Banca de QUALIFICAÇÃO: JOAO MARIA ARAUJO DO NASCIMENTO

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : JOAO MARIA ARAUJO DO NASCIMENTO
DATE: 17/06/2021
TIME: 14:30
LOCAL: meet.google.com/wiw-ycpw-snm
TITLE:

Diagnosis of Operating Conditions and Sensor Failures in Wells Operating by Mechanical Pumping Using Machime Learning

KEY WORDS:

Sucker-rod pumping, Machine Learning and Dynamometric cards

PAGES: 69
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUBÁREA: Eletrônica Industrial, Sistemas e Controles Eletrônicos
SPECIALTY: Automação Eletrônica de Processos Elétricos e Industriais
SUMMARY:

In oil fields with many wells operating by Sucker-rod pumping, due to the lack of
early diagnosis of operating conditions or sensor faults, several problems can go unnoticed.
These problems can generate large losses, such as increased downtime, increased
OPEX (operating expenses), decreased efficiency and lost production. In practice, the
identification and diagnosis of operating conditions are carried out from downhole dynamometric
cards, using pre-established standards, with human visual effort in the operation
centers. This task requires a lot of time and work, in addition to requiring deep experience,
as it can be influenced by subjective factors. However, in recent years, with the facilities
inherent to Machine Learning (ML) algorithms, several researches on the subject have
achieved good results in the diagnosis of operating conditions, showing that ML can be
used for this purpose. However, it is still common to have doubts about the difficulty level
of the dynamometer card classification task, the best algorithm, the best shape descriptor,
the best metrics and what is the impact of the imbalanced datasets. In the search for
answers to these questions, this work used real data from 38 wells operating by sucker-rod
pumping in the region of Mossoró, RN, Brazil. More than 50,000 cards have been classified
by specialists and distributed in eight modes of operation and two common sensor
faults in this field. Sixty tests were performed and divided into seven groups. Three algorithms
were tested (with and without hyperparameter tuning): Decision Tree, Random
Forest and XgBoost, in addition to pipelines provided by Automated Machine Learning
(AutoML). The descriptors used were: Fourier descriptors andWavelet descriptors, in addition
to the load values of the downhole dynamometric card. Balanced and imbalanced
datasets were also tested. The results that will be presented confirm the feasibility of applying
ML for diagnosis of operating conditions and sensor faults in sucker-rod pumping
systems, since 75% of the tests reached accuracy above 92% and a maximum accuracy
was 99.84%, higher than most of the reviewed papers.

BANKING MEMBERS:
Presidente - 350693 - ANDRE LAURINDO MAITELLI
Externo ao Programa - 2453033 - ANDERSON LUIZ DE OLIVEIRA CAVALCANTI
Externa ao Programa - 6350734 - CARLA WILZA SOUZA DE PAULA MAITELLI
Externo à Instituição - OSCAR GABRIEL FILHO - PETROBRAS