Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Gryzlov, Anton; Schiferli, W.; Mudde, Robert F.

doi:10.1016/j.flowmeasinst.2013.09.002

Cited by 21 publications

(6 citation statements)

References 21 publications

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“…Where, m is the length of binary string descriptors, 256 is taken in this paper; j τ is a binary code, as shown in (5). ORB descriptors are independent of location and pixel value of selected random points, and they are related to the pixel values difference of two points.…”

Section: Orb Feature Extraction Of Oil Phasementioning

confidence: 99%

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Han

Wang

Yao

et al. 2020

Measurement Science Review

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Velocity is an important parameter for fluid flow characteristics in profile logging. Particle tracking velocimetry (PTV) technology is often used to study the flow characteristics of oil wells with low flow velocity and high water cut, and the key to PTV technology is particle matching. The existing particle matching algorithms of PTV technology do not meet the matching demands of oil drops in the oil phase velocity measurement of oil-water two-phase flow with low velocity and high water cut. To raise the particle matching precision, we improved the particle matching algorithm from the oriented FAST and the rotated BRIEF (ORB) feature description and the random sample consensus (RANSAC) algorithm. The simulation and experiment were carried out. Simulation results show that the improved algorithm not only increases the number of matching points but also reduces the computation. The experiment shows that the improved algorithm in this paper not only reduces the computation of the feature description process, reaching half of the computation amount of the original algorithm, but also increases the number of matching results, thus improving the measurement accuracy of oil phase velocity. Compared with the SIFT algorithm and the ORB algorithm, the improved algorithm has the largest number of matching point pairs. And the variation coefficient of this algorithm is 0.039, which indicates that the algorithm is stable. The mean error of oil phase velocity measurement of the improved algorithm is 1.20 %, and the maximum error is 6.16 %, which is much lower than the maximum error of PTV, which is 25.89 %. The improved algorithm overcomes the high computation cost of the SIFT algorithm and achieves the precision of the SIFT algorithm. Therefore, this study contributes to the improvement of the measurement accuracy of oil phase velocity and provides reliable production logging data for oilfield.

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Section: Orb Feature Extraction Of Oil Phasementioning

confidence: 99%

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Han

Wang

Yao

et al. 2020

Measurement Science Review

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“…Various methods have been proposed in the literature in order to tackle the soft sensing problem based on the measurements collected from downhole sensors. For instance, Bloemen et al (2006) used the conventional extended Kalman filter (EKF) for soft sensing in gas-lift wells; Gryzlov et al (2010), Leskens et al (2008), Lorentzen et al (2010aLorentzen et al ( , 2010b adopted the more recently emerged ensemble Kalman filter (EnKF, see, for example, Aanonsen et al 2009, Evensen 2006, Naevdal et al 2005 to estimate the flow rates based on the temperature and/or pressure measurements. To better address the issue of nonlinearity in the process of soft sensing, a more sophisticated method, called the auxiliary particle filter (APF, see, for example, Pitt and Shephard 1999), is employed in a recent work of Lorentzen et al (2014a).…”

Section: Inverse Problemsmentioning

confidence: 99%

Toward an enhanced Bayesian estimation framework for multiphase flow soft-sensing

Luo¹,

Lorentzen²,

Stordal³

et al. 2014

Inverse Problems

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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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“…Several data-driven models are derived through identification (online or offline) using observers (see [ 10 , 11 ]), or machine learning techniques where the basic assumption is that the outputs are uniquely determined by the nonlinear projection of the inputs and/or the dynamics of the systems that approximate some natural behavior (see [ 12 , 13 ]). Many models are integrated using optimization procedures, such as random forest and generic algorithm [ 14 ] as well as Kalman filters and Extended Kalman filters covering certain stochastic properties of measurements and noisy environments (see [ 15 , 16 ]).…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Design via Switching Observers

Koumboulis

Fragkoulis

Kouvakas

et al. 2023

Sensors

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The goal of the paper is the design of soft sensors for single input single output (SISO) nonlinear processes. This goal is of essential importance for process monitoring, fault detection and fault isolation. The observer-based technique, being a fruitful direction in soft sensor design, is followed to develop soft sensors for nonlinear processes with known dynamics and unknown physical parameters. A new and general approach, based on the identified I/O linear approximant system descriptions, around prespecified operating points, and a bank of switching linear observers, will be developed. The system property of the I/O reconstructability of the state space linear approximant of a nonlinear model is presented. The design of each observer is based on the I/O measurements and structural characteristics of the nonlinear process. Observer-oriented target areas are introduced, and the respective dense web principle is formulated. The design is completed by the design of a data-driven rule-based system, providing stepwise switching among the observers of the bank. The number of observers of the bank is equal to the number of the linear approximants of the nonlinear process model and is equal to the number of the respective target operating areas. The target operating areas are required to satisfy the dense web principle. The information provided by the soft sensor is the estimation of the non-measured variables of the process. The information used by the soft sensor is the identified I/O approximants of the process as well as the real time values of the measurement variables. The efficiency of the design scheme is illustrated through symbolic and numerical simulation results for a chemostat. The nonlinear model of the chemostat is initially approximated by a set of ten linear approximants. After, the I/O approximants are identified, the respective observers are designed and the target operating areas are determined, where several cases of the satisfaction of the dense web principle are investigated. The soft sensor is composed in terms of the designed observers. Simulation results illustrate the satisfactory performance of the designed soft sensor.

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Soft-sensors: Model-based estimation of inflow in horizontal wells using the extended Kalman filter

Cited by 21 publications

References 21 publications

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Oil Phase Velocity Measurement of Oil-Water Two-Phase Flow with Low Velocity and High Water Cut Using the Improved ORB and RANSAC Algorithm

Toward an enhanced Bayesian estimation framework for multiphase flow soft-sensing

Soft Sensor Design via Switching Observers

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