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In this article, we address the problem of fault reconstruction in delayed systems by introducing a time-shifted sliding mode observer (SMO). While time-varying delays of arbitrary duration are considered in the measured output signal, the actuator fault is parametrized as a weighted sum of known regressor functions with unknown coefficients. The prediction scheme utilizes the variation of constants formula to obtain the present time estimate of the unmeasured state. The fault is also identified at present time by means of the continuous-time Least Squares approaches. Ideal sliding mode can be guaranteed in theory, even in the presence of such adverse delays, since there is no chattering in the output estimation error of the SMO. An application to petroleum engineering with numerical simulations is presented to show the effectiveness of the proposed method.

Scale formation in downhole tubular-flow passages can cause partial to complete plugging that will affect production or injection rates adversely. In an intelligent-well completion in which the interval-control-valve (ICV) opening must be changed to control flow rate, the completion will become ineffective if plugging of clearances prevents valve actuation. To mitigate these problems, a method to predict the potential rate of scale formation under realistic conditions has been developed.This empirical method allows prediction of tool performance under scale-forming conditions for downhole applications, and uses chemical data and flow fields generated by computationalfluid-dynamics (CFD) models for downhole tools. Chemical data are obtained from laboratory tests on coupons by use of brines matching the chemistry of connate fluids. Tests were conducted in a high-pressure, corrosion-resistant vessel over a range of high pressures (100 to 10,000 psi) and high temperatures (75 to 150 C) to simulate downhole well conditions. Two test sets were conducted, each with fluid at rest and with an impeller generating low velocity in the reaction vessel, ranging from 4 hours to 4 days, with scaling rates determined from coupon-weight gain. Concentrations in the range of 50 to 125% of the typical connatefluid concentration were used.The weight-gain data obtained from the coupon tests and from a tube-plugging test were used to develop an empirical model for scale-growth rate at a given point on a solid surface with pressure, pressure gradient, temperature, fluid velocity, and brine concentration as independent variables. Artificial-intelligence methodology was used to develop this model, which can be used to predict the scale-growth rate for any arbitrary geometry. By use of the internal geometry of any tool to be modeled, a CFD model is prepared and the pressure, fluid velocity, and pressure-gradient data are generated for the entire internal solid surface of the tool for a given flow rate through the tool. These data are fed into the empirical model to calculate the scale-growth rate, which is integrated to obtain scale thickness at each point of the internal solid boundary. To verify accuracy, scale formation in a 4.5-in. ICV was predicted at high-pressure, high-temperature conditions at a low flow rate. Laboratory tests on the valve matched the model predictions well enough, which enabled Petrobras to design a better completion and fluid-handling system for a presalt well. BackgroundInorganic-scale formation associated with brine solutions from oil and gas wells has been a major issue, leading to production restrictions and costly downtime to remove the scale. In the past few decades, there have been a significant number of studies conducted to understand the mechanism of scaling at elevated temperatures and pressures that correspond to well operating conditions and to develop models to predict the change in scaling

No abstract

Scale formation in downhole tubular-flow passages can cause partial to complete plugging that will affect production or injection rates adversely. In an intelligent well completion in which the interval control valve (ICV) positions must be changed in order to control flow rate; the completion will become ineffective, if plugging of clearances prevents valve actuation. To mitigate these problems, a method to predict the potential rate of scale formation under realistic conditions has been developed. This paper describes this method, which allows prediction of tool performance under scale-forming conditions for downhole applications. This semi-empirical method uses chemical data and flow fields generated by computational fluid dynamics (CFD) models for downhole tools. Chemical data are obtained from laboratory tests on coupons using brines matching the chemistry of connate fluids. Tests in a high-pressure, corrosion-resistant vessel over a range of high pressures (100 to 10,000 psi) and high temperatures (75 to 150°C) to simulate downhole well conditions have been conducted. Two test sets each with fluid at rest and where an impeller generates low velocity in the reaction vessel were conducted, ranging from 4 hours to 4 days with scaling rates determined from coupon weight gain. Concentrations in the range of 50% to 125% of the typical connate fluid concentration were used. The laboratory test data are used with velocity field data to develop an artificial-intelligence-based mathematical model to determine scale formation rates. The model can be applied to any tool geometry as long as the operating conditions are within allowable limits of the model. The model also provides some insight into the mechanism of scale formation. To verify accuracy, scale formation in a 4.5-inch interval control valve was predicted at high-pressure, high-temperature conditions at a low flow rate. Laboratory tests on the valve matched the model predictions reasonably well, enabling Petrobras to design a better completion and fluid-handling system for a pre-salt well.