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Collaborating Authors
Invensys
Establishing a Digital Oil Field Data Architecture Suitable for Current and Foreseeable Business Requirements
Cramer, R.. (Shell) | Krebbers, J.. (Shell) | van Oort, Eric (Shell) | Lanson, Tony (Shell) | Palermo, Bob (Shell) | Murthy, Ajith (Shell) | Duncan, Peter (Microseismic) | Sowell, Tim (Invensys)
The Digital Oil Field (DOF) real time data structure as applied to drilling, reservoirs, wells surface production facilities, pipelines and downstream systems has evolved as bit of a muddle with little overall design and structure and little thought given to the underlying data foundational requirements. This has lead to disintegrated systems and inefficiency in attempts to integrate the multi-various systems and components. Current real time data standards are based on a combination of downstream and upstream proprietary vendor standards that are growing more and more higgledy-piggeldy as more systems are deployed. Aggravating the problem is the ever growing volumes of data which needs to be transformed into useful information to facilitate better and more timely decision-making. Hence the purpose of this paper is fourfold to: - Define the problem in terms of the current over-abundance of data systems and standards; - Document current and foreseeable data business requirements; - Define the required integrated data foundation capable of handling the ever growing data volumes and providing appropriate, timely and accurate information to those that need to know; - Identify the business value that can be attained with this more structured and standardized approach. The ultimate aim is to provide a solid foundation upon which the Digital Oil/Gas field can grow and flourish and a corresponding business justification.
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (21 more...)
Abstract PRODuction xML (PRODML™) was started jointly by BP, Chevron, ExxonMobil, Shell, and Statoil in early 2005 as a data exchange mechanism to support production optimization within a 'digital oil field' context. These companies have been joined by Aspentech, ConocoPhillips, Euriware, Halliburton, InfoSys, Invensys, Kongsberg Intellifield, Matrikon, OSISoft, P2ES, Pioneer, Petroleum Experts, Schlumberger, TietoEnator, and Weatherford. Energistics® has stewardship of PRODML and fosters further development. There is significant industry interest in implementing digital oil field strategies. Corporate and government initiatives anticipate significant, sustained improvements in recovery and operating efficiencies while maintaining safe operations. This will require robust, trustworthy, implementation of measurement, optimization and automation technologies. Version 1.0 of the PRODML standard, released in 2006, enables a range of production optimization use cases to handle an information hierarchy which includes time series data. This lays a foundation for adaptive optimization involving interaction between applications and data stores from multiple vendors. Such optimization is important both for situations with low-frequency changes, such as waterfloods, and for those requiring agility, such as compliance with pipeline, liquefied natural gas, and power-generation customer-export schedules that may cycle within a day. PRODML V1.0 provides a means of transferring data between applications incorporated in simple, common use cases. However, it did not address the task of accommodating changes to the physical configuration of the network, such as the addition of a well or a sensor, without having to manually reconfigure applications. Such changes are commonplace. In 2007, the PRODML work group focused on managing changes in production network configuration and in the capabilities of system components. The result enables optimization and reporting architectures and data management processes to adapt to changes faster with less effort and fewer errors. PRODML has therefore become a tool which can be used in implementing robust, trustworthy optimization and automation processes. Several example use-cases are included to illustrate how PRODML can be applied. Introduction The production system of an oil field changes during its life. Wells are added and removed, and gathering and injection systems and other facilities are modified and often expanded. The capability of equipment also changes over time. The changes may be gradual, such as a change in compressor efficiency or the performance of a well zone, or it may be a step change after equipment is overhauled or modified during maintenance. Measurements and reference parameters abound and vary over time for a given production installation. The sensors might include downhole pressure and temperature gauges, distributed temperature systems, and other equipment instrumentation throughout the production facilities. Flowmeters may use different reference temperatures, and a single optimization system incorporating information from several sources must deal with this variation. Managing this environment therefore involves accounting for changes in the physical asset, represented by the information hierarchy, and in the data available for the asset. In production optimization, a single architecture must often support a diverse set of use cases. For example, one process might require information for only the higher levels of the hierarchy while another might require the most detailed information available at the lowest levels of the hierarchy.
A Multi-vendor Data Exchange Format To Support Digital Oilfields
Weltevrede, Ben (Shell) | Foreman, Russell D. (BP) | Morneau, Richard (Chevron) | Rugland, Bjoern (Statoil ASA) | Booth, Jake Ernest (ExxonMobil Exploration Co.) | DeVries, Stanley George (Invensys) | Little, Todd Eric (Landmark Graphics Corporation) | Ormerod, Laurence (Weatherford) | Doniger, Alan (POSC - Energy eStandards)
Abstract Over the past decade the rapid evolution of Information Technology has enabled oil companies to much more effectively exploit hydrocarbon reserves than was possible up to now. These technologies all rely on an extensive set of instrumentation and controls. The expected benefits of this novel approach to oilfield management are very high, but can only be harvested by means of an appropriate IT infrastructure and data exchange protocols. PRODML (PRODuction xML) is a proposed data exchange mechanism which will facilitate the integration between software tools that are used in combination to turn raw production data into control actions. PRODML intends to be an industry standard XML-based exchange format for production data. The scope of the first version of PRODML will be determined by what can reliably be delivered in a one year period. This first version will support data exchange between applications in the office domain with emphasis on near-real time optimisation. In this context, near-real time optimisation is defined as optimisation that can be achieved by making changes in the existing production configuration that can be effectuated within one day. The overall approach follows the successful example of the WITSML project, which established a similar set of specifications for the drilling domain. PRODML was initiated jointly by BP, Chevron, ExxonMobil, Shell and Statoil in early 2005. The initiative has since been joined by Halliburton, Invensys, OSIsoft, Petex, Schlumberger, Sense-Intellifield, Tietoenator and Weatherford, and is now in the process of developing the standard. POSC has agreed to take over stewardship of the effort once work on the first version has been completed and to foster further developments. The paper represents the work of the entire team. Introduction Many oil companies have begun to exploit the benefits of highly instrumented fields for optimal operation of their assets. This approach relies on much-increased use of data streaming from field to office. Improvements in infrastructure for data handling and a common data exchange format as a ‘lingua franca’ between applications are prerequisites to robust and efficient dataflows. Many of the software tools used to process and monitor the data flowing from the field are provided by a number of independent software companies and service providers. The current commercial landscape is characterised by a relatively large number of companies, each providing a piece of the solution. The majority of these tools do not stand on their own, but require information from other tools. An efficient means of interoperability between these tools is essential. In this commercial setting it is in the interest of both users and providers of tools that a viable open industry standard for a data exchange format be established. Such a standard levels the playing field by assuring some level of compatibility between vendor products, allowing them to focus on delivering innovative, distinguishing functionality. From an operator's perspective the standard will accelerate the delivery of integrated solutions to end-users and decrease the costs of connecting and supporting the various parts. The evolution of the Internet has had a profound impact on the manner in which data is being processed. The technologies, although still developing, have matured over the past years to the extent that they can now be used reliably for routine operations. Internet-based IT architectures are being adopted by most companies and will be incorporated in the PRODML design. PRODML will help oil companies reap the benefits of highly instrumented fields.
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production data management (0.55)
- Management > Asset and Portfolio Management > Field development optimization and planning (0.50)
- (2 more...)
Abstract The Digital Oilfield needs to distribute information to personnel in many locations, companies and situations while satisfying requirements for privacy, protection of intellectual property, and suitability for new business processes.All of this must be accomplished with an appropriate set of standards.A key example is an escalating condition, such as a flow assurance problem, that requires the operating company to share information with one of several competing service companies.How does the access easily change based on the situation and supervisor decisions?How does a busy, remote, mobile expert know when to look at this information?How is this activity tracked?How does each organization protect their intellectual property?This transcends the domain of information technology and petroleum engineering expertise.This paper examines the three main challenges and proposes two new approaches to address them. Introduction Today's production information management exhibits the following generally acknowledged limitations:Low visibility into field and well performance 60–80% of time spent finding and preparing data. Real-time data are available but unintelligible Time is spent reacting to problems instead of anticipating them No systems to alert for discrepancies. Workers need to perform analysis to detect Events often result in groups of workers mobilizing to solve instead of just the appropriate experts Difficulty to comply with regulations and company policies on sharing information and record-keeping. These limitations have two common causes: inappropriate structure and inappropriate distribution of information for today's working environment.Experts now have less time to browse or use an application, because they are more mobile and remote; the information must go to them, but only when it is appropriate.Often the appropriate user is outside the country, and sometimes he or she is outside the company.Access must be temporary and limited in scope. Many information management installations focus on driving desktop displays and comprehensive information analysis by processing historical and real-time information by aggregating and reducing streams of data.But key users don't have time and can't get the information that they need.How do you find and compare two similar events or transitions? To address these limitations, several organizations have outlined a new approach to information management for the Digital Oilfield based on US Air Force Colonel (deceased) John Boyd's Observe-Orient-Decide-Act or OODA loop.Notable examples include the Value Loops of Shell's Smart Field™ and BP's Field of the Future.This loop is shown in Figure 1.This approach has several challenges, including the questions posed above. Several operating companies have also encountered restrictions from host countries and their corporate policies of distributing information outside the country and/or outside the company. Two approaches address these challenges: organize and capture production information as sets of transitions, and adapting information access with situations. Challenges There are three main challenges in overcoming these limitations:Making the OODA loop sufficiently useful for oil & gas production Adapting information access to situations and supervisor decisions Maintaining sufficient information quality
- North America > United States > Texas (0.28)
- North America > United States > Arkansas (0.24)