A Functional Networks-Type-2 Fuzzy Logic Hybrid Model for the Prediction of Porosity and Permeability of Oil and Gas Reservoirs

2010 ◽  
Author(s):  
Fatai Adesina Anifowose ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Fuzzy Logic ◽  
Hybrid Model ◽  
Oil And Gas ◽  
Functional Networks ◽  
Gas Reservoirs ◽  
Porosity And Permeability ◽  
Oil And Gas Reservoirs

FRACTURE MODELING IN OIL AND GAS RESERVOIRS USING IMAGE LOGS DATA AND PETREL SOFTWARE

2015 ◽  
Vol 75 (11) ◽  
Author(s):  
Mostafa Alizadeh ◽  
Zohreh Movahed ◽  
Radzuan Junin ◽  
Rahmat Mohsin ◽  
Mehdi Alizadeh ◽  
...  
Keyword(s):  
Oil And Gas ◽  
Fracture Permeability ◽  
Gas Reservoirs ◽  
New Information ◽  
Fracture Modeling ◽  
Fracture Intensity ◽  
Major Fault ◽  
Oil And Gas Reservoirs ◽  
Minimum Stress ◽  
Longitudinal Fractures

The purpose of modelling the fractures is to create simulation properties with the power to predict the reservoir behaviour. Petrel software is one of the best softwares in the market that can do this task very well, but there is no available educational paper for every researcher. Therefore, in this work, a fracture modelling job was done in one of the most important Iranian fields using Petrel software and image log data. The purpose of this work was  to determine the new information of the fractures in Gachsaran field and also to prepare a valuable educational paper for other researchers who are interested to learn about the fracture modelling. This work revealed that in this field, the longitudinal fractures had been parallel to minimum stress (Zagros trend), fracture intensity was the nearest to the major fault and northern flank, fracture porosity was 0-7%, fracture permeability was 0-6000 MD, and more valuable information is provided in this paper.

scholarly journals A robust fuzzy logic-based model for predicting the critical total drawdown in sand production in oil and gas wells

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250466
Author(s):  
Fahd Saeed Alakbari ◽  
Mysara Eissa Mohyaldinn ◽  
Mohammed Abdalla Ayoub ◽  
Ali Samer Muhsan ◽  
Ibnelwaleed A. Hussein
Keyword(s):  
Fuzzy Logic ◽  
Correlation Coefficient ◽  
Oil And Gas ◽  
Maximum Error ◽  
Physical Parameters ◽  
Sand Production ◽  
Gas Reservoirs ◽  
The North ◽  
Sand Control ◽  
North Adriatic Sea

Sand management is essential for enhancing the production in oil and gas reservoirs. The critical total drawdown (CTD) is used as a reliable indicator of the onset of sand production; hence, its accurate prediction is very important. There are many published CTD prediction correlations in literature. However, the accuracy of most of these models is questionable. Therefore, further improvement in CTD prediction is needed for more effective and successful sand control. This article presents a robust and accurate fuzzy logic (FL) model for predicting the CTD. Literature on 23 wells of the North Adriatic Sea was used to develop the model. The used data were split into 70% training sets and 30% testing sets. Trend analysis was conducted to verify that the developed model follows the correct physical behavior trends of the input parameters. Some statistical analyses were performed to check the model’s reliability and accuracy as compared to the published correlations. The results demonstrated that the proposed FL model substantially outperforms the current published correlations and shows higher prediction accuracy. These results were verified using the highest correlation coefficient, the lowest average absolute percent relative error (AAPRE), the lowest maximum error (max. AAPRE), the lowest standard deviation (SD), and the lowest root mean square error (RMSE). Results showed that the lowest AAPRE is 8.6%, whereas the highest correlation coefficient is 0.9947. These values of AAPRE (<10%) indicate that the FL model could predicts the CTD more accurately than other published models (>20% AAPRE). Moreover, further analysis indicated the robustness of the FL model, because it follows the trends of all physical parameters affecting the CTD.

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