bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enterprise SCADA Case Study: Pipeline, Plants, Production
Authors: Seale Steven R. , Cousins Barrett , Hanna Ron 
Published: June 2014

Abstract
As companies working in the oil and gas industry move into the Digital Energy age, they often have to deal with legacy SCADA platforms that fail to meet the challenges of supporting their growing enterprise. Intelligent asset management across a company’s entire network of pipelines, plants, and producing fields requires the acquisition, management, and manipulation of large volumes of data. Without the proper enterprise strategy in place, a company often finds itself with fragmented data and applications all across the enterprise and unusable or unmanageable IT systems. These fragmented solutions often become a liability when trying to manage an asset’s technology, equipment, and people.
A short list of the Technical and People Challenges include: Addressing SCADA systems that historically were managed by I&E field personnelMultiple SCADA vendors, solutions, and versions deployedChallenges associated with the IT responsibilities being outsourced to multiple integratorsA multiple integrator and system approach, creating installations that are unique and independent from each otherAddressing a network infrastructure that allows for enterprise visibilityHistorical data warehousing that was never in scope with future needs
This paper discusses an enterprise approach to SCADA that supports distributed assets across multiple vertical sectors (plants, pipelines, producing facilities). The approach has been implemented in several CO2 enhanced oil recovery (EOR) projects by Denbury Resources, as it solves the requirements for support of the process and business network, data replication, system failover, redundant control centers, and data historization and management across the enterprise. Denbury is deploying the enterprise SCADA system in a growing number of its CO2 EOR assets, which includes both projects that are being converted and new assets being added through the acquisition of properties.
ISA99 Compliant Architecture for Realtime Drilling Data Capture and Remote Monitoring
Authors: Callander M. , Hashim M. , Pickering J. 
Published: June 2014

Abstract
This paper gives an overview of the innovative technology and ISA 99 compliant architecture that was used to implement a high availability realtime drilling data hub on a recent project in the Caspian along with proposed refinements for future deployments.
Real-time monitoring of key surface and down-hole parameters is becoming an essential part of gaining the privilege to operate a drilling campaign. In 2009 a pilot project was started to develop a new architecture to transform the way drilling data was captured and stored. The project was successful and began operation in Q4 2013. The main objectives of the project were: Implement an ISA99 (cyber security) compliant architecture for data capture and remote accessProvide a vendor neutral single source for all realtime and historic drilling data on the platformMake this data available to anyone anywhere within the companyDrive the implementation of WITSML for all interfaces
The objectives were met and the result is a consistent history of all wells that is now forming the basis of learning and continuous improvement. The final solution is based on a virtualised architecture with a fully redundant infrastructure engineered from the ground up to maximise the amount of remote support and minimise the impact on the offshore organisation.
Removing Risk and Cost from Remote Operations through Intelligent Practices
Authors: Dickson David 
Published: June 2014

Abstract
The Arctic is the latest O&G frontier, opening new fields in North West and North Central Russia, Norway, Greenland, Alaska and Canada. The locations imply cold, remote, dark and harsh environments that bring about new challenges to operate in a safe environment while optimizing production and productivity. Furthermore, staffing these assets can provide additional challenges, with increased costs associated with transportation, personnel safety, and often-times high turnover rates.
Vast improvements in operations are possible by adding intelligence locally in the producing asset and linking the asset into an enterprise-wide collaborative work environment. This has the effect of greatly reducing the personnel and systems footprint on remote platforms, wellheads and subsea; thus removing risk and cost from operations while improving reliability in such environments. Furthermore, these environments enable institutionalization of best practices between assets, with the goal of true knowledge management
We will examine the latest advances in Remote Operations and Remote Collaboration infrastructure and tools and examine the benefits that they bring to operating companies. We will also articulate how these concepts can be incorporated into the project design cycle to improve delivery and how this can influence the supplier / operator relationship during the commissioning phase, but also over the entire life cycle of the asset.
Samarang Integrated Operations (IO): Well Performance Workflows Enable Continuous Well Status and Performance Monitoring
Authors: B Ahmad M Zul , Sayung Colinus Lajim , Wong Lee Hin , Salim Muzahidin Muhamed , Mohd Som Mohamad Kasim , Kurniawan Rudy , Biniwale Shripad 
Published: June 2014

Abstract
Samarang field is located offshore Sabah, East Malaysia with first oil in 1975. Majority of the platforms are provided with fully pneumatic instrumentation / control and shutdown systems, with no provision for remote monitoring/control. This has caused limitation in acquiring data for monitoring and analyzing hence optimizing the asset. The field is undergoing a major redevelopment project with Integrated Operations (IO) being part of initiatives that covers re-instrumentation, telemetry and telecommunication infrastructure installation, Collaborative Working Environment construction and Workflows implementation.
The limitation of status quo instrumentation situation has posed challenges for Samarang asset to monitor flowing status of the wells. Consequently, a number of indirect inferences were being used to determine the status and uptime of the wells including sporadic well head observation (i.e. "feeling" or "touching" the well head assembly for vibrations) and well head fluid sampling. Added to the complexity is the flow from multiple strings and wells commingling in production headers in each of the well platforms, it is not uncommon to have cases wherein the individual string or well’s actual status and production contribution is unknown for many days (or even weeks). At best, Well Testing, which is a direct measurement of the well or string’s deliverability, is carried out once a month.
This paper describes a novel methodology of implementing workflow to achieve continuous well status monitoring objective. Workflow that combines the data feeding and processing through data driven logics as well as well models driven logics has enabled continuous well status awareness. The workflow has enabled the followings: Continuous well rate estimation using nodal analysis well model by automating data input and result output processWell flowing or quitting status inference by using wellhead pressure, temperature, valve status, gas lift injection pressure, MIG, pump voltage and frequency for natural, gas lifted as well as ESP wellsAutomated notification of well status through Tri-node Collaborative Working EnvironmentFaster reaction and decisionReduction of production deferment and increase awareness to the well status and performance
Real-Time Surveillance: How System Integration Allows One Company to Minimize Deferment, Optimize Production, Maximize Test Unit Capacity, and Track the Operating Envelopes of its Wells
Authors: Al-Balushi M. , Konwar A. , Abdullah Z. , Sahu S. , Briers J. 
Published: June 2014

Abstract
A key issue in upstream E&P operations is how to manage wells and reservoirs more effectively. Oil and gas production from onshore and offshore wells can be relatively difficult to manage, not only because of multiphase nature of the well production, but also due to lack of a continuous, reliable measurement of well/reservoir performance. Reporting continuous production from the wells and performing hydrocarbon allocation with credible accuracy is an area with many challenges in upstream E&P operations.
Shell’s Production Universe (PU) is a novel software application developed by Shell to address some fundamental gaps in the management and surveillance of oil and gas production systems. Using data driven models, it is designed to estimate and optimize the production (and injection) of wells continuously in real-time, based on the available wellhead instrumentation. PU provides improved well surveillance, more accurate hydrocarbon accounting, and automatic production reporting and production optimization. The data from PU can also be used for quick analysis to safeguard the technical integrity of wells and reservoirs. It is also cost-effective in that it requires minimal commodity instrumentation and IT systems, much of which may already be present in field operations.
Brunei Shell Petroleum (BSP) in South East Asia had adopted the PU technology just after it was launched in 2004. Today, BSP continues to deploy PU in all of its assets. Implementation of PU in BSP has helped in exception based surveillance, analysis and troubleshooting; and hence has helped optimized resources being directed towards production optimization activities. It has also improved data quality and reporting.
This paper discusses how Production Universe has been instrumental in helping BSP in the above operational and production areas. In particular, the application of PU in various facets of WRFM (Wells, Reservoir and Facility Management) to achieve operational excellence is discussed.
A Shared Asset Model to Enable Navigation of Data in Continuously Changing Production Assets
Authors: McKenzie W. , Segura D. , Hollier M. , Crawford M. L. , Iype T. , Ormerod L. 
Published: June 2014

Abstract
The Shared Asset Model (SAM) is a software system specification that enables the discovery and navigation of information about producing upstream assets. It is part of the PRODML industry standard. Using a SAM makes it possible to store and discover asset information, such as: a registry of all the components that comprise the asset (e.g., wells, pipelines, etc.) together with their identities; multiple hierarchies of such components; the nature, location, and identity of additional information about these components (e.g., well completion data), and representations of the various flow networks in which the components may be connected.
For a typical producing asset, data exists in many diverse systems, for example, production allocation and accounting, well services, well modeling, surveillance, completion data, etc. Discovering and then integrating this data can be a significant overhead and can form a barrier to the exploitation of best practices and digital oilfield (DOF) innovations. Furthermore, a producing asset is rarely static. The configuration either of the real asset or of the data systems supporting it change frequently. This dynamic nature makes the task of sustaining any DOF system reliant on data a continuous and intensive process.
A SAM acts as a gateway into asset data, presenting the user with a consistent and easy-to-navigate interface to discover details about the asset and to be "directed" to its underlying data. The specification and testing of SAM capabilities demonstrate that it satisfies the needs of many common workflows in terms of managing configuration and accessing data.
The SAM specification is expected to be widely deployed, either through a new generation of software applications or as a "façade" or gateway into existing systems. It will enhance DOF projects in terms of deployment speed, reliability, flexibility, and cost reduction over the full life cycle.
Advances in Integrated Environmental Monitoring
Authors: Wesenberg Harald , Hepsø Vidar , Hermansen Anders 
Published: June 2014

Abstract
The term Integrated Environmental Monitoring (IEM) refers to the tight coupling of sensor based real-time environmental monitoring with day-to-day operations. In that perspective IEM extends the traditional meaning of the term environmental monitoring by including discharge control, leak detection and remote sensing. Using offshore operations as an example, environmental monitoring has often been perceived to mean off-line sampling of seabed sediments and the water column during annual and triennial campaigns by ship. Whereas such monitoring has its place in a total monitoring programme, advances in technology and methodology open up for more flexible and real-time sensor based monitoring which can be tied closely to daily operations in the oil and gas industry.
Statoil has been working for the past few years to develop an integrated approach to environmental monitoring (IEM). Now, the first version of the software, developed together with partners, is ready for use during drilling operations to monitor dispersion of drill cuttings and resulting risks to cold water corals and other sensitive areas subsea. Both the planning, execution and post-operation phase of environmental assessment related to such operations are covered. In this paper we take a look at the key components of the system (sensors, data integration, models/analytics and presentation) and review the implications for future drilling operations in sensitive areas.
Decision Support for Daily Production Optimization: In Genuine Real-Time
Authors: Ursin-Holm S. , Sandnes A. , Gunnerud V. , Foss B. A. , Conn A. R. , Zenith F. , Teixeira A. F. 
Published: June 2014

Abstract
The presence of a complex bottleneck structure makes it non-trivial to find the best production strategy, and the competence of the production engineer makes a significant difference. Over the last decade, the number and quality of computer based tools to support the production engineers in their decision, has increased. The contribution from such tools can be divided into two.
The current state of the system– valuable information about the current state of the production system is often not easily accessible through measurements. A prime example is the flow of oil, gas and water from each well. Software able to estimate flow rates from each well with acceptable real-time accuracy are very valuable to the production engineer.
Improving the systems performance– the greatest value of knowing the current state of the production system is that it acts as a platform for improvement. However, knowing your current state is not enough. One also needs to know how the effect of changes, for example in a choke valve, affects the bottlenecks of the system. These effects could be complex, and only relying on human interpretation when searching for the best production strategy is clearly suboptimal, and indeed, sometimes dangerous. Computer based optimization systems that are able to assist the production engineer in these decisions, are therefore extremely valuable. Unfortunately, the time needed to compute recommendations has until now often been prohibitively high and thus impractical to efficiently contribute to the production engineer's workflow.
This paper discusses challenges and opportunities related to the development and use of such advanced online decision support tools. It highlights the potential of fast response times that enables totally different workflows, as a result of the fact that instant re-optimizations could be conducted in a well optimization decision meeting. The discussions are supported by presenting two most promising contributions from the IO Center, within this domain.
Closing the Integration Gap for the Next Generation of Drilling Decision Support Systems
Authors: Nybø Roar , Sui Dan 
Published: June 2014

Abstract
This paper identify the lack of integration between drilling decision support systems and their users as a barrier towards better decision support and increased drilling automation. In part one of this paper we outline the workings of a next generation of decision support systems that reduce this gap. In part two we present our preliminary results with a time series anomaly detection technique, which the decision support systems we have described would require.
Enhanced Data Quality and Lowered Risk Utilizing Remote Operations
Authors: Thorsen Arve K. , Saeverhagen Erland , Svensson Inge 
Published: June 2014

Abstract
The development of remote operational centers has improved data quality due to the increased focus on data acquisition and real-time usage of data.
Data quality has different connotations to various participants in the oilfield. Data streaming and continuous flow of data is the main focus for the technical part in delivering and receiving data. Scientists focus on the accuracy of each data point. Most surface sensors measure milliamps with calibrations for accuracy. Reservoir measurements have porosity and permeability as the main reservoir properties, values that are not measured directly and derived from other sources.
In automation processes data streaming must be flawless. Through remote operations the surveillance of data streaming quality and accuracy is performed. In most areas the response time on the network is too narrow to stream data from the well to a remote location and to then stream a command or solution back for full closed-loop control.
During the acquisition, aggregation, distribution and finally visualization there are room for changes in the data point s. These changes are from uncertainty, stacking, filtration, unpacking, transmission, and any other data handling process for data sharing.
For the digital oil field, data are evaluated in two different settings, real-time and after the event. The interpretation is performed either remotely or at the wellsite. There is room for improvement in all areas, depending on objectives in the process: -Automation in the operational phase-Interpretation based on a model update-Automated quality control
This paper illustrates the differences and similarities between real-time operations and processes performed on the data later and how combined local and remote operations enhance data quality. We will follow up by making improvement suggestions in all areas moving into the digital oil field.
Enhancing Wellwork Efficiency with Data Mining and Predictive Analytics
Authors: Sidahmed Mohamed , Ziegel Eric , Shirzadi Shahryar , Stevens David , Marcano Maria 
Published: June 2014

Abstract
Effective well management and a productive wellwork program are valuable and integral business objectives. Wellwork involves various well interventions and optimisation activities for enhancing and extending hydrocarbon production. These remedial processes involve substantial CAPEX and OPEX, as well as other resource allocations.
Failure to prioritize objectives and improper selection of candidate wells can have significant implications on both derived value and potential risk. A primary challenge is to ensure that wellwork is delivering production growth while maintaining cost efficiency. Well-by-well reviews with actionable decision support information will provide the best method for identifying potential production improvements. The selection and prioritisation of candidate jobs is a critical investment decision.
This paper addresses the business problem of reducing the uncertainty of well work program outcomes’ so that more informed choices can be made from all the options such that the benefits and value of an overall well work program is enhanced and optimized.
It illustrates the use of data-driven models for estimating key performance indicators for wellwork jobs and predicting the likely outcome for a new planned job using pre-determined success criteria. Nine different machine learning and advanced analytics learning schemes were applied to the training dataset of wellwork history. The competing models performance was evaluated on a separate validation data set for a balance between best fit and prediction accuracy.
The application of developed models provided intelligence augmentation for the decision-making process. This methodology embeds learning from past wellwork activities to streamline and guide complex workflows. The business value for embedding quantitative predictions into strategic and operational decision-making processes is realized in reducing less-favorable investments and maximizing the value of wellwork.
Collaborative Work Environments Deployed at Global Scale
Authors: van den Berg Frans G. , McCallum Giles A. , Graves Matt , Heath Elizabeth 
Published: June 2014

Abstract
Collaborative Work Environments (CWEs) have provided significant benefits to Shell in production, field management and maintenance. The CWEs enable faster and better decision making by improved communication and easier sharing of relevant information and data. Business benefits include increased production, higher equipment reliability, lower operating cost and improved HSE. Considerable learning was derived that helps future implementations and ensures sustainability.
The Integration of Drilling Sensor Real-Time Data with Drilling Reporting Data at Saudi Aramco using WITSML
Authors: Khudiri M. , James J. , Amer M. , Otaibi B. , Nefai M. , Curtis J. 
Published: June 2014

Abstract
The Wellsite Information Transfer Standard Markup Language (WITSML) is a global open standard for the exchange of geotechnical data in the upstream oil and gas industry.
Saudi Aramco has embarked upon a project to implement the transfer and storage of all wellsite real-time data using the latest released WITSML specification. This project is called the Real-Time Data Transmission and Viewer Service (RTDTVS). Through this project the data from all rigs will be captured and transmitted back to the Drilling Real-Time Data Hub (DRTDH), which runs on an Oracle platform with triple redundancy.
Drilling sensor data has not in the past been transmitted back for monitoring purposes; instead, Saudi Aramco has previously left the drilling contractor to manage the drilling process. On exploration wells a mud logging contractor is often used, and real-time data has been available to the different asset teams; but on development wells, a mud logging contractor is not used, making the need to capture the drilling sensor data vital not only for safety monitoring, but also for the optimization of the drilling process to reduce well drilling costs.
To make accurate calculations of drilling performance indexes it is necessary to have access to some information that is typically not transmitted as part of a real-time data stream, but is available instead through daily reporting and other mechanisms. This paper discusses how the use of the WITSML Open Standard has enabled drilling sensor data stored in the DRTDH to be integrated with report data available through the Saudi Aramco Drilling Knowledge Database (SADK) to provide more efficient and safer drilling practices.