Banca de DEFESA: ANTONIO PAULO AMANCIO FERRO

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : ANTONIO PAULO AMANCIO FERRO
DATE: 21/03/2024
TIME: 09:00
LOCAL: Defesa de mestrado - Antonio Ferro Quinta-feira, 21 de março · 09:00 até 12:00 Fuso horário: America
TITLE:

Predictive models for ROP as support for real-time optimization of operational drilling parameters in oil well drilling

 

 

 

KEY WORDS:

Drilling. Rate of Penetration. Machine Learning. Real-Time.

PAGES: 150
BIG AREA: Engenharias
AREA: Engenharia Civil
SUBÁREA: Estruturas
SPECIALTY: Mecânica das Estruturas
SUMMARY:

The Rate of Penetration (ROP) is a parameter of great interest in real-time drilling optimization. A
higher ROP reduces drilling time and can lead to significant cost reductions in well construction.
Predictive models for ROP are applied to predict ROP based on measured data while drilling,
enabling the determination of optimal operational parameters such as RPM, WOB, and fluid
flow when combined with optimization techniques. Obtaining more accurate ROP models is a
challenging task due to the large number of factors interacting nonlinearly. This study involves the
examination of different ROP models, including traditional ones, like Bourgoyne & Young and
Specific Mechanical Energy, and their adaptations, compared with machine learning models such
as Artificial Neural Networks (ANN) and Random Forests (RF). Public data from 7 wells were
structured into a dataset with relevant information for evaluating the performance of different
models in estimating ROP, including operational parameters, drill-bit data, lithology, geophysical
logging data, pore pressure gradient, and unconfined compressive strength. In comparative
analyses, error metrics such as Mean Absolute Error (MAE) and Root Mean Square Error
(RMSE) are compared among the different models for each of the 7 wells. Statistical significance
analysis is performed with the Bourgoyne & Young model to understand more significant effects
on ROP. The interpretability of traditional models, along with hyperparameter tuning, is adopted
to employ machine learning models with more meaningful inputs and greater predictive capacity.
Next, two strategies found in the literature for using predictive models for ROP in real-time
optimization are compared: models trained with offset well data or with the well’s own data,
simulating a gradual data acquisition process (continuous learning). The results indicate better
performance of machine learning models compared to traditional models. The RF model shows
overall better performance in comparative analyses, with smaller errors and lower computational
cost. The relevance of torque and the inclusion of formation data (Delta-T compressional) in
machine learning models is identified. Also, continuous learning strategy can achieve lower
errors, although both strategies are capable of generating appropriate predictions.

COMMITTEE MEMBERS:
Presidente - 1121075 - ALINE DA SILVA RAMOS BARBOZA
Interno(a) - 1121260 - EDUARDO NOBRE LAGES
Externo(a) à Instituição - CHARLTON OKAMA DE SOUZA - PETROBRAS