| Autor: |
Ahmed Assem, Ahmed Farid Ibrahim, Matt Sinkey, Tom Johnston, Shabnam Marouf |
| Rok vydání: |
2023 |
| Zdroj: |
Day 2 Tue, March 21, 2023. |
| DOI: |
10.2118/212405-ms |
| Popis: |
Shale well performance depends mainly on stimulated reservoir volume (SRV) which is generated from the hydraulic fracture job. The hydraulic fracture requires a mixture of fluid and proppant per feet to increase well recovery factor. Production rate of shale wells is related to the stimulated rock volume during the fracture treatments. Horizontal well spacing and depletion also play a significant role in optimizing SRV for each well. This paper presents a method of improving and measuring SRV during a frac job by using neural network technology to guide frac operation in achieving the maximum SRV per injected fluid volume. Also, this technique can detect well-to-well interference, in case of a parent/child completion, and optimize frac hits. The guided system makes frac time more efficient and generates an efficient SRV with high connectivity to wellbore. The generated SRV per each stage is integrated with leak-off SRV to confirm the SRV volume and conductivity of each fracture system. Actual field cases from oil and gas shale frac data are presented with live measurements of SRV for each stage followed by leak-off connected stimulated surface volume. The new method demonstrates that this concept can be used to improve completion design, well spacing, and placement strategies The paper proposes a technology that will help shale producers optimize and measure SRV during frac operation without paying for or installing extra equipment after the pressure gauge. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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