bluuebunny/crossref_metadata_2025 huggingface dataset:
Well Integrity Tests for Geological CO2 Storage Using Large-Scale Experimental Setup
Authors: Nassan T. H. , Baganz D. , Alkan H. , Opedal N. , Amro M.
Published: June 2024

Well integrity plays an important role in any subsurface operation since the wellbore connects the target formation with surface facilities. Any compromising of well integrity may lead to unsafe operation, harm the environment, and may lead to complete failure of the project. This study introduces new built setup to study well integrity through permeability measurement of different wellbore components allowing new insights on the risk management of the geologic carbon storage projects.

To study wellbore size sections like of casing-cement, cement-rock, or even casing-cement-rock, a large- scale facility was built at our underground laboratory at 150 m underground. The facility weighs approx. 3 tons with a length of 2 m, and a diameter of 0.7 m. It can handle samples of size 80 cm length and 30 cm diameter. Working pressure and temperature are up to 20 MPa, and 100 °C, respectively. The facility is equipped with two chamber real-time permeability measurement system that can determine the effective permeability of wellbore components based on transient method. Effective permeability of casing-cement composites is measured using CO2 at ambient and subzero temperatures to ensure the well integrity during CO2 injection in depleted oil and gas reservoirs.

Preparation of casing-cement samples requires special conditions to preserve integrity of the samples and avoid damage. The preliminary results show that the tested samples exhibit expected values of permeability at ambient temperatures (lower than 1×10−17 m2). Subzero temperatures, down to -11 °C have no effect on original permeability i.e., no increase in permeability after thawing. Pressure cycling experiments showed that the effective pressure and exposure time to CO2 play the most important role in permeability degradation. Furthermore, small pure cement cylinders (10 cm× 10 cm) were prepared and tested on a small-scale facility (analogous to the large-scale setup) to compare the results with the composites. In addition, the permeability of the composites was measured -before and after CO2 exposure- with H2 to account for the reduction in permeability due to the geochemical reactions. It was concluded that the permeability has decreased at least in one order from 10−18 m2 to 10−19 m2 owing to the reaction.

The large-scale facility has been used for HYPOS (Germany) project and currently working for RETURN (EU) project as well. Its uniqueness is in the broad range of pressure and temperature working conditions and handling wellbore sections of the size 1:1. The results provide new insights into the risk management of storage projects saving time and cost.
A Robust Method to Distinguish Oil and Gas in Depleted Reservoirs: Success Implementation in Oseberg Sor field
Authors: Yerkinkyzy Gulnar , Donnadieu Sandrine , Bravo Maria Cecilia , Ungar Frode , Fristad Paal , Bekkouche Yasmine , Yang Tao
Published: June 2024

Hydrocarbon phase determination in depleted fields has been a major challenge. This main uncertainty affects completion decisions and, consequently, oil recovery. As the field matures, the reservoir fluids start to exist in a two-phase configuration due to gas released from reservoir oil or gas trapped due to aquifer movements during production. A traditional petrophysical evaluation, neutron density separation, can mislead under such conditions. An oil reservoir can be interpreted as gas by showing a big separation in neutron density logs. However, a fundamental question arises: Are these apparent "gas zones" genuinely filled with free gas, or could a substantial portion contain oil?

This paper introduces an integrated work process between petrophysical logs and Gas Oil Ratio (GOR) prediction based on advanced mud gas, as well as historical fluid samples and field production history. The effectiveness of this method is demonstrated through a case study from the Oseberg Sor field, a late-life production field on the Norwegian Continental Shelf. The fluid identification technology based on mud gas data predicts a large oil saturation in reservoirs. In contrast, interpretations derived from conventional petrophysical logging tend to overemphasize minimal gas saturation and incorrectly predict the reservoir fluid type. This critical interpretation from mud gas has enabled the completion of the well in the oil zone. With the successful implementation of this innovative approach, questionable "gas zones" is reinterpreted as oil zones, making them viable production prospects.

The integrated methodology reveals a robust solution for a longstanding industry challenge and emphasizes incorporating a comprehensive understanding of petrophysical logs, advanced mud gas (AMG) data, and PVT data from nearby wells. This approach remains unaffected by minor quantities of released or entrapped gas within reservoirs, offering the potential to complete a higher number of oil zones for production. Given the increasing activity in matured fields, the business value of this innovative method is substantial.
An Extended JBN Method to Determine the Flow Function and Relative Permeabilities from Composite Core Flooding Experiments – Preliminary Results
Authors: Andersen P. Ø.
Published: June 2024

Core flooding is a standard experiment where for example water is injected to displace oil, and the production, pressure drop, efficiency and ultimate recovery of the process are parameters of interest. Transient pressure drop, and production data can be used to calculate relative permeability functions characterizing the porous media. An analytical technique for this calculation is the JBN method, which assumes immiscible, incompressible fluids, a homogeneous system and negligible capillary and gravity forces. These conditions may be met for oil-water systems flooded at high rate or in long horizontal cores. However, in many cases, composite cores are used experimentally, i.e. several short cores with different properties (lengths, porosities and permeabilities) are put together to form one long core during flooding. The JBN method then calculates incorrect relative permeabilities.

This work derives theory for an extended JBN method to interpret unsteady state flooding experiments of composite cores accounting for the spatial variation in core properties. The method calculates outlet saturation, fractional flow function and relative permeabilities based on production and pressure drop data, for a composite core with known porosity and permeability distribution.

The saturation profile in the composite core can be calculated by taking the Buckley Leverett profile of a uniform core and converting the spatial axis. Assuming negligible capillarity and compressibility, the saturation profiles in composite cores depend on the porosity distribution, not the permeabilities. However, a saturation arrives at the outlet after the same number of pore volumes injected, regardless of porosity distribution. Thus, the resulting breakthrough, producing flow fraction, average saturation and outlet saturation reported in terms of pore volumes injected are the same and only depend on the fractional flow function. The fractional flow function can be estimated accurately between the front saturation and the highest obtained outlet saturation from composite core flooding experiments. The method is validated on synthetical data using CO2-water primary drainage literature curves where the relative permeability curves.
Migration, Leakage, and the EU Directive on Geological Storage of Carbon Dioxide
Authors: Harrison B. , Falcone G.
Published: June 2024

An escape of injected carbon dioxide (CO2) from a subsurface storage site is not a leak according to the Council and Parliament of the European Union (EU), who suggest it is migration of CO2 into the surrounding storage complex. This definition is given in the EU Directive 2009/31/EC for geological storage of CO2, which has been adopted and adapted by the countries that border and operate in the North Sea. However, when assessing the potential capacity of a deep saline aquifer of large areal extent, how do stakeholders delineate the storage complex? Can the monitoring grid size change over time? And how might this impact the monitoring of the injected CO2 volume? From data available in the public domain, and from discussions with a Norwegian Operator and the Regulator of Norway, the authors explore the assumptions and perceptions of the storage potential and containment risks of identified sites in the North Sea. Examples are given of a pre-FEED project and an ongoing storage project in the region and how the risk of lateral containment of the injected CO2 is perceived and how it may be mitigated. With only a limited database of examples, it is a challenge to assign values to the likelihood of CO2 leakages occurring. This paper offers some suggestions on how the directive on the geo-sequestration of CO2 might be improved to define a storage site vs. a storage complex, leveraging lessons learnt from case studies which now exist.
Pulsed Neutron Logging for Underground Hydrogen Storage Monitoring in Depleted Reservoirs
Authors: Pirrone M. , Machicote S. , De Lisio S.
Published: June 2024

This paper deals with the quantitative evaluation of the diagnostic capability of various cased-hole Pulsed Neutron Logging (PNL) measurements for fluid identification and saturation monitoring purposes in Underground Hydrogen Storage (UHS) projects. The main target are depleted reservoirs. The methodology aims at properly understanding hydrogen plume development far from the injector/withdrawal wells, discriminating working and cushion gases, and early detecting undesired leaks at the so-called spy-wells.

The starting point is the analytical and numerical modeling of typical PNL responses of mixtures of water, cushion gas, reservoir gas and hydrogen. These sensitivities include fast neutron interactions, inelastic/elastic scatterings, and capture, together with their dependence on pressure, temperature, and acquisition environment. In detail, fit-for-purpose curves and their physics-based models are selected. Next, a joint inversion is performed to reconcile models and actual measurements to solve for the different desired volume fractions. Moreover, the uncertainty of the outputs is also quantified by means of an ad-hoc Monte Carlo approach, propagating the standard uncertainties of the input PNL data.

A case study of PNL monitoring in a possible usage scenario is discussed for a selected depleted gas reservoir where hydrogen charge/discharge numerical modeling has been performed. Several PNL simulations have been carried out to forecast the deviation from pre-injection baselines during the injection/production cycles. These encompass the expected plume behavior, its variable composition, possible changes in water saturation, and the different pressure/temperature regimes coming from the available dynamic reservoir model. Therefore, random errors have been generated for the simulated PNL curves to be used for the subsequent uncertainty quantification in obtaining water saturation and reservoir/cushion gas-hydrogen relative concentrations, mimicking future time-lapse interpretations. The latter represents a useful template to understand the real PNL capabilities in such environment and the best subset of neutron interactions to exploit for the purpose of plume monitoring, characterization and, if needed, leak detection.

This novel approach is the very first deep study on the potentiality of selected cased-hole logs for hydrogen monitoring. In addition, the outcomes are fundamental for several aspects in UHS projects, including reservoir modeling calibration, geomechanics, geochemistry, and overall risk management.
Numerical Study of Sealing Performance of CO2 Hydrate Cap in Sub-Sea Sediments Considering Flow, Thermal, and Geomechanical Processes
Authors: Zhang Jiecheng , Moridis Geroge J. , Lu Hailong , Yang Jin , Zhang Laibin
Published: June 2024

CO2 sequestration in sub-sea sediments, aided by the creation of a CO2 hydrate cap, is proposed as a possible solution to reduce greenhouse gas emissions. Earlier numerical studies have considered only flow and thermal processes, neglecting geomechanics, which is crucial for understanding long-term storage processes, because of the lack of appropriate simulators designed for CO2 hydrate. This study enhances the capabilities of the pTOUGH+HYDRATE V1.5 to support CO2-H2O-NaCl systems, resulting in the development of the pTOUGH+CO2 HYDRATE V1.5. Coupled with the geomechanical software RGMS, this enables comprehensive simulation of flow, thermal, and geomechanical processes during CO2 sequestration. Buoyancy causes CO2 to migrate upward, forming a CO2 hydrate cap upon reaching the hydrate formation zone. Initially, the low saturation of the CO2 hydrate cap allows limited hindrance to CO2 migration. However, as saturation increases, hindrance becomes more pronounced, leading to enhanced lateral migration of CO2 and the formation of a larger CO2 hydrate cap, thereby improving sealing performance. Injected CO2 exhibits poor initial mobility, resulting in pore pressure accumulation and increased effective stress in sub-sea sediment. Careful consideration of injection rates is necessary during this stage. Furthermore, a comparison was conducted between the results obtained from (a) pT+CH V1.5 and (b) the coupled pT+CH V1.5 and RGMS models. Minimal differences between the models were observed, possibly attributed to relatively high permeabilities. Future research should explore diverse geological scenarios and investigate the depth of CO2 injection.
Enhancing the Passive Monitoring of the Rock Damage Process
Authors: DeLandro G. , Vanorio T. , Guo T.
Published: June 2024

This study explores the effectiveness of double-difference event location methods in detecting early signs of cracking, leading to leaks from structurally trapped CO2. Leaks carry the potential to escalate into bursts, highlighting the importance of proactive pore-pressure management measures. While the double-difference method is frequently applied for precise identification of fluid-induced seismic events, its reliance on a constant velocity model poses limitations, especially in dynamic environments potentially undergoing fracturing. Consequently, integrating velocity variations within a fractured medium into time-lapse seismic localization remains a significant challenge. In this study, we investigate how point-in-time average velocity changes influence the accuracy of double-difference event location. We analyze acoustic emissions (AEs) data gathered from laboratory experiments to explore this relationship. We performed a triaxial mechanical test on Berea sandstone, monitoring stress, strain, acoustic emissions (AEs), and variations in acoustic velocity as damage accumulated over time. AE event locations were determined using P-wave arrival times and magnitudes derived from source-receiver distances. The test revealed a significant surge in AE activity during the stages of damage progression, characterized by crack propagation ultimately leading to failure. Conventional double-difference technique, which assumes constant velocity, led to clustered distributions of acoustic emissions (AEs) that inaccurately represented fracture geometry. However, integrating point-in-time velocity changes improved the alignment of AE distributions with fracture geometry, unveiling a clear time-lapse imaging of crack initiation, propagation, and coalescence along the primary stress direction. This study highlights the crucial role of temporal velocity variations in ensuring precise event mapping, particularly in the monitoring of CO2 leaks. It proposes that average velocity fluctuations be monitored during injection or estimated from rock physics models for continuous and cost-effective monitoring. Ultimately, integrating time-lapse velocity changes improves our capacity to monitor crack progression and mitigate risks associated with CO2 leakage.
Estimation of Real-Time Bottomhole Parameters in CO2 Injection Wells During Operations by Means of an Ensemble of Neural Networks
Authors: Figueroa J. , Baraldi P. , Chouybat I. , Ursini F. , Vignati E. , Zio E.
Published: June 2024

Effective management of CO2 storage operations in subsurface reservoirs demands real-time estimation of well performance to identify deviations from expected operating conditions and ensure compliance with the requirements of Carbon Capture and Storage monitoring plans. To overcome the limitations of analytical tools in accurately estimating bottomhole flowing parameters, particularly during two-phase flow, this work aims to develop an empirical mapping between monitored parameters at various well sections and the bottomhole flowing parameters. Specifically, an ensemble of neural networks (NNs) is developed for the estimation of the bottom hole pressure (FBHP) and temperature (FBHT) in a CO2 injection well during operations. A feature selection approach based on trial-and-error is employed for the selection of the model inputs among the monitored parameters. The data used to train the NNs are generated using a thermo-fluid dynamic numerical simulator. Diversity between the NNs is achieved during the training phase by using different subsets of data and different weight initializations. The final FBHP and FBHT estimations are the median of the individual NN outcomes. The methodology has been applied to several cases in all flow regimes and different regions of the pure CO2 phase diagram, including single-phase, supercritical and two-phase transition. The proposed ensemble of NNs achieves accurate estimations of FBHP and FBHT, with root mean square errors (RMSE) below 1 bar and 0.1 °C, respectively, significantly outperforming an individual NN. This is due to the diversity among the NNs of the ensemble, which allows obtaining individual models that compensate for their errors, and the use of the median for the aggregation of the NN estimations, which make the ensemble robust towards the presence of possible outlier estimations provided by individual NNs. To our knowledge, this is the first application of machine learning solutions to real-time estimate bottomhole properties in CO2 injection wells. The satisfactory performance achieved on simulated data in a wide range of operating conditions, including two-phase transition, paves the way for the deployment of an ensemble of NNs for field application during operations.
Techno-Economic Evaluation of the Integration of High-Temperature Electrolysis With Geothermal Energy for Low-Carbon Hydrogen Production
Authors: Molho Simone , Roccaro Ernesto , Lainati Andrea , Stefanucci Michele
Published: June 2024

In this paper, the integration of geothermal energy with high temperature electrolysis to produce low-carbon hydrogen is investigated. Geothermal energy provides a stable energy flux, allowing to achieve high electrolyzer utilization factors, the compatibility with industrial processes and reduced load oscillations on electrolyzers. If steam-fed high temperature electrolysis is employed a highly efficient low-temperature heat exploitation process is made possible, with an electrolysis electrical efficiency above 80 % on LHV basis. The study identifies the best design for the integration of high temperature SOEC (Solid Oxide Electrolysis Cell) electrolysis technology and geothermal energy and provides a techno-economic assessment, computing its performance and the Levelized Cost of Hydrogen (LCOH). To feed the 3 MW electrolyzer the system exploits a hot geothermal fluid, both to generate steam and power (via ORC). A model of the system is set up with Aspen Plus software. A set of low-enthalpy geothermal resources ranging from 85 °C to 160 °C are considered. The two technologies work synergistically, leading to an efficient and stable hydrogen production. The use of SOEC electrolysis allows to produce 34 % to 38 % more hydrogen than alkaline electrolysis. The economic evaluation shows an exponential increase in the geothermal plant specific Capex for decreasing geothermal resource temperature, leading to an analogous trend in the LCOH. A benchmark case is defined coupling a PEM electrolyzer with a PV plant. The LCOH grows from a value 15 % lower than the benchmark in the 160 °C geothermal brine case, to a value 20 % higher than the benchmark in the 85 °C case. The breakeven point is found at 98 °C. This promising result is even more valuable considering the high dispatchability and stability of the output.
Cornegliano or How a New Porous Ugs Asset Can Rapidly Provide Multiple Services to the Grid
Authors: Gervasi G. , Ton A. , Lombardi S. , Mrad C. Abou , Lange A. , Egermann P. , Delsante F. , Salazar D.
Published: June 2024

A new Underground Gas Storage (UGS) has been developed from the Cornegliano gas depleted field, located in the Po Valley, South-East of Milan. The gas field was originally discovered in the 1950s, produced 2.4 Bsm3 of gas from 13 wells until 1997 and was converted in December 2018. This paper presents more particularly how the ramp-up operations and commercialization were combined in an optimal manner, since they have complementary objectives. By taking into account the reservoir specificities and the wells / surface facilities capabilities, wide range of commercial services were rapidly provided to the Italian gas system while the long-term UGS performance increased continuously. The capacity of Cornegliano UGS to offer rapidly both seasonal and flexible short-term services also illustrates the future role of porous assets to provide relevant services also for hydrogen storage.

The first part of this paper describes the technical aspects of the first injection and withdrawal sequences, with a particular emphasis on the pressure and water management at the reservoir and well scales. An optimal ramp-up requires both injection of the appropriate volume of cushion gas for maximizing the reservoir performance and a pressurization of the reservoir to make pore space available for gas by pushing out the aquifer. All the data collected during the ramp-up were key to update regularly the reservoir model and to gain rapidly confidence in its forecasts.

The second part is focused on the commercial products design, according to the technical specificities of the UGS and the expected needs of the market. Along the ramp-up, the standard seasonal product was improved continuously in terms of both working gas volume and flowrate. Interestingly, many short-term additional capacities are offered on regular basis depending on the real-time behavior of the customers and the reservoir performance. Additionally, several specific products were also designed to respond to other market needs. For short-term market needs, the 5+2 product enables to reverse the physical flow in the UGS during the withdrawal campaign and to reinject a significant volume during the week-end which can be produced during the 5 following working days on top of the seasonal product. Various forms of "parking" product are also proposed to anticipate or postpone either injection or withdrawal in order to respond to longer term needs of the market.

As a conclusion, the UGS ramp-up and the commercial activities have been conducted successfully in parallel with a continuous development rate of the seasonal product (volume multiplied by 2.5 in 5 years) and increasing flexible short-term activities (+70% in addition to seasonal volume). Diversified and increasing services can be provided to the Italian gas system with a 100% reliability, which is valuable for both the current natural gas and future hydrogen markets.
Assessment of Reservoir Performance for Alternative Cushion Gases for Underground Hydrogen Storage
Authors: Correnti A. , Verlaan M.
Published: June 2024

Underground Hydrogen Storage (UHS) is an emerging area of interest in the space of a future hydrogen economy to match fluctuating supply and demand. The European Commission highlights the development of large-scale hydrogen storage facilities in the upcoming years in the Hydrogen Strategy for a Climate-Neutral Europe (European Commission, 2020). Storing large volumes of hydrogen for extended periods will require safe, reliable, and economically feasible underground storage in addition to pipeline and surface storage, which will fall far short of providing the storage volumes that are expected to be required in a scenario of large-scale hydrogen implementation. Underground hydrogen storage capability builds on existing oil, gas and carbon dioxide storage, including subsurface and wells expertise, to provide a safe and cost-effective solution that ensures continuity of hydrogen customer supply.

Building upon an initial reservoir engineering modelling study that was done to assess the viability of UHS in depleted gas fields (Huisman, et al., 2023), the analysis in this report assesses the technical viability, from a reservoir engineering perspective, of totally or partially replacing the hydrogen in the cushion gas by alternative gases such as methane, nitrogen, or carbon dioxide. This analysis aims to evaluate the options of total or partial replacement with a cheaper gas to reduce the hydrogen storage cost.

The impact of the different alternatives was evaluated by:

the resulting back produced gas compositions. the amount of tail gas to be reinjected/recirculated. The tail gas is formed when contaminants are separated at surface to comply with hydrogen export specifications.

To understand if the performance of the cushion gas option could be improved, some other variables were also assessed such as an alternative cushion gas injection strategy using a more distant well to reduce gas mixing and back produced gas contamination, and the impact of less stringent export gas quality specifications. It was observed that even though less stringent specifications slightly reduce tail gas reinjection rate, the carbon dioxide option in all cases results in a higher flowrate of tail gas reinjection to the reservoir. On the other hand, the assessed alternative cushion gas injection strategy had a relatively minor impact on results. Even though reservoir performance results are impacted when reservoir dip angles change, reservoir dip did not have an effect on the relative performance of the different cushion gases.

Overall analysis suggests that a partial replacement of the hydrogen cushion gas with methane is the best option. Integrated project economics should be assessed to provide a final recommendation on which cushion gas alternative is best from an economic point of view.
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

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

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

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

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

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

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

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

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