Estimation of the Reservoir Permeability by Petrophysical Information Using Intelligent Systems

Ahmadi, Morteza; Saemi, Mohsen; Asghari, Koorosh

doi:10.1080/10916460701675173

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…A few years later, in 1990, Chilingrian et al also presented an equation in which the permeability equation is a logarithmic equation and a function of a specific surface, porosity, and water saturation. The reported equations are as follows (equation 3) (Kamali et al, 2022): The direct measurement method is a method that takes a lot of time and money, and the empirical equation method cannot explain the heterogeneity of permeability for the reservoir in any condition (Marshall, 1958;Morgan and Gordon, 1970;Ahmadi et al, 2008). Therefore, many intelligent calculation methods can predict permeability with high accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A new development algorithm for permeability prediction: A new milestone

Sun

Liang

2022

Front. Ecol. Evol.

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Permeability is one of the most important reservoir rock parameters in petroleum engineering, reservoir, and exploitation. This parameter causes the movement of hydrocarbon reserves in the reservoir rock. Therefore, it is an important parameter from the economic point of view because it greatly impacts the amount of extraction from the reservoir rock. In this study, the combined RBFNN-GA algorithm and 200 data sets collected from a field in the Middle East were used to predict permeability. Water saturation, porosity, and specific surface are the input variables used in this study. GA has advantages such as solving complex optimization problems of continuous functions and multi-objective problems. The advantages of RBF neural networks are that they are easy to design, strongly tolerant to input noise, and have good generalization. The RBFNN-GA model has the advantages of both algorithms. RBFNN-GA algorithm and experimental models have been compared in terms of performance accuracy. The results show that RBFNN-GA with STD = 89.8 and R-square = 0.9011 for the total data set obtained from a field in the Middle East has better accuracy and performance in predicting permeability than experimental models. Compared to other neural network methods, the RBFNN-GA model has a higher performance accuracy and is efficient for predicting other parameters. Oil researchers and engineers can use this method to predict other parameters in their studies and research.

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Section: Literature Reviewmentioning

confidence: 99%

A new development algorithm for permeability prediction: A new milestone

Sun

Liang

2022

Front. Ecol. Evol.

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“…Reservoir permeability is either obtained from direct measurement of core and also well testing analysis or from empirical equations of well log parameters. On the contrary, a direct measurement from core and well test have shown to be costly and time-consuming method if carried out on all wells while empirical equation obtained from well logs cannot fully account for the heterogeneity of permeability in all reservoir conditions [7,8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

et al. 2020

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Permeability is an important petrophysical parameter that controls the fluid flow within the reservoir. Estimating permeability presents several challenges due to the conventional approach of core analysis or well testing, which are expensive and time-consuming. On the contrary, artificial intelligence has been adopted in recent years in predicting reliable permeability data. Despite its shortcomings of overfitting and low convergence speed, artificial neural network (ANN) has been the widely used artificial intelligent method. Based on this, the present study conducted permeability prediction using the group method of data handling (GMDH) neural network from well log data of the West arm of the East African Rift Valley. Comparative analysis of GMDH permeability model and ANN methods of the back propagation neural network (BPNN) and radial basis function neural network (RBFNN) were further explored. The results of the study showed that the proposed GMDH model outperformed BPNN and RBFNN as it achieved R/root mean square error (RMSE) value of 0.989/0.0241 for training and 0.868/0.204 for predicting, respectively. Sensitivity analysis carried out revealed that shale volume, standard resolution formation density, and thermal neutron porosity were the most influential well log parameters when developing the GMDH permeability model.

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“…In the latter, GIS are used as platforms, from which spatial analysis functions are used, and with which mathematical models are integrated to complete reservoir evaluation. In terms of mathematical models, fuzzy mathematics (Schrader, Balch, & Ruan, ; Taheri, ; Zoveidavianpoor, Samsuri, & Shadizadeh, ), gray clustering (Denney, ), artificial neural networks (Ahmadi, Saemi, & Asghari, ; Elshafei & Hamada, ; Wang, Wang, & Chen, ), gray multivariate correlation analysis (Naseri, Khishvand, & Sheikhloo, ; Shi, ), rough sets (Wu, Li, Dai, & Feng, ), support vector machines (Ahmadi, ), and geostatistical methods (Tamaki, Suzuki, Fujii, & Sato, ) are studied and/or integrated with GIS.…”

Section: Introductionmentioning

confidence: 99%

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

Chen

Wang

Jun-wen³

2018

Transactions in GIS

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Delineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case-based reasoning for oil-gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP-ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs6.38% and 46.81% better than BP-ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.

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Estimation of the Reservoir Permeability by Petrophysical Information Using Intelligent Systems

Cited by 11 publications

References 6 publications

A new development algorithm for permeability prediction: A new milestone

A new development algorithm for permeability prediction: A new milestone

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

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