Faults/fractures role in oil exploration is invaluable. In this study, all determinative seismic attributes (with different insight to the data) were applied. To achieve an image including the results of all attributes, the geographical information system was used to integrate the resultant images of different attributes. To do this, the Laplacian filter was applied to sharpen the linear structures on computed attributes images. Then, a principal component analysis was applied to increase the clarity of fracture. Finally, comparing the result of the author's study with the output of neural network analysis proves that each method has its own advantages and their integration will fulfill the research objective.

Prediction Of Effective Porosity Over The Field Using 3D Seismic Multiattributes, Seismic Acoustic Impedance And Well-Driven Effective Porosity

Seismic Attributes Integration by GIS for Fracture Detection

Upscaled Pseudo-porosity Extracted from Impedance Volume Can Effectively Predict Gas Fingering in a Dynamic Model – a Gas Injection Case Study

Upscaled Pseudoporosity Estimated From Seismic-Acoustic-Impedance Volume Can Effectively Predict Gas Fingering in a Dynamic Model: A Gas-Injection Case Study

Prediction Of Effective Porosity Over The Field Using 3D Seismic Multiattributes, Seismic Acoustic Impedance And Well-Driven Effective Porosity

Citations

Seismic Attributes Integration by GIS for Fracture Detection

Upscaled Pseudo-porosity Extracted from Impedance Volume Can Effectively Predict Gas Fingering in a Dynamic Model – a Gas Injection Case Study

Upscaled Pseudoporosity Estimated From Seismic-Acoustic-Impedance Volume Can Effectively Predict Gas Fingering in a Dynamic Model: A Gas-Injection Case Study

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