CO2 Plume Research Articles

Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

History matching is crucial for reliable numerical simulation of geological carbon storage (GCS) in deep subsurface aquifers. This study focuses on inferring highly complex aquifer permeability fields with multi- and intra-facies heterogeneity to improve the characterization of CO2 plume migration. We propose a deep learning (DL)-based parameterization strategy combined with the ensemble smoother with multiple data assimilation (ESMDA) algorithm to formulate an integrated inverse framework. The DL model is employed to parameterize non-Gaussian permeability fields using low-dimensional latent variables in a Gaussian distribution, thereby mitigating the non-Gaussianity issue faced by the ensemble-based ESMDA inverse method and simultaneously alleviating the computational burden of high-dimensional inversion. The efficacy of the integrated DL-ESMDA inverse framework is demonstrated using a 3-D GCS model, where it estimates the non-Gaussian permeability field characterized by multi- and intra-facies heterogeneity. Results show that the DL model is able to represent the highly complex and high-dimensional permeability fields using low-dimensional latent vectors. The DL-ESMDA framework sequentially updates these low-dimensional latent vectors instead of the original high-dimensional permeability field to obtain posterior estimations of the permeability field. The resulting CO2 plume migration closely matches historical measurements, suggesting a significantly improved model reliability after history matching. Additionally, a substantial reduction in uncertainty for future plume migration predictions beyond the history matching period is observed. The proposed framework provides an effective approach for reliable characterization of CO2 plume migration in highly heterogeneous aquifers, enhancing GCS project operation and risk analysis.

Geophysical Monitoring Technologies for the Entire Life Cycle of CO2 Geological Sequestration

Geophysical monitoring of CO2 geological sequestration represents a critical technology for ensuring the long-term safe storage of CO2 while verifying its characteristics and dynamic changes. Currently, the primary geophysical monitoring methods employed in CO2 geological sequestration include seismic, fiber optic, and logging technologies. Among these methods, seismic monitoring techniques encompass high-resolution P-Cable three-dimensional seismic systems, delayed vertical seismic profiling technology, and four-dimensional distributed acoustic sensing (DAS). These methods are utilized to monitor interlayer strain induced by CO2 injection, thereby indirectly determining the injection volume, distribution range, and potential diffusion pathways of the CO2 plume. In contrast, fiber optic monitoring primarily involves distributed fiber optic sensing (DFOS), which can be further classified into distributed acoustic sensing (DAS) and distributed temperature sensing (DTS). This technology serves to complement seismic monitoring in observing interlayer strain resulting from CO2 injection. The logging techniques utilized for monitoring CO2 geological sequestration include neutron logging methods, such as thermal neutron imaging and pulsed neutron gamma-ray spectroscopy, which are primarily employed to assess the sequestration volume and state of CO2 plumes within a reservoir. Seismic monitoring technology provides a broader monitoring scale (ranging from dozens of meters to kilometers), while logging techniques operate at centimeter to meter scales; however, their results can be significantly affected by the heterogeneity of a reservoir.

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

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

Optimization of pressure management strategies for geological CO2 storage using surrogate model-based reinforcement learning

Injecting greenhouse gas (e.g. CO2) into deep underground reservoirs for permanent storage can inadvertently lead to fault reactivation, caprock fracturing and greenhouse gas leakage when the injection-induced stress exceeds the critical threshold. It is essential to monitor the evolution of pressure and the movement of the CO2 plume closely during the injection to allow for timely remediation actions or rapid adjustments to the storage design. Extraction of pre-existing fluids at various stages of the injection process, referred as pressure management, can mitigate associated risks and lessen environmental impact. However, identifying optimal pressure management strategies typically requires thousands of simulations, making the process computationally prohibitive. This paper introduces a novel surrogate model-based reinforcement learning method for devising optimal pressure management strategies for geological CO2 sequestration efficiently. Our approach comprises of two steps. The first step involves developing a surrogate model using the embed to control method, which employs an encoder-transition-decoder structure to learn dynamics in a latent or reduced space. The second step, leveraging this proxy model, utilizes reinforcement learning to find an optimal strategy that maximizes economic benefits while satisfying various control constraints. The reinforcement learning agent receives the latent state representation and immediate reward tailored for CO2 sequestration and choose real-time controls which are subject to predefined engineering constraints in order to maximize the long-term cumulative rewards. To demonstrate its effectiveness, this framework is applied to a compositional simulation model where CO2 is injected into saline aquifer. The results reveal that our surrogate model-based reinforcement learning approach significantly optimizes CO2 sequestration strategies, leading to notable economic gains compared to baseline scenarios.

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

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

Exploring the influences of salinity on CO2 plume migration and storage capacity in sloping formations: A numerical investigation

Accurate evaluation of CO2 plume migration, potential leakage, and storage capacity are the main challenges for CO2 geological storage (CGS). This study develops a suite of numerical models, focusing on the Shiqianfeng Formation of the Shenhua-Carbon Capture and Storage (CCS) demonstration project in the Ordos Basin, China, aiming to numerically evaluate the influence of combining salinity, dip angle, and faulting factors on CO2 plume migration, storage amount, conversion of gas and dissolved phase states. Simulation results show that lower salinity and larger dip angles result in earlier CO2 leakage. Increasing salinity from 0.00 to S in a 10° sloping formation causes later CO2 leakage (205–260 years). Lower salinity enhances CO2 plume migration, injection amount, and dissolved-phase CO2. As the salinity decreased from S to 0.00 in a 3° sloping formation over 120 years of CO2 migration, the dissolved CO2 plume migration increased by 9.1 %, the total CO2 amount increased by 70.0 %, and the proportion of dissolved CO2 increased by 13.07 %. Compared to formation dip angle, salinity has a more significant impact on the plume migration of dissolved CO2. However, CO2 storage safety decreases with increasing dip angle. The conversion proportion (1.88 %–9.75 %) of gas-phase to dissolved CO2 increased with an increasing dip angle and decreasing salinity for the first 100 years after injection ceased. Thus, we conclude that salinity, dip angle and faulting are important factors during CGS, and this study may provide a practical reference for further studies for evaluating CO2 storage potential.

Learning CO[formula omitted] plume migration in faulted reservoirs with Graph Neural Networks

Deep-learning-based surrogate models provide an efficient complement to numerical simulations for subsurface flow problems such as CO2 geological storage. Accurately capturing the impact of faults on CO2 plume migration remains a challenge for many existing deep learning surrogate models based on Convolutional Neural Networks (CNNs) or Neural Operators. We address this challenge with a graph-based neural model leveraging recent developments in the field of Graph Neural Networks (GNNs). Our model combines graph-based convolution Long-Short-Term-Memory (GConvLSTM) with a one-step GNN model, MeshGraphNet (MGN), to operate on complex unstructured meshes and limit temporal error accumulation. We demonstrate that our approach can accurately predict the temporal evolution of gas saturation and pore pressure in a synthetic reservoir with impermeable faults. Our results exhibit a better accuracy and a reduced temporal error accumulation compared to the standard MGN model. We also show the excellent generalizability of our algorithm to mesh configurations, boundary conditions, and heterogeneous permeability fields not included in the training set. This work highlights the potential of GNN-based methods to accurately and rapidly model subsurface flow with complex faults and fractures.

Dynamic industrial risks assessment for enhanced safety in the oil and gas industry: a methodological innovations and applications

In recent years, continued and accelerated growth in the oil and gas sector has significantly contributed to catastrophic accidents in various regions of Algeria, especially those with high population densities. Therefore, assessing the risks associated with these industries is crucial to prevent such disasters. This study introduces a methodology integrating several Quantitative Risk Assessment techniques, such as Failure Modes and Effects Analysis, Fault Tree Analysis, Event Tree Analysis, and the Bow-Tie method, coupled with simulation tools (ALOHA and FLUENT). This integration created a Dynamic Industrial Risk Assessment Methodology (DIRAM), a comprehensive approach to assessing industrial risk consequences in the oil and gas industries. The study demonstrates DIRAM's effectiveness in understanding the dynamics of catastrophic events. It provides a visual representation of CO2 plume and heat dispersion after a Boiling Liquid Expanding Vapor Explosion (BLEVE), emphasising the importance of spatial-temporal awareness in risk assessment and emergency response. DIRAM offers a valuable basis for preventing and managing potential risks and reinforcing safety measures at hydrocarbon plants.

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

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

Optimizing Injection Well Trajectory to Maximize CO2 Storage Security and Minimize Geomechanical Risk

Summary The work demonstrates an optimal well design for a potential geological carbon storage (GCS) project in Kern County, California (USA). Carbon dioxide (CO2) plume shape, size, and pressure response history in the subsurface are outcomes. We created a toolbox (pyCCUS) to standardize the well design optimization process and it is applicable to different carbon storage assets. This toolbox is helpful to maximize storage security and minimize geomechanical risk. The numerical model of the storage formation features two-way coupled transport and geomechanical deformation. It honors a predefined injection scheme with injection rates that ramp up and then decline for a total of 12.3 MtCO2 injection in 18 years. The peak injection rate is greater than 1 MtCO2/yr, whereas the post-injection monitoring period is 100 years. We propose to develop a long, deviated injection well to best address the injectivity and plume migration challenges for this complex, heterogeneous, dipping formation. The chosen well trajectory improves injectivity while minimizing formation pressure buildup. The well design optimization successfully reduces the pressure buildup by 54% over the base design while only increasing the areal extent of the plume by 21%. We quantify the CO2 plume shape and size at the land surface. The plume grows rapidly during injection, but it increases only slightly after shut-in due to slow updip migration driven by buoyancy. The plume becomes stationary within the post-injection monitoring period. The optimal injector design balances the optimization goals of CO2 plume size, pressure increase, and pressure buildup at geological faults. The optimal injection well design is robust under uncertainties from injection schemes and geological model realizations. Rock deformation due to the pressure buildup is also computed. The model estimates 2.1 cm of uplift that occurs during the year of the peak annual injection rate. Land surface uplift strongly correlates with the subsurface pressure response.

A Lab-scale Investigation of the Mars Kieffer Model

The Kieffer model is a widely accepted explanation for seasonal modification of the Martian surface by CO2 ice sublimation and the formation of a “zoo” of intriguing surface features. However, the lack of in situ observations and empirical laboratory measurements of Martian winter conditions hampers model validation and refinement. We present the first experiments to investigate all three main stages of the Kieffer model within a single experiment: (i) CO2 condensation on a thick layer of Mars regolith simulant; (ii) sublimation of CO2 ice and plume, spot, and halo formation; and (iii) the resultant formation of surface features. We find that the full Kieffer model is supported on the laboratory scale as (i) CO2 diffuses into the regolith pore spaces and forms a thin overlying conformal layer of translucent ice. When a buried heater is activated, (ii) a plume and dark spot develop as dust is ejected with pressurized gas, and the falling dust creates a bright halo. During plume activity, (iii) thermal stress cracks form in a network similar in morphology to certain types of spiders, dendritic troughs, furrows, and patterned ground in the Martian high south polar latitudes. These cracks appear to form owing to sublimation of CO2 within the substrate, instead of surface scouring. We discuss the potential for this process to be an alternative formation mechanism for “cracked” spider-like morphologies on Mars. Leveraging our laboratory observations, we also provide guidance for future laboratory or in situ investigations of the three stages of the Kieffer model.

Tuning effect in time-lapse seismic inversion for CO2 plume monitoring at Sleipner field

Over 20 million tons of CO2‌ have been injected into the sandy Utsira formation of the Sleipner field in the North Sea basin. The thin layering of sands, CO2-saturated sands, and intra-formation thin shales cause interference effects in the seismic response of the monitor surveys. Initial analysis of the amplitude change suggests over 60 % change in the relative acoustic impedance of the reservoir between the 1996 and 2010 surveys. This study follows a multi-stage inversion scheme applied to time-lapse seismic monitoring of the Sleipner field. At each stage, the errors and uncertainties caused by noise and tuning are deeply analyzed. Time-shift estimation from seismic data shows spurious features caused by tuning, specifically using the window-based methods. The time-strain inversion builds a low-frequency initial model for the subsequent model-based inversion but is contaminated by remnants of noise and interference. The synthetic wedge modelling and analysis provides the origins and severity of the tuning error in time-shift and time-strain estimations at the Sleipner field. The model-based inversion removes noise and injects high-frequency components into the results, improving the outcome of time-strain inversion. However, it fails to fully eliminate the tuning imprints and leaves strong traces of error on the results. Afterward, the time-lapse Bayesian seismic inversion slightly adjusts the outcome and shows how deep the influence of interference effect on the time-lapse inversion is. In addition, the complementary discussion on rock physical models in estimating the saturation changes highlights how the tuning error can lead to flawed quantitative interpretation.

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

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

Integrated modelling of CO2 plume geothermal energy systems in carbonate reservoirs: Technology, operations, economics and sustainability

The rising CO2 content in the atmosphere calls for urgent decarbonization efforts. Carbon capture and storage (CCS) have proven effective in storing large volumes of CO2 in geological reservoirs. Nevertheless, challenges persist in identifying sustainable energy sources and efficient storage sites, especially in carbonate reservoirs. This study aims to characterize the Miocene carbonate reservoir in the Jintan Megaplatform, for CO2 storage and geothermal energy utilization. By integrating advanced technologies such as CO2 Plume Geothermal (CPG) with 3D seismic techniques, borehole analysis and numerical modelling, the study reveals the distribution of critical reservoir characteristics, including architecture, heterogeneity, karstification, fractures, porosity, permeability, temperature and pressure that are paramount for CO2 plume and energy production. The reservoir is estimated to store over 31 Gt of CO2, produce 3.824 × 1018 J of geothermal energy and ⁓44.3 GW of power. This power output can be utilized for electrification of the CCS operations, industrial and municipals, potentially generating over $1.4 billion in revenue. A flow-through model has been proposed to improve energy efficiency, reservoir management, and risk mitigation. This study advances to achieving net-zero targets and the United Nations Sustainable Development Goals on clean energy, climate action and sustainable communities.

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

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

Physicochemical behavior and impact of CO2 and CH4 plumes during gas-rich water leakage in a shallow carbonate freshwater aquifer

Carbon capture and storage (CCS) is a promising technology for reducing CO2 emissions. Significant concerns have emerged about the potential leakage of CO2 into shallow aquifers, highlighting the risk to water quality and environmental safety. This underscores the importance of finding monitoring tools suitable for different geological scenarios. If leakage occurs in the context of depleted reservoirs being used for CO2 storage, residual CH4 from the storage complex will likely be entrained together with CO2. However, few studies have addressed the implications of CH4 presence and its potential as a monitoring parameter during CO2 leakage.To address this gap, we simulated a leakage event by injecting water enriched with CO2 and CH4 into a shallow limestone aquifer. The impact of the injection was monitored using a combination of laboratory measurements on water samples and in-situ sensors located downstream from the injection well.All parameters were affected by the simulated leakage. Some monitoring tools allowed us to differentiate the leakage event from natural variations. A key finding of this study was that at 7 m from the injection well, the CH4 breakthrough occurred roughly one day before the CO2 breakthrough, highlighting the potential of CH4 as an early indicator of CO2 leakage and suggesting interesting prospectives for industrial-scale sites. However, further research is needed to confirm the potential of CH4 as a CO2 leakage indicator at industrial scales, due to potential methane oxidation and loss of the signal with longer times and distances.This study contributes to a better understanding of the potential risks and effective monitoring strategies associated with CO2-CH4 leakage in carbonate aquifers.

Key indicators of caprock sealing assessment with consideration of faults in potential CO2 geological storage sites in Subei Basin, China

Good caprock sealing is crucial for ensuring the long-term safety and security of carbon dioxide (CO2) geological storage. However, concealed faults within caprocks and reservoirs can impact the transport and accumulation of CO2 plumes. Due to the uncertainties associated with the geometric structure, physical properties, and combined relationships among reservoir, caprock and fault, accurately evaluating caprock sealing is challenging. To address the challenge of identifying key indicators for caprock sealing considering faults, this study focuses on the one potential CO2 geological storage field in the Subei Basin, China. First, this study establishes a hydraulic-mechanical (HM) coupled finite element model considering a combination of caprock, reservoir and fault. Then, 22 research indicators, including fault offset, fault dip, caprock thickness, burial depth, permeability and mechanical properties, are analysed to evaluate their influence on caprock sealing via the tornado analysis and response surface methods. Finally, key indicators are determined based on evaluation criteria, such as pore pressure increment (ΔPP) and Coulomb failure stress (CFS), in the fault plane. The research results indicate that fault dip, fault offset, fault permeability, burial depth, and reservoir permeability are key indicators. The caprock thickness, caprock permeability, and caprock Young's modulus significantly influence the caprock sealing capacity. The ΔPP and CFS increase with increasing fault dip and offset. When the fault dip exceeds 45°, the ΔPP and CFS reach maximum values at the interface between the reservoir and caprock. Fault permeability has a greater impact than reservoir permeability and caprock permeability. The research results may provide guidance for the evaluation of caprock sealing capacity for CO2 geological storage.

The impacts of geothermal gradients on compressed carbon dioxide energy storage in aquifers

Compressed CO2 energy storage in aquifers (CCESA) is new low-cost large scale energy storage technology. To further improve the energy efficiency of CCESA, we propose to combine the geothermal system with CCESA. In order to study the influence of geothermal energy on CCESA, aquifers with large vertical interval and different geothermal gradients from 0.026–0.07 °C/m are designed. The 3d wellbore-reservoir grid of underground part of CCESA was established. The simulation covering 100 days of intermittent initial gas fill and 50 days of cyclic injection and production was conducted. The hydrodynamic and thermodynamic behaviors and energy efficiency of the system were analyzed, comparatively. In the gas fill period, the higher the geothermal gradient, the farther the front of CO2 plume diffuses in the target aquifer, and thus the smaller the pressure accumulation. The temperature, pressure and energy efficiency of produced CO2 increases with the increase of geothermal gradient, while the density of released fluid decreases. The energy efficiency is greater than 1.0 under 6 geothermal gradients. The average energy efficiency of high- and low-pressure reservoirs can reach 1.282 and 1.061 under the geothermal gradient of 0.07 °C/m, which is 21.79 % and 5.5 % higher than that of 0.026 °C/m. The total energy efficiency of the joint system finally can reach 95.1 %. This research well confirms that the geothermal energy can dramatically improve the efficiency of CCESA and can enhance its application.

A Probabilistic Study of CO2 Plume Geothermal and Hydrothermal Systems: A Sensitivity Study of Different Reservoir Conditions in Williston Basin, North Dakota

The exploration of alternative energy sources has gained significant traction in recent years, driven by the urgent need to mitigate greenhouse gas emissions and transition towards sustainable energy. Among these alternatives, CO2 plume geothermal and hydrothermal systems have emerged as promising options due to their potential for providing clean, renewable energy. This study presents a probabilistic investigation into the sensitivity of CO2 plume geothermal and hydrothermal systems under various reservoir conditions in the Williston Basin, North Dakota. In addition to elucidating the impact of reservoir conditions on system performance, the study utilizes probabilistic methods to assess energy output of CO2 plume geothermal and hydrothermal systems. Insights derived from this probabilistic investigation offer valuable guidance for the working fluid selection, systems design and optimization in the Williston Basin and beyond. Results from the sensitivity analysis reveal the profound influence of reservoir conditions on the behavior and efficiency of CO2 plume geothermal and hydrothermal systems. Our case study on Red River Formation and Deadwood Formations shows a potential of 34% increase and 32% decrease in heat extraction based on varying reservoir conditions. Our investigations in the Beaver Lodge field within the Red River Formation yielded arithmetic mean values for CO2 best case resources, hydrothermal resources and the CO2 worst case as 6.36 × 1018 J, 4.75 × 1018 J and 3.24 × 1018 J, respectively. Overall, this research contributes to advancing the knowledge and understanding of CO2 plume geothermal and hydrothermal systems as viable pathways towards sustainable energy production and carbon sequestration. By highlighting the sensitivity of these systems to reservoir conditions, the study provides valuable insights that can inform decision-making processes and future research endeavours aimed at fostering the transition to a low-carbon energy landscape.

The Analysis of Permeability Influence on CO2 Plume based on Reservoir Simulation

Abstract Permeability is critical in site characterization and geoscience studies for carbon capture and storage projects. There is significant uncertainty of CO2 movement in the subsurface caused by permeability. This study aims to analyze the influence of permeability on the movement of CO2 plumes using a synthetic single-porosity model based on reservoir simulation. By simplifying geomechanics, employing tornado experimental design, as well as utilizing Pearson and Spearman correlations from 30,000 neural network proxy models based on Latin Hypercube and Monte Carlo experimental design, this research successfully explains the impact of permeability on CO2 movement. The reservoir can withstand pressures up to 6500 psi without rock failure. The total CO2 injection with a 90% probability is ∼1.3 TSCF equivalent, with an injection rate of 30 MMSCFD, optimized for a duration of 120 years. Initially, the CO2 movement tends to be upward during injection due to gravitational effects, but over time, it tends to move laterally. Vertical permeability exhibits a negative correlation with total CO2 injection, lateral distribution of CO2 plumes, and CO2 solubility in water. On the other hand, horizontal permeability shows a positive correlation with total CO2 injection. Factors such as perforation location and layer thickness also influence the extent of permeability’s role. However, the lateral and vertical movement of CO2 in this study has not been fully identified, and more complex quantification formulas are needed in commercial simulators. Despite the limitations in data and computational resources, this research successfully explains the movement of CO2 plumes and highlights the importance of other factors such as porosity, perforation location, layer thickness, among others. The recommendations of this study include the use of more complex quantification formulas to model the movement of CO2 plumes, along with increasing the complexity of the model.