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 Models (HMM). The proposed approach is based on the principle of learning by mistakes. A HMM model is trained using the confusion matrices which are normally used to measure the classification accuracy. Misclassified samples are the basis of learning process. Our approach improves the classification accuracy by executing a second cycle of classification taking into account the temporal relations in the 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. Consequently,instead of finding single isolated patterns, we focus on understanding the relationships between these patterns. The proposed approach was evaluated with a case study. The target of the case study was to classify real drilling data generated by rig sensors. Experimental evaluation proves the feasibility and effectiveness of the approach.
Abstract. Multivariate time series data often have a very high dimensionality. Classifying such high dimensional data poses a challenge because a vast number of features can be extracted. Furthermore, the meaning of the normally intuitive term "similar to" needs to be precisely defined. Representing the time series data effectively is an essential task for decision-making activities such as prediction, clustering and classification. In this paper we propose a featurebased classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry. Our approach encompasses two main phases: representation and classification.For the representation phase, we propose a novel representation of time series which combines trend-based and value-based approximations (we abbreviate it as TVA). 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. The TVA representation improves both the accuracy and the running time of the classification process by extracting a set of informative features suitable for common classifiers.For the classification phase, we propose a memory-based classifier which takes into account the antecedent results of the classification process. The inputs of the proposed classifier are the TVA features computed from the current segment, as well as the predicted class of the previous segment.Our experimental results on real-world multivariate time series show that our approach enables highly accurate and fast classification of multivariate time series.
Several mathematical ROP models were developed in the last five decades in the petroleum industry, departing from rather simple but less reliable R-W-N (drilling rate, weight on bit, and rotary speed) formulations until the arrival to more comprehensive and complete approaches such as the Bourgoyne and Young ROP model (BYM) widely used in the petroleum industry. The paper emphasizes the BYM formulation, how it is applied in terms of ROP modeling, identifies the main drilling parameters driving each subfunction, and introduces how they were developed; the paper is also addressing the normalization factors and modeling coefficients which have significant influence on the model. The present work details three simulations aiming to understand the approach by applying the formulation in a presalt layer and how some modification of the main method may impact the modeling of the fitting process. The simulation runs show that the relative error measures can be seen as the most reliable fitting verification on top ofR-squared. Applying normalization factors and by allowing a more wide range of applicable drillability coefficients, the regression could allow better fitting of the simulation to real data from 54% to 73%, which is an improvement of about 20%.
Different researches have been developed during the past years aiming addressing challenges still faced in petroleum exploration. Rate of penetration and specific energy studies have been the main focuses in trying to boost operational efficiency. The combination of both techniques accomplished with a preoperational test (drill-rate test) may allow as a new trending tool the best side of the drillability optimization to take place. Further, by having these implemented in real-time, an interesting methodology result in seeing the penetrating processes as a step-by-step where drilling mechanics parameters can be more dynamically adjusted. Thus, the main focus of this paper is to address a combination of rate of penetration together with a specific energy formulation in parallel with a possible dynamic and real-time drill-rate test plotted graphs for sake of the drilling optimization enhancement.
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