Mathematical Prediction of Oil Recovery Factor for Nanoparticles Assisted Polymer Flooding through Statistical Analysis and ANN Modelling

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Authors

Abdelaziz El-Hoshoudy¹

1. Egyptian Petroleum research Institute

Conference / event

4th International Conference of Pure and Applied Chemistry, July 2024 (Virtual)

Poster summary

Conventional Polymer flooding is a promised CEOR technique. However, there are some shortages associated with molecular architecture destruction in severe reservoir environments. The target of this work is to develop an empirical model using an Artificial neural network (ANN) to predict the incremental oil recovery for polymeric nanofluid. The investigation results indicated that NPs assisted polymer flooding may result in an incremental recovery of 18% of oil-in-place. In addition, the results revealed that core-flooded viscous oil needs high permeable rock to guarantee successful flooding. Coefficient of determination (R2) between the real and calculated incremental oil from the ANN model was established to be 0.953 and 0.952 with an error of 5.6 and 8.7% respectively for the training and testing approaches. Such statistical investigations and ANN approaches afford new insights and guidelines for preliminary assessment, designing, and execution Nanohybrid polymer upcoming projects in field scale.

Keywords

Polymer flooding, Enhanced oil recovery, Statistical analysis, Artificial neural network (ANN)

Research areas

Geotechnical Engineering, Nanoengineering, Chemical Engineering

References

1. Salem, Khalaf G., et al. "Key aspects of polymeric nanofluids as a new enhanced oil recovery approach: A comprehensive review." Fuel 368 (2024): 131515
2. Khattab, Hamid, et al. "Assessment of a Novel Xanthan Gum-based Composite for Oil Recovery Improvement at Reservoir Conditions; Assisted with Simulation and Economic Studies." Journal of Polymers and the Environment (2024): 1-29
3. Salem, Khalaf G., et al. "Nanoparticles assisted polymer flooding: comprehensive assessment and empirical correlation." Geoenergy Science and Engineering 226 (2023): 211753
4. Gomaa, Sayed, et al. "Development of artificial neural network models to calculate the areal sweep efficiency for direct line, staggered line drive, five-spot, and nine-spot injection patterns." Fuel 317 (2022): 123564

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2024 El-Hoshoudy. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.