Determination of heavy oil viscosity as a funtion of the ratio of log means of T1to T2NMR relaxation times distributions

2009 ◽  
Author(s):  
Pedro A. Romero ◽  
Benito Saavedra ◽  
Hyung T. Kwak ◽  
Gabor Hursan
Keyword(s):  
Heavy Oil ◽  
Relaxation Times ◽  
Oil Viscosity

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

scholarly journals Designing pressure build-up test on heavy oil well by alternating oil viscosity

2019 ◽  
Vol 1402 ◽  
pp. 022029 ◽  
Author(s):  
M T Fathaddin ◽  
R H K Oetomo ◽  
N Hisanah
Keyword(s):  
Heavy Oil ◽  
Oil Well ◽  
Oil Viscosity

Resin from Liaohe Heavy Oil: Molecular Structure, Aggregation Behavior, and Effect on Oil Viscosity

2017 ◽  
Vol 32 (1) ◽  
pp. 306-313 ◽  
Author(s):  
Tao Li ◽  
Jun Xu ◽  
Run Zou ◽  
Hao Feng ◽  
Li Li ◽  
...  
Keyword(s):  
Molecular Structure ◽  
Heavy Oil ◽  
Aggregation Behavior ◽  
Oil Viscosity
Sign in / Sign up

Export Citation Format

Share Document