Adaptive Prediction of Enhanced Oil Recovery by N₂ huff-n-puff in Fractured-Cavity Reservoir Using an FNN-FDS Hybrid Model

Abstract

N2 huff-n-puff has proven to be a promising technique to further improve oil recovery in naturally fractured-cavity carbonate reservoirs. The effect of enhanced oil recovery (EOR) by N2 huff-n-puff is significantly affected by various dynamic and static factors such as type of reservoir space, reservoir connectivity, water influx, operational parameters, and so on, typically leading to a significant increase in oil production. To reduce the prediction uncertainty of EOR performance by N2 huff-n-puff, an adaptive hybrid model was proposed based on the fundamental principles of fuzzy neural network (FNN) and fractional differential simulation (FDS); a detailed prediction process of the hybrid model was also illustrated. The accuracy of the proposed FNN-FDS hybrid model was validated using production history of N2 huff-n-puff in a typical fractured-cavity carbonate reservoir. The proposed model was also employed to predict the EOR performance by N2 huff-n-puff in a naturally fractured-cavity carbonate reservoir. The methodology can serve as an effective tool to optimize developmental design schemes when using N2 huff-n-puff to tap more remaining oil in similar types of carbonate reservoirs.

Keywords

• N₂ huff-n-puff
• fractured-cavity reservoir
• adaptive performance prediction
• fuzzy neural network
• fractional differential simulation