Evolving Neural Conditional Random Fields for drilling report classification

Autor(es)

Ribeiro, Luiz C. F. ^[1] ; Afonso, Luis C. S. ^[2] ; Colombo, Danilo ^[3] ; Guilherme, Ivan R. ^[4] ; Papa, Joao P. ^[1]

Afiliação do(s) autor(es)

^[1] UNESP Sao Paulo State Univ, Sch Sci, Sao Paulo - Brazil

^[2] UFSCar Fed Univ Sao Carlos, Dept Comp, Sao Carlos - Brazil

^[3] Cenpes Petroleo Brasileiro SA, Rio De Janeiro - Brazil

^[4] UNESP Sao Paulo State Univ, Inst Geosci & Exact Sci, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING; v. 187, APR 2020.
• Citações Web of Science: 0

Resumo

Oil and gas prospecting is an important economic activity, besides being expensive and quite complex, thus requiring close monitoring to avoid work accidents and mainly environmental damages. An essential source of information concerns the daily drilling reports that contain operations technical interpretations and additional information from rig sensors. However, only a few works have focused on mining textual information from such reports for providing intelligent-based decision-making mechanisms to aid safety and efficiency concerns in drilling operations. This work proposes a contextual-driven approach based on Recurrent Neural Networks to recognize events in drilling reports that can outperform other related techniques. We also introduce a novel approach based on evolutionary computing to combine partially trained models using cyclical learning rates. Experiments conducted on two unbalanced datasets provided by Petrobras (Petroleo Brasileiro S.A.) show that our model improved Macro-F1 scores over the baseline by more than 47%. Besides, the proposed ensembling technique further enhanced these values by another 3% in the best scenario. Such promising results can shed light over new research directions in the field.(1) (AU)

• 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
• 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