New model for standpipe pressure prediction while drilling using Group Method of Data Handling
出版年份 2021 全文链接
标题
New model for standpipe pressure prediction while drilling using Group Method of Data Handling
作者
关键词
-
出版物
Petroleum
Volume 8, Issue 2, Pages 210-218
出版商
Elsevier BV
发表日期
2021-04-22
DOI
10.1016/j.petlm.2021.04.003
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Machine Learning Methods for Herschel–Bulkley Fluids in Annulus: Pressure Drop Predictions and Algorithm Performance Evaluation
- (2020) Abhishek Kumar et al. Applied Sciences-Basel
- Prediction of CO2 diffusivity in brine using white-box machine learning
- (2020) Menad Nait Amar et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Prediction of Lattice Constant of A2XY6 Cubic Crystals Using Gene Expression Programming
- (2020) Menad Nait Amar et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Modeling solubility of sulfur in pure hydrogen sulfide and sour gas mixtures using rigorous machine learning methods
- (2020) Menad Nait Amar INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
- Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm
- (2020) Mohamed Riad Youcefi et al. Earth Science Informatics
- Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network
- (2019) Salaheldin Elkatatny Arabian Journal of Geosciences
- Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: Application to thermal enhanced oil recovery processes
- (2019) Nait Amar Menad et al. FUEL
- On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment
- (2018) Abdolhossein Hemmati-Sarapardeh et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models
- (2018) Cesar Soares et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids
- (2017) Vitor Diego da Silva Bispo et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Application of general regression neural network (GRNN) for indirect measuring pressure loss of Herschel–Bulkley drilling fluids in oil drilling
- (2016) Reza Rooki MEASUREMENT
- Toward reliable model for prediction Drilling Fluid Density at wellbore conditions: A LSSVM model
- (2016) Mohammad Ali Ahmadi NEUROCOMPUTING
- Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared
- (2016) Rassoul Khosravanian et al. Journal of Natural Gas Science and Engineering
- Estimation of Pressure Loss of Herschel–Bulkley Drilling Fluids During Horizontal Annulus Using Artificial Neural Network
- (2014) Reza Rooki JOURNAL OF DISPERSION SCIENCE AND TECHNOLOGY
- Evolving smart approach for determination dew point pressure through condensate gas reservoirs
- (2013) Mohammad Ali Ahmadi et al. FUEL
- An evolutionary-based hyper-heuristic approach for optimal construction of group method of data handling networks
- (2013) J. Gascón-Moreno et al. INFORMATION SCIENCES
- Annular Frictional Pressure Losses During Drilling—Predicting the Effect of Drillstring Rotation
- (2013) Arild Saasen JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
- Laminar, transitional and turbulent flow of Herschel-Bulkley fluids in concentric annulus
- (2008) K. Founargiotakis et al. CANADIAN JOURNAL OF CHEMICAL ENGINEERING
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