EVALUATING MACHINE LEARNING TECHNIQUES FOR DETECTION OF
FLOW INSTABILITY EVENTS IN OFFSHORE OIL WELLS
Name: BRUNO GUILHERME CARVALHO
Publication date: 04/10/2021
Advisor:
Name |
Role |
|---|---|
| FLÁVIO MIGUEL VAREJÃO | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| CELSO JOSE MUNARO | Internal Examiner * |
| FLÁVIO MIGUEL VAREJÃO | Advisor * |
| RICARDO EMANUEL VAZ VARGAS | Co advisor * |
Summary: Flow instability is an abnormal operational state in offshore oil wells. For the oil and gas
industry, methods to detect and classify faults as soon as possible are crucial to reduce
downtime and increase efficiency. The application of machine learning algorithms has
been extensively applied in an industrial context, proven to be a viable way to tackle this
kind of problem. In this study, an evaluation is performed on the application of machine
learning techniques for the detection and classification of pressure and temperature sensor
readings related to flow instability. Firstly, a custom cross-validation splitting strategy
is defined and compared to the classical equal split. Results are shown to be much more
realistic when checked on previous publications. Next, grid search is chosen to evaluate
whether hyperparameter tuning could increase the classifiers performance. Results were
not satisfactory. Then, feature selection is applied to reduce problem dimension and
circumvent the curse of dimensionality. Three different methods were used: sequential
feature selection, hybrid ranking wrapper, and genetic algorithm. Only a few methods
have shown a decrease in the number of features selected while improving classification
performance measured with F1. The genetic algorithm was one of those, proving to be
a robust selector even when the similarity bias is removed. Finally, an analysis of the
results from all experiments is performed to find which of the statistical features are more
relevant and from what sensor they come from. Standard deviation and variance from the
P-MON-CKP sensor are found much frequently than the others.
Keywords: flow instability; machine learning; cross-validation; feature selection; genetic
algorithm.
