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.
Highlights
The main reservoir spaces of typical fractured-cavity reservoirs consist of karst caves, high-angle fracture networks, and many dissolved pores [1,2]
The factors greatly affecting the degree of increased oil production by N2 huff-n-puff in a fractured-cavity carbonate reservoir mainly include geological characteristics of reservoir body and various operation parameters of N2 huff-n-puff
There is good evidence that permeability strongly correlates with porosity in a typical fractured-cavity reservoir [39,40], permeability is enough to describe the effect of reservoir properties on increasing oil production by N2 huff-n-puff
Summary
The main reservoir spaces of typical fractured-cavity reservoirs consist of karst caves, high-angle fracture networks, and many dissolved pores [1,2]. It is of great importance to make an accurate prediction about N2 huff-n-puff in order to explore its potential in fractured-cavity carbonate reservoirs. By integrating advantages of grey wolf optimization and adaptive neuro-fuzzy inference system, an improved ANFIS is proposed by Dehghani et al [16], showing better prediction capability. The model is highly accurate and can overcome the class ratio test restrictions of the conventional grey model (GM) (1,1) It demonstrated that the fractional differential simulation (FDS) can greatly improve the prediction ability of differential simulation [26]. The fractional order grey models were widely used with the improved GM (1,1) model [37,38] These previous studies show that the prediction ability is strong when the fuzzy neural network (FNN) and fractional differential simulation (FDS). N2 huff-n-puff in a naturally fractured-cavity carbonate reservoir
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