Monitoring Of CO2 Sequestration Research Articles

Assessing the potential for CO2 storage in shallow coal seams using gas geochemistry: A case study from Qinshui Basin, North China

Shallow coal seams are a target reservoir for CO2 sequestration. So far efforts to trace CO2 sequestration by coal beds have largely focussed on short-term monitoring or theoretical modelling. To evaluate the feasibility of safe long-term storage of CO2 in shallow coal beds, we report the first systematic gas geochemical study at a shallow injection site in Shizhuang, Qinshui Basin, one year after CO2 injection ceased. The injected CO2 is isotopically distinctive and appears to be enriched in heavy noble gases (Kr and Xe). The high CO2 content and light δ13CCO2 of gases from production wells confirm the migration of injected gas to the northeast and east of the main injection well previously tracked by geophysical studies. The migration of injected gas to the southeast has not been identified before, and exceeds that to the northeast, implying that gas geochemistry provides a more robust method of tracking migration. Less than 1 % of the total injected CO2 has been extracted from production wells in the year since injection ceased. We estimate that the target coal seam in the study area can store about 0.08 billion tonnes of CO2. Scaling up we estimate that the Qinshui Basin has the potential to store about 19 billion tonnes of CO2. This is significantly more than previous estimates. This study demonstrates the importance of geochemical tracers in quantifying and monitoring CO2 sequestration and also implies that unmined coal seams have high potential for the long-term storage of CO2.

Impacts of magnetic permeability on electromagnetic data collected in settings with steel-cased wells

SUMMARY Electromagnetic methods are increasingly being applied in settings with steel infrastructure. These include applications such as monitoring of CO2 sequestration or even assessing the integrity of a wellbore. In this paper, we examine the impacts of the magnetic permeability of a steel-cased well on electromagnetic responses in grounded source experiments. We consider a vertical wellbore and simulate time and frequency domain data on 3-D cylindrical meshes. Permeability slows the decay of surface electric fields in the time domain and contributes to a phase shift in the frequency domain. We develop our understanding of how permeability alters currents within, and external to, the casing by focussing first on the time domain response and translating insights to the frequency domain. Following others, we rewrite Maxwell’s equations to separate the response into terms that describe the magnetization and induction effects. Magnetic permeability impacts the responses in two ways: (1) it enhances the inductive component of the response in the casing and (2) it creates a magnetization current on the outer wall of the casing. The interaction of these two effects results in a poloidal current system within the casing. It also generates anomalous currents external to the casing that can alter the geometry and magnitude of currents in the surrounding geological formation. This has the potential to be advantageous for enhancing responses in monitoring applications.

Machine-learning predictions of solubility and residual trapping indexes of carbon dioxide from global geological storage sites

Ongoing anthropogenic carbon dioxide (CO2) emissions to the atmosphere cause severe air pollution that leads to complex changes in the climate, which pose threats to human life and ecosystems more generally. Geological CO2 storage (GCS) offers a promising solution to overcome this critical environmental issue by removing some of the CO2 emissions. The performance of GCS projects depends directly on the solubility and residual trapping efficiency of CO2 in a saline aquifer. This study models the solubility trapping index (STI) and residual trapping index (RTI) of CO2 in saline aquifers by applying four robust machine learning (ML) and deep learning (DL) algorithms. Extreme learning machine (ELM), least square support vector machine (LSSVM), general regression neural network (GRNN), and convolutional neural network (CNN) are applied to 6811 compiled simulation records from published studies to provide accurate STI and RTI predictions. Employing different statistical error metrics coupled with supplementary evaluations, involving score and robustness analyses, the prediction accuracy of the models proposed is comparatively assessed. The findings of the study revealed that the LSSVM model delivers the lowest RMSE values: 0.0043 (STI) and 0.0105 (RTI) with few outlying predictions. Presenting the highest STI and RTI prediction scores the LSSVM is distinguished as the most credible model among all the four models studied. The models consider eight input variables, of which the time elapsed and injection rate displays the strongest correlations with STI and RTI, respectively. The results suggest that the proposed LSSVM model is best suited for monitoring CO2 sequestration efficiency from the data variables considered. Applying such models avoids time-consuming complex simulations and offers the potential to generate fast and reliable assessments of GCS project feasibility. Accurate modeling of CO2 storage trapping indexes guarantees successful geological CO2 storage operation, which is, in fact, the cornerstone of properly controlling and managing environmentally polluting gases.

Adjoint Q tomography with central-frequency measurements in viscoelastic medium

SUMMARY Accurate Q (quality factor) structures can provide important constraints for characterizing subsurface hydrocarbon/water resources in exploration geophysics and interpreting tectonic evolution of the Earth in earthquake seismology. Attenuation effects on seismic amplitudes and phases can be included in forward and inverse modellings by invoking a generalized standard linear solid rheology. Compared to traditional ray-based methods, full-waveform-based adjoint tomography approach, which is based on numerical solutions of the visco-elastodynamic wave equation, has the potential to provide more accurate Q models. However, applications of adjoint Q tomography are impeded by the computational complexity of Q sensitivity kernels and by strong velocity-Q trade-offs. In this study, following the adjoint-state method, we show that the Q (P- and S-wave quality factors QP and QS) sensitivity kernels can be constructed efficiently with adjoint memory strain variables. A novel central-frequency difference misfit function is designed to reduce the trade-off artefacts for adjoint Q tomography. Compared to traditional waveform-difference misfit function, this new central-frequency approach is less sensitive to velocity variations, and thus is expected to produce fewer trade-off uncertainties. The multiparameter Hessian-vector products are calculated to quantify the resolving abilities of different misfit functions. Comparative synthetic inversion examples are provided to verify the advantages of this strategy for adjoint QP and QS tomography. We end with a 3D viscoelastic inversion example designed to simulate a distributed acoustic sensing/vertical seismic profile survey for monitoring of CO2 sequestration.

Effect of CO2 on coal P-wave velocity under triaxial stress

As P-wave velocity is sensitive to the variations in coal reservoir parameters, it is possible to monitor the injected CO2 through P-wave velocity during CO2 sequestration in coal. However, the effects of CO2 on the coal P-wave velocity under triaxial stress are not clearly discerned. In the present study, different boundary conditions and gases were utilised to investigate the factors affecting the P-wave velocity after the interaction of coal with CO2. Experiments with helium indicated that the pore pressure primarily affected the P-wave velocity by altering the effective stress. Experiments with CH4 and CO2 indicated that matrix swelling induced-cleats porosity decline significantly promoted P-wave velocity. Moreover, CO2 caused a wider scale and severe weakening of coal matrix than CH4, thereby significantly decreasing the P-wave velocity, and the decline in P-wave velocity increases with vitrinite content. Furthermore, experiments under different boundary conditions showed that with the boundary condition having more constraints, the decrement of pore pressure on P-wave velocity is more weaken, whereas the improvement of matrix swelling on P-wave velocity is more evident. This study contributes to understanding the mechanism of effect of CO2 on P-wave velocity under triaxial stress condition and provides guidance for monitoring CO2 sequestration in coal.

Quantifying the impact of effective stress on changes in elastic wave velocities due to CO2 injection into a depleted carbonate reef

AbstractThe interpretation of time‐lapse seismic monitoring of CO2 sequestration has typically focused on mapping the saturation and pressure changes. However, elastic wave velocity and associated seismic attribute variation also depend on the poroelastic response to CO2 injection. In this study, we investigated the impact of effective stress changes on elastic wave velocity responses during CO2 storage within a carbonate reef of the Michigan basin. The geomechanical multiphase flow modeling combined with the experimental measurements were used to investigate the impact of effective stress on changes in velocities. We measured the ultrasonic compressional (Vp) and shear (Vs) wave velocities in cores acquired from the carbonate formations under different stress‐pressure scenarios. Experimental scenarios were applied by changing the effective stress and pore pressure conditions simulating injection into the depleted reservoir. Experimental results show that incorporating stress changes of injection avoids overestimation of changes in velocities due to considering only pore pressure effects. Coupled multiphase flow – geomechanical simulations were performed to assess stress and saturation change during CO2 injection. We discussed expected changes in elastic wave velocities using combined simulation results with experimental ones. The simulation results show that both effective stress and saturation changes contribute to changes in velocities in the main reservoir. Effective stress is the dominant parameter that contributes to changes in velocities in the upper formations. Results of this work show that quantifying the poroelastic effect of stress on elastic wave velocities changes can lead to a better interpretation of changes in seismic attributes during CO2 injection. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

A Novel System for Real-Time, In Situ Monitoring of CO2 Sequestration in Photoautotrophic Biofilms.

Climate change brought about by anthropogenic CO2 emissions has created a critical need for effective CO2 management solutions. Microalgae are well suited to contribute to efforts aimed at addressing this challenge, given their ability to rapidly sequester CO2 coupled with the commercial value of their biomass. Recently, microalgal biofilms have garnered significant attention over the more conventional suspended algal growth systems, since they allow for easier and cheaper biomass harvesting, among other key benefits. However, the path to cost-effectiveness and scaling up is hindered by a need for new tools and methodologies which can help evaluate, and in turn optimize, algal biofilm growth. Presented here is a novel system which facilitates the real-time in situ monitoring of algal biofilm CO2 sequestration. Utilizing a CO2-permeable membrane and a tube-within-a-tube design, the CO2 sequestration monitoring system (CSMS) was able to reliably detect slight changes in algal biofilm CO2 uptake brought about by light–dark cycling, light intensity shifts, and varying amounts of phototrophic biomass. This work presents an approach to advance our understanding of carbon flux in algal biofilms, and a base for potentially useful innovations to optimize, and eventually realize, algae biofilm-based CO2 sequestration.

Comparison of seismic interferometry techniques for the retrieval of seismic body waves in CO2 sequestration monitoring

AbstractAmbient noise seismic interferometry performed by cross-correlation has been proven to be a potential cost-effective technique for geological studies. To improve the resolution of images created by interferometry, additional techniques using deconvolution and cross-coherence have been introduced. While all three methods have previously been evaluated using surface wave data for shear-wave imaging of the near surface, comparatively little study has been devoted to assess the three methods for the retrieval of body waves in reflection surveys for time-lapse application. Moreover, although the application of seismic interferometry to CO2 sequestration by cross-correlation has been investigated by many researchers, to our knowledge, similar time-lapse studies have not been conducted using deconvolution and cross-coherence methods. We evaluate the three methods of cross-correlation, deconvolution and cross-coherence for the retrieval of phase information contained in virtual seismic records by applying seismic interferometry to synthetic data, using a model reservoir before and after CO2 injection. By examining two approaches of regularization and smoothing factors to suppress spurious reflection events observed on the deconvolution and cross-coherence results, we note that both approaches provide similar results. We investigate noise effects by adding random noise independently at each geophone. Finally, we apply these techniques to field data recorded near the CO2 storage site in Ketzin, Germany. For both our numerical and field data studies, we find that the cross-coherence technique retrieves the phase information of body-wave data more effectively than the cross-correlation and deconvolution techniques, and is less sensitive to uncorrelated noise from shallow sources.

Predicting solubility of CO2 in brine by advanced machine learning systems: Application to carbon capture and sequestration

Carbon dioxide (CO2) capture and sequestration in saline aquifers have turned into a key focus as it becomes an effective way to reduce CO2 in the atmosphere. The solubility of CO2 in brine is of vital role in monitoring CO2 sequestration. In this study, based on molality of NaCl, pressure and temperature, modeling of CO2 solubility in brine has been carried out utilizing multilayer perceptron (MLP) and radial basis function neural network (RBFNN). Levenberg-Marquardt (LM) algorithm was implemented to optimize the MLP model, while genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony (ABC), were applied to optimize the RBFNN model. To this end, a widespread experimental databank including 570 data sets gathered from literature was considered to implement the proposed models. Graphical and statistical assessment criteria were considered to investigate the performances of these models. The obtained results revealed that all the proposed techniques are in excellent correspondence with experimental data. In addition, the performance analyses showed that RBFNN-ABC model exhibits the higher accuracy in the prediction of CO2 solubility in brine compared with the other proposed smart approaches and the existing well-known models. The RBFNN-ABC model yields a root mean square error (RMSE) value of 0.0289 and an R2 of 0.9967. Finally, the RBFNN-ABC model validity was confirmed and a small number of probable doubtful data was detected.

4D seismic monitoring of CO2 sequestration

WA is poised to embark on several major new energy developments. These include $250+ Billion AUD investment in new gas reserves that will begin production in the Carnarvon, Browse and Bonaparte basins. Developing these new gas reserves will require handling many Millions of tons per year in CO2 released as a natural byproduct of the LNG process. To avoid venting this natural CO2 into the atmosphere, which may be bad for both the environment and business, the CO2 will have to be disposed of safely. One of the best available options is to accelerate nature's course by re-injecting and storing CO2 into deep rock formations, termed geo-sequestration. Geophysical monitoring of producing gas reservoirs will thus play an important role in two ways; (1) enhancing the gas recovery factor of these projects by improving the reservoir model and understanding geologic flow complexity, and (2) monitoring any required CO2 injection to ensure it is being safely stored in the subsurface for the longterm. In addition to petroleum applications, there is a strong interest in developing clean-coal initiatives by capturing the CO2 generated at coal-fired power plants and injecting it into the subsurface. Geophysical techniques will therefore also play a key role in monitoring and verification strategies for clean-coal CO2 sequestration projects.

Quantitative monitoring of CO2 sequestration using thermal neutron detection technique in heavy oil reservoirs

CO2 enhanced oil recovery (CO2-EOR) project is of significance for CO2 sequestration and heavy oil recovery. Quantitative monitoring of CO2 saturation (SCO2) is essential to recognizing and understanding the migration and distribution of CO2 injected into the geological formations. In this paper, based on the difference in the neutron moderation ability of CO2, water and heavy oil, thermal neutron detection technique is applied in heavy oil reservoirs to monitor CO2 sequestration. By Monte Carlo simulation, the responses of thermal neutron count ratio versus different porosities and CO2 saturation were studied. Then, a mathematical model of CO2 saturation versus thermal neutron count ratio and formation porosity was established to quantitatively calculate CO2 saturation. Besides, the effects of formation pressure and temperature, heavy oil density, lithology, and other factors on the method were studied. Results show that variations of formation pressure, formation temperature, and density of heavy oil have little impact on the CO2 saturation measurement. However, the change of formation lithology results in larger CO2 saturation errors and needs corrections. In addition, the method has a low discrimination between CO2 and CH4 gas, and the results are easily affected by the CH4 content. Finally, a simulated case demonstrates the application of the method. For the heavy-oil sandstone with different porosities, the method shows a perfect performance: the SCO2 errors are less than 1% for the high and low gas saturated formation. This research provides an effective strategy to monitor CO2 storage and residual oil saturation in CO2-EOR reservoirs.

CO2 Storage Site Characterisation at Harvey, Western Australia Using Non-Conventional Seismic Survey Geometries

This stage of the South West Hub Carbon Capture and Storage (CCS) project aims to produce an in-depth integrated study of the fault and fracture network surrounding the Harvey 3 well to obtain an understanding of their propensity to act as conduits for the injected CO2. An additional interest to the seismic component of the study was to find an optimum CO2 sequestration monitoring methodology. This was of key importance since acceptance by the farming community across the area of the potential reservoir extent is crucial for successful completion of the project. These two factors were taken into account for the survey design that was comprised of more conventional components consisting of simultaneous recorded 3D surface and 3D VSP data sets and an alternative component that recorded multi-offset VSP (MOVSP) survey along the public roads. A comparative analysis of seismic imaging achieved with different geometries is discussed. One of the important results was that MOVSP surveys produced good results and hence this technique may be preferred for monitoring CO2 sequestration in this area. This was since it caused no disturbance to the farming ground and it was much more likely to gain acceptance with the landowners.

Geologic CO2 sequestration monitoring design: A machine learning and uncertainty quantification based approach

Monitoring is a crucial aspect of geologic carbon dioxide (CO2) sequestration risk management. Effective monitoring is critical to ensure CO2 is safely and permanently stored throughout the life-cycle of a geologic CO2 sequestration project. Effective monitoring involves deciding: (i) where is the optimal location to place the monitoring well(s), and (ii) what type of data (pressure, temperature, CO2 saturation, etc.) should be measured taking into consideration the uncertainties at geologic sequestration sites. We have developed a filtering-based data assimilation procedure to design effective monitoring approaches. To reduce the computational cost of the filtering-based data assimilation process, a machine-learning algorithm: Multivariate Adaptive Regression Splines is used to derive computationally efficient reduced order models from results of full-physics numerical simulations of CO2 injection in saline aquifer and subsequent multi-phase fluid flow. We use example scenarios of CO2 leakage through legacy wellbore and demonstrate a monitoring strategy can be selected with the aim of reducing uncertainty in metrics related to CO2 leakage. We demonstrate the proposed framework with two synthetic examples: a simple validation case and a more complicated case including multiple monitoring wells. The examples demonstrate that the proposed approach can be effective in developing monitoring approaches that take into consideration uncertainties.

Geophysical monitoring technology for CO2 sequestration

Geophysical techniques play key roles in the measuring, monitoring, and verifying the safety of CO2 sequestration and in identifying the efficiency of CO2-enhanced oil recovery. Although geophysical monitoring techniques for CO2 sequestration have grown out of conventional oil and gas geophysical exploration techniques, it takes a long time to conduct geophysical monitoring, and there are many barriers and challenges. In this paper, with the initial objective of performing CO2 sequestration, we studied the geophysical tasks associated with evaluating geological storage sites and monitoring CO2 sequestration. Based on our review of the scope of geophysical monitoring techniques and our experience in domestic and international carbon capture and sequestration projects, we analyzed the inherent difficulties and our experiences in geophysical monitoring techniques, especially, with respect to 4D seismic acquisition, processing, and interpretation.

Multipoint Pressure and Temperature Sensing Fiber Optic Cable for Monitoring CO2 Sequestration

A highly accurate multi-point pressure measurement system based on MEMS pressure sensors spliced into a fiber optic cable and suitable for downhole deployment in a CO2 sequestration well was designed, developed and tested in the laboratory. An interrogator system based on a pulsed laser excitation was shown to be capable of multiple (potentially 60+) point sensor measurements on a single fiber. The interrogator was interfaced with the GE PredixTM industrial internet to demonstrate a remote monitoring system. Sensor packages were fabricated and tested at high temperatures and pressures in supercritical CO2. Environmental and stress testing of the sensor components and package indicated areas in which the design of the package should be further improved.

MCSEM inversion for CO2 sequestration monitoring at a deep brine aquifer in a shallow sea

Carbon dioxide injection monitoring in offshore environments is a promising future application of the marine controlled-source electromagnetic (mCSEM) method. To investigate whether the mCSEM method can be used to quantitatively monitor variations in the distribution of the injected CO2, we developed a mCSEM inversion scheme and conducted numerical analyses. Furthermore, to demonstrate the monitoring capability of the mCSEM method in challenging environments, we used a deep brine aquifer model in shallow sea as an injection target. The mCSEM responses of the injected CO2 in the deep brine aquifer were severely decayed and heavily masked by the air wave due to the proximity of the free space. Therefore, the accurate computation of small mCSEM responses due to the injected CO2 and the proper incorporation into the inversion process are critically important for the mCSEM method to be successful. Additionally, in monitoring situations, some useful a priori information is usually available (e.g. well logs and seismic sections), and the proper implementation of this to our inversion framework is crucial to ensure reliable estimation of the distribution of the injected CO2 plume. In this study, we developed an efficient 2.5D mCSEM inversion algorithm based on an accurate forward modelling algorithm and the judicious incorporation of a priori information into our inversion scheme. The inversion scheme was tested with simplified and realistic CO2 injection models and successfully recovered the resistivity distributions of the injected CO2, although it still required the presence of a considerable amount of the injected CO2. Based on these inversion experiments, we demonstrated that the mCSEM method is capable of quantitatively monitoring variations in the distribution of injected CO2 in offshore environments.

Time-lapse pre-stack seismic inversion with thin bed resolution for CO2 sequestration from Cranfield, Mississippi

Time-lapse surface seismic surveys have been used for CO2 sequestration monitoring at Cranfield, Mississippi. The 3D time-lapse seismic data were recorded both before (2007) and after (2010) CO2 injection. The injection interval is the lower Tuscaloosa sandstone formation, which appears as a thin layer and displays weak signature due to CO2 injection in the post-stack seismic amplitudes. Previous studies have reported inversion of time-lapse acoustic impedances for CO2 plume mapping. However, the acoustic impedances lack elastic information, which are more sensitive to the fluid variation. To address this, we applied a basis pursuit pre-stack inversion on time-lapse Amplitude Versus Angle (AVA) datasets to obtain elastic properties (Vp, Vs, density and Vp−Vs ratio). The inverted elastic properties show improved resolution and provide reasonable fits to the well-log data. The temporal changes of inverted elastic properties provide a basis for mapping the CO2 plume after three years’ injection, demonstrating their effectiveness for a CO2 sequestration study.

Assessing sensitivity to well leakage from three years of continuous reservoir pressure monitoring during CO2 injection at Cranfield, MS, USA

Continuous reservoir fluid pressure monitoring is a technique being deployed and evaluated for monitoring CO2 sequestration projects for storage integrity. Pressure data are known to reveal information about bulk reservoir properties and performance, and the pressure magnitude trajectory through time is commonly used as a metric for demonstrating numerical flow model conformance with observed injection history (i.e. pressure history match). Identification of well leakage using a pressure history approach can be difficult due to the subtle effects such leaks can have on the characteristic pressure magnitude evolution. Using a combination of theory, an analytically-derived synthetic reservoir pressure history example, and analysis of field pressure data, we develop a broadly applicable technique for analyzing reservoir pressure transients in continuous time series and illustrate how that technique can be used to quantify sensitivity to wellbore leakage in the field. Analysis presented here focuses on information provided by second derivative of the continuous pressure time series (d2P/dt2). We show that d2P/dt2 transients reveal consistent relationships with known (theoretical case) or induced (field case) pressure perturbations that are independent of prior pressure history, making them a very useful diagnostic tool for identifying and evaluating any observed transients of unknown origin (i.e. sudden well leakage events unrelated to injection schedule). To the degree that the onset of well leakage is expressed as a pressure transient similar to decreases in injection rates, we are able to identify the source functions (changes in flow rates from variable distances) that our gauges are (and are not) sensitive to. Observational data indicate that sensitivities in the field can be much lower than theoretical expectations, mostly a result of noisy and heterogeneous natural systems. Our analysis can be applied to other field scenarios with different reservoir properties for refining observation well placement design with predictable and quantifiable sensitivity.

Electromagnetic monitoring of CO2 sequestration in deep reservoirs

Michael S. Zhdanov, Masashi Endo, Noel Black, Lee Spangler, Stacey Fairweather, Andrew Hibbs, George A. Eiskamp and Robert Will present a feasibility study of permanent electromagnetic (EM) monitoring of CO2 sequestration in deep reservoir using a novel borehole-to-surface EM (BSEM) method.

Elucidating geochemical response of shallow heterogeneous aquifers to CO2 leakage using high-performance computing: Implications for monitoring of CO2 sequestration

Predicting and quantifying impacts of potential carbon dioxide (CO2) leakage into shallow aquifers that overlie geologic CO2 storage formations is an important part of developing reliable carbon storage techniques. Leakage of CO2 through fractures, faults or faulty wellbores can reduce groundwater pH, inducing geochemical reactions that release solutes into the groundwater and pose a risk of degrading groundwater quality. In order to help quantify this risk, predictions of metal concentrations are needed during geologic storage of CO2. Here, we present regional-scale reactive transport simulations, at relatively fine-scale, of CO2 leakage into shallow aquifers run on the PFLOTRAN platform using high-performance computing. Multiple realizations of heterogeneous permeability distributions were generated using standard geostatistical methods. Increased statistical anisotropy of the permeability field resulted in more lateral and vertical spreading of the plume of impacted water, leading to increased Pb2+ (lead) concentrations and lower pH at a well down gradient of the CO2 leak. Pb2+ concentrations were higher in simulations where calcite was the source of Pb2+ compared to galena. The low solubility of galena effectively buffered the Pb2+ concentrations as galena reached saturation under reducing conditions along the flow path. In all cases, Pb2+ concentrations remained below the maximum contaminant level set by the EPA. Results from this study, compared to natural variability observed in aquifers, suggest that bicarbonate (HCO3−) concentrations may be a better geochemical indicator of a CO2 leak under the conditions simulated here.