bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wells Efficiency Program - Early Conception and Technology Incorporation for Buzios Field Wells Campaign
Authors: Diab Alexandre José , de Souza Ana Paula M. , dos Santos Anderson R. , Castilho Carlos Alexandre B. , Coccoli Flávio C. , e Silva Jayme Eduardo M. , Abdu João Paulo S. , Leite Julio Cesar C. , Tolfo Marcos C. , Santos Rodrigo O. , Silva Tiago S. 
Published: April 2024

Abstract
This paper outlines a strategic approach to early-stage well configuration design, incorporating cutting-edge technologies to maximize value throughout Buzios Field wells campaign, with a target of 30% cost reduction with a strong emphasis on enhancing well safety, reliability, and cost efficiency, this process, named SELEPOQO, was initiated during Project Phase 1 (Opportunity Identification). SELEPOQO efficiently identifies optimal well configurations from a range of technologically mature options, aligning with production timelines to ensure maximum field value.
Our approach for the proposed paper involves a comprehensive review of the "Enhancing Well Efficiency " program within the context of the Buzios oil field development. We'll focus on detailing the key methodologies employed to incorporate innovative technologies. We'll analyze the achieved milestones, presenting both qualitative and quantitative results to demonstrate the program's impact on reducing well construction costs and improving equipment reliability. Additionally, we'll discuss the broader implications of the program for Petrobras, the oil industry, and future offshore well construction projects.
The results of the paper highlight the remarkable achievements of the "Enhancing Well Efficiency " Program in the development of the Buzios oil field. The program successfully incorporated cutting-edge technologies, resulting in a substantial reduction of well construction costs, in line with the ambitious 30% target. Equipment reliability improved significantly, leading to more efficient reservoir management, and positioning Petrobras as a leader in offshore well construction. The observations reveal that the early integration of technology selection, thorough planning, and collaboration with research centers enabled Petrobras to address challenges and expedite the implementation of innovative solutions. The results illuminate the importance of efficient processes in the adoption of technology, providing valuable insights for organizations seeking to expedite their technological integration efforts. The conclusions drawn from this study underscore the importance of proactive planning and technology integration in complex deepwater projects. The Enhancing Well Efficiency program model, with its focus on cost reduction and enhanced reliability, has set a benchmark for the industry, demonstrating the feasibility of early-stage technology adoption, even in challenging environments. This initiative not only strengthens Petrobras' competitive position but also underscores the significance of continuous innovation in optimizing project economics and ensuring sustainable operations in the oil and gas sector.
Application of Artificial Intelligence in Predicting Cement Thickening Time
Authors: Shamsan Abdulmalek. , Jimenez Walmy Cuello , Jandhyala Siva Rama Krishna 
Published: April 2024

Abstract
Cementing operations consists of placing cement slurries between casing-casing and casing-formation to achieve multiple objectives including zonal isolation, corrosion resistance, isolation of water bearing formations, etc. Cement slurries are tailored in the laboratory environment simulating downhole conditions to achieve the required placement times (i.e. thickening time) for the slurry to remain in a liquid state during pumping. This study presents an artificial intelligence model developed to predict the cement slurries thickening time based on historical experimental data, along with the ability of the model to continue learning based on the flow of output results.
Collected data sets of different input parameters were gathered and studied to estimate cement thickening time. Based on statistical analysis, only five inputs (Pressure (psi), measured depth (ft),bottom hole circulating temperature (°F), retarder additive concentration (%), and slurry density (ppg)) had a positive impact on the correlation coefficients between the inputs and target properties. A model was developed using Python programming language to predict the thickening time. The data was fed into the model to train and test the input factors impacting the target property using a neural network method.
The model expressed an integrated relationship between the training and validation data which resulted in an optimum learning performance revealed by a loss function plot. As a result, the scatter plot for prediction vs actual has illustrated the prediction of the thickening time with an average accuracy of 83% confirmed by the model box plot. The model validation consisted of employing a set of data to predict the thickening time for more than 11 slurries with an average absolute error of 6%.
Model implementation indicated that over 200 hours per month were saved due to reduced number of thickening time test iterations required to be run before executing the job which positively impacting operational efficiencies associated with cost, resource allocation, and investigation assessment. The outcome results from this model and the process used herein could be scaled up to consider other properties, i.e. ultra-sonic cement analyzer UCA, fluid loss, free fluid, etc.
Interpretation of Pressure Transient Measurements in Realistic Geological Models Using Bayesian Inversion and Surrogate Modeling
Authors: Jiang Lei , Kristensen Morten , Rajan Santa Kumar Teaga , Motaei Eghbal , Prioul Romain , Daungkaew Saifon , Dubost Francois-Xavier 
Published: April 2024

Abstract
Deep transient pressure testing, using a state-of-the-art formation testing platform, allows deeper investigation into a subsurface formation compared to previous wireline-conveyed testing techniques. Given the associated interaction of the pressure with more varied geological features, numerical reservoir models and simulations are generally required to capture the reservoir or formation heterogeneity that may be encountered during a test. However, the long computation time of such numerical simulation poses challenges for some critical interpretation tasks, such as model inversion.
We propose a novel method for the parameter estimation in geologically complex reservoirs by conducting Bayesian inversion with surrogate models. To account for the geology complexity, we utilize surrogate models constructed through the polynomial chaos expansion (PCE) method, to substitute for the numerical simulators. It allows simulating the pressure response in a timely manner while at the same time providing global sensitivity analysis for each uncertain parameter in the model. The Markov chain Monte Carlo (MCMC) method is then employed with the surrogate models for conducting the Bayesian inversion with pressure transient measurements.
We analyze the properties of PCE surrogate models and demonstrate that, for typical pressure transient interpretation tasks, sufficiently accurate surrogates can be constructed from an ensemble of 200 to 500 numerical model evaluations. These evaluations are performed concurrently in a cloud-based environment thus reducing the time-cost for surrogate model training to less than an hour. We then perform the Bayesian inversion on the pressure measurements and effectively characterize the model parameters with their associated uncertainties via the posterior distributions. The problem of MCMC inversion is solved in a few minutes using the surrogate models.
Through our studies, we demonstrate that the adoption of surrogate models considerably reduces the computation time required, allowing the Bayesian inversion to be completed within minutes, which was unachievable with the numerical simulators. Furthermore, this new method offers accurate parameter estimations and provides posterior distributions of uncertain parameters, as well as unveiling correlations among the parameters for the interpretation of measurement. These capabilities were lacking in the current inversion process utilizing numerical simulators. Finally, the new method lends itself well to workflow automation for history matching, thus reducing the workload on petrotechnical experts and addressing today's imperatives of faster and more cost-efficient field development.
Novel Machine Learning Approach for Predicting the Gel Strength of the Drilling Fluid While Drilling
Authors: Gowida Ahmed , Elkatatny Salaheldin 
Published: April 2024

Abstract
Accurately estimating gel strength is paramount for optimizing drilling operations and preventing cuttings from settling at the wellbore's bottom. Traditional methods rely on rotational viscometers, which are time-intensive, equipment-dependent, and lack real-time monitoring capabilities. This study investigates the use of machine learning (ML) techniques to forecast drilling fluid gel strength. A dataset comprising surface drilling parameters and laboratory gel strength measurements was gathered to construct ML models. Selected drilling parameters, such as mud weight and Marsh funnel viscosity, were chosen as model inputs due to their accessibility and cost-effectiveness during drilling. A neural network-based model was trained and assessed using statistical measures like R-squared and average absolute error (AAPE). Results showcased neural networks' ability to predict gel strength accurately, achieving an AAPE below 6.76%. Model validation using an unseen dataset demonstrated close alignment with actual gel strength values, boasting a prediction accuracy surpassing 93% and a low AAPE of 7.21%. Statistical scrutiny affirmed the reliability of the developed neural networks model for real-time gel strength forecasting. This study underscores the feasibility of leveraging machine learning as a practical tool for predicting drilling fluid gel strength, offering real-time monitoring and precise predictions to enhance drilling efficiency, safety, and automation initiatives.
Lufeng 22-1 Oil Field Redevelopment - Witness the Subsea Technology Evolution
Authors: Gao Shuang , Li Xiang , Chen Xiaoyan , Dong Hongkun , Steinnes Erling , Xiao Xiaolong 
Published: April 2024

Abstract
The Lufeng 22-1 oil field is in the South China Sea, at a water depth of approximately 330 m. It was first developed with a floating production storage and offloading (FPSO) unit, subsea wells, and the first electrical submersible pump in the industry, moving subsea processing a giant step forward. After producing for 12 years, it became China’s first abandoned offshore field.
Recently, the field had its first oil for the second time, when the owner re-developed the same field using a different concept with a 19-km subsea tieback and new subsea boosting technology. In combination with an offshore platform, it enabled the economic development of the area for marginal fields.
The paper begins with a discussion of the development challenges of this field, and provides a holistic review of different development options, including a standalone development vs. a tieback development. It was pioneering act to adopt the first subsea boosting system, which has advanced significantly over the last two decades. In the past, the field was initially developed using five subsea pumps, with one for each subsea well. However, as a result of significant technological advances and maturation in the years since, it was more recently redeveloped using a single pump to process all flows. The comparison of the two development concepts allows for a rare opportunity to assess the impact of the technology advancement over time. The values brought by the technology are analyzed from both technical and operational point of view.
The re-adoption of the subsea boosting technology with a subsea tieback is of great significance to the offshore field development in China, where many marginal fields and long tieback fields can be enabled.
Prediction of Pile Run During Pile Driving - Analytical Model and Field Observations
Authors: Thijssen R. , Roelen S. 
Published: April 2024

Abstract
In offshore construction, geotechnical risk assessment of pile installation during the preparatory phase is of major importance. Pile driveability analyses are part of these preparations and provide, primarily, insights into the relation between hammer blow count, driving stresses, and the static soil resistance during driving (SRD). In case the total weight of the "pile driving system" (i.e. the combination of pile, follower and hammer) exceeds the SRD, a phenomenon referred to as "pile run" or "drop fall" might occur. Usually, at this point in wave equation analysis software, no hammer blow count is calculated until a depth is found where the SRD increases sufficiently to support the weight of the pile driving system again. For many (offshore) projects, with ever increasing weight of piles and hammers, the consequences of pile run to equipment and foundations could be significant. Moreover, once offshore, options for mitigation are limited or are met with high costs and/or significant delays. This paper elaborates on a model that was developed to predict the extent of pile run and velocity of pile and hammer during a drop fall. The model includes an approach that allows for assessment of effects from liquefaction during high velocity pile runs. A correction for strain rate dependent shear strength is also included. The model was validated by means of field observations of multiple controlled pile run events during construction of an offshore wind farm. More recently, pile run field observations from a monopile installation project became available to the authors, for which the model also proved satisfactory. The observed and analyzed pile run for this specific case is also reported. Use of the model provides insight into the extent of pile run and could provide a basis for assessment of mitigating measures. Sensitivity analyses were performed to determine which model parameters are key and what uncertainty bandwidth in pile run predictions should be taken into account.
Mineral Sulfite Scale Issues: Water Compatibility and Mitigation Assessment
Authors: Brenas T. , Vincent A.-L. , Lesage N. 
Published: April 2024

Abstract
This paper aims to study the compatibility of two mineral waters (containing sulfites and calcium) likely to produce large quantity of sulfites scale if they are mixed. This scale formation impacts the production facilities and is hardly recoverable. Consequently, this work is assessing the scale risks simulation and simple laboratory tests. The results of both methods show very high risks of precipitation likely to disturb process operations. The scale quantity is also very sensitive to pH modification.
Then, three different scale mitigations solutions were evaluated to prevent any deposit formation by working either on the sulfites of the first water or on the cations of the second one. These measures are: The formation water divalent cations sequestration thanks to Ethylenediaminetetraacetic acid (EDTA) as complexing agent.The early precipitation of sulfites thanks to CaCl2 or CaCO3 addition.The chemical scale mitigation by antiscalant injection.
The injection of scale inhibitors did not show any efficiency whatever the scale inhibitor and the concentration (30, 60 and 120 ppm) for process water at pH = 8 and 12. The sequestration requires a high amount of EDTA (5.9 g EDTA/g divalent cations) to be fully effective due to stochiometric conditions necessity. The early precipitation with 120 g.L-1 of CaCl2 remains the most effective solution and enables the easy separation of sulfites salts by physical methods such as settling. The most promising solution consists in a mix of early precipitation, acidification and scale inhibitor addition.
Could One Size Fit Most? A Right Sized Vessel for DSM Exploration in Remote Regions
Authors: Smit H. , Meyer L , van Eck G 
Published: April 2024

Abstract
The Cook Islands (CI) possesses within its Exclusive Economic Zone (EEZ) a massive field of polymetallic nodules representing one of the world’s largest undeveloped cobalt deposits, along with large quantities of other metals critical to achieving global energy transition targets. In February of 2022 the Seabed Minerals Authority (SBMA) of the CI granted Moana Minerals a license to conduct our nodule exploration program. This paper describes how Moana Minerals chose to approach conducting exploration activities, one of the greatest challenges to Deep Sea Mining (DSM) development and the securing of the social license to advance to eventual mining of the resources, in an unconventional manner.
The remote location of the project poses logistical challenges in mobilizing to and operating in the region and is a significant cost driver. Historically, exploration campaigns mobilized large vessels from mainland Europe, the Americas, and Asia as this model works well when contemplating long duration and widely temporally spaced campaigns without the need to revisit. Moana Minerals, when considering these challenges determined a multi-year deep sea mining (DSM) exploration program conducted in such a remote location can be better optimized by the deployment of a smaller yet highly capable "exploration optimized" vessel that is dedicated to the task and is based full time in the Cook Islands.
Enhancement of Health-related Quality of Life among School-going Adolescent Girls with Improvement in Menstrual Hygiene Knowledge and Practices
Authors: Goyal Akanksha , Agarwal Sunita 
Published: April 2024

Background and Aim:
In India, among adolescent girls, the problem associated with menstrual hygiene management (MHM) has a deep impact on health-related quality of life (HRQoL). Improvement in knowledge and menstrual hygiene practices may lead to enhancement in HRQoL. Therefore, the aim of the study was to enhance the HRQoL of adolescent school girls by improvement in menstrual hygiene knowledge and practices.

Materials and Methods:
The present study was an interventional, cross-sectional, questionnaire study conducted among adolescent school-going girls. The study was conducted among randomly selected five government secondary schools in Ajmer city and was conducted among adolescent girls of 7th–10th Class with the age group of 14–16 years. A total of 538 adolescent girls were surveyed in the pretest, and 503 was the sample size in the posttest with an attrition of 6.5%. Moreover, after the pretest, a workshop was conducted to educate adolescent girls about MHM.

Results:
A majority of the study participants (328 [61%]) had poor HRQoL after the pretest, which was improved to 195 (39%) of the study participants with good HRQoL after 3 months of the workshop during the posttest. The mean general health scores ([15.35 ± 0.38]) after 3 months of the workshop were significantly (P = 0.01*) higher among the study participants compared to the pretest scores ([10.93 ± 1.63]).

Conclusion:
From the above results, it has been concluded that there has been a significant improvement in knowledge, practice, and HRQoL among adolescent school-going girls of age 14–16 years when assessed after 3 months of the workshop on MHM.
Dietary Iodine Intake and Pattern of Thyroid Disorders among the Ethnic Population of Eastern Himalayan Ranges of India
Authors: Sonowal Trisha , Sarmah Jatin , Sarma Paresh Kumar 
Published: April 2024

Background and Aim:
In the present study, dietary iodine intake and thyroid functioning were assessed among the ethnic adolescent group of Eastern Himalayan range with the hypothesis that insufficient iodine intake negatively affects normal thyroid functions. The aim of the study was to see the prevalence and relation between iodine deficiency and thyroid functioning.

Materials and Methods:
It is a hospital-based study conducted in the health care facilities of Dima Hasao district, Assam, from April 2021 to March 2022. The study included 821 subjects including both male and female of adolescent age group (10–19 years). The thyroid profiling, anti-Thyroid peroxidase antibody (anti-TPO Ab), and urinary iodine content (UIC) were carried out. Thyroid hormone analysis was carried out by chemiluminescence assay. The anti-TPO Ab was determined using the electrochemiluminescence immunoassay analyzer. The dietary iodine intake was measured on the basis of UIC and was determined by arsenic cerium catalytic spectrophotometry method by the Sandell-Kolthoff reaction.

Results:
The prevalence of thyroid disorder among the studied population was found to be 13.8%. Among different conditions of thyroid disorders, hypothyroidism was present in 10.6% and hyperthyroidism in 3.13%. 4.18% had elevated anti-TPO Ab. Among the euthyroid subjects with different thyroid disorders, 1.48% and 21% had anti-TPO Ab positivity, respectively. The median UIC was 99.97 µg/l. 25.78% of the population had iodine deficiency. Thyroid disorders were common in subjects with iodine deficiency (61.34%) than iodine sufficient subjects (20%).

Conclusion:
The work was an attempt to demonstrate how the dietary iodine intake influences thyroid functioning among the adolescent ethnic population of Eastern Himalayas which is manifested as high prevalence of subclinical hypothyroidism with iodine deficiency. The work underscores region specific recommendation on iodine supplementation for populations living in geographically remote locations.
Knowledge and practices among nurses and laboratory technicians towards biomedical waste management
Authors: Chitroda Sonal Mayur , Purohit Drashti 
Published: April 2024

Background:
Unmanaged sanatorium waste carries a severe threat of nosocomial infections, which are contagious. Improper disposal of biomedical waste poses a significant environmental and health threat to healthcare facilities, staff and communities, highlighting the growing challenges of managing this waste.

Aims and Objectives:
The study was aimed at evaluating the adequacy of knowledge and practice concerning biomedical waste management amongst nurses and laboratory technicians.

Materials and Methods:
It was a cross-sectional study involving 101 participants, including 50 nurses and 51 laboratory technicians, from June 2022 to 2023, using a validated self-administered Google Form questionnaire. The study used the Chi-square approach to conduct intergroup comparisons amongst nurses and laboratory technicians.

Results:
Statistically significant P values were found for age, sex, qualification and years of experience in a survey, with the highest qualification being postgraduation (46.5%). However, 45.5% had <1 year of experience, indicating a diverse demographic distribution.

Conclusions:
Continuous learning and professional development are crucial in healthcare, with tailored interventions addressing knowledge gaps and waste management guidelines, ensuring successful implementation and accessibility for a diverse workforce.