The exploration and production of oil and gas lead to many logistics challenges. In the case of t... more The exploration and production of oil and gas lead to many logistics challenges. In the case of the Brazilian offshore production, the operation size and the fact that the exploration occurs up to 300 km apart from the coast make these challenges even greater. There are many thousands of different types of materials, hundreds of suppliers, as well as hundreds of different destinations served by distinct routes. In short, this is a complex system, in which it is necessary to deal with an intrinsically combinatorial problem of sharing resources, such as warehouses, ports and means of transportation. Logistics processes need to deliver the materials in time, making sure that the production is not affected. In addition, operational costs and immobilized capital must be minimized. It is crucial to evaluate distribution probabilities for lead-times, identify bottlenecks and predict the effect of adopting specific policies for supply, picking and transportation. In order to address these issues, it is necessary to answer complex what-if queries that take into account the temporal relations, either specified or dictated by the process dynamics, between the events. Since all operations are logged, a substantial amount of historical data is generated. However, these data do not necessarily cover all situations, simply because certain feasible and relevant combinations of events may not have occurred during the period that data is logged. This motivates the use of simulations that generate huge amounts of data augmenting the (logged) historical data, and making big data analytics necessary. In this paper, we briefly describe the process of model building based on historical data as well as the construction of a simulation engine that permits efficient large scale simulations. The simulation results, together with the logged historical data are subjected to big data analytics in order to create global prediction models. The proposed methodology aims to answer complex what-if queries about the logistics processes with a high degree of efficiency and prediction accuracy. The software tool, based on this methodology, is designed so that a decision maker can interactively detect critical situations and study the global effect of changes in policies. Some examples of queries that can be supported by this research are: (i) estimation of distribution probabilities for lead-times under varied circumstances; and (ii) probability of critical materials shortage during periods of high demand.
Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2020
Classifying sentences in industrial, technical or scientific reports can enhance text mining and ... more Classifying sentences in industrial, technical or scientific reports can enhance text mining and information retrieval tasks with useful machinereadable metadata. This paper describes a search engine that employs sentence classification so as to search for abstracts from scholarly papers in Petroleum Engineering. The sentences were classified into four classes, based on the popular IMRAD categories. We produced a dataset containing more than 2,200 manually labeled sentences from 278 scholarly articles in the field of Petroleum Engineering in order to be used as training and testing data. The classifier with best results was logistic regression, with an accuracy of 86.4%. The information retrieval system built on top of the classification system yielded a mAP of 0.80.
Big data analytics, applied in the industry to leverage data collection, processing and analysis,... more Big data analytics, applied in the industry to leverage data collection, processing and analysis, can allow a better understanding of production system's abnormal behavior. This knowledge is essential for the adoption of a proactive maintenance approach instead of conventional time-based strategies, leading to a paradigm shift towards Condition-Based Maintenance (CBM) since decision is now based on the usage of a huge, diverse, and dynamic amounts of data as a means to optimize operational costs. This paper reports an investigation of the emerging aspects in the design and implementation of big data analytics solutions for offshore installations in order to allow predictive maintenance practices. Condition-based maintenance focuses on performing interventions based on the actual and future states (health) of a system by monitoring the underlying deterioration processes. One of the building blocks of a CBM design and implementation is the prognostic approach/system, which aims to...
Figure 1: Our approach enables creating multimedia annotations and provides a tree-based structur... more Figure 1: Our approach enables creating multimedia annotations and provides a tree-based structure to support traceability in decision making. (a) An annotation in the VE, created by user "Obama". It also shows how to create a new annotation. (b) Annotation as it is playing. It relies on camera movements (synchronized with audio) to show an anomaly in the model. (c) The discussion structure. Balloons indicate annotations' type and colors denote arguments' type (Pro, Con, +Info). The user is adding a (fourth) positive argument (Pro) with an audio annotation, as it can be seen by active buttons. Abstract Globalization has transformed engineering design into a worldwide endeavor pursued by geographically distributed specialist teams. Widespread adoption of VR for design and the need to act and place marks directly on the objects of discussion in design reviewing tasks led to research on annotations in virtual collaborative environments. However, conventional approache...
The exploration and production of oil and gas lead to many logistics challenges. In the case of t... more The exploration and production of oil and gas lead to many logistics challenges. In the case of the Brazilian offshore production, the operation size and the fact that the exploration occurs up to 300 km apart from the coast make these challenges even greater. There are many thousands of different types of materials, hundreds of suppliers, as well as hundreds of different destinations served by distinct routes. In short, this is a complex system, in which it is necessary to deal with an intrinsically combinatorial problem of sharing resources, such as warehouses, ports and means of transportation. Logistics processes need to deliver the materials in time, making sure that the production is not affected. In addition, operational costs and immobilized capital must be minimized. It is crucial to evaluate distribution probabilities for lead-times, identify bottlenecks and predict the effect of adopting specific policies for supply, picking and transportation. In order to address these issues, it is necessary to answer complex what-if queries that take into account the temporal relations, either specified or dictated by the process dynamics, between the events. Since all operations are logged, a substantial amount of historical data is generated. However, these data do not necessarily cover all situations, simply because certain feasible and relevant combinations of events may not have occurred during the period that data is logged. This motivates the use of simulations that generate huge amounts of data augmenting the (logged) historical data, and making big data analytics necessary. In this paper, we briefly describe the process of model building based on historical data as well as the construction of a simulation engine that permits efficient large scale simulations. The simulation results, together with the logged historical data are subjected to big data analytics in order to create global prediction models. The proposed methodology aims to answer complex what-if queries about the logistics processes with a high degree of efficiency and prediction accuracy. The software tool, based on this methodology, is designed so that a decision maker can interactively detect critical situations and study the global effect of changes in policies. Some examples of queries that can be supported by this research are: (i) estimation of distribution probabilities for lead-times under varied circumstances; and (ii) probability of critical materials shortage during periods of high demand.
Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2020
Classifying sentences in industrial, technical or scientific reports can enhance text mining and ... more Classifying sentences in industrial, technical or scientific reports can enhance text mining and information retrieval tasks with useful machinereadable metadata. This paper describes a search engine that employs sentence classification so as to search for abstracts from scholarly papers in Petroleum Engineering. The sentences were classified into four classes, based on the popular IMRAD categories. We produced a dataset containing more than 2,200 manually labeled sentences from 278 scholarly articles in the field of Petroleum Engineering in order to be used as training and testing data. The classifier with best results was logistic regression, with an accuracy of 86.4%. The information retrieval system built on top of the classification system yielded a mAP of 0.80.
Big data analytics, applied in the industry to leverage data collection, processing and analysis,... more Big data analytics, applied in the industry to leverage data collection, processing and analysis, can allow a better understanding of production system's abnormal behavior. This knowledge is essential for the adoption of a proactive maintenance approach instead of conventional time-based strategies, leading to a paradigm shift towards Condition-Based Maintenance (CBM) since decision is now based on the usage of a huge, diverse, and dynamic amounts of data as a means to optimize operational costs. This paper reports an investigation of the emerging aspects in the design and implementation of big data analytics solutions for offshore installations in order to allow predictive maintenance practices. Condition-based maintenance focuses on performing interventions based on the actual and future states (health) of a system by monitoring the underlying deterioration processes. One of the building blocks of a CBM design and implementation is the prognostic approach/system, which aims to...
Figure 1: Our approach enables creating multimedia annotations and provides a tree-based structur... more Figure 1: Our approach enables creating multimedia annotations and provides a tree-based structure to support traceability in decision making. (a) An annotation in the VE, created by user "Obama". It also shows how to create a new annotation. (b) Annotation as it is playing. It relies on camera movements (synchronized with audio) to show an anomaly in the model. (c) The discussion structure. Balloons indicate annotations' type and colors denote arguments' type (Pro, Con, +Info). The user is adding a (fourth) positive argument (Pro) with an audio annotation, as it can be seen by active buttons. Abstract Globalization has transformed engineering design into a worldwide endeavor pursued by geographically distributed specialist teams. Widespread adoption of VR for design and the need to act and place marks directly on the objects of discussion in design reviewing tasks led to research on annotations in virtual collaborative environments. However, conventional approache...
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Papers by Ismael Santos