Application of fuzzy logic and neural networks for porosity analysis using well log data: an example from the Chanda Oil Field, Northwest Pakistan

Khan, Natasha; Rehman, Khaista

doi:10.1007/s12145-021-00679-2

Cited by 4 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The statistical methods commonly used at present include the fuzzy clustering, BP neural network, Bayes stepwise discriminant and its derivative methods, such as: Rogistiv discriminant, multivariate statistical, multiple population stepwise discriminant analysis, selforganizing neural grid, etc. (Abdulaziz et al, 2019;Khan and Rehman, 2021). Compared with the fuzzy clustering and BP neural network methods, the Bayes stepwise discriminant method is a statistical analysis method that integrates effective parameter selection and quantitative recognition functions (Liao and Zhang, 2004;Sames and Saussus, 2010;Rimstad and Avseth, 2012;Zhang and Du, 2021).…”

Section: Quantitative Methods Of Classification and Discrimination Of...mentioning

confidence: 99%

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K‐means Clustering and Bayesian Theory

Fang

Zhu²,

Yang³

et al. 2023

Acta Geologica Sinica (Eng)

View full text Add to dashboard Cite

Reservoir classification is a key link in reservoir evaluation. However, traditional manual means are inefficient, subjective, and classification standards are not uniform. Therefore, taking the Mishrif Formation of the Western Iraq as an example, a new reservoir classification and discrimination method is established by using the K-means clustering method and the Bayesian discrimination method. These methods are applied to non-cored wells to calculate the discrimination accuracy of the reservoir type, and thus the main reasons for low accuracy of reservoir discrimination are clarified. The results show that the discrimination accuracy of reservoir type based on K-means clustering and Bayesian stepwise discrimination is strongly related to the accuracy of the core data. The discrimination accuracy rate of Type I, Type II, and Type V reservoirs is found to be significantly higher than that of Type III and Type IV reservoirs using the method of combining K-means clustering and Bayesian theory based on logging data. Although the recognition accuracy of the new methodology for the Type IV reservoir is low, with average accuracy the new method has reached more than 82% in the entire study area, which lays a good foundation for rapid and accurate discrimination of reservoir types and the fine evaluation of a reservoir.

show abstract

Section: Quantitative Methods Of Classification and Discrimination Of...mentioning

confidence: 99%

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K‐means Clustering and Bayesian Theory

Fang

Zhu²,

Yang³

et al. 2023

Acta Geologica Sinica (Eng)

View full text Add to dashboard Cite

show abstract

“…The adaptive algorithms were used to determine the best combination of the network structure of the artificial neural network (number of hidden layers, number of neuron layers, training function, transfer function), The experimental results showed that the neural network optimized by the adaptive difference algorithm had higher accuracy than the traditional artificial neural network algorithm. Navid Kardani [27] (2021) combined the equivalent optimization and improved equivalent optimization with two traditional machine learning algorithms, extreme neural network (ELM) and artificial neural network (ANN) to develop a new hybrid model. The experimental results showed that the combination of ANN, ELM and meta heuristic search algorithm can better predict the properties of carbonate reservoir.…”

Section: Introductionmentioning

confidence: 99%

WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Sun

Zhang

et al. 2022

Energies

View full text Add to dashboard Cite

Porosity is a vital parameter in reservoir research. In the process of oil exploration, reservoir research is very important for oil and gas exploration. Because it is necessary to take cores for indoor test in order to accurately obtain the porosity value of cores, this process consumes significant manpower and material resources. Therefore, this paper introduces the method of machine learning to predict the porosity by using logging curves. This paper creatively develops a WOA (whale optimization algorithm) optimized Elman neural network model to predict porosity through logging parameters PE, DEN, M2R1, AC, GR, R25, R4 and CNL. Porosity measurement is constructed by taking cores for indoor experiments. It contains a total of 328 sample points. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity measured in the laboratory are used as the output parameter. In order to evaluate the performance of the model, RMSE, R2, MAE and VAF evaluation indexes are introduced to evaluate. This paper also introduces the non-optimized Elman neural network and BP neural network to compare with this optimization model. The research shows that the WOA algorithm optimizes the super parameters of the Elman neural network, so that the performance of the WOA–Elman model is better than the Elman neural network model and the BP neural network model.

show abstract

Machine learning - a novel approach to predict the porosity curve using geophysical logs data: An example from the Lower Goru sand reservoir in the Southern Indus Basin, Pakistan

Hussain

Luo

Ali

et al. 2023

Journal of Applied Geophysics

View full text Add to dashboard Cite

Application of fuzzy logic and neural networks for porosity analysis using well log data: an example from the Chanda Oil Field, Northwest Pakistan

Cited by 4 publications

References 26 publications

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K‐means Clustering and Bayesian Theory

Quantitative Method of Classification and Discrimination of a Porous Carbonate Reservoir Integrating K‐means Clustering and Bayesian Theory

WOA (Whale Optimization Algorithm) Optimizes Elman Neural Network Model to Predict Porosity Value in Well Logging Curve

Machine learning - a novel approach to predict the porosity curve using geophysical logs data: An example from the Lower Goru sand reservoir in the Southern Indus Basin, Pakistan

Contact Info

Product

Resources

About