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NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock

Author

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  • Naser Golsanami

    (State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China
    College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Xuepeng Zhang

    (State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China
    College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Weichao Yan

    (Department of Well Logging, School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, China)

  • Linjun Yu

    (No.12 Oil Production Plant, Changqing Oilfield Company, PetroChina, Xi’an 710200, China)

  • Huaimin Dong

    (Department of Well Logging, School of Geosciences, China University of Petroleum (Huadong), Qingdao 266580, China)

  • Xu Dong

    (Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development, Ministry of Education, Northeast Petroleum University, Daqing 163318, China)

  • Likai Cui

    (Institute of Unconventional Oil and Gas, Northeast Petroleum University, Daqing 163318, China)

  • Madusanka Nirosh Jayasuriya

    (College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shanilka Gimhan Fernando

    (College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Ehsan Barzgar

    (State Key Laboratory of Petroleum Resources and Prospecting, and Unconventional Petroleum Research Institute, China University of Petroleum, Beijing 102249, China)

Abstract

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T 2 ) curves were synthesized artificially. This task was done by dividing the area under the T 2 curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T 2 curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.

Suggested Citation

  • Naser Golsanami & Xuepeng Zhang & Weichao Yan & Linjun Yu & Huaimin Dong & Xu Dong & Likai Cui & Madusanka Nirosh Jayasuriya & Shanilka Gimhan Fernando & Ehsan Barzgar, 2021. "NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock," Energies, MDPI, vol. 14(5), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1513-:d:513807
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    References listed on IDEAS

    as
    1. Zhang Qiang & Qamar Yasin & Naser Golsanami & Qizhen Du, 2020. "Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan," Energies, MDPI, vol. 13(2), pages 1-19, January.
    2. Xuexian Gao & Haidong Zheng & Yan Zhang & Naser Golsanami, 2019. "Tax Policy, Environmental Concern and Level of Emission Reduction," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
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    Cited by:

    1. Golsanami, Naser & Jayasuriya, Madusanka N. & Yan, Weichao & Fernando, Shanilka G. & Liu, Xuefeng & Cui, Likai & Zhang, Xuepeng & Yasin, Qamar & Dong, Huaimin & Dong, Xu, 2022. "Characterizing clay textures and their impact on the reservoir using deep learning and Lattice-Boltzmann simulation applied to SEM images," Energy, Elsevier, vol. 240(C).
    2. Bin Gong & Ruijie Ye & Ruiqi Zhang & Naser Golsanami & Yujing Jiang & Dingrui Guo & Sajjad Negahban, 2023. "The Failure Mechanism of Methane Hydrate-Bearing Specimen Based on Energy Analysis Using Discrete Element Method," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    3. Weichao Yan & Fujing Sun & Jianmeng Sun & Naser Golsanami, 2021. "Distribution Model of Fluid Components and Quantitative Calculation of Movable Oil in Inter-Salt Shale Using 2D NMR," Energies, MDPI, vol. 14(9), pages 1-17, April.
    4. Naser Golsanami & Bin Gong & Sajjad Negahban, 2022. "Evaluating the Effect of New Gas Solubility and Bubble Point Pressure Models on PVT Parameters and Optimizing Injected Gas Rate in Gas-Lift Dual Gradient Drilling," Energies, MDPI, vol. 15(3), pages 1-25, February.
    5. Marta Skiba & Barbara Dutka & Mariusz Młynarczuk, 2021. "MLP-Based Model for Estimation of Methane Seam Pressure," Energies, MDPI, vol. 14(22), pages 1-12, November.

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