IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p486-d310557.html
   My bibliography  Save this article

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

Author

Listed:
  • Zhang Qiang

    (Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), 66th Changjiang West Road, Qingdao 266580, China
    Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
    Research Institute of Exploration and Development, Shengli Oilfield Branch, SINOPEC, Dongying, Shandong 257015, China)

  • Qamar Yasin

    (Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), 66th Changjiang West Road, Qingdao 266580, China
    Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China)

  • Naser Golsanami

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

  • Qizhen Du

    (Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China), 66th Changjiang West Road, Qingdao 266580, China
    Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China)

Abstract

This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:486-:d:310557
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/486/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/2/486/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pinghe Sun & Junyi Zhu & Binkui Zhao & Xinxin Zhang & Han Cao & Mingjin Tian & Meng Han & Weisheng Liu, 2019. "Study on the Mechanism of Ionic Stabilizers on Shale Gas Reservoir Mechanics in Northwestern Hunan," Energies, MDPI, vol. 12(12), pages 1-11, June.
    2. Abdulrauf R. Adebayo & Lamidi Babalola & Syed R. Hussaini & Abdullah Alqubalee & Rahul S. Babu, 2019. "Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir," Energies, MDPI, vol. 12(22), pages 1-27, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. Seyedalireza Khatibi & Azadeh Aghajanpour, 2020. "Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field," Energies, MDPI, vol. 13(14), pages 1-16, July.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Reza Rezaee, 2022. "Editorial on Special Issues of Development of Unconventional Reservoirs," Energies, MDPI, vol. 15(7), pages 1-9, April.
    2. Aliya Mukhametdinova & Andrey Kazak & Tagir Karamov & Natalia Bogdanovich & Maksim Serkin & Sergey Melekhin & Alexey Cheremisin, 2020. "Reservoir Properties of Low-Permeable Carbonate Rocks: Experimental Features," Energies, MDPI, vol. 13(9), pages 1-25, May.
    3. Pinghe Sun & Meng Han & Han Cao & Weisheng Liu & Shaohe Zhang & Junyi Zhu, 2020. "Development and Performance Evaluation of Solid-Free Drilling Fluid for CBM Reservoir Drilling in Central Hunan," Energies, MDPI, vol. 13(18), pages 1-16, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:486-:d:310557. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.