bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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

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

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

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

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.
A Successful Approach to Developing Digital Oilfield Opportunities
Authors: Wiemers Suzan , Kocian Eric , Wright Andrew , Stewart Steven
Published: June 2014

A successful approach to developing digital oilfield opportunities in asset management for specific production sites has been achieved with early engagement of asset leaders and cross-discipline engineering and operations work forces that directly benefit from enabling new capabilities. After workflow limitations are summarized, vetted and prioritized, a vision for technology-enabled improved asset performance is developed based on identified opportunities. An execution plan is agreed upon and a multi-discipline steering team comprised of members from the production unit, the asset team and digital technology advisors stewards the effort to ensure a satisfactory deliverable.

Critical success factors to ensure digital technologies are embedded and sustained include: 1) supportive leadership, 2) targeted review of asset business drivers and technical engagement practices, 3) comprehensive change management plan for work transformation and new behaviors, 4) robust user support model, and 5) process for identification and capture of key performance indicators, including benefits reported by users. Successful results of this ‘targeted business case approach’ are shared with other production sites to enable quick-wins for other assets. Learnings from each implementation experience are assessed and used to improve deployment at the next site and achieve a strategic global deployment model.

This paper will examine digital technology implementations where business leaders were engaged to improve targeted business cases for improved asset surveillance. Benefits, challenges and learnings from experiences to develop digital oilfield opportunities will be highlighted.
Reservoir Simulations in a High Performance Cloud Computing Environment
Authors: Eldred Morgan Edward , Orangi Abdollah , Al-Emadi Ahmed Abubakr , Ahmad Asma Aboubakr , O"Reilly Thomas James , Barghouti Nedal
Published: June 2014

High Performance Cloud Computing (HPCC), which is mature in many industries, is not a new technology, but rather a new method of delivering two resource components of storage capacity and computational power. Within the upstream area of the oil and gas industry, Cloud Computing is very immature, as the industry as a whole has always been challenged by storage and computational capability. However, there is recent evidence to consider using HPCC due to the promise of several benefits, such as flexibility, accessibility and cost reduction (pay-per-use). HPCC can be categorised into private, public or a hybrid model.

Scenario-based modeling, the probabilistic approach for uncertainty analysis, risk quantification for existing brown field reservoirs and new perspective green field reservoirs all require a significant amount of computational power and storage capabilities. HPCC may create an opportunity for small to mid-size upstream companies that do not want to invest in infrastructure needed for evaluating scientific applications.

In this paper a pilot project was commenced, where Amazon Web Services was used as a public HPCC, with an industry standard reservoir simulation software, known as Eclipse has been used. Multiple simulation cases have been launched to the cloud while transferring large amounts of data between simulated offices. The project involved designing and developing a secure lean agile technology model, which was software vendor agnostic. It was believed that this would drive efficiencies and reliability by being able to dynamically scale up or down computing clusters depending on needs.

This paper presents the finding of the project and presents a recommended development methodology for creating both tactical and strategic roadmaps for leveraging trends in HPCC to further unlock potential in the industry by driving innovation and business value.
"Smart Decision Making Needs Automated Analysis" Making Sense out of Big Data in Real-Time
Authors: Crockett Brian , Kurrey Kshitiz
Published: June 2014

Problem Statement

Due to the availability of large quantities of real-time data, many operators are faced with the challenge of extracting meaningful information from it. In many cases, analysis of the data, the updating of models for Wells, Reservoirs and Facilities requires human intervention. When WRFM teams do not have analysed information, there are delays in the decision making to process required for optimisation.

Objectives

To demonstrate how auto analysis can reduce time consuming and repetitive tasks of comparing real time data with operating envelopes. Exception based applications and the updating of multiple operating envelopes and models without human intervention, enables the analyst to concentrate on anomalies and opportunities to optimise. The constant visualisation of analysed information should assist the decision making process.

Method

Results

Conclusions

The analysing of data by automated methods means that all wells and facilities are under surveillance and updated models are available for production system optimisation. Analysts have more information and time to focus on critical issues rather than mundane tasks.

Applications

This approach can be used for wells, reservoirs and facilities surveillance and model updates.

Innovations

Significance of Subject Matter

This demonstrates how very large amounts of data can be analysed and translated into meaningful information for managers to take key decisions.
A Functional Networks Softsensor for Flowing Bottomhole Pressures and Temperatures in Multiphase Flow Production Wells
Authors: Bello Oladele , Asafa Tesleem
Published: June 2014

Many production and injection wells completed with intelligent (smart) systems are operating around the world. These wells are more economical and efficient than conventional wells. Computational tools for predicting bottomhole flowing pressure and temperature can provide valuable information for engineers and operators when making decisions regarding the use of production optimization technologies and reservoir management operations in the future. As such, improved predictions of bottomhole flowing pressures and temperatures are quite critical in minimizing well production losses. This paper presents functional networks as a novel modeling method to forecast bottomhole flowing pressures and temperatures in vertical multiphase production wells using over 700 multiple field data. The new approach helps to overcome the most common limitations of the existing predictive techniques such as empirical correlations, multiple regressions and artificial neural networks.

The functional network models were trained and tested using 70% and 30% of the available datasets, respectively. Trainings were conducted with associativity functional networks models with families of linearly independent learning functions such as polynomial, logarithm, Fourier and exponential basis. By using backward-forward search based on minimum description length criterion or the least-square optimization technique, the best functional networks models were selected and tested. To demonstrate the robustness of the developed models, the optimized networks were used for time series analysis (using data obtained every three minutes, every hour and every six hours) and trend evaluation to forecast and monitor the influence of changing input values. After optimization, logarithm basis of order three gave the best line-of-fit with correlation coefficients R2 > 0.99 for training and testing sets and for bottomhole flowing pressure and temperature data. The models results are accurate, reliable and can be used for forecasting. For the times series analyses, the models perform excellently with R2 > 0.99 for the hourly and 6-hourly data while R2 > 0.96 for the data obtained every three minutes. In addition, trend analysis shows that the predictive models are physically correct and justified by the field data. As examples, temperature and pressure decrease with increased oil flow rate or with increased gas flow rate, while they are less significantly influenced by increased water flow rate. Finally, the current models outperform the artificial neural network models in both time series and trend analyses.

This is the first reported study where functional network is used to simultaneously forecast bottomhole flowing pressures and temperatures with such a high accuracy. This work also holds a significant contribution to authenticating operational state and diagnosing future malfunction of downhole pressure and temperature sensors in intelligent well system operations in petroleum and geothermal industry.
Reducing Production Deferrals from Rotating Equipment Using a Standardised Approach across Multiple Assets
Authors: Hickey Greg , Gamblin Bryan , Hocking Paul , Grange Brett
Published: June 2014

In 2005, BP realised that the upstream operating business, with many assets, needed a set of standardised tools and processes for managing rotating equipment deferrals. As part of the Field of the Future programme, the Equipment Run Time Tracker (ERTT) was developed to automate the gathering of availability and reliability information which are the key performance indicators for rotating equipment. Where poor reliability is identified, other tools and methods including condition monitoring are used to understand the root cause of poor performance and fix the problem.

This technology is applied across the segment to all types of surface facilities wherever rotating equipment is in use. This includes onshore, offshore, oil or gas producers and every geographical location. The solution is standardised, as is the deployment process to cover the whole range of upstream operations.

This paper describes how automated reliability monitoring has been applied at scale across multiple assets as the first step in the management of production deferrals. It describes the BP Field of theFuture programme’s experience of the development and delivery of Equipment Run Time Tracker, which has become the standard platform for monitoring reliability in a growing footprint of over 30 assets. Poor performers have been identified using the tool, along with instances of poor operation and control regimes. There are also examples where equipment has been moved to a condition based maintenance regime.

Equipment Runtime Time Tracker is a significant development for the digital oilfield because it is a standardised solution for automatically capturing availability and reliability information across multiple assets at scale. As production deferrals are a major source of lost business value in oilfield operations and rotating equipment is a key source of these deferrals in the upstream oil business, addressing this problem at scale in a cost effective way is a strong driver of business value.
Getting More Value of Your Production Data by Using Datamart
Authors: Pedersen Lars Petter , Tran Tram T.
Published: June 2014

Great diversity and number of data sources represents real challenges to every organization, when trying to combine these sources into one common, optimized and streamlined source supporting specific business needs.

In Statoil our challenge was to manage data from a multitude of production data sources from operated fields on the Norwegian Continental Shelf effectively; retrieving and structuring these data in a format that not only makes sense to the different production/exploration teams and departments, but also for other usage within and outside the organization. A project was initiated to create one common and shared data source, which would provide all users with the same type of data for day-to-day production and exploration activities. The goal of this project is to improve data availability, efficient management of data, security and access to data, present in a streamlined manner to both end users and other clients with similar data needs, thus protecting end users from any underlying IT systems changes in the future.

This was accomplished by harmonizing the hundreds of views from multiple data sources; developed partitioned tables and procedures shared through a common interface.

The result is a solution that provides enhanced security for the various data sources; improved efficiency; a best practice case as it extracts intrinsic value to the organization as a whole and a seamless access to data when required.
Enabling Best Work Practices With Electronic Procedures
Authors: Molin Arthur , Kortenkamp David , Schreckenghost Debra , Bell Scott , Kusy Kevin
Published: June 2014

To transform work practices, it is necessary to clearly communicate the best practices to the work force, and to track actual performance to enable review and training. The National Aeronautics and Space Administration (NASA) has developed protocols for following best practices in its Manned Space Flight program by developing detailed standard operating procedures. NASA has recently completed a transition from a paper-based procedure infrastructure to an electronic procedure infrastructure.

NASA uses procedures to define tasks and communicate those tasks to the astronauts and flight controllers who perform them. These procedures are used to train and to perform work on the space vehicles that NASA operates. Procedures are written to define tasks of all kinds, from dealing with emergency conditions to flushing the zero-gravity toilet. Through the Shuttle program these procedures were written and distributed as paper documents, but recently NASA has transitioned to providing these procedures as electronic documents.

Electronic procedures have a number of definite advantages over paper procedures. By delivering the procedures on hand- held devices, the procedures are available at the point where work is being performed; paper documents are often left on a shelf somewhere. The electronic documents can be searched more easily than paper documents, facilitating maintenance of the procedures in the face of changing systems. The electronic procedures can be updated and the updates transmitted to the work force in real time; this is particularly important when work is performed in remote locations where it is difficult to deliver updates to paper documents.

By providing the procedures as electronic documents a number of new features can be provided. Displays can merge the procedures with data from the system being operated. Hazard conditions can be highlighted. Interaction with procedures can be logged, making it possible to reconstruct how the procedure was executed after the fact. Finally, performance of the procedures can be monitored for compliance and improved training. The authors have participated on a series of NASA projects to create and deploy technologies to enable electronic procedures, and we see them as transformative technologies for creating and sustaining changes in the way work is performed.
Remote Operations for Drilling Services in Deepwater Environments: An Industry Breakthrough
Authors: Menezes Ana Leticia , Kolomiets Alexander , Toco Leonardo
Published: June 2014

The remote drilling operations concept has been widely utilized and executed around the world on high volume, land-based jobs. Applying the same concept in the offshore environment has largely been avoided because of job complexity and high operational cost; in this environment, even a small failure could lead to substantial consequences. Ironically, however, offshore environments have bigger issues than are encountered in land-based operations with regard to personnel availability, significant logistical challenges and related risks, the burden of rig space to accommodate workers, and many other constraints and challenges and which could be alleviated by implementing remote operations concepts. Brazil is at the forefront of deepwater oil exploration and has been facing new challenges in developing discoveries much farther from traditional offshore areas, providing many opportunities for streamlining operations through innovation. After achieving a sustainable stage in real-time drilling projects through remote monitoring, modeling, and optimizing processes on deepwater operations, it was decided to take a further step and conduct a feasibility review for advancing efforts in remote drilling operations in these deepwater environments. The project was approved by the operator to be executed on specific development wells for activities such as measurement-while-drilling (MWD) tool programming, acquisition system configuration, prejob quality checks, recorded mode data processing, and daily reporting. The remote operations project began with a "shadow job" to test the process of combining and aligning the resources in terms of people, processes, reliable communications infrastructure, and remote control technologies. Following the success of the shadow job, the remote operations center performed 12 bit runs with different services provided on 6 different platforms. Importantly, there was not a single hour of NPT caused by remote operations during entire project.
Instituting Data Quality Into a Real-Time Decision Support Infrastructure
Authors: Tjostheim Sigurd , Andresen Per Arild
Published: June 2014

In several of the past SPE conferences, as well as other forums there has been a call for dialog around data quality and the value it would bring to the drilling operations community for understanding the validity of real time data. This paper continues those conversations with a discussion into some of the basic mechanisms that can be employed to understand the quality of data received in real-time. It will expand on the ideas around how this can be implemented generically in a real- time operational support system and the benefits of such a system that can be deployed regardless of the process or naming conventions used by at the original point of acquisition. This paper will explore some of the available ways to monitor and measure the quality of real-time data as well as the practical value of understanding each of these measurement’s validity. It will also touch on a few of the benefits that the end-consumers will see in having a standardized mechanism of determining the quality of the data they are using in calculations, monitoring and reporting. This road to data quality however has several limitations when only using open standards such as WITSML, therefore we will explore the options that are available as well as a few of the practical limitations imposed by these standards.
Table That Table: Intuitive Dashboard Design for Oil and Gas
Authors: Stiffel Zoltan , Beach Jaynee , Arntzen Jarl Frode
Published: June 2014

In the Oil & Gas industry, tables and spreadsheets are the software user interface (UI) norm that has persisted beyond its value. Although users ranging from field engineers to geologists to physicists typically use spreadsheets on a day to day basis, the advent of social media and mobile devices has evolved the presentation of large amounts of data in a visual manner. The table has become cumbersome and tedious in contrast.

Dashboards are visual displays that are usually a combination of text and graphics with an emphasis on graphics. The reason being graphical representation, handled expertly, can often communicate with greater efficiency and richer meaning (Few, 2006). To benefit the user it should display the most important high-level information in order to complete the task or task(s) at hand.

Presenting data visually from day-to-day well operations in a simple-to-consume format provides customer value, technical value and business value. Why bury data in columns and rows when graphs and charts would convey the same information visually? Users have already embraced the mobile UI (think iPad, Android tablet) in every day use, which has shortened the adoption rate of a typical user in this industry. They are already accustomed to gathering information they need quickly and efficiently, in scannable, at-a-glance data. The value gains that have already been realized in the use of ordinary applications are opportunities waiting to be realized in the use of data presentation from well operations.

However, implementing this kind of user experience can be challenging and will take some convincing. Attitudes tend to fall within the "but we have always done it that way" mentality. The intent of this paper is to cover the challenges of user experience engineering as it pertains to Oil & Gas software and the possibility of shared UI concepts between the consumer-driven UIs from mobile and social media applications.
Integrated Operation Collaboration Technologies - Remaining Challenges and Opportunities
Authors: Sarshar S. , Rindahl G.
Published: June 2014

Integrated Operation (IO) in the petroleum industry has for many years included distributed collaboration as a key support capability. The technologies used to enable distributed collaboration, e.g. between onshore and offshore, have evolved from phone calls and teleconference to video conferencing rooms, to desktop solutions and lately towards more mobile solutions. The authors report on observations over the last five years from five petroleum companies operating on the Norwegian Continental Shelf of how technology solutions both enable and sometimes hamper collaboration in an IO setting.

The study includes both structured observations of meetings and work sessions where collaboration between distributed locations is required and findings from industry projects for development of collaboration environments or IO training. A general setting in which collaboration technologies are used is presented. Then different solutions are talked through with focus on technology aspects such as usefulness, usability and literacy. Observations with examples from the industry are given on the solutions used for collaboration and information sharing. These include lessons learned with respect to how well they support the agenda and purpose of the collaboration session as well as the organizational workflow.

Technology can support IO to different degrees. Technologies developed with a different purpose can be tweaked to fit, technology may have been developed or selected to support IO, and in some cases, technology choices may induce or enforce a desired IO work practice. The authors identified several areas where the focus on technology solutions appears not yet adequate; the technology still does not support the work flows as intended. Some challenges observed in 2008 are still valid while new challenges and opportunities have arisen as IO has moved from being a new concept into "the way we operate around here". The results from these observations can help to focus the attention on collaboration technologies towards supporting the work flow (rather than forcing it upon the organisation). In many cases, the result of inadequate strategies has been added constraints rather than enabling solutions.
Pattern Recognition and Data-Driven Analytics for Fast and Accurate Replication of Complex Numerical Reservoir Models at the Grid Block Level
Authors: Amini S. , Mohaghegh S. D. , Gaskari R. , Bromhal G. S.
Published: June 2014

Reservoir simulation models are used extensively to model complex physics associated with fluid flow in porous media. Such models are usually large with high computational cost. The size and computational footprint of these models make it impractical to perform comprehensive studies which involve thousands of simulation runs. Uncertainty analysis associated with the geological model and field development planning are good examples of such studies.

In order to address this problem, efforts have been made to develop proxy models which can be used as a substitute for a complex reservoir simulation model in order to reproduce the outputs of the reservoir models in short periods of time (seconds).

In this study, by using artificial intelligence techniques a Grid-Based Surrogate Reservoir Model (SRMG) is developed. Grid- based SRM is a replica of the complex reservoir simulation models that is trained, calibrated and validated to accurately reproduce grid block level results. This technology is applied to a CO2 sequestration project in Australia.

This paper presents the development of the reservoir simulation model and the Grid-based SRM. The SRM is able to generate pressure and gas saturation at the grid block level. The results demonstrate that this technique is capable of generating the reservoir simulation output very accurately within seconds.
Overcoming the Technology Barrier - Experience and lessons learned from deploying a chemical management system into multiple operating assets
Authors: Grange Brett , Sutton Rob , Richardson Dave
Published: June 2014

Delivery of new technology on assets with already established work practices can face many challenges and even fail for various reasons. The Chemical Management System, delivered by BP’s Field of the Future programme, is designed to help manage and monitor chemical injection systems to ensure the efficient delivery of operations and integrity based chemicals. As part of this technology, a cross-regional prototype trial program was implemented which faced a number of challenges around the successful embedding and sustained usage of this new solution.

This study, which came from the delivery of the trial program, highlights some of the main challenges and barriers that are seen with the delivery of new technology. Running the trial program across two operating assets in different regions helped to ensure that sustained global delivery of the technology was achievable. The range of challenges and lessons learned covered in the scope of this paper run from difficulties faced with embedding new work flows, through to the actual physical software implementation.

The field trial highlighted a number of areas of improvement, lessons learned and success stories. These became key recommendations for enhancing future technology trials and are included in this paper. Once the recommendations were identified, processes were then put in place which has helped the success of this technology when delivered globally across BP’s asset base. Through applying these lessons and the recommendations to future technology programmes, we can ensure the continued success of technology within BP and our industry. This will deliver long-term sustainable technology which can generate the expected value throughout the technology lifecycle.
RESQML Version 2 Uses New Technologies to Improve Data Exchange for Subsurface Modeling Processes
Authors: Verney Philippe , Gautreau Christian , Rainaud Jean-François , Deny Laurent , Magsipok Joey , Marcotte Donna
Published: June 2014

Subsurface modeling workflows are complex, data intensive, and iterative, requiring many different software packages to repeatedly exchange data. Improving the speed and accuracy of data exchange in model development can improve the speed, accuracy, and reliability of analyzing these models, ultimately supporting better decisions for asset development and economics over the life of a field.

RESQML is an XML/HDF5-based, data-exchange specification that allows data to be transferred efficiently between the many different software packages used in subsurface modeling workflows. Key Version 2 enhancements include: a richer, more detailed RESQML data model with more domain data-objects (e.g., wells), and the ability to group related data-objects (e.g., faults, horizons, grids, etc.) and exchange them as a complete model. While these enhancements support more accurate and reliable models, they also presented challenges that were solved using new technological approaches new to RESQML.

To design the richer yet more complex data model, the RESQML Special Interest Group (SIG) has moved from a hierarchical view of the data model to an entity-relationship view. The SIG is now using UML modeling tools to visualize the data model and produce both class and instance diagrams. The class diagrams are then used to generate XML schema definitions in a more automated and consistent manner.

The Open Packaging Conventions_a container-file technology that stores a combination of files and their relationships to form a single entity for transfer in one compressed (ZIP) document format_are being used to group together independent data-objects (files) as a complete model.

This paper presents an overview of RESQML Version 2 enhancements and capabilities, and explains how these new technology solutions are being used and their impact on subsurface modeling, the design process, future maintenance, and in helping software developers integrate RESQML into their own products.
Data Security Promotes Improved Intelligent Drilling
Authors: Cavazos Catherine
Published: June 2014

As drilling becomes more intelligent, barriers associated with data security are increasingly encountered. Such data security barriers often proliferate many times faster than the drilling technology itself. The drilling industry needs to be able to efficiently and effectively adapt and expand to address these barriers, protect their assets, and continue to meet the needs of both operators and contractors.

Rig assets, in this case, data, are extremely valuable, and any loss, corruption, or theft can translate into potential safety hazards, significant monetary damages, and negative impacts to their business. Today’s climate brings to the forefront hacktivists and nation state hackers who infringe on these assets. As technology improves, the accuracy, rapidity, and remote accessibility of data must be capitalized on to ensure data security. Furthermore, education, training, and utilizing the "right" standards according to the needs of the rigsite are critical to the safety and production of the well and its data. There is a great need to eliminate security obscurity. Educating users at the rig and knowing how to operate under a particular set of standards or security measures can make all the difference in having a compromised or protected rig. Understanding the type of monitoring and security needed for 10,000 users/employees is different than for 100 users. The collaboration between the management and groups working to secure the assets must adapt adequately to address and prepare for the potential obstacles that may occur down the road.

This paper addresses the issues organizations must be recognizing to avoid this obscurity. Implementing the right training, education, and security practices will enable real--time security of data management and monitoring to be more sound and robust, permitting the intelligent decision--making required to ensure that the rigsite’s needs are met and the information required for analysis continues to be acquired uninterruptedly and securely.
Fiber Optic Sensing: Evolution to Value
Authors: Kamal S. Zafar
Published: June 2014

Harsh environments and access limitations continually challenge well surveillance in oil and gas production. Knowledge of subsurface characteristics, such as fluid flow, helps mitigate safety and operational risk while enhancing economic viability. As operators pursue technically challenging reservoirs, the development and use of advanced sensor technology has become crucial to exploring, developing and producing hydrocarbon reserves - while maintaining system integrity from the producing zone to transportation. Research and technical commercialization in fiber optic sensing has the potential to enhance many aspects of flow integrity and assurance, including barrier monitoring, reservoir and flow characterization, leading to increased safety, production protection, and production enhancement.

Fiber optic sensing has evolved over time at BP. This is with respect to diversity of applications and realization of benefits, while increasing our learning from implementation challenges. We will use this context to highlight the research, commercial ecosystem and business aspects of fiber optic sensing– particularly to provide a perspective on future uses of this technology. Using an integrated approach, the goal of this paper is to increase awareness and promote adoption. We do this by making a case for accelerating integration, collaboration and standardization among the operators, service providers, equipment manufacturers and systems integrators to stimulate and to drive efficiencies and value.