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- W3090235055 abstract "Uniaxial Compressive Strength (UCS) is the ultimate stress that a sample can stand beforebreaking or failing.The traditional method of for finding Uniaxial Compressive Strength is known to becostly, time consuming and destructive to the core samples being tested. In this project, a Neural Networkwas produced to correlate between UCS and different rock physical properties for sandstone and limestonesamples from various data collected from previous research. Different models were created to be able toinvestigate the best conditions under which the network is most optimized. The final network was createdusing 18 hidden neurons with input parameters being porosity, p-wave velocity and rock type and the outputbeing UCS. The correlation coefficient from this network was found to be 0.975. The main factors found tosignificantly affect the efficiency of the correlation or network were formation age, input parameters beingused, and the type of model used for statistical analysis. Results produced identified that narrowing the rangeof difference in age between the samples being tested significantly improves the strength of the networkspecifically from 0.870 to 0.968. Also, density was found to have the least correlation with UCS with acorrelation of coefficient of 0782 followed by p-wave velocity at 0.818 and the strongest correlation foundbetween UCS and proosity at a correlation coefficient of 0.862. Comparing Simple Regression model(SRM), Multiple Regression Model (MRM) and Artificial Neural Network(ANN) the use of a NeuralNetwork was found to produce the best correlation coefficient and best RMSE values." @default.
- W3090235055 created "2020-10-08" @default.
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- W3090235055 date "2020-01-31" @default.
- W3090235055 modified "2023-09-23" @default.
- W3090235055 title "Evaluation of Uniaxial Compressive Strength from Physical Properties of Rocks to Obtain Formation Hardness And Bit Design Selection" @default.
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