Carlos Magno Couto Jacinto

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This paper intends to show how two different methodologies, a Monte Carlo simulation method and a connectionist approach can be used to estimate the total time assessment in drilling and completion operations of oil wells in deep waters. The former approach performs a Monte Carlo simulation based on data from field operations. In the later one, correlations(More)
Nearly every well installation process nowadays relies on some sort of risk assessment study, given the high costs involved. Those studies focus mostly on estimating the total time required by the well drilling and completion operations, as a way to predict the final costs. Among the different techniques employed, the Monte Carlo simulation currently stands(More)
Scale deposition can damage equipment in the oil & gas production industry. Hence, the reliable and accurate prediction of the scale deposition rate is critical for production availability. In this study, we consider the problem of predicting the scale deposition rate, providing an indication of the associated prediction uncertainty. We tackle the problem(More)
This paper presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference(More)
The present work focuses on the development of a simulation method which provides an engineering tool for managing the risks associated with the development of an oil field. The developed method consists of performing discrete simulation based on data from field operations. The paper reports and discusses the simulation results of a real field development(More)
Data-driven learning methods for predicting the evolution of the degradation processes affecting equipment are becoming increasingly attractive in reliability and prognostics applications. Among these, we consider here Support Vector Regression (SVR), which has provided promising results in various applications. Nevertheless, the predictions provided by SVR(More)
Failure Prediction of Oil Wells by Support Vector Regression with Variable Selection, Hyperparameter Tuning and Uncertainty Analysis Isis Lins*, Márcio Moura, Enrique Droguett, Enrico Zio, Carlos Jacinto Center for Risk Analysis and Environmental Modeling, Federal University of Pernambuco, Av. da Arquitetura, s/n, Cidade Universitária, 50740-550, Recife,(More)
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