Intelligent decisions to stop or mitigate lost circulation based on machine learning
暂无分享,去创建一个
[1] Emad A. El-Sebakhy,et al. Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme , 2009 .
[2] A. Abbas,et al. Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis , 2018, Day 1 Mon, December 10, 2018.
[3] Holger R. Maier,et al. The Development of an Optimal Artificial Neural Network Model for Estimating Initial Water Saturation - Australian Reservoir , 2005 .
[4] Lloyd Heinze,et al. Implementing Artificial Neural Networks and Support Vector Machines in Stuck Pipe Prediction , 2012 .
[5] Yingfeng Meng,et al. Prediction of wellbore and formation temperatures during circulation and shut-in stages under kick conditions , 2015 .
[6] Hooman Adib,et al. Support Vector Machine based modeling of an industrial natural gas sweetening plant , 2013 .
[7] Ferat Sahin,et al. A survey on feature selection methods , 2014, Comput. Electr. Eng..
[8] Y. Xiong,et al. Wellbore temperature distribution during circulation stage when well-kick occurs in a continuous formation from the bottom-hole , 2018, Energy.
[9] Arild Saasen,et al. Updated criterion to select particle size distribution of lost circulation materials for an effective fracture sealing , 2017 .
[10] Vamegh Rasouli,et al. The influence of perturbed stresses near faults on drilling strategy: A case study in Blacktip field, North Australia , 2011 .
[11] Luis Puigjaner,et al. Performance assessment of a novel fault diagnosis system based on support vector machines , 2009, Comput. Chem. Eng..
[12] Mohsen Hadian,et al. Using artificial neural network predictive controller optimized with Cuckoo Algorithm for pressure tracking in gas distribution network , 2015 .
[13] Yan Gao,et al. Maximization of energy absorption for a wave energy converter using the deep machine learning , 2018, Energy.
[14] Ildar Z. Batyrshin,et al. Fuzzy expert system for solving lost circulation problem , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).
[15] Z. You,et al. Friction coefficient: A significant parameter for lost circulation control and material selection in naturally fractured reservoir , 2019, Energy.
[16] Mortadha Alsaba,et al. Modeling Rate of Penetration for Deviated Wells Using Artificial Neural Network , 2018 .
[17] Gunnar Rätsch,et al. An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.
[18] Arild Saasen,et al. Lost Circulation Materials Capability of Sealing Wide Fractures , 2014 .
[19] A. Ramezanzadeh,et al. Machine learning technique for the prediction of shear wave velocity using petrophysical logs , 2019, Journal of Petroleum Science and Engineering.
[20] Amin Gholami,et al. Support vector regression based determination of shear wave velocity , 2015 .
[21] Sumit Roy,et al. An experimental based ANN approach in mapping performance-emission characteristics of a diesel engine operating in dual-fuel mode with LPG , 2016 .
[22] Md. Rafiul Hassan,et al. Hydraulic unit prediction using support vector machine , 2013 .
[23] Fionn Iversen,et al. Drilling Automation: Potential for Human Error , 2013 .
[24] Ehsan Khamehchi,et al. A novel approach to sand production prediction using artificial intelligence , 2014 .
[25] Majid Amidpour,et al. A hybrid artificial neural network and genetic algorithm for predicting viscosity of Iranian crude oils , 2014 .
[26] Vladimir Ceperic,et al. Short-term forecasting of natural gas prices using machine learning and feature selection algorithms , 2017 .
[27] Ajoy Kumar Das,et al. A comparative study of GEP and an ANN strategy to model engine performance and emission characteristics of a CRDI assisted single cylinder diesel engine under CNG dual-fuel operation , 2014 .
[28] Hamid Taghavifar,et al. Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices , 2014 .
[29] Sanjeev S. Tambe,et al. Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst , 2004 .
[30] Mostafa Keshavarz Moraveji,et al. Experimental and field test analysis of different loss control materials for combating lost circulation in bentonite mud , 2017 .
[31] Yanbin Yuan,et al. Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine , 2017 .
[32] Saeed Salehi,et al. Casing collapse risk assessment and depth prediction with a neural network system approach , 2009 .
[33] Alireza Bahadori,et al. Prediction of natural gas flow through chokes using support vector machine algorithm , 2014 .
[34] Mostafa Alizadeh,et al. Performance prediction model of Miscible Surfactant-CO2 displacement in porous media using support vector machine regression with parameters selected by Ant colony optimization , 2016 .
[35] Ralph E. Flori,et al. Learning From Experience: An Automatic pH Neutralization System Using Hybrid Fuzzy System and Neural Network , 2018 .
[36] R. Jahanbakhshi,et al. Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields , 2012 .
[37] Sunday O. Olatunji,et al. Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization , 2015 .
[38] A. Abbas,et al. Laboratory analysis to assess shale stability for the Zubair Formation, Southern Iraq , 2018, Journal of Natural Gas Science and Engineering.
[39] Pezhman Kazemi,et al. A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking , 2017 .
[40] Seyed Reza Shadizadeh,et al. Modeling and Optimizing Rate of Penetration Using Intelligent Systems in an Iranian Southern Oil Field (Ahwaz Oil Field) , 2011 .
[41] Mohammad Hassan Khalid,et al. Computational intelligence modeling of granule size distribution for oscillating milling , 2016 .
[42] Farshad Rashidinejad,et al. Prediction of penetration rate of rotary-percussive drilling using artificial neural networks – a case study / Prognozowanie postępu wiercenia przy użyciu wiertła udarowo-obrotowego przy wykorzystaniu sztucznych sieci neuronowych – studium przypadku , 2012 .
[43] Vamegh Rasouli,et al. Practical application of failure criteria in determining safe mud weight windows in drilling operations , 2014 .