Banca de DEFESA: JOSENÍLSON GOMES DE ARAÚJO

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : JOSENÍLSON GOMES DE ARAÚJO
DATE: 24/02/2022
TIME: 09:00
LOCAL: meet.google.com/mtv-vtpt-eff
TITLE:

APPLICATION OF DEEP LEARNING MODELS IN THE ESTIMATION OF OIL FLOWRATES IN OFFSHORE WELLS WITH ELECTRICAL SUBMERSIBLE PUMPING

KEY WORDS:

fluid flowrate measurement; virtual flow metering (VFM); electric submersible pumps (ESP); deep learning; LSTM neural networks; Industry 4.0.

PAGES: 83
BIG AREA: Engenharias
AREA: Engenharia Química
SUBÁREA: Tecnologia Química
SPECIALTY: Petróleo e Petroquímica
SUMMARY:

The fluid flow measurement is a fundamental activity for the oil and gas industry. The correct produced volumes mensuration provides a good reservoirs management, reducing production losses, guiding plans of the production system optimization and of the lift and production flow methods. The use of flow estimation techniques in real time using Virtual Flow Metering (VFM) has shown to be a promising field due to the provided results precision and their low-cost implementation. Deep learning models have been applied successful in the oil and gas industry. Combining technological advances and the great importance of fluid measurement for the oil industry, the study aims to develop a model for the flowrate of liquid applying an approach combined of Long Short-Term Memory (LSTM) models and hydrodynamical modelling. The data used were power, frequency, pressure and were collected from two offshore wells with electric submersible pumps (ESP) in Northeast region of Brazil. The LSTM results compare favorably with the results of hydrodynamical modeling and increases its powerful, they can be useful joint to accurately estimate the flowrate behavior in real time in transient and steady states and to forecast the flowrate for a sequence of future time instants, supporting better production management. It is expected that the results obtained with the LSTM neural networks can be integrated with other technologies of Industry 4.0 and contribute to the digital transformation of the oil and gas industry.

BANKING MEMBERS:
Presidente - 6350734 - CARLA WILZA SOUZA DE PAULA MAITELLI
Interno - 347628 - ADRIAO DUARTE DORIA NETO
Externa à Instituição - TATIANA ESCOVEDO - PUC - RJ
Externo à Instituição - FABIO SOARES DE LIMA - PETROBRAS