bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Integrated System for Drilling Real Time Data Analytics
Authors: Alsalama Ahmed M. , Canlas Joseph P. , Gharbi Salem H. 
Published: August 2016

Abstract
Drilling operations is one of the potential areas where data mining and advanced analytics technology can be used to improve the company's bottom line. With real time drilling technology, huge amounts of data can be generated and captured. Exploiting this data and transform it into knowledge will help oil and gas companies to increase revenue, lower costs, and reduce risks. Building a data mining and analytics engine that uses drilling real time data is a difficult challenge. Data transmission, retrieval, storage, data accuracy, format of the data and the timeliness of the analysis are major considerations for developing a data mining and analytics solution. Most of the current analytical engines require more than one tool to have a complete system. Several tools will be needed for data transmission, retrieval, storage, and data formatting. In addition, separate tools for building data models and visualization are required. Therefore, adopting an integrated system that combines all required tools will significantly help an organization to address the above challenges in a timely manner.
In this paper, we propose an architecture of an integrated system that combines all required tools of building a data mining and analytics engine for drilling real time data. Our architecture uses a single system that retrieve and preprocess data, apply data analytics models, extract the knowledge and visualize it, and store the results for further analysis. The advantage of such a system is the speed of the delivery of the analysis, which is crucial in drilling operations. The proof of concept experiments show that our proposed architecture can address and overcome the challenges of building a data mining and analytics engine for drilling real time data.
Human Factors in Control Room Design & Effective Operator Participation
Authors: Pikaar Ruurd , de Groot Niels , Mulder Erik , Remijn Bas 
Published: August 2016

Abstract
Human Factors Engineering aims at optimizing work systems. The short definition "user-centred design" expresses a focus on both human beings and design. In a control centre design project several related topics need to be addressed: 1) job content and operator workload, 2) workplace arrangement and design, 3) process graphics and interaction design, 4) alarms, and 5) CCTV. Experienced operators have an extensive knowledge of the processes. Therefore, they should be considered a valuable asset. They act adequately in case of unexpected events, provided they have an excellent overview of the process status.
A Human Factors (HF) approach is well documented in ISO 11064 - Ergonomic Design of Control Centres. It requires professional HF skills to carry out this approach, including a task analysis, job design, and functional workplace design. In addition, a HF professional may fill the gap between engineering disciplines and operations. The aim of this paper is to show the impact and benefits of a systematic HF involvement in control centre design in oil & gas. Topics are:How to include HF in systems design.The move from off-shore to on-shore control centres, including changing operator tasks.Good process graphics design and developing powerful process overviews.Organizing effective operator participation and change management.Outcomes and lessons learned from HF interventions.
ESP Health Monitoring KPI: A Real-Time Predictive Analytics Application
Authors: Gupta Supriya , Nikolaou Michael , Saputelli Luigi , Bravo Cesar 
Published: August 2016

Abstract
The electrical submersible pump (ESP) is currently the fastest growing artificial-lift pumping technology (Bates, Cosad et al. 2004). Deployed across 15 to 20 percent of oil-wells worldwide, ESPs are an efficient and reliable option at high production volumes and greater depths (Breit and Ferrier 2008). However, ESP performance often declines without much warning and reaches the point of service interruption due to factors like high gas volumes, high temperature, and corrosive environments. The financial impact of ESP service interruption is substantial, from both lost production and replacement costs. Therefore, ESP performance in extensively monitored, and numerous workflows exist to suggest actions in case of service interruption events such as trippings or breakdowns. However, such workflows are reactive in nature, i.e. action is taken after an event occurs. Furthermore, with the emerging trend in the E&P industry of using downhole sensors for real-time surveillance of parameters impacting ESP performance, there is an opportunity to leverage real-time data obtained from sensors for predicting and preventing ESP shutdowns using data analytics.
This paper presents an analytical framework towards proactive ESP health monitoring based on data-driven predictive modeling and analysis. This framework can be used to automatically identify real-time patterns and assess ESP health in real time, thus offering to engineers and field personnel early detection of impending problems well before they occur, for mitigation and prevention of ESP service interruptions.
Risk-Based Prioritization Abandonment Strategy for Inactive Subsea Wells
Authors: Oladipo Abimbola , Houlbrook Adrian 
Published: August 2016

Abstract
Well abandonment, although an activity as old as the art of well construction itself, undoubtedly is perceived as a high cost, revenue-eroding commitment for exploration and production companies. Nonetheless the fulfillment of this obligation is non-negotiable for an operator, and thus the application of an efficient methodology to prioritize the abandonment of inactive wells, and ultimately reduce risk exposure is fundamental for a cost effective abandonment campaign. This paper describes how an exploration and production company ("the Company"), working in conjunction with an asset risk management consultancy, successfully developed and applied a qualitative risk-based abandonment prioritization strategy for an inventory of inactive subsea wells.
The exercise entailed a preliminary identification of the inactive subsea well inventory, after which a qualitative assessment methodology was devised using twenty-five well integrity related criteria calibrated to allow for various well types, well function, condition, age, design, history etc. An aggregate of weighted scores was subsequently applied to the Company's corporate risk assessment matrix, for consistency, based on the escalation potential in the event of a loss of containment.
The results clearly highlighted the wells with the highest risks and thus formed one of the components (including abandonment phase and operational complexity) of an integrated abandonment campaign, ultimately incorporated into the drilling schedule.
An efficient prioritization strategy applied to legacy wells served as an improved risk management and proactive cost allocation tool in the hands of senior decision makers for managing medium to long term liabilities, ultimately leading to reduced risk exposure and long term capital savings.
Using RealTime Data for Well Design Optimization, Not Just Drilling Optimization
Authors: Andersen Øystein 
Published: August 2016

Abstract
In recent years, amount of downhole drilling dynamics data has increased significantly. To some extent, the velocity of which the data becomes available for evaluation / correction has also increased, especially with the introduction of wired drill pipe. But even without this technology, it is evident that the amount of data one can evaluate to optimize performance, has increased substantially from only a decade ago.
There is a large focus on adjusting drilling parameters to enhance drilling progress / ensuring wellbore stability, but experience shows that the data tends to be "forgotten" after a well is finished. This is partly due to the volume of data, but mostly the engineering team's inherent trend to design wells again from scratch based on scarce "lessons learned" spreadsheets from the previous well and theoretical simulations; simulations which often have large uncertainty built-in.
Contradicting many people's belief, drilling engineering is not an exact science. The operations take place several thousands of feet below the ground and one only get hints of what is happening downhole. It is up to the engineering department to make as much out of these hints as possible, often referred to as "listening to the hole" in order to ensure a successful drilling operation.
With lots of parameters affecting the drilling performance (such as e.g. mud properties, bit/BHA design and trajectory), there is no clear indication that one can find the optimum strategy for a drilling a hole section by changing only one factor between one well and the next. Even so, changing drilling parameters to optimize hole performance during operation is only a part of the optimization, it is in the planning phase one makes the decisions that can really affect if a well will be successful or not.
For a field development (or any other area with several wells being nearby each other) it is recommended to change the method of designing wells. Instead of leaning towards sophisticated theoretical simulations, one should instead have a much higher focus on having the historic data "at your fingertips" when designing a new well. But in order to do this analysis, it is crucial that the data available is of high quality. This means having log data in similar format and well details & operational timings entered correctly in the daily drilling reporting system.
It is, however, not enough only to compare the drilling parameters / well designs, one also needs to assess what operations were successful/unsuccessful for each wellbore together with the relevant data. Examples of this can be annotation of formation instability with mud parameters or annotation of bit damage together with drilling parameters.
From offset well information, one can utilize a probabilistic approach for well design. This includes what risk factors to consider and expected ranges operational timings. From this, one can easily see where the largest risk factors and opportunities for improvements are.
Drilling Process Simulation: Status, Outlooks and Comparisons to Other Industries
Authors: Bhardwaj Vivek Kushal , Bello Opeyemi , Aragall Roger , Oppelt Joachim 
Published: August 2016

Abstract
Technological strides are reshaping the world each day. Computational advancements are challenging the conventional ways of carrying out our day to day tasks. The process of simulation has been absorbed by industries all across as the next natural step for growth and the drilling industry is no exception to it. The rate of acceptance and innovation in drilling domain has not been able to keep up pace with other industries. Simulation in drilling is facing challenges which are not unique therefore spin off technologies from other industries can be borrowed to bridge the gap.
The paper discusses the present state of simulation in the drilling industry as an aid to improving the overall performance along with making new technological advancements possible. The present state of practices and how they are bound to play a major role in the future are discussed. The report further enlists the industries analogous to drilling process and points out similarities with industries like aviation or acoustics, so that their learnings can be applied to the drilling processes and thereby shortening its learning curve. Whereas hindrances to successful implementation ranging from high table to rotary table time span for a technology to the cultural barriers still exist, expectations from simulation are bound to increase as the industry shifts from the conventional well on paper to a well on simulator approach. The way ahead for the drilling industry is to be open to changes and embrace them for their own good.
Monitoring Through Analyzing Real Time Build-Up Data from an Intelligent Field
Authors: Al-Wehaibi Bandar A. , Issaka Mohammed B. , Abuhasson Amer 
Published: August 2016

Abstract
Over the last several years, the petroleum industry has experienced a significant increase in the number of new fields and increments being equipped with the intelligent field technology. This includes remote monitoring and control of wells in real time, as well as the acquisition of high frequency pressure, temperature and rate data from the integrated-monitoring systems, including the permanent down-hole monitoring systems. Using field cases, this paper demonstrates the added values of this technology in the pressure transient analysis of real-time data.
As part of this study, real-time, build-up tests have been monitored and interpreted remotely for two oil wells conducted in one of intelligent fields. The objectives of these tests are to characterize the reservoir and investigate the presence of reservoir boundaries and heterogeneities around the wells. The results demonstrated how the intelligent field technology provides the real-time reservoir characterization and guarantees achieving the overall objectives of the tests. Monitoring the tests in real-time significantly improves the decision on when to call off the test. It also allows the shut-in times to be extended until sufficient pressure transient data has been acquired, to ensure that the late time responses have been captured.
This paper also illustrates how the intelligent field technology helps in the modeling and analysis of the pressure transient responses of wells, by incorporating the effects of the offset wells in a multi-well numerical model. This strengthens the interpretation outcomes of the analysis and eliminates any other less-likely interpretations or scenarios due to the late time responses.
Business Models and KPIs as Drivers for Drilling Automation
Authors: Iversen F. P. , Thorogood J. L. , Macpherson J. D. , Macmillan R. A. 
Published: August 2016

Abstract
Industries other than the drilling industry have largely adopted process automation, up to the level of full autonomy. Drilling automation technologies are becoming available, but uptake is slow. One major challenge is successfully motivating all actors in the complex work organization that is drilling. The authors believe that a change is required in business models and ways of measuring success in order to achieve this.
Using existing computerized process models, tools may be generated for automatic optimization of drilling performance. The same methodology may be used for evaluating drilling operations to determine the quality of the operation. In comparing with optimal performance, key performance indicators (KPIs) may directly reflect the benefits of automation. An analysis of known automation functions is performed, covering application areas and the effect on safety and efficiency in the drilling process. Key attributes of the same automation functions are evaluated in order to gain proper understanding of the complexities in developing good business models.
Automation technology built on optimization methods allows for development of new KPIs that are a direct measure of the quality of the process. However, current automation efforts reveal value conflicts in the work organization. Such conflicts must be resolved if the potential of automation is to be fulfilled. Once such conflicts are resolved, the building of fresh contractual models and the standardization of KPIs will make it easier for the industry to cooperate and move forward in using new automation technology.
The novelty in the proposed approach lies in resolving goal conflicts in the work organization with reference to overall value, based on which business and contractual models properly reflect the benefits and value of drilling systems automation technology applied in the current work organization.
The Internet of Things in Upstream Oil and Gas - How Can Decisions Be Made in Real-Time and Safely Manage Risk?
Authors: Crockett Brian J 
Published: August 2016

Abstract

Problem Statement
The current climate in the upstream Oil and Gas industry is making us face up to the challenge of radically reducing our OPEX but how can we achieve this without compromising on safety.
Improvements in Technology and the tsunami of Big Data has left our decision makers Drowning in Data and Thirsting for Knowledge. How can we provide the necessary information for decisions to be taken and shorten the time from detection to implementation? Is near real-time decision making possible?

Objectives
Demonstrate how the "Internet of Things" can be applied to the Human Machine Interface (HMI) to enable people to speed up responses.
Map out the route to decision making maturity by looking at where we start and what needs to be achieved along the road. We examine the basic components required for efficient decisions and discover how thing have changed over the years as pilot schemes have been developed into mature operational facilities.
Show how digital technology has speeded up decision making to near real-time and how it is paving the way for automatic processing of repetitive problems.
Finally we will look at where collaborative decision making is going and investigate the trends and advances in the industry.

Method
Harnessing the power of big data and how we make sense of it Using Exception Based Surveillance and how we can Increase Production and Reduce Deferment Predictive Analytics and Risk Based Maintenance. We will use examples from the Middle East where decisions were made "in the cloud."

Results
Early attempts relied on technology and failed to develop the HMI. Lessons were learned Continuous improvement was evident There is an appetite on the part of operators to embrace this way of working

Conclusions
What started out as an IT solution has now been focused on enabling people to work within automated processes to make decisions faster.
The tangible benefits of faster decision making include increased production and reduced deferment.
The intangible benefits have provided better working environments and a more motivated workforce.

Applications
This approach can be used for subsurface and surface teams and encourage cross functional cooperation. Examples are given for the Middle East, UK North Sea, Africa and the Far East.

Innovations
Monitor by exception and deliver better visualisation. Big data solutions in the connectivity of disparate databases. Data and information fed to the operator through automated workflows. Standard non critical decisions made automatically.

Significance of Subject Matter
This should be the normal way of working to take real-time data and translate it into meaningful information for managers to take key decisions.
Integrated Planning to Optimize Field Development Strategies and Real Time Decision Making During Project Execution for Large Scale Shale Resource Projects
Authors: Mitschanek M. , Prohaska M. , Thonhauser G. 
Published: August 2016

Abstract
Integrated planning unveils insight into a large project and all its bottlenecks for every discipline. In order to optimize utilization of facilities, optimize scheduling and its subsequent operations, an integrated planning tool was developed especially for large scale and complex shale resource projects.
The best option to come up with a suitable planning tool for such projects was found to be an Agent Based Model (ABM). Flexible definition of the individual components of the model, which comprises the subsurface, surface data, and the physical representation of wells, pipelines and equipment, allows effective strategy testing of various scenarios. With the chosen approach, results could be obtained fast and also numerous variations of the original development strategy could be tested and included.
It can be shown that through optimization of facilities and optimized scheduling for an asset, economical benefits can be found effectively. It tremendously reduces the time for strategy testing of conventional approaches and also updates due to new development strategies as new information is available. Scheduling and an analysis of various drilling strategies allow a further reduction of economics.
It can be concluded that this approach allows more effective testing of strategies for large assets, which involves cross-functional decision making. It allows effective communication and immediately shows possible shortcomings of any strategy which otherwise would be overlooked with any other approach that does not involve everything from the resource itself, to the production system, contractual conditions as well as a representation of the actual field operations.
How Can We Turn Intelligent Energy into Profitable Operations?
Authors: Crompton Jim 
Published: August 2016

Abstract
The Digital Oilfield philosophy has been with us for over a decade now. This philosophy has focused on leveraging the increasing "digitization" of the oil field and the interconnectedness of sensors, process control systems, smart equipment, and smart wells with remote decision support centers. Many innovative solutions have been developed to build upon field automation and real-time data to analyze, model and optimize specific oil field processes. The emergence of new digital technologies shows no sign of slowing. Some argue that we can now go forward from this foundation and focus now on analytics and the theme of PD2A (petroleum data-driven analytics).
The industry is now facing a new commercial reality, low oil prices. It is not business as usual and it may be lower for longer. Aging infrastructure (facilities, wells and pipelines), the "big crew change", increasing regulation and more complex reservoirs all shape the near term future. Developing creative solutions and successfully deploy them using the data we have collected to gain better insights into operational inefficiencies, bottlenecks in our supply chain., and most difficult of all, to help our culture embrace new ways of working that bring out value in terms of lower costs of operations, safer operations and more environmentally benign operations.
In adopting digital oilfield solutions, the industry has come up against a number of barriers including information silos, organization change, lack of trust, skill set shortages and ultimately the difficulty of building a business case for digital oilfield projects. This paper will explain why these barriers exist and will suggest alternative approaches to overcome them.
In difficult economic times with lower budgets and fewer people, the digital oilfield can either be a way to turn data into insights which drive understanding and efficiencies; or it can be a complex source of yet more data which is too overwhelming to make a difference. It is a critical time for the digital oilfield to stand up and create value.
Collaborative Working - Harvesting Value and Evolving with New Technologies
Authors: van den Berg Frans G. , Weiss Cristopher , van Dijk Jaap , Tulalian Robert , Amos Martins , Alharthy Said , Awobadejo Mayowa 
Published: August 2016

Abstract
Collaborative Work Environments (CWEs) were implemented in the majority of Shell's assets, now covering over 55% of the production. Collaborative working has evolved beyond production and surveillance processes, includes new mobility and visualization technologies and supports the cost reduction drive.
The paper provides: Business Value and Lessons Learnt from global CWE deployment;Examples from three assets in different countries and environments;Evolution of the collaboration model;Use of new technologies in mobility, visualisation and exception based surveillance.