bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psychometric Properties of the Vocational Rehabilitation Engagement Scale When Used With People With Mental Illness in Clubhouse Settings
Authors: Fitzgerald Sandra , Deiches Jonathan , Umucu Emre , Brooks Jessica , Muller Veronica , Wu Jia-Rung , Chan Fong 
Published: August 2016

Purpose:The purpose of this study was to validate the Vocational Rehabilitation Engagement Scale (VRES) for use in the Clubhouse Model of Psychosocial Rehabilitation.Method:There were 124 individuals with serious mental illness recruited from 8 Clubhouse programs in Hawaii. Measurement structure of the VRES was evaluated using exploratory factor analysis.Results:Exploratory factor analysis of the Clubhouse version of the VRES yielded 2 reliable factors (cognitive-affective engagement and behavioral engagement). Both the cognitive-affective engagement and behavioral engagement factors were found to correlate with other self-determination theory constructs including autonomy support, relatedness, and outcome expectancy.Conclusions:The Clubhouse version of the VRES is a brief, reliable, and valid instrument for assessing vocational rehabilitation (VR) engagement and contributes to the use of self-determination as a paradigm for improving recovery outcomes in psychiatric rehabilitation.
Efficacy of Local and Systemic Antimicrobials in the Non‐Surgical Treatment of Smokers With Chronic Periodontitis: A Systematic Review
Authors: Chambrone Leandro , Vargas Miguel , Arboleda Silie , Serna Maritza , Guerrero Marcela , de Sousa Jose , Lafaurie Gloria Inés 
Published: August 2016

Background: The aim of this systematic review is to evaluate whether use of local or systemic antimicrobials would improve clinical results of non‐surgical periodontal therapy for smokers with chronic periodontitis (CP).Methods: Medical Literature Analysis and Retrieval System Online, Excerpta Medica Database, and The Cochrane Central Register of Controlled Trials were searched up to and including March 2016. Randomized clinical trials of duration of at least 6 months were included if they reported on treatment of smokers (≥10 cigarettes per day for minimum 12 months) with CP with non‐surgical periodontal therapy either alone or associated with local or systemic antimicrobials. Random‐effects meta‐analyses were undertaken to evaluate mean differences in probing depth (PD) and clinical attachment level (CAL).Results: Of 108 potentially eligible articles, seven were included. Most individual studies (75%) testing locally delivered antibiotics reported that smokers benefited from this treatment approach. Pooled estimates found additional PD reduction of 0.81 mm (P = 0.01) and CAL gain of 0.91 mm (P = 0.01) at sites with baseline PD ≥5 mm. Conversely, meta‐analysis on systemic use of antimicrobials failed to detect significant differences in mean changes from baseline, and only one trial supported their use.Conclusions: In smokers with CP, adjunctive use of local antimicrobials improved efficacy of non‐surgical periodontal therapy in reducing PD and improving CAL at sites presenting PD ≥5 mm before treatment. Current evidence does not demonstrate similar gains when scaling and root planing plus systemic antimicrobial/antibiotics were associated with therapy.
Correlation of domination parameters with physicochemical properties of octane isomers
Authors: Hosamani Sunilkumar M. 
Published: August 2016

Abstract
In this paper, we find the values of three important domination parameters namely, connected domination number, total domination number and total edge domination number of molecular graph of octane isomers. Further, we show that these parameters are highly correlated with physical properties of octane isomers. Finally we carry out QSPR (Quantitative Structure-Property Relationship) analysis using several physicochemical properties of octane isomers.
O&G Mergers and Acquisitions in the Digital Age
Authors: Prakash Amit 
Published: August 2016

Abstract
Technology has always been a major success factor when it comes to M&A deals in the Oil and Gas Industry. A systematic approach towards handling the Technology side of the equation is essential in making M&A deals a success through identification of integration opportunities, disjointed processes, preparation of the unified platform for companies to merge and also provide the required support for unified platforms to split and build separate processes, system instances and applications.
However, in a traditional set up most of the deal making team’s attention goes into the financial due-diligence, Asset Valuation, Operational Integration and strategic alignment while technology is left behind. This obviously needs to change for the better not only because Technology has always had a major role to play in M&A deals but also because of the rising trends of Digital and new technologies in Oil and Gas. The new trends have enabled Technology supported by Digital to be one of the most valuable tools in making M&A deals a success.
The plethora of technologies Digital brings to the table allows it to play a major role in identifying, shaping and finally closing any M&A deal through accurate and timely data using the power of connectedness and real time data availability. A new structure will soon come into effect encompassing Digital as a major player in the Oil and Gas M&A space.
Securing the Future: A Structured Approach for Early Engagement With Major Capital Projects to Embed IE Solutions and Add Value
Authors: Gillenwater J. , Gilman H. , Lukehart C. J. 
Published: August 2016

Abstract

Objectives/Scope:
The future of Intelligent Energy (IE) is tied to the success of our Major Capital Projects (MCPs). As exploration pushes into more remote and challenging environments, IE provides technology and processes that can help make these projects viable. However, the largest MCPs have a project lifecycle of several years, and it is hard to find the right moment to introduce IE concepts – too early, and there is too much uncertainty about the overall project philosophy and scope; too late, and the project schedule, design, and budgets have already been frozen.

Methods, Procedures, Process:
We present a structured approach for IE engagement that has been developed using learning from the early IE initiative at Chevron and is now used successfully to help MCPs manage challenges associated with designing for a future that can be five or more years away. This allows MCPs to maintain flexibility and avoid being locked in to aging technologies, while managing risk and avoiding expensive scope or schedule changes. IE concepts are introduced early to help influence the operating philosophy and design and to allow for consideration of research and development activities. This results in a structured program to deliver the selected IE solutions at the right time – some of which may be before first production.

Results, Observations, Conclusions:
Throughout the paper, we will use examples from MCPs in the Chevron portfolio to illustrate how this approach has been successful in engaging early, selecting appropriate solutions and delivering value.

Novel/Additive Information:
We explain how the approach is aligned with the MCP project management process, assurance and documentation requirements. In each phase, there are specific requirements and deliverables, including a rigorous deployment program for the selected solutions and structured handover to the operating asset.
Recovery Factor Prediction for Deepwater Gulf of Mexico Oilfields by Integration of Dimensionless Numbers with Data Mining Techniques
Authors: Srivastava Priyank , Wu Xingru , Amirlatifi Amin , Devegowda Deepak 
Published: August 2016

Abstract
The objective of this paper is to integrate "Big Data" concept with petroleum engineering knowledge for the prediction of recovery factor in Deepwater Gulf of Mexico (dGOM) oilfields. Recovery factor is affected by many geological and engineering factors; as a result, there is no explicit approach to accurately calculate the recovery factor. This is particularly true for deepwater development as the parameters associated with the recovery factor estimation have significant uncertainties and usually extremely costly to obtain. Typically, the recovery factor of a field is estimated using analogs, material balance, decline curve or numerical simulation. These deterministic approaches requires representative geological models. However, enough information is often not available to capture the realistic flow. In addition, the estimated recovery factor can be very different using different methods. Reservoir engineers are faced with the challenging task of estimating recovery factor by optimizing a large number of parameters with limited, sometime inaccurate information. This dilemma calls for an alternative approach in handling the noisy data.
Data mining and classification identify hidden patterns in unstructured data and tend to be fairly robost in the presence of noisy data. Using a database of 395 Deepwater Gulf of Mexico (dGOM) oilfields with 84 attributes, a set of dimensionless numbers are calculated for 59 oilfields with water drive. This helps in dimensionality reduction and scaling of reservoir models for comparison. Based on the distribution of dimensionless numbers, data mining techniques like K-means clustering followed by principal component analysis (PCA) are used for classifying oilfields into four categories. Subsequently, partial least square (PLS) regression is used for relating dimensionless numbers to recovery factor from sparse data in dGOM, which gives good coefficient of correlation for some clusters.
This paper shows that dimensionless numbers together with data mining techniques provides a new, easy to implement method for predicting recovery factor for large datasets where application of other methods are limited due to requirement of high computational cost and time.
Enhancing Well Delivery Performance Through Discipline Integration
Authors: Davies R. , Bruland J. , Busto I. Del , Willumsen M. 
Published: August 2016

Abstract

Objectives
Demonstrate how to improve well delivery performance by strengthening discipline integration at key decision points. This draws on insight from implementing a well delivery process at Maersk Oil.

Methods
This paper analyses well delivery team performance, identifying issues and success factors through 30+ interviews and feedback from 100+ practitioners. It identifies five decision points with the greatest performance improvement potential. The study consistently found Subsurface/Wells integration challenges at the heart of issues, decisions and success stories.
It presents methods to systematically strengthen integration at each decision to improve performance. It defines enablers for effective integration, including learnings from prior projects and subject matter experts. The approach was implemented globally in all 2015/16 projects.

Results and Conclusions
Integration was strengthened through activities leading up to each decision. This covers deliverables, peer reviews, freezes and gates. For deliverables, this required clearer expectations and divisions of responsibilities. Peer reviews were adjusted to force trade-off debates and fulfil sharper requirements. Freeze points were created representing leadership commitment to minimise re-work. The gates were reinforced to drive cross-discipline integration and finalise decisions.
These integration activities aren't effective in isolation. They depend on enablers: building a robust case for integration, up-front collaboration, communicating the intent and cross-discipline leadership commitment. The paper describes best practices for each and how they fit together.
By strengthening integration and implementing enablers, project teams have improved efficiency, risk management and decision quality. The results are quantified with metrics and supported by examples and interviews.

Novel Information
Novelty lies in the systematic approach to strengthening cross-discipline integration at decision points. This goes beyond existing literature in scope and specificity. Operators can apply this to improve performance. The approach can be applied to other decision points, disciplines and capital project processes.
Looking Back 2006 - 2016 - Ten Years of Data Driven Well Rate Estimates for Real-Time Surveillance and Optimization
Authors: Jan Briers , Keat-Choon Goh , Ann Sniekers , Dave Schotanus , John Hofland , David Adun 
Published: August 2016

Abstract
On the tenth anniversary of the first Intelligent Energy Conference held in 2006, it is appropriate to look back at some of the technologies introduced at the time, and report out on how these have been progressed. This paper discusses one of the technologies: the use of data driven models for well rate estimation, to support and enable real-time surveillance and optimization.
In the early 2000s real-time data from oil and gas fields became available in abundance on engineering central office desktops via process data historians and wide area / global communications networks. A key challenge to production management, then, as now, was: "What are the wells producing?" The April 2006 SPE Paper 99963 introduced data driven modelling for continuous well production surveillance leveraging on the suddenly abundant and often very revealing PI data.
Today, ten years later, a stream of success stories on the use real time well rate estimates based on data driven models continue to be reported, and these tools are seen as best practice in many different operational scenarios. This paper reviews the key concepts introduced in 2006, and extensions and applications of the technology to various aspects of production surveillance and optimization. As with all innovations, the most challenging elements in the journey have been related to people and processes. The paper discusses these issues in relation to the important role played by technology integration themes such as Smart Fields.
The Smart Digital Platform for Effective Well Control Management
Authors: Jose S. , Sookdeo T. , Says I. , Carreno Y. , Ferrero R. 
Published: August 2016

Abstract
The oil and gas industry has increased its efforts in preventing and mitigating risks associated with loss of well control and loss of primary containment. Initiatives have primarily focused on increasing the awareness and education of field and operations personnel. The foundation of this strategy rests on the belief that increased awareness of threats, risks and enhanced training will lower well control risks and eliminate well control events. Despite this renewed focus, industry data show that well control events and high-potential near-misses have not diminished. In addition, findings from incident investigations point to human-factor-related causes, including lack of procedural discipline, non-compliance errors and cognitive errors.
For organizations to deliver flawless execution at the wellsite while effectively preventing or mitigating well control or loss of primary containment events, a robust closed-loop methodology that leverages smart risk detection and mitigation systems must be employed. This paper analyzes the critical process safety requirements in the industry and provides solutions centered on a smart integrated digital platform. This platform, built on technologies such as precursor sensor and alarming technologies, barrier and equipment health monitoring, wellsite performance analysis, sophisticated workflow management and video and audio analytics, will effectively coordinate and manage these dynamic risks.
A data management workflow that uses automated risk assessments, threat detection, structured and nonstructured contextual data will minimize the impacts of human factors and drive operational efficiency, process assurance, reduction in nonproductive time and project cost. These enhancements will enable global organizations to proactively drive effective risk management of well control and loss of primary containment events. This paper will explore the application, methodology and value of this smart, integrated digital platform through the presentation of case studies.
Novel Solutions and Interpretation Methods for Transient, Sandface Temperature in Vertical, Dry Gas Producing Wells
Authors: Dada Akindolu O. , Muradov Khafiz M. , Davies David R. 
Published: August 2016

Abstract
Pressure-Transient-Analysis (PTA) is a technique that is routinely used by both Production and Reservoir Engineers for a variety of applications during the lifetime of a producing well. It is initially used for reservoir characterisation and, later on, for well performance monitoring and (wider) reservoir surveillance. The application of high precision, downhole, temperature sensors has resulted in PTA being complemented or replaced by Temperature-Transient-Analysis (TTA).
Recent TTA research has shown that comprehensive information on the state of the near-wellbore zone and fluid flow rates and composition can potentially be derived from such measurements. However, the derivation and use of TTA solutions is challenging, due to both the small value of the measured temperature change and the more complex nature of the governing physics and equations. In particular, analysis workflows for wells producing gas or gas-liquid mixtures are still lacking since most published liquid TTA solutions cannot be applied in the presence of gas.
This paper addresses the missing workflow for a (dry) gas producing well. It presents the derivation of novel analytical transient sandface temperature solutions together with the development and application of workflows for interpreting transient sandface temperature data in vertical, dry gas wells. Estimation of either the flow rate or the permeability. thickness (kh) product is demonstrated using a linearized analytical solution.
Further, the radius and permeability of a near-wellbore zone of reduced permeability (due to formation damage) can be determined by tracking changes in the temperature transient using the thermal radius of investigation concept. The complete interpretation methodology has been validated by a bespoke numerical model and its application illustrated by case studies.
The developed solution and workflows provides a simple and fast method for interpreting sandface temperature data. They will be invaluable for well testing and monitoring applications. It is a major step forward in the longer-term project of developing a full-spectrum of TTA methods for multiphase (gas-liquid) producing wells.
Intelligent Monitoring and Control: Essential Guidance for Critical Infrastructure Security
Authors: Tarter Alex 
Published: August 2016

Abstract
Energy companies recognize that they have become high-value targets and that their operations can be disrupted using only cyber means. Supervisory Control and Data Acquisition (SCADA) systems, Distributed Control Systems (DCS), and Process Control Systems (PCS) are of specific interest to attackers as these operate critical infrastructure. Risk management is extremely difficult because every risk has a high cost attached and there are no reliable statistics to assess the likelihood of a risk occurring. The challenge is to understand an organization's risk profile and tailor its risk management strategy accordingly.
Industrial networks facilitate the free flow of messages that can allow poison packets to transmit with potentially disastrous effect. For instance, Distributed Network Protocol (DNP3) Secure Authentication does not protect against implementation failures in the device that can be exploited through malformed packets, therefore making protocol-aware validation and restrictions essential. A comprehensive approach to restricting what can and does occur within an industrial control network will help effectively mitigate the systemic risks that are inherent within the industrial control environment.
To comprehensively protect the critical infrastructure that powers civilian businesses and homes, the intelligent approach demands a secure-system design that controls and monitors all entities within the control system from users to vulnerability-prone endpoints and protocols. Not all attacks occur from external sources and so boundary protections such as data-diodes and firewalls are not enough. Such solutions, however, also must preserve operational efficiency to assure that the cure is not worse than the malware, in relation to operational efficiency.
Successful risk management requires an effective assessment of the inherent risks and the most viable mitigation strategies for those risks. This paper outlines these systemic industrial control specific cyber-related risks, and offers an approach for intelligently monitoring and controlling cyber activity to reduce a cyber-related impact. This approach promotes systems-based methods that focus on architectures that leave critical operations uninterrupted.
Leveraging Data-driven Exception Based Surveillance to Maximize Returns in Times of Low Oil Prices
Authors: Agrawal Amol , Lie Joel , van Leeuwen Peter , Adun David , Briers Jan , Pei-Huey Phua , Sniekers Ann , Enns Rick 
Published: August 2016

Abstract
With current economic realities, now is the time to produce "more with less". Exception Based Surveillance (EBS) helps eliminate waste in an engineer's day, by removing unnecessary analysis and allowing the engineer to focus on the highest value tasks. For the surveillance of oil wells, an efficient and effective way to achieve this is to use an automated Exception Based Surveillance System, often augmented by data-driven well rate estimates.
Specifically, this paper provides examples from Shell operations in Gabon, Malaysia, and the Netherlands on how EBS systems have been set up to address day to day production challenges. The multiple EBS Systems to be described here have been achieved via the tight integration of real-time data in Well, Reservoir and Facilities management (WRFM) workflows and the automation of complex calculations and rule sets. This paper also describes the WRFM "Next Generation surveillance tool" (NGT) currently being rolled out in several Shell assets (Clinton 2016).
The work described here regarding enhanced Exception Based Surveillance Systems and integrated Portals go beyond just deploying tools. To be sustainable and value adding over existing practices, the introduction of these systems requires the transformation of roles, processes and tools to fully and efficiently leverage and gain value from now mature technological capabilities.