A Combined Wavelet Transform and Recurrent Neural Networks Scheme for Identification of Hydrocarbon Reservoir Systems From Well Testing Signals

Abstract

Oil and gas are likely the most important sources for producing heat and energy in both domestic and industrial applications. Hydrocarbon reservoirs that contain these fuels are required to be characterized to exploit the maximum amount of their fluids. Well testing analysis is a valuable tool for characterization of hydrocarbon reservoirs. Handling and analysis of long-term and noise-contaminated well testing signals using the traditional methods is a challenging task. Therefore, in this study a novel paradigm that combines wavelet transform (WT) and recurrent neural networks (RNN) is proposed for analyzing the long-term well testing signals. The WT not only reduces dimension of the pressure derivative (PD) signals during feature extraction, but it efficiently removes noisy data. The RNN identifies reservoir type and its boundary condition from the extracted features by WT. Results confirmed that five-level decomposition of the PD signals by the Bior 1.1 filter provides the best features for classification A two-layer RNN model with 9 hidden neurons correctly detects 3202 out 3298 hydrocarbon reservoir systems. Performance of the proposed approach is checked using smooth, noisy, and real field well testing signals. Moreover, comparison is done among predictive accuracy of WT-RNN, traditional RNN, conventional multilayer perceptron (MLP) neural networks, and couple WT-MLP approaches. The results confirm that the coupled WT-RNN paradigm is superior to the other considered smart machines.

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

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A universal methodology for reliable predicting the non-steroidal anti-inflammatory drug solubility in supercritical carbon dioxide

“…This study focuses on five artificial neural networks (ANN), four hybrid neuro-fuzzy types, and three kinds of support vector regression (SVR) to simulate anti-inflammatory drug solubility in supercritical CO 2 . The considered ANN models are multilayer perceptron neural network (MLPNN) 46 , 47 , cascade feedforward neural network (CFFNN) 48 , recurrent neural network (RNN) 49 , 50 , general regression neural network (GRNN) 48 , and radial basis function neural networks (RBFNN) 51 . The efficiency of the support vector regression with the linear kernel (LSSVR-L) 52 , polynomial kernel (LSSVR-P) 52 , and Gaussian kernel (LSSVR-G) 53 are also evaluated over the considered purpose.…”

Design of a Neuro‐Based Computing Paradigm for Simulation of Industrial Olefin Plants

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