Numerical Simulation of Hydraulic Fracturing in Unconventional Reservoirs

Authors

  • Yasir Mukhtar Sudan University of Science & Technology
  • Elhassan M. A. Mohammed
  • Irtiza Amjad

DOI:

https://doi.org/10.54938/ijemdcsai.2022.01.2.131

Keywords:

Hydraulic Fracturing, Numerical Simulation, Unconventional Reservoirs, Oil Production

Abstract

Hydraulic fracturing is a well-stimulation technique that uses a large amount of water mixed with proppants at high pressure to enhance and boost oil and gas production. Proppants, in conjunction with water, facilitate the cracking process, acting as a catalyst for the oil and gas to pass through the formation and into the producing well. The primary goal of this research is to look into the parameters that influence hydraulic fracturing quality. Moreover, such parameters that improve the productivity of oil in unconventional reservoirs are evaluated. The field properties of the Eagle Ford Shale were used to create a geological model on the Navigator software for this purpose. This software is used to simulate three different horizontal and vertical wells to observe the production of oil and gas. Then, hydraulic fracturing is performed on the same wells with different scenarios using three parameters, namely the length of fracture, the width of fracture, and the height of fracture. These parameters are selected because the usage of proppants can be dependent on them. The findings are quite convincing and demonstrate the importance of hydraulic fracturing in an unconventional reservoir. Besides, it is observed that the greater the width, length, or height of the fracture, the greater the productivity of oil and gas in an unconventional reservoir due to the increment of the seepage area of the hydrocarbons. Thus, hydraulic fracturing can make any potential unconventional reservoir economically viable.

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Published

2022-09-30

How to Cite

Mukhtar, Y., Elhassan M. A. Mohammed, & Irtiza Amjad. (2022). Numerical Simulation of Hydraulic Fracturing in Unconventional Reservoirs. International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence, 1(2), 97–118. https://doi.org/10.54938/ijemdcsai.2022.01.2.131

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Research Article

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