bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Adsorption of CO2 Soluble Amine Surfactants for Foam Assisted Carbon Storage: An Industry-First Quantification Under Reservoir Conditions
Authors: Wu Qianhui , Zhao Lun , Zhao Wenqi , Wu Xuelin , Fan Zifei 
Published: April 2024

Abstract
The success of foam-assisted CO2 enhanced oil recovery and carbon sequestration processes is largely determined by the adsorption level of surfactants in the field. However, the dynamic adsorption behavior of CO2 soluble surfactants, predominately the amine surfactants, remains a challenge due to their unique solubility in brine under most of the reservoir conditions.
The reliability of static surfactant adsorption tests is questionable, especially for switchable amine surfactants, because the testing conditions greatly deviate from the reservoir conditions. For the first time in the oil and gas industry, dynamic adsorption tests of switchable amine surfactants were conducted by injecting a proper amount of CO2 with the surfactant solution at reservoir conditions, where CO2 would be completely solubilized. The dynamic adsorption tests were conducted on dolomite at reservoir conditions, where the effluents were collected every 0.05 PV for high-performance liquid chromatography and pH analyses.
It is found that the pH of the CO2-saturated brine and the charge of the mineral surface are heavily dependent on CO2 pressure, which can therefore affect the surfactant adsorption behavior on minerals. The desired co-injected volume of CO2 under specific reservoir conditions can be predicted by the Peng-Robinson equation of states model and the iPhreeqc geochemistry module. The pressure gradient during the dynamic adsorption test is almost equal to brine injection at the same flow rate, indicating that all the injected CO2 could be essentially solubilized into the aqueous phase. By using this approach, the pH, salinity, brine compositions (including the potential determining ions), surface charge, and mineral wettability can be largely preserved, being capable of reproducing all the essential geochemistry reactions at reservoir conditions during CO2 injection. The pH of effluent indicates that CO2 has been dissolved in brine and good pH control necessary for surfactant solubility has been attained in the core. From the dynamic adsorption tests, the adsorption of amine surfactants on dolomite is small (0.089 mg/g) in the presence of high-pressure CO2, while the static adsorption tests may significantly overestimate the adsorption levels (0.350-0.510 mg/g). It is also found that the static adsorption tests may overestimate the adsorption levels.
Foam-assisted carbon storage using CO2-soluble surfactants has recently attracted great interest in the oil and gas industry. This study provides a robust approach for estimating the adsorption of switchable amine surfactants with good reproducibility, which is more accurate than the existing method of static adsorption. This novel approach is of great importance for decision-makers to quantify the economic feasibility of the foam-assisted CO2 storage process.
Effect of Pressure, Initial Water Saturation, Rock Type and Injection Rate on Oil Recovery and Displacement Efficiency of CO2 Injection in Carbonate Reservoirs
Authors: Masalmeh S. K. , Farzaneh S. A. , Sohrabi M. , AlHammadi M. 
Published: April 2024

Abstract
Miscible gas injection is the most widely applied enhanced oil recovery (EOR) method in light oil carbonate reservoirs, both as tertiary and secondary method. Miscible gas has high displacement efficiency and usually results in a low residual oil saturation (Sorm) in the part of the reservoirs that is contacted with gas. Accurate determination of Sorm and understanding the parameters that affect displacement efficiency are crucial for successful miscible gas EOR projects.
This paper presents a comprehensive experimental program designed to investigate the effect of experimental pressure, pore volume injected, injection rate, rock type and initial water saturation on the displacement efficiency, Sorm and recovery factor of miscible/near-miscible CO2 injection. The CO2 injection experiments were performed using live crude oil at pressures above the minimum miscibility pressure (MMP) using reservoir core samples of up to 1 ft long and 2 in. diameter. All CO2 injection experiments were performed using vertically oriented cores, with gas injection from the top unless stated otherwise.
The experimental results show that: 1-Oil recovery decreases as pressure decreases with Sorm increases by more than 20 saturation units as the pressure decrease from 4250 psi to 2700 psi; 2-CO2 breakthrough was much earlier at lower pressure which leads to more CO2 recycling, 3-The recovery factor is strongly affected by pore volume injected, however, the effect of the CO2 injection volume is much more significant at lower pressure; 4-The injection rate has insignificant effect on oil recovery and Sorm for miscible or near miscible CO2, due to the low IFT between oil and CO2, 5-Rock heterogeneity has a strong effect on oil recovery and CO2 breakthrough and hence on CO2 recycling and economy of the projects., and 6-The presence of mobile water at the beginning of CO2 injection resulted in lower displacement efficiency and increased Sorm. However, this water blocking effect should be determined experimentally for a given reservoir rock/fluid system. The results of this study cannot be generalized for other reservoirs.
The results of this study have important implications for the design and performance predictions of CO2 gas injection. Starting CO2 injection at higher reservoir pressure is recommended due to its superior displacement efficiency and less CO2 recycling due to later breakthrough. However, higher pressure may negatively impact the required CO2 volume, the compression cost and project economics.
Autonomous SRP Optimization Using an Edge-Based Data-Driven Solution
Authors: Gambaretto A. , Yermekova M. , Srivastava S. , Hyder Z. 
Published: April 2024

Abstract
Sucker Rod Pumps (SRPs) are the leading Artificial Lift System (ALS) worldwide for low oil-producing wells. Yet, SRPs have been overlooked in the ongoing wave of digitalization within oil and gas production. SRP operation and optimization processes are outdated and still based on simple field legacy ‘rule of thumb’ practices. This paper presents a simple yet innovative solution that uses an Industrial Internet of Things (IIoT) framework to autonomously optimize SRP production while minimizing pump shutdowns.
The SRP optimization algorithm, rule-based in nature, gathers trending operational data from the controller via a time-moving window. This time window represents a well snapshot that is used to evaluate performance indicators at different operating pump frequencies. These calculated performance indicators, namely Production Indicator (PI), Combined Indicators (CI) and Combined Indicator-Quadratic Penalty (CI-QP), are mainly a function of pump fillage, strokes per minute, and well shutdowns. The PI prioritizes higher well production, while the CI and CI-QP penalizes shutdowns to reduce pump failure rates. The algorithm dynamically evaluates the well performance indicators to recommend and implement an optimal frequency setpoint to the pump controller. This solution has been dockerized and developed as an Edge Application, capable of running directly on-site in an IIoT Gateway device. This application has been tested on eight SRP wells for several months with excellent results. These wells were tested in both PI and CI modes. As a result, inferred production increased by 15% and shutdowns reduced by 29% in average, in all tested wells. The solution provides an easy yet powerful tool that can be scaled to manage multiple wells by providing optimal setpoints based on pump-specific conditions. Additionally, features like user-configurable optimization cycle duration can be included for faster well optimization.
With the advent of the digital oilfield, the solution evaluates trending data by leveraging IIoT to optimize SRP operation using real-time performance indicators of well production and shutdowns. This completely autonomous, edge-based solution requires no manual intervention and can be scaled to hundreds of wells.
Successful Application of a Low Dose Surfactant Injectivity-Aid in a Canadian Polymer Flood
Authors: Roostapour A. , Ramsden-Wood A. , Tudor E. , He K. , Lee J. 
Published: April 2024

Abstract
To achieve a successful polymer EOR application, it is crucial to maintain high polymer injectivity below the fracture pressure. This helps to prevent fracture propagation in the reservoir, which can cause fast breakthroughs, while still enabling the fastest recovery of oil. Laboratory and field testing of the addition of surfactant into a polymer injection system has shown that it can reduce the interfacial tension between injected fluids and reservoir oil, enabling more efficient oil displacement and enhanced sweep efficiency. Additionally, tailored surfactant can modify rock wettability and mitigate polymer retention in the reservoir, thereby improving polymer injectivity.
In a previous paper related to this work, He et al (2024) conducted a detailed laboratory study to select an effective low dose surfactant injectivity-aid for the Atlee Buffalo field in Canada. The study comprised of fluid characterization, thermal stability, interfacial tension (IFT), emulsion tendency, polymer compatibility, and core flood performance testing. It was concluded from the laboratory core flood experimental results that the addition of a unique surfactant, at low concentrations, did indeed enhance both injectivity and oil recovery. This paper explores the results from the field pilot application of the selected surfactant from their study into an ongoing polymer flood project. Changes to the injectivity of wells moving from waterflood to polymer flood, and again from polymer flood to polymer-surfactant flood, have been assessed.
Review of the initial field pilot trial data shows a positive impact of the surfactant injectivity-aid mixed within the polymer solution that was injected as part of an EOR polymer scheme. The injection data demonstrates that even at low dosage, the addition of surfactant to polymer solutions can stabilize and improve the injection process.
Lean Start-Up Procedure for Optimized Heavy Oil Well Operations
Authors: Alfoudari M. , Johar M. , Al-Naqi Y. , Khan S. B. , Pandey D. , Williams D. 
Published: April 2024

Abstract
The South Ratqa Heavy Oil field in Kuwait, a key initiative in the nation's thermal recovery endeavors, employs the cyclic steam stimulation (CSS) method within its challenging sandstone formations. The field's operational efficiency was hampered by the original well layout, which necessitated the dismantling of critical components such as the 4-inch production flowline, 2-inch casing gas vent line, and 2 1/16-inch wellhead sensors for maintenance and intervention access by the flushby unit (FBU). This process significantly extended downtime, contributing to a monthly production deficit of around 5,000 barrels and impacting the field's goal of 60 MBOPD.
In 2020, an initial process modification allowed the 4-inch production flowline to remain in place, reducing downtime from 10 days to 5 by avoiding its removal. Building on this improvement, a Management of Change (MOC) implemented by the Fields Development Heavy Oil Team (FDHO) in 2022 further optimized operations. The MOC enabled the immediate operation of pumps post-installation, without waiting for the reconnection of the dismantled casing gas vent line and wellhead sensors. These components could be reconnected later, aligning with the maintenance crew's schedule, thus minimizing non-productive time (NPT). Adopting this approach, rooted in lean methodology principles of efficiency and waste reduction, reduced well downtime further from 5 days to less than 24 hours, markedly improving production rates and reducing overall well downtime. This initiative, projecting annual savings of $4,900,000, has redefined operational efficiency and cost management in the South Ratqa Heavy Oil field.
The optimized flowline configuration and wellhead layout, validated through this project, will guide the design of future expansions, ensuring operational efficiency from the outset.
A Comprehensive EOS Model for Extra-Heavy Oil Blended with Liquid Solvents: Development, Assessment and Validation
Authors: Al-Murayri M. T. , Akther S. , Al-Otaibi B. , Abu Taleb R. , Al-Busari A. , Delamaide E. , Soltani A. , Garifullin R. , Bekri S. , Rousseau D. 
Published: April 2024

Abstract
Work is in progress to de-risk and mature liquid solvent injection for extra-heavy oil extraction from the Lower Burgan Abdali (ADBG) reservoir in North Kuwait. This is a deep reservoir (i.e. 9,000 ft) that is not believed to be amenable to steam-based EOR methods. This paper presents a workflow to create a holistic equation of state (EOS) describing the thermophysical properties of the selected solvents, extra-heavy oil, and their blend at different conditions. Such a model is essential to capture subsurface-to-surface variations in the mixture properties, thus allowing proper assessment of solvent-assisted oil production to design a fit-for-purpose pilot for further validation.
Two multi-component liquid solvents and representative extra-heavy downhole oil samples were investigated using high-pressure laboratory testing. Different conditions were considered to measure fluid and density. Thereafter, a cubic EOS model was built to reproduce the extra-heavy oil lab-based PVT data. On a parallel track, another EOS model was also developed for the two selected solvents, namely: diesel and Solvarex. These two EOS models were merged into a unified model to generate reliable extra-heavy oil-solvent mixture predictions. A validation technique was suggested to verify the impact of lumping and pseudo-component overlapping on oil recovery and injected fluid breakthrough.
Significant differences in the molecular weight of C36+ and the measured viscosity were observed through lab evaluation using representative downhole oil samples with negligible dissolved gas. Upon creating a single EOS model using multi-fluid regression, it was realized that the heaviest fraction had to be split into two pseudo-components, coexisting in both samples, but at different molar fractions. The components existing in extra-heavy oil and solvent were lumped mainly to avoid overlapping. The resulting EOS model was found to be robust as it can accurately reproduce the phase and volumetric behaviour of extra-heavy oil, solvent and their blend at the reservoir temperature and different pressures.
Furthermore, it was established that, with such a robust EOS model, an arbitrary number of pseudo-components can be defined for the crude oil and solvents PVT models, without a considerable impact on oil recovery or injected fluid breakthrough.
Study on Digital Twin Technology to 3D Seismic Iterative Processing and Geological Modeling for Reservoir Development
Authors: Yang Xuesong , Cao Bin , Wang Huilai , Zhou Wei , Zhang Xu , Li Jiye , Li Chenya , Zhang Bingxuan , Huang Xingning 
Published: April 2024

Abstract
For an already established 3D geologic model, the accuracy of model correction only by seismic and well data is not high. Seismic-driven modeling, a modeling method that fully integrates seismic data, can fully explore the potential information in seismic data and convert seismic residuals into corrections of model errors through seismic driving to realize the updating and correction of the model. Based on this, this paper proposes the seismic forward loop iterative model correction technique and describes the technical process of this method with the example of the actual work area. The qualitative and quantitative evaluations of the model correction results are carried out by seismic comparison and well comparison techniques, and the results show that this method can reduce the cumulative error of the model correction, make the model closer to the real value, and better characterize the non-homogeneity of the reservoir and the distribution law of natural fractures. Based on the shale gas production well area of Changning Company, this project focuses on the research of different natural fracture prediction and the reliability evaluation method of the prediction results and explores the influence of different natural fractures on drilling and fracturing to form a more perfect and effective prediction and evaluation system of natural fractures in deep shale gas. The research results will guide the prediction of natural fractures in shale gas production zones, which will help improve the scientificity and adaptability of the design of engineering parameters in study area.
Field Development Plan (FDP) Cloud-Based Data Integration Between Geology & Drilling Software Using Python Tools
Authors: Mwansa P. , Repilloza H. , Hassan M. A. 
Published: April 2024

Abstract
The oil and gas industry generates vast amounts of data throughout its operations, from exploration to production. Collecting, connecting, and optimally utilizing this data is key to maximizing efficiency, accuracy, and access to new disruptive technologies.
In a typical well-planning cycle, an engineer will spend a significant amount of time looking for the data they require to do their jobs efficiently. The data are typically locked away in silos - trajectories in one data platform, Pore Pressure Gradient, Fracture Gradient or Targets in another, and so on.
A major Middle Eastern NOC and Two Norwegian software service companies teamed up to develop Proof of Concept (PoC) for a new workflow that integrates subsurface and drilling data between on-premises Geology E&P software and Drilling software through a proprietary Python Tool plug-in and Python library. This integration enables a streamlined connection to a cloud-based drilling and well planning software, facilitating collaboration among teams involved in well planning. The project's key challenges are the lack of a standardized communication, integration, and automation of data flows between subsurface and drilling teams, as well as the inability of engineers to access necessary data due to scattered information and access restrictions. The project utilizes a proprietary data science suite, named Cegal's Prizm, which allows easy configuration to integrate data from various applications, sources, and platforms. A proprietary Python Tool is used to merge data from various application silos and data sources, enabling enriched investigation. The process involves connecting to the Geology E&P software retrieving domain objects using the proprietary Python Tool, and converting these domain data objects into common Python data structures. The project aims to develop an innovative workflow that provides easier access to data for experts throughout the organization, leading to better decision-making during the well-planning cycle. This not only makes it easier, but it also ensures collaboration between the G&G and Drilling teams involved in new well planning
Novel WAG Method with Nanotechnology in a Heterogeneous Carbonate Reservoir
Authors: Jang H. C. , Ko S. M. , Park H. R. 
Published: April 2024

Abstract
In the WAG process, adding nanoparticles to the water phase (referred to as NWAG) is a method advantageous for enhancing oil recovery and CO2 storage by improving wettability. Previous research had limitations in not reflecting actual site conditions, as it did not account for heterogeneity and hysteresis. However, most reservoirs exhibit different permeability distributions, and CO2 can be trapped due to hysteresis. Therefore, it is essential to consider petrophysical properties, such as heterogeneity and relative permeability hysteresis, for accurate predictions. In this study, a 3-D heterogeneous reservoir model with a relative permeability hysteresis model was designed. Based on this model, a sensitivity analysis of the NWAG method was conducted for each factor to investigate its impact on oil recovery and CO2 storage. This analysis was performed using the CMG-GEM simulator, and the results were compared with the findings obtained for each different factor. A Dykstra-Parson coefficient of 0.4 was selected to represent the reservoir's heterogeneity. Furthermore, the capillary trapping of CO2 during WAG injection was calculated using Larsen and Skauge's three-phase relative permeability hysteresis model. The sensitivity analysis considered factors such as injection fluid-gas ratio, slug size, injection period, injection cycle, and nanofluid concentration. The simulation results indicated that the heterogeneity and hysteresis model have an impact on oil recovery and CO2 storage. Specifically, when considering both heterogeneity and hysteresis, oil recovery and CO2 storage exhibited differences of 0.8% each. In the simulation model that incorporated heterogeneity and hysteresis, the difference in oil recovery was 0.8%, while the difference in CO2 storage was 15.2%. Comparing nanofluid injection to CO2-WAG, it was observed that nanofluid injection can alter wettability by reducing permeability. This mechanism suggests that the use of nanofluids can enhance oil recovery and expand CO2 storage, resulting in an efficiency improvement of up to 5% in both oil production and CO2 storage when compared to the conventional WAG process. The NWAG method can offer new insights into the highly effective exploitation of oil reservoirs. Considering a limited supply of CO2 from industry and a constant source, the NWAG method is expected to be efficient way to enhance oil recovery and increase CO2 storage capacity.
Fit for Purpose H2S and Oil Removal System for a Sustainable Zero Liquid Discharge (ZLD) Produced Water Management Technology
Authors: Ayirala S. , Saleh S. H. , Ahmed S. , Haider S. , Yousef A. , Saeed Q. , Muhammad M. , Saif Suha , Gendron Blake , Akram A. 
Published: April 2024

Abstract
The study focuses on Zero Liquid Discharge (ZLD) produced water desalination, an increasingly important process for recycling hypersaline wastewater and promoting a circular water economy in upstream operations. A key challenge in ZLD desalination is pre-treating produced water streams to remove hydrogen sulfide (H2S) and oil, as H2S is a harmful toxic gas and oil can cause operational issues in desalination units.1,2
The study aimed to develop and evaluate a fully automated pre-treatment system for removing H2S and de-oiling produced water streams. This system included a series of chemical dosing, integrated degassing, and de-oiling unit, and scrubbing units. The degassing unit, using a controlled nitrogen supply, efficiently removed H2S from the water. Chemical dosing was employed to aid in removing H2S, and oil, while the scrubbing unit targeted residual H2S in the vapor phase.
Results indicated that the automated system was highly effective in both H2S removal and de-oiling, achieving up to 99% efficiency in both processes. Key factors contributing to this efficiency were the amount of chemical dosing, the residual time, and the controlled nitrogen supply in the degassing unit. The study demonstrates the feasibility of using a fully automated pre-treatment system for H2S and oil removal in ZLD produced water desalination projects. This system is particularly useful for treating sour produced water in both remote and populated locations.
Additionally, the study highlighted the system's scalability to accommodate varying water volumes and its capability for in-line monitoring of treated water and scavenged H2S. This versatility allows real-time optimization of chemical loading, making the system a novel solution for H2S and oily produced water treatment. However, further research is necessary to optimize the system for different produced water streams and assess its long-term efficiency.