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Statistical features are calculated from the dataset using the sliding window method. Merging the results from both, Expectation Maximization and Piecewise Linear Approximation, gives the suggested algorithm the dynamic ability to detect all drilling events and activities. In the second phase, the prepared time series data are transformed into a compact representation. Detailed data analysis shows that the surface sensors measurements can be considered as a main source of information about drilling operations. In this paper many feature weighting and selection algorithms were tested to find which statistical measures clearly distinguish between many different rig operations. Discrete polynomial moments are used as a tool to extract specific features moments from drilling sensors data.

Drilling experts compare the results to manually classified operations and the results show high accuracy. Discrete polynomial moments are used as a tool to extract specific features moments from drilling sensors data. The objective of the proposed approach is to utilize the strengths of Hidden Markov Models dealing with temporal data to complement the weaknesses of other classification techniques. The first part of the suggested method is based on the Expectation Maximization algorithm which is used to decompose Gaussian mixture models in the sensor data set. If you provide more information about the problem, data, features, and classes, then maybe selecting the best one will be possible. The given time series should be segmented to different-length segments, and for each segment a label class should be assigned.

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SVM with large feature space? In addition, Piecewise Linear Approximation algorithm slices standpipe pressure; pump flow rate; rotational speed and torque of top drive motor into labeled segments low-level segmentation. Moreover, it shows that this approach enables highly accurate recognition process. Experimental evaluation proves the feasibility and effectiveness of the approach. An outlier is any value x that is at least 1.

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The target of the case study was to classify real drilling data generated by rig sensors. Sensor measurements as well as a set of derived feature channels were used as input to the models. As network architecture the completely connected perceptron was applied in combination with parallel learning. Operations Recognition at Drill-Rigs. Check the precision of the numbers and calculations. In this paper we propose a feature-based classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry.

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Operations Recognition at Drill-Rigs. It combines both the computational power of computers and the experience of domain experts to analyze and gain insights into large data. Question - Future of machine learning - what are your thoughts? Many techniques have been developed to analyze time series and understand the system that produces them. Question - In which cases can I use the decision tree classifier? Question - What is good starting point to learn about practical speech recognition and the theory behind it?

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Try the following simple and trivial approach:. Time series data are ubiquitous and being generated at an unprecedented speed and volume in many fields including finance, medicine, oil and gas industry and other business domains. Using these measurements, it is sometimes possible to reconstruct the processes by segmenting the respective time series data into intervals that correspond to the constituent activities. If drilling operations are classified accurately, detailed performance reports not only on drilling crews but also on drilling rigs can be produced. One of these equations should be satisfied:.

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How can we solve an overfitting problem? A very useful paper:. In most cases, ensemble method is useful. IQR is calculated by this equation: One of these equations should be satisfied:.

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Question - In which cases can I use the decision tree classifier? Therefore, it combines various machine learning techniques, and exploits all available drilling data to provide valuable and accurate outcome. Why Support Vector Machine can deal with a huge number of features? Finally, the selected features are exploited to train a classifier that is used for final pattern recognition. Discrete polynomial moments are used as a tool to extract specific features moments from drilling sensors data. It is depend on the number and data type of the features.

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We have to use ML to predict the answer: The threshold values calculation concept is heavily depending on the likelihood probabilities of each data cluster. Diagnosing drilling problems using visual analytics of sensors measurements. Several sensor measurements collected from drilling rig during oil well drilling process. The principal operation states can be considered as an intermediate layer between sensor data and high level drilling operations. Question - Which is more powerful:

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The compact representation of the data consists of a set of statistical features extracted by sliding a window across the time series. The goal of the this project was to provide a framework for knowledge extraction from rig sensor data as well as daily morning reports. In addition, Piecewise Linear Approximation algorithm slices standpipe pressure; pump flow rate; rotational speed and torque of top drive motor into labeled segments low-level segmentation. What are the disadvantages of moving average filter when using it with time series data? Genetic Programming or Artificial Neural Networks?

In the second phase, those principal states will be combined to a set of drilling operational states. Distributed recognition system for drilling events detection and classification. It is depend on the number and data type of the features.

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  1. Why it works perfectly in Text classification where usually we have a hung amount of features e. The developed framework is based on the idea that there is no single technique outperforms all others over the full range of problems. The target of the case study was to classify real drilling data generated by rig sensors. How can we select a feature subset from a huge amount of features around features that will produce the highest possible classification accuracy?
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  2. Automatic threshold tracking of sensor data using Expectation Maximization algorithm. These problems can occur for various reasons and can exhibit varying symptoms, which make them difficult to identify or prevent automatically. Our approach is simple but effective, where for each sensor data channel a list of statistical features will be extracted, then features selection algorithms will be used to select the most informative features, and finally, a classifier will be trained based on these features. We have to use ML to predict the answer: A very useful paper:. Then we use these moments for each drilling operation as pattern descriptor to classify similar operations in drilling time series.
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Usually real-time measurements of the following sensors data are available as surface measurements: The goal of the this project was to provide a framework for knowledge extraction from rig sensor data as well as daily morning reports. It produces a compact representation of the time series which consists of symbolic strings that represent the trends and the values of each variable in the series. Several sensor measurements collected from drilling rig during oil well drilling process. How can I select the most informative features from a big feature set? Our experimental results on real-world multivariate time series show that our approach enables highly accurate and fast classification of multivariate time series.

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Software available at http: HMM is used when you have a state-machine system and you don't know the states hidden states , but you know the observations that produced from that states. Those sensors measurements are considered as indicators to monitor different states of drilling process. This paper develops a novel algorithm for detecting drilling events and operations in sensor data of drilling rig. How to use HMM for Multivariate time series classification.

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Our experimental results on real-world multivariate time series show that our approach enables highly accurate and fast classification of multivariate time series. How does one choose which algorithm is best suitable for the dataset at hand? Moreover, it shows that this approach enables highly accurate recognition process. How to make a classifier forget some wrong cases without re-training the whole system? Operations Recognition at Drill-Rigs. Question - What is good starting point to learn about practical speech recognition and the theory behind it?

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Statistical features are calculated from the dataset using the sliding window method. The streaming data from the rig site is gathered and analyzed, the main clusters in the sensor data are identified and monitored as in a real life case. Try the following simple and trivial approach:. How to make a classifier forget some wrong cases without re-training the whole system? Question - Is it possible to obtain negative values for approximate entropy? The proposed approach consists of two phases.

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