bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collaborative IE Inside and Across Functional Silos - A Software Solution that Really Works
Authors: Fjellheim R. , Lohne H. P. , Sivertsen S. , Sølvberg A. , Thorsen T. 
Published: August 2016

Abstract
Intelligent Energy solutions have been successfully adopted by many operators with documented benefits. Still challenges remain, such as weak cross-discipline collaboration, sub-optimal decision making, and an inability to effectively learn from experience. Hence, enterprise IE - achieving better collaboration across discipline silos - remains an important challenge. "Top down" ways of obtaining enterprise IE include co-locating function teams, integrated planning, business process management, and information management and standardization.
In this contribution we describe a pragmatic "bottom up" software solution that supports collaborative IE on an enterprise level, serving both departmental functional silos and their need to coordinate with other parts of the organization (both onshore and offshore). The solution builds on well proven enterprise social media technology, augmented with a carefully designed set of integrated collaboration tools. The latter include case management, information sharing, document management, task and workflow support, Outlook/Lync integration, decision support, and more.
The solution was successfully deployed at the onshore operations center of a major North Sea field. We provide details and experience from rollouts in three separate but interlinked areas:
– Monitoring the condition of offshore gas turbines and supporting maintenance decisions – Supervising drilling operations, logging and following up well interventions – Managing and optimizing production from a large multi-well field
In each case we describe how the IE solution supports the natural way of working of the involved personnel, making their work day more productive and enjoyable. We also describe how the solution enables improved coordination on a practical level across functions, e.g. when maintenance decisions need to be coordinated with production plans. Finally, we assess operational and business related benefits of the IE solution, and discuss possible future extensions of our work.
Real Time Decision Making - Incorporating Dynamic Risk Management
Authors: Hartigan Andrew , Thrasher Darryl , Adlam Robin 
Published: August 2016

Abstract
This paper introduces Dynamic Risk Modeling and reports on a 2016 proof-of-concept demonstration of the emergent capability. While Dynamic Risk modeling can apply in any high risk environment, the demonstration focuses on the High Pressure, High Temperature (HPHT) drilling environment and uses 2010 Macondo cumulative risk as a demonstration scenario.
Dynamic Risk Modeling integrates the effect of human decisions with technical conditions; it enables three evolutionary capabilities in risk management: The awareness of cumulative and current risk based on input from multiple points and times of originThe quantitative insight of the coupling effect of multiple hazards when barriers or controls change conditions or failThe incorporation of real-time data into risk awareness, management and decisions
The paper demonstrates enhancement of the digital oilfield by applying real-time data to predictive decision making. The model receives continuous well data, processes it through the model with other environmental and human factors, and translates it into real-time risk awareness and insight.
The demonstration of Dynamic Risk Modeling is applied to the Deepwater Horizon drilling environment in April, 2010 to show how a real-time representation of current and cumulative risk could have aided decision makers with critical information and insight.
Big Data Analytics for Prognostic Foresight
Authors: von Plate Moritz 
Published: August 2016

Abstract

Objectives/Scope
With the rise of big data analytics in petroleum engineering, a plethora of data collectors, sensors, transmission devices, and software tools is entering our lives at the personal and professional levels. Data is extracted, transferred, processed, and utilized - be it from wearable devices, refrigerators, or motorcycles. It also spans areas such as public facilities, transportation networks, and major industrial assets.
In the sphere of petroleum asset management data analytics is certainly not a new concept. For decades, equipment condition data has been explored and exploited. Internal data networks, repositories, and historians, along with the condition monitoring, predictive diagnostics, and performance optimization applications show a long tradition of utilizing data to gain useful insight.

Methods, Procedures, Process
Current asset management technologies can identify what is going on, where, why there are challenges, and how they can be resolved. Yet, new prognostic analytics emphasizes data-driven forecasts addressing "when" questions, such as "When will my compressor have a malfunction?". Presently, such forecasts have no real proxy in petroleum asset management. The questions "When will my bearing fail?", "When is my last chance for plant revision?", and "When will turbine replacement be cheaper than refurbishment?" are widely asked but rarely answered.

Results, Observations, Conclusions
We will review herein which major challenges prognostics faces in the petroleum industry sphere, how they are currently being resolved, and what benefits can be achieved. We present, in addition, a detailed example of an application of prognostics in a petrochemical manufacturing plant. Condition and process data of a cracked gas compressor is utilized to generate remaining useful life distributions and future malfunction risk profiles. The main objective of the operator is to reduce both downtime and maintenance costs.
Smart Oilfield Safety Net: An Integrated System for Prediction of Asset Integrity Opportunities
Authors: Killen N. , House R. , Sankur V. , Thigpen B. , Chelmis C. , Prasanna V. K. , Neumann U. , Raghavendra R. , Yao K. , You S. , Ershaghi I. 
Published: August 2016

Abstract
We present an integrated system that automatically collects historical and current data from heterogeneous sources, performs analytics to identify telltale signatures of Loss of Containment (LOC) events, and makes asset behavior predictions as an asset ages. Our semantically enhanced system of record stores and manages heterogeneous data introduced by a variety of collection mechanisms and frequencies, and also captures subject matter knowledge and experience to automate LOC prevention decision making. Analytics provide a "learning by example" platform; LOC patterns can be quickly identified even in new facilities by utilizing other facilities LOC facts. Machine learning techniques coupled with rule-based systems and advanced visualization environments can not only determine an asset’s health, but also minimize labor intensive and error prone data comprehension and decision-making.
Reservoir Uncertainty Analysis: The Trends from Probability to Algorithms and Machine Learning
Authors: Ani Maureen , Oluyemi Gbenga , Petrovski Andrei , Rezaei-Gomari Sina 
Published: August 2016

Abstract
For over fifty years, reservoir development around the world has covered different reservoir types and environments with vast technology, expertise and a growing variety of approaches. However, the predominant challenge from which a myriad of other field development issues arise has been on how to accurately characterise reservoir parameters because the obtained results are largely associated with uncertainties due to subsurface geological complexities.
This paper focuses on the evolving advances and current practices in reservoir uncertainty modelling and gives insight into the future trends. This work critically examines the foremost statistical reservoir uncertainty analysis approaches, the current probabilistic and stochastic uncertainty modelling workflows which are typically based on various numerical models, and the very recent development of embedding some artificial intelligence algorithms (which include genetic algorithms, artificial neural networks, Bayesian networks amongst others) in reservoir uncertainty modelling, which now points to a future of using more sophisticated machine learning systems for achieving reservoir models and parameters with higher confidence.
These evolving trends and approaches are discussed in more detail in this paper; with an in-depth analysis of the associated workflows, fundamental principles, strengths, weaknesses, robustness and economics of each approach. Also, reconciliation between the statistical, probabilistic, stochastic and artificial intelligence methods present a deep insight into the prospects of using artificial intelligence for optimising the modelling of reservoir uncertainties beyond the capabilities of conventional methods. Thus saving time and cost by quantifying the uncertainties in reservoir properties as well as regenerating new best-fit reservoir attributes using the robust uncertainty analysis networks and the pattern-recognition ability of machine learning networks.
Hence, this paper presents a comprehensive review of the various uncertainty analysis methods, and also analyses the confidence of artificial intelligence applications which are increasingly pushing the frontiers to improved uncertainty modelling.
Data to Language - Leveraging Software to Understanding and Communicate Well Integrity Risk Throughout the Organisation
Authors: Mundie Gavin , Wilson Sean 
Published: August 2016

Abstract
It is inevitable that during a wells life cycle there will be failures or degradation that introduce risk. By applying process safety principles, there are three questions that an operator should ask themselves:Do we understand what could go wrong?What systems remain to prevent this happening?Are we getting the right information to assure us that these systems are working effectively?
In terms of well integrity management, questions one and two are dealt with in major accident hazard reviews, leading to well designs that ensure there are adequate barriers between the hazards in the well and the environment.
Question three is managed by the verification and assurance of the identified barriers and systems that are aligned with corporate or industry performance standards.
This paper suggests a fourth question:
4. Is risk communicated to the relevant people to ensure effective decisions are made, and that the risk is understood and mitigated?
Traditionally, a well failure model approach can be used to optimise the risk assessment process with recommended response times to repair specific failures. However, it does not account for the whole asset and the overall level of risk is posed to the installation. Further, the scoring methodology needs to be understood by a specialist and aligned with the corporate risk matrix used by asset managers.
This paper describes how software systems can be leveraged to provide effective communication in the organisations "language of risk" and helps to visualise the accumulation of risk from wells that are aligned with the corporate matrix. This small step change vastly improves the understanding and communication of well integrity risk throughout the organisation enabling installation management early challenge, better resource utilisation and the monitoring of creeping accumulative change.
A Case Study on Remotely Operated Intelligent Bridge Plugs for Plug and Abandonment Operations
Authors: Arackakudiyil Zac Suresh , Thomson Barry , Førde Carl Henning 
Published: August 2016

Abstract
This paper demonstrates a new method of using remotely operated intelligent bridge plugs to replace conventional plugs, thereby eliminating the need for multiple wireline runs. This reduces operational expenses and eradicates potential health, safety, and environment (HSE) challenges resulting from additional personnel involvement in the pressure control equipment rig up and testing.
Use of traditional plug-and-prong barriers and retrievable bridge plugs is integral in plug and abandonment (P&A) operations. However, multiple wireline runs are required in wells with considerable gas buildup.
The well in this paper had a considerable amount of gas buildup in the annulus that required periodic circulating. Use of an intelligent bridge plug offered the potential to open and close the plug remotely because the plug uses onboard decision-making electronics that monitor well condition. The plug is programmed to either open or close whenever a specified condition (known as a trigger) is detected.
The intelligent bridge plugs allowed the operator to communicate with the tool remotely from the surface using applied pressure commands, and the plugs were opened and closed periodically to circulate the gas buildup out of the annulus. The plugs were later closed remotely to act as a barrier while the christmas tree was removed and the blowout preventer (BOP) was installed. The plugs were finally opened and pulled along with the tubing, saving further wireline runs.
The operator was able to circulate all of the gas buildup in this mature well and continue the P&A operation. The well discussed in the case study used the intelligent plug to remotely open and close nine times. This saved 18 wireline runs in comparison to a conventional retrievable bridge plug. Considerable financial savings were achieved by removing intervention runs, and HSE risks involved with rigging up and running wireline were also eliminated in this case. Because of the benefits provided by using the intelligent plug, the operator plans to perform future P&A operations without mobilizing a wireline unit to the rig.
The industry is beginning to experience an increase in P&A operations. Minimizing costs without sacrificing safety is of utmost importance. The groundbreaking use of remotely operated intelligent bridge plugs for P&A operations could be a timely supplement to existing solutions.
Integration of Petroleum and Reservoir Engineering Workflows in Production Database Management Systems
Authors: Talbot David , Jeffs Paul , Hyde Andrew , Kohok Abhimanyoo , Rossi David 
Published: August 2016

Abstract
The Breagh tight gas field is located in the Southern North Sea and currently delivers gas from 8 wells via a normally unmanned installation (NUI) to a processing facility in Teesside. In the coming years there is scope to install a second NUI and drill an additional nine wells. Platform and onshore sensors deliver high-frequency pressure, temperature, and flow rate data for each well, in addition to key facility data.
The high volume and variable quality of this high frequency and real time data directly affects the way in which daily engineering activities are performed. Originally, the engineering team used conventional methods of data handling, storage and management, including manual manipulation, and spreadsheet computations. This was a time consuming exercise which led to excessive replication of data and a lack of audit trail behind the data used for any analysis.
Allocation of field production to individual wells is a key production engineering workflow. The flow rate data is the prime input for every piece of analysis conducted by the reservoir engineering team; decline curve analysis, rate and pressure transient analysis, material balance and history matching the numerical simulation model. The results of this analysis directly impacts upon routine production strategy & optimisation, workovers, well planning, future development strategy and reserves. The platform Venturi flow meters have proven unreliable throughout the production life of Breagh, leading to the evaluation of several alternative allocation methods, all of which are manual and time intensive.
This paper presents workflows for production data management and production allocation. Various production allocation methods are used and this paper demonstrates the impact of data accuracy on long-term development decisions, and short term interventions due to flow assurance issues. Thereby, the asset team can shift from a reactive, to a proactive position and generate value for the business.
Smart and Automated Workover Candidate Selection
Authors: Zangl Georg , Neuhofer Lisa , Zabel David , Tippel Philipp , Pantazescu Cosmin-Ionut , Krcmarik Vladimir , Krenn Lisa , Hachmöller Barbara 
Published: August 2016

Abstract
A probabilistic expert system assisting workover candidate selection and increasing the technical and economic success rate of workovers, has been developed in cooperation with a major Central-European operator.
In order to streamline and standardize the workover candidate selection process across 240 fields using a corporate-wide software solution, expert knowledge, data analytics and already existing workflows and methods have been integrated. The underlying logic is capable of detecting a comprehensive set of well integrity and deliverability problems for active and shut-in wells.
A problem detection logic based on historic workover performance provides risk, cost and success estimates. Well production gain through proposed workovers is calculated automatically based on reservoir and neighbourhood potential. Reservoir and well performance KPIs and NPV calculations enable flexible ranking of candidates with regard to technical and economic aspects.
This paper describes the applied procedures and methods, like data analytics, machine learning and reasoning tools like Bayesian Belief Networks. The system additionally integrates petroleum engineering and knowledge discovery techniques. The screening logic works as a repeatable and automated process that can be scheduled or be executed on demand. The screening of several thousands of wells takes less than an hour. Hence the process can be executed more often and much quicker, leading to an intensified monitoring of possible business opportunities.
Over the last 2 years, several studies in more than 30 fields - with a total number of 7,600 wells - representing a wide variety of reservoir and well characteristics have been performed. In the course of these studies and in independent blind tests the correct and precise functionality was successfully tested.
The smart and automated workover candidate selection process leads to reduced costs, helps to remove non value adding work through automation and improves the overall workover success.
A Step by Step Approach to Improving Data Quality in Drilling Operations: Field Trials in North America
Authors: Ashok Pradeepkumar , Ambrus Adrian , Ramos Dawson , Lutteringer John , Behounek Michael , Yang Yueheng Lisa , Thetford Taylor , Weaver Terrence 
Published: August 2016

Abstract
Multiple literature studies have indicated that a significant amount of data collected during drilling operations is unreliable. To move towards better data quality, two critical hurdles need to be overcome. First, the case for the value of good data needs to be made, so that resources can be allocated towards improving data quality. Second, a process needs to be established within the operator company to measure and improve the quality of data. This paper is a case study in addressing these challenges. In this work, we focus on eight core surface sensor measurements essential to drilling operations (block position, hook load, rotary speed, rotary torque, pump strokes per minute, flow rate out, standpipe pressure and pit volume) and attempt to assess/improve their quality. The first step involves identifying how much of each measured data deviates from their accepted values. This is most economically accomplished using automated data validation software. Once the root cause is identified, steps can be taken to rectify the problem. Four rigs in North America were identified for this trial conducted over a six-month period. The goal is to establish a data quality improvement loop that continually accesses data, identifies issues, and implements corrective actions. This paper explains this process and how it has been applied to improve the quality of drilling data.
Advanced Decision Support for Deepwater Offshore Drilling Incorporating Insights from Nuclear Power and Aerospace
Authors: Nelson William R. 
Published: August 2016

Abstract
Experience gained from catastrophic events such as Three Mile Island, Challenger, Columbia, and Fukushima has highlighted the critical importance of effective decision making to assess the situation when abnormal conditions are first detected and to formulate an effective response to terminate the event and minimize the consequences. A common thread across these incidents has been the failure to recognize that critical safety barriers have been defeated.
A systematic approach for decision support has been developed using the concept of dynamic barrier management. This approach combines bow tie diagrams from the offshore industry with the success path concept from the nuclear power industry. By combining bow tie diagrams - which provide information about the barriers that can intervene in the progression of an accident, with response trees - which provide information on actions needed to maintain or restore the barriers, a comprehensive, robust approach for dynamic barrier management can be realized.
This paper summarizes lessons learned from major accidents in the nuclear, aerospace, and offshore industries. The dynamic barrier management approach for decision support is described, and experience from offshore drilling and production applications is summarized. Decision support for dynamic barrier management combines effective methods from the nuclear power, aerospace, and offshore industries to form a powerful approach for increasing offshore safety.
How Do We Accelerate Uptake and Fulfill the Value Potential of Intelligent Energy?
Authors: Gilman H. , Lilleng T. , Nordtvedt J. E. , Unneland T. 
Published: August 2016

Abstract
The objective of this paper is to identify ways to accelerate the uptake and fulfill the value potential of Intelligent Energy (IE). The paper is co-authored by a cross-industry group drawn from operators, service provides and product vendors, all of whom have been involved in the IE arena for 10 years or more. We have analyzed the past experience to identify ways in which IE has been successful, and improvements that could be made to add value across a broader scale amid the challenges of today's commercial environment. In this paper, assessments are given on IE implementations to identify practical ways in which we can expand deployment and deliver results more quickly, including the importance of collaboration and competition in the IE domain, and how longer-term business models and new organizational ideas could improve the industry's uptake of IE. We have identified two areas where we believe changes to our approach could deliver significant benefits, through the expanded use of integrated workflows and shared Subject Matter Expert services. We discuss the benefits and challenges of this integrated approach to solution design, work processes, technology, skills and competencies. Field cases from two major operators are given as best practice examples on advanced use of Intelligent Energy in the oil and gas industry.