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PETROMAKS2-Stort program petroleum

MultiFlow SUITE: Smart Utilization of Data for CondItion Monitoring, Operational OpTimization, and Tie-in DEsign

Alternative title: MultiFlow SUITE: Smart Utnytting av data for tIlstandsovervåkning, operasjonell optimalisering og Tie-in dEsign

Awarded: NOK 12.0 mill.

MultiFlow SUITE contributes to digitalization of the Norwegian Continental Shelf. Large amounts of data will be exploited in development and training of digital tools that reflects how gas, oil, and water flow through pipelines. The data will in concert with mathematics and statistics move current technologies forward towards hybrid analytics for improved monitoring and optimization of production, and more effective planning of new pipelines. The following project elements are considered novel, thus representing important advancements: • A machine learning (ML) framework for flow monitoring, flow prediction, and automated flow regime identification. • A general objective method for quantifying pipe flow model performance/quality, addressing important shortcomings of traditional methods. • A stochastic virtual flow meter, capable of determining flow rates and the associated uncertainties in production systems. How can such activity have any positive environmental impact? Firstly, it reduces the need for work-overs and drilling of new wells in ongoing production. Secondly, it reduces subjectivity, computational time and uncertainty in simulations leading to reduced investments and operating costs, shorter well delivery time, and accelerated production, all which in turn reduces Green House Gas emissions. The project puts great effort into education and knowledge transfer. NTNU students at the Bachelor, Master and PhD-level will be offered education via ordinary courses at NTNU, and the project participants will be invited to give guest lectures hosted by NTNU open to the public. A postdoctoral research fellow will work across the project work packages. Webinars will be arranged regularly, and the relatively new form of dissemination Massive Open Online Course (MOOC) will be used. The research partners SINTEF, IFE and NTNU continue their longstanding collaborative effort to meet the ambitious national goals for the Norwegian Oil & Gas sector.

MultiFlow SUITE is aiming at exploiting large amounts of lab and industrial multiphase pipe flow data available to the consortium as part of digitalization of the NCS. The data, in concert with statistical processing methods, machine learning (ML), uncertainty quantification, and predictive physics-based models, will move current technologies towards hybrid analytics for improving condition monitoring, production optimization and more effective tie-in design. The research partners SINTEF, IFE and NTNU continue their longstanding collaborative effort to meet the ambitious national goals for the Norwegian Oil & Gas sector. A postdoctoral research fellow will work across work packages aligned with the project objectives and own interest/proficiency. Lab data will be used in combination with real field data to train ML tools for monitoring of pipe flow, as well as for predicting changes to the flow regime or flow structure based on adjustments to key parameters such as superficial velocities or pipe inclination. Furthermore, a general method/framework for model tuning will be developed. The framework can be used for tuning real-time monitoring systems and/or virtual flow meters, as well as commercial multiphase pipe flow simulators. The target is to move forward from the often subjective, time-consuming and in part arbitrary workflows present in the industry today. In addition, a Stochastic Virtual Flow Meter (SVFM) will be developed tailored to available lab or field use cases employing the model tuning framework and the ML algorithms developed in the project. The project puts great effort into education and knowledge transfer. NTNU students at the Bachelor, Master and PhD-level will be offered education via ordinary courses at NTNU, and the project participants will be invited to give guest lectures hosted by NTNU open to the public. Webinars will be arranged regularly, and the relatively new form of dissemination Massive Open Online Course (MOOC) will be used.

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Funding scheme:

PETROMAKS2-Stort program petroleum