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Title: Novel methods for active reservoir monitoring and flow rate allocation of intelligent wells
Author: Malakooti, Reza
ISNI:       0000 0004 5993 6735
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2015
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The value added by intelligent wells (I-wells) derives from real-time, reservoir and production performance monitoring together with zonal, downhole flow control. Unfortunately, downhole sensors that can directly measure the zonal flow rates and phase cuts required for optimal control of the well’s producing zones are not normally installed. Instead, the zonal, Multi-phase Flow Rates (MPFRs) are calculated from indirect measurements (e.g. from zonal pressures, temperatures and the total well flow rate), an approach known as soft-sensing. To-date all published techniques for zonal flow rate allocation in multi-zone I-wells are “passive” in that they calculate the required parameters to estimate MPFRs for a fixed given configuration of the completion. These techniques are subject to model error, but also to errors stemming from measurement noise when there is insufficient data duplication for accurate parameter estimation. This thesis describes an “active” soft-sensing technique consisting of two sequential optimisation steps. First step calculates MPFRs while the second one uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve positions during a routine multi-rate test in an I-well. This novel approach maximises the accuracy of the calculated reservoir properties and MPFRs. Four “active monitoring” levels are discussed. Each one uses a particular combination of available indirect measurements from well performance monitoring systems. Level one is the simplest, requiring a minimal amount of well data. The higher levels require more data; but provide, in return, a greater understanding of produced fluids volumes and the reservoir’s properties at both a well and a zonal level. Such estimation of the reservoir parameters and MPFRs in I-wells is essential for effective well control strategies to optimise the production volumes. An integrated, control and monitoring (ICM) workflow is proposed which employs the active soft-sensing algorithm modified to maximise I-well oil production via real-time zonal production control based on estimates of zonal reservoir properties and their updates. Analysis of convergence rate of ICM workflow to optimise different objective functions shows that very accurate zonal properties are not required to optimise the oil production. The proposed reservoir monitoring and MPFR allocation workflow may also be used for designing in-well monitoring systems i.e. to predict which combination of sensors along with their measurement quality is required for effective well and reservoir monitoring.
Supervisor: Davies, David ; Muradov, Khafiz Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available