Predicting Shale Volume from Seismic Traces Using Modified Random Vector Functional Link Based on Transient Search Optimization Model: A Case Study from Netherlands North Sea

[1]  Jitendra Singh Bhadoriya,et al.  A novel transient search optimization for optimal allocation of multiple distributed generator in the radial electrical distribution network , 2021, International Journal of Emerging Electric Power Systems.

[2]  David A. Wood,et al.  Predicting Formation Pore-Pressure from Well-Log Data with Hybrid Machine-Learning Optimization Algorithms , 2021, Natural Resources Research.

[3]  H. Pan,et al.  Machine learning - A novel approach of well logs similarity based on synchronization measures to predict shear sonic logs , 2021 .

[4]  A. Nair,et al.  Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study , 2021 .

[5]  M. Nabih.  Reliability of velocity-deviation logs for shale content evaluation in clastic reservoirs: a case study, Egypt , 2021, Arabian Journal of Geosciences.

[6]  I. Zahmatkesh,et al.  Integration of well log-derived facies and 3D seismic attributes for seismic facies mapping: A case study from mansuri oil field, SW Iran , 2021, Journal of Petroleum Science and Engineering.

[7]  U. Iturrarán-Viveros,et al.  Machine Learning as a Seismic Prior Velocity Model Building Method for Full-Waveform Inversion: A Case Study from Colombia , 2021, Pure and Applied Geophysics.

[8]  Kewen Xia,et al.  An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems , 2021, Symmetry.

[9]  Runhai Feng,et al.  Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm , 2021 .

[10]  M. Abd Elaziz,et al.  Prediction of power consumption and water productivity of seawater greenhouse system using random vector functional link network integrated with artificial ecosystem-based optimization , 2020 .

[11]  Hany M. Hasanien,et al.  Optimal Transient Search Algorithm-Based PI Controllers for Enhancing Low Voltage Ride-Through Ability of Grid-Linked PMSG-Based Wind Turbine , 2020, Electronics.

[12]  Q. Du,et al.  Application of machine learning tool to predict the porosity of clastic depositional system, Indus Basin, Pakistan , 2020 .

[13]  Taher A. Shehabeldeen,et al.  Utilization of Random Vector Functional Link integrated with Marine Predators Algorithm for tensile behavior prediction of dissimilar friction stir welded aluminum alloy joints , 2020 .

[14]  Ammar H. Elsheikh,et al.  Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer , 2020, Journal of Intelligent Manufacturing.

[15]  Hany M. Hasanien,et al.  Transient search optimization: a new meta-heuristic optimization algorithm , 2020, Applied Intelligence.

[16]  Hany M. Hasanien,et al.  Transient search optimization for electrical parameters estimation of photovoltaic module based on datasheet values , 2020 .

[17]  Mohamed Abd Elaziz,et al.  Enhancing thermal performance and modeling prediction of developed pyramid solar still utilizing a modified random vector functional link , 2020 .

[18]  U. Strecker,et al.  Direct prediction of petrophysical and petroelastic reservoir properties from seismic and well-log data using nonlinear machine learning algorithms , 2019 .

[19]  M. Nabih,et al.  New approach for releasing uranium radiation impact on shale content evaluation in shaly sand formations: A case study, Egypt. , 2018, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine.

[20]  Aurobinda Routray,et al.  Well-Log and Seismic Data Integration for Reservoir Characterization: A Signal Processing and Machine-Learning Perspective , 2018, IEEE Signal Processing Magazine.

[21]  Plamen P. Angelov,et al.  Parsimonious random vector functional link network for data streams , 2017, Inf. Sci..

[22]  Efkan Kabaca.  Seismic stratigraphic analysis using multiple attributes - an application to the f3 block, offshore Netherlands , 2018 .

[23]  Kevin P. Dorrington,et al.  Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction , 2004 .

[24]  M. Kamel,et al.  Estimation of shale volume using a combination of the three porosity logs , 2003 .

[25]  B. Schroot,et al.  Expressions of shallow gas in the Netherlands North Sea , 2003, Netherlands Journal of Geosciences - Geologie en Mijnbouw.

[26]  B. Schroot.  North Sea Shallow Gas as a Natural Analogue in Feasibility Studies on CO2 Sequestration , 2002 .

[27]  Gert Jan Weltje,et al.  The Late Cenozoic Eridanos delta system in the Southern North Sea Basin: a climate signal in sediment supply? , 2001 .

[28]  Olaf Michelsen,et al.  High-frequency sequence stratigraphy of Upper Cenozoic deposits in the central and southeastern North Sea areas , 1997 .

[29]  C. Laban.  The Pleistocene glaciations in the Dutch sector of the North Sea. A synthesis of sedimentary and seismic data , 1995 .

[30]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[31]  Crain,et al.  Log analysis handbook , 1986 .