Water Intrusion Characterization in Naturally Fractured Gas Reservoir Based on Spatial DFN Connectivity Analysis

Pengyu Chen, Mauricio Fiallos-Torres, Yuzhong Xing, Wei Yu, Chunqiu Guo, Joseph Leines-Artieda, Muwei Cheng, Hongbing Xie, Haidong Shi, Zhenyu Mao, Jijun Miao and Kamy Sepehrnoori
Additional contact information
Pengyu Chen: The Research Institute of Petroleum Exploration and Development CNPC, Beijing 100083, China
Mauricio Fiallos-Torres: Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Yuzhong Xing: The Research Institute of Petroleum Exploration and Development CNPC, Beijing 100083, China
Wei Yu: Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Chunqiu Guo: The Research Institute of Petroleum Exploration and Development CNPC, Beijing 100083, China
Joseph Leines-Artieda: Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Muwei Cheng: The Research Institute of Petroleum Exploration and Development CNPC, Beijing 100083, China
Hongbing Xie: Sim Tech LLC, Houston, TX 77494, USA
Haidong Shi: The Research Institute of Petroleum Exploration and Development CNPC, Beijing 100083, China
Zhenyu Mao: Sim Tech LLC, Houston, TX 77494, USA
Jijun Miao: Sim Tech LLC, Houston, TX 77494, USA
Kamy Sepehrnoori: Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA

Energies, 2020, vol. 13, issue 16, 1-37

Abstract: In this study, the non-intrusive embedded discrete fracture model (EDFM) in combination with the Oda method are employed to characterize natural fracture networks fast and accurately, by identifying the dominant water flow paths through spatial connectivity analysis. The purpose of this study is to present a successful field case application in which a novel workflow integrates field data, discrete fracture network (DFN), and production analysis with spatial fracture connectivity analysis to characterize dominant flow paths for water intrusion in a field-scale numerical simulation. Initially, the water intrusion of single-well sector models was history matched. Then, resulting parameters of the single-well models were incorporated into the full field model, and the pressure and water breakthrough of all the producing wells were matched. Finally, forecast results were evaluated. Consequently, one of the findings is that wellbore connectivity to the fracture network has a considerable effect on characterizing the water intrusion in fractured gas reservoirs. Additionally, dominant water flow paths within the fracture network, easily modeled by EDFM as effective fracture zones, aid in understanding and predicting the water intrusion phenomena. Therefore, fracture clustering as shortest paths from the water contacts to the wellbore endorses the results of the numerical simulation. Finally, matching the breakthrough time depends on merging responses from multiple dominant water flow paths within the distributions of the fracture network. The conclusions of this investigation are crucial to field modeling and the decision-making process of well operation by anticipating water intrusion behavior through probable flow paths within the fracture networks.

Keywords: water intrusion; fractured gas reservoirs; natural fractures; embedded discrete fracture model; water breakthrough; shortest path (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/16/4235/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/16/4235/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:16:p:4235-:d:399736

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().