Statistical Time Estimates for Deepwater Completions in the Gulf of Mexico: Integration of Neural Networking and Distribution Modeling

Аннотация

Predicting completion time in deepwater wells is an imminent necessity in the modern well construction cycle. The primary objective of this thesis is to present a novel integrated approach of statistical analysis and neural networking to identify well characteristics for their impact on total time to complete a well. Using a neural network, fifteen crucial attributes from the Dodson Database were used in this study and analyzed for relative impact with respect to time. These attributes included primary parameters such as well depth and interval number. Wells in the database were assigned a value, depending on their fifteen attributes, that correlated to length of time to complete.