Automated microseismic event detection with machine learning

Abstract

Microseismic monitoring is essential to image and map hydraulic fractures during and after the hydraulic fracturing stimulations for unconventional oil and gas reservoirs. Insights into the underlying reservoir geology and structure can be obtained and effectiveness of hydraulic fracturing engineering parameters can be evaluated through located microseismic events and interpreted hydraulic fractures. There are many important steps in a typical microseismic data processing workflow, including preprocessing, microseismic event detection, microseismic event location, etc. These steps can be implemented either automatically or manually. Automatic microseismic event detection is of particular interest in this thesis. Automatic microseismic event detection involves algorithms and/or workflows to discriminate genuine microseismic events, either P-wave events or S-wave events or both of them, from noise. From algorithm or workflow perspective, microseismic event detection methods can be classified into three major categories, including arrival-time picking, migration-based and waveform-based detection. Most of state-of-the-art arrival-time picking and migration-based methods are characteristic function and threshold based. Limitations in these traditional methods are that user-defined threshold imposes too much impact on the detection accuracy and inappropriate pre-set threshold is prone to bring about low detection accuracy, especially if the signal-to-noise ratio (SNR) of a given microseismic dataset is relatively low.Recently, machine learning and deep learning based methods have been investigated to overcome the drawbacks of these traditional physical model based methods. This thesis aims to develop a workflow that leverages the support vector machine (SVM) classifier to realize automatic microseismic event detection and investigate how to train a robust SVM classifier in order to improve the microseismic event detection accuracy. Here, a classifier is considered to be robust if its performance has the following property: it achieves “similar” performance on a testing sample and a training sample that are “close”. In this thesis, we proposed a “Classification Is Detection” strategy, where a machine learning based approach, specifically SVM classifier referred to as microseismic event detector (MED), was used to distinguish genuine microseismic events from noise. Thus, microseismic detection was cast as a supervised classification. Experiments in this work indicated a well-trained MED is able to achieve comparable, if not better, event detection accuracy with traditional methods.To improve the detection accuracy of a MED, enhanced feature engineering was investigated. We added more 1D features, including time, frequency and multi-channel domain features, into existing feature set published by other researchers. These features were referred to as “ZZ features” in this work. The multi-channel domain features, for example cross-correlation, proved to be effective in improving event detection accuracy. We introduced matched filter analysis (MFA) to enhance the 2D features through firstly applying matched filter to the low to ultra-low SNR dataset and then extracting 2D features from the MFA data. The results indicated that a MED trained with 2D features extracted from MFA data obtained higher detection accuracy than one trained with 2D features extracted from raw data, especially when low to ultra-low SNR dataset was presented. We also studied the impact of SNR on feature selection by carrying out many experiments with variable-SNR training datasets.These experiments indicated that 2D features were important for all training sets, regardless of their SNR, however, 1D features gained more importance weights when training a SVM using features extracted from higher-SNR training sets. As 2D features were more important to train a robust MED, we investigated if adding more 2D features, for example 2D features extracted from raw data, will improve the MED performance. The result suggested that 2D features in ZZ features were sufficient to obtain a robust MED. Lastly, the impact of SNR discrepancy between training and test sets on MED performance was investigated. It was found that a MED can only perform well when it was trained and tested with similar noise level datasets.In practice, both P-wave events and S-wave events are present in a individual seismic trace and an event detection algorithm needs to differentiate these two phases in order to feed them to following location processes with different velocity models and wave travel times. To further differentiate P-wave and S-wave, we leveraged the existing multiclass SVM classifier to cast the two-phase microseismic event detection problem into a multiclass SVM classification problem. In multiclass classification, we introduced the multivariate time series (MTS) concept to take the 3C microseismic data as MTS data and ZZ features were expanded into 3C-ZZR features by extracting ZZ features from X, Y and Z component of raw training dataset. We next used both One-vs-Rest (OVR) and One-vs-One (OVO) strategies to train and test multiclass SVM classifiers. Both synthetic and field examples indicated that multiclass SVM classifiers were still able to achieve acceptable detection accuracy, while the overall event detection performance cannot compete with the aforementioned binary SVM classifiers. During this course, we also found that 1D features gained more feature importance in feature selection process and a MED trained with 1D features only was able to achieve comparable performance with a MED trained with both 1D and 2D features in 3C-ZZR features. As mentioned, both the training and test data are either synthetic or field data in all of previous examples. However, it is a MED trained with synthetic data and tested on field data that is of particular interest to the industry. To find out if a MED trained by synthetic data can perform well on field data, we carried out feasibility study of applying a MED trained by synthetic data to field data. We compared the experiment in which a MED was trained with white Gaussian noise (WGN) polluted synthetic data and tested on field data with the experiment in which a MED was trained with field noise polluted synthetic data and tested on field data. It is found that the MED trained with ZZ features extracted from WGN polluted synthetic data achieved comparable high event detection accuracy with the MED trained with ZZ features extracted from field noise polluted synthetic data, though both of these MEDs cannot compete with the MED trained with field data in terms of event detection accuracy. Furthermore, the results of experiments in which less features were used in the training phase indicated that a MED trained with field noise polluted synthetic data was superior to a MED trained with WGN in terms of event detection accuracy.The machine learning based microseismic event detection methods and the robust MEDs developed and presented in this thesis can be utilised as standalone or concurrent microseismic event detection processes within a standard microseismic data processing workflow. These MEDs provides remedies to the aforementioned limitations of the conventional characteristic function and threshold based methods. Contributions made in this thesis offer an improvement on existing automatic microseismic event detection techniques and offer a new avenue for future research. Some of the key improvements provided by this research are that we developed a new feature set that was able to develop a robust MED and obtain improved event detection accuracy and we found a SVM classifier trained with features extracted from field noise polluted synthetic data can achieve comparable event detection accuracy with a classifier trained by field data.