bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing CCS Well Design in Depleted Reservoirs
Authors: Konganuntragul P. , Boonsri K. , Buapha S. , Vattanapornpirom K. , Nopsiri N. 
Published: April 2024

Abstract
The decarbonization of the energy section urges a radical transformation to meet the net zero carbon emission target. In the last decades, many countries around the world have initiated the Carbon Capture and Storage (CCS) as one of the pilot projects aiming to permanent and environmentally safe storage of CO2 underground. However, only a few projects have strived and is eventually executed. Ones of the biggest challenges are technicality and commerciality to drive the success of the project. Thus, the objective of this paper is to mainly focus on the CCS well construction, design consideration, and optimization in Southeast Asia offshore CCS projects during the detailed planning phase.
The process will start with the reservoir injection targets. Each well is planned to inject one single target to minimize the complexity of well architecture and completion equipment. Inevitably as being common in this area, stacked reservoirs or multi-target reservoirs are exceptional to achieve the intended CO2 injection target with a higher complexity level of completion equipment.
Wellhead platform is a huge capital investment. Reuse of existing platform or new wellhead platform brings advantages and disadvantages. The key concerns include slot recovery issues, collision risk, fatigue concern on platform structure, and so on. The detailed consideration has been laid out to ensure the platform integrity and meets the expected design life.
Well construction process is similar to normal oil and gas wells, but the main differences are CO2 injecting fluid and load cases analysis. Thus, material selection plays a major role for the well construction process to align with flow assurance study result and expected load case when majority of the worst-case scenario might occur in the late life injection. Material optimization is a key to bring the well construction cost down. Material testing has been constructed as per the expected downhole condition for the verification and optimization purposes. Detailed study of each type of wells are necessary to ensure the casing and completion equipment is fitted for purpose during the well life cycle.
The Measurement, Monitoring and Verification (MMV) equipment is embedded since the designed phase with the ranking technique to comply with conformance and containment criteria.
Process Simulation and Optimization of Hydrogen Liquification Using Reinforcement Learning
Authors: Zirrahi M. , Santiago C. , MacFarlan K. 
Published: April 2024

Abstract
Hydrogen liquification is one of the main steps for using hydrogen in obtaining carbon neutralization. This process consists of cooling hydrogen to approximately 20 K and converting ortho-hydrogen to para-hydrogen. This paper presents a study in which the authors simulate the Linde-Hampson hydrogen liquification process using a commercial process simulator. Then, the process optimization is evaluated using a reinforcement learning method.
In the first step, the Linde-Hampson process is simulated in a steady-state mode using a commercial process simulator. The process uses liquified natural gas (LNG) as a pre-cooling medium. Process optimization is conducted in the next step to minimize the energy consumption of the hydrogen liquification process. A reinforcement learning (RL) method, using a deep deterministic policy gradient algorithm (DDPG), is implemented for process optimization. The reward for the RL optimizer is defined to minimize the operating cost for hydrogen liquification according to the cost and rate of consumption of electricity and LNG.
An Artificial Neural Network (ANN) is used to construct the Actor-Critic sections of the RL algorithm. Capability of the RL algorithm is evaluated versus a classic optimization method. Case studies at different electricity and LNG prices were conducted to evaluate the trained RL model versus the base process optimized by the process simulator. The comparison between hydrogen liquification operating cost of the trained model with base model showed improved performance for the RL model.
Multifaceted Smart Completion Application: A Gulf of Mexico Case Study
Authors: Manning J. P. , Carter B. J. , Bannister A. L. , Saripally I. P. 
Published: April 2024

The Gulf of Mexico (GOM) is one of the world’s most prolific hydrocarbon basins, with more than 80 years of development since the first offshore drilling began. GOM is the primary source of the United States (U.S.) offshore oil and natural gas production. According to data published by the United States EIA, approximately 15% of the U.S. domestic oil production came from offshore oil and natural gas fields in federal waters, with over 99% of that production coming from the Gulf of Mexico over the most recent year of data available at the time of this paper’s publishing (Q4 2022-Q3 2023) [1]. Nonetheless, GOM reservoirs have complex depositional settings. Reservoir heterogeneity, stacked pay system, reservoir compartmentalization, high-temp and pressure are some of the common challenges seen in these fields. The use of conventional completion design in vertical wells with commingled production form multiple zones has led to multiple operational challenges. In this paper, we explore the use of intelligent completions in the deepwater GOM as a solution to this problem. Specifically, we will show how intelligent completions have allowed for increased production, reduction in operating expenses, enhanced overall reservoir recovery and improved safety and environmental performance. A case study of intelligent completions in GOM is with sliding sleeves that are hydraulically controlled from the production facility.
In this paper we will be focusing on one of these deepwater oil fields located in the northern GOM. The field is comprised of a subsalt reservoir with best-in-class Pliocene age sands (main target reservoir). These conventional sands have good porosities and permeabilities up to 1 Darcy. The target producing reservoir sands are comprised of multiple, stacked, turbidite sands. This has led to the standard field development in this area to be vertical wells with commingled production from multiple zones. The field has been producing back to a deepwater facility for more than 5 years through multiple subsea wells. Initial production was over 10,000 barrels of oil per day (bopd) per well. The producing reservoirs are supported with strong aquifer drive and leads to rapidly increasing water rates in wells post water breakthrough. Riser base gas lift is utilized in all producing risers to help optimize production.
A Tracer Technology Applied for Inflow Profiling While Monitoring Individual AICD/ICD Performance. A Case Study from Offshore Horizontal Wells in the South China Sea
Authors: Husein Nadir , Wang Guangyu , Belova Anna , Upadhye Vishwajit 
Published: April 2024

Abstract
A tracer technology system was integrated with AICD/ICD completions in 2 horizontal offshore wells with a dual objective to assess performance of each individual inflow control device and to obtain long-term dynamic data of the well inflow profile.
As a measure of water control, ICD (Inflow Control Device) technique is widely applied in the eastern oilfield in the South China Sea, AICD (Autonomous Inflow Control Device) technology is also gaining wider recognition. At the same time, it is very difficult to evaluate the application effect of ICD/AICD, and there is no unified evaluation criteria and methods. This solution assumes equipping each inflow control device with a set of marker indicators (tracers of unique signatures) enabling to obtain inflow profile in numerical terms with unique identification of a source of water breakthrough (specific ICD/AICD) in the horizontal lateral of the well.
Selected technologies showed good compatibility. The design of ICD/AICD allows easy incorporation of tracer material. Inflow profile can indicate each individual device working condition over a long period. The tracer analysis provides quantitative data showing the flow composition and each liquid phase contribution from every device. The large number of unique marker signatures allowed to trace every single device (25 with ICD and 37 with AICD in the study wells). Data combined from the performance of all ICDs/AICDs within one zone of the well horizontal section (3 zones in total of the study well) shows production profile of the well and helps with reservoir characterization to provide recommendations for improving field development management efficiency.
The key feature of the method is the possibility to measure the performance of each inflow control device and production contribution and phase composition coming from each zone of the well. Being a long term interventionless monitoring solution the combination of technologies (ICD/AICD + marker monitoring) allows to understand what impact different operational modes and working regimes of AICD have on the production and zonal contribution and select the most optimal regime to achieve production goals.
Optimizing Operational Decisions in Steam Flood Oilfields: A Machine Learning Model for Steam Injection Pressure Analysis
Authors: Wibawa R. , Faturrahman M. S. , Siregar A. O. , Reza M. , Muzammir M. 
Published: April 2024

Abstract
Efficient steam injection management is vital for successful operation of large-scale steam flood fields, such as the "D" field, which consists of more than 6000 wells located in 13 distinct areas. Maintaining steam injection pressure within the target range of 500-600 psi in both injection areas and Gathering Stations (GS) is a significant challenge. Steam supply originates from multiple sources scattered around the field that need to be managed to anticipate decreasing pressure due to operational problems, for example, equipment maintenance or fuel gas shortage. Steam injection pressure exhibits various behaviors across different areas because of several factors such as pipe configuration and choke sizing in injector wells.
This project aims to develop a Machine Learning (ML) model to simulate pressure behaviors in both steam injector areas and GS during any steam rate changes in each steam generator unit. Applying Machine Learning to modelling pressure in interconnected steam distribution network is a novel approach provided by the project. The utilized model is Nonlinear Autoregressive with eXogenous inputs (NARX), which incorporates historical time series data from both endogenous and exogenous inputs. Steam contribution factor for each source was also determined to augment information to the model about implicit factors that might impact the pressure at each area. To test the model’s robustness against actual data, a moving cross validation dataset was implemented. In addition, alignment with the physical sense was also tested by conducting sensitivity analysis of pressure against controllable parameters such as steam rate and equivalent opening choke area. The model outperformed the naïve forecast as a baseline performance. Furthermore, a dedicated web-based dashboard is constructed, providing engineers and operation teams with a user-friendly graphical interface to facilitate monitoring and decision-making processes. Lastly, the model has been used to improve steam rate management by avoiding oil loss due to decreasing producer well performance that equivalent to $250,000.
Applications of Advanced Multifunction Pulsed Neutron Measurements for Cased-Hole Formation Evaluation in a Mature Field
Authors: Al-Jubouri M. , Alward W. , Zongfa L. , Zhaowei L. , Wenyin Z. , Xiaori X 
Published: April 2024

Abstract
Efficient formation evaluation in heterogenous carbonate and clastic reservoirs is indispensable for estimating reserves and optimizing the testing and completion scheme of wells. E&P companies rely heavily on open hole logs to acquire a wide spectrum of measurements necessary for this objective. Effective petrophysical analysis provide quantification of key formation elements including, but not limited to, mineralogy, porosity, reservoir fluids saturation and typing, and rock permeability. Nonetheless, well control issues can sometimes pose considerable limitations to measurement acquisition in open hole leading to partial or complete compromise of data acquisition programs and subsequent loss of essential information. In such case, acquiring alternative measurements specifically designed to work in cased-hole environment is the only mitigation strategy.
A widely known cased-hole logging solution is pulsed neutron measurements that have been introduced to the Oil and Gas industry decades ago. Recent advances in pulsed neutron tools technology greatly improve the quality of this type of cased-hole measurement and enrich the spectrum of its applications, bringing it closer to open hole logs in terms of robustness. In time domain, the measurement includes formation Sigma, for saturation in saline environment, thermal neutron porosity (TPHI), and inelastic fast neutron cross section (FNXS) for gas identification. In energy domain, the measurement includes Inelastic-Capture elemental yields for mineralogy and Total Organic Carbon (TOC), and Inelastic Carbon & Oxygen ratio for salinity-independent saturation estimation.
These advanced measurements were deployed in a mature field to compensate for the absence of open-hole logs by providing comprehensive set of inputs for complete petrophysical analysis. The measurement was additionally implemented to evaluate the gravel pack integrity in certain producer wells that targeted clastic reservoirs where this type of completion is utilized.
The study presents the results of this successful deployment and discusses the associated interpretation workflow that helped optimizing the testing/completion design and characterizing the reservoir fluids across different formations. The study will also discuss the workflow used for verifying the gravel pack integrity with the same measurement.
Stability of closed and needle-punctured vials of Porvac ® subunit vaccine against classical swine fever subjected to thermal stress
Authors: Sardina-González Talía , Vargas-Hernández Milagros , Sordo-Puga Yusmel , Naranjo-Valdéz Paula , Rodríguez-Moltó María Pilar , Méndez-Orta Mary Karla , Hernández-García Mara Laura , Santana-Rodríguez Elaine , Pena-Guimaraes William , Moreira-Rubio Alain , Mateu-Hernández Rosaili , Cabrales-Rico Ania , Duarte Carlos A. , Pérez-Pérez Danny , Suárez-Pedroso Marisela 
Published: April 2024

Abstract

Background
Classical Swine Fever (CSF) is still one of the most economically important viral diseases of pigs. In endemic countries, the disease is controlled mostly through vaccination, hence, the availability of safe and effective vaccines is of utmost importance. Vaccines intended for application in developing countries must also be thermally stable, since the infrastructure needed to maintain a cold chain in those countries is usually lacking. Porvac® is a second-generation subunit marker vaccine against CSF that has demonstrates to be safe and protective. Previous studies have also shown that the vaccine is stable for 1 week at 37 oC and have a shelf life of at least 36 months at 2–8 oC. The aim of this work was to further explore the accelerated stability of Porvac® by assessing the physicochemical properties of the emulsion, and the safety and efficacy of the vaccine subjected to more drastic conditions of thermal stress: (1) 25 oC for 12 months; (2) 30oC and 37 oC for one month and (3) 15 days at 37°C after the cap of the vials had been needle-punctured.
Results
The vaccine subjected to all these conditions did not show significant changes in the physicochemical properties of the emulsion; did not produce local or systemic adverse reactions in pigs, and the chromatographic profile of the recovered antigen was preserved. All vaccinated swine developed protective neutralizing antibody titers ≥ 1:1000 at 28 days post vaccination.
Conclusions
Porvac® is stable in all the experimental conditions tested, even after cap puncture, and retains the capacity to induce protective neutralizing antibodies. These results reinforce the robustness of the vaccine, and support its use as a very attractive alternative to modified live vaccines in developing countries endemic for CSF.
Research on the Factors Influencing the Fertility Intentions of Women of Childbearing Age under the Three-Child Policy: Empirical research based on CGSS 2021 data
Authors: Zhou Ting , Chen Yijun , Liu Taotao 
Published: April 2024

Abstract

Objective
The fertility intentions of women of childbearing age in China has declined to very low levels, The purpose of this study is to explore the fertility intention for having a third child under the three-child policy and the factors that influence these intentions.
Methods
Based on the 2021 China General Social Survey (CGSS) database, a sample of 864 individuals was selected according to the research objectives. we systematically analyzed the third child fertility intention using descriptive statistical analysis, t-tests or chi-square tests, and binary logistic regression were used for analysis.
Results
Among the 864 women aged 20 to 49, 28.819% expressed a willingness to have a third child. Binary logistic regression analysis revealed that personal education level, family annual income, affordable kindergarten services, and parental support in childcare are factors influencing the fertility intentions of women of childbearing age (P < 0.05). Women with higher education level have a lower willingness to have a third child. Conversely, women with lower education level, higher family income, access to affordable kindergarten services nearby, and support from their parents in childcare have a higher willingness to have a third child.
Conclusions
Implementing the three-child policy necessitates the development of comprehensive support measures, the promotion of various forms of beneficial services, and the strengthening of comprehensive supervision of childcare services to reduce the costs of childbirth, upbringing, and education, hence alleviating family concerns and better unleashing fertility potential, facilitating attainment of a moderate fertility level.
Controlled Synthesis of Fly Ash-Based Zeolite X/A for Ammonia- Nitrogen Removal
Authors: Yan Zheng , Wu Yaling , Yu Lingmeng , Qiao Hongwei , Hu Siqi , Wang Yongguang , Liu Dan , Meng Yan 
Published: April 2024

Abstract

Fly ash is a kind of industrial solid waste, which mainly comes from thermal power plants using coal as power fuel. The lack of effective disposal and utilization of fly ash has led to its not only destroying and polluting the ecological environment but also seriously endangering human health. In addition, the treatment of ammonia nitrogen wastewater is an important issue in environmental pollution control, so the study of efficient and low-cost separation materials has become a challenging topic. This study utilises the hydrothermal method to convert fly ash into fly ash-based zeolite, which is capable of treating ammonia nitrogen wastewater. The successful synthesis of Zeolite X, zeolite A and zeolite A-X are confirmed through various characterisation techniques, including XRD, TG-DSC, TEM, BET, and FT-IR. Simultaneously, the synthesized zeolite is tested for its capacity to adsorb and regenerate ammonia nitrogen wastewater. The pure zeolite with the largest adsorption capacity, simpler preparation process, good stability, and high regeneration capacity is selected.
Identification of Lernaea Species in Icthyofauna in District Mardan KPK, Pakistan
Authors: Ahmad Ashfaq 
Published: April 2024

Abstract
Lernaea cyprinacea, a parasitic cyclopoid copepod, is found to parasitize in the brood stock, nursery and culture system in the freshwater hatchery of Mardan regions. The fish were severely infected on their body surface showed dull and sluggish movements. This research, carried out in the district Mardan Khyber-Pakhtunkhwa, Pakistan, spanning from February to August 2023, delves into the prevalence and infestation dynamics of Lernaea parasites in freshwater fish in river Kalpani. Through a comprehensive analysis of 159 specimens collected from various aquatic sources, the study identifies the presence of five parasite species, notably with Saprolegnia as the predominant one. Utilizing a categorization method, the investigation discerns varying degrees of infestation, underscoring the susceptibility of local fish populations to Lernaea. These findings corroborate with earlier research, accentuating the pressing need for targeted mitigation strategies and robust conservation initiatives. Additionally, the study unravels the profound impact of Lernaea cyprinacea on 103 fish, emphasizing its pivotal role as a primary cause of fish mortality. This research not only contributes crucial insights for shaping effective conservation measures but also proves imperative for sustaining the overall health and biodiversity of freshwater ecosystems in the region.
Luminous Harmony: An Easy Approach to Amino Acid Detection through Fluorophore Sensing in Aqueous Micellar Systems
Authors: Malik Nisar AHMAD , Yaseen Sameera , Majeed Tameeza , Jan Nusrat , Khan Javed Masood 
Published: April 2024

Abstract
The fluorophore, Pyrene aldehyde, PA, with micelles of different surfactants is used to detect the amino acids using fluorescence. The fluorescence of the ensemble is utilized for obtaining the different spectra’s and the data obtained is analyzed for various types of interactions and detection of some amino acids in aqueous medium. The fluorophore (PA) when dissolved in PF-127 and Tween-80 micelle solutions, the fluorescence emission becomes more stable. The quenching efficiency determined in case of, Cetrimide (CET), CET + PA and Tx-100 + PA is much smaller than that of PF-127 and Tween-80. It was observed that Tween-80 sensor system has a good selectivity towards valine and PF-127 shows an increasing trend as Aspartic acid < Histadine < Valine.
Research on the degradation of hydrogen sulfide by sulfur oxidase using Ruegeria pomeroyi DSS-3
Authors: Sun Baoxin , Peng Jing , Shao Zhao'an , Zheng Xin , Zhang Xinli , Hu Xin 
Published: April 2024

Abstract

Hydrogen sulfide (H2S) is the main toxic sulfur-containing odorant in water bloom, posing a great threat to the public health and drawn increasing attention. Marine Ruegeria pomeroyi DSS-3 is the dominant species in coastal sediments and harbors various sulfide-oxidizing genes. Herein we check the ability for H2S removal of R. pomeroyi DSS-3 and study the enzymes responsible for H2S removal. Using the fluorescent probe psGFP, we found that R. pomeroyi DSS-3 is able to oxidize H2S to polysulfides, then converting to thiosulfate and eventually to sulfate. By expression of sulfur oxidase in R. pomeroyi DSS-3, we found that SQR and FccB play synergistic roles in sulfur oxidation. Given that the heterotrophic R. pomeroyi DSS-3 bacteria are abundant and ubiquitous in marine environment such as water bloom, they can make a considerable contribution to sulfide removal. This study provides a novel green microbial deodorization approach for H2S biodegradation.