bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing Cement Integrity in CO2 Sequestration Wells - Advanced Strategies and Implications for Environmental Regulatory Standards
Authors: Meng M. , Zhou L. , Baldino S. , Liu Y. , Mehana M. , Chen B. , Frash L. , Carey J. William 
Published: September 2024

Abstract
Geological carbon sequestration requires injecting CO2 into the deep formation through wellbores with an injection rate as high as ~1 Mt/year. This process can lead to substantial temperature drops near the wellbore, potentially causing the cement debonding from the casing or formation, resulting in severe leakage. In this paper, we first analyzed the transient wellbore temperature and pressure profile across the well's depth using both an analytical model and one dynamic multiphase flow simulator, with cross validation. We then adapted these results for the subsequent well integrity analysis using a fully coupled thermoporoelastic model with transient solutions. Our analysis shows that the cooling effect depends on injection rate, surface CO2 temperature, and reservoir pressure. For a simulated Class VI well, a combination of high injection rate, low surface CO2 temperature, and depleted reservoir could result in the wellbore temperature that close to the bottom dropping by 60 ℃. Similar significant cooling scenarios have been observed at the Aquistore field located in Canada, the Alwyn field, and the Goldeneye field in the U.K. The maximum allowable cooling temperature for a typical cement formulation with 10 GPa elastic modulus, and 0.15 Poisson's ratio is 40 ℃. Therefore, this cooling effect could compromise wellbore integrity and cause well leakage through cement-casing debonding. Multiple practical strategies have been found to enhance cement integrity during CO2 injection, including modifying cement formulation to be more ductile and resilient, enhancing the initial state of stress in the cement using a pre-stressing method or expansive additive, and adding an insulated coating layer to the tubing. Pre-heating CO2 at the surface is effective but can be expensive and impractical. Using a protective annulus fluid with a lower thermal conductivity and a new tubing material with low thermal conductivity have been found to be ineffective. Overall, our comprehensive analysis enables us to assess the long-term impacts of CO2 injection on well integrity and promote sustainable and effective geological carbon sequestrations with proper environmental protection protocols.
Efficacy Gain From a Deep Neural Network-Based History-Matching Workflow
Authors: Yan Bicheng , Zhang Yanhui 
Published: September 2024

Abstract
Reservoir history-matching is essential for understanding subsurface uncertainty in rock properties (e.g., porosity and permeability). However, conventional history-matching methods usually require a large number of forward model evaluations and heavily rely on the prior estimation of uncertain model parameters. To tackle these issues, we resort to cutting-edge deep learning (DL) technologies for their universal approximation capability in forward and inverse modeling based on automatic differentiation. In this study, we develop a Deep Neural Network-based History-Matching (DNN-HM) workflow with enhanced efficiency and scalability for solving complex history-matching problems. Conditioned on the available observational data from various sources (e.g., well pressures, flow rates, and saturation maps), our approach couples two customized deep neural networks, a DL-based forward surrogate model ℕℕf for fast and accurate prediction of the multiphase flow in porous media, and an inference network ℕℕg that intakes prior subsurface knowledge and performs history matching based on the pretrained ℕℕf. We examine the performance of the DNN-HM workflow with a two-phase water-flooding problem in a 2-dimensional (2D) heterogeneous reservoir. After trained on 800 physics-based simulations, the forward model ℕℕf achieves high prediction accuracy, with R2 scores of 0.9935 for well grid pressure and of 0.9965 for water saturation Sw. The observational data comprise the bottom-hole pressure (pwfinj) measurements from 5 injection wells obtained via bottom-hole pressure gauges, the water and oil production rates (qwprod,sc, qoprod,sc) in one production well measured by flowmeters, and the saturation maps of Sw estimated from seismic inversion. The history-matching results show that even starting with a homogeneous prior permeability field, the DNN-HM workflow based on ℕℕg successfully deduces a synthetic heterogeneous permeability field with a low relative error. Moreover, the forward model accurately forecasts qwprod,sc, qoprod,sc, pwfinj and Sw plume propagation in the future after history matching. Through sensitivity analysis of the observation duration, we find that extending the observational data collection duration enhances history-matching results consistently. Besides, the DNN-HM workflow has a descent tolerance of noise in the observational data. The superiority of the proposed method is further demonstrated by its transfer-learning capability and higher efficiency compared with the conventional gradient-based history-matching (CG-HM) method. Therefore, our DL-HM workflow pioneers a direct deep learning-based approach for efficient history matching, showcasing two innovations: direct use of deep learning for reservoir history matching and automatic integration of diverse sources of observational data with prior geological knowledge of subsurface reservoirs.
Decision Criterion for Stimulation Method: Proppant Fracturing, Acid Fracturing, or Matrix Acidizing?
Authors: Schwalbert M. P. , Alves I. N. , Hill A. D. 
Published: September 2024

Abstract
Most oil and gas wells are stimulated. In unconventional reservoirs, proppant fracturing is the preferred stimulation technique. However, in higher permeability conventional carbonate reservoirs, matrix acidizing and acid fracturing can also result in stimulated wells, usually with lower risk and cost. This article presents a decision criterion for the preferrable stimulation technique considering a combination of parameters, including permeability, stress, geomechanical properties, mineralogy, injection rate, proppant, fracturing fluid, and acid concentrations and volumes.
This article is an extension of a previous study, in which a decision criterion was presented comparing only acid stimulation methods - matrix acidizing and acid fracturing. The present study developed a similar methodology to include proppant hydraulic fracturing in the analysis. The proposed methodology was developed using analytical derivations and validated using numerical simulators for both stimulation operations and production. The results were also validated and adjusted using field results and operational constraints. The final decision criterion is analytical and simple enough to be used in initial phases of conceptual design using a spreadsheet.
The developed decision criterion was implemented in a simple software and has been used in conceptual design phase for several projects in offshore Brazil carbonates for a few years. It is especially useful in early conceptual design for two main reasons: (1) in this early stage there is usually not enough information available to justify using more complex and detailed numerical simulators, and (2) the well completions are often defined in this early stage, which requires a predefinition of the stimulation technique. Due to lower cost, risk, and simpler operations, acid stimulation methods are usually preferred when they yield similar results. Matrix acidizing is simpler and less risky than acid fracturing, which in turn is simpler and less risky than proppant fracturing. Agreeing with field observations, the presented calculation method shows that in low confining stress and hard rocks, acid fracturing can yield better results than matrix acidizing. However, acid fracturing is less effective in high permeability, high confining stress, or soft rocks. Proppant fracturing can usually result in more stimulated wells than the acid stimulation techniques in low permeabilities, but not in high permeabilities - despite higher cost and operational risk.
This calculation method presents an agile quantitative estimate of the gain in productivity index that can be achieved by each technique, allowing an engineered quantitative decision for the stimulation method and corresponding well completion. It was observed that, although there is a good collection of stimulation models and software available, this simple method and its analytical equations provide insight and agility that cannot be replaced by software.
Application of Artificial Intelligence to Model Stresses and Failure Parameters in Anisotropic Formations
Authors: Yousuf W. , Kim J. 
Published: September 2024

Abstract
AI-driven solutions have transformed computational applications, offering a competitive edge over traditional analytical and numerical methodologies. While the initial training of these proxy models may be time-intensive, once trained, they execute tasks in almost real-time. This rapid execution is especially beneficial for operations that demand real-time responses, like drilling operations.
Rocks generally encountered in oil and gas drilling operations are anisotropic due to their mineral texture, laminations and pre-existing cracks and fractures that develop during depositional and later rock maturing processes. In this work, we perform an in-depth investigation of proxy formulations for analytical Lekhnitskii-Amadei (LA) solution to model stresses around circular cavities in anisotropic rocks. First, we create the forward model by implementing the LA solution for a variety of input parameters on a 2D grid and then post-process the resultant 2D stress states with an iterative algorithm that provides a workflow to estimate compressive and tensile failure limits to establish secure well pressure gradient or mud-weight window.
We follow this up with 3 distinct proxy solution approaches that can model stress states on 2D grid, 1D stress vectors around the borehole wall and single output parameters related to LA solution. Specifically, our workflow includes 3 steps. Step 1: We provide a new deep-learning-based reduced-order modeling (ROM) framework for modeling stress distributions around boreholes in 2D grid. Step 2: Realizing the need to model the stresses at the wellbore wall only for most wellbore stability applications, we provide modifications to DL-ROM framework to handle 1D stress vectors at the wellbore wall. Step 3: We then perform a Machine Learning based study to develop proxy solutions for single-value outputs including induced maximum and minimum principal stress at the wellbore, far-field maximum and minimum horizontal stress, shear and tensile wellbore pressure limit for failure as well.
CO2 Foam Generation and Strength in the Presence of Oil at High Pressure and Temperature
Authors: Halsøy H. , Graue A. , Alcorn Z. P. 
Published: September 2024

Abstract
Foam is a field proven and laboratory verified technique to reduce CO2 mobility and mitigate the impacts of reservoir heterogeneity in CO2 enhanced oil recovery (EOR) and CO2 storage processes. However, foams are unstable and can breakdown in the presence oil and at elevated temperatures. Therefore, foam generation and stability must be screened at reservoir pressure/temperature and in the presence of oil. This work investigates the effect of oil and temperature on foam generation and stability at a range of foam qualities and injection velocities. Foam quality and rate scans using Bentheimer sandstone cores were conducted in the presence and absence of mineral oil (n-Decane) and a crude oil (Troll blend) at reservoir temperature (40°C and 60°C) and pressure (180 bar). The foam quality scans co-injected supercritical CO2 and nonionic foaming solution (0.1wt%) with increasing foam quality from 30% to 100% to determine the optimal foam quality. The optimal foam quality was then used in the rate scans to determine the effect of injection velocity on foam strength.
Overall, foam was generated both in the presence and absence of both oils. However, both n-Decane and Troll blend crude oil had detrimental effects on foam strength with Troll crude oil reducing foam apparent viscosity by 93%, compared to a baseline scan without foaming solution. The optimal foam quality in the presence of both oils was between 60% and 70%. Foam rate scans in the absence and presence of residual oil revealed shear-thinning foam rheology, which is favorable for maintaining field-scale injectivity. In addition, foam recovered oil more efficiently (i.e. with less pore volumes injected), compared to experiments without foaming solution. Foam was generated at both 40°C and 60°C with optimal foam qualities of 70%. However, at 60°C the foam was weaker, compared to 40°C, likely related to changes in fluid properties. Few studies have used supercritical CO2 at reservoir pressure and temperature in the presence of oil. This work provides critical insights into the effect of oil and temperature on foam strength and stability at reservoir conditions, important parameters for ensuring field-scale success.
The Impact of Survey Frequency on the Accuracy of Measurement Informed Methane Emissions Inventory
Authors: Xia Haojun , Strayer Alan , Ravikumar Arvind P. 
Published: September 2024

Abstract
Methane emission distributions are highly skewed, where a small portion of large emitters could contribute over 50% of total emissions. Studies have found that some of the large emitters are intermittent, and they only last as short as a few hours. However, leak detection and repair programs (LDAR) are only conducted periodically. Using the snapshot measurement information to extrapolate the annualized emission inventory brings uncertainties. In this work, we quantified the uncertainties that arise from the survey frequency. This study considers the impact of survey frequency on two major scenarios – without repair programs and including repair programs. The results show that survey frequency has a significant impact on the accuracy of measurement-informed inventory (MII). However, the impact of survey frequency on different scenarios varies.
Top-Of-Line Corrosion in Legacy and Abandoned Wells for CCS Applications
Authors: Gupta S. S. , Puthenkalathil R. , Ratnakar R. R. 
Published: September 2024

Abstract
One of the risks associated with carbon capture and storage (CCS) strategy is the integrity issue of the legacy/abandoned wells, where the condensation of acidic gases may lead to the top-of-line corrosion within the wellbore. The main objective of this work is to assess this problem with the help of physics-based models for phase behavior, pH, heat transfer and corrosion rates that could help in screening and risk-assessment of the candidate fields for CCS.
First, the thermodynamics of formation brine upon ingression of CO2 in abandoned well is modelled using the PR78 equation of state (EOS) with asymmetric Huron-Vidal mixing rule for CO2-brine system. An empirical concentration/pressure dependent expression for the interaction coefficient for CO2-salt system are developed and validated for the three salts: NaCl, KCl and CaCl2. Second, a pH model is developed based on the dissociation constants of carbonic acids. Third, a heat transfer model is employed to obtain the condensation rates at given reservoir conditions. Finally, the condensation rate models are used in an empirical corrosion rate model for a representative reservoir scenario. The main results are as follows:
The prediction of CO2 solubility in brine from the phase equilibria model (developed here) matches very well with experimental data, within the accuracy of 5.34% for single salt and 4.14% for mixed salts. The water condensation rate (WCR) across the wellbore height changes with the brine composition. With a given ionic strength (of 3 molality), the highest WCR value (0.45 g/m2/s) corresponds to the case of pure CaCl2-based brine while the lowest WCR value (0.35 g/m2/s) corresponds to the case of pure NaCl-based brine. Most importantly, the models presented here are shown to be valid for a wide range of pressure, temperature and salinity conditions, so as to be directly applicable to assess the candidacy of the field for CCS applications.
The novelty of this work lies in the development a fast, convenient, and easy-to-implement physics-based modeling framework to evaluate the corrosion rates within legacy/abandoned wells. Especially, the thermodynamic modelling of brine solution containing a mixture of NaCl, KCl and CaCl2 salts, where a simple concentration/temperature dependent binary interaction between CO2 and salt component are determined. While the framework is developed for screening the candidacy of the field for CCS applications, it could easily be extended to screening for geothermal and other subsurface applications.
Novel ML Modeling Approach for Fatigue Failure of Hydrogen-Transporting Pipelines
Authors: Ahmed Nayem , Ahmed Ramadan , Teodoriu Catalin , Gyaabeng Michael 
Published: September 2024

Abstract
The challenge of data scarcity significantly impedes machine learning (ML) applications in specialized domains, such as research on hydrogen embrittlement in carbon steel, posing a significant obstacle to repurposing existing natural gas networks for hydrogen transportation. Hence, this study aims to develop an ML model to predict hydrogen-assisted fatigue failures in hydrogen-transporting gas pipelines.
A novel approach is developed to establish an accurate and widely applicable ML model that leverages data from one set of mechanical tests to improve the prediction of other relevant mechanical parameters. The new approach involves employing data extracted from primary literature sources that conducted the fatigue crack growth (FCG) test. This data is then utilized in an existing ML model, previously trained with tensile test data, to predict the reduction of area (RA) of materials subject to hydrogen embrittlement. Building upon the aforementioned approach, the model-generated RA is further integrated as an input parameter to train a new ML model aimed at predicting FCG rates. In parallel, a conventional ML model is developed that excludes model-calculated RA as an input parameter, focusing solely on fatigue test variables. The traditional approach is undertaken to evaluate the efficacy of incorporating model-generated RA into the model. Data sets from one source (i.e., 96 data points) are completely removed from the model training data sets to compare the models. Afterward, model predictions are compared with the removed data set to assess model performance and accuracy.
Testing the models with the removed data sets shows the traditional model has a maximum relative error (MRE) of 11.65, while the RA-based model has an MRE of 10.06. Moreover, the model with RA is 15.65% more accurate than the traditional model in terms of maximum relative error after data removal. Furthermore, both models are assessed using standard statistical performance parameters such as the coefficient of determination (r-square), and they show similar performance. Despite the similarity in conventional performance metrics for both models, the analysis of maximum error and the scatter in the cross-correlation plots of model predictions clearly indicates that the model incorporating the calculated RA demonstrates closer alignment with actual values. This outcome not only underscores the merit of this innovative approach but also contributes significantly to mitigating data scarcity challenges in the evolving field of hydrogen energy.
Data scarcity challenges in hydrogen embrittlement studies are addressed by developing a novel approach that uses data from one type of mechanical test to improve the prediction accuracy of a model of other relevant mechanical parameters. The method developed in this study can be extended for other ML modeling applications when data scarcity is the limiting factor.
Triaxial Hydraulic-Mechanical-Chemical Coupled Experiments for Hydrocarbon Production in Unconventional Shale Reservoirs
Authors: Meng M. , Wigand M. , Frash L. P. , Welch N. J. , Li W. , Carey J. W. 
Published: September 2024

Abstract
Unconventional oil and gas play a key role in the global transition to clean energy. Advances in horizontal drilling and hydraulic fracturing have enabled substantial hydrocarbon extraction from shale formations. However, the initial high production rates from these formations are not sustained over time, leading to a significantly lower recovery rates compared to conventional reservoirs. Hydrocarbon production in shale formations is governed by complex, multi-physical coupled mechanisms, including fluid diffusion in the tight matrix, multiphase flow in fractures, and stress dependent interactions between matrix and fracture. To address these challenges, we developed a high pressure ‘quad-pore’ triaxial cell and an in situ experimental protocol to simulate the live oil production scenarios. Under fully in-situ conditions, we saturated Wolfcamp shale with live oil, induced fractures, and conducted soaking, pressure drawdown, long term production, and stress dependent permeability evaluation. Through multiple tests, we found that shale gas production is mainly driven by pressure drawdown, long term production, and fracturing, while oil production was mainly produced during the long-term production and pressure drawdown. Soaking enhances hydrocarbon production through imbibition. Complex, branched fractures with larger surface areas are more effective in producing shale gas. As pressure decreases, the produced hydrocarbons becomes heavier, with higher concentrations of intermediate-chain and longer-chain hydrocarbons. Heavier oil dominates in the late production stage. The bubble point marks a critical threshold, beyond which a large amount of oil and gas are produced. These findings promote the development of a better strategy for unconventional oil and gas production and contribute to achieving important economic, energy security, and environmental benefits.
Similitude-Based Technology to Study Wellbore Hydraulics
Authors: Mehrabian Amin , Wu Yidi , Khodaparast Pooya 
Published: September 2024

Abstract
Accurate estimation of the wellbore equivalent circulation density (ECD) is crucial for effective well planning and drilling. This paper introduces a portable and compact hardware designed to study the two-phase annular hydraulics of arbitrarily inclined wellbores, providing detailed specifications and practical applications. The hardware design enables automated adjustments of the inner pipe eccentricity, rotation speed, total flowrate, and annular flow inclination. Measurements of solids and fluid flowrates along the hardware flow loop are separately conducted using a Coriolis meter. Control and data logging are facilitated through an integrated set of programmable controllers and data communication system with a built-in human-machine interface. The upscaling of measured pressure gradients along the flow loop annulus to those pressure gradients that would occur in a simulated wellbore annulus is achieved through a set of scaling equations. These scaling equations are developed using dimensional analysis and the simultaneous application of geometric, dynamic, and rheological similarities between the two scales of annular flow. A test case is presented to illustrate the workflow for estimating upscaled frictional pressure loss in a wellbore annulus.