Soft Multiphase Flow Metering for Accurate Production Allocation

Lorentzen, Rolf Johan; Sævareid, Ove; Nævdal, Gunnar

doi:10.2118/136026-ms

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Linear Kalman Filter (Kalman, 1960), and its extension to nonlinear problems such as Extended Kalman Filter (EKF), Ensemble Kalman Filter (EnKF) and Unscented Kalman Filter, are the most widely used of the stochastic based optimisation methods to find the optimum solution for multi-phase flow rate allocation problems. Initial works by Lorentzen et al (2010a) and Lorentzen et al (2010b) developed a framework with a transient well model and EnKF to estimate the flow rates using high frequency pressure and temperature data. Later, Lorentzen et al (2014) used a more sophisticated method, the Auxiliary Particle Filter, which could predict both discrete and continuous variables in a Bayesian framework that preserved the physical properties of the well model.…”

Section: Introductionmentioning

confidence: 99%

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Malakooti¹,

Muradov²,

Davies³

et al. 2015

All Days

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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 directly measure the flow rates and phase cuts required for optimal control of the well's producing zones are not normally installed. Instead, Multi-zone, Multi-phase Flow Metering (MFM) parameters are calculated from indirect measurements (e.g. from zonal pressures, temperatures, and well flow rates).To-date all published techniques of zonal flow rate allocation in multi-zone I-wells are "passive" in that they calculate the MFM parameters 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 paper describes an "active" monitoring technique which uses a direct search method based on Deformed Configurations to optimise the sequence of Interval Control Valve (ICV) positions during a routine multi-rate test in a "intelligent" well. This novel approach maximises the accuracy of the calculated reservoir properties and MFM parameters.Four "active monitoring" levels are available. Each one uses a particular combination of down-hole and surface measurements depending on the available 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 the in-flow performance, the volumes of produced fluids and the reservoir's properties at both a well and a zonal level.Our "active monitoring" workflow can be extended to a wide range of production and injection allocation problems for any well completion type. The workflow may also be used to improve the accuracy of production flow rate measurements and, ultimately, to maximise oil production rate and recovery.

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Section: Introductionmentioning

confidence: 99%

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Malakooti¹,

Muradov²,

Davies³

et al. 2015

All Days

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“…Based on the collected data, one can estimate the flow rates in the well. Such an exercise is often called "soft sensing" or "soft metering" (see, for examples, Bloemen et al, 2006;de Kruif et al, 2008;Leskens et al, 2008;Lorentzen et al, 2010b;Wrobel and Schiferli, 2009). Information obtained from soft sensing can then be used to support decision-making, e.g., choosing ICV operation strategies for the purpose of production optimization.…”

Section: Introductionmentioning

confidence: 99%

Toward an Enhanced Bayesian Estimation Framework for Multiphase Flow Soft-sensing

Luo¹,

Lorentzen²,

Stordal³

et al. 2014

Proceedings

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In this work the authors study the multiphase flow soft-sensing problem based on a previously established framework. There are three functional modules in this framework, namely, a transient well flow model that describes the response of certain physical variables in a well, for instance, temperature, velocity and pressure, to the flow rates entering and leaving the well zones; a Markov jump process that is designed to capture the potential abrupt changes in the flow rates; and an estimation method that is adopted to estimate the underlying flow rates based on the measurements from the physical sensors installed in the well.In the previous studies, the variances of the flow rates in the Markov jump process are chosen manually. To fill this gap, in the current work two automatic approaches are proposed in order to optimize the variance estimation. Through a numerical example, we show that, when the estimation framework is used in conjunction with these two proposed variance-estimation approaches, it can achieve reasonable performance in terms of matching both the measurements of the physical sensors and the true underlying flow rates.

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“…Initial work focused on determining either gas or water breakthrough from the relative magnitude of the observed layer flowing bottomhole temperatures from either vertical or horizontal producers (Pinzon et al 2007;Yoshioka et al 2007). Subsequently, several investigators have attempted to regress upon layer permeability and flow rates using coupled reservoir and wellbore models (Li and Zhu 2010;Duru and Horne 2010;Lorentzen et al 2010;Ramazanov et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Impact of Reservoir Permeability on Flowing Sandface Temperatures: Dimensionless Analysis

App

Yoshioka

2013

SPE Journal

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Layer flow contributions are increasingly being quantified through the analysis of measured sandface flowing temperatures. It is commonly known that the maximum temperature change is affected by the magnitude of the drawdown and the Joule-Thomson expansion coefficient of the fluid. Another parameter that strongly impacts layer sandface flowing temperatures is the layer permeability. Aside from determining the drawdown, the layer permeability also affects the ratio of heat transfer by convection to conduction within a reservoir. The impact of permeability can be represented by the Péclet number, which is a dimensionless quantity representing the ratio of heat transfer by convection to conduction. The Péclet number is directly proportional to reservoir permeability. Through dimensionless analysis, it will be shown that for a given drawdown (based on a dimensionless Joule-Thomson expansion coefficient JT D ) the temperature change diminishes at low Péclet numbers and increases at high Péclet numbers. This implies that for low-permeability reservoirs such as shale gas or tight oil, the temperature changes will be minimal (less than 0.18F) despite the large drawdowns in many instances. Dimensionless analysis is performed for both steady-state and transient thermal models. Results from multilayer transient simulations illustrate the ability to identify contrasting permeability layers on the basis of the Péclet number effect.

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Soft Multiphase Flow Metering for Accurate Production Allocation

Cited by 9 publications

References 18 publications

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Flow Control Optimisation to Maximise the Accuracy of Multi-phase Flow Rate Allocation

Toward an Enhanced Bayesian Estimation Framework for Multiphase Flow Soft-sensing

Impact of Reservoir Permeability on Flowing Sandface Temperatures: Dimensionless Analysis

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