This paper considers the use of extended Kalman Filtering as a soft-sensing technique for gas-lift wells. This technique is deployed for the estimation of dynamic variables that are not directly measured. Possible applications are the estimation of flow rates from pressure measurements or the estimation of parameters of a drift-flux model. By means of simulation examples different configurations of sensor systems are analyzed. The estimation of drift-flux model parameters is demonstrated on real data from a laboratory set-up. Introduction The smart well paradigm involves the instrumentation of wells with sensors and actuators, which can be used for monitoring and control purposes. From a monitoring point of view, the use of sensors that measure different properties at several locations is preferred. However, because of practical and economical reasons such demands are unrealistic. Some measurements, like pressure measurements, are more readily available than others (e.g. oil flow rate). To have access to the unmeasured variables, the concept of soft sensing is used in this paper: the unmeasured dynamic variables are estimated from the measured ones by fitting a model to the measurements using extended Kalman Filtering [6]. In this paper a gas-lift well is considered. Possible applications of soft sensing for gas-lift wells are the estimation of gas and oil flow rates from pressure measurements and the parameter estimation for models that describe the multiphase flow phenomena. The use of soft sensing for well operations has been described in e.g. [9], [10]. In [9], [10] ensemble Kalman Filtering is used as the soft sensing algorithm, whereas in this paper extended Kalman Filtering is used. The main difference between these two algorithms is the prediction of the state-covariance matrix: ensemble Kalman filtering uses an ensemble of nonlinear state predictions to construct the predicted state-covariance matrix, whereas extended Kalman Filtering uses a locally linearized model to predict the state-covariance matrix. For models that are moderately nonlinear, in the sense that the change of the dynamics is small within two subsequent sampling times, extended Kalman filtering works well since in such cases the linear approximation between two sampling times is accurate. For highly nonlinear models this approximation is no longer accurate, and the use of an ensemble of nonlinear predictions may improve the predicted state covariance matrix [4]. However, the nonlinearity of the gas-lift model considered in this paper proved to be modest in the investigated operating region, which justifies the use of local linearizations. According to [9] a disadvantage of the extended Kalman Filter is the large computational demand of the numerical linearizations, which require a number of model evaluations that is of the same order as the number of state variables. However, in the ensemble Kalman Filter in [9] and [10] the number of nonlinear model predictions is set to 100, which is also of the same order as the number of state variables (159 in [10]: a discretization of 20 meters for a 1000 meter well results in 50 sections and each section consists of 3 states, additionally 9 states are used for the estimation of model parameters). Drawing the ensemble members randomly from a distribution introduces a stochastical component in the prediction of the state-covariance matrix of an ensemble Kalman Filter. This stochastic dependency on the random realization of the ensemble can be circumvented by choosing the realizations as suggested in [4] and [5], but this results in a number of ensemble members that is twice the number of state variables. Thus with respect to the computational load the extended Kalman Filter may be preferred over the ensemble Kalman Filter in the case of the application for gas-lift wells. The organization of the paper is as follows: first brief descriptions are given of both the model for the gas-lift well and of the extended Kalman Filter. Next, different measurement configurations are analyzed by means of simulations: the use of pressure measurements along the tubing, and the use of topside measurements from the annulus and the tubing. These configurations can be used for the on-line estimation of the gas and oil flow rates in the tubing, acting as a multiphase-flow soft-sensor. Besides, unknown model parameters can be estimated on-line in order to keep the model on track. For the estimation of unknown parameters of a drift-flux model, the soft sensor is tested on experimental data from a laboratory set-up.

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Summary This paper considers the use of extended Kalman filtering as a soft-sensing technique for gas lift wells. This technique is deployed for the estimation of dynamic variables that are not directly measured. Possible applications are the estimation of flow rates from surface and downhole pressure measurements or the estimation of parameters of a drift-flux model. By means of simulation examples, different configurations of sensor systems are analyzed. Finally, the estimation of drift-flux model parameters is demonstrated on real data from a laboratory setup. Introduction During the last 10 years, the industry has seen different downhole actuation technologies (commonly known as intelligent completions or under different trademarks) coming into existence. The goal of these technologies is ultimately to maximize the value of an asset by applying "right-time" optimization concepts borrowed from control engineering. Depending on the specific economics of the asset, this can be translated into more specific objectives such as speeding up of production, stabilization of unstable production, deferment of production of unwanted fluids, maximizing ultimate recovery, or a combination of some of the aforementioned short- and long-term objectives. Control theory concepts of optimization by means of a feedback loop require means for determining the deviation between the actual response and the desired response of the system. In wells, this often boils down to some sort of multiphase flow measurement. Different accurate multiphase-measurement technologies have been matured during the last decade, and the industry seems to be crossing the chasm between the early-adopter and the early-follower stages. Often for control purposes, direct measurements with high absolute accuracy are not required, as long as the measurements give a good indication of the relative change in the property that needs to be optimized. In different process industries, soft-sensing techniques were developed to determine variables where it is either impossible to directly measure the variables of interest or where it is economically not justifiable. In this paper, the concept of soft sensing is used; unmeasured dynamic variables (such as flow rates) are estimated from measured ones (i.e., pressures) by fitting a sufficiently accurate numerical model to the available measurements. We have looked at the gas lifted well application, where the lift gas rate may be controlled. Ideally this control would be based on directly measured multiphase flow rates, but in reality one often finds that this information is not available. Other measurements, such as surface and downhole pressure and temperature measurements, are more readily available and may be used for soft sensing. The paper is organized in the following manner: first, the model of the gas lifted well is described; then, the soft-sensing concepts are explained; and, finally, different examples and configurations are shown in which this technology is applied for estimating multiphase flows.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn order to develop smart well control systems for unstable oil wells, realistic modeling of the dynamics of the well is essential. Most dynamic well models use a semi-steady state inflow model to describe the inflow of oil and gas from the reservoir. On the other hand, reservoir models use steady state lift curves for modeling of the wells. When producing oil from thin oil rims, this description does not sufficiently describe the well behavior observed in practice. For this reason, a model was built that describes both the dynamic flow of oil and gas towards the well bore and the dynamic flow inside the well. The integrated model provides a realistic description of the well dynamics on a time scale of minutes, which is the time scale that is required for development of a control system. As a result, the integrated model allows the development of model based gas coning control or water coning control schemes, as well as model based interpretation of well data.

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