Comparative analysis of filtering methods for measurement data from complex well configurations

Abstract

This article presents a comparative analysis of various filtering methods for synthetic measurements that simulate data from well test analysis (WTA). The main objective of this work is to identify the most effective filtering methods for noisy WTA data, with the aim of preserving useful information and facilitating the subsequent interpretation of the results. The initial dataset consisted of 200 synthetic pressure drawdown (PDD) and pressure buildup (PBU) curves with varying levels of artificially introduced noise. Both classical filtering methods (Kalman filter, Savitzky–Golay filter, one-dimensional Gaussian filtering) and numerical methods based on neural networks (autoencoders) and machine learning (support vector machines) were considered for data filtering. The comparative analysis demonstrated that the performance of different filtering methods depends on the type of curve (PDD or PBU) and the well characteristics. The best results in terms of signal-to-noise ratio (SNR) and root mean square error (RMSE) were achieved using modern autoencoder-based methods. The conclusion is that the choice of an optimal filtering method requires a detailed analysis of the specific problem and the characteristics of the input data. A combination of different filtering methods is proposed to improve the quality of processing and interpretation of WTA data for complex well designs. The obtained results have practical significance, as they can simplify the segmentation of PDD and PBU curves, which is necessary for the correct identification of various operating periods of the well during the investigation process.