Autor(es)

Sousa, G. J. ; Pedronette, D. C. G. ; Baldassin, A. ; Privatto, P. I. M. ; Gaseta, M. ; Guilherme, I. R. ; Colombo Cenpes, D. ; Afonso, L. C. S. ; Papa, J. P. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2018-01-01.

Resumo

Well drilling monitoring is an essential task to prevent faults, save resources, and take care of environmental and eco-planning businesses. During drilling, it is required that staff fill out a log to keep track of the activities that are currently occurring. With such data analyzed and processed, it is possible to learn how to prevent faults and take corrective actions in real-time. However, the most important information is usually stored in a free-text format, thus complicating the task of automated text mining. In this work, we introduce the Optimum-Path Forest (OPF) for sentence classification in drilling reports and compare its results against some state-of-art results. We show that OPF combined with text-based features are a compelling source to learn patterns in drilling reports. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático