Layer | Fill | Outline |
---|
Map layers
Theme | Visible | Selectable | Appearance | Zoom Range (now: 0) |
---|
Fill | Stroke |
---|---|
Collaborating Authors
Results
Quantitative Prediction of Lost Circulation Risk Ahead of Bit Using Leaky Noisy-Or Gate Bayesian Networks and Seismic Attributes
Yuan, Duo (Sinopec Research Institute of Petroleum Engineering) | Lu, Baoping (Sinopec Research Institute of Petroleum Engineering) | Haider, Syed Tabish (Sinopec Tech Middle East R&D Center)
Abstract Quantitative prediction of lost circulation risk ahead of bit is of great significance for mud loss prevention and well control. This paper provides a method that is able to not only locate the potential mud loss points along well trajectory to drill, but also estimate their severity and occurrence probability using leaky Noisy-OR gate Bayesian network and seismic-well data mining. All the risk factors of lost circulation are identified and then they are mapped into a Leaky Noisy-OR Gate Bayesian network to generate a conditional probability distribution table, so as to assess the lost circulation risk quantitatively ahead of bit. This method has been tested and applied successfully and these predicted results provide decision basis for preventing and dealing with the problems of lost circulation.
- Asia (0.47)
- North America > United States (0.29)
- North America > United States > Texas > Meramec Formation > Meramec Formation > Mississippi Chat > Mississippi Lime > St. Louis Formation (0.98)
- North America > United States > Texas > Meramec Formation > Meramec Formation > Mississippi Chat > Meramec Formation > St. Louis Formation (0.98)
- North America > United States > Texas > Meramec Formation > Meramec Formation > Meramec Formation > Mississippi Lime > St. Louis Formation (0.98)
- (21 more...)
- Well Drilling > Pressure Management > Well control (1.00)
- Well Drilling > Drilling Fluids and Materials > Drilling fluid management & disposal (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (1.00)