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 the 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 the 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 the 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 nine hidden neurons correctly detects 3202 out of 3298 hydrocarbon reservoir systems. Performance of the proposed approach is checked using smooth, noisy, and real field well testing signals. Moreover, a 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.
Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation
