Abstract
In oil and gas and geothermal installations, open channels followed by sieves for removal of drill cuttings, are used to monitor the quality and quantity of the drilling fluids. Drilling fluid flow rate is difficult to measure due to the varying flow conditions (e.g., wavy, turbulent and irregular) and the presence of drilling cuttings and gas bubbles. Inclusion of a Venturi section in the open channel and an array of ultrasonic level sensors above it at locations in the vicinity of and above the Venturi constriction gives the varying levels of the drilling fluid in the channel. The time series of the levels from this array of ultrasonic level sensors are used to estimate the drilling fluid flow rate, which is compared with Coriolis meter measurements. Fuzzy logic, neural networks and support vector regression algorithms applied to the data from temporal and spatial ultrasonic level measurements of the drilling fluid in the open channel give estimates of its flow rate with sufficient reliability, repeatability and uncertainty, providing a novel soft sensing of an important process variable. Simulations, cross-validations and experimental results show that feedforward neural networks with the Bayesian regularization learning algorithm provide the best flow rate estimates. Finally, the benefits of using this soft sensing technique combined with Venturi constriction in open channels are discussed.
Highlights
One of the important phases in extracting oil and gas is drilling from the surface down to the reservoir
Due to high temperature and pressure conditions in the bottom-hole, there is a high risk of failure while drilling
The fluid is viscoplastic in nature with a density of kg/m3, and its viscosity values are within 23–180 cP for corresponding shear rates within
Summary
One of the important phases in extracting oil and gas is drilling from the surface down to the reservoir. The primary functions of drilling fluid circulation are stabilizing the wellbore, the cleaning borehole and transporting rock cuttings These functions are dependent on the properties of drilling fluid, among which density, viscosity and flow rate are the most important ones. The usage of the Ensemble Kalman Filter (EnKF) for estimating non-Newtonian fluid flow in an open channel is studied in [19] This mathematical approach presented in [18,19] is computationally demanding and is only applicable to a slow system with a large sampling time. The simplified equation (Equation (2)) can be used to estimate the flow rate using a set of spatial samplings of the open surface of the fluid in the Venturi channel, leading to a set of level measurements.
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