Jinze Xu
Zhangxing Chen
Jinghong Hu
Keliu Wu
Desheng Zhou- 1Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB, Canada
- 2College of Petroleum Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, China
- 3Beijing Key Laboratory of Unconventional Natural Gas Geology Evaluation and Development Engineering, China University of Geosciences, Beijing, China
- 4State Key Laboratory of Petroleum Resources and Prospecting in China University of Petroleum (Beijing), Beijing, China
Editorial on the Research Topic
Advances in reservoir modelling and simulation
In light of the current energy crisis, the development of oil and gas resources must adhere to a higher standard of both efficiency and precision. Reservoir simulation and modelling has been a significant tool in increasing oil and gas recovery. Engineers are able to better visualize the subsurface environment, study fluid dynamics, and come up with ideas for production improvement by applying reservoir simulation and modelling (Fanchi, 2005; Islam et al., 2016). The growing complexity of reservoirs promotes the further advancement of reservoir modelling and simulation. In recent years, there has been a significant development in the investigation of reservoir modelling and simulation, particularly in the fields of heavy oil, shale gas, and shale oil. For instance, in the numerical simulation of heavy oil reservoirs, large-scale reservoir models directly serve production, and certain heterogeneity, such as lean zone, can also be successfully examined (Xu et al., 2014; Xu et al., 2016). The flow law of gas at the micro and nano scales is transferred to the real field size in the numerical modelling of the shale gas reservoir (Xu et al., 2017; Xu et al., 2018; Xu et al., 2019). The simulation technology of in-situ conversion is emerging at a quick pace with considering the effects of thermochemical reaction and thermophysical reaction on the production process (Xu et al., 2021).
This special issue gives a summary of the most current advancements in numerical reservoir modelling. We shall outline briefly to present this special issue:
To improve gas reservoir development, Xiao et al. built a CBM (coalbed methane) prediction model for wells. In this study, the influence of coal characteristics and reservoir geology conditions on CBM production was examined. They discovered that a larger absorption capacity increases CBM production.
Wang et al. used numerical and physical simulation to investigate the water control process in a gas field. In addition, their research revealed that the water-blocking and water-sensing capacities affect the water control process. The use of a continuous packer is suggested based on simulation findings.
Using panel data modelling and simulation methods, Shi et al. evaluated China’s oil resources. In this study, the Malmquist-Luenberger Index was used to more accurately measure the static efficiency of time nodes. Their research demonstrated that the development of oil fields in China’s eastern area is superior to the west.
Cai et al. used deep learning into the indicator modelling and simulation. In this study, the AlexNet model was enhanced by adding more convolution layers. The comparison to actual working situations revealed that the model presented in this study is greatly enhanced in terms of estimation precision.
The influence of natural gas hydrates on the pressure of a deep-water pipeline was anticipated by Mo et al. In this study, a mathematical model connecting pipeline flow and hydrate volume is constructed. The modelling findings indicate that hydrate development will result in a more severe pipeline obstruction.
Deng et al. analyzed the production of a multilayer heterogeneous reservoir using a production model that took experimental data into account. Their research shown that an increase in the permeability gradient increases oil output, with the high-permeability layer contributing more to oil recovery.
To analyze the flow efficiency, Li et al. devised a gas/water relative permeability model based on fractal theory. In this study, the authors linked the geomechanical equation, the flow equation, and the fractal equation to demonstrate that stress and slip flow jointly govern the gas/water flow efficiency. Also noted is the link between wettability and relative permeability.
Liu et al. conducted research on the mechanism of hydraulic fracture propagation in infill horizontal wells. Based on geomechanical models, they examined the interactions between original fractures and artificial fractures. Their research revealed that fracture spacing and well spacing influence the propagation trajectory.
Author contributions
JX-Draft manuscript; ZC, JH, KW, and DZ-Review and revision.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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References
Islam, M. R., Abou-Kassem, J. H., Hossain, M. E., Mousavizadegan, S. H., and Mustafiz, S. (2016). Advanced petroleum reservoir simulation: Towards developing reservoir emulators. Hoboken, NJ: John Wiley & Sons.
Xu, J., Chen, Z., and Li, R. (2014). 3D Geological modeling and uncertainty analysis of pilot pad in the Long Lake field with lean zone and shale layer.GeoConvention
Xu, J., Chen, Z., Wu, K., Li, R., Liu, X., and Zhan, J. (2019). On the flow regime model for fast estimation of tight sandstone gas apparent permeability in high-pressure reservoirs. London, UK: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–12.
Xu, J., Chen, Z., Zhou, D., Nie, W., and Li, R. (2021). Review on the characteristics of pyrolysis during in-situ conversion of oil shale. J. Southwest Petroleum Univ. Sci. Technol. Ed. 43 (5), 220.
Xu, J., Pan, Y., and Chen, Z. (2016). “Understanding impacts of lean zones on thermal recovery in view of mobile water,” in SPE Canada Heavy Oil Technical Conference (Calgary, Canada: OnePetro).
Xu, J., Wu, K., Li, Z., Pan, Y., Li, R., Li, J., et al. (2018). A model for gas transport in dual-porosity shale rocks with fractal structures. Ind. Eng. Chem. Res. 57 (18), 6530–6537. doi:10.1021/acs.iecr.8b00021
Keywords: reservoir simulation, reservoir modelling, unconventional reservoir, shale gas, shale oil
Citation: Xu J, Chen Z, Hu J, Wu K and Zhou D (2022) Editorial: Advances in reservoir modelling and simulation. Front. Earth Sci. 10:1106622. doi: 10.3389/feart.2022.1106622
Received: 24 November 2022; Accepted: 02 December 2022;
Published: 09 December 2022.
Edited and Reviewed by:
Alexander Kokhanovsky, German Research Centre for Geosciences, GermanyCopyright © 2022 Xu, Chen, Hu, Wu and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jinze Xu, amluenh1QHVjYWxnYXJ5LmNh