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Calculation and prediction pressure drop in vertical multiphase flow is needed to get an efficient and optimum production strategy. One of the most widely used correlations in pressure drop calculation in vertical tubing is Hagedorn Brown correlation. This study presents an Artificial Neural Network...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/31421 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
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id-itb.:314212018-05-21T11:51:05Z#TITLE_ALTERNATIVE# SURYANI PUTERI (NIM : 12208058), USWAH Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/31421 Calculation and prediction pressure drop in vertical multiphase flow is needed to get an efficient and optimum production strategy. One of the most widely used correlations in pressure drop calculation in vertical tubing is Hagedorn Brown correlation. This study presents an Artificial Neural Network (ANN) model for predicting Hagedorn-Brown bottom hole pressure in vertical multiphase flow fast and still accurate. This ANN model was developed using 513 hypothetical data and was built using Mathworks-Matlab R2011a software. The hypothetical data was simulated using Schlumberger-Pipesim 2008. These data sets were divided into training, testing, and validation sets in the ratio of 5:1:1. By doing sensitivity to the number of hidden neurons, the best model that is get is the model with 8 hidden neurons and the architecture of the model is 11-8-1. The result showed high in accuracy with R in training data set is 0.98033 and MSE is 1238278.9. text |
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Indonesia |
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Calculation and prediction pressure drop in vertical multiphase flow is needed to get an efficient and optimum production strategy. One of the most widely used correlations in pressure drop calculation in vertical tubing is Hagedorn Brown correlation. This study presents an Artificial Neural Network (ANN) model for predicting Hagedorn-Brown bottom hole pressure in vertical multiphase flow fast and still accurate. This ANN model was developed using 513 hypothetical data and was built using Mathworks-Matlab R2011a software. The hypothetical data was simulated using Schlumberger-Pipesim 2008. These data sets were divided into training, testing, and validation sets in the ratio of 5:1:1. By doing sensitivity to the number of hidden neurons, the best model that is get is the model with 8 hidden neurons and the architecture of the model is 11-8-1. The result showed high in accuracy with R in training data set is 0.98033 and MSE is 1238278.9. |
| format |
Final Project |
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SURYANI PUTERI (NIM : 12208058), USWAH |
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SURYANI PUTERI (NIM : 12208058), USWAH #TITLE_ALTERNATIVE# |
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SURYANI PUTERI (NIM : 12208058), USWAH |
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SURYANI PUTERI (NIM : 12208058), USWAH |
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https://digilib.itb.ac.id/gdl/view/31421 |
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