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Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits

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  • Edyta Puskarczyk

    (Department of Geophysics, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

The main purpose of the study is a detailed interpretation of the facies and relate these to the results of standard well logs interpretation. Different methods were used: firstly, multivariate statistical methods, like principal components analysis, cluster analysis and discriminant analysis; and secondly, the artificial neural network, to identify and discriminate the facies from well log data. Determination of electrofacies was done in two ways: firstly, analysis was performed for two wells separately, secondly, the neural network learned and trained on data from the W-1 well was applied to the second well W-2 and a prediction of the facies distribution in this well was made. In both wells, located in the area of the Carpathian Foredeep, thin-layered sandstone-claystone formations were found and gas saturated depth intervals were identified. Based on statistical analyses, there were recognized presence of thin layers intersecting layers of much greater thickness (especially in W-2 well), e.g., section consisting mainly of claystone and sandstone formations with poor reservoir parameters (Group B) is divided with thin layers of sandstone and claystone with good reservoir parameters (Group C). The highest probability of occurrence of hydrocarbons exists in thin-layered intervals in facies C.

Suggested Citation

  • Edyta Puskarczyk, 2020. "Application of Multivariate Statistical Methods and Artificial Neural Network for Facies Analysis from Well Logs Data: an Example of Miocene Deposits," Energies, MDPI, vol. 13(7), pages 1-18, March.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1548-:d:337399
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    References listed on IDEAS

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    2. Solomon Asante-Okyere & Chuanbo Shen & Yao Yevenyo Ziggah & Mercy Moses Rulegeya & Xiangfeng Zhu, 2018. "Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability," Energies, MDPI, vol. 11(12), pages 1-13, November.
    3. Chuanbo Shen & Solomon Asante-Okyere & Yao Yevenyo Ziggah & Liang Wang & Xiangfeng Zhu, 2019. "Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques," Energies, MDPI, vol. 12(8), pages 1-16, April.
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    Cited by:

    1. Tadeusz Kwilosz & Bogdan Filar & Mariusz Miziołek, 2022. "Use of Cluster Analysis to Group Organic Shale Gas Rocks by Hydrocarbon Generation Zones," Energies, MDPI, vol. 15(4), pages 1-14, February.
    2. Stanisław Baudzis & Joanna Karłowska-Pik & Edyta Puskarczyk, 2021. "Electrofacies as a Tool for the Prediction of True Resistivity Using Advanced Statistical Methods—Case Study," Energies, MDPI, vol. 14(19), pages 1-18, September.
    3. Sebastian Waszkiewicz & Paulina I. Krakowska-Madejska, 2021. "Vitrinite Equivalent Reflectance Estimation from Improved Maturity Indicator and Well Logs Based on Statistical Methods," Energies, MDPI, vol. 14(19), pages 1-16, September.
    4. Guangjuan Fan & Ting Dong & Yuejun Zhao & Yalou Zhou & Wentong Zhao & Jie Wang & Yilong Wang, 2023. "Establishment and Application of a Pattern for Identifying Sedimentary Microfacies of a Single Horizontal Well: An Example from the Eastern Transition Block in the Daqing Oilfield, Songliao Basin, Chi," Energies, MDPI, vol. 16(20), pages 1-19, October.
    5. Marcin Kremieniewski, 2022. "Improving the Efficiency of Oil Recovery in Research and Development," Energies, MDPI, vol. 15(12), pages 1-7, June.

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