bluuebunny/crossref_metadata_2025_split huggingface dataset:
Upstream/Downstream Real Time Surveillance and Optimization Systems: Two Sides of the Same Coin, or Never the Twain Shall Meet
Authors: Cramer Ron , Mehrotra Shailendra , Goh K C , Stoever Matt , Berendschot Leo 
Published: March 2013

Abstract
Downstream Refineries and Chemical Plants have benefited from real time optimization systems (RTO) for the last 30 years. Downstream RTO is a well established and permanent fixture in many plants – the "way we do things ‘round here!". Upstream E&P operations have "come to this party" much more recently and are using RTO more sparingly, even though the economic and HSSE benefits can be very significant.
There are key differences between downstream and upstream. For example, downstream facilities do not deal with sub-surface uncertainties, multiphase flow and isolated/harsh environments; while upstream operations do not usually have to deal with complex chemical processes.
Integrated Oil Companies run upstream and downstream operations and integration of tools/practices across both regimes is often perceived to be of significant value. Hence, the purpose of this paper is to compare and contrast downstream and upstream RTO learnings with a view to identifying and describing:
similarities in production unit operations e.g. fluid separation, compression etc.; key differences between production unit operations; cultural differences between operations; RTO activities from a technical perspective; RTO business benefits and how these might be leveraged and sustained in both directions.
What will emerge from this analysis will be a comparison, highlighting points of commonality and differences, leading to a better understanding of how RTO can be more effectively exploited in the upstream business – the cheapest oil available!
Specifically, it is concluded that RTO in upstream operations is feasible and lucrative, but is relatively rare with sustainability a challenge. Downstream RTO is more common and sustainable, significantly less lucrative, but a "must do" to compete in a highly competitive, margin constrained business.
IT Infrastructure Architectures to Support Drilling Automation
Authors: Annaiyappa Pradeep 
Published: March 2013

Abstract
Drilling automation is gaining momentum as seen by the increasing participation at workshops. Most of the early efforts of drilling automation have focused on incorporating specialized algorithms by both oil companies and service companies that try to improve the drilling performance. As drilling automation efforts try to scale to all rig types and all drilling operations there will be an increasing need to integrate and connect equipment from different vendors with different control systems to create complete automated work flow. Implementing drilling automation on existing rigs requires integration of equipment owned by the drilling contractor, service company and even the operator. This paper will investigate different architectures that have been successfully used in other domains to solve this key technology challenge to support Automation on a drilling rig. Cloud computing has been used as a way to provision services on demand. There are several public cloud services that are available via the internet. The focus of this paper will be the implementation of a community cloud infrastructure that can be implemented on a drilling rig, provisioning various services key to the successful implementation of drilling automation.
Big Data in E&P: Real-Time Adaptive Analytics and Data-Flow Architecture
Authors: Brulé Michael R. 
Published: March 2013

Abstract
Big Data Analytics are most effective when data-in-motion are combined with data-at-rest. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing (CEP) and for scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data whether semistructured or unstructured, which can be used to support the E&P industry's many physics-based methods in modeling and simulation, over a wide range of disciplines from geology & geophysics to reservoir, production, & facilities engineering. High-volume data of many different types can be landed in Hadoop and analyzed without extensive transformation into a relational database model. Combining Stream computing with Hadoop Map/Reduce or massively parallel processing relational data warehousing (MPP DW) enables the analysis of high-frequency data scored against continuously updated models, known as "Real-Time Adaptive Analytics." This design pattern provides a low-latency "Real-Time Data-Flow Architecture," which enables decisions during operational events and also at the speed of the overall business. Big Data Analytics also depends on tooling for building modeling and simulation applications and for performing analytics, as much as it depends on infrastructure. Augmenting the industry's many decades of progress in physics-based modeling and simulation, powerful empirical models can be developed with tools for statistical analytics, text analytics, AI, and machine-learning and then used as scoring and data-validation models by implementing stream computing on-site where the data are generated in oilfield operations. This paper will explore RealTime Adaptive Analytics and the Real-Time Data-Flow Architecture combining stream computing, Hadoop/NoSQL, and MPP data warehousing. Several compelling use cases in drilling and in production will be reviewed.
Integrated Operations (IO) Model for Samarang Field Conceptual Study
Authors: Abd Razak C. A. , Mohd Som M. , Shuhaimi M. A. , Mazlan E. , Muhamed Salim M. , Wong L. , Traboulay I. 
Published: March 2013

Abstract
Integrated Operations (IO) was indentified as part of the redevelopment program in Samarang field. The IO framework defined was focusing on Asset Management Decision Support through increasing levels of monitoring and surveillance; fast, medium and slow loop diagnosis, transformation of operations, and optimization of asset values.
The aim of Samarang IO is to achieve the followings:
Effective and efficient working environment through streamlined and automated workflows Quality information acquisition and dissemination across the organization Collaborative expertise across multiple disciplines and locations Optimum asset value through increased production and reserve recovery
As the first step of project execution process, an IO study team was formed to carry out a conceptual study. The Samarang Integrated Operations (IO) study was conducted within the context of an Integrated Operations Model developed by Schlumberger. This model is used to frame, plan, and execute the IO Conceptual Study for Samarang.
The methodology, scope of work, and project deliverables for the conceptual study, is based on the general description of the IO model. In general the IO model describes how asset decisions will be supported by new measurements and equipment proposed for installation on existing and new platforms. It also describes how decision process will be enabled by software applications and how collaborative decision making can be improved.
An innovative parallel design approach was used to fast track the design process by integrating IO solution conceptual design in parallel to EPC FEED design for instrumentation with interface management as the main quality controller.
Upon completion of the conceptual study, a total of 2010 man-hours were logged from interviews, workshops, peer reviews and offshore survey involved interaction with different stakeholders. The extensive time invested in the data gathering has enabled the team to complete the project deliverability inclusive of some additional scope.
Enabling Numerical Simulation and Real-Time Production Data to Monitor Waterflooding Indicators
Authors: Al-Jasmi A. , Nasr H. , Carvajal G. A. , Cullick A. S. , Vanish D. , Wang F. , Velasquez G. , Villamizar M. , Md-Adnan F. , Betancourt D. , Urrutia K. 
Published: March 2013

Abstract
Intelligent digital oilfield (iDOF) operations include the transfer, monitoring, visualization, analysis, and interpretation of real-time data. Enabling this process requires a significant investment to upgrade surface, subsurface, and well instrumentation and also the installation of a sophisticated infrastructure for data transmission and visualization. Once upgraded, the system then has the capability to transfer massive quantities of data, converting it into real information at the right time.
The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to here as "smart flows," which assist engineers in their daily well surveillance activities, helping make them more productive and improve decision making. A major oil and gas operator in the Middle East has invested in such an infrastructure and is developing a set of smart flows for key activities and workflows for its production operations, with the ultimate goal of improved asset performance.
The Sabriyah Mauddud limestone of north Kuwait is a giant depletion-drive oil reservoir currently under the waterflooding process and is being monitored as part of this iDOF operation. A "smart flow" has been developed for visualization and analysis of the waterflooding performance that uses real-time production data to derive and update daily water saturation and reservoir pressure from numerical simulation. The ultimate goal of the smart flow is to identify and define actions to improve water injection and production over the long term.
This paper explains how this smart flow works-how it automatically runs a reservoir simulator to estimate water saturation and reservoir pressure and iteratively calculates the waterflooding indicators to provide advice to change the water injection on a monthly basis.
New Generation of Petroleum Workflow Automation: Philosophy and Practice
Authors: Al-Abbasi A. , Al-Jasmi A. , Goel H. K. , Nasr H. , Cullick A. S. , Rodriguez J. A. , Carvajal G. A. , Velasquez G. , Scott M. , Bravo C. 
Published: March 2013

Abstract
Intelligent digital oilfield (iDOF) operations have gained momentum in the past few years, transformed from being merely a vision to real-world projects with quantifiable value. Challenges such as increased energy demand and diminishing new discoveries, coupled with a lack of specialist-domain expertise and trained personnel to efficiently operate assets, have forced operators to rethink the traditional way of asset management to increase productivity and operational efficiency.
The amount of information that asset managers now have to make decisions has increased dramatically in the last few years. More data about a problem can lead to improved decisions, but it also increases the complexity of the decision-making process. Asset teams need tools and technologies to help them quickly and efficiently analyze and understand all this data so they make better, faster decisions.
To help asset teams meet these challenges, a new generation of petroleum workflow automation integrates real-time data with asset models, helping team members to collaborate so they can better analyze data and more fully understand asset problems. We're calling this new generation of automated, intelligent workflows "smart flows."
This approach is cutting-edge, but also more complex. The complexity is addressed with the use of artificial intelligence technology, such as proxy models and neural networks, coupled with a visualization engine to provide an effective visual data mining tool. The objective of this new generation of petroleum workflow automation is to provide integrated solutions to asset opportunities and guide the operations with instructions based on smart analysis and integrated visualization.
This paper provides an overview of a workflow automation environment that is being implemented for a major operator.
A Real-Time Automated "Smart Flow" to Prioritize, Validate, and Model Production Well Testing
Authors: Al-Jasmi A. , Nasr H. , Goel H. K. , Lopez C. , Carvajal G. A. , Vanish D. , Cullick A. S. , Yong Z. , Moricca G. , Campbell I. , Querales M. , Rodriguez J. A. , Velasquez G. , Villamizar M. , Mahajan A. 
Published: March 2013

Abstract
Intelligent digital oilfield (iDOF) operations include the transfer, monitoring, visualization, analysis, and interpretation of real-time data. Enabling this process requires a significant investment to upgrade surface, subsurface, and well instrumentation and also the installation of a sophisticated infrastructure for data transmission and visualization. Once upgraded, the system then has the capability to transfer massive quantities of data, converting it into real information at the right time.
The transformation of raw data into information is achieved through intelligent, automated work processes, which are referred to here as "smart flows," which assist engineers with key well operations and monitoring, helping make them more productive and improve decision making. A major oil and gas operator in the Middle East has invested in such an infrastructure and is developing a set of smart flows for key activities and work flows for its production operations, with the ultimate goal of improved asset performance.
Well testing and production allocation are vital for identifying and resolving problem wells and for identifying candidate wells to optimize production and maximize a field's potential and asset value. But with traditional tools and methods, this work is time-consuming and requires many low-value tasks (data gathering, cleaning, etc.) leaving little time for the high-value analysis work. To expedite, prioritize, and simplify well testing and related analyses, the Well Performance Evaluation (WPE) smart flow has been developed.
This paper presents and describes the WPE, how it works, and how it is delivering value today. WPE features advanced data visualization that includes interactive charts, enabling production engineers to execute well testing daily, select the most critical wells for testing based on an expert system, verify well test data with advanced data filtering and qualification technology, execute and fit updated nodal analysis models on demand, in seconds, to recommend the optimal well output, and provide recommended actions to operations personnel to increase production.
Child and Adolescent Behavior Inventory (CABI): A New Instrument for Epidemiological Studies and Pre-Clinical Evaluation
Authors: Cianchetti Carlo , Pittau Andrea , Carta Valeria , Campus Grazia , Littarru Roberta , Ledda Maria Giuseppina , Zuddas Alessandro , Fancello Giuseppina Sannio 
Published: March 2013

Background:
Some questionnaires have already been elaborated to collect information from parents of children and adolescents, both as preparation for clinical evaluation and for screening and epidemiological studies. Here a new questionnaire, the CABI, is proposed, and it is validated in a population of 8-10 year-old children. Compared to existing questionnaires, the CABI has been organized so as to be of medium length, with items concerning the most significant symptoms indicated by the DSM-IV-TR for the pertinent disorders, and covering a wider range than existing instruments. There is no charge for its use.

Methods:
The answers of the parents of 302 children in the last 3 years of primary school provided the normative data. A discriminant validation was done for internalizing and externalizing disorders and as a comparison with self-administered anxiety and depression scales. Exploratory factor analysis and internal consistency were also performed.

Results:
Distribution of scores on the main scales in the normal population shows positive skewness, with the most frequent score being zero. A highly discriminant capability was found in regard to the sample of children with internalizing and externalizing disorders, with high correlation with the self-administered anxiety and depression scales.

Conclusion:
The CABI appears to be capable, at least for 8-10 year-old children, of effectively discriminating those with pathological symptoms from those without. Compared with the widely- used CBCL, it has the advantages of a lower number of items, which should facilitate parental collaboration especially in epidemiological studies, and of being free of charge.
The behavioural ecology of animal movement: reflections upon potential synergies
Authors: Liedvogel Miriam , Chapman Ben B. , Muheim Rachel , Åkesson Susanne 
Published: March 2013

Abstract
Animal movement acts at multiple scales: it can shape the destiny of
individuals and populations, govern community and ecosystem structure,
and influence evolutionary processes and patterns of biodiversity. Recent
technological advances, such as the revolutionary developments in tracking
technology and remote sensing, provide fresh insights and the possibility to
collect detailed data on where and how animals travel through space, how
they react to and/or interact with their environment and conspecifics as
well as their predators and prey. Scientists from various disciplines ranging
from physics to psychology develop and apply ever improving analytical
techniques to observe, assess and archive animal movement across scales.
As in any other field, standardising data collection is a key prerequisite in
order to combine and extend dataset collections, many of which may further
be utilized by behavioural ecologists to answer questions on the function and
significance of animal movements. Large-scale manipulative experimental
approaches have also shed new light on old questions in animal movement,
and opened new and previously inaccessible perspectives to study animal
movement in the context of behavioural ecology. Animal movements are
intrinsic to all behavioural processes, and analysis of movement phenomena
within the framework of behavioural ecology has provided rich insights into
the mechanisms and functions of animal behavior for some decades. We
convened an international symposium to reflect on the behavioural ecology
of animal movement, asking how these two related disciplines can produce
new insights and synergies. Our symposium provided a platform that brought
together a diverse range of researchers working on animal movement
on different taxa and a range of spatial scales to discuss how behavioural
ecology can integrate with the nascent discipline of movement ecology. In
this short paper we summarise the key points from this meeting, and call for
a renewed focus on the behavioural processes involved in the movements of
animals.
A simple culture method for epithelial stem cells derived from human hair follicle
Authors: Hilmi Abu , Halim Ahmad , Noor Norhayati , Lim Chin , Idris Zamzuri , Pohchi Abdullah , Asma Hassan , Wahab Shaik , Tiede Stephan , Paus Ralf 
Published: March 2013

Abstract
The challenge arises among researchers when hair follicle stem cells (HFSCs) derived from a human hair follicle remain poorly expanded in defined culture medium. In this study, we isolated the HFSCs and they became confluent after 10 days of cultivation. Comparing the viability of HFSCs cultured in defined keratinocytes serum free medium (KSFM) in a coated plate and CnT07 medium in an uncoated plate, the number of HFSCs cultured in CnT07 was significantly higher at days 2, 4, 6 and 8 (P=0.004). The population doubling time of HFSCs was 21.48±0.44 hours in non-coated plates with CnT07 and 30.73±0.75 hours in coated plates with KSFM. Our primary HFSC cultures were positive for CD200 and K15 with brownish color. Flow cytometry analysis showed that the percentage of HFSCs expressing CD200 and K15 were 65.20±3.16 and 72.07±6.62 respectively. After reaching 100% confluence, the HFSCs were differentiated into an epidermal layer in vitro using CnT02-3D defined media. HFSCs were differentiated into an epidermal layer after 2 weeks of induction. Involucrin- and K6-positive cells were detected in the differentiated epidermal layer. This method is a simple technique for HFSC isolation and has a lower cost of processing and labor, and it represents a promising tool for skin tissue engineering.
Characteristics of transmembrane proton transport in the cells of Lupinus polyphyllus
Authors: Darginavičienė Jūratė , Jurkonienė Sigita 
Published: March 2013

Abstract
The aim of this work was to examine H+-ATPase hydrolytic activity and the stability of transmembrane electrochemical gradients in membrane vesicles isolated from seedlings and leaf cells of an invasive Washington lupine (Lupinus polyphyllus Lindl.) and compare them with non-invasive yellow lupine (Lupinus luteus L.). Temperatures of 25 and 30°C, keeping in mind possible climate warming, were used. For harder stress conditions, short term cold treatment (−8°C in vivo) was used. Plasmalemma-, tonoplast-, and endoplasmic reticulum-enriched membrane fractions were obtained from a sucrose density gradient and identified. Differences in ATPase hydrolytic activity were significant only between lupine species and were more obvious in plasmalemma-enriched fractions. Preincubation of seedlings and leaves at −8°C for 15 min to 24 h showed that microsomic fraction membranes of invasive lupine were more stable (according to Na+-diffusive potential) at low temperature compared to yellow lupine ones. The level of transmembrane electrochemical potential, mainly evoked by ATP-dependent active proton transport, was almost equal in both lupine species. Supposedly, the cells of invasive lupine are able to maintain transmembrane electrochemical potential by the employment of lower hydrolytic activity of H+-ATPase, thus saving energy for growth.
Analysis of the Essential Oil Compounds of Origanum Syriacum L.
Authors: Farhat M. , Tóth J. , Héthelyi B. É. , Szarka Sz. , Czigle Sz. 
Published: March 2013

Abstract
Syrian oregano (Origanum syriacum L., Lamiaceae) is a very popular culinary herb that has been used through the ages in traditional medicine mainly in Lebanon and the Arab world. The subject of this study was the microscopic analysis of the herbal drug Origani syriaci folium, as well as the chemical analysis of its essential oil components. Plant materials originated from Arabsalim - South Lebanon and were collected in 2010. The presence of very dense clusters of multicellular covering trichomes (510 μm × 18.47 μm) and peltate secretory glands (∅ 87.95 μm) with eight cells were observed. The essential oil of Origani syriaci folium was isolated and quantified using hydrodistillation according to SPhC 1997. The content of essential oil was 95 ml/1000 g, i.e. 9.5% (V/m). The volatile constituents of Origani syriaci folium were qualitatively and quantitatively evaluated using SPME GC/MS. The two dominant constituents identified in Syrian oregano leaves were carvacrol (78.4%) and thymol (17.9%).
New possibility of objective evaluation of yarn appearance: part II
Authors: Moučková Eva , Jirásková Petra 
Published: March 2013

Abstract

In this article, the possibility of new objective evaluation of yarn appearance in area employing selected spatial
statistical functions is presented. The yarns wound on the boards were used for the experiment. Appearance of
standard yarn boards from the standard CSN 80 0704 and real yarn boards with faultless and faulty yarns were
converted into grayscale images. Fluctuation in degrees of grayness was evaluated between square fields in the
image using statistical function called the area variation curve. In addition, the method was applied to the simulated
yarn board appearance generated by the Uster Tester apparatus. Behavior of area variation curves in dependence
on the result of visual evaluation of yarn board appearance was discussed. The generated appearances from the
Uster Tester device were also evaluated by other statistical function called semivariogram. It was found out that the
area variation curve is not a suitable tool for evaluation of yarn board appearance. The semivariogram seems to be
a more suitable tool. The paper extends the knowledge on the issue of objective evaluation of yarn appearance and
directly follows the author’s work [1].