An Artificial Neural Network Approach for the prediction of Water-Based Drilling Fluid Rheological Behaviour

Year 2018, Volume: 2 Issue: 2, 124 - 131, 15.08.2018

Emine Avcı

Abstract

It is well known that high temperatures, which change
the rheological properties of the drilling fluid and can frequently cause
problems in deep wells, is a major problem during drilling. The importance of
the estimation and control of the rheological parameters of the drilling fluid
and the hydraulics of the well  increases
as the depth of the well drilled is being increased to explore new oil, gas or
geothermal reserves. Since it is difficult to measure these parameters with
standard field and laboratory viscometers, different conventional measurements
and regression-analysis techniques are routinely used to approximate the true
rheological parameters. In this study,  water-based
drilling fluid was initially prepared and rheological properties of the fluids
were measured under elevated temperatures using high temperature rheometer (Fann
Model 50 SL). Then, the shear stresses of drilling fluid are predicted using
artificial neural network (ANN) method depending on the elevated temperature
and shear rate. The results obtained from the high temperature rheometer and
artificial neural network were compared with each other and analyzed.
Consequently, it is observed that the artificial neural network could be used
with good engineering accuracy to directly estimate the shear stress of
drilling fluids without complex procedures. The testing process shows that the
average percentage error was found to be approximately 2% for the prediction of
shear stress values. Hence, rheological parameters of the drilling fluid could
be determined quickly and controllability was facilitated using artificial
neural network structure developed. 

Keywords

Artificial Neural Network, Drilling Fluids, Rheology, Temperature

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