A Critical Review of Surrogate Reservoir Modelling for Uncertainty Quantification in Thin Oil Rim Reservoirs

• Yetunde M. ALADEITAN

  Author

• Damilola V. ABRAHAM

  Author

• Abdulmojeed O. OLUOGUN

  Author

• Abdulwasiu ABDURRAHMAN

  Author

The Surrogate Reservoir Models are becoming popular as a computationally efficient alternative to performing full-field numerical simulations for problems of complex reservoir models where uncertainty analysis is extensive. The low recovery factors for thin oil rim reservoirs (40 percent or less) with oil columns between large water and gas caps is a challenge that must be addressed during development. This review presents the methodology, application and validation of Surrogate Reservoir Models for uncertainty quantification in thin oil rim reservoirs from the basic studies in the Niger Delta to the recent development of machine learning-based proxy modelling up to 2026. The paper critically reviews the use of Surrogate Reservoir Models and shows how experimental design, response surface methodology and Monte Carlo simulation are combined to prioritize uncertain parameters and provide a production forecast, which is probabilistic. Key findings include the fact that the horizontal well length is the largest uncertainty factor and that horizontal well placement just above the oil-water contact had the best results than in the mid-rim or gas-oil contact. The review highlights some key challenges such as the lack of standard protocols for validation, the limited scale of physics-informed neural networks being adopted in industry, and the lack of a real-time updating framework. The best modelling strategy is the response surface methodology used to optimize the model in initial screening followed by Monte Carlo simulation for probabilistic modelling validation, with correlation greater than 0.99 when compared with history matched field data.

• 3_Aladeitan et al. (2026)

Copyright (c) 2026 Yetunde M. ALADEITAN, Damilola V. ABRAHAM, Abdulmojeed O. OLUOGUN, Abdulwasiu ABDURRAHMAN (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.