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SPE’s first Intelligent Energy conference, to be held 11–13 April in Amsterdam, will focus on the upstream industry’s drive to improve oilfield performance through the use of digital-age technologies. More than 60 technical, three plenary, and 14 poster sessions will address the challenges of integrating these new technologies across the different workflows and areas of activity in the oil and gas industry.

Program organizers chose five papers that will be presented at the conference as examples of some of the best uses of digital technology operating in the oil and gas sector today. The technical papers discussed in this article are listed below.

Accelerating Mars Hydrocarbon Development

In SPE paper 99482, R. Peterson et al. state, “Real value in reservoir geoscience and engineering lies in the ability to optimize the coupling between static and dynamic components at both appraisal and development stages.” Elaborating on a joint Shell-Schlumberger project to upgrade capabilities and global processes on deepwater Gulf of Mexico fields such as Mars, the authors outline how an integrated solution enables the asset team (driven by group consensus) to optimize resources on the right reservoir scenarios and the most relevant sources of risk.

Fig. 1—Catalog listing of uncertainties.

The project targets the area where there is the highest possibility of making decisions that could possibly erode significant value from the field—the reservoir-modeling and concept-solution phase. This area requires a high level of input from multiple asset team members. According to the authors, because an exhaustive number of uncertainties (Fig. 1) and a variety of development options must be considered, evaluation time can take years. Also, sometimes reservoir models are not developed to the appropriate level of detail to support the required decision making. Not only do the models take a long time to build, but they also identify uncertainties, and the resulting assumptions are understood only on a discipline-by-discipline basis.

The authors detail how technology enablers (e.g., scenario-options evaluation, decision support systems, collaborative environments) and organizational change (intelligent workflows, R&D, and business models) are used to improve execution of hydrocarbon-development and integrated-reservoir-management (IRM) processes. A solution embodying a combination of real-time R&D coupled with strong asset team project alignment to support “right-time” decisions is being leveraged on Shell’s Mars field. Solution elements include a Smart Workflow System, an Uncertainty Management Tool, and a Smart Collaborative Environment (Fig. 2) joined with new collaborative work processes.

Fig. 2—Collaborative environment.

The joint project scope will take data-management issues beyond existing architecture and functionality. Also, a prototype Smart Workflow System extends integrated framework capabilities and addresses such shortfalls as nontransparent tracking, poor audit trail of the multidisciplinary decision rationale, and lack of guides and prompts/best practices from within the application suite. The prototype system employs a Web-based environment that supports human workflow and automated processes.

Previous processes have not allowed users to capture and manage uncertainty in a qualitative way. Because information such as rationale, assumptions, and confidence levels behind uncertainties is lost during the modeling process, the decision-making process is impaired. Also, uncertainties are case dependent; often they can be matched to a particular stage of development.

The creation of a Smart Collaborative Environment, a major component of the joint project, improves execution of the hydrocarbon-development and IRM processes through establishment of a common real-time team view. According to the authors, rework is minimized, efficiency and effectiveness of technical and business reviews are improved, and best practices are captured for global use. Development efforts call for the Smart Collaborative Environment to comprise secure desktops, an information hub and workspace linked to real-time drilling and production surveillance centers, satellite team rooms, and high-end 3D visualization facilities.

Expectations following the implementation of these intelligent-field solutions include a better understanding of the relationship between decisions and uncertainties, technical review visibility, virtual expert collaboration, and greater asset-team cooperation.

Transforming San Ardo Asset Operations

Paper SPE 99548 documents a case study of how Chevron is bringing the industry’s vision of a true digital field into reality. The authors describe the preimplementation activity necessary for achievement of an i-field to enable more-reliable and -efficient well operation and execution of reservoir-management targets in California’s San Ardo field (Fig. 3). The i-field project is nearing the end of the planning and front-end engineering phases, with project execution beginning this year. One of a number of i-field projects being undertaken by Chevron, the San Ardo implementation combines steamflood processes with high-reliability water management.

Fig. 3—The San Ardo field in California, where Chevron is implementing an i-field program.

Production optimization in fields of the future is no longer only about technology. Intelligent workflows are essential to managing operability and efficiency. The steamflood project in Monterey County offers a case in point.

This case study is about transforming operations in the Lombardi unconsolidated-sandstone heavy-oil reservoir for better decision making for this particular asset and streamlining work processes for heat, wells, and water management. Here the i-field integration effort has journeyed across the asset-management value chain from reservoir through production optimization to day-to-day steamflood and facilities decisions and work processes. It also has crossed the technology threshold. For example, reservoir surveillance signposts were created and used with computer models to traffic decisions along correct trajectories for executing reservoir heating and dewatering.

Chevron plans to increase San Ardo production through a major capital project. The capital project also will enhance the database infrastructure, offering a perfect opportunity to design new workflows.

Thus far, 21 work processes have been identified. According to the authors, for each work process, the project team included recommended necessary hardware, instrumentation, decision support software, workflow automation, collaboration, change management, and new-technology requirements. Value measures for the project are injury- and incident-free operations, increasing production and minimizing lost or deferred production, reducing operating expense per barrel, and further developing workforce capability.

“The i-field will play a key role in ensuring that the concurrent dewatering and steamflooding of the Lombardi reservoir tracks the Lombardi Expansion Project plan. In addition, the visualization in an operations center environment will provide a common view of performance. This will enhance collaboration and tracking of the many facilities and well activities to minimize down time,” said Ouimette.

The San Ardo integration project provides one of the early implementations of the Asset Decision Environment (ADE). The i-field recommended eight preferred ADE components for this asset, including a proposed operations center. During the next project phase, decision support software will be integrated with improved instrumentation, workflow automation, and data architecture.

Of course, technology remains an important part of the equation. That is apparent with Chevron’s investment in terms of capital and personnel in the Center for Interactive Smart Oilfield Technologies (CiSoft). It is an integral component of Chevron’s i-field initiative launched in 2002 and focuses on research and development of integrated technologies targeted to the operations of instrumented, intelligent oil and gas fields.

Collaborative environments are also part of the i-field solution. A prototype visualization environment is under development, and the pilot visualizer will use Epsis ERA proprietary software to deploy work processes in an operation center environment. Also in a separate but connected project, a headquarters Decision Support Center (DSC) is under development for the San Joaquin Valley Business Unit. The DSC is intended to support field operations by collectively leveraging the knowledge of subject-matter experts. Steam management is the first business-unit-wide practice to be supported with a DSC.

Transforming Data Into Control Actions

Reporting on the status of a joint-industry project to accelerate development of a data exchange format in paper SPE 99707, Shell, BP, Chevron, ExxonMobil, Statoil, Halliburton, POSC, Invensys, and Weatherford present the reasoning and benefits of this high-profile project.

In past years, the rapid evolution of electronic instrumentation and information technology (IT) has enabled oil companies to exploit hydrocarbon reserves more efficiently than was previously possible. These technologies all use an extensive set of instruments and control devices. The expected benefits of this approach in oilfield management are extensive, but can be effectively harvested only with appropriate IT infrastructure and data-exchange protocols. The Production xML (ProdML) initiative in early 2005 addressed the integration aspect of using analytical tools to turn raw production data into control actions.

“The project was initiated by energy companies that are actively pursuing the usage of highly instrumented fields,” said Shell Smart Fields Consultant Ben Weltevrede. “Independently, these companies arrived at the same conclusion: the need for an efficient integration framework. We took learnings from the Wellsite Information Transfer Standard Markup Language (WITSML) project—i.e., the energy companies will provide the majority of funding, the software companies provide resources and know-how, and we develop proof-of-concept projects jointly. Because we are aiming for an open industry standard, external input is essential. To this end, we have set up a public website (www.prodml.org), where early versions and experiences will be published and feedback solicited.”

A team made up of BP, Chevron, ExxonMobil, Shell, Statoil, Halliburton, Invensys, OSIsoft, Petroleum Experts, Schlumberger, Sense Intellifield, TietoEnator, Weatherford, and POSC is currently in the process of developing an open industry extensible markup language (XML) -based standard exchange protocol for production data. POSC will take custody of the project once the first version has been completed and will foster further development.

“In this year’s second quarter, proof-of-concept projects will be set up by participant companies,” said Rusty Foreman, BP Program Manager–Service Oriented Architecture.

Exploiting the benefits of highly instrumented producing assets for optimal operation depends upon increased use of data streaming from the producing assets to the office. Infrastructure improvements for data handling and a common data-exchange format are deemed essential for successful completion.

Many of the software tools used to process and monitor the data flowing from the field are provided by independent software companies and service providers. The current commercial landscape is characterized by a relatively large number of companies, each providing a piece of the solution. Most of the pieces do not stand on their own, but require information from other tools. An efficient means of interoperability between tools is essential. “While the industry is making progress in the areas of field instrumentation, reliable models, and efficient stimulation and optimization tools, we’ve been missing the connectivity between these components to make them operate efficiently,” said Rick Morneau, Chevron Transformational Information Technology Manager. “Establishing the needed critical mass to move at pace was missing until the ProdML initiative was created. We have learned from developing WITSML and observing the pace of other standards that a standard is accelerated by active sponsorship from a group of end users and meaningful commitment on the part of companies who develop and market parts of the system. We believe we now have the critical mass (both partners and suppliers) to deliver and deploy significantly faster than most standards efforts.”

In this commercial setting, it is in the interest of tool users and providers alike that an open industry standard for a data-exchange format, which can be readily and cost-effectively implemented, is established. Such a standard accelerates the usability of solutions to end-users and decreases the costs of interfacing the various parts of the solution set. “Benefits from the adoption of the ProdML standard include, among many things, improved business performance, integration with partners, rapid deployment of internal tools, and new market access for vendors,” said Stan DeVries, Invensys Solutions Director–Upstream Solutions.

Fig. 4 shows how applications interact with data from the field. Data are generated by sensors in the process-control domain and are used directly by systems in the field for real-time control. A large portion of these data is stored locally in short-term historians, and a subset is transferred from the process-control domain to the office domain for storage in long-term historian databases. The historian data are used by applications for monitoring, alerting, trending, reporting, optimization, and history matching.

Fig. 4—High-level data-exchange architecture.

Some applications use raw data; other applications process the data and store derived values in proprietary databases. Some applications send control data to the process-control domain. Some applications need not only the raw data from the field, but also data that have been processed and stored for other applications. Key characteristics of possible service-oriented architecture are:

• Data are stored at various places and in various formats in the system, and data flow between various applications, in many cases from different vendors.

• Different architectural solutions can be put in place to enable interoperability between applications. Out of several options, a common data-exchange protocol and a service-oriented architecture were selected by the ProdML team, using XML as the technology basis.

Direct Link to Reduced Failure Rate and Operating Costs

The deployment of a real-time field-surveillance and well-services-management system, Life of Well Information Software (LOWIS), at an onshore field in California has established that a fully intelligent solution that integrates remote devices, communications networks, and workflow management software can be successfully achieved on large mature fields. Paper SPE 99949 authors document planning, implementation, and results phases; they point out the change-management programs critical to project success. Required technologies for the project included the software systems plus integration of these systems with remote intelligent-field sensors and data-transmission systems.

The introduction of the online LOWIS system for surveillance and management in the Cymric field (well count of 1,750) and the other six fields in Chevron’s San Joaquin Valley Business Unit (also with high well counts) was successful in driving down operating costs by reducing failure rates and repair costs (Figs. 5 and 6). According to Ormerod et al., the improvement in failure rate over the time of deployment was from 0.15 to 0.1 failures per well per year in the Cymric field pilot, corresponding to a direct cost saving for repairing failures of approximately U.S. $0.5 million per year. Scaling this performance up to include the entire business unit represents an annual savings of $6 million. System functionality includes managing not only real-time and historical information, but also the well-services planning activity.

Fig. 5—Decreases in Cymric field well annual failure rates as a result of the LOWIS system. Failure trend rose to 0.3 in 1995–97 before action was taken, and improved to 0.1 after the LOWIS project was initiated.

Fig. 6—Estimated costs of a single failure between direct repair and deferred production in the San Joaquin Valley Business Unit. Deferred production cost is split into (from left to right) detection time, time to schedule repair, and actual repair time.

This system makes a unique contribution to the intelligent-field puzzle. “The projects took in the use of real-time data right through into process management, which we believe is pretty unique, in maintenance applications and elsewhere,” the authors wrote. “The LOWIS system takes automation to the next level, in which not only are models used for diagnosis, but also the actual workflows are managed to make sure that this surveillance is used. The workflows to take the action and measure the results are all defined and set up in the software. Such a level not only enables ‘best practice’ engineering with real-time data, it ensures that the right processes are followed and it tracks them.”

The implementation or rollout of LOWIS was prefaced by a year-long definition phase during which standard operating procedures were documented in detail. Specifications were set by reference to the desired business processes that the software would have to support and not the other way around. While the aims of the business case were apparent, people issues related to managing the change constituted a large portion of the actual project. Senior management at the business unit and corporate levels strongly supported the project, setting out the vision, making resources available, and actively pushing for necessary changes. By the end of the deployment phase, there were more than 500 regular business-unit users.

While the concept of LOWIS could be described as being similar to an enterprise resource planning system rather than a traditional upstream technical application confined to a specific area of operation or engineering, there has not been an upstream-system precedent.

“The concept was demanding in that nothing existed that would do what was required, and it had to be developed from scratch, very much building on experience, cultures, and business drivers that supported the innovation,” the authors wrote. “It was clear from past experiences that the solution lay at least as much in how the transformation to ‘e-processes’ was handled as in the technology content.”

Adding to successful results of system implementation was the ability to ascertain whether the wells were being fixed in the economically optimal order. Another emergent benefit has been that of enabling new processes not envisaged at the project start but that have emerged to create additional value through the use of the LOWIS system. Since the initial San Joaquin Valley project, Chevron has rolled out the LOWIS system across the U.S., covering the midcontinent and Gulf of Mexico.

Future of Integrated Forecasting

Paper SPE 99979 presents a prototype model-based tool kit for oil production and forecasting—which is a key workflow in Integrated Asset Management (IAM). Partially funded by CiSoft, the model is the result of a joint effort between the U. of Southern California/Chevron Center of Excellence for Research and Academic Training on Interactive Smart Oilfield Technologies. The authors offer a direct, clear-cut explanation of the conceptual logic and architecture of the GIFT tool. GIFT is short for “GME-based Integrated Forecasting Toolkit,” where GME (Generic Modeling Environment) is a configurable domain-specific modeling environment developed by Vanderbilt U.

With capability to allow the end-user to rapidly specify and evaluate various scenarios corresponding to different production controls on reservoir volume elements, the tool requires only a few seconds to read model information and produce a forecast. Time to output the forecast in desired formats is dependent on forecast duration, field size, and format criteria. The model parameters and other information are stored in the simple, human-readable XML format. The choice of XML as the underlying representation for data storage is motivated by the objective of eventual alignment with data schemas and application programming interfaces being defined by POSC.

“Flexibility afforded by the application of model-integrated computing principles to the framework design enables extensions, such as periodic recalibration of input data (i.e., recovery curves based on real-time production data),” said Will Da Sie, Chevron Engineering Consultant. “Unique attributes of the tool kit, such as a clean separation of the model database from the modeling environment, supports a long-term vision. We expect to continuously refine the modeling language based on experience with other workflows and other asset types.”

The IAM framework for this system is based on a modeling language that is representative of the generic oilfield domain. Structured model information can be inspected using an intuitive graphical interface built using the GME tool suite developed by the Inst. for Software Integrated Systems at Vanderbilt U. Model data also can be accessed programmatically by software agents (“model interpreters”). Such a modular, extensible framework allows information exchange between digital workflows without requiring a tight coupling of producers and consumers.

The authors point out that objects are currently classified within the IAM framework as physical and nonphysical. Physical components include wells, reservoir volume elements, separators, and compressors; nonphysical components include production controls, field constraints, drilling schedules, and reliability models. This paper uses the concepts of “inventory” and “scenarios” to enable rapid specification and execution of this workflow as these concepts separate the concerns of asset modeling from scenario definition and analysis. An inventory acts as a library of building blocks corresponding to asset components and their properties. Building blocks from the inventory are assembled into different configurations, termed “scenarios,” where each scenario could represent a possible operational strategy, the effect of which on oil production is to be evaluated through the forecasting tool.

Design objectives of GIFT were generic and reusable architecture, a single version of the “truth,” single view of information, tool integration through loose coupling, and standards-based implementation. The long-term goal was defined as a seamless transition into a completely service-oriented architecture where all components, including visual modeling, will interact with other components through well-defined service interfaces using platform-independent standards and protocols.

The GIFT-user experience is driven almost entirely through a point-and-click graphical interface that guides the user through the workflow (Fig. 7). According to the authors, the details of how and where the model data are stored and how the integrated tools are configured and invoked are completely hidden from the user, allowing a consistent user experience while the implementation of the framework may undergo possibly radical changes.

Fig. 7—A typical integrated forecasting workflow using the GIFT framework.

Future developments include support for sophisticated scenario management and versioning, mechanisms for data assembly from heterogeneous data stores, and event-triggered automatic workflow execution. Plans also exist to define Web-service interfaces for the model data and for the integrated tools to ensure platform-independent, universal interoperability between disparate applications.

For more information on the conference, please visit www.ie06.com.

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