An intelligent identification method of interlayers in deep clastic rock – An example of Donghe Sandstone in Hade Oilfield, Tarim Basin

[1]  David A. Wood.  Optimized feature selection assists lithofacies machine learning with sparse well log data combined with calculated attributes in a gradational fluvial sequence , 2022, Artificial Intelligence in Geosciences.

[2]  David A. Wood,et al.  Performance evaluation of boosting machine learning algorithms for lithofacies classification in heterogeneous carbonate reservoirs , 2022, Marine and Petroleum Geology.

[3]  V. Srivardhan.  Adaptive boosting of random forest algorithm for automatic petrophysical interpretation of well logs , 2022, Acta Geodaetica et Geophysica.

[4]  David A. Wood.  Carbonate / siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning , 2022, Earth Science Informatics.

[5]  M. Riahi,et al.  Reservoir facies classification based on random forest and geostatistics methods in an offshore oilfield , 2022, Journal of Applied Geophysics.

[6]  Yi-Qing Zhang,et al.  Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption , 2022, Journal of Hydroinformatics.

[7]  Xiaoyong Yang,et al.  Genesis of interlayer oxidation zone-type uranium deposit in the channel conglomerates, Beisantai area, Junggar Basin: An insight into uranium mineralization , 2021, Ore Geology Reviews.

[8]  D. Grana,et al.  An unsupervised deep-learning method for porosity estimation based on poststack seismic data , 2020 .

[9]  Haifeng Li,et al.  Application of EEMD-HHT Method on EEG Analysis for Speech Evoked Emotion Recognition , 2020, 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR).

[10]  Prediction and Analysis of Pollutant using Supervised Machine Learning , 2020, Regular.

[11]  Liuqing Yang,et al.  Deep learning reservoir porosity prediction based on multilayer long short-term memory network , 2020 .

[12]  Mingliang Liu,et al.  Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework , 2019, Comput. Geosci..

[13]  Hongqi Li,et al.  The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling , 2019, Journal of Petroleum Science and Engineering.

[14]  G. Beroza,et al.  CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection , 2018, Scientific Reports.

[15]  Vandana Roy,et al.  Enhanced Empirical Mode DecompositionApproach toEliminateMotion Artifacts in EEG using ICA and DWT , 2016 .

[16]  Yih Jeng,et al.  A data-driven multidimensional signal-noise decomposition approach for GPR data processing , 2015, Comput. Geosci..

[17]  Guochang Wang,et al.  Application of Artificial Intelligence on Black Shale Lithofacies Prediction in Marcellus Shale, Appalachian Basin , 2014 .

[18]  T. Jiang,et al.  Feature, origin and distribution of calcareous interlayers: A case of Carboniferous Donghe sandstone in Hade Oil Field, Tarim Basin, NW China , 2014 .

[19]  Sun Longde,et al.  Unsteady reservoir in Hadson Oilfield, Tarim Basin , 2009 .

[20]  Christopher D. White,et al.  Multivariate statistical log log-facies classification on a shallow marine reservoir , 2008 .

[21]  Norden E. Huang,et al.  A review on Hilbert‐Huang transform: Method and its applications to geophysical studies , 2008 .

[22]  Saumen Maiti,et al.  Automatic detection of lithologic boundaries using the Walsh transform: A case study from the KTB borehole , 2005, Comput. Geosci..

[23]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[24]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[25]  Zhao Shue.  A method for quantitative division of sequence stratigraphy with high-resolution using logging data and its application , 2013 .

[26]  Ren Jin-fen.  A method for quantitative division of sequence stratigraphy with high-resolution based on wavelet transform and its application , 2013 .

[27]  Chang Lun-jie.  SEDIMENTARY STRUCTURE AND SEQUENCE BOUNDARY IDENTIFICATION BASED ON FORMATION DIPLOG , 2008 .