
Quantification of sand fraction from seismic attributes using NeuroFuzzy approach
In this paper, we illustrate the modeling of a reservoir property (sand ...
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Finding hiddenfeature depending laws inside a data set and classifying it using Neural Network
The logcosh loss function for neural networks has been developed to comb...
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Performance Analysis Of Neural Network Models For Oxazolines And Oxazoles Derivatives Descriptor Dataset
Neural networks have been used successfully to a broad range of areas su...
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Artificial Neural Network Surrogate Modeling of Oil Reservoir: a Case Study
We develop a datadriven model, introducing recent advances in machine l...
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Deep neural network for pier scour prediction
With the advancement in computing power over last decades, deep neural n...
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RealTime Well Log Prediction From Drilling Data Using Deep Learning
The objective is to study the feasibility of predicting subsurface rock ...
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Greenhouse Gas Emission Prediction on Road Network using Deep Sequence Learning
Mitigating the substantial undesirable impact of transportation systems ...
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Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
This paper proposes a complete framework consisting preprocessing, modeling, and postprocessing stages to carry out well tops guided prediction of a reservoir property (sand fraction) from three seismic attributes (seismic impedance, instantaneous amplitude, and instantaneous frequency) using the concept of modular artificial neural network (MANN). The data set used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India. Firstly, the acquired data set is integrated and normalized. Then, well log analysis and segmentation of the total depth range into three different units (zones) separated by well tops are carried out. Secondly, three different networks are trained corresponding to three different zones using combined data set of seven wells and then trained networks are validated using the remaining test well. The target property of the test well is predicted using three different tuned networks corresponding to three zones; and then the estimated values obtained from three different networks are concatenated to represent the predicted log along the complete depth range of the testing well. The application of multiple simpler networks instead of a single one improves the prediction accuracy in terms of performance metrics such as correlation coefficient, root mean square error, absolute error mean and program execution time.
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