Real-Time Determination of Rheological Properties of Spud Drilling Fluids Using a Hybrid Artificial Intelligence Technique

[1]  Mario Zamora Drilling Fluid Yield Stress: Measurement Techniques for Improved Understanding of Critical Drilling Fluid Parameters , 2003 .

[2]  J. J. Azar,et al.  Experimental Study of Drilled Cuttings Transport Using Common Drilling Muds , 1983 .

[3]  Dennis Denney Artificial-Intelligence Approach for Well-To-Well Log Correlation , 1998 .

[4]  Shan Sung Liew,et al.  An optimized second order stochastic learning algorithm for neural network training , 2015, Neurocomputing.

[5]  Jong-Se Lim,et al.  Artificial-intelligence approach for well-to-well log correlation , 1998 .

[6]  Salaheldin Elkatatny,et al.  Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique , 2018 .

[7]  Salaheldin Elkatatny,et al.  Real-Time Prediction of Rheological Parameters of KCl Water-Based Drilling Fluid Using Artificial Neural Networks , 2017 .

[8]  Mohamed Mahmoud,et al.  Development of New Permeability Formulation From Well Log Data Using Artificial Intelligence Approaches , 2018 .

[9]  Muhammad M. Saggaf,et al.  Estimation of Lithologies And Depositional Facies From Wireline Logs , 1998 .

[10]  Si Le Van,et al.  Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks , 2018 .

[11]  Goshtasp Cheraghian,et al.  Application of Nano-Particles of Clay to Improve Drilling Fluid , 2017 .

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Olivier Francois Allain,et al.  A Practical Artificial Intelligence Application in Well Test Interpretation , 1992 .

[14]  H. N. Marsh,et al.  Properties and Treatment of Rotary Mud , 1931 .

[15]  Saeed Salehi,et al.  Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach , 2015 .

[16]  E. C. Bingham Fluidity And Plasticity , 1922 .

[17]  Arvind Kumar,et al.  Artificial Neural Network as a Tool for Reservoir Characterization and its Application in the Petroleum Engineering , 2012 .

[18]  H. D. Outmans Mechanics of Differential Pressure Sticking of Drill Collars , 1958 .

[19]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[20]  George R. Gray,et al.  Composition and Properties of Drilling and Completion Fluids , 1988 .

[21]  Dennis Denney,et al.  Reservoir Characterization by Integrating Time-Lapse-Seismic and Production Data , 1998 .

[22]  Djebbar Tiab,et al.  Application of Artificial Intelligence to Characterize Naturally Fractured Reservoirs , 2003 .

[23]  Mahmood Hemmati,et al.  Application of TiO2 and fumed silica nanoparticles and improve the performance of drilling fluids , 2014 .

[24]  Bernt S. Aadnoy .... contributors Fundamentals of drilling engineering , 2011 .

[25]  Georg Zangl,et al.  Innovative Approach to Assist History Matching Using Artificial Intelligence , 2006 .

[26]  Gonzalo Jesus Olivares Velazquez,et al.  Production Monitoring Using Artificial Intelligence , 2012 .

[27]  A. Saasen,et al.  Particle Size Distribution of Top-Hole Drill Cuttings from Norwegian Sea Area Offshore Wells , 2013 .

[28]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[29]  Abdulazeez Abdulraheem,et al.  Estimating Dewpoint Pressure Using Artificial Intelligence , 2012 .

[30]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[31]  Robert Balch,et al.  How Artificial Intelligence Methods Can Forecast Oil Production , 2002 .

[32]  Xinhua Yang,et al.  An improved self-adaptive differential evolution algorithm and its application , 2013 .

[33]  M. J. Pitt,et al.  The Marsh Funnel and Drilling Fluid Viscosity: A New Equation for Field Use , 2000 .

[34]  Salaheldin Elkatatny,et al.  Determination of the total organic carbon (TOC) based on conventional well logs using artificial neural network , 2017 .

[35]  Ş. Niculescu Artificial neural networks and genetic algorithms in QSAR , 2003 .

[36]  Masoud Afrand,et al.  Effect of a novel clay/silica nanocomposite on water-based drilling fluids: Improvements in rheological and filtration properties , 2018, Colloids and Surfaces A: Physicochemical and Engineering Aspects.

[37]  Shahab D. Mohaghegh,et al.  Artificial Intelligence (AI) Assisted History Matching , 2014 .

[38]  Abdulazeez Abdulraheem,et al.  Profiling Downhole Casing Integrity Using Artificial Intelligence , 2015 .

[39]  J. Wiener,et al.  Predict permeability from wireline logs using neural networks , 1995 .

[40]  O. P. Houze,et al.  A Hybrid Artificial Intelligence Approach in Well Test Interpretation , 1992 .

[41]  Habib Rostami,et al.  Application of Artificial Neural Network–Particle Swarm Optimization Algorithm for Prediction of Gas Condensate Dew Point Pressure and Comparison With Gaussian Processes Regression–Particle Swarm Optimization Algorithm , 2016 .

[42]  Jamal Shahrabi,et al.  Automatic well-testing model diagnosis and parameter estimation using artificial neural networks and design of experiments , 2017, Journal of Petroleum Exploration and Production Technology.