scholarly journals Performance evaluation of machine learning algorithms in predicting dew point pressure of gas condensate reservoirs

2020 ◽  
Vol 2 (12) ◽  
Author(s):  
Princewill Ikpeka ◽  
Johnson Ugwu ◽  
Paul Russell ◽  
Gobind Pillai
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Learning Algorithms ◽  
Gas Condensate ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Gas Condensate Reservoirs ◽  
Point Pressure ◽  
Dew Point Pressure

Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs

2014 ◽  
Vol 92 (5) ◽  
pp. 891-902 ◽  
Author(s):  
Seyed Mohammad Javad Majidi ◽  
Amin Shokrollahi ◽  
Milad Arabloo ◽  
Ramin Mahdikhani-Soleymanloo ◽  
Mohsen Masihi
Keyword(s):  
Machine Learning ◽  
Gas Condensate ◽  
Dew Point ◽  
Learning Approach ◽  
Accurate Model ◽  
Gas Condensate Reservoirs ◽  
Model Based ◽  
Machine Learning Approach ◽  
Point Pressure ◽  
Dew Point Pressure

A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms

2021 ◽  
Author(s):  
Thitaree Lertliangchai ◽  
Birol Dindoruk ◽  
Ligang Lu ◽  
Xi Yang
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Compositional Data ◽  
Machine Learning Algorithms ◽  
Empirical Correlation ◽  
Dew Point ◽  
Pvt Data ◽  
Point Pressure ◽  
Input Variables ◽  
Dew Point Pressure

Abstract Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.

Hybrid machine learning algorithms to predict condensate viscosity in the near wellbore regions of gas condensate reservoirs

2021 ◽  
pp. 104210
Author(s):  
Abouzar Rajabi Behesht Abad ◽  
Seyedmohammadvahid Mousavi ◽  
Nima Mohamadian ◽  
David A. Wood ◽  
Hamzeh Ghorbani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Gas Condensate ◽  
Machine Learning Algorithms ◽  
Gas Condensate Reservoirs ◽  
Hybrid Machine

scholarly journals Robust correlation to predict dew point pressure of gas condensate reservoirs

Petroleum ◽  
2017 ◽  
Vol 3 (3) ◽  
pp. 340-347 ◽  
Author(s):  
Mohammad Ali Ahmadi ◽  
Adel Elsharkawy
Keyword(s):  
Gas Condensate ◽  
Dew Point ◽  
Gas Condensate Reservoirs ◽  
Point Pressure ◽  
Dew Point Pressure

GA-RBF model for prediction of dew point pressure in gas condensate reservoirs

2016 ◽  
Vol 223 ◽  
pp. 979-986 ◽  
Author(s):  
Adel Najafi-Marghmaleki ◽  
Afshin Tatar ◽  
Ali Barati-Harooni ◽  
Mohammad-Javad Choobineh ◽  
Amir H. Mohammadi
Keyword(s):  
Gas Condensate ◽  
Dew Point ◽  
Gas Condensate Reservoirs ◽  
Point Pressure ◽  
Dew Point Pressure
Sign in / Sign up

Export Citation Format

Share Document