become a plausible technology to help meet these decarbonization aspirations. In this work, we performed a simulation to compare the use of methyl-ethanolamine (MEA) and methyl-di-ethanolamine/piperazine (MDEA/PZ) aqueous mixtures for chemical temperature swing separation of CO₂ on-board a ship. We studied the main MCC system integration and design parameters and demonstrated that MDEA/PZ could represent a more efficient and cheaper path than MEA due to the heat availability limitations from the exhaust stream of the ship. Specifically, MDEA/PZ could enable 10 % savings in heat demand at the reboiler compared to MEA (3.3 vs 3.7 GJ/tCO₂). Additionally, we showed that an MDEA/PZ amine-based CO₂ capture and storage unit on-board the ship could lead to as low as 191$/ton CO₂ avoided compared to 281$/ton of CO₂ when using MEA

The scientific community has become increasingly interested in geological CO₂ sequestration and CO₂ enhanced oil recovery (EOR). The tracking of the CO₂ propagation in both space and time during geologic sequestration is necessary to ensure the secure and effective handling of a site for CO₂ injection. Our objective is to develop efficient and novel models and monitoring techniques for visualizing CO₂ plumes using field measurements. As a first step, the streamline-based data integration approach is extended to include data from distributed temperature sensors (DTS). The DTS and pressure data are then jointly history matched using a hierarchical workflow combining evolutionary and streamline methods. As a final step, we will create maps that visualize CO₂ propagation during the sequestration process based on saturation and streamline maps. We validate the extended streamline-based inversion method using a synthetic model. An application of the hierarchical workflow is then made to the CO₂ geologic storage test site in Michigan, USA. Monitoring data includes bottom-hole pressure of the injection well, DTS data at the monitoring well, and distributed pressure measurements from several downhole sensors along the monitoring well. Based on the history matching results, the CO₂ movement is largely limited to the zones intended for injection, which is in agreement with an independent warmback analysis of the temperature data. The novelty of this work is the extension of the streamline-based inversion algorithm for the DTS data, its field application to the Department of Energy regional carbon sequestration project, and potential extensions to other CO₂-EOR and/or associated geological storage projects.

The intensifying global climate change has prompted the imperative implementation of CO₂ capture and storage (CCS) projects as a mitigation strategy. Ensuring the safety and reliability of these projects requires meticulous validation, including the establishment of geological models and conducting numerical simulations. In CO₂ geological storage initiatives, the limitation of well data during the initial stages leads to data deficiency. This scarcity compromises the precision of geological and numerical models, hindering their ability to accurately depict actual subsurface conditions. Meanwhile, parameters related to heterogeneity significantly also impact storage effectiveness and safety. This study addresses these challenges by utilizing the Shenhua CCS demonstration project as a case study. Various heterogeneous parameters are selected, and local and global sensitivity analysis methods are subsequently introduced to determine the ranges and sequences of these parameters in numerical simulations. The simulation results can aid in assessing the influence of various heterogeneous parameters on the CO₂ plume and bottom hole pressure. The study establishes the importance ranking of various heterogeneous parameters under different temporal and spatial conditions through sensitivity analysis. The findings reveal the following key points:

1. During the small-scale injection period, the CO₂ plume is particularly sensitive to variations in net-to-gross and vertical permeable properties.

2. During and after larger-scale injections, the net-to-gross significantly impacts plume evolution, while bottom hole pressure is predominantly influenced by variations in vertical permeable properties.

3. Both the CO₂ plume and well bottom pressure are primarily affected by changes in sand body morphologies, especially at low net-to-gross scenarios.

These conclusions assist in prioritizing the collection of critical parameter data in CCS projects, facilitating the establishment of more precise and reliable geological and numerical simulation models. The heightened accuracy and reliability of these models contribute to improving their predictive capabilities, ultimately guiding engineering practices.