Journal
APPLIED SOFT COMPUTING
Volume 47, Issue -, Pages 63-75Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2016.05.052
Keywords
Well testing data; Reservoir model detection; Dimension reduction; Discrete wavelet coefficients; Multilayer perceptron network
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Well testing analysis is performed for detecting oil and gas reservoir model and estimating its associated parameters from pressure transient data which are often recorded by pressure down-hole gauges (PDG). The PDGs can record a huge amount of bottom-hole pressure data, limited computer resources for analysis and handling of these noisy data are some of the challenging problems for the PDGs monitoring. Therefore, reducing the number of the recorded data by PDGs to a manageable size is an important step in well test analysis. In the present study, a discrete wavelet transform (DWT) is employed for reducing the amount of long-term reservoir pressure data obtained for eight different reservoir models. Then, a multi-layer perceptron neural network (MLPNN) is developed to recognize reservoir models using the reduced pressure data. The developed algorithm has four steps: (1) generating pressure over time data (2) converting the generated data to log-log pressure derivative (PD) graphs (3) calculating of the multi-level discrete wavelet coefficient (DWC) of the PD graphs and (4) using the approximate wavelet coefficients as the inputs of a MLPNN classifier. Sensitivity analysis confirms that the most accurate reservoir model predictions are obtained by the MLPNN with 17 hidden neurons. The proposed method has been validated using simulated test data and actual field information. The results show that the suggested algorithm is able to identify the correct reservoir models for training and test data sets with total classification accuracies (TCA) of 95.37% and 94.34% respectively. (C) 2016 Elsevier B.V. All rights reserved.
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