bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficacy Gain From a Deep Neural Network-Based History-Matching Workflow
Authors: Yan Bicheng , Zhang Yanhui
Published: September 2024

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

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

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

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

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

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

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

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

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.
Development of a Geological Hydrogen Storage Simulator For Cyclic Injection and Withdrawal Processes: Coupled Flow and Geomechanics With Capillary Hysteresis
Authors: Sarkar P. , Kim J.
Published: September 2024

We have developed a high-fidelity, coupled flow and geomechanics simulator specifically designed for geological hydrogen storage. This simulator integrates non-isothermal multiphase multicomponent flow of hydrogen and water with geomechanics, accounting for elastoplastic behavior such as Mohr-Coulomb failure, essential for maintaining the geomechanical integrity of storage reservoirs. Additionally, the simulator addresses capillary hysteresis resulting from repeated drainage and imbibition cycles induced by periodic hydrogen injection and production. To model capillary hysteresis robustly, we employ a thermodynamically consistent return mapping algorithm utilizing the van Genuchten model for the capillary pressure curve. The coupling between flow and geomechanics is achieved through the fixed-stress sequential method. We validated our simulator using three test cases (1D, 2D, and 3D) to assess its numerical stability and capabilities. In the 1D case, we evaluated numerical stability during repeated hydrogen injection and production, where both capillary hysteresis and geomechanical failure occur simultaneously. The results demonstrated stability, accurately capturing Mohr-Coulomb failure and capillary hysteresis curves. For the 2D and 3D cases, which focused on wellbore integrity and field-scale simulation, respectively, the simulator consistently provided reasonable and stable results for capillary hysteresis and effective stress, confirming its applicability to real field scenarios.
Accurate Measurement of Relative Permeability as Crucial Step for Carbon Capture, Utilization, and Storage: Case Study
Authors: de Itriago Yani C. Araujo , Devier Sarah , Singletary Phillip , Watkins Henderson
Published: September 2024

Carbon Capture, Utilization, and Storage is one of the key technologies that can help meet the goal of reducing carbon emissions in the atmosphere by pulling out CO2 emissions from different industries. CO2 storage can provide a way to continue using hydrocarbons while minimizing their environmental impact, making the process valuable for the energy transition. To implement a CO2 storage project, it is necessary to meet existing regulatory framework for planning, implementation, and monitoring, which includes the evaluation of rock-fluid properties. These properties are necessary to ensure that the selected geological formation is suitable for long-term CO2 storage, to predict the CO2 movement through the reservoir during the life of the process, to anticipate changes in the rock, to monitor the evolution of the CO2 and make sure that its injection is efficient and safe. This study was performed aimed to measure CO2-brine relative permeability as one of the most complex rock-fluid properties to comply with current CCUS regulations.

In this study, the experimental results of the relative permeability measurements performed in core plugs from a well located in the Gulf of Mexico and six synthetic sandstones are presented and discussed. Well permeability ranges from 2.36 mD to 763.00 mD and porosities between 18.44% and 28.43% respectively. The permeability of the synthetic cores was between 25.65 mD to 1389.76 mD with an average porosity of 21.81%. Reservoir samples were taken from the target areas where the CCS project is planned. The basic petrophysical properties were measured at net confining stress. Supercritical CO2 and Synthetic Brine were used. A proposed CO2 injection scheme was simulated in the laboratory to evaluate the injectivity. Relative permeability tests were performed by SteadyState method with In-Situ Saturation Monitoring (ISSM) by Linear X-ray attenuation. Parameters used in the experimental design were the initial water saturation, temperature, reservoir pressure, brine composition, and flow rate.

A good injectivity associated with low pressure drop was observed in the reservoir target zones. The results obtained in this study show that during CO2 flooding, the CO2-Brine relative permeability decreases as a function of the absolute permeability. The resulting curves have different shapes and end points depending on the permeability, which indicates the importance of performing the evaluation of this property in the selected zones and not only in zones with high permeability. A map created with the end points shows the behavior of the relative permeability with brine and CO2 saturation. The saturation map can be used as a tool to select the more feasible zone for CO2 storage minimizing risks of leakage and environmental problems in long-term applications.
Capillary Dynamics of Nanoconfined Water and CO2 Induced by Time-Dependent Wettability Alteration
Authors: Pan Ziqing , Zhang Kaiqiang
Published: September 2024

Formation wettability alteration (WA) is ubiquitous in nature and relevant to many applications spanning from geological CO2 storage to enhanced oil/gas recovery. WA and the induced capillary dynamics are of great significance in caprocks and shale reservoirs. However, the impact of nanoconfinement effect has rarely been studied. In this work, we developed a new dynamic capillary pressure model for nanoconfined water and CO2 considering time-dependent WA. A sorption-based WA model was adopted to describe the variation of wettability over time. Dynamic capillary pressure model was then established by coupling the time-dependent WA model and numerical modelling of H2O-CO2 drainage and imbibition in nanoscale bundle-of-tubes. To adapt to the nanoconfined environment, slip and entrance/exit loss modified Lucas-Washburn flow model was developed by molecular dynamics (MD) simulations and interfacial tension (IFT) was predicted using nanoscale-extended Cubic-Plus-Association equation of state and the density gradient theory. Cycles of H2O-CO2 drainage and imbibition were implemented in the nanoscale bundle-of-tubes, until the wettability of all the tubes reached its final state. Based on the results, a quantitative dynamic capillary pressure model was established with a term characterizing the speed and extent of change. The impact of nanoconfinement was investigated, where slip and entrance/exit loss effect accelerated the capillary pressure alteration, while IFT shift due to fluid-wall interaction slowed it down. The influence of pore structure was also examined. The capillary pressure of pores with triangular cross-sections changed fastest, followed by those with square cross- sections, and circular cross-sections; tortuosity reduced the speed of capillary pressure change. This new nanoscale-extended dynamic capillary pressure model accounts for the rock-fluid interaction and long-term WA induced by WA agent adsorption, which are usually ignored by conventional capillary pressure models.
A Hybrid Tabular-Spatial-Temporal Model with 3D Geo-Model for Production Prediction in Shale Gas Formations
Authors: Wang Muming , Wang Hai , Chen Shengnan , Hui Gang
Published: September 2024

The evolution of shale gas production has reshaped North America's energy profile. Utilizing the vast amounts of data generated from production and operations, machine learning offers significant advantages in production forecasting and performance optimization. This study proposed a pioneering hybrid model integrating tabular, spatial, and temporal modalities to enhance production forecasting in unconventional shale gas reservoirs. Despite traditional methods such as artificial neural networks (ANN) and XGBoost, which rely solely on tabular data for training and prediction, this study proposes a novel 3D-parameterization method. This approach tokenizes the formation property distribution into 3-axis tensors, enabling a more comprehensive representation of spatial data. Then, a 3D-convolutional neural network (3D-CNN) with the attention mechanism module was established to process the created spatial data. For temporal modality, the long short-term memory (LSTM) module was used to accept the dynamic input and predict the monthly production simultaneously. A total of 677 wells data from Duvernay formation was collected, pre-processed and fed into the according module based on their modality. The results show that the model combined three modalities achieved an impressive level of accuracy, with a coefficient of determination (R2) of 0.8771, surpassing the tabular (0.7841) and tabular-spatial (0.8230) modalities models. Additionally, global optimization was applied to further enhance the model performance by optimizing the architecture of each module and model hyperparameters, and a 1.88% improvement was achieved from the empirical design. These advancements set a new benchmark for predictive modelling in unconventional shale gas reservoirs, highlighting the importance of utilizing data from different modalities in improving production forecast prediction.
Exploring Feasibility and Optimization of CO2 Sequestration in Depleted Methane Hydrate Reservoirs
Authors: Sambo Chico , Gupta Ipsita
Published: September 2024

With the climate crisis intensifying, reducing carbon dioxide (CO2) emissions is critical. Current CO2 sequestration methods face limitations due to geological integrity risks and slow reaction rates, hindering long-term reliability. This underscores the urgent need for innovative, sustainable solutions. Increasing interest is growing in underground solid-state CO2 storage, particularly in depleted CH4 hydrate reservoirs, which are less prone to leakage and offer a more reliable option for long-term storage.

This study developed a Thermal-Hydrological-Chemical (THC) model to simulate the reactions involved in the formation and dissociation of CO2 and CH4 hydrates. Initially, the model was used to simulate methane hydrate production through depressurization. After 15 years of methane hydrate production and depletion, CO2 injection was initiated and continued for 30 years.

The simulation results indicated that the depressurization method effectively induced the dissociation of methane hydrates, leading to significant changes in reservoir properties such as porosity, hydrate concentration, permeability, and temperature. These changes facilitated methane gas production from methane hydrate, which in turn enhanced CO2 storage capacity.

To further understand these dynamics, the developed model was used to conduct a sensitivity analysis, investigating the impact of porosity, permeability, reaction frequency factor, and bottom hole flowing pressure on methane hydrate production and CO2 storage. The analysis revealed that low porosity, a high reaction frequency factor, and high permeability result in higher methane production from methane hydrate reservoirs. Improved methane recovery was also correlated with increased 0ϋ2 storage capacity. However, in this particular study, some factors seemed not to affect the storage capacity significantly because the amount of CO2 injected was much lower than the amount of methane recovered from methane hydrates.

The study found that CO2 injection for 30 years was feasible in almost all cases explored, especially when the injection rate was below 5,000 m3 and the bottom hole pressure was less than 55.5% of the initial pressure in the methane hydrate formation. It is possible that at higher injection rates, the injectivity of CO2 storage might be compromised.

Overall, the results of this work indicate that injecting CO2 into depleted methane hydrate reservoirs is a feasible and effective method for long-term CO2 storage. This approach is particularly suitable when the production well operates with minimal bottom hole pressure, leading to the recovery and dissociation of a significant amount of methane hydrates.
Detection of Low Contrast, Low Resistivity Pay (lclr) Challenge in Shaly Sand Gas Reservoir Through Acoustic Data in the on Shore Nile Delta, Egypt
Authors: Abdelwareth A. Wafik , Abdelrahman M.
Published: September 2024

In the onshore Nile Delta gas fields, the Upper Miocene Abu Madi clastic reservoir show significant challenges for effective gas production, development, and exploration. The Abu Madi Formation contain unconventional reservoirs, including thin-bedded intervals and low resistivity zones, which are usually overlooked by normal petrophysical evaluation techniques and conventional logging tool analyses. These challenges are mostly clear in areas with significant facies changes, typical of the complex depositional environment of the Nile Delta, or in exploratory wells. Detecting these hidden Abu Madi reservoirs is difficult due to their high shale content, which complicates formation evaluation. The maximum deep resistivity reading in these wet sand reservoirs can reach up to 1.6 ohm.m., while pay intervals exhibit low resistivity readings of less than 2.58 ohm.m. due to high water content. This narrow resistivity range often leads to misinterpretations and affects the pay calculations, reducing the reserve estimation for the whole field. The volume, distribution, and type of clay minerals present in the shale significantly affect the readings which come from conventional logging tools, especially resistivity reading. The conductive nature of clay can mask high resistivity values indicative of gas-bearing intervals, especially when these intervals are thinner than the vertical resolution of the conventional resistivity tools. This study explains that combining conventional logging data (such as gamma ray, resistivity, density, and neutron data) with acoustic data (such as shear and compressional sonic data) can effectively detect these hidden pay reservoirs. This integrated approach depends on the theory that says, while the compressional sonic waves are influenced by reservoir fluids, the shear waves are not affected by it. This methodology helps to mitigate the masking effects of shale conductivity and establishes a strong framework for detecting these hidden reservoirs. Identifying and analyzing these hidden pay zones can significantly increase commercial reserves and production, which is necessary for maintaining low water cut values and ensuring the long-term production of these gas fields. This integrated assessment ensures that valuable resources within low resistivity and thin-bedded intervals are not overlooked, thereby enhancing the identification and understanding of various unconventional reservoirs, including tight formations and shaly sand.
The Role of Miscible Gas Mixing on CO2-Enhanced Methane Recovery
Authors: Yamada Kenta , Delshad Mojdeh , Lake Larry W. , Sepehrnoori Kamy , Fernandes Bruno Ramon Batista , Farajzadeh Rouhi
Published: September 2024

Depleted gas reservoirs are viable choices for large-scale CO2 storage and to displace remaining methane volumes to further increase the storage capacity (EGR). However, deployment of such projects depends on an informed knowledge of the magnitude of mixing of the miscible gases, efficiency in displacing in-situ methane by CO2, composition of the produced gas, and CO2 storage capacity. This study focuses on the fundamental analysis of mixing during CO2-EGR using a numerical approach.

We propose to conduct very fine grid compositional simulations to provide insights into the mixing of CO2 and methane in a gas reservoir at different reservoir and operational conditions. We first analyze a stratified layer model to understand the basic mechanisms of scale-dependency of dispersion and the significance of reservoir heterogeneity on fluid mixing. To consider more realistic reservoir heterogeneity, a two-dimensional stochastic reservoir model is analyzed to estimate dispersivity generated as fluids flow in porous media at different scales. Reservoir heterogeneity is represented by the Dykstra Parsons coefficient (VDP) and autocorrelation length, and fluid properties are modeled depending on pressure and temperature conditions. Field-scale simulation is also performed to discuss the way dispersion is modeled in reservoir simulation affects simulated gas recovery.

Our study shows that the variance of permeability and convective spreading are the primary causes of fluid mixing at any scale. In addition, molecular diffusion is not always negligible in gas mixing even in large-scale heterogeneous reservoirs since gas has much larger diffusivity than liquid. Furthermore, the mechanism of fluid mixing during CO2-EGR is complex with the interplay between convective spreading, transverse dispersion (including molecular diffusion), and gravity segregation. Although geoscientists often assume numerical dispersion can represent physical dispersion, our study indicates this is an oversimplification and could cause significant errors in calculated gas recovery. Permeability heterogeneity is essential for the dispersion growth process and the final displacement behavior. Reservoir heterogeneity should be modeled with high-resolution grid models to analyze mixing behaviors more accurately.
A Digital Rock Physics Approach to Modeling Permeability Changes with Solid Phase Formation in Reservoir Porous Media
Authors: Rathmaier D. , Zerpa L. E.
Published: September 2024

This paper illustrates the application of pore-scale numerical simulation to evaluate permeability changes due to solids precipitation with Digital Rock Physics (DRP). A DRP modeling workflow is used to quantify permeability changes due to solid phase formation (precipitation of gas hydrates) based on their deposition morphology on the pore scale. Virtual representations of a sandstone sample from the Bentheimer outcrop are created using micro-computer-tomography (micro-CT) scanning images and state-of-the-art segmentation algorithms. The binarized images (pores and grains) are systematically altered with morphological operations, i.e., erosion and dilation, to represent precipitation of solids in a pore filling and a grain/pore coating morphology, respectively. The Lattice Boltzmann Method (LBM) for single-phase fluid flow simulations is used to calculate the absolute permeability of the original and the altered 3D rock models directly on the pore structure. The changes of permeability are plotted against the solid saturation to compare to empirically derived and idealistic, mathematical descriptions. We verify the suitability of the high-resolution micro-CT images for fluid flow simulations using guidelines from the technical literature. Due to the high permeability and porosity of the sample, a threshold algorithm for image binarization based on the Otsu criterion is used and yields a sample porosity of 0.28 and permeability of 4.77 Darcy in z-direction. These values for porosity and permeability are validated from measurements using a CMS-300 apparatus on the actual rock sample. Erosion is applied to the segmented pore space to represent the pore filling deposition morphology and dilation is applied to the segmented grains to represent the grain/pore coating deposition morphology of methane hydrates. A third-degree polynomial trend relating the permeability reduction with the hydrate saturation is established for the first time using the actual pore structure rather than idealized and empirically derived laws, which are non-unique, depending on which theoretical method is chosen. The presented methodology could be used to evaluate the effect of solids precipitation on scenarios like the productivity of petroleum reservoirs, and injectivity in carbon sequestration or gas storage projects.
Three-Phase Flowback Rate Transient Analysis of Shale Gas Reservoirs
Authors: Yang Chia-Hsin , Emami-Meybodi Hamid , Zhang Fengyuan , Kordestany Amin
Published: September 2024

We present a three-phase flowback rate transient analysis (RTA) of the multi-fractured horizontal wells (MFHWs) in shale gas reservoirs by considering condensate dropout once the hydraulic fracture (HF) pressure drops below the dewpoint. We have developed a semi-analytical three-phase flowback model that considers multiphase flow in the HF and two-phase water and gas flow in the matrix to analyze flowback data. Three HF flow regimes are considered, including two-phase water and gas infinite-acting linear flow (IALF), two-phase boundary-dominated flow (BDF), and three-phase water, gas, and oil BDF that appears once the HF pressure falls below the dewpoint. Meanwhile, IALF is considered in the matrix domain. Pressure diffusivity equations for the water, gas, and oil-phase flow are derived, and the material balance approach is used to obtain the average HF pressure with the modification of the matrix distance of investigation (DOI). We define new pseudo-variables for pressure and time to linearize the governing equations and construct the diagnostic plot for flow regime identification and the specialty plot for HF properties characterization. We obtain a diagnostic plot for each phase using the proposed semi-analytical solution. A half-slope straight line is observed in the early flowback period for the water and gas-phase diagnostic plots, indicating the IALF regime. This is followed by a unit slope line, implying the BDF regime. Furthermore, a unit slope line representing BDF is examined for the oil phase diagnostic after the HF pressure reaches the dewpoint. The numerical simulations are conducted to validate the developed flowback RTA model. Estimated HF permeability and half-length from the three-phase flow model closely match the set values in the numerical model with relative errors < 10%, confirming the proposed model's accuracy. The proposed three-phase flowback model is useful in understanding the early stages of HF dynamics and its attributes.
The Advent of a Novel Selective Zonal Isolation with Thermosetting Gels
Authors: Sherief M. H. , Abdulal H. J. , Almohsin A. M. , Alabdrabalnabi M. I.
Published: September 2024

Chemical shut-off operations have the capability to remediate complex sweep unconformities particularly in mature reservoirs. Planning and executing those jobs require through understanding of sweep profile, wellbore dynamics and setting clear job objectives. Many chemicals were tried in the past decades, however, in recent years, thermosetting gels gained industry attention due to their superb physical characteristics. Those characteristics opened the door not only for great innovative applications but also making operations quicker and safer.

This article delves into the practical and essential aspects to ensure successful and safe treatment targeting water and gas shut-off operations, as well as casing repairs utilizing thermosetting gels.

Based on shut-off objectives, the gel type and required treatment volume are determined. The performance of the selected gel is then thoroughly evaluated in the laboratory and validated through yard testing to ensure that lab results can be replicated at the well site.

During the intervention and prior to delivering the treatment volume, the treated zone is flushed to maximize the gelation process efficiency concurrently while conducting an injectivity test to assess the impact of the pumping rate on wellbore temperature. Based on observed wellbore temperature transients, the pumping schedule is optimized accordingly which is one of the critical advents of this new deployment method. Another key innovation in this new approach is to achieve simultaneous zone isolation while delivering the treatment to leave no residual gel or solid plugs that may restrict the wellbore or require post-treatment interventions. In addition to eliminating risk of getting stuck or plugging the wellbore, it reduces operation time significantly from days and weeks to less than a day and the well can be produced immediately after treatment.

Overall, this approach represents a substantial advancement in chemical shut-off operations with thermosetting gels promoting efficiency, safety while meeting the evolving demands of mature reservoirs management.
ESP Wells Dynamic Survival Analysis and Lifespan Prediction Using Machine Learning Algorithms
Authors: Han Guoqing. , Lu Xin. , Zhang He. , Sui Xianfu. , Wang Biao. , Ling Kegang.
Published: September 2024

The electric submersible pump (ESP) is the primary artificial lift method for offshore oil wells. Due to the high operation and maintenance costs, it is crucial to avoid operations and conditions that negatively impact ESP wells' lifespan and seek ways to prolong their run life. However, the lifespan of ESP is affected by many factors, and it isn't easy to quantify the effects of these factors by conventional methods. To improve the lifespan of ESP and identify critical factors affecting it, this study thoroughly utilizes the wealth of downhole and wellhead measurement parameters available for ESP wells, establishes a dynamic survival model based on Cox proportional hazards survival analysis, and further constructs a Long Short-Term Memory (LSTM) network that can predict the remaining life based on survival modeling. Field application results indicate that the LSTM network can accurately predict the remaining life of ESP and that the factors influencing ESP lifespan can be identified and quantified by the survival analysis model.
Facilitating Carbon Capture and Storage in Louisiana: The Implications of Environmental Protection Agency's Final Rule Granting Primacy for Class VI Wells
Authors: Wartman Philip , Ettehadi Ali , Kiran Raj , Ostrander Clay , Mokhtari Mehdi
Published: September 2024

The Environmental Protection Agency's (EPA) final rule granting primacy for Class VI wells in Louisiana represents a pivotal advancement for carbon capture and storage (CCS) projects within the state. This paper explores the regulatory framework and its implications for CCS, highlighting the benefits and challenges posed by this rule. Louisiana, with its extensive experience in oil and gas activities, presents a favorable environment for CCS deployment due to its robust underground storage options and regulatory infrastructure. However, the state's history of legacy wells, many inadequately sealed or documented, poses significant risks for CO2 sequestration initiatives.

This study delves into the administrative policies, compliance requirements, and the comprehensive assessment process mandated by the EPA to ensure safe and effective CO2 storage. A significant focus is placed on detailed case studies of existing CCS projects in Louisiana, which provide valuable insights into the practical challenges and technical wellbore problems encountered during CCS implementation. These case studies highlight issues such as well integrity, CO2 plume migration, and potential leakage pathways, offering lessons learned and best practices for future projects. The analysis further examines the geological formations of interest, including the Lower Tuscaloosa, Frio, Lower Miocene, and Wilcox sands, and assesses their suitability for long-term CO2 storage. The findings underscore the importance of thorough risk assessment, stakeholder engagement, and ongoing regulatory support to maximize the benefits of CCS deployment while mitigating associated risks.

This paper aims to contribute to the understanding of CCS regulatory practices and offer a detailed examination of the technical, environmental, and policy considerations critical to advancing CCS projects in Louisiana. By synthesizing regulatory dynamics, economic considerations, and empirical evidence from case studies, we provide actionable recommendations for policymakers, industry stakeholders, and economic decision-makers to capitalize on the transformative potential of CCS deployment.