A Statistical Feature-Based Approach for Operations Recognition in Drilling Time Series

Recognizing patterns in time series has become a necessary machine learning task in many fields including medicine, finance, business and oil and gas industry. In this paper we propose a feature-based approach to recognize patterns in drilling time series data. Our approach consists of four phases which are: data preparation, feature extraction, feature selection and classifier training. In the first phase, the sensor-generated data required for building the recognition models are collected and prepared. In the second phase, the prepared time series data are transformed into a compact representation. The compact representation of the data consists of a set of statistical features extracted by sliding a window across the time series. In the third phase, numerous feature selection algorithms are applied to select a subset of most informative features from the statistical features set. Finally, the selected features are exploited to train a classifier that is used for final pattern recognition. Numerous feature weighting and selection algorithms were tested to find which statistical measures clearly distinguish between several different patterns. In addition, many classification techniques were employed to find the best one in terms of accuracy and speed. Experimental evaluation with real data shows that our approach has the ability to extract and select the best features and build accurate classifiers. Four different real-world drilling scenarios were used in the experiments. The performance of the classifiers was evaluated by using the cross-validation method.