bluuebunny/crossref_metadata_2025_split huggingface dataset:
Real-Time Physical Models with Learning Feedback as a Digital Twin Architecture
Authors: Dannenhauer Cristiano Edio , Bastos Baptista Gustavo Luiz , Szwarcman Dilza , Martins da Silva Ramises , Martins Plucenio Daniel 
Published: April 2020

AbstractThe use of numeric simulation tools has been a traditional approach for determining the behavior of equipment and complex engineering operations. These same techniques have been used in the oil & gas industry over the years to help improve efficiency and reduce cost, allowing the exploration of areas that were previously economically unfeasible. Currently, with the increasing development of sensors and communication technologies, real-time numerical simulation is becoming a key tool for a better understanding of drilling dynamics, allowing the prediction of critical events and making operations more efficient and secure. The goal of this work is to present a computational tool that applies the concept of digital-twin to oil drilling systems by proposing the use of physical models to simulate, in real-time, phenomena such as well hydraulics, torque and drag, cuttings concentrations and bed formation. Data collected in real-time from the rig is used as input to the physical models, whose outputs can flow into other models, allowing a multi-physics analysis. The data collected from the sensors and the results provided by the digital-twin tool are stored in a time series database, which is used for the continuous evaluation of errors and efficiency improvement, implementing a learning feedback into the physical model calculations of drilling operations. Enhancements on the accuracy of model calculations are shown, leveraging the aforementioned architecture to monitor well operations, such as hydraulic, torque and drag and cuttings transport, and by the validation of the results by subject matter experts. This work presents how the proposed approach incrementally improves closing equations coefficients with learning feedback, i.e. having historical data from sensors and calculated data continuously being stored into the system, using big data technologies for handling the large volume data.
Intelligent Pressure Control System on Drilling Process
Authors: Zhu John , Zeng Liam 
Published: April 2020

Abstract
MPD (managed pressure drilling) is an important technique for challenging drilling operations especially in narrow operational windows, the IPC (Intelligent Pressure Control) system is equipped with the ultra-compact MPD manifold (L3.6m*W2.3m*H2.8m) with complete functionality of measurement & automatic control system, benefit the operators on footprint reducing for limited field space especially offshore operations. The 3D interface of IPC software provides operator with a perceptual intuition about current status of the equipment.
IPC system doesn’t rely on PWD sensor to acquire the downhole pressure data, with the unique algorithm integrated in iPWD module, the real time downhole pressure data could be generated without any downhole PWD sensor, the deviation between iPWD data and real PWD data is within 3%, which was proven in over 40 wells operation.
Nebula system is an add-on feature for IPC system, using cloud and IoT (Internet of Things) technologies, the Nebula could track the equipment’s specific location, working status and drilling parameter, providing statistical diagnosis based on data collected from field operations, which helps operators to make decisions quickly. The data uploaded to cloud could generate different reports according to end user’s requirements, to analyze drilling operation challenges or difficulties, this could be an intelligent tool which helps operators learn and understand the features of different blocks smarter via big data analysis. You can receive all data provided by Nebula system on your cellphone and PC at any time anywhere once connected to cloud station, always keep an eye on field operation and get the latest drilling parameters.
Flow Assurance Risks Impact on Kuwait Offshore Field Development Plans
Authors: Al Safran Eissa M. , Al Ansari Sara I. 
Published: April 2020

AbstractKuwait Oil Company (KOC) offshore exploration and development plans are underway to boost its production capacity to the future target rate. However, the selection of offshore field development scheme is critical due to the associated flow assurance risks, which impact project economics, safety, and sustainability. The objective of this study is to simulate and evaluate two offshore field development schemes, namely subsea multiphase flow, and platform schemes. The evaluation is based on the associated flow assurance risks, project economics, and environmental impact of each development schemes. The analysis covers simulation of each scheme, prediction of flow assurance risks, and prevention/mitigation of the risks during the entire life of the field. Results revealed that although the flow assurance risks in the subsea multiphase flow scheme are significant, it eliminates the large investment cost of platform, which improves the project economics significantly, and minimize environmental impact. Conversely, although the platform development scheme has low flow assurance risk, platform capital investment cost may impair the project economics, safety, and environmental impact. This study investigates each scheme by simulating, economically evaluating, and environmental impact assessing both schemes, to enable the right decision and selection of field development plan.
Data Conditioning and Forecasting Methodology using Machine Learning on Production Data for a Well Pad
Authors: Bagheri Maryam , Zhao Haoran , Sun Manyang , Huang Li , Madasu Srinath , Lindner Peggy , Toti Giulia 
Published: April 2020

Abstract
A new machine learning (ML)/statistical-based methodology for conditioning and predicting production data for a well pad has been developed. Typically, data conditioning involves outlier detection, missing data, data imputation, and smoothing. Time-series production data prediction can be challenging because the target (wellbore oil production) depends on large-scale, high-dimensional data sets with unknown distributions and is influenced by missing data and outliers. Hence, data conditioning is key for accurate predictions. The current work is the first attempt at using ensemble ML and statistical techniques, such as multilayer perceptron (MLP), principal component analysis (PCA), and support vector regression (SVR), for well pad data conditioning using recently disclosed subsurface and production data from a field in the southern area of the Norwegian North Sea. The time-series forecasting based on large-scale, high-dimensional conditioned and cleaned data sets is also presented.
The data with an oil production rate greater than 10 Sm3 have been retained for data cleansing, which reduced the size of the production well data set by 14.9%. Outliers are detected using the z-score method. The missing values are predicted using a trained ML model on all available nonmissing data. The procedure first predicts the downhole missing values from all the wells, including the available neighboring wells, and then uses these features to predict other missing values for the well pad. In this paper, the two approaches implemented and compared for prediction of missing data are MLP and SVR, and PCA is performed to extract the most important data features.
Production data with 12 related variables (i.e., dates, hours, temperature, pressure, etc.) are used to explore the complex nonlinearity of features and estimate wellbore oil production with ML and deep-learning models. Conventional SVR and MLP methods are implemented as the benchmark.
During this work, more than 60% of the missing and abnormal data from the field data set are detected and imputed using advanced ML methods, such as MLP and SVR with radial basis function kernel. More than 6% of data are outliers and are removed using the z-score method. The modified SVR with time-series data structure and long short-term memory (LSTM) algorithms are used for the comparisons. An R-squared (R2) of 98% is achieved for both the algorithms; however, LSTM has the lowest root mean square error (RMSE) results compared to SVR.
Data conditioning is conventionally performed using statistical techniques, but here, an ensemble of ML techniques is used depending on the available data. This paper presents a new methodology to perform data conditioning and production prediction for a well pad using ML and neighboring well data. The ML algorithms used are highly efficient, as demonstrated by the results.
Qualification Of 20k Choke & Kill Unbonded Flexible Pipes For Drilling Applications
Authors: Bernard Gary , Peronne Simon , Vera Damien , Leborgne Flavien 
Published: April 2020

Abstract

Objectives/Scope
For thirty-five years, TechnipFMC has been providing high pressure and high temperature flexible pipes to the drilling industry. These unbonded flexible pipes rated up to 15k psi working pressure are used worldwide as part of the choke and kill system of onshore and offshore drilling facilities or units. Following recent high-pressure reserve discoveries and to enable future exploration of formations with anticipated higher pressures, the drilling industry has started upgrading drilling safety equipment like blowout preventers to higher pressure ratings. Unbonded flexible pipe technology is following this trend, striving to continue providing the industry with reliable high-pressure choke and kill flexible pipes.

Methods, Procedures, Process
The information presented in this document is the outcome of a full-scale test campaign performed within company owned facilities or in collaboration with worldwide recognized test laboratories.

Results, Observations, Conclusions
This paper will present the trials performed on the presently available 15k psi rated product and the results of the testing toward qualification of the first choke and kill unbonded flexible pipes with a 20k psi internal pressure rating. In addition to the high-pressure upgrade, progress was made in the fire performance domain by improving flexible pipe product knowledge with regard to the fire resistance. This has been achieved through a large fire test campaign performed over the years. The document will detail the tests performed, the procedures followed, and the understanding of the different failure modes observed.

Novel/Additive Information
Major achievements in the development of high pressure choke and kill unbonded flexible pipe technology will be presented to the industry with a focus on qualification testing and fire performance.
Critical Offshore Field Development Projects –Why Work Outside of Strict Timelines?
Authors: Arukhe James Ohioma , Al-Ghamdi Saleh Abdullah , Al-Nafisah Musa Mohammed 
Published: April 2020

Abstract
Overcoming environmental challenges to develop offshore oil fields remains daunting for engineers who must balance economic feasibility with environmental protection, often within limited timelines before first oil commitments. A critical examination of Saudi Arabia's field development cases reveals a 56-year timeline (which seems interminable) between offshore field discovery in 1957 and realization of an environmentally friendly field development in 2013. If teams desire to accomplish Aegian projects with the sustenance of core values in perspective (whatever the core values might be), such as to gain the economic advantages of offshore projects while preserving the environment or honoring some other constraint, it is imperative to work with more robust timelines. Whereas strictly time-bounding projects may help with releasing adrenalin for performance, engineers may require adequate time for innovative concept designs to conduct necessary pilots and qualification.
Collaboration between cross-functional project teams – construction, drilling, testing, and production – is examined from the economic delivery of several challenging wells under various environmental challenges and constraints. An overarching theme from each team was to apply environmental solutions. Colonization of hard coral species and presence of several fish species were observed on project completion. For example, a 70% increase in growth of Seagrass and artificial coral reefs was observed near the causeway built for the Manifa Field, which could become a hot spot of biodiversity. Water circulation, as measured by an acoustic doppler current profiler (ADCP) improved from 17 days before the project to 11 days on project completion. When engineers persevere, collaborate and have enough time to plan intensively until they find the technology solutions to advance, society benefits from an environmental return.
As the worldwide study cases reveal, several projects are threatened globally because engineers may be unable to find viable solutions under rigid timelines. Often companies must be willing to wait until they acquire the technologies to responsibly advance complex environmentally sensitive projects. If timelines are flexible and sufficiently realistic, management's unwavering support to avail necessary resources would be critical for success.
Advanced Supervised Machine Learning Algorithms for Efficient Electrofacies Classification of a Carbonate Reservoir in a Giant Southern Iraqi Oil Field
Authors: Al-Mudhafar Watheq J 
Published: April 2020

Abstract
Understanding the vertical discrete electrofacies distributions in wells is a vital step to preserve the reservoir heterogeneity. Predicting the electrofacies distribution at all wells is commonly conducted manually or with the use of some graphing approaches, but recently different machine learning techniques have been adopted to categorize electrofacies. In this paper, two supervised machine-learning techniques were implemented to model electrofacies given well logging data for a well in order to predict the distributions in all other wells (classification) in a carbonate reservoir in a giant southern Iraqi Oil Field.
The available data included open-hole and CPI well logging records in addition to the routine core analysis. The well discrete electrofacies distribution for the entire reservoir thickness has been obtained in our paper [OTC-29269-MS] using the Ward Hierarchical Clustering Analysis. For electrofacies classification, two supervised machine-learning techniques, K-Nearest Neighbors (KNN) and Random Forests (RF), were adopted to model the resulting electrofacies given the CPI well logging data for a well to predict at other wells that have missing data. These two supervised learning techniques were implemented as non-linear and non-parametric classifiers, which are imperative attribute due to the non-linearity of the electrofacies properties and the geological reservoir control.
The results of this research illustrated that the reservoir electrofacies can be predicted through the use of the supervised learning techniques when well logging records and core data are available. The two adopted classification algorithms were analyzed and compared based on confusion table, transition probability matrix and total percent correct (TCP) of the identified electrofacies that reveal the accuracy of the classification. RF was observed to be the optimum approach as it led to better electrofacies classification in this carbonate reservoir than the KNN.
The application of supervised machine learning techniques enhanced the accuracy and reduced the time spent in electrofacies classification. The two machine learning algorithms were implemented by R software, the most powerful statistical programming language.
Changing pattern of adult external abdominal hernias in Zaria
Authors: Abur Peter Pase , Daniyan Muhammad , Nwabuoku Stanley Emeka , Yusufu Lazarus M. D. , Odigie Vincent I. 
Published: April 2020

Background:
External abdominal hernias are very common diseases encountered in surgical practice. A previous report from this centre 21 years ago documented the pattern of adult external abdominal hernias. However, there is an observed changing pattern. The aim of the study was to document the changing pattern, mode of presentation, treatment and outcome.

Methods:
It was a 5-year prospective study from January 2011 to December 2015. Adult patients with external abdominal hernia at our institution were studied. Information documented included patients' sociodemographic information, type of hernia, mode of presentation, treatment and outcome.

Results:
Six hundred and thirty-seven out of 4,083 patients with general surgical cases had external abdominal hernias (15.6%), with a male:female ratio of 3.1:1. The types of hernia were inguinal (451 [70.8%]), umbilical (83 [13.0%]), incisional (54 [8.5%]), epigastric (31 [4.9%]), femoral (14 [2.2%]) and others (4 [0.6%]). The common modes of presentation for inguinal hernias were simple (364 [80.7%]) and strangulated (42 [9.3%]). The most common mode of treatment for inguinal hernias was modified Bassini (265 [58.8%]). The common post-operative morbidities for groin hernias were wound infection (18 [3.9%]) and acute urinary retention (10 [2.2%]). The 3-year recurrence rate for groin hernias was 14 (3.0%). Mortality was three (0.5%) patients.

Conclusion:
The pattern of external abdominal hernias in our institution has changed with the descending order of occurrence as follows: inguinal, umbilical, incisional, epigastric and femoral. This is in contrast to previous reports where femoral was the second most common. Modified Bassini was the preferred method of repair of inguinal hernia due to its simplicity.
The influence of water vapor anomalies on clouds and their radiative effect at Ny-Ålesund
Authors: Nomokonova Tatiana , Ebell Kerstin , Löhnert Ulrich , Maturilli Marion , Ritter Christoph 
Published: April 2020

Abstract. The occurrence of events with increased and decreased integrated water vapor (IWV) at the Arctic site Ny-Ålesund, their relation to cloud properties, and the surface cloud radiative effect (CRE) is investigated. For this study, we used almost 2.5 years (from June 2016 to October 2018) of ground-based cloud observations processed with the Cloudnet algorithm, IWV from a microwave radiometer (MWR), long-term radiosonde observations, and backward trajectories FLEXTRA. Moist and dry anomalies were found to be associated with North Atlantic flows and air transport within the Arctic region, respectively. The amount of water vapor is often correlated to cloud occurrence, presence of cloud liquid water, and liquid water path (LWP) and ice water path (IWP). In turn, changes in the cloud properties cause differences in surface CRE. During dry anomalies, in autumn, winter, and spring, the mean net surface CRE was lower by 2–37 W m−2 with respect to normal conditions, while in summer the cloud-related surface cooling was reduced by 49 W m−2. In contrast, under moist conditions in summer the mean net surface CRE becomes more negative by 25 W m−2, while in other seasons the mean net surface CRE was increased by 5–37 W m−2. Trends in the occurrence of dry and moist anomalies were analyzed based on a 25-year radiosonde database. Dry anomalies have become less frequent, with rates for different seasons ranging from −12.8 % per decade to −4 % per decade, while the occurrence of moist events has increased at rates from 2.8 % per decade to 6.4 % per decade.
Assessment of the quality of TROPOMI high-spatial-resolution NO2 data products in the Greater Toronto Area
Authors: Zhao Xiaoyi , Griffin Debora , Fioletov Vitali , McLinden Chris , Cede Alexander , Tiefengraber Martin , Müller Moritz , Bognar Kristof , Strong Kimberly , Boersma Folkert , Eskes Henk , Davies Jonathan , Ogyu Akira , Lee Sum Chi 
Published: April 2020

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) aboard
the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a
nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet,
visible, near-infrared, and shortwave infrared spectral ranges. The measured
spectra are used to retrieve total columns of trace gases, including
nitrogen dioxide (NO2). For ground validation of these satellite
measurements, Pandora spectrometers, which retrieve high-quality NO2
total columns via direct-sun measurements, are widely used. In this study,
Pandora NO2 measurements made at three sites located in or north of the
Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO2 data
products, including a standard Royal Netherlands Meteorological Institute
(KNMI) tropospheric and stratospheric NO2 data product and a TROPOMI
research data product developed by Environment and Climate Change Canada
(ECCC) using a high-resolution regional air quality forecast model (in the
air mass factor calculation). It is found that these current TROPOMI
tropospheric NO2 data products (standard and ECCC) met the TROPOMI
design bias requirement (< 10 %). Using the statistical
uncertainty estimation method, the estimated TROPOMI upper-limit precision
falls below the design requirement at a rural site but above in the other
two urban and suburban sites. The Pandora instruments are found to have
sufficient precision (< 0.02 DU) to perform TROPOMI validation work.
In addition to the traditional satellite validation method (i.e., pairing
ground-based measurements with satellite measurements closest in time and
space), we analyzed TROPOMI pixels located upwind and downwind from the
Pandora site. This makes it possible to improve the statistics and better
interpret the high-spatial-resolution measurements made by TROPOMI. By using
this wind-based validation technique, the number of coincident measurements
can be increased by about a factor of 5. With this larger number of
coincident measurements, this work shows that both TROPOMI and Pandora
instruments can reveal detailed spatial patterns (i.e., horizontal
distributions) of local and transported NO2 emissions, which can be
used to evaluate regional air quality changes. The TROPOMI ECCC NO2
research data product shows improved agreement with Pandora measurements
compared to the TROPOMI standard tropospheric NO2 data product (e.g.,
lower multiplicative bias at the suburban and urban sites by about 10 %),
demonstrating benefits from the high-resolution regional air quality
forecast model.