Impact of geological variables in controlling oil-reservoir performance: An insight from a machine-learning technique
Predicting oilfield performance is extremely challenging because of the large number of
variables that can influence and control it. Traditional methods such as decline‐curve …
variables that can influence and control it. Traditional methods such as decline‐curve …
Estimating hydrocarbon recovery factor at reservoir scale via machine learning: Database-dependent accuracy and reliability
This study aims to estimate recovery factor (RF), a key property for exploration, from other
reservoir characteristics, such as porosity, permeability, pressure, and water saturation via …
reservoir characteristics, such as porosity, permeability, pressure, and water saturation via …
[HTML][HTML] Machine learning for recovery factor estimation of an oil reservoir: A tool for derisking at a hydrocarbon asset evaluation
Well-known oil recovery factor estimation techniques such as analogy, volumetric
calculations, material balance, decline curve analysis, hydrodynamic simulations have …
calculations, material balance, decline curve analysis, hydrodynamic simulations have …
Machine-learning algorithm for estimating oil-recovery factor using a combination of engineering and stratigraphic dependent parameters
The methods used to estimate recovery factor change through the life cycle of a field. During
appraisal, prior to development when there are no production data, we typically rely on …
appraisal, prior to development when there are no production data, we typically rely on …
Applicability of LAMDA as classification model in the oil production
This work analyzes the utilization of classification models in the context of the oil industry
and presents examples of application. Particularly, we analyze three case studies, two to …
and presents examples of application. Particularly, we analyze three case studies, two to …
Opening new opportunities with fast reservoir-performance evaluation under uncertainty: Brugge field case study
RR Torrado, DE Ciaurri, UT Mello… - SPE Economics & …, 2015 - onepetro.org
Decision making under uncertainty can be quite challenging, especially when complex
numerical simulations are considered in the work flow and the decision has to be made …
numerical simulations are considered in the work flow and the decision has to be made …
New proxy models for predicting oil recovery factor in waterflooded heterogeneous reservoirs
M Al-Jifri, H Al-Attar, F Boukadi - Journal of Petroleum Exploration and …, 2021 - Springer
To predict the recovery factor (RF) in waterflooded layered oil reservoirs, two empirical
relationships were derived. Both correlations use four independent variables. These are …
relationships were derived. Both correlations use four independent variables. These are …
Using data analytics on dimensionless numbers to predict the ultimate recovery factors for different drive mechanisms of Gulf of Mexico oil fields
G Talluru, X Wu - SPE Annual Technical Conference and Exhibition?, 2017 - onepetro.org
The ultimate recovery factor is strongly affected by petrophysical parameters, engineering
data, structures, and drive mechanisms. The knowledge of the recovery factor is needed for …
data, structures, and drive mechanisms. The knowledge of the recovery factor is needed for …
Estimating oil and gas recovery factors via machine learning: Database-dependent accuracy and reliability
With recent advances in artificial intelligence, machine learning (ML) approaches have
become an attractive tool in petroleum engineering, particularly for reservoir …
become an attractive tool in petroleum engineering, particularly for reservoir …
Estimating oil recovery factor using machine learning: applications of XGBoost classification
In petroleum engineering, it is essential to determine the ultimate recovery factor, RF,
particularly before exploitation and exploration. However, accurately estimating requires …
particularly before exploitation and exploration. However, accurately estimating requires …