Zhao et al., 2017 - Google Patents
Machine learning–based trigger detection of drilling events based on drilling dataZhao et al., 2017
- Document ID
- 7201197543472846979
- Author
- Zhao J
- Shen Y
- Chen W
- Zhang Z
- Johnston S
- Publication year
- Publication venue
- SPE Eastern Regional Meeting
External Links
Snippet
A method is developed to detect the precursors of drilling events based on drilling data such as surface data, wellbore geometry data, lithology (formation characteristics), and downhole measurements from various downhole tools. The drilling events refer to interesting behavior …
- 238000005553 drilling 0 title abstract description 59
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhao et al. | Machine learning–based trigger detection of drilling events based on drilling data | |
US9582764B2 (en) | Real-time risk prediction during drilling operations | |
Unrau et al. | Machine learning algorithms applied to detection of well control events | |
Magana-Mora et al. | AccuPipePred: A framework for the accurate and early detection of stuck pipe for real-time drilling operations | |
Ahmed et al. | Stuck Pipe Early Warning System Utilizing Moving Window Machine Learning Approach | |
US20130341090A1 (en) | Detecting Stick-Slip Using A Gyro While Drilling | |
Meor Hashim et al. | Utilizing artificial neural network for real-time prediction of differential sticking symptoms | |
WO2015060865A1 (en) | Real-time risk prediction during drilling operations | |
Li et al. | Incipient fault detection for geological drilling processes using multivariate generalized Gaussian distributions and Kullback–Leibler divergence | |
Maidla et al. | Drilling analysis using big data has been misused and abused | |
Othman et al. | Application of machine learning to augment wellbore geometry-related stuck pipe risk identification in real time | |
Salminen et al. | Stuck pipe prediction using automated real-time modeling and data analysis | |
Meor Hashim et al. | Case Studies for the Successful Deployment of Wells Augmented Stuck Pipe Indicator in Wells Real Time Centre | |
Mao et al. | An automated kick alarm system based on statistical analysis of real-time drilling data | |
Ambrus et al. | Self-learning probabilistic detection and alerting of drillstring washout and pump failure incidents during drilling operations | |
Stone | Introducing predictive analytics: Opportunities | |
Chang et al. | Application of machine learning in transient surveillance in a deep-water oil field | |
Kaneko et al. | Hybrid approach using physical insights and data science for early stuck detection | |
Wong et al. | Advances in Real-Time Event Detection While Drilling | |
Brankovic et al. | A data-based approach for the prediction of stuck-pipe events in oil drilling operations | |
Lawal et al. | Real-time prediction of mud motor failure using surface sensor data features and trends | |
Robinson et al. | Real-Time estimation of downhole equivalent circulating density ECD using machine learning and applications | |
Nybø et al. | Closing the integration gap for the next generation of drilling decision support systems | |
Alzahrani et al. | Novel Stuck Pipe Troubles Prediction Model Using Reinforcement Learning | |
Zhang et al. | Advanced Realtime Early Kick and Loss Detection (EKLD) Through a Hybrid Data-Driven and Physics-Constrained Approach |