Bianca Zadrozny

One of the most critical activities for the oil and gas industry is the discovery of new possibles reserves. Geoscientists must rely on indirect measures of the subsurface to scrutinize huge areas looking for leads of hydrocarbon reservoirs. Usually, to study the Earth's crust, geoscientists examine seismic images. Although deep learning has become popular in the last decade, only a few published results have demonstrated the application of such techniques to seismic images. In this paper, we… Mostrar más

One of the most critical activities for the oil and gas industry is the discovery of new possibles reserves. Geoscientists must rely on indirect measures of the subsurface to scrutinize huge areas looking for leads of hydrocarbon reservoirs. Usually, to study the Earth's crust, geoscientists examine seismic images. Although deep learning has become popular in the last decade, only a few published results have demonstrated the application of such techniques to seismic images. In this paper, we present deep neural models specifically for the task of seismic facies analysis, using state-of-the-art concepts and tools to train and classify seismic facies efficiently. Our results show that we can train a neural network in 4 minutes using less than 5% of the dataset, and yet obtain 88% of accuracy. Moreover, we can reach up to 97% of accuracy in 30 minutes using 60% of the dataset. Mostrar menos

We present a new methodology for modeling the cumulative oil and gas production of horizontal wells in a shale play given two types of well completion parameters: lateral length and proppant intensity, we also consider the location of the wells and the shut-in days of wells. We show experimental results evaluating the predictive accuracy of this methodology on hold-out data and compare it to standard data-driven estimation procedures.

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like… Mostrar más

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like Precision and Recall; and also have developed many different measures specifically for the multi-label paradigm, like Hamming Loss and Subset Accuracy. However, these evaluation measures have been used arbitrarily in multi-label classification experiments, without an objective analysis of correlation or bias. This can lead to misleading conclusions, as the experimental results may appear to favor a specific behavior depending on the subset of measures chosen. Also, as different papers in the area currently employ distinct subsets of measures, it is difficult to compare results across papers. In this work, we provide a thorough analysis of multi-label evaluation measures, and we give concrete suggestions for researchers to make an informed decision when choosing evaluation measures for multi-label classification. Mostrar menos

In many important application domains such as text categorization, biomolecular analysis, scene classification and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research on feature selection methods that allow the identification of relevant and informative features for multi-label classification. However, the methods proposed for this task are… Mostrar más

In many important application domains such as text categorization, biomolecular analysis, scene classification and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research on feature selection methods that allow the identification of relevant and informative features for multi-label classification. However, the methods proposed for this task are scattered in the literature, with no common framework to describe them and to allow an objective comparison. Here, we revisit a categorization of existing multi-label classification methods and, as our main contribution, we provide a comprehensive survey and novel categorization of the feature selection techniques that have been created for the multi-label classification setting. We conclude this work with concrete suggestions for future research in multi-label feature selection which have been derived from our categorization and analysis. Mostrar menos