bluuebunny/crossref_metadata_2025_split huggingface dataset:
CO2 Injection Well Design Considerations: Completion Design to Injectivity Analysis
Authors: Bagci A. S. , Jonnalagadda A. , Hernandez-Diaz D. 
Published: September 2024

Abstract
Carbon Capture and Underground Storage (CCUS) has been identified as a viable method for reducing industrially sourced greenhouse gases. In this paper, the methodology and workflow were developed to provide insight on the feasibility of CO2 injection and storage potential in proposed injection well to sustain the target injection rates with proper designed well construction under operating conditions.
Inflow Performance Relationships (IPR) and Vertical Lift Performance curves (VLP) were developed for predicting the temperature and pressure profiles from the wellhead to sandface under well operating conditions (injection pressure and temperature, injection rate). CO2 phase envelope was developed by using proposed CO2 stream composition and plugged-in well modeling. Dynamic multiphase transient analysis software was used for wellbore dynamics to predict pressure and temperature of CO2 at the wellhead, as well as CO2 phase behavior in the wellbore during well transient operations. Proper well construction was applied to ensure that CO2 was safely injected into and contained within the targeted injection zone for the protection of drinking water zones by considering well integrity must be maintained during CO2 injection.
Developed pressure and temperature profiles from well modeling were superimposed on the CO2 phase envelope for designed CO2 injection rates to evaluate CO2 phase changes within the wellbore during CO2 injection. Understanding the steady-state and transient multiphase flow behavior of CO2 within the wellbore is crucial for appropriate well design and operational risk assessment. In transient analysis, well models were developed to represent start-up, shutdown, and re-start of the CO2 injection during life of the well. Casing and tubing stress analysis was performed on the suitability of the proposed casing and tubing under the injection conditions required and with a given set of safety factors using industry recognized software. This analysis enabled to predict how the well construction will respond to various operational conditions, thus ensuring its reliability over the course of the CCUS project.
In this paper, CO2 phase envelope development, well modeling for steady-state and transient analysis in CO2 injection methodologies were combined with proper well construction for designing the wells in CCUS project.
Quantifying Permeability Changes Due to Gas Hydrate Formation in Undersaturated Oil Sandstone Reservoirs: A Numerical Reservoir Simulation Study
Authors: Rathmaier D. , Zerpa L. E. 
Published: September 2024

Abstract
Numerical simulation is used to quantify the resulting permeability effects due to solid phase formation/precipitation during fluid flow in an oil reservoir. The case study consists of a sandstone reservoir saturated with live-oil above the bubble-point pressure that is subject to cold water injection. Due to the relatively low temperature of this over-pressurized shallow reservoir, gas hydrate formation can arise when the reservoir temperature falls below 7 °C, reducing permeability as observed in previous coreflooding tests. Digital representations of the core samples used in the experiments were created with a commercial reservoir simulator to follow the experimental procedures. These include tracing a cooling temperature ramp starting outside of the hydrate stability curve towards inside the hydrate forming region and differential pressure changes due to hydrates plugging the pore space, also implying a permeability alteration. The numerical simulations are compared with the experimental results and explore the hurdles and limitations of modeling this reactive fluid flow with a commercial reservoir simulator on the continuum scale. Reservoir simulation is suitable to model the thermodynamic hydrate formation conditions that result in a realistic spatial hydrate distribution but is not able to capture the microscopic amount of solids precipitation that provide a quantitative analysis of permeability reduction on the centimeter scale. The upscaling of the laboratory coreflooding scale simulations to the field-scale for counter-acting this microscopic behavior resulted in a localized permeability reduction of up to around 40%.
Assessing Risk In Long-term CO2 Storage Under Uncertainty Via Survival Analysis-based Surrogates
Authors: Gurwicz A. , Chen J. , Gutman D. H. , Gildin E. 
Published: September 2024

Abstract
Reservoir simulations for CO2 sequestration are computationally expensive because they run for centuries or millennia. Shortened, cheaper simulation timelines prevent the observation of gas leakages that might occur over a longer duration. In the statistics community, the exclusion of these leakages is called censorship. We propose a fast simulator surrogate that captures these potentially unobservable long-term risks. The crux of our approach is survival analysis, a branch of statistics tailored to handle censored data.
Our proposed framework begins by sampling scenarios with varied injection and production rates from a number of geological and flow uncertainty realizations. The potentially censored time to gas leakage is recorded for each simulated scenario. We then leverage varied survival analysis methods, ranging from Kaplan-Meier to Random Survival Forests, to create a computationally cheap, and highly interpretable, simulator surrogate. The surrogate can predict the risk of CO2 leakage in new scenarios for significantly shorter simulations than usual in carbon sequestration studies, or no new simulations at all. This eases the computational burden of centuries-long, expensive simulations.
In order to validate our methodology, we constructed an exploratory case study with a shortened monitoring window. The proposed framework is implemented within a compositional simulation model where CO2 is injected into a saline aquifer. To assess the risk of leakage and caprock integrity, we simulated scenarios where we inject CO2 for permanent storage using 4 injector wells, while 5 producer wells are used for pressure maintenance. This model is run for 2000 days under varied permeability realizations to monitor CO2 breakthrough from the production wells. We then infer the occurrence of leakage in new scenarios and compare these results to full simulations via appropriate statistical metrics such as hypothesis testing, metrics tailored for the censored data context and usual prediction metrics. Initial results show that the proposed method predicts time to gas leakage with good accuracy without the need for any new simulations at all.
To the best of the authors’ knowledge, this is the first paper to approach the carbon storage optimization issue with survival analysis, a clear fit due to the presence of censored data coming from shortened simulations. Our unique, novel framework yields a simulator surrogate built with techniques never-before-seen in this context. Also, we fill the gap other approaches leave open by focusing on interpretability, a model quality that is paramount to decision-making under high uncertainty.
Improving Injectivity of CO2 in Saline Aquifers Through Chemical Additives
Authors: Penny G. S. , Bhagwat S. M , Shuchart C. E. , Gupta V. P. 
Published: September 2024

Summary
A key metric for Carbon Capture and Storage (CCS) projects is the rate of injection of supercritical CO2 (sCO2) into the subsurface and maximizing injectivity will be important for success. The sCO2 injection rate into saline aquifers is limited by formation permeability and residual water saturation held in place by capillary pressure. The objective of this work is to explore the application of additives to lower the capillary pressure and the water saturation in the near wellbore region and thereby increase the relative permeability to sCO2. The intended benefit will be the ability to inject CO2 at higher rates while maintaining injection pressure below the desired level/fracture gradient or injecting at reduced pressures for the same rate.
Screening tests identified several types of additives (Nonionic/Cationic/Anionic/Amphoteric) that reduced brine-CO2 IFT by a factor of 10 from 20-40 mN/m to 2-4 mN/m at 1000-2000 psi and modified contact angle between CO2, brine and sandstone and dolomite cores from 20-25 degrees with 2% KCl alone to 30-40 degrees with additives at 1000 to 2000 psi. In brine displacement tests in 100 md cores, the best additives when used in a concentration range of 0.1% to 0.2% decreased the water saturation by 10-30% and reduced the displacement pressures by ∼50% over the control, resulting in proportional increase of the relative permeability to sCO2.
Well model results show that doubling the relative permeability in the near wellbore region can increase the CO2 injectivity by ∼20%. Recommendations on treatment volumes and procedures for candidate injection wells are provided. The approach presented here can provide a low-cost option to increase injectivity and other benefits including compensating for injection well downtime, providing higher injection rates in lower permeability rocks, and reducing injection pressures for safer and more cost-efficient operations.
Leveraging the Successful Integration of New Technologies to Solve the Key Drilling Challenge in the Eastern Amazon Region, Onshore Brazil
Authors: Quinones H. , Fernandes G. , Harada L. , Arruda G. , Carvalho J. , Chaves L. , Medeiros T. , Sena H. , S.A. Eneva , Schmidt A. , Aguiar R. , Bonilla P. 
Published: September 2024

Abstract
While conducting a wells campaign in North Brazil, the Operator suffered from significant non-productive time in the 8 ½-in deviated intermediate hole section, caused by BHA component failures while drilling hard diabase rock layers. Based on this outcome, an integrated Wells-Contractor team devised a plan to deploy new tools and technologies to efficiently drill the intermediate hole section in a single run, while controlling/mitigating shocks and vibrations, and preventing the re-occurrence of tool failures.
The strategy encompassed a detailed engineering analysis to refine the drilling system, integrating new tech PDC bits with geometrical cutting structure, an at-the-bit sensor module for downhole dynamics recording, a more robust mud motor technology, and an instrumented sub for real-time high-frequency downhole dynamics monitoring. For the initial implementation, a 3-well sequence was selected to map the frequency and magnitude of shocks and vibrations throughout the area of operations, thus contributing to an enhanced understanding of the phenomenon for further modeling of the most suitable drilling system to address the challenges of the section.
From the outset, well 4 had immediate significant improvement, with the first 8 ½-in BHA drilling through over 200 meters of diabase rock, more than doubling the meterage of the previously longest recorded run in well 3 and keeping drilling dynamics and tool integrity concerns in check. For the next 2 wells, the team further capitalized on the success of the refined BHA. Enticed by performance in well 4, the team started deepening the intermediate hole section to accommodate specific well design requirements. In well 6, the first BHA drilled through 328 meters of diabase rock, slightly surpassing the single BHA run meterage from the previous well and improving ROP. And in well 7, a single BHA drilled 1062 meters "shoe-to-shoe", including 300 meters of diabase rock, yielding a 33% ROP improvement against the average of the previous 2 best-performing wells.
On a shoe-to-shoe basis, the implementation yielded a significant 100% performance improvement in the overall section meterage rate compared to the previous campaign. This, in turn, contributed to a 50% reduction of the effective metric cost and reduced risk exposure by eliminating trips and associated BHA handling.
Well construction activities in North Brazil have their share of logistical and technical challenges. For the latter, the systematic integration of fit-for-purpose technologies and downhole measurements enabled the team to finally crack the code of hard rock drilling performance and dynamics in the Eastern Amazon basin, thus yielding great overall value for the project. As a result, the Operator plans to carry on the momentum and promote the next well design-related step changes across the region.
Deep Learning-Driven Acceleration of Stochastic Gradient Methods for Well Location Optimization Under Uncertainty
Authors: Eltahan Esmail , Sepehrnoori Kamy , Alpak Faruk Omer 
Published: September 2024

Abstract
We have developed the deep-learning-accelerated-gradient (DLAG) algorithm, a novel solution for well location optimization (WLO) problems that leverages data collected from the explored parameter space to accelerate optimization. During optimization, we collect data to train a deep neural network (NN), creating a map from well-location parameters to the objective function. We utilize the analytical gradient of the NN, resulting in an effective search direction while saving the computational cost associated with stochastic-gradient perturbations. Our method features a novel NN architecture, the Spatial Pairwise Interaction Network (SPINet) with independent and contextual neural pathways (NPs), designed to capture the primary well characteristics, and its complex interactions with the neighboring wells. For the contextual NP, we explore using the popular Attention mechanism and simpler mechanism with weight-sharing Multilayer Perceptron (MLP) layers.
To evaluate the architectures, we designed the Bird Ensemble (BE) test problem which resembles the structure of the WLO problem. The comparison between DL architectures reveals that the NP with a weight-sharing mechanism has superior performance in terms of MSE error and gradient accuracy. The weight-sharing structure allows the model to efficiently model relationships with shared parameters while maintaining invariance to input permutations. For the test function, utilizing DLAG drastically improves computational efficiency, reducing the number of function evaluations required to achieve the same level of optimization by one to two orders of magnitude. Following validation of this test problem, we successfully applied our method to optimizing locations of injection and production wells in the Egg reservoir model. To alleviate random artifacts and the inevitable possibility of encountering local minima, we conducted 20 iterations of the WLO problems both with and without DLAG. The results reveal that, on average, our DLAG optimization method is more efficient than the traditional approach. This efficiency gain is particularly impactful for real-world applications where project timelines are tightly constrained.
The U-Net Enhanced Graph Neural Network for Multiphase Flow Prediction: An Implication to Geological Carbon Sequestration
Authors: Tariq Zeeshan , Hoteit Hussein , Sun Shuyu , Abualsaud Moataz , He Xupeng , AlMajid Muhammad , Yan Bicheng 
Published: September 2024

Abstract
Monitoring CO2 pressure buildup and saturation plume movement throughout the operation of Geological Carbon Sequestration (GCS) projects is crucial for ensuring environmental safety. While the movement of CO2 plumes can be predicted with high-fidelity numerical simulations, these simulations are often computationally expensive. However, through training on readily available simulation datasets, recent advancements in data-driven models have made it possible to predict CO2 movement rapidly. In this study, we adopt the U-Net Enhanced Graph Convolutional Neural Network (U-GCN) to predict the spatial and temporal evolution of CO2 plume saturation and pressure buildup in a saline aquifer reservoir. Utilizing the U-Net architecture, which incorporates skip connections, enables U-GCN to capture high-level features and fine-grained details concurrently. First, we construct physics-based numerical simulation models that account for both GCS injection and post-injection periods. By employing Latin-Hypercube sampling, we generate a diverse range of reservoir and decision parameters, resulting in a comprehensive simulation database comprising 2000 simulation cases. We train and test the U-GCN model on a two-dimensional (2D) radial model to establish a U-GCN code benchmark. We utilize Mean Squared Error as the loss function throughout the U-GCN training process. The U-GCN model demonstrates robust performance on the radial model, achieving an R2 score of 0.993 for saturation prediction and an R2 of 0.989 for pressure buildup prediction based on the blind testing dataset. Notably, the Mean Absolute Percentage Error (MAPE) for all mappings consistently hovers around less than 5%, indicating the effectiveness of the trained models in predicting the temporal and spatial evolution of CO2 gas saturation. Moreover, the prediction CPU time for the DL models is significantly lower (0.02 seconds per case) than the physics-based reservoir simulator (on average, 45 to 60 minutes per case). This underscores the capability of the proposed method to provide predictions as accurate as physics-based simulations while reducing substantial computational costs.
Determination of Dispersion Coefficient of Solvent in Heavy Oil/Bitumen Under Reservoir Conditions
Authors: Zhao Wenyu , Yang Shikai , Yang Daoyong 
Published: September 2024

Abstract
In this work, the dispersion coefficient of solvent in heavy oil/bitumen is innovatively determined by employing an inverse approach to accurately reproduce the measured temperature, pressure, and production profiles in a heated vapour extraction (H-VAPEX) process, thereby significantly advancing our insights into the dispersion physics under reservoir conditions. Monitoring and surveillance data, including temperature profiles, solvent injection rates, and fluid production rates, from a large three-dimensional (3D) physical experiment for an H-VAPEX test have been meticulously collected, analyzed, and processed. More specifically, the evolving geometries of the solvent chamber have been precisely delineated by integrating the advection-dispersion equation (ADE) with a modified Peng-Robinson equation of state (PR EOS), where the solvent chamber interface (SCI) is theoretically formulated as a function of temperature and solvent concentration gradient. As dispersion physics significantly impacts the SCI advancement, the dispersion coefficient in the presence of porous media can be inversely determined by assimilating the measured and calculated morphological contours of the solvent chamber. Such an integrated model has been rigorously validated with the measured temperature profile of a steam-assisted gravity drainage (SAGD) process from a one-dimensional (1D) physical model collected from literature, and subsequently extended to a large-scale 3D application. In addition, sensitivity analysis has been performed to analyze and identify the key parameters dominating the H-VAPEX performance. Not only does the injection velocity of solvent and its concentration gradient at the SCI exert a significant influence on its anisotropic dispersion in various spatial dimensions, but also the injection velocity dictates the solvent propagation over time. A higher injection velocity is found to accelerate the expansion of solvent chamber, thereby promoting solvent dispersion and resulting in a more pronounced solvent concentration gradient beyond the SCI. Under reservoir conditions and with high injection velocities, the dispersion coefficient is found to substantially exceed the diffusion coefficient by several orders of magnitude. Nevertheless, a large reduction in the viscosity of heavy oil/bitumen saturated with solvent results in a great increase in fluid mobility, facilitating the solvent injection as well as SCI propagation. Through the repeatable and consistent H-VAPEX experiments within the large 3D physical model, this systematic and robust method enables us, for the first time, to not only inversely determine the dispersion coefficient of solvent under reservoir conditions, but also to accurately evaluate and optimize the growth and propagation dynamics of the solvent chamber.
The Role of Petroleum Engineers in the Energy Transition: Developing Workforce through an Introductory Camp
Authors: Enab Khaled , Tashtoush Tariq , Khasawneh Mahmoud , Elmasry Youssef , Pena Julian 
Published: September 2024

AbstractThis study highlights the benefits of a hands-on four-day camp on the recruitment of high school students for petroleum engineering programs. The majority of participating students in this camp were Hispanic students who are underrepresented in engineering professions in general, including the oil and gas industry, despite being well represented in the labor technician side of the discipline. The introductory camp sought to introduce students to the role of petroleum engineers in the energy sector and Net Zero Carbon vision, in addition to stimulating their interest in petroleum engineering as a potential career path. The four-day camp was designed for a total of 8 contact hours per day that were scheduled into two four-hour segments per day. The first 4-hour segment focused on teaching students the fundamentals of petroleum engineering, and the second 4-hour segment focused on teaching students additive manufacturing and its role in the design and modification of tools. Throughout the third and fourth day of the camp, students completed a team-based project that connected concepts from petroleum engineering and additive manufacturing under the context of innovating drilling capabilities to address future energy demand. Twenty-eight students from three high schools participated in the short camp (27 Hispanic and 11 female). Participants completed pre-and post-surveys to evaluate the impact of the camp on their understanding of the role of petroleum engineers in the energy transition and decarbonization efforts, as well as their interest in joining the oil and gas industry. The outcomes reflect the effectiveness of the camp in clarifying the role and work of petroleum engineers in the oil and gas sector and their importance for future energy sustainability. This approach attempts to clarify misconceptions about petroleum engineering, especially in relation to environmental concerns, while stoking interest in the discipline, to address workforce needs in the oil and gas sector. The novelty of this work is providing a hands-on, application-based approach to guide young students from underrepresented communities to a rewarding yet exciting career. Giving students more exposure and direct contact with mentors from technical disciplines is of paramount importance at a critical stage of selecting a lifelong career. The proposed approach can be adapted to undecided first-year college students. In this paper, we provide a detailed breakdown of the proposed camp that is easy to follow and replicate.This camp was sponsored by The US Department of Education – Minority Science and Engineering Improvement Program – Grant # P120A230056.