Research graph
References from A new prediction model of bottomhole weight on bit in offshore extended-reach drilling based on the integration of intelligent algorithms and the physical models of rate of penetration. Local targets link to admitted publications; unresolved targets remain external evidence.
Nikaitchuq extended-reach drilling: designing for success on the north slope of Alaska
10.2118/149778-pa · 2012 · External reference
Comprehensive experimental investigation of hole cleaning performance in horizontal wells including the effects of drill string eccentricity, pipe rotation, and cuttings size
10.1115/1.4052102 · 2022 · External reference
Integrated machine learning and analytical modeling for real-time lost circulation in fractured formations
2025 · External reference
Optimization of drilling parameters while drilling surface holes using machine learning and differential evolution
2025 · External reference
Semi-analytical models on the effect of drilling fluid properties on rate of penetration (ROP)
2011 · External reference
Automated lithology classification from drill core images using convolutional neural networks
10.1016/j.petrol.2020.107933 · 2021 · External reference
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
10.1016/j.ress.2014.09.014 · 2015 · External reference
Rate of penetration (ROP) model for PDC drill bits based on cutter rock interaction
2020 · External reference
XRF-FTIR data fusion and machine learning models for mineralogical analysis of solid mineral mixtures and solid dispersions in drilling fluids
2025 · External reference
Penetration rate prediction models for core drilling
2021 · External reference
Unresolved reference
1965 · External reference
Enhancing the drilling efficiency through the application of machine learning and optimization algorithm
10.1016/j.engappai.2023.107035 · 2023 · External reference
Real-time prediction and optimization of drilling performance based on a new mechanical specific energy model
10.1007/s13369-014-1376-0 · 2014 · External reference
Real-time optimization of drilling parameters based on mechanical specific energy for rotating drilling with positive displacement motor in the hard formation
10.1016/j.jngse.2016.09.019 · 2016 · External reference
A real-time drilling parameters optimization method for offshore large-scale cluster extended reach drilling based on intelligent optimization algorithm and machine learning
10.1016/j.oceaneng.2023.116375 · 2024 · External reference
Dynamic depth correction to reduce depth uncertainty and improve MWD/LWD log quality
10.2118/103094-pa · 2008 · External reference
Investigation and analysis of influential parameters in bottomhole stick–slip calculation during vertical drilling operations
10.3390/en17030622 · 2024 · External reference
Real-time solution for down hole torque estimation and drilling optimization in high deviated wells using artificial intelligence
2023 · External reference
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
10.1016/j.neunet.2005.06.042 · 2005 · External reference
LSTM: a search space odyssey
10.1109/tnnls.2016.2582924 · 2016 · External reference
A drilling rate model for roller cone bits and its application
2010 · External reference
Using trees, bagging, and random forests to predict rate of penetration during drilling
2015 · External reference
Investigating the impact of drillpipe’s rotation and eccentricity on cuttings transport phenomenon in various horizontal annuluses using computational fluid dynamics (CFD)
10.1016/j.petrol.2017.06.059 · 2017 · External reference
Self-attentive sequential recommendation
2018 · External reference
Measurement, prediction, and modeling of the drilling specific energy by soft rock properties during the drilling operation
10.1016/j.measurement.2023.113679 · 2023 · External reference
ImageNet classification with deep convolutional neural networks
10.1145/3065386 · 2017 · External reference
Research progress and the prospect of intelligent drilling and completion technologies
2023 · External reference
Integrating mechanics and machine learning for build-up rate prediction
10.1016/j.geoen.2024.213594 · 2025 · External reference
Characteristics and suggestions of global oil and gas exploration
2020 · External reference
Research progress in attention mechanism in deep learning
2021 · External reference
Analysis of spiraled-borehole data by use of a novel directional-drilling model
10.2118/167992-pa · 2014 · External reference
Drilling optimization applying machine learning regression algorithms
2021 · External reference
Comparing vision transformers and convolutional neural networks for image classification: a literature review
10.3390/app13095521 · 2023 · External reference
Prediction of penetration rate for PDC bits using indices of rock drillability, cuttings removal, and bit wear
10.2118/204231-pa · 2021 · External reference
Improved drilling efficiency technique using integrated PDM and PDC bit parameters
10.2118/141651-pa · 2010 · External reference
Combining insight from physics-based models into data-driven model for predicting drilling rate of penetration
2020 · External reference
Torque-on-bit (TOB) prediction and optimization using machine learning algorithms
10.1016/j.jngse.2020.103623 · 2020 · External reference
Multivariable sales prediction for filling stations via GA improved BiLSTM
10.1016/j.petsci.2022.05.005 · 2022 · External reference
Coupled drilling optimization based on hybrid physics-machine learning models
2025 · External reference
Developing a machine learning-based methodology for optimal hyperparameter determination—A mathematical modeling of high-pressure and high-temperature drilling fluid behavior
10.1016/j.ceja.2024.100663 · 2024 · External reference
A unique computer simulation model well drilling: part II—The drilling model evaluation
1986 · External reference
Stuck-pipe prediction by use of automated real-time modeling and data analysis
10.2118/178888-pa · 2017 · External reference
Experimental investigation into rate of penetration in carbonated rocks
10.1504/ijogct.2016.075086 · 2016 · External reference
Sequence to sequence learning with neural networks
2014 · External reference
Deep learning method for improving rate of penetration prediction in drilling
10.2118/219746-pa · 2024 · External reference
Attention is all you need
2017 · External reference
Intelligent prediction model of downhole weight on bit based on GA-Bi-LSTM and its application
2024 · External reference
Current status and prospects of offshore oil and gas drilling technology development in China
2023 · External reference
Comparative analysis of machine learning techniques for predicting drilling rate of penetration (ROP) in geothermal wells: a case study of FORGE site
10.1016/j.geothermics.2024.103028 · 2024 · External reference
Research on dynamic prediction of tubular extension limit and operation risk in extended-reach drilling
10.1016/j.jngse.2022.104542 · 2022 · External reference
Review of convolutional neural network
2017 · External reference
