Modelling and Optimisation in BP Exploration and Production; Case Studies and Learnings

Stenhouse, Bryn John

doi:10.2118/112148-ms

Cited by 14 publications

(1 citation statement)

References 2 publications

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“…System Optimization: the final element is aimed at making the best possible use of the real time information by using model-based and advanced analytical techniques to drive towards full optimization of production and recovery for the asset or system (Stenhouse, 2008). Some of the operating solutions identified can then be addressed by implementing automated engineering solutions, such as closed-loop controllers.…”

Section: Introductionmentioning

confidence: 99%

The BP Field of the Future Programme: The Continuing Mission To Deliver Value

Dickens¹,

Latin²,

Verra³

et al. 2010

Proceedings of SPE Intelligent Energy Conference and Exhibition

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BP's digital oilfield initiative, the Field of the Future Technology Flagship, has been developing and deploying distinctive operational capabilities since it's inception in 2005. This paper describes the growing track record of value delivery, achieved through pace and scale deployment. These capabilities, which continue to be developed, support a relentless drive for continuous improvement in our asset operations, aiming at delivery of significant production (100mboed BPnet) and reserve (1bnbbl) targets by 2017. We report significant progress toward meeting these targets and highlight new key lessons from our growing body of experience.The Field of the Future concept is now a routine part of how BP builds projects and operates fields across much of our portfolio. It impacts a significant and growing proportion of net production and routinely supports >80% of our top 100 wells. A range of monitoring, analytical, optimization and collaborative solutions are applied according to asset needs and value opportunities.The in-house solutions have delivered over a third of the production target so far -ahead of plan. When integrated with general bought-in solutions, base infrastructure and collaboration benefits, we are routinely adding more than 50mboed gross production per annum.The Paper presents examples of the variety of capability, technology and solution applications that have underpinned this continuing successful value delivery.Key to this success has been:• Establishing an effective, balanced approach to R&D, deployment and value realization;• Developing clear linkage to business needs and functional priorities as a basis for effectively engaging the customer.Areas of continuing challenge include:• Sustaining uptake and usage when faced with issues and barriers arising from technical, business and organizational diversity in the asset portfolio; • Competing or aligning with other business priorities and initiatives, especially in a cost challenged environment.Looking forward, the common challenge all operators face is firmly on making it an industry-wide reality, and to fully embed "intelligent energy" in all that we build and operate.

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Section: Introductionmentioning

confidence: 99%

The BP Field of the Future Programme: The Continuing Mission To Deliver Value

Dickens¹,

Latin²,

Verra³

et al. 2010

Proceedings of SPE Intelligent Energy Conference and Exhibition

View full text Add to dashboard Cite

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Virtual Flow Metering using B-spline Surrogate Models

Grimstad¹,

Robertson²,

Foss³

2015

IFAC-PapersOnLine

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Existing optimization-based virtual flow metering solutions use advanced, blackbox process models directly in the optimization problem. This approach has many potential disadvantages, for example: non-smooth models and lack of derivative information may hamper the optimization solver. In this paper a new approach to optimization-based virtual flow metering using B-spline surrogate models is presented. In this approach the black-box process models are replaced with smooth B-spline approximations, with gradients readily available to the solver. We show that the approximation can be done without any significant loss of accuracy. By using surrogate models the optimization solver can be decoupled from the process simulator, saving I/O-operations and evaluations of the process model, resulting in reduced solution times. Another beneficial feature of the problem formulation is that poorly calibrated models may be identified and weighed less in the optimization problem. Some insight on how to select measurement noise and model error weights is shared with the reader.

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