bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of Complex Event Processing for Intelligent-Field
Authors: Al-Gosayir Muhammad , Al-Nammi Moayad , Awadh Waleed , Al-Bar Abdullah , Nasser Nasser 
Published: June 2014

Abstract
One of the main challenges for Intelligent Field engineers is handling large amount of data stream in real-time. Finding meaningful patterns among this huge amount of data is just like trying to find a needle in a haystack. Every day reservoir and production engineers are being bombarded with streams of massive amount of real-time data coming from all kinds of Intelligent Field equipment’s including but not limited to Permanent Downhole Monitoring Systems (PDHMS), Multiphase Flowmeters (MPFM), MicroMotion Meters, Wellhead Gauges, Smart Well Completion (SWC), and Electrical Submersible Pumps (ESP). They spend significant amount of time and effort looking at the trends and analyzing the data to find anomalies. Moreover, the existing systems for data cleansing and summarization are based on batch processing, hence, engineers cannot make the right decision on time and they do not have the mechanism to instantly detect interesting patterns as data coming in streams.
The objective of this paper is to share Saudi ARAMCO experience with Complex Event Processing (CEP) as an emerging technology that is designed for low-latency and high-throughput event processing for data stream. This paper addresses the architecture, the implementation, and the benefits of CEP as a solution for Intelligent Field. The implementation will cover three common applications of CEP namely real-time data cleansing, pattern detection, and Event-Driven computation. Data cleansing covers handling out-of-bound, negative, and frozen values. Patten detection enables the detection of anomalous behavior in the data stream, such as unusual buildup or drawdown in well pressure in real-time. Event-Driven computation is trigged by events in the field, such as change in downhole pressure to perform advanced calculation logic, such as average reservoir pressure. Implementing CEP will help reservoir and production engineers obtain clean data in real time and receive notification in case of any significant event detected.
Fuzzy Logic Application to Monitor and Predict Unexpected Behavior in Electric Submersible Pumps (Part of the KwIDF Project)
Authors: Bermudez F. , Carvajal G. A. , Moricca G. , Dhar J. , Md Adam F. , Al-Jasmi A. , Goel H. K. , Nasr H. 
Published: June 2014

Abstract
Implementing asset-wide intelligent digital oilfield (iDOF) solutions is more complex than simply "integrating technology." For iDOF to be truly successful and achieve expectations, concepts from different fields, such as artificial intelligence (AI) or soft computing techniques, must be used to help orchestrate the technology and required functionality to deliver the "intelligence" required to meet the challenges associated with optimizing oil production. Fuzzy logic (FzL) is a type of probabilistic logic that is approximate as opposed to being precise or exact. FzL can have a range of truth values or determine a likelihood of being true.
The process of diagnosis for artificial lift systems has always relied solely on the engineer’s criteria when analyzing data. It is not uncommon for two engineers with similar backgrounds to have, on the basis of the same data, differing opinions of a well problem. It is, however, uncommon that both engineers are either completely incorrect or correct in their judgment, which begs the question as to why this occurs. When traditional binary logic (true or false) is used for well analysis, the outcomes must be either this or that, simply because of the nature of binary logic. Unfortunately, most of the time, the results of well diagnoses, especially when attempting to use a predictive approach, cannot be properly represented with a yes-no approach, which means both engineers would be correct to a certain degree; in FzL, this is called the "degree of truth." FzL incorporates the rationale of having different degrees of truth into the world of well diagnosis and adds tremendous value by raising the issue of different conditions having certain degrees of truth of being in development.
The FzL in this solution was built on a set of worldwide expert rules and converts these rules to their mathematical equivalents. Production operation’s expert rules are used to formulate the conditional statements that comprise FzL (e.g., if the motor temperature exceeds a certain limit, the pump can burn out).
This paper discusses how the AI tool determines trends by calculating a slope for a predetermined period of time for the different sensor signals and compares these sets of trends with pre-established sets of rules. The closeness of the measured trends to the models is expressed as "conditioners" that represent the likelihood of each trend occurring over a period of time.
This technique has proven to be very useful in real-time monitoring for production engineers, helping them to perform fast predictions and pump diagnostics to predict unexpected problems, such as pump wear, gas interference, tubing leaks, viscous forces, and solids-plugged intakes. Several case histories are discussed in this paper. The ultimate goal of this smart flow is to deliver field-level actions that extend pump life and reduce pump downtime.
Applying Analytics to Production Workflows: Transforming Integrated Operations into Intelligent Operations
Authors: Bravo César , Rodriguez Jose , Saputelli Luigi , Rivas Francklin 
Published: June 2014

Abstract
Several industry technologies support integrated operations, such as intelligent completions, real-time systems, surface-sub- surface models, workflow automation systems, etc. Each of these technologies provides relevant data pertaining to one specific part of the asset. The integration, correlation, and analysis of this data (current and historic) help the operator to understand the current state of the asset, as well as make inferences about future behavior. Such capabilities are provided by a set of tools and techniques known within the industry as analytics. Operating and service companies are using new and improved analytics to support oil and gas operations and management processes. Additionally, several analytics commonly used in the foreign market are being applied to industry operations and management workflows. This has led to more robust and effective solutions for oil and gas production operations. However, the analytics value added is limited if implemented in an isolated fashion; the real value is obtained when analytics are immersed within comprehensive production workflows, which aid in analysis, processing, and modeling of the production process. Workflows enhanced using analytics can transform integrated operations into intelligent operations. This paper presents an analysis of the primary analytics techniques and how they have been applied to support intelligent operations. To support this analysis, several application examples of analytics in oil and gas intelligent operations are described, and several case studies of real applications of analytics are referenced.
Social Networking Technologies: Can They Help Increase Production and Improve Operations?
Authors: Morneau Richard , Brink James 
Published: June 2014

Abstract
Approximately $1 trillion in yearly value across the commercial value chain could be created by social technologies according to a July 2012 McKinsey Global Institute analysisi. Believe that? No, we did not think so. The report finds that two-thirds of the potential value can be found in improving collaboration. Within the Oil and Gas industry, we have been chasing and improving on cross-functional collaboration for the past two decades. And, whatever the actual value generation number is associated with social networking technologies, we believe that greater levels of and more timely collaboration can only have a positive impact on production, increased efficiency and more reliable operations.
This paper will take a work process view of production operations and consider where, when and how social networking technologies can be a benefit. But a quick and limited Twitter-type of response has its drawback, which also needs to be considered and explored. We will consider what is and is not a beneficial social network and what constitutes a value- adding social network response.
The purpose of this paper is to cut through the hype and focus on where, when and how social networking technologies can and will increase production and improve safe and reliable operations. While we do not have a yearly dollar estimate associated with value creation, the same McKinsey Global Institute analysis stated: "Most importantly, we find that social technologies, when used within and across enterprises, have the potential to raise the productivity of the high-skill knowledge workers that are critical to performance and growth in the 21st century by 20 to 25 percent." which we think is a worthy goal regardless of the actual dollar amount.
Intelligent Field Infrastructure: Distributed Intelligence and Retention Based System
Authors: Almadi Soloman 
Published: June 2014

Abstract
The continuous technology development in different industries and Information Technologies invites opportunities for porting these capabilities to the upstream domain. Also, the goal of increasing overall reservoir performance and operational environment mount higher pressure on the operating companies. Hence, different attempts are made to explore new solutions, optimize processes, and/or introducing new enhancements. One approach is to establish parallelism between the mature industries such as Downstream and Information Technology in pursuit of Intelligent Field Infrastructure (IFI) enhancements. This shall speed up and increase the flexibility in digitizing the oil and gas fields.
The IFI success in delivering intended objectives requires embedded intelligence at all layers; from sub-surface, surface, and to the engineer’s desk top. A model that maps the intelligence for these layers to ensure a coherent end-to-end system is required. The model depends on hardware, software, and integrated data exchanges that may result in new process and/or altering the conventional operation. In this paper, the authors share systematic approach in the concept of technology parallelism and mapping intelligence at each of the IFI layers. The outcomes resulted in actual enhancements by modifying and introducing new capabilities in the IFI infrastructure’s algorithm, hardware and software. This required close collaboration with the different Service Companies. The final outcome resulted in standard feature capabilities part of the standard Saudi Aramco’s IFI solution for Intelligent Field. Case study with empirical data is used in this paper to demonstrate the enhancement outcomes on the overall operation improvements.
Digital Oilfield Implementation: Learning From the Ghostbusters
Authors: Feineman David R 
Published: June 2014

Abstract
Implementing digital oilfield concepts and realizing value from their mainstream use and operations has been discussed openly for the last six years or more, and there is now some evidence to suggest the value portrayed in early success stories could not be sustained. Case studies from Intelligent Energy Conferences underscore that digital oilfield maturity remains highly variable between and within organizations. This paper attempts to investigate what it takes to improve digital oilfield maturity, and is a companion paper to SPE-167832 which deals directly with what the key attributes of digital oilfield maturity are and its use across a portfolio of projects. To find guidance in how to deliver high digital oil field maturity projects, this paper suggests that some relevant lessons were built into the 1984 supernatural comedy film "Ghostbusters". The movie provides parallels to the issues of digital oilfield maturity related to people, process, technology, strategy, and governance. Insights span issues ranging from moving to higher levels of maturity from a new vision driven business model, adoption of new step out technologies, customer engagement and change management challenges, demands for results in a world of complex operational decisions, all interacting in a political environment.
Assessing the Maturity of Digital Oilfield Developments
Authors: Feineman David R 
Published: June 2014

Abstract
Ten years of Digital Oilfield (DOFF) implementation have left recognizable differences in adoption of the new ways of working and their enabling technologies across companies. These differences, in the state of maturity of digital oilfield activity, exist even though the concept is no longer new. Case studies of successful implementations of one or more DOFF solutions, in both greenfield and brownfield assets, are well documented. Understanding the actual state of digital oil field implementation across a portfolio of projects and assets in a single operator, or across operators today, is almost impossible due to the lack of consistent definitions of what constitutes a digital oilfield. There are differences in the R&D activity between companies in response to specific business challenges, and differences between base level and advanced capabilities needed to support different resource types. This paper examines the concepts, challenges, and benefits for establishing a maturity assessment tool for benchmarking multiple digital oilfield projects. Key insights include: The maturity model concept has been used to understand solution quality in multiple domainsThere is no agreed and consistent DOFF maturity framework or model todayA workable maturity model would have strategic and tactical implications: maturity assessment supports analyzing the gap between where we are against where we want to beMaturity assessment is an evergreen activity: assets that score high in a dimension of maturity today will see that lead erode over time unless they identify and close gaps as part of a continuous renewal process
How IO Leaders Can Use Technology to Enhance Risk Perception and Communication
Authors: Taylor C. , Sarshar S. , Larsen S. 
Published: June 2014

Abstract
Safe, reliable and efficient operation is a common goal for all organizations in the petroleum industry. To achieve this, the organizations must manage the risks inherent in this high hazard industry. Risk management is one example of a capability where leadership can, and does, have an impact on safe, reliable and efficient operation. In fact, poor leadership has been cited as a contributing factor in a number of major accidents, such as the Texas City refinery explosion and the Deepwater Horizon oil spill.
However, in the North Sea petroleum industry, risk management is shared across a number of disciplines and geographical locations, and so it may be difficult for individuals to maintain an adequate picture of risk during everyday activities. Risk identification, analysis and modeling are typically performed onshore by engineers with little or no offshore experience. Meanwhile, the planning of preventative and corrective maintenance tasks, including local Safe Job Analysis prior to execution of the task, is performed by personnel who may have little or no knowledge of the higher-level qualitative and quantitative risk models.
This paper seeks to identify the potential impact of this distributed approach to risk management and the implications in terms of leadership roles and responsibilities. The paper will also examine the challenges for leaders in cultivating and maintaining a good level of risk perception and communication between geographically dispersed teams.
Work is ongoing within the Center for Integrated Operations (IO) in the Petroleum Industry in Norway to investigate the solutions that IO can offer to overcome these leadership challenges. This paper will describe a case study looking at how leaders can use innovative IO software tools to enhance risk perception and communication of risk between the different disciplines and across geographical boundaries.
Overcoming Constraints with a Visualisation Environment; How such a Facility has Begun to Change the Way We Work
Authors: Scholtz E. , Wood L. 
Published: June 2014

Abstract
A quote from Eliyahu Goldratt’s "Necessary but not Sufficient" concisely defines how we should correctly approach the deployment of technology; "Technology is a necessary condition, but it's not sufficient."
The method provides for two types of conditions for the application of concepts, namely necessary (what are the required attributes) and sufficient(the minimum required attributes to qualify). In this discourse, participants jointly exert themselves through a process of ongoing reductive questioning to identify the necessary and sufficient conditions for the submission of a specific concept.
Other than extensive investment in technology, managers must ensure a variety of other aspects are under control in order to ensure that they maximise the business benefit available from their exploration applications (Nor, 2001). This is crucial when deploying a visualisation environment in an exploration department – a facility designed to enhance collaboration and decision making. Goldratt’s mantra applies closely here where the considerable investment in technology is indeed essential, but if treated in isolation will not deliver the expected business benefits to the exploration department, nor the operating enterprise.
"To reap the benefits at the time new technology is installed, we must also change the rules that recognize the existence of the limitation"; Goldratt was stating that the introduction of improved technology must be complimented with a consideration of existing work practices and values, to ensure maximum value is extracted from the improved capability. To this end, throughout the planning and design of PetroSA’s new Collaboration and Visualization Environment (CVE) - the first of its kind in South Africa - this high technology facility was positioned as a change enabler.
This paper describes how the new CVE has begun to embed change in the way geoscientists and drilling teams interact, thereby significantly improving the performance associated with a number of processes in the exploration and operations divisions of this National Oil Company.
Four examples of identified constraints are described in detail, supporting Goldratt’s hypothesis of championing new processes and working practices in order to create demonstrable operational improvement; in this case in a geoscience visualisation facility. Outlines are given as to how such change in working practices was undertaken, the importance of ongoing measurement of effectiveness and how such a change management journey was undertaken with the establishment of a Management Plan, in order to ensure that improvement is continuous.
This paper describes this journey from the Theory of Constraints point of view, whilst conveying the understanding that it is acceptable to know an end goal, but accept that the journey to success will alter depending on several aspects during design and implementation, whilst the goals remain non-negotiable.
Implementation of an Integrated Digital Field Project: Intricacies of Project Management and Change Management Processes for Successful Project Execution
Authors: Al-Jasmi Ahmad Kh. , Choudhuri Amit , Goel Harish 
Published: June 2014

Abstract
Several years back, Kuwait Oil Company (KOC), the National Oil Company (NOC) of the State of Kuwait initiated multiple projects on Kuwait Integrated Digital field (KwIDF). There was a significant amount of planning before launching the KwIDF program, however, the projects involved lots of intricacies and the implementation process involved novel experiences of obstacles and setbacks caused due to introduction of innovative work and business process which were overcome over the project cycle. The paper will delve upon the positive facilitators enabling implementation of a complex project of this nature, lessons learnt from the project and necessities of an excellent change management and project management program to successfully execute the program.
The key drivers for the projects, team structure for program management and lessons learnt will be discussed during course of this paper. There will be a short description of the KwIDF program, technical workflows implemented and production optimization runs along with conceptualization of the new concept of an integrated surface-sub surface workflows.
Building Effective Teams to Enable Project Success
Authors: Lyden John , Zeringue Amy 
Published: June 2014

Abstract
A comprehensive research study conducted by the Construction Industry Institute reported that the two most important contributors to project success are front end loading (FEL) and team development.
FEL has to do with the "hard" or technical aspects of the project. FEL defines the concept for the project, the project economic rationale or feasibility, and the depth and breadth of the project scope.
Team building and team development concern the "soft" aspects of a project — the activities and initiatives undertaken to help a group of individuals work effectively together and produce superior project results. When people are combined into teams, the results can be highly unpredictable. Even teams with great people and great skills can be unsuccessful. The ultimate success or failure of a project is often strongly linked to the way in which project leaders and the project team handle the soft issues. In fact, it has often been said that "the soft stuff is really the hard stuff."
In intelligent energy projects involving experts, team members and practitioners across distance and function, teamwork challenges can be magnified. Finding ways to build alignment, and productive collaboration in a complex project environment can require dedicated effort and, if not done properly, can have detrimental effects on team and project success.
This paper presents: A definition and assessment of successful teamsPrinciples for a well-designed teamTeam development—start with trust, end with trust; everything else goes in the middleTrust always affects two outcomes: speed and cost.Building a collaborative teamAddressing team challengesCase Study: Digital oilfield solution project teamTransportable lessons