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Several sensor measurements are collected from drilling rig during oil well drilling process. These measurements carry information not only about the operational states of the drilling rig but also about all higher level operations and activities performed by drilling crew. Automatic detection and classification of such drilling operations and states is considered as a big challenge in drilling industry...
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. Many techniques have been developed to analyze time series and understand the system that produces them. In this paper we propose a hybrid approach to improve the accuracy of time series classifiers by using Hidden Markov...
One of the major challenges in the drilling industry is the quick detection of problems that can occur during drilling a deep well due to high cost implications. These problems can occur for various reasons and can exhibit varying symptoms, which make them difficult to identify or prevent automatically. Visual Analytics has emerged as an alternative approach for data analysis. It combines both the...
Up to very recently, the applications of machine learning in the oil & gas industry were limited to using a single machine learning technique to solve problems in-hand. As the complexity of the demanded tasks being increased, the single techniques proved insufficient. This gave rise to intelligent systems that are hybrids of several machine learning techniques to solve the most challenging problems.
Operations classification is one of the most needed tasks in the oil & gas industry. It provides the engineers with detailed information about what is happening on the rig site. In this paper we propose an approach to classify drilling operations automatically using machine learning techniques. This approach takes as input the sensors data in a specific time range, and predicts the drilling operation...
In this paper we present a novel method for automatic threshold handling and tracking of sensor data at drilling rigs. A hybrid system for automated drilling operation classification is extended by the Expectation Maximization algorithm in combination with the Bayes' theorem to find automatically threshold values required by a rule based system used in an automated drilling operations classification...
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