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Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented Kalman Filter Technique
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2005
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EngineeringIndustrial EngineeringKalman FilterDrillingState EstimationNonlinear System IdentificationUncertainty QuantificationWell LoggingNumerical SimulationManaged Pressure DrillingSystems EngineeringModeling And SimulationDrilling EngineeringProcess MeasurementPetroleum EngineeringMechatronicsReservoir SimulationMechanical SystemsProcess ControlBusinessFormation EvaluationIndustrial Process ControlMpd OperationsComputer Model Parameters
Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented Kalman Filter Technique J. E. Gravdal; J. E. Gravdal RF-Rogaland Research Search for other works by this author on: This Site Google Scholar R. J. Lorentzen; R. J. Lorentzen RF-Rogaland Research Search for other works by this author on: This Site Google Scholar K. K. Fjelde; K. K. Fjelde RF-Rogaland Research Search for other works by this author on: This Site Google Scholar E. H. Vefring E. H. Vefring RF-Rogaland Research Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2005. Paper Number: SPE-97028-MS https://doi.org/10.2118/97028-MS Published: October 09 2005 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Gravdal, J. E., Lorentzen, R. J., Fjelde, K. K., and E. H. Vefring. "Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented Kalman Filter Technique." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2005. doi: https://doi.org/10.2118/97028-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search Abstract In order to manage the annular pressure profile during Managed Pressure Drilling (MPD) operations, simulations performed with advanced computer models are needed. To obtain a high degree of accuracy in these simulations it is crucial that all parameters describing the system are as correct as possible. A new methodology for real time updating of key parameters in a well flow model by taking into account real time measurements, including measuring uncertainty, is presented. Key model parameters are tuned using a recently developed estimation technique based on the traditional Kalman Filter.The presented methodology leads to a more accurate prediction of well flow scenarios. Although the present study is motivated by applications in MPD, the idea of tuning model parameters should be of great importance in a wide area of applications.The performance of the filter is studied, both using synthetic data and real measurements from a North Sea High-Pressure-High-Temperature (HPHT) drilling operation. Benefits by this approach are seen by more accurate downhole pressure predictions which are of major importance for safety and economic reasons during MPD operations. Keywords: filter technique, model parameter, estimation, friction factor, artificial intelligence, kalman filter, van der merwe, prediction, friction calibration factor, machine learning Subjects: Pressure Management, Information Management and Systems, Managed pressure drilling, Well control Copyright 2005, Society of Petroleum Engineers You can access this article if you purchase or spend a download.