Applied Drilling Engineering by Jr. Adam T. Bourgoyne, Keith K. Millheim, Martin E.

By Jr. Adam T. Bourgoyne, Keith K. Millheim, Martin E. Chenevert, Jr. F. S. Young

An and educational normal, utilized Drilling Engineering offers engineering technological know-how basics in addition to examples of engineering functions related to these basics. appendices are integrated, in addition to quite a few examples. solutions are integrated for each end-of-chapter query. Contents: Rotary drilling - Drilling fluids - Cements - Drilling hydraulics - Rotary drilling bits - Formation pore strain and fracture resistance - Casing layout - Directional drilling and deviation keep an eye on - Appendix: improvement of equations for non-Newtonian beverages in a rotational viscometer - Appendix: improvement of slot stream approximations for annular stream for non-Newtonian fluids.

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BACK-PROPAGATION 14 ,. 12 class=00100 10 / 8 ! 1. A scatterplot of data points and their corresponding classification. The data can be used as input to a neural network for supervised training. The class boundaries indicate that the problem is not linearly separable and therefore appropriate for a non-linear network such as back-propagation. Input Pattern 9 ,..... 2. A sample training file. The input pattern is represented by x- and y-coordinates of data points. The corresponding output pattern is a classification of the datum location.

1 shows a scatter plot where the points have been classified into one of three possible classes. The class boundaries are drawn as straight lines to help separate the points on the plot. The classes have been assigned a binary code that can be used for network training. 1 with two input values representing x- and y-coordinate values and five output values representing five possible classes for the input data points. The output coding is referred to as "l-of-n" coding since only one bit is active for any pattern.

CHAPTER 3. 3. A feed-forward multi-layer Perceptron architecture typically used with a backpropagation learning algorithm. 2. 2) t=l where Sumj represents the weighted sum for a PE in the hidden layer. The connection weight vector ~ represents all the connections weights between a PE in the hidden layer and all of the input PEs. The input vector Y contains one element tbr each value in the training set. 0. 6. In subsequent equations the bias connection weights will be assumed to be part of the weight vector Y and will not be shown as a separate term.

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