Knowing the amount of cuttings that accumulate inside horizontal and highly-inclined wellbores is part of the information that is essential for controlling bottom-hole pressure, preventing stuck pipe, minimizing the circulation time for cleaning the wellbore, etc.

A dimensional analysis is conducted using basic drilling information such as pump rates, fluid densities and viscosity, drilling rate, and wellbore geometry. By using these drilling variables, three dimensionless groups (Reynolds Number, Froude Number and cuttings concentration at the bit) are developed for estimating the height of stationary cuttings beds deposited in horizontal and highly-inclined wellbores for a wide range of drilling fluids, including foams and compressible drilling fluids for underbalanced drilling.

A series of cuttings transport tests were conducted within the annular test section of Tulsa University's Low- Pressure/Ambient-Temperature Flow Loop. The test fluids included water, polymer muds and foam with a wide range of pump rates and rates of cuttings injection. The experimental data are used to develop two different models. The first model is a traditional least-squares fit of the dimensionless groups to the data. The result is an equation with four empirical constants. This equation calculates bed heights that are within 15% of measurements for all fluids. One of the disadvantages of this traditional approach is that different correlations are needed for different flow regimes, e.g., turbulent flow requires a correlation that is different from one for laminar flow.

The second model is an Artificial Neural Network (ANN) program that uses the same dimensionless groups but has been "trained" by using the test data. The ANN model predicts bed heights with an error of less than 10% over the entire range of measured data. One of the advantages of the ANN model is that it can accommodate data from all flow regimes and provide equally good results. It can also be more easily extended to include other effects such as drillstring rotation.

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