Geologic Sequestration Projects Research Articles

An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements

Underground carbon dioxide (CO2) sequestration is widely accepted as a proven and established technology to respond to global warming from greenhouse gas emissions. It is crucial to monitor the CO2 plume effectively throughout the life cycle of a geologic CO2 sequestration project to ensure safety and storage efficacy. We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site.Our proposed workflow integrates field measurements, including distributed pressure and temperature at wells to visualize spatial and temporal migration of the CO2 plume in the subsurface. The deep learning framework has two key concepts that enhance the efficiency of the training and robustness of the CO2 plume prediction: first, instead of using multiple saturation images as output of the deep-learning model, a single Time of Flight (TOF) image are used to represent CO2 plume propagation; second, we use variational autoencoder-decoder (VAE) for high dimensional image data compression considering uncertainties in the predicted images. Time-of-Flight (TOF) is the travel time of a neural particle from the injection point, which is calculated along streamline based on the velocity field by considering reservoir heterogeneity and driving forces. The latent variables of VAE are estimated through a deep neural network model with inputs of distributed pressure and temperature measurements. Subsequently, the decoder part of the VAE expands the estimated latent variables to the original dimension of the TOF images. We demonstrate the efficacy of the proposed method by comparison with the more traditional pareto-based multi-objective Genetic Algorithm (MOGA) for the assimilation of the field measurements and forecasting of the CO2 plume migration.The power and utility of our proposed workflow are demonstrated by application to the Illinois Basin-Decatur Project (IBDP). The IBDP is a large scale 3-year CO2 storage test and is part of the Midwest Regional Carbon Sequestration Partnership (MRCSP). The field measurements include bottom-hole pressure and distributed temperature sensing (DTS) data at the injection well, and the distributed pressure measurements at several locations along the monitoring well. The dynamic model is a compositional model with 1.7 million cells. First, we identify the influential parameters using sensitivity analysis based on the pressure and temperature responses. Next, the sensitive parameters are sampled from a pre-specified range and a comprehensive training dataset is generated, including the pressure and temperature measurements and TOF images based on flow simulation results from a variety of geological model realizations. The trained neural network model can identify geological model realizations that capture the spatial and temporal migration of pressure and CO2 saturation based on Euclidean distance within the VAE latent space. For comparison with traditional history matching workflow, the selected sensitive parameters are also tuned using MOGA with the pressure and temperature measurements. A reasonable agreement is obtained for the predicted CO2 plume migration between two workflows. The proposed deep learning workflow shows significant speed-up compared to the traditional multi-objective optimization-based history matching workflow (5 h for training and seconds for prediction; compared to several days/weeks for the MOGA).The novelty of this work is the application of an efficient deep learning-based workflow to a large-scale CO2 storage test site for assimilating field measurements and predicting CO2 plume propagation in a near real-time and comparison with traditional history matching approach.

An efficient deep learning-based workflow for CO2 plume imaging considering model uncertainties with distributed pressure and temperature measurements

Monitoring CO2 plumes throughout the operation of geologic CO2 sequestration projects is essential to environmental safety. The evolution of underground CO2 saturation can be predicted using high-fidelity numerical simulations. However, high-fidelity simulations can be prohibitively expensive to compute. As a result of recent developments in data-driven models, rapid predictions of the CO2 plume can now be made using readily available pressure and temperature measurements. This study presents a novel deep learning-based workflow for efficiently visualizing CO2 plumes in near real-time while considering their uncertainties.In our deep learning workflow, we visualize the CO2 plume images in the reservoir as a propagating saturation front, represented by ‘onset time’, using field measurements, including downhole pressure and temperature. At a given location, the ‘onset time’ is the calendar time when the CO2 saturation exceeds a certain threshold. Therefore, a single image of the CO2 front propagation is captured using the ‘onset time’ rather than storing multiple CO2 saturation images at different time steps. The use of ‘onset time’ significantly reduces memory and computational cost of deep learning-based framework, enabling large-scale field applications.We use a variational autoencoder-decoder (VAE) network to compress high dimensional ‘onset time’ images into low dimensional latent variables while considering uncertainties of the predicted images. The use of VAE and onset time, simplifies the overall neural network architecture and significantly enhances the training efficiency. To estimate the latent variables of the VAE network, we train a feed forward neural network model that incorporates available monitoring data such as downhole pressure and temperature measurements. The estimated latent variables are then fed into a trained decoder network to generate 3D onset time images, visualizing the propagation of CO2 plume in near real time.The proposed workflow is applied to both synthetic and field cases, where the field application is a large-scale geological carbon storage project in a carbonate reef reservoir in the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The field measurements include distributed temperature sensing (DTS) and distributed pressure responses from down-hole gauges along the monitoring well. The predicted CO2 plume images provided by the proposed workflow are shown to be consistent with the simulation results of traditional history matched model using genetic algorithm. Our deep learning-based framework can predict the CO2 front propagation in terms of onset time in seconds, making it well-suited for real time decision-making and operational optimization.

A Study of the Effect of CO2-Water-Rock Reaction on Caprock Mechanical Sealing Ability

In this paper, the Johansen formation in the Norwegian CO2 geologic sequestration project is taken as the research object, and a multi-field coupled chemical-seepage-stress model is established to analyze the effects of CO2-water-rock reaction on the mechanical structural parameters, such as the pore pressure and the effective stress. Tensile safety factor and shear safety factor of the caprock are selected as evaluation indexes to describe the evolution of the caprock mechanical sealing ability over time dynamically. The simulation results show that the mechanical parameters change a lot during 0-50 years, and the change of mechanical parameters near the injection wells is larger than that far away from the injection wells, indicating that mechanical damage is more likely to occur near the injection wells. However, in 200 years, the tensile safety factor and shear safety factor of the caprock are always greater than 0, indicating that no tensile damage or shear damage occurs in the caprock, and the caprock sealing ability is good.

An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging With Distributed Pressure and Temperature Measurements

Summary Geologic carbon dioxide (CO2) sequestration has received significant attention from the scientific community as a response to global warming due to greenhouse gas emissions. Effective monitoring of CO2 plume is critical to CO2 storage safety throughout the life cycle of a geologic CO2 sequestration project. Although simulation-based techniques such as history matching can be used for predicting the evolution of underground CO2 saturation, the computational cost of high-fidelity simulations can be prohibitive. Recent development in data-driven models can provide a viable alternative for rapid CO2 plume imaging. Here, we present a novel deep learning–based workflow that can efficiently visualize CO2 plume in near real time. Our deep learning framework utilizes field measurements, such as downhole pressure, distributed pressure, and temperature, as input to visualize the subsurface CO2 plume images. However, the high output dimension of CO2 plume images makes the training inefficient. We address this challenge in two ways: First, we output a single CO2 onset time map rather than multiple saturation maps at different times; second, we apply an autoencoder-decoder network to identify lower-dimensional latent variables that compress high-dimensional output images. The “onset time” is the calendar time when the CO2 saturation at a given location exceeds a specified threshold value. In our approach, a deep learning–based regression model is trained to predict latent variables of the autoencoder-decoder network. Subsequently, the latent variables are used as inputs of the trained decoder network to generate the 3D onset time image, visualizing the evolving CO2 plume in near real time. The power and efficacy of our approach are demonstrated using both synthetic and field-scale applications. We first validate the deep learning–based CO2 plume imaging workflow using a 2D synthetic example. Next, the visualization workflow is applied to a 3D field-scale reservoir to demonstrate the robustness and efficiency of the workflow. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at a monitoring well, flowing bottomhole pressure (BHP) data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. Our approach is also extended to efficiently evaluate the uncertainty of predicted CO2 plume images. Additionally, an efficient workflow for optimizing data acquisition and measurement type is demonstrated using our deep learning–based framework. The novelty of this work is the development and application of a unique and efficient deep learning–based subsurface visualization workflow for the spatial and temporal migration of the CO2 plume. The efficiency and flexibility of the data-driven workflow make our approach suitable for field-scale applications.

Carbonation-induced properties of alkali-activated cement exposed to saturated and supercritical CO2

Carbonation of well cement under high-temperature and high-pressure conditions is a significant concern in geological sequestration projects. This study evaluates the carbonation-induced properties of low to high calcium-based one-part alkali-activated cements (AACs) as an alternative to Portland oil well cement under reservoir conditions. Fly ash/slag-based AACs exposed to saturated (SAT) and supercritical (SUP) CO2 for 28 days were characterised using compressive strength, XRD and SEM tests. The strength properties of AACs increased with increasing slag content from 40% (low calcium AACs) to 100% (high calcium AACs) by weight of the binder under both SAT and SUP conditions. Aragonite is the major polymorph of CaCO3 in all AACs, irrespective of the type of CO2 exposure, and the highest aragonite content was observed in 100% slag AAC. The amorphous phase content decreased while the crystalline CaCO3 increased in carbonated AACs with increasing slag content, resulting in a dense microstructure in high calcium AACs. Sealing of microcracks and pores was observed in high calcium AACs with the accelerated precipitation of CaCO3 by reducing total porosity. Low calcium AACs resulted in a more highly porous carbonated microstructure than high calcium AACs under SAT and SUP conditions.

Skin factor and potential formation damage from chemical and mechanical processes in a naturally fractured carbonate aquifer with implications to CO2 sequestration

In this study, we investigate formation damage due to acidization and water injection tests into the naturally fractured carbonate Middle Duperow Formation at Kevin Dome, Montana, potentially diminishing the chance of a future successful Geological Carbon Sequestration (GCS) project. Multiple well-test analytical models, correlated with core description and lithology data, are used to determine flow behavior and communication between the water injection interval and surrounding formations. An improved three-dimensional (3D) geologic model with dual-continuum matrix and fracture properties is constructed based on most recent seismic, core, and water sample measurements. Brine injection is simulated to verify the interpretation from the analytical models, followed by CO2 injection simulation. Geochemical calculations are performed to understand the in-situ processes that led to formation damage. Our findings suggest: (1) there are two possible scenarios that could lead to a positive total effective skin factor and permeability decline: partial penetration and formation damage; (2) analytical models indicate a positive total skin factor, contradicting results of a previous study suggesting that the well was mildly stimulated; (3) numerical simulation supports the formation damage hypothesis by matching the pressure buildup observed during the latter two brine injection tests; (4) several mechanical and chemical processes may have occurred during injection to clog the matrix/fracture system: anhydrite fines migration and/or calcite precipitation. We then make preventative suggestions for future GCS projects into carbonate reservoirs and remediation recommendations for GCS operation at the Kevin Dome.

A screening model for predicting injection well pressure buildup and plume extent in CO2 geologic storage projects

We present a new screening model for predicting injection well pressure buildup and CO2 plume migration for CO2 geologic sequestration projects. The model requires only limited information and is quite accurate when compared to detailed simulation results. Such models can assist project developers during the early days of project planning (e.g., for 45Q related projects), and also help regulators perform simple checks against detailed numerical models. The screening model consists of two correlations: one for injectivity index in terms of the slope of the CO2 fractional flow curve, and the second for total storage efficiency within the footprint of plume as a function of gravity number and slope of the CO2 fractional flow curve. Using these two correlations, as well as a knowledge of some basic reservoir characteristics and estimates of fluid properties from standard correlations, the injection-well pressure buildup and CO2 plume extent in the formation can be readily estimated. The new correlations show a good match with the results of the underlying simulations, and also provide good agreement with independent calculations for two example problems.

Temperature Dependence of the Permeability of Sandstone Under Loading and Unloading Conditions of Confining Pressure

Rock permeability is a key parameter in evaluating the CO2 storage capacity and injectivity in geologic CO2 sequestration projects. To investigate the influences of confining pressure and testing temperature on rock permeability, seepage tests were carried out on four cylindrical sandstone specimens using a newly developed triaxial permeability measurement system. In this study, the confining pressure was loaded and unloaded stepwise between 10 and 30 MPa at different temperatures (25–90 °C). The experimental results showed that as the effective confining pressure increased in the loading process, sandstone permeability decreased nonlinearly. As the effective confining pressure decreased in the unloading process, permeability increased nonlinearly. Elevated temperature decreased the sandstone permeability, and the percentage reduction in permeability decreased with increasing temperature. Micropore space closure and thermal expansion were evidence of the permeability changes as the confining pressure and testing temperature were varied. The experimental results obtained in the seepage tests under the different confining pressures and elevated testing temperatures provide a reference for evaluating rock permeability in underground engineering.

Carbonation of Portland-Zeolite and geopolymer well-cement composites under geologic CO2 sequestration conditions

Degradation of Portland cement under supercritical CO2 conditions (scCO2) is one of the major concern in geological sequestration projects. This paper presents the research on two novel types of binders considered to display a CO2 corrosion resistance potential: (1) Portland cement partially replaced with 20, 30 and 40 wt % of zeolite and blended with Styrene-Butadiene (SB) Latex and (2) geopolymer based on lime-slag and lime-pozzolan blends. Specimens were placed in an autoclave, covered with water, heated up to 100 °C and pressurized with 7 MPa CO2 in order to simulate CO2 environment in the Croatian wellbore. On behalf of tests on compressive strength, porosity, permeability and alkalinity, as well as thermogravimetry, X-ray diffraction and SEM microstructural analyses of samples at various penetration depths, carbonation mechanisms were discussed. Portland-Zeolite composites have been argued as potential CO2 resistant cement systems, with an replacement rate below the minimally tested addition of 20 wt %, as excessive replacement rates resulted in an increase in porosity and permeability. Geopolymer composites based on lime activation unfortunately haven't exhibited required properties for application in well cementing of CO2 injection wells. Calcium carbonate polymorphs precipitated throughout the whole specimen thickness of all samples, but preferably in mixtures based on Ca-richer raw materials. A re-precipitation of calcium carbonate filled in the porosity preferentially at the 0.5 mm surface layer of PC-based specimens, due to the outward diffusion of calcium at the early stage of carbonation.

Prediction of CO2/water/quartz wettability behavior during CO2 storage in deep saline aquifers

ABSTRACT In the context of geological CO2 sequestration projects, CO2/water/rock wettability directly affects the capacities of residual and structural trapping mechanisms. Therefore, presenting an accurate model that can simulate the wettability behavior of CO2/water/rock under different environmental conditions is necessary. In this study, a new model based on adaptive neuro-fuzzy interference system (ANFIS) was developed for estimation of CO2/water/quartz contact angle as a function of influencing parameters, such as pressure, temperature, salinity, and salt type. Results of the proposed model were compared to the actual data and it was found that the presented model has immense potential with high correlation coefficient (R 2) of 0.991 and average absolute relative error (AARE) of 2.010 for predicting the CO2/water/quartz contact angle. Finally, a sensitivity analysis was performed on the influencing factors. Results of this analysis demonstrated that salinity and pressure have the highest impact on the wettability of CO2/water/quartz.

A metric for evaluating conformance robustness during geologic CO2 sequestration operations

A metric for quantifying the robustness of a designation of conformance of a geologic CO2 sequestration (GCS) project during its operational phase is developed and demonstrated. Conformance in this context is a measure of the degree to which the sequestration system is understood and can be accurately modeled along with the degree to which the storage system is performing as designed. The robustness of conformance quantifies the degree to which parameter values can deviate from their current nominal estimates and still produce model forecasts that meet the performance criteria for the GCS operation. We develop and demonstrate the approach on a simplified scenario to illustrate the concept using a single uncertain parameter (homogeneous reservoir permeability) and a single performance criterion (critical pressure at a monitoring well in the reservoir; i.e., one that may displace brine from the reservoir to an overlying drinking water aquifer for example). Increased confidence in conformance assessment as more monitoring data are obtained is incorporated through the standard error of the coefficient (reservoir permeability in the case presented here), which we designate as the concordance metric. As more monitoring data become available during the course of the GCS operation, the standard error of the coefficient decreases (in general), thereby leading to increased conformance robustness as a larger deviation from nominal is required to fail to meet performance criteria. Increasing conformance robustness over time builds confidence that a GCS project will continue to meet performance criteria during the life-span of the project, thereby supporting designations of conformance. A lack of conformance robustness provides a critical warning that the performance criteria of the GCS operation are not robust against probabilistic and non-probabilistic uncertainty in model conceptualization and/or model parameters.

Effect of Capillary Induced Flow on CO2 Residual Trapping

Residual trapping is one of the main mechanisms for immobilizing CO2 after the injection phase of a geological sequestration project. Residual trapping results from capillary forces at the pore scale which lead to snap-off and bypass of CO2. For heterogeneous systems, there is, in addition to the capillary potential at the pore scale, a capillary potential at the scale of the heterogeneity which will result in capillary induced flows and trapping. We investigate the impact of capillary induced flow on multiphase flow behavior and its implications for residual trapping of CO2 by performing experimental and numerical core-flood tests. Results show that the magnitude and spatial extent of the heterogeneity impact the local capillary forces and, therefore, the capillary pressure and saturation distribution. In certain cases, even in the capillary dominated regime, local capillary disequilibrium was observed, leading to capillary induced flow when the system relaxed. This work shows that for layered rock with small variations in permeability, laminations direction has minimal impact on the local capillary forces and does not effect the residual trapping potential. Further research is needed to investigate if lamination direction impacts the residual trapping potential in the case of layered rocks with larger variations in permeability.

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.

Simulating the Cranfield geological carbon sequestration project with high-resolution static models and an accurate equation of state

A field-scale carbon dioxide (CO2) injection pilot project was conducted as part of the Southeast Regional Sequestration Partnership (SECARB) at Cranfield, Mississippi. We present higher-order finite element simulations of the compositional two-phase CO2-brine flow and transport during the experiment. High-resolution static models of the formation geology in the Detailed Area Study (DAS) located below the oil-water contact (brine saturated) are used to capture the impact of connected flow paths on breakthrough times in two observation wells. Phase behavior is described by the cubic-plus-association (CPA) equation of state, which takes into account the polar nature of water molecules. Parameter studies are performed to investigate the importance of Fickian diffusion, permeability heterogeneity, relative permeabilities, and capillarity. Simulation results for the pressure response in the injection well and the CO2 breakthrough times at the observation wells show good agreement with the field data. For the high injection rates and short duration of the experiment, diffusion is relatively unimportant (high Péclet numbers), while relative permeabilities have a profound impact on the pressure response. High-permeability pathways, created by fluvial deposits, strongly affect the CO2 transport and highlight the importance of properly characterizing the formation heterogeneity in future carbon sequestration projects.

Applicability of aquifer impact models to support decisions at CO2 sequestration sites

The National Risk Assessment Partnership has developed a suite of tools to assess and manage risk at CO2 sequestration sites. This capability includes polynomial or look-up table based reduced-order models (ROMs) that predict the impact of CO2 and brine leaks on overlying aquifers. The development of these computationally-efficient models and the underlying reactive transport simulations they emulate has been documented elsewhere (Carroll et al., 2014a,b; Dai et al., 2014; Keating et al., 2016). In this paper, we seek to demonstrate applicability of ROM-based analysis by considering what types of decisions and aquifer types would benefit from the ROM analysis. We present four hypothetical examples where applying ROMs, in ensemble mode, could support decisions during a geologic CO2 sequestration project. These decisions pertain to site selection, site characterization, monitoring network evaluation, and health impacts. In all cases, we consider potential brine/CO2 leak rates at the base of the aquifer to be uncertain. We show that derived probabilities provide information relevant to the decision at hand.Although the ROMs were developed using site-specific data from two aquifers (High Plains and Edwards), the models accept aquifer characteristics as variable inputs and so they may have more broad applicability. We conclude that pH and TDS predictions are the most transferable to other aquifers based on the analysis of the nine water quality metrics (pH, TDS, 4 trace metals, 3 organic compounds). Guidelines are presented for determining the aquifer types for which the ROMs should be applicable.

Experimental investigation of seismic velocity behavior of CO2 saturated sandstones under varying temperature and pressure conditions

AbstractSubsurface storage of CO2 into geological formations is considered an important strategy to mitigate increasing atmospheric CO2. Time‐lapse seismic monitoring is an integral component of a geological CO2 sequestration project because the seismic behavior of the rock is a function of both mineralogical composition and pore fluid properties. At the uppermost kilometer of the sedimentary basin, CO2 can be present at gaseous, liquid, and supercritical states, with the supercritical and liquid states preferred in CO2 storage operations due to the higher sweep efficiency. In this study, the seismic velocities [both compressional (Vp) and shear (Vs) waves] of two CO2‐saturated sandstone core plugs (Red Wildmoor and Knorringfjellet formations) have been measured under a range of temperatures and pressures in which CO2 phase transitions occur. The experiments were done using a uniaxial hydrostatic cell equipped with seismic wave transmitting and receiving transducers. The experimental investigation illustrated that seismic velocities (both Vp and Vs) decreased until the critical point was reached. Further increases in the CO2 pressure above the critical point led to a gradual increasing of Vp while the Vs remained unchanged. The effect of CO2 on the seismic velocity of the sandstone was compared with the effects of N2 and distilled water at the same conditions. It was further indicated that the seismic velocity changes were mainly connected to significant changes of CO2 density and the corresponding bulk rock moduli over the critical point. The observed velocities are in good agreement with Gassmann‐predicted velocities as well as literature data. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd

Gas content derivative data versus diffusion coefficient

The study of the gas diffusion process has a main role in both coalbed methane (CBM) production and CO2 injection in geological sequestration projects. The accurate determination of gas diffusion coefficients in unconventional reservoirs such as coal seams requires a consistent mathematical approach. The study of the gas diffusion process in coal seams was carried out using sorption isotherms. The Langmuir model for individual gases and the extended Langmuir model for multicomponent gas mixtures were applied to fit sorption isotherm data. “Gas content derivative data” and “gas content changes” emerged as crucial mathematical parameters to accurately study the gas diffusion process. The main goal of this paper is to define the degree of interaction between the gas content derivative data and the gas diffusion process. Experiments were performed on three samples selected from two different coals, which were submitted to three different gas compositions, viz 99.999% CH4; 99.999% CO2; and a gas mixture containing 74.99% CH4 + 19.99% CO2 + 5.02% N2, at 35℃, and at pressures ranging from 0 up to 50 bar. Experimental results obtained from the three samples indicate that during adsorption/desorption processes, the diffusion coefficients increase and the gas content changes decrease when the pressure decreases, due to the sample saturation degrees and to the kinetic mechanisms increase. Additionally, the “gas content derivative data” scattering is slightly lower during the desorption process than during the adsorption process. These behaviours are clearly identified when using methane, but are even more evident when using CO2 and the gas mixture, due to the CO2 interaction with coal porous structure, which induces a considerable resistance to CO2 release. The results show that sample B (CH4 + CO2 + N2) displays higher diffusion coefficient values (this behaviour is mainly related to the presence of N2) than sample C (CH4) and than sample A (CO2).

Experimental study of two-phase flow properties of CO2containing N2in porous media

In preliminary analyses, the co-injection of CO2 with H2S, SO2 and N2 impurities has been shown to reduce total carbon capture and storage (CCS) cost. The multiphase flow properties of impurities in the CO2–brine system in porous media are the key to understanding the mechanisms and nature of geological CO2 sequestration projects. In this study experiments were performed on the multiphase flow process of CO2/N2/brine system at conditions similar to aquifer pressure and temperature using the X-ray CT technique. Experiments at various rates of CO2 injection that affect saturation and spatial distribution of injected gas were conducted in this experiment. The results indicate an strong relationship between gas saturation and porosity distribution in porous media, and the increasing capillary number leads to lower saturation in downward injection. Small capillary numbers and higher fractional flows in the gas phase both result in uniform saturation maps in the core. The CO2 clusters seem larger at high capillary numbers and the high CO2 collection regions extend based on the saturation distribution in the lower CO2 fraction as the flow pattern stays similar as the same capillary number. CO2 containing N2 tends to retain more correlative relationships at different gas injection rates compared with the pure CO2 stream. Even though both distribution and saturation are storage concerns, the N2 component has little effect on gas distribution, whereas it brings about an overall increase in the saturation for most experiments. Thus the N2 enhanced the storage performance of CO2.

The coal cleat system: A new approach to its study

After a general analysis regarding the concept of coal “cleat system”, its genetic origin and practical applications to coalbed methane (CBM) commercial production and to CO2 geological sequestration projects, the authors have developed a method to answer, quickly and accurately in accordance with the industrial practice and needs, the following yet unanswered questions: (1) how to define the spatial orientation of the different classes of cleats presented in a coal seam and (2) how to determine the frequency of their connectivites. The new available and presented techniques to answer these questions have a strong computer based tool (geographic information system, GIS), able to build a complete georeferentiated database, which will allow to three-dimensionally locate the laboratory samples in the coalfield. It will also allow to better understand the coal cleat system and consequently to recognize the best pathways to gas flow through the coal seam. Such knowledge is considered crucial for understanding what is likely to be the most efficient opening of cleat network, then allowing the injection with the right spatial orientation, of pressurized fluids in order to directly drain the maximum amount of gas flow to a CBM exploitation well. The method is also applicable to the CO2 geological sequestration technologies and operations corresponding to the injection of CO2 sequestered from industrial plants in coal seams of abandoned coal mines or deep coal seams.

Commercial-scale CCS Project in Decatur, Illinois – Construction Status and Operational Plans for Demonstration

Abstract United States Department of Energy (DOE) and Archer Daniels Midland Company (ADM) has made substantial progress in the development and construction of the largest saline storage project in the U.S. This commercial-scale project is located at the ADM's agricultural processing and biofuels complex in Decatur, Illinois. The Office of Fossil Energy's National Energy Technology Laboratory manages this project. Detailed design, installation of the CO2 compression, dehydration, and transmission system, and installation of related piping, electrical, and instrumentation was completed and commissioning of this system was initiated. The construction of a 100-MW electrical substation, which will supply power to the compressors and other equipment, is in progress. A 2206 m deep monitoring well and a 1083 m geophysical well were drilled. The U.S. Environmental Protection Agency (EPA) issued a draft permit for a Class VI injection well with a capacity to inject 3000 tonnes of CO2 per day. This is expected to be the first geological sequestration project to operate with EPA's Class VI well permit in the U.S. The project is scheduled to begin CO2 injection into the Mount Simon Sandstone, a deep saline reservoir, early 2015. The project team members include Schlumberger Carbon Services, Illinois State Geological Survey (ISGS)-University of Illinois, and Richland Community College (RCC). Public education and outreach for CCS is an integral part of this ICCS project and to this end, the project has established the National Sequestration Education Center (NSEC) at RCC in Decatur. NSEC is implementing a new associate degree program, first in the United States, with an emphasis on CCS. This project was recognized by the Carbon Sequestration Leadership Forum.