Improved Robustness In Long-term Pressure Data Analysis Using Wavelets and Deep Learning

Abstract

AbstractThe high frequency and long duration of well production data have high value for inferring reservoir properties, monitoring reservoir conditions and detecting changes in the well flow conditions. Unlike traditional well test data where the well flow-rate and the pressure response are carefully monitored, long-term pressure data is subject to abrupt changes in flow-rate, noise caused by hardware failures, missing data and physical changes in the reservoir. In this work, methodology to interpret long-term flow-rate and pressure data was developed using wavelet multiresolution analysis in combination with deep learning algorithms. The methodology requires little to no data preprocessing, no explicit knowledge of a physical model and has high robustness to noise.The proposed methodology was found to exploit the ability of wavelets to capture and synthetize the relevant behavior of the reservoir performance history at multiple scales. This was achieved by applying the Maximal Overlap Wavelet Transform (MODWT) to long-term flow-rate data and using the output of the MODWT as the input for a Recurrent Neural Network (RNN) which creates a function map between flow-rate and pressure as a function of time. For the training of the RNN, synthetic well data were used.The application of wavelet transforms allowed the neural network to perform an automatic noise filtering and simplified the training process while requiring no knowledge of noise levels, little to no manual preprocessing of data and no knowledge of the reservoir's physical model. Moreover, the mathematical formulation of the wavelet transforms allows the neural network to take advantage of the multiresolution properties, opening the door to executing the whole analysis procedure in a simplified way.The proposed methodology aids in the inference of effects such as changes in skin factor, pressure depletion or changes in the reservoir model from the available production data, even when those data are noisy or incomplete. By using production data, the inference can be done without loss of production and used to support oil production enhancement operations. Moreover, the coupling of a neural network with multiresolution analysis eliminates the need for feature engineering and performs automatic denoising of the data, which were found to be extremely desirable properties for a methodology to be scalable and applicable to real well datasets.