Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Characterization of Rheological Properties of Drilling Fluids Using Ultrasonic Waves
Drilling fluid rheology is important for drilling safety and has to be constantly monitored and adjusted during drilling operations. The goal of this thesis is to attempt to create a model with neural networks that can estimate rheological properties of drilling fluids based on dampening and travel time of ultrasonic waves. The current way of measuring rheology consists of sampling and use of offline rheological measurements using lab equipment, and an online measurement system would allow for faster corrections. Experiments have been planned and carried out accordingly to create data for training and testing the neural network models, and this data has been used for training the models along with previously gathered data. The neural network models have been created with TensorFlow in Python, with Adam Optimiser, relu6 and sigmoid activation functions, and square error loss function. Models have been created for Density, Yield Point, Gel Strength and Plastic Viscosity. The best models for each output, in the same order, have an RMSE of 2.7%, 2.2%, 1.7% and 3.0% with all available data based on two different drilling fluids gradually diluted, and 5.1%, 3.6%, 3.7% and 3.8% with data gathered in this thesis based on one type of drilling fluid gradually diluted, where the best models were selected based on mean square error. These models were the best out of more than 250 models each that were trained with the same datasets for the same output variable.
Characterization of Rheological Properties of Drilling Fluids Using Ultrasonic Waves
Drilling fluid rheology is important for drilling safety and has to be constantly monitored and adjusted during drilling operations. The goal of this thesis is to attempt to create a model with neural networks that can estimate rheological properties of drilling fluids based on dampening and travel time of ultrasonic waves. The current way of measuring rheology consists of sampling and use of offline rheological measurements using lab equipment, and an online measurement system would allow for faster corrections. Experiments have been planned and carried out accordingly to create data for training and testing the neural network models, and this data has been used for training the models along with previously gathered data. The neural network models have been created with TensorFlow in Python, with Adam Optimiser, relu6 and sigmoid activation functions, and square error loss function. Models have been created for Density, Yield Point, Gel Strength and Plastic Viscosity. The best models for each output, in the same order, have an RMSE of 2.7%, 2.2%, 1.7% and 3.0% with all available data based on two different drilling fluids gradually diluted, and 5.1%, 3.6%, 3.7% and 3.8% with data gathered in this thesis based on one type of drilling fluid gradually diluted, where the best models were selected based on mean square error. These models were the best out of more than 250 models each that were trained with the same datasets for the same output variable.
Characterization of Rheological Properties of Drilling Fluids Using Ultrasonic Waves
Hafredal, Morten (Autor:in)
01.01.2018
298
Hochschulschrift
Elektronische Ressource
Englisch
DDC:
624
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