Autor(es): Ribeiro, Luiz C.F. [UNESP] ; Afonso, Luis C.S. ; Colombo, Danilo ; Guilherme, Ivan R. [UNESP] ; Papa, João P. [UNESP]
Data: 2020
Identificador Persistente: http://hdl.handle.net/11449/201430
Origem: Oasisbr
Assunto(s): Conditional Random Fields; Drilling reports classification; Natural Language Processing; Conditional Random Fields; Conditional Random Fields; Drilling reports classification; Drilling reports classification; Natural Language Processing; Natural Language Processing
Made available in DSpace on 2020-12-12T02:32:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-04-01
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Petrobras
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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 (Petróleo 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
UNESP - São Paulo State University School of Sciences
UFSCar - Federal University of São Carlos Department of Computing
Cenpes - Petróleo Brasileiro S.A.
UNESP - São Paulo State University Inst. of Geosciences and Exact Sciences
UNESP - São Paulo State University School of Sciences
UNESP - São Paulo State University Inst. of Geosciences and Exact Sciences
FAPESP: #2013/07375-0
Petrobras: #2014/00545-0
FAPESP: #2014/12236-1
FAPESP: #2016/19403-6
CNPq: #307066/2017-7
CNPq: #427968/2018-6
