Application of Artificial Intelligence Techniques in Estimating Oil Recovery Factor for Water Derive Sandy Reservoirs

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

[2]  R. C. Craze,et al.  A Factual Analysis Of The Effect Of Well Spacing On Oil Recovery , 1945 .

[3]  Di Chai,et al.  Pseudo Density Log Generation Using Artificial Neural Network , 2016 .

[4]  Mohamed Mahmoud,et al.  New insights into the prediction of heterogeneous carbonate reservoir permeability from well logs using artificial intelligence network , 2017, Neural Computing and Applications.

[5]  Ahmad M. AlMaraghi,et al.  Automatic Reservoir Model Identification using Artificial Neural Networks in Pressure Transient Analysis , 2015 .

[6]  Salaheldin Elkatatny,et al.  Real time prediction of drilling fluid rheological properties using Artificial Neural Networks visible mathematical model (white box) , 2016 .

[7]  Salaheldin Elkatatny,et al.  Prediction of Bubble Point Pressure Using Artificial Intelligence AI Techniques , 2016 .

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

[9]  R. K. Guthrie,et al.  The Use of Multiple-correlation Analyses for Interpreting Petroleum-engineering Data , 1955 .

[10]  Mohamed Mahmoud,et al.  New Technique to Determine the Total Organic Carbon Based on Well Logs Using Artificial Neural Network (White Box) , 2017 .

[11]  T. Ertekin,et al.  Characterization of Partially Sealing Faults from Pressure Transient Data: An Artificial Neural Network Approach , 2002 .

[12]  Rick L Gulstad The determination of hydrocarbon reservoir recovery factors by using modern multiple linear regression techniques , 1995 .

[13]  M. Muskat,et al.  Effect of Reservoir Fluid and Rock Characteristics on Production Histories of Gas-drive Reservoirs , 1946 .

[14]  Salaheldin Elkatatny,et al.  A New Approach to Predict Failure Parameters of Carbonate Rocks using Artificial Intelligence Tools , 2017 .

[15]  Tarek Ahmed,et al.  Reservoir Engineering Handbook , 2002 .

[16]  W. W. Weiss,et al.  Fractured Reservoir Characterization and Performance Forecasting Using Geomechanics and Artificial Intelligence , 1995 .

[17]  David C. Gardner,et al.  Analyzing Well Production Data Using Combined Type Curve and Decline Curve Analysis Concepts , 1998 .

[18]  J. J. Arps,et al.  The Effect of the Relative Permeability Ratio, the Oil Gravity, and the Solution Gas-Oil Ratio on the Primary Recovery From a Depletion Type Reservoir , 1955 .

[19]  Ahmed Ouenes,et al.  Fractured Reservoir Characterization Using Streamline-Based Inverse Modeling and Artificial Intelligence Tools , 2000 .

[20]  Salaheldin Elkatatny Determination the Rheological Properties of Invert Emulsion Based Mud on Real Time Using Artificial Neural Network , 2016 .

[21]  Zeeshan Tariq,et al.  A New Artificial Intelligence Based Empirical Correlation to Predict Sonic Travel Time , 2016 .

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

[23]  M. F. Hawkins,et al.  Applied Petroleum Reservoir Engineering , 1991 .

[24]  Shahab D. Mohaghegh,et al.  A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks , 1994 .

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

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

[27]  Zeeshan Tariq,et al.  A Holistic Approach to Develop New Rigorous Empirical Correlation for Static Young's Modulus , 2016 .