Porosity and permeability prediction from well logs using an adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir

Vardian, Mojtaba; Nasriani, Hamid Reza; Faghihi, Reza; Vardian, A.; Jowkar, Somayeh

doi:10.1080/15567036.2011.592923

Cited by 11 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some other self-adaptive methods were also developed in previous oil and gas studies [20,21]. Vardian et al [22][23][24] concluded that the ANFIS system can serve as an excellent model with relatively few errors. The ANFIS model has a strong training capability, which is akin to the ANN.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Janiga et al and Sayyafzadeh [20,21] give the different self-adaptive methods. Vardian et al [22] obtained an excellent ANFIS model with low error, which presented all data influencing the reservoir characteristics to the ANFIS. Haghiabi et al [23] predicted the discharge coefficient of triangular labyrinth weir using the multilayer perceptron neural network and adaptive neurofuzzy inference system.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Oilfield‐Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

Chen

Liu

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A novel modified adaptive neurofuzzy inference system with smoothing treatment (MANFIS) is proposed. The MANFIS model considered the smoothing treatment of initial data basing on the adaptive neurofuzzy inference system, and we used it to predict oilfield-increased production under the well stimulation. Numerical experiments show the prediction result of the novel considering smoothing treatment is better than that without smoothing treatment. This study provides a novel and feasible method for prediction of oilfield-increased production under well stimulation, and it can be helpful in the further study of oilfield development measure planning.

show abstract

Hybrid Connectionist Models to Investigate the Effects on Petrophysical Variables for Permeability Prediction

Miah

Abir

2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Porosity and permeability prediction from well logs using an adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir

Cited by 11 publications

References 3 publications

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

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

Prediction of Oilfield‐Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

Hybrid Connectionist Models to Investigate the Effects on Petrophysical Variables for Permeability Prediction

Contact Info

Product

Resources

About