Hybrid Approach Using Physical Insights and Data Science for Stuck-Pipe Prediction

Early sign detection for the stuck pipe scenarios using unsupervised deep learning

(2021) Konda Reddy Mopuri et al.   JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING

Early Stuck Pipe Sign Detection with Depth-Domain 3D Convolutional Neural Network Using Actual Drilling Data

(2020) Naoki Tsuchihashi et al.   SPE JOURNAL

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Stuck-Pipe Prediction by Use of Automated Real-Time Modeling and Data Analysis

(2017) Kent Salminen et al.   SPE DRILLING & COMPLETION

A survey of methods for time series change point detection

(2016) Samaneh Aminikhanghahi et al.   KNOWLEDGE AND INFORMATION SYSTEMS

Early Symptom Detection on the Basis of Real-Time Evaluation of Downhole Conditions: Principles and Results From Several North Sea Drilling Operations

(2012) Eric Cayeux et al.   SPE DRILLING & COMPLETION

Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

(2012) Reza Jahanbakhshi et al.   SPE DRILLING & COMPLETION

Perspectives on system identification

(2010) Lennart Ljung   ANNUAL REVIEWS IN CONTROL