By Shahab D. Mohaghegh (Ed.), Saud M. Al-Fattah (Ed.), Andrei S. Popa (Ed.)
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Extra info for Artificial Intelligence & Data Mining Applications in the E&P Industry
We suggest looking at algorithms, such as the split-andmerge algorithm. 3,4 The objective of the split-and-merge algorithm is to find the minimum number of segments in a plot where, on each of the segments, the data points are approximated by a piecewise linear function with an error norm less than a prespecified quantity. Nonlinear functions can also be used in the previously mentioned algorithm to approximate each segment. It is important to realize that the segmentation that is produced by the split-and-merge algo- 238 rithm depends on a pre specified error tolerance.
Otherwise, the learning may not be general. The combined effect of using a different number of subparts in scaling patterns of similar models and having corresponding segments of different slopes could be significant. Understanding the extent of such an effect requires further investigation. References 1. : "Discussion of Using Artificial Neural Nets To Identify the Well-Test Interpretation Model," SPEFE (June 1994). 2. U. : "Using Artificial Neural Nets To Identify the Well-Test Interpretation Model," SPEFE (Sept.
D-I, Model A has four distinct parts (two horizontal lines and two slope lines). If we modify Eq. , by 2 and 3). However, we found this scaling method to be undesirable in our neural-net program. To illustrate this, consider a hypothetical well-test response model, Model A (28151) (D-4) = xXj-Xmin -x. max mm (1)Ii' .............................. (5) where n is the number of subparts, the resulting scaling of patterns in Fig. D-I is identical (Fig. D-3). Here, we let y;=O when the denominator becomes 0 in Eq.
Artificial Intelligence & Data Mining Applications in the E&P Industry by Shahab D. Mohaghegh (Ed.), Saud M. Al-Fattah (Ed.), Andrei S. Popa (Ed.)