Deploying Field of the Future on Major Projects

Thomson, Alan; Law, Timothy

doi:10.2118/112117-ms

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The BP initiative to implement a digital oilfield is called the FIELD OF THE FUTURE programme. Tools and processes developed as part of this programme have been successfully deployed in a number of existing assets, demonstrating and returning real value to BP (Thomson, 2008). Stenhouse, 2008, adds that recent successes, at a number of BP assets, has shown that there is a huge potential for increased production from using the predictive capability of existing models in daily operations, once these models have been combined with real-time data and delivered in a form that actively supports the production optimization process.…”

Section: British Petroleummentioning

confidence: 99%

“…Past experience in BP, with modelling and optimization technologies, has shown that there is a huge potential for increased production from the use of models in support of daily operations, once the quality of these models has been assured (Stenhouse, 2008) . BP wants to maximize the value by exploiting the technologies available, including those from the FIELD OF THE FUTURE programme (Thomson, 2008).…”

Section: Field Of the Future Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Operations: Value and Approach in the Oil Industry

Lima¹,

Lima²,

Quelhas³

et al. 2015

Braz. J. of Operations & Production Manag.

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Today's society is increasingly susceptible to global economic crisis. The energy is still extremely dependent on oil, whose extraction scenarios usually have great complexity. The technology in the oil industry has undergone a vertiginous development in recent times. Advances in information, data and telecommunications have been reflected in the connectivity of systems and people with tremendous impacts on improvements of operational and business decisions. In this context, came to light an important movement of Integrated Operations (IO), which has systematized the implementation of this concept in the oil and gas operators and service providers. Each company has created its program, with peculiarities and challenges. This paper, including an intensive literature search, analyzed the objectives, concepts and architectures of Integrated Operations deployments in five major operators. Through a reference framework, the differences and similarities of these models were discussed. There was also an analysis in recent bibliography in order to better understand the value for Integrated Operations.

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Section: British Petroleummentioning

confidence: 99%

Section: Field Of the Future Objectivesmentioning

confidence: 99%

Integrated Operations: Value and Approach in the Oil Industry

Lima¹,

Lima²,

Quelhas³

et al. 2015

Braz. J. of Operations & Production Manag.

View full text Add to dashboard Cite

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FIELD OF THE FUTURE Digital Infrastructure and IT Architecture

Otto

Verra²

2008

All Days

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In order to successfully generate benefits from programmes such as BP's FIELD OF THE FUTURE, the supporting IT architecture and connectivity must meet the specific needs and challenges of the digital oil field. Developing IT architecture that can be efficiently deployable at scale across a global and dynamic organization is fundamental. BP has extensive experience in researching and deploying large scale IT architecture for the digital oil field. This paper will discuss, and illustrate with examples:

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Application of Differential Evolution as a New Method for Automatic History Matching

2009

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This paper presents a novel approach in automatic history matching using a differential evolution algorithm. Differential evolution is becoming a popular global optimization method and has been widely applied in many challenging engineering problems outside the oil industry. Some advantages of differential evolution are its simplicity in structure which leads to ease of coding and straightforward parallelisation, and its few control parameters making it easy to use in an operational context. These advantages make it important to evaluate differential evolution for use in automatic history matching frameworks where reservoir engineers require a powerful, yet easy to work tool with little effort to find optimum algorithm parameters. We have applied differential evolution approach to obtain history matched models of a reservoir simulation case in Gulf of Mexico. The effect of different tuning parameters on the performance of differential evolution has been studied and the algorithms sensitivity to initial starting conditions investigated. Results confirm that differential evolution is a good optimization tool in obtaining multiple good history matched models. We have also compared the match quality of models obtained using differential evolution with the Neighbourhood Algorithm (NA). This comparison shows that differential evolution obtains comparable or better models in terms of matching misfit. Finally, we studied the effect of the number of observation data points used in history matching with differential evolution algorithm. We show how additional information helps the algorithm in finding the promising regions of the search space and narrowing down the uncertainty intervals of the predictions. Introduction For the past 50 years finding models that can reproduce the history of the reservoir is one of the topics where researchers within petroleum engineering community have focused. From the very first attempts [Kruger, 1960][Jacquard and Jain, 1965] the goal in history matching was to automatically and quickly adjust the parameters of the reservoir simulation model in a way that it can reproduce the past production or pressure profile of the reservoir. This is essentially an inverse problem, where no unique answer exists and many combinations of the reservoir model can result in a good match. Finding these alternate models for history matching later became the main target. With this change in the research focus, we were no longer looking for a single set of parameters which can yield a good match; instead we are now searching for many other realizations of the reservoir simulation model with different geological and petrophysical properties that can reproduce history of reservoir. This ensemble of multiple history matched models enables us to realistically quantify uncertainty for future performance of reservoir. On the other hand, in real life problems, single reservoir simulation may take hours or even days to complete. This indeed makes another challenge when we are looking at automatic history matching algorithms. We need to obtain diverse history matching solutions in minimum possible iterations.

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Deploying Field of the Future on Major Projects

Cited by 6 publications

References 12 publications

Integrated Operations: Value and Approach in the Oil Industry

Integrated Operations: Value and Approach in the Oil Industry

FIELD OF THE FUTURE Digital Infrastructure and IT Architecture

Application of Differential Evolution as a New Method for Automatic History Matching

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