Prediction of Particle Settling Velocity in Newtonian and Power-Law Fluids Using Artificial Neural Network Model
Source abstract
Abstract
In petroleum engineering, accurately predicting particle settling velocity during various stages of a well’s life cycle is vital. This study focuses on settling velocities of both spherical and non-spherical particles in Newtonian and non-Newtonian fluids. Utilizing a dataset of 931 experimental observations, an artificial neural network (ANN) model with a 7-42-1 architecture is developed (one input layer, one hidden layer with 42 neurons, and one output layer). This model effectively incorporates particle settling orientation and the inclusion of the settling area ratio, enhancing its predictive accuracy. Achieving an average absolute relative error (AARE) of 8.51%, the ANN model surpasses traditional empirical correlations for settling velocities in both Newtonian and power-law fluids. Key influencing factors, such as the consistency index and particle equivalent diameter, were identified. This approach in ANN model construction and data analysis represents a significant advancement in understanding particle dynamics.
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Research context
OpenAlex topics: Hydraulic Fracturing and Reservoir Analysis · Petroleum Processing and Analysis · Drilling and Well Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Jianghan Machinery Research Institute Limited Company of CNPC, Wuhan 430024, China
School of Energy Resources, China University of Geosciences, Beijing 100083, China
School of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
Funding
National Natural Science Foundation of China · China National Petroleum Corporation Scientific Research and Technology Development Project “200C/105MPa Continuous tube drilling and grinding/Jet operation Tool Development” · China National Petroleum Corporation Limited major technology promotion project “Full series Continuous pipe operation Technology Promotion”
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