A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks

Mohaghegh, Shahab D.; Arefi, Reza; Ameri, S.; Hefner, M.H.

doi:10.2523/28394-ms

Cited by 25 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The penetration of neuro computing in the petroleum industry is accredited to Mohaghegh, who proposed a method to study different rock properties in heterogeneous reservoirs by analyzing the data of geophysical well logs. To this end, he designed a three-layer, feed-forward, Back-Propagation Neural Network, using general regression neural networks which predicted rock permeability, porosity, and the level of oil/gas saturation (Mohaghegh et al 1994).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Data mining applications in hydrocarbon exploration

et al. 2010

View full text Add to dashboard Cite

This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term "intelligent" is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Data mining applications in hydrocarbon exploration

et al. 2010

View full text Add to dashboard Cite

show abstract

“…The ANNs are able to deal with nonlinear problems, and once trained can perform prediction and generalization rapidly (Bean and Jutten, 2000). Previous investigations (Mohaghegh et al, 1996) have revealed that neural network is a powerful tool for identifying the complex relationship among permeability, porosity, fluid saturations, depositional environments, lithology, and well log data.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network

Kaydani

Hagizadeh

Mohebbi

2013

Petroleum Science and Technology

View full text Add to dashboard Cite

Dew point pressure (DPP) is one of the most important parameters to characterize gas condensate reservoirs. Experimental determination of DPP in a window pressure-volume-temperature cell is often difficult especially in case of lean retrograde gas condensate. Therefore, searching for fast and robust algorithms for determination of DPP is usually needed. This paper presents a new approach based on artificial neural network (ANN) to determine DPP. The back-propagation learning algorithms were used in the network as the best approach. Then equations for DPP prediction by using weights of the network were generated. With the obtained correlation, the user may use such results without a running the ANN software. Consequently, this new model is compared with results obtained using other conventional models to make evaluation among different techniques. The results show that the neural model can be applied effectively and afford high accuracy and dependability for DPP forecasting for the wide range of gas properties and reservoir temperatures.

show abstract

“…Many researchers had been predicted permeability with artificial neural networks from well logs data [3,4], generation of synthetic wireline logs from other logs [5,6], identification of lithological and depositional facies via competitive neural network and fuzzy logic [7], as well as estimation of reservoir permeability using integration of genetic algorithm and a coactive neuro-fuzzy inference system [8]. Archie in 1942 found the correlation between porosity (PHI), and resistivity formation factor (F).…”

Section: Introductionmentioning

confidence: 99%

ANN-based prediction of cementation factor in carbonate reservoir

Kadhim

Samsuri

Al-Dunainawi

2015

2015 SAI Intelligent Systems Conference (IntelliSys)

View full text Add to dashboard Cite

since carbonate reservoirs are a heterogeneous in nature, therefore the behaviour of petrophysical properties of these reservoirs is a highly nonlinear. There is no close conventional statistical model can describe the behaviour of the relation between cementation factor and rock properties. Artificial Neural Network technique is used in many applications to predict variable that usually cannot be measured in linear modelling. Depending on well logs data, the Interactive Petrophysics software had been used to calculate the petrophysical properties of studied oilfield. In this study, the data sets used for training and testing neural network are provided from well number three of Nasiriya oilfield in the south of Iraq. The neural network model was trained using two different training algorithms; Gradient Descent with Momentum and Levenberg -Marquardt. Porosity, permeability and resistivity formation factor relationships to cementation factor are proposed using artificial neural network model. An efficient performance of excellent prediction of cementation factor has been obtained with less than (1*10 -4 ) mean square error (MSE).

show abstract

A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks

Cited by 25 publications

References 0 publications

Data mining applications in hydrocarbon exploration

Data mining applications in hydrocarbon exploration

A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network

ANN-based prediction of cementation factor in carbonate reservoir

Contact Info

Product

Resources

About