bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Modelling of Bio-Geochemical Aspects in Underground Hydrogen Storage: Implications for Reservoir Selection and Performance
Authors: Shojaee A. , Ghanbari S. , Wang G. , Mackay E. G. 
Published: June 2024

Abstract
Hydrogen offers a potential replacement for conventional fossil fuels as a sustainable energy vector. Despite this promise, its large-scale storage is one of the main bottlenecks. Utilizing depleted gas reservoirs for hydrogen storage could present a viable solution. However, introduction of hydrogen into the subsurface may induce microbial and geochemical reactions, resulting in possible hydrogen loss. Therefore, understanding the microbial and geochemical risks associated with underground hydrogen storage is essential for appropriate reservoir selection.
To explore the bio-geochemical behaviour of subsurface hydrogen storage, we developed a coupled numerical model using PHREEQC. This model includes both geochemical and microbial reactions, with the former assumed to be at equilibrium and the latter governed by kinetics. The model incorporates three metabolic pathways: Methanogenesis, Acetogenesis, and Sulphate Reduction modelled by the Dual-Monod approach. Inputs such as reservoir mineralogy and brine composition determine the reservoir type for geochemical reactions, while kinetic drives microbial reactions. This adaptable model enables batch simulations across various reservoir types, contributing to a comprehensive understanding of hydrogen storage dynamics in subsurface environments. This understanding may then be applied to specific reservoir systems.
The preliminary findings reveal a significant interplay between microbial and geochemical reactions, underscoring the substantial impact of reservoir choice - specifically mineralogy and initial brine composition - on microbial reactions. Storage performance and hydrogen loss are particularly sensitive to these reservoir characteristics. Developing on these initial insights, a comprehensive case study was undertaken, assessing hydrogen storage performance in some depleted/operating gas reservoirs in the North Sea with specified formation mineralogy and brine compositions. Observations indicate that reservoir type substantially drives hydrogen storage performance, with variations tied to the presence of calcite, dolomite, quartz, and anhydrite, and to the initial brine composition, as well as to the activity of microbial life (kinetic). This illustrates the need for a rigorous reservoir selection process to ensure optimal storage efficacy and purity of recovered hydrogen.
This study offers novel predictive insights into the microbial and geochemical dynamics within any given reservoir during underground hydrogen storage projects, thereby facilitating screening processes.
Watch the Reservoir! Improving Short-Term Production Forecast Through Transformers
Authors: Werneck R. , Lusquino Filho L. A. , Lustosa A. , Loomba A. , Gonçalves M. M. , Esmin A. , Salavati S. , Morais E. , Mendes Junior P. Ribeiro , Zampieri M. , Amaral M. , Linares O. C. , Castro M. , Moura R. , Schiozer D. J. , Ferreira A. Mello , Davolio A. , Rocha A. 
Published: June 2024

Abstract
Data-driven methodologies have been used in reservoir management and production forecasting, particularly demonstrating remarkable efficacy in short-term oil production forecasts. However, there is space to improve its prediction, especially in tackling the complexities of challenging reservoirs, such as the heterogeneous carbonate reservoirs from Brazilian Pre-salt fields. Methods for oil production forecasting in the petroleum literature generally consider linear correlations or recurrent neural networks (RNNs). In this paper, we propose a new strategy to improve short-term forecasting for oil production through attention mechanisms that boost state-of-the-art methods. Traditional data-driven techniques generally do not consider static data or planned activities. However, we address this critical gap by leveraging the Temporal Fusion Transformer (TFT) to integrate such information into our short-term forecasting. Transformers, the architectural inspiration behind ChatGPT, employ attention mechanisms to establish relationships between different time series data points, assigning weights to these connections. We jointly explore oil, gas, and water production, pressure, and the ratios between them. This method includes static data (e.g., geographical coordinates) as well as known side reservoir information. Such side information can be, for instance, another predicted future production or planned well shut-ins. We also investigate which side information improves the obtained forecasting. This paper presents two main findings. First, it shows how using certain side information can improve the overall predictive capability of a model. For example, using predicted gas production as side information can significantly improve the oil production forecast. This is logical and in line with expectations, as there is an intimate connection between oil and gas production. In the second application of TFT, we considered well closures as the side information. We used an anomaly detection tool to identify well closures in the history period and converted it to usable side information for the TFT model. The distribution of these well closures is used as a guide to predict our target oil production. As we considered the distribution of well closures as side information, we framed our results in terms of cumulative oil production rather than daily forecast rates. The results of this work show that the cumulative production gets very close to the ground-truth data, better than linear and proposed baselines. In summary, the second key result shows and underscores the significance of incorporating side information within our TFT approach.
Rough Gas Storage Site – Redeveloping and Making it Hydrogen Ready
Authors: Jutila H. A. , Cullen M. , Hayhurst S. , Howell K. , Fullarton L. , Heydari E. , Orley M. 
Published: June 2024

Abstract
The Rough Field UKCS SNS started gas production in 1975 and was converted to gas storage in 1985. In 2017 it was partially decommissioned, and it entered a blowdown phase. In early 2022 the Rough Field was brought back into storage operation due to changing energy requirements. However, due to the age of the Rough facility, a redevelopment was required, and it was decided to future-proof the facility and wells by making Rough ‘Hydrogen-Ready’.
A new static reservoir model was built to enable a calibrated dynamic model to optimise future well placement for both gas and hydrogen storage. The initial dynamic modelling was done using black oil formulation to speed up the calibration phase and the final optimisation for well placement was carried out using compositional modelling.
The static model was expanded to a ‘whole earth’ model for thermal and geomechanical analysis to assess the risk of potential hydrogen and or natural gas leak path generation.
After several iterations of the static model, testing various uncertainties, a calibrated dynamic model was achieved with small variations to the static properties porosity, permeability, and transmissibility between facies and across the faults in the reservoir. To achieve a good pressure match, a ‘ghost’ gas volume was required otherwise the amplitude of the pressure cycle during injection/withdrawal cycles would have been impossible to achieve. Possible sources of the ‘ghost’ gas volume could be a larger than expected Carboniferous volume, limited pressure support from the surrounding aquifer or an isolated gas accumulation adjacent to the field. The future well count to achieve injection and withdrawal targets is 16 wells, this is less than the historical 36 due to the introduction of horizontal well technology. The model calibration was challenged by less than perfect data gathering during the storage phase and the bottomhole pressures were estimated using tubing head pressure data. The temperature and geomechanical modelling indicated that past operations past had most likely not caused any damage to the reservoir integrity.
The study highlights the requirement for comprehensive data gathering during the operation of any field. The assumption that a reservoir behaves like a tank is an oversimplification and can lead to incorrect assumptions of the reservoir mechanism (the requirement for the additional volume is not evident in material balance models). A ‘whole earth’ model would be ideal for any reservoir study and the perceived cost is justified by a much-improved reservoir understanding.
Biogeochemical Modelling of HP-HT Bioreactor Systems for Enhanced Microbial Risk Assessment in Underground Hydrogen Storage
Authors: Vasile N. S. , Suriano A. , Bellini R. , Bassani I. , Vizzarro A. , Coti C. , Barbieri D. , Scapolo M. , Viberti D. , Verga F. , Pirri F. , Menin B. 
Published: June 2024

Abstract
Underground hydrogen storage (UHS) can be a valuable solution for efficient and environmentally friendly energy storage but it introduces complex microbial and geochemical interactions that pose unique challenges. This research leverages advanced biogeochemical modeling to accurately replicate these interactions, reproduced within a lab-scale bioreactor system that mimics the high-pressure and high-temperature conditions typical of many underground gas storages. Utilizing a dual-platform approach, we employed COMSOL Multiphysics and CMG-GEM, augmented by supplementary simulation tools like PHREEQC, to perform an in-depth analysis of the evolution of microbial populations and gas and liquid composition, and of the hydro-chemical processes in geological formations. Our comparative study demonstrates the effective application of these platforms in modeling the complex dynamics of heat and fluid dynamics, mass transfer, and biochemical reactions. The models were meticulously validated against experimental data, displaying high accuracy in kinetic parameter fitting and the ability to replicate the observed phenomena. The simulation results from COMSOL and CMG-GEM showed remarkable agreement, with differences in the respective outcomes under 3-5%, confirming the reliability and robustness of the simulations across different computational environments. The research highlights the benefits of integrating multiple simulation platforms to achieve a comprehensive and comparative understanding of biogeochemical processes at various scales. This approach not only enhances our predictive capabilities but also facilitates the transfer of biochemical and geochemical kinetics from bioreactor-scale to reservoir-scale models, to make the implementation of hydrogen storage possible. These findings underscore the potential of the modeling tools to support the assessment and management of microbial risks associated with hydrogen storage, contributing to fully assessing the storage feasibility. By providing a detailed comparison of two leading software platforms, we established an essential methodological framework for advancing the UHS technology toward safe implementation.
Deep Learning for Geothermal Reservoir Characterization: Estimating Rock Properties from Seismic Data Using Convolutional Neural Networks
Authors: Shreif Mariam , de Chizelle Julien Kuhn , Turner Adam , Bhattacharjee Saurav , Madani Ali 
Published: June 2024

Abstract
Estimating rock properties is a crucial aspect of geothermal reservoir characterization, which plays a pivotal role in the efficient harnessing of geothermal energy. Rock properties include hydraulic properties, such as porosity and permeability, and elastic properties such as Poisson’s ratio, P-wave, S-wave velocity, bulk modulus, and acoustic impedance. Accurate determination of these properties allows geoscientists and reservoir engineers to assess and optimize the reservoir performance and assess the long-term stability of geothermal projects. Seismic inversion is the process of deriving these rock properties from seismic data. Conventional seismic inversion can be time-consuming and costly. Machine learning can effectively estimate rock properties which reducesthe need to rely on conventional seismic inversion, expensive lab experiments, and well logging data. This study aims to estimate keyrock properties (acoustic impedance, bulk modulus, density, permeability, Poisson’s ratio, and porosity) from the SCAN dataset using a convolutional neural network. The proposed U-net architecturewas used to develop models that rely on a full-stack seismic dataset as inputs to the model. Mean SquaredError (MSE) with a regularization factor was considered as a loss function when training the model and Mean Absolute Error (MAE) to assess the performance of the model. Results reveal an effective performance of the developed models in the estimation of rock properties with low MAE values ranging between 0.5-3 %. The higher MAE observed for the porosity and permeability estimation is attributed to poor data coverage in the ground truth data.This study demonstrates the potential of convolutional neural networks to predict rock properties from seismic data for efficient reservoir characterization.
Landing and Navigating Thin Reservoirs for UGS by Means of Integrated LWD Measurements and Reservoir Modelling
Authors: Sudiro P. , Mantegazzi A. , Pozzovivo V. , Codegone G. 
Published: June 2024

Abstract
Underground Gas Storage (UGS) reservoirs are often a late development of hydrocarbon reservoirs converted to storage after reservoir depletion. This conversion commonly requires the drilling of new wells tailored to the needs of UGS and the deployment of services like geosteering to optimize the well placement in the target UGS reservoir. However, UGS is an application with specific technical challenges. Modelling of underground conditions can be difficult due to lack of suitable data, or due to the changing fluid properties connected to the cyclicity of charging and depletion of the reservoir, adding additional uncertainty to the challenges posed by geological variability.
This case history details the integration of Logging While Drilling (LWD) data to land and geosteer two infilling wells for UGS activity optimization.The target for both wells was a sand layer, a few meters-thick, with optimal petrophysical properties at the top of the reservoir sequence. The results of the geosteering operations confirmed the uncertainties regarding the structural trend and formation properties in a peripheral area of the field. Unexpected variations in the reservoir architecture required rapid changes of wellbore trajectory to achieve the landing objectives. After landing, both wellbores were geosteered within the few meters-thick target interval, although lateral formation changes generated anomalous tool responses compared to the data measured in the offset wells. After achieving the operational target length, Well A exited the reservoir through the roof due to a rapid steepening of the formation dip, exceeding the project’s dogleg limitations. TD was called without further extension of the hole section. Well B, instead, was successfully geosteered beyond the planned TD.
Technical Insights into the Compatibility of Salt-Saturated Cement for Underground Storage in Salt Caverns
Authors: Iorio V. S. , Cracolici F. , Massara E. Previde , Consonni A. , Parrozza F. , Lainati A. 
Published: June 2024

Abstract
The need for a sustainable and decarbonized energy system has led to a renewed interest in Underground Hydrogen Storage (UHS). These caverns, formed by solution mining of underground salt deposits, offer large storage volumes and high structural integrity. Furthermore, salt caverns exhibit containment properties, with their impermeable salt formations ensuring long-term storage without significant degradation of the hydrogen. This paper investigates the compatibility of salt-saturated cement for Underground Hydrogen Storage in salt caverns.
The primary aim is to scrutinize how hydrogen interacts with cement in this unique environment, with implications for the safety and efficiency of hydrogen storage in salt caverns. Therefore, it is important to investigate the interactions between the cement-salt slurry and hydrogen gas, including potential chemical reactions or degradation processes that may occur over time. This information is crucial for designing a storage system that maintains its integrity and performance throughout the storage duration. This abstract discusses the results of aging for 28 days at 100 bar 50°C of one real composition of salt-saturated cement already used in oil and gas wells.
The investigation into the compatibility of salt-saturated cement with hydrogen has shown encouraging preliminary results. Mechanical testing indicated only marginal reductions in compressive strength, well within acceptable limits for underground storage structures. Chemical analyses revealed no significant alterations in the cement's composition, suggesting good chemical stability. Observations from real-time aging tests reinforce the material's resilience over prolonged exposure to hydrogen. While these findings are promising, further analyses and long-term studies are warranted to establish definitive conclusions. In summary, the study points to the potential viability of salt-saturated cement for hydrogen storage. However, further analyses are essential to solidify these initial observations and comprehensively understand the material's behavior in salt caverns over time.
This paper significantly adds to the current body of knowledge by focusing on the specific challenges posed by salt-saturated environments in hydrogen storage. The detailed exploration of salt-saturated cement compatibility with hydrogen contributes novel insights not widely available in existing literature. This knowledge is pivotal for advancing the understanding and practical implementation of salt caverns for hydrogen storage, marking a distinctive and valuable contribution to the field.
Critical Parameters for Caprock Tensile Failure Induced by CO2 Injection Into Aquifers
Authors: Gillioz A. , Verga F. , Deangeli C. 
Published: June 2024

Summary
CO2 geological storage, where supercritical CO2 is injected into underground porous formations for long-term sequestration, is one of several mitigation strategies that have been proposed to control the level of CO2 in the atmosphere. CO2 can be injected into several formation types; deep saline aquifers are the most promising mainly because of their large storage capacity. However, safe and effective CO2 geological storage requires an understanding of the potential failure mechanisms that may lead to a loss of containment. In fact, injection of a fluid into an aquifer increases pore pressure above the initial value and this may trigger unwanted geomechanical effects such as fault reactivation or microseismicity. Additionally, the caprock overlaying the aquifer must be able to withstand the expected pressure increase without fracturing. While previous work has explored the mechanism underlying caprock tensile fracturing, an investigation was carried out to understand which parameters are more susceptible to trigger this mode of failure. This study presents comprehensive sensitivity analyses to highlight the relative contribution of different parameters to caprock tensile fracturing. A simple quasi-2D model comprising an aquifer, caprock and monitoring layer was first set up using the compositional reservoir simulator GEM. A total of 22 parameters among aquifer properties, caprock properties, simulation options and injection parameters were then selected as input variables, and simulations were run by varying one parameter at a time. The target variable was chosen as the maximum CO2 mass that could be injected before tensile fractures start opening in the caprock. Findings were then summarized in a tornado plot. For the range of values considered, the aquifer matrix permeability, caprock Young modulus and number of injection wells were found to have the highest impact on the target variable. Furthermore, results showed that higher quantities of stored CO2 can be achieved without caprock tensile failure in systems where the aquifer has a high Young modulus and Poisson ratio and the caprock has a low Young modulus and Poisson ratio. While this research focused on a simple model, these findings can help refine the current criteria for the selection of aquifers suitable for CO2 geological storage
Comparison of Constrained Life-Cycle Production Optimization Algorithms Based on Stochastic Simplex Approximate Gradients
Authors: Lutfu Toktas Omer , Minh Nguyen Quang , Onur Mustafa 
Published: June 2024

Abstract
This study focuses on the optimization of net present value (NPV) with respect to well controls such as injection rates and producing bottomhole pressures (BHPs), subject to the nonlinear constraints of field liquid production rate (FLPR) and water rate for producers (WWPR). The core challenge is to maximize NPV while adhering to both linear and nonlinear constraints, crucial for effective reservoir management. The research presents a detailed comparison of three sequential quadratic programming (SQP)-based optimizers—TU-NLCPO, our in-house nonlinear production optimizer, and two publicly available optimizers; namely Sequential Least SQuares Programming optimizer (SLSQP), and Sparse Nonlinear OPTimizer (SNOPT)—highlighting their application in constrained life-cycle production optimization, a key aspect of closed-loop reservoir management. These optimizers are evaluated based on their ability to handle optimization variables like BHPs and well injection rates (WIRs) over fixed control steps. Stochastic simplex approximate gradients (StoSAG) is applied across the three algorithms for a fair comparison. This comparison is performed using the Brugge model for waterflooding, providing a rigorous real-world assessment of each optimizer’s performance. The findings reveal that TU-NLCPO consistently achieves higher NPV compared to SLSQP and SNOPT, indicating its superior optimization capability. Additionally, TU-NLCPO and SLSQP are found to be robust against different initial guesses, demonstrating consistent NPV values, whereas SNOPT shows variability despite reasonable overall performance. When it comes to adhering to nonlinear constraints, SLSQP exhibits superior performance, especially in managing FLPR within set limits. The study also explores the impact of varying the number of perturbations for stochastic gradient generation on optimizer performance, noting an improvement across all methods with more perturbations.