CO2 Plume Research Articles

Influence of impurities on reactive transport of CO2 during geo-sequestration in saline aquifers

Carbon capture and storage (CCS) is a promising technology to reduce greenhouse gas emissions by storing CO2 in geological formations. Storing less pure CO2, which has low percentages of impurities, can reduce the cost of the process. However, impurities can affect the dynamics of CO2 propagation and its trapping mechanisms in porous media, potentially impacting the overall effectiveness of the storage process. Therefore, this study investigates the effect of impurities on the reactive transport of CO2 during geo-sequestration in a saline aquifer using a reliable reactive transport model. The effect of co-injected H2S and CH4 in the CO2 stream on CO2 plume migration, CO2 trapping mechanisms, minerals dissolution and precipitation, and cap rock integrity, was studied here. To do so, reactive transport modeling was performed using a compositional simulator based on actual data from Sleipner storage site. The model was run for pure CO2 and mixtures (CO2 with 5 wt% H2S and CO2 with 5 wt% CH4). The results showed that the impurities co-injected with CO2 affect the CO2 plume migration in the aquifer. For instance, injecting the CO2-CH4 mixture increased the CO2 horizontal migration below the cap rock compared to the pure CO2 injection case. However, the buoyancy effect of the injected gas was slightly reduced by adding H2S to the CO2 stream. The impurities influenced the pH of the system, and hence minerals dissolution and precipitation, and cap rock porosity changes. The impurities (CH4 and H2S) injected with CO2 were found to suppress an anticipated increase in the cap rock porosity and accelerated the mineral trapping of CO2 compared to pure CO2 storage. While, among the studied cases, the CO2-CH4 mixture case had the highest structural trapping and the lowest solubility trapping of CO2. Thus, this study suggests that storage of impure CO2 can enhance mineral trapping, maintain cap rock sealing, and change CO2 distribution in porous media while reducing separation costs.

Studying the impact of reservoir temperature, water salinity and CO2 dryness on CO2 injectivity during geological CO2 sequestration

Carbon capture and storage (CCS) is proved to be effective measure for reducing CO2 emissions. whilst the world still highly depends on the use of fossil fuel energy, this method is necessary for reaching the world’s 1.5 °C goal. Saline aquifers among all possible underground formations are most common targeted ones for CO2 storage due to their frequent presence, and large storage capacity. However, this storage option suffers from sufficient well injectivity to inject large volumes of CO2 at acceptable rates through a minimum number of wells. The injectivity impairment/reinforcement happens through mineral dissolution, fine particle movement, salt precipitation and hydrate formation (known so far). Each of these mechanisms will be more dominant in injectivity alteration at different distance from the injection point depending on reservoir pressure and temperature, formation water salinity, rock mineralogy, and flow rate of CO2 injection as well as its dryness. In this study we have chosen a commercial software Eclipse 300 together with an open-source code to investigate the impact of formation characteristics, CO2 -Brine-Rock interaction, pressure, temperature as well as injection rate on injectivity alteration. The goal for this work is to provide a workflow which can help predicting injectivity alteration using the existing tools. Simulation results show that permeability is affected severely by salt precipitation during CO2 injection. Combined static and dynamic parameter study demonstrate that the injection rate plays a crucial role in size and expansion of CO2 plume as well as growth rate of dry out zone length, amount of salt precipitation and length of equilibrium region. The higher the injection rate, the quicker activation of the capillary and gravity force which leads to drag more brine to near well-bore resulting in higher volume fraction of salt precipitation. However, low injection rate could result in smaller CO2 plume, shorter dry out zone and longer equilibrium region in term of distance from injection point. Thus, optimizing the injection rate regarding reservoir parameters i.e., temperature, pressure and in-situ salinity, will lead to higher storage capacity as well as well performance and maintenance.

Deep-learning-generalized data-space inversion and uncertainty quantification framework for accelerating geological CO2 plume migration monitoring

Efficient coupling of high-fidelity simulation models and history-monitored data to predict future migration behavior of CO2 plume is crucial for leakage risk management and assessment during the process of geological CO2 sequestration (GCS). However, the conventional model-based inverse modeling and forecasting procedure generally presents intensive computation-cost due to numerous high-fidelity model simulations and thus hinders its practical applications. We present a novel data-driven direct forecast and uncertainty quantification framework that hybridizes deep convolutional autoencoder and deep residual neural network to represent spatial distribution of subsurface fluid by latent space features and achieve predictions of future fluid dynamic behavior given history-monitored data, respectively. The linear-type data-space inversion approaches based on Bayesian theorem are generalized to non-linear model applications through deep-learning, i.e., referred to as DSI-DL. Compared to the conventional learning-based direct forecast approach (LDFA) that numerous training samples are required to guarantee predictability of the supervised machine-learning models, our proposed DSI-DL employs a data-augmentation strategy to automatically generate sufficient training samples from a relatively small ensemble of high-fidelity model simulations. The performance DSI-DL method is demonstrated on a synthetic 2D models and a practical 3D model for deep saline aquifer GCS applications. The deep-learning surrogate directly predicts the latent features of future spatial CO2 plumes given history-monitored CO2 plumes. Comparisons between LDFA and DSI-DL confirm that the proposed DSI-DL method obtains more robust and better results in predicting future CO2 plume migration, especially when solely a few number of large-scale high-fidelity model simulations is available at hand and large observation errors exist. The proposed deep-learning-based direct forecast approach contributes to an efficient computation framework to manage uncertainty of the long term process of GCS.

Evaluation of simulated CO2 power plant plumes from six high-resolution atmospheric transport models

Abstract. Power plants and large industrial facilities contribute more than half of global anthropogenic CO2 emissions. Quantifying the emissions of these point sources is therefore one of the main goals of the planned constellation of anthropogenic CO2 monitoring satellites (CO2M) of the European Copernicus program. Atmospheric transport models may be used to study the capabilities of such satellites through observing system simulation experiments and to quantify emissions in an inverse modeling framework. How realistically the CO2 plumes of power plants can be simulated and how strongly the results may depend on model type and resolution, however, is not well known due to a lack of observations available for benchmarking. Here, we use the unique data set of aircraft in situ and remote sensing observations collected during the CoMet (Carbon Dioxide and Methane Mission) measurement campaign downwind of the coal-fired power plants at Bełchatów in Poland and Jänschwalde in Germany in 2018 to evaluate the simulations of six different atmospheric transport models. The models include three large-eddy simulation (LES) models, two mesoscale numerical weather prediction (NWP) models extended for atmospheric tracer transport, and one Lagrangian particle dispersion model (LPDM) and cover a wide range of model resolutions from 200 m to 2 km horizontal grid spacing. At the time of the aircraft measurements between late morning and early afternoon, the simulated plumes were slightly (at Jänschwalde) to highly (at Bełchatów) turbulent, consistent with the observations, and extended over the whole depth of the atmospheric boundary layer (ABL; up to 1800 m a.s.l. (above sea level) in the case of Bełchatów). The stochastic nature of turbulent plumes puts fundamental limitations on a point-by-point comparison between simulations and observations. Therefore, the evaluation focused on statistical properties such as plume amplitude and width as a function of distance from the source. LES and NWP models showed similar performance and sometimes remarkable agreement with the observations when operated at a comparable resolution. The Lagrangian model, which was the only model driven by winds observed from the aircraft, quite accurately captured the location of the plumes but generally underestimated their width. A resolution of 1 km or better appears to be necessary to realistically capture turbulent plume structures. At a coarser resolution, the plumes disperse too quickly, especially in the near-field range (0–8 km from the source), and turbulent structures are increasingly smoothed out. Total vertical columns are easier to simulate accurately than the vertical distribution of CO2, since the latter is critically affected by profiles of vertical stability, especially near the top of the ABL. Cross-sectional flux and integrated mass enhancement methods applied to synthetic CO2M data generated from the model simulations with a random noise of 0.5–1.0 ppm (parts per million) suggest that emissions from a power plant like Bełchatów can be estimated with an accuracy of about 20 % from single overpasses. Estimates of the effective wind speed are a critical input for these methods. Wind speeds in the middle of the ABL appear to be a good approximation for plumes in a well-mixed ABL, as encountered during CoMet.

Expansion of Geological CO2 Storage Capacity in a Closed Aquifer by Simultaneous Brine Production with CO2 Injection

Structural trapping is the primary mechanism for intensive CO2 sequestration in saline aquifers. This is the foundation for increasing global CO2 storage; gradual switch to preferable trapping mechanisms, such as residual saturation, dissolution, and mineral trapping, will require a long-time scale. The major constraints limiting the storage capacity of structural trapping are formation pressure and structure size. Over-pressure owing to CO2 injection causes a disruption of seal integrity indicating a failure in geological sequestration. The other constraint on storage capacity is a spill point determining geological storage volume. Overflowing CO2, after filling the storage volume, migrates upward along the aquifer geometry with buoyancy. This study proposes a methodology to maximize CO2 storage capacity of a geological site with a substructure created by an interbedded calcareous layer below spill point. This study provides various conceptual schemes, i.e., no brine production, simultaneous brine production and pre-injection brine production, for geological CO2 storage. By the comparative analysis, location of brine producer, production rate, and distance between injector and producer are optimized. Therefore, the proposed scheme can enhance CO2 storage capacity by 68% beyond the pressure and migration limits by steering CO2 plume and managing formation pressure.

Capillary pressure equilibrium theory mapping of 4D seismic inversion results to predict saturation in a gas-water system

Understanding the effect of fluid concentration in rocks is crucial to characterize a hydrocarbon reservoir. Monitoring of fluid concentration specifically becomes challenging during a sequestration/enhanced oil recovery program because the objective is to understand the amount of initial fluid replaced by liquid/gas injection and identify the probability of leakage with time. The prior assumption of uniform or patchy type of distribution of gas in a rock-physics theory leads to large uncertainty in the prediction of saturation. As a result, we have used the capillary pressure equilibrium theory (CPET) for creating a reservoir model that matches the physics of capillary-induced fluid invasion and avoids the uncertainty related to the type of distribution of gas in pores. We create a CPET model using the reservoir parameters of the clean and unconsolidated sandstone formation of the Sleipner field, North Sea, which is the world’s first industrial-scale CO2-injection project, assuming that there is no significant change in the rock frame throughout the field. The model is then used to analyze the fluid content from the time-lapse seismic inversion results of the Sleipner field. CO2 at higher quantities, according to our research, is analogous to a uniform distribution, whereas CO2 at lower concentrations is mostly in between patchy and uniform distribution or slightly patchy type. We predict maximum CO2 saturation from a quantitative interpretation of six time-lapse seismic data from 1999 to 2010 using the CPET as 75% of the pore space, and the footprint of the CO2 plume in the topmost layer is spreading from zero in 2001 to 7 × 105 m2 in 2010. Our model finds that injected CO2 from all layers below will migrate to the top layer approximately 50 years after the commencement of the injection.

Biomass burning CO, PM and fuel consumption per unit burned area estimates derived across Africa using geostationary SEVIRI fire radiative power and Sentinel-5P CO data

Abstract. We present the first top-down CO fire emissions inventory for Africa based on the direct relation between geostationary satellite-based fire radiative power (FRP) observations and polar-orbiting satellite observations of total column carbon monoxide (TCCO). This work significantly extends the previous Fire Radiative Energy Emissions (FREM) approach that derived total particulate matter (TPM) emission coefficients from FRP and aerosol optical depth (AOD) observations. The use of satellite-based CO observations to derive biome-specific CO emission coefficients, ECCOb, addresses key uncertainties in the use of AOD observations to estimate fire-generated CO emissions including the requirement for a smoke mass extinction coefficient in the AOD to TPM conversion and the large variation in TPM emission factors – which are used to convert TPM emissions to CO emissions. We use the FREM-derived CO emission coefficients to produce a pan-African CO fire emission inventory spanning 2004 to 2019. Regional CO emissions are in close agreement with the most recent version of GFED(v4.1s), despite the two inventories using completely different satellite datasets and methodologies. Dry matter consumed (DMC) and DMC per unit burned area are generated from our CO emission inventory – the latter using the 20 m resolution Sentinel-2 FireCCISFD burnt area (BA) product for 2019. We carry out an evaluation of our FREM-based CO emissions by using them as input in the WRF-CMAQ chemical transport model and comparing simulated TCCO fields to independent Sentinel-5P TROPOMI TCCO observations. The results of this evaluation show FREM CO emissions to generally be in good agreement with these independent measures – particularly in the case of individual fire-generated CO plumes, where modelled in-plume CO was within 5 % of satellite observations with a coefficient of determination of 0.80. Modelled and observed total CO, aggregated over the full model domain, are within 4 % of each other, though localised regions show an overestimation of modelled CO by up to 50 %. When compared to other evaluations of current state-of-the-art fire emissions inventories, the FREM CO emission inventory derived in this work shows some of the best agreement with independent observations. Updates to previously published FREM TPM emissions coefficients based on this methodology are also provided, along with a similar evaluation as conducted for CO. The methodology described in this work is forming the basis of a forthcoming near-real-time fire emissions product from Meteosat to be issued by the EUMETSAT LSA SAF (https://landsaf.ipma.pt/en/, last access: 19 December 2022).

4D Seismic Used Effectively in Subsurface CO2 Plume Monitoring

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206162, “4D Seismic in Subsurface CO2 Plume Monitoring: Why It Matters,” by Pankaj K. Tiwari, SPE, Debasis P. Das, and Parimal A. Patil, SPE, Petronas, et al. The paper has not been peer reviewed. _ Carbon dioxide (CO2) sequestration in depleted carbonate reservoirs requires the incorporation of comprehensive and innovative monitoring technologies. 4D time-lapse seismic is necessary for monitoring, measurement, and verification (MMV) planning to demonstrate the migration of CO2 plumes within geological storage. An adaptive, site-specific MMV plan for monitoring CO2 plumes is critical to minimize possible subsurface and project-integrity risks. In the complete paper, the authors explore the migration of CO2 plumes within depleted carbonate reservoirs and the expected effects of CO2 saturation and pressure buildups during injection on time-lapse 4D seismic for conformance monitoring. Integrated Reservoir Dynamic Simulation In the current study, a depleted carbonate reservoir gas field was identified for near-future CO2 sequestration offshore Sarawak. The storage site has been studied thoroughly for both overburden and reservoir integrity by adopting work flows comprising integration of subsurface integrity analysis, pore-pressure analysis, and subsidence modeling, along with a health check of existing wells. All parameters were cross-correlated. Qualitative risk categorization was performed to determine the robustness of the reservoir for long-term CO2 storage. Integrated reservoir dynamic simulation was performed by coupling compositional reservoir dynamic/geomechanics/geochemistry 3D models to assess the integrity of the field as a CO2 storage site and to demonstrate the periodic development of CO2 saturation and plumes within the gas-depleted carbonate reservoir. Computation of the cumulative effect of changes occurring in all three models was essential. Changes to reservoir rock properties caused by dissolution and precipitation, as well as compaction during injection, were interpreted by 3D coupled modeling simulation and integrated with seismic modeling. Apart from storage capacity, results from the coupled modeling study showed that reservoir pressure at the end of the injection period was approximately 400 psia lower than the initial reservoir pressure. Modeling also showed that the entire incoming permeate stream rate could be injected with three injection wells. Fig. 1 shows the CO2 plume migration after 2, 10, and 20 years of injection from all three injectors. Because injection is planned from a predrill liner in the full reservoir column, the CO2 plume migrates upward in a funnel shape around the well first. In the initial years of injection, most of the CO2 migrates laterally in the shallower formation of the reservoir, then migrates downward once the shallower formation of the reservoir section is fully CO2-saturated.

Uncertainty Quantification of the CO2 Storage Process in the Bunter Closure 36 Model

The UK plans to bring all greenhouse gas emissions to net-zero by 2050. Carbon capture and storage (CCS), an important strategy to reduce global CO2 emissions, is one of the critical objectives of this UK net-zero plan. Among the possible storage site options, saline aquifers are one of the most promising candidates for long-term CO2 sequestrations. Despite its promising potential, few studies have been conducted on the CO2 storage process in the Bunter Closure 36 model located off the eastern shore of the UK. Located amid a number of oil fields, Bunter is one of the primary candidates for CO2 storage in the UK, with plans to store more than 280 Mt of CO2 from injections starting in 2027. As saline aquifers are usually sparsely drilled with minimal dynamic data, any model is subject to a level of uncertainty. This is the first study on the impact of the model and fluid uncertainties on the CO2 storage process in Bunter. This study attempted to fully accommodate the uncertainty space on Bunter by performing twenty thousand forward simulations using a vertical equilibrium-based simulator. The joint impact of five uncertain parameters using data-driven models was analysed. The results of this work will improve our understanding of the carbon storage process in the Bunter model before the injection phase is initiated. Due to the complexity of the model, it is not recommended to make a general statement about the influence of a single variable on CO2 plume migration in the Bunter model. The reservoir temperature was shown to have the most impact on the plume dynamics (overall importance of 41%), followed by pressure (21%), permeability (17%), elevation (13%), and porosity (8%), respectively. The results also showed that a lower temperature and higher pressure in the Bunter reservoir condition would result in a higher density and, consequently, a higher structural capacity.

CO2 leak detection threshold using vertical seismic profiles

At the Containment and Monitoring Institute Field Research Station in Alberta, Canada, CO2 is being injected into a saline aquifer at 300 m depth to simulate a shallow leak from a deep carbon sequestration operation. The injection interval is a 7 m thick sandstone with a porosity of 11%. The gas-phase CO2 plume was detected with time-lapse vertical seismic profiles (VSP) after 32 tonnes of CO2 had been injected. The VSP surveys had high repeatability with permanent borehole geophones and carefully repeated source locations. A data processing workflow was developed to produce directly comparable baseline and monitor amplitudes while preserving subtle amplitude changes caused by partial CO2 saturation. Seasonal variations in the physical properties of the near-surface glacial tills introduced spectral differences between baseline and monitor datasets that were successfully mitigated by deterministic deconvolution and selected band-pass filters. The 32 t detection threshold represents a lower limit of CO2 detectability with borehole geophones in this geological setting. These results provide a rare field data example of CO2 monitoring at shallow to intermediate depths and demonstrate that VSP surveys are capable of identifying loss of containment in industrial-scale CO2 sequestration operations.

Data-assimilated time-lapse visco-acoustic full-waveform inversion: Theory and application for injected CO2 plume monitoring

Continuous seismic monitoring for quantifying CO2 plume migration and detection of any potential leakages in the subsurface is essential for the security of long-term anthropogenic carbon dioxide geologic storage. Traditional time-lapse full-waveform inversion (TLFWI) methods aim to map the CO2 distribution by estimating seismic velocity changes, but recent studies find that CO2-induced attenuation is an important complement to seismic velocity for tracking the CO2 plumes and even quantifying the CO2 saturation. We have developed a novel data-assimilated TLFWI method to construct high-resolution time-lapse velocity and attenuation changes from dense time-lapse monitoring data. This method consists of two theoretical developments: visco-acoustic full-waveform inversion (QFWI) and multiparameter hierarchical matrix-powered extended Kalman filter (mHiEKF). The method is capable of (1) posing temporal constraints to retrieve time-lapse information from dense monitoring data by using mHiEKF, (2) accurately recovering high-spatial-resolution velocity and attenuation perturbations using first-order equation system-based QFWI, and (3) providing the model uncertainty by estimating their model standard deviation. With numerical examples, we first find the effectiveness of the new QFWI on estimating accurate velocity and attenuation models simultaneously. Then, a CO2 leakage case and a realistic Frio-II CO2 monitoring case are presented to find the advantages and applicability of our data-assimilated QFWI method for estimating time-lapse changes using dense time-lapse monitoring surveys. By assimilating time-lapse seismic monitoring data over time, our data-assimilated QFWI method can improve the resolution of velocity and attenuation changes and decrease their model uncertainties.

Integrated thermo-poro-mechanical characterization for CO2 Sequestration at deep aquifer conditions

Modelling and forecasting of injected CO2 plume behaviour is an essential step in the baseline, monitoring, and verification [BMV] process in the CO2 sequestration lifecycle. The goal of reduction of uncertainty through forecasting models, can be better realized by accounting for the thermo-poro-mechanical nature of the deep subsurface reservoir systems. The current study focusses on developing and refining a laboratory workflow which will help in generating representative static and dynamic datasets at ambient and deep aquifer conditions. The workflow involves characterizing the poroelastic Biot coefficient and mechanical properties at ambient, high temperatures and at reservoir representative stress conditions. This information will be combined with the dataset from a CO2 flood experiment which replicates the displacement of brine by super critical CO2 at ambient and high temperatures and at reservoir representative stresses. Resistivity and acoustic signals will be monitored throughout the flood experiment. Existing analytical models for fluid substitution such as the Biot-Gassmann-Brie populated with representative data will be evaluated for finding the best description of the experimental observations. The integrated results of the workflow are meant to help develop better informed static and dynamic models improving the confidence in the BMV process of CCUS.

Time-Lapse Seismic Matching for CO₂ Plume Detection via Correlation-Based Recurrent Attention Network

Time-Lapse Seismic Matching for CO₂ Plume Detection via Correlation-Based Recurrent Attention Network

Derisking geologic carbon storage from high-resolution time-lapse seismic to explainable leakage detection

Geologic carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Among the different monitoring modalities, seismic imaging stands out due to its ability to attain high-resolution and high-fidelity images. However, these superior features come at prohibitive costs and time-intensive efforts that potentially render extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology in which time-lapse images are created by inverting nonreplicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high-fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of realistic synthetic noisy time-lapse seismic data sets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse data sets are subsequently inverted to produce time-lapse difference images that are used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data with reasonable accuracy. We consider the use of this classifier as a first step in the development of an automatic workflow designed to handle the large number of continuously monitored CO2 injection sites needed to help combat climate change.

A deep learning-based direct forecasting of CO2 plume migration

Accurate and timely forecasts of CO2 plume evolution in geological reservoirs are crucial for CO2 migration detection, leakage risk assessment, and operation decision support. Conventional forecasting usually adopts a two-step strategy, first calibrating reservoir model parameters against observations using iterative inverse modeling (or history matching) and then applying the calibrated model for predictions. This method impedes real-time forecasts due to the heavy computational demand in inverse modeling and may suffer from poor prediction accuracy because of the limited observation data. In this work, we propose a deep learning-based latent space mapping framework to forecast CO2 plume migration directly by avoiding the inverse modeling. We first use the convolutional autoencoder to map the high-dimensional complex plume extents onto low-dimensional latent space. Next, we use neural networks to learn the relationship between the observation variables and the prediction latent variables. And then for given observation data, we infer the prediction values directly. This one-step direct forecasting is computationally efficient which requires a few number of parallelizable reservoir simulations and it can provide accurate predictions with limited observations by learning the observation-prediction relationship in the reduced dimension. Therefore, our proposed method enables an in-time forecast of dynamic CO2 plume distributions. We demonstrate the effectiveness and accuracy of our method in predicting the CO2 plume migration using four metrics such as plume area, centroid movement distance, and plume spreading in the primary and secondary directions. And the spatio-temporal evolution patterns of plume migration under diverse geological complexities are also accurately quantified.

4D Seismic Monitoring with Diffraction-Angle-Filtering for Carbon Capture and Storage (CCS)

Carbon capture and storage (CCS) is a key technology that directly removes industry driven CO2 to achieve carbon neutrality. In the process of CCS, it is necessary to monitor whether injected CO2 is properly stored and not leaking. The behavior of CO2 can be investigated using a 4D seismic survey that compares seismic data before and after injection. We proposed a two-step monitoring with diffraction-angle filtering (DAF) to effectively locate the CO2 plume. Because DAF allows us to control wavenumber components, the gradient of full-waveform inversion (FWI), which is the first step, is composed of low-wavenumber components, and reverse time migration (RTM) for seismic imaging is carried out with high-wavelength components. To verify our method, we implemented FWI and RTM with and without DAF using the velocity model in the Volve oil field in the North Sea. Numerical examples show that the CO2 plume is properly detected from the difference between baseline and post-injected survey and the extension of the reflective boundary is improved compared to the results of the conventional method. With our proposed method, local minima problem is mitigated in FWI, and the boundaries between layers can be clearly distinguished in RTM.

Assessing a potential site for offshore CO2 storage in the Weixinan Sag in the northwestern Beibu Gulf Basin, northern South China Sea

AbstractGeologic carbon storage (GCS) activities, especially offshore, are still far insufficient worldwide. Sedimentary basins in the northern South China Sea (nSCS) are identified to be favorable to offshore GCS deployment. This study investigates by numerical reservoir simulations the performance of the Weixinan Sag in the northern depression of the Beibu Gulf Basin (BGB), which is considered the most promising area for CO2 offshore GCS in saline formations. Simulations of CO2 injection at a potential site with a normal fault indicate that the pressure buildup induced by CO2 horizontal injection could penetrate extremely low‐permeability faults or caprock formations. The CO2 plumes under higher injection pressures are more constrained in the horizontal direction and hence appear thicker while CO2 tends to spread out horizontally in conditions of low pressure. The impermeable fault renders the CO2 plume about 200 m smaller in size horizontally. Assuming the presence of a fault with a highly permeable core causes leakage to occur after about 30 years of injection, which accounts for only 0.04% the injected amount. For the vertical‐well injection case, the potential CO2 leakage only accounts for around 0.28% of the injection amount, which is far less than the criterion (i.e., 2%) required to make GCS worthwhile. The storage capacities of the formations are mainly controlled by the depths and thicknesses since both their porosities and permeabilities are comparable. The formation Jiaowei‐2 and Xiayang have the largest and second largest storage capacities, respectively, when using a fully perforated well for vertical injection. The average storage capacity of the studied site is 13.04 kg m−3, which is comparable to that of the formation Xiayang. Average injectivities of formations Jiaowei‐2, Xiayang, Weizhou‐1, and Weizhou‐3 are 8.29 × 10−5, 1.75 × 10−4, 6.58 × 10−5, and 2.99 × 10−3 kg s−1 Pa−1, respectively. © 2022 The Authors. Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

Estimation of OH in urban plumes using TROPOMI-inferred NO2 ∕ CO

Abstract. A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15 % to 30 % in summer and 40 % to 50 % in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10 % with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by ∼ 52 ± 14 % and reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowing one to analyze day-to-day variability in OH, NOx and CO emission.

The impact of geological heterogeneity on coupled CO2 storage and geothermal extraction in inclined reservoirs

CO2 plume geothermal (CPG) production is an emerging CO2 utilization approach coupled with CO2 geological sequestration. Geological heterogeneity plays an important role in CO2 fluid and heat flow in the formation. Although the impact of heterogeneity has been investigated during CO2 injection for many sequestration projects, few studies have been reported for CO2-circulation and associated heat extraction. This study aims to numerically assess the impact of spatially correlated heterogeneity on both CO2 plume development (CPD) and subsequential CPG operations in inclined and thinly layered geothermal reservoirs. The simulated field is simplified from a typical depleted oil/gas reservoir block located in a potential geothermal area in North Oman, which had undergone horizontal water flooding as the second oil recovery phase. Formation slope and heterogeneity properties, including variance, azimuth, and correlation lengths, are sampled using the Latin-Hypercube approach in a high dimensional parameter space defined by their ranges. A suite of numerical models simulating CPD-CPG operations by a horizontal well doublet is set up using the slope and heterogeneity parameter samples. Performance metrics for CPD-CPG, including CPD time, CPG time, net injected CO2, maximum pressure decline, minimum CO2 mass fraction in produced fluids, total recovered heat energy, and heat extraction rate, are calculated from each of the numerical models. A response surface model mapping the input (slope and heterogeneity parameters) and output (performance metrics) is developed using these model results. Using the response surface, global sensitivity analysis is efficiently conducted to quantitatively evaluate the impact of slope and heterogeneity on the performance of CPD-CPG operations. It is found that steep slopes can enhance CO2 injectivity, facilitate high CO2 fraction in produced fluids and heat extraction rate; However, it causes early cold CO2 breakthrough to the production well and reduce total recovered heat energy. High variance and x-correlation length of heterogeneous fields create prominent high-permeable preferential paths in the major flow direction (x-axis). Low-permeable lenses created by large y-correlation lengths are perpendicular to the x-axis, acting as flow barriers to both CO2 and brine towards the production well. As for z-correlation length, 15 m is found to be the critical value relative to 20 m of the reservoir thickness. Large z-continuity scale facilitates fluid flow owing to the thick preferential paths when it is below 15 m, but low-permeable lenses tend to significantly block flow as they approximate to the reservoir thickness.

Characteristics of SH-wave propagation during oil reservoir excitation using BEM formulation in half-plane model representation

The application of elastic waves induced through seismic excitation (stimulation) to increase the oil recovery from a hydrocarbon reservoir as an enhanced oil recovery (EOR) technique is currently in its infancy. Seismic stimulation is a cost-effective and efficient method for enhancing oil production from waterflood reservoirs by increasing areal sweep performance and decreasing water cuts, hence extending the productive life of a mature oilfield. It has great prospect in offshore fields where technological difficulties and restrictions hinder the deployment of other EOR methods. Herein, this study investigates the effects of a direct impact of seismic SH-wave on a 2D transient behaviors of an oil reservoir with a half-plane model. In the time domain, a half-plane model containing a poroelastic oil reservoir is formulated using a boundary element method (BEM). The model considers complete seismic SH-wave propagation using Ricker wavelet, from a down-hole source via a partially saturated oil reservoir to ground surface receiving points. The model is based on 2D elastodynamic and Biot's dynamic poroelasticity equations. A DASBEM program is incorporated into MATLAB (2022a) to develop a model of wave propagation. The result showed that variable incidence angle affects the frequency and time domain responses, while the depth of the reservoir and porosity influence the amplitude of oscillation. Synthetic seismograms at stations above the oil reservoir indicated attenuation and numerous diffracted waves with various time delays. Multiple SH-wave reflections in the reservoir amplify 3D signals, while at higher dimensionless frequencies, effect of porosity amplify the distant locations. This technique can be utilized as fluid indicator to monitor wave based EOR using SH-wave attenuation, and to detect and visualize the permeability changes in reservoir (impermeable boundaries) during CO2 plume storage for the purpose of monitoring the safety of CO2 geo-sequestration.