The objective of this work is to implement a pseudo-forward equation which is called PFE to transform data (similarity attribute) to model parameters (porosity) in a gas reservoir in the F3 block of North Sea. This equation which is an experimental model has unknown constants in its structure; hence, a least square solution is applied to find the best constants. The results derived from solved equations show that the errors on measured data are mapped into the errors of estimated constants; hence, Tikhonov regularization is used to improve the estimated parameters. The results are compared with a conventional method such as cross plotting between acoustic impedance and porosity values to validate the PFE model. When the testing dataset in sand units was used, the correlation coefficient between two variables (actual and predicted values) was obtained as 0.720 and 0.476 for PFE model and cross-plotting analysis, respectively. Therefore, the testing dataset validates relatively well the PFE optimized by Tikhonov regularization in sand units of a gas reservoir. The obtained results indicate that PFE could provide initial information about sandstone reservoirs. It could estimate reservoir porosity distribution approximately and it highlights bright spots and fault structures such as gas chimneys and salt edges.

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This work integrated selective principal component analysis (SPCA) with a singularity fractal model to map hydrothermal alterations of argillic, phyllic, and propylitic in the north-west of Kerman city in Iran. SPCA results were provided for short wave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to map phyllic and propylitic alterations. Also, the bands 5-7 were applied to map the argillic alteration zone. SPCA results could present useful information for alteration mapping but it does not show the purest pixels of alteration. Therefore, a fractal model of singularity was applied to highlight the different alteration pixels in the Kerman Cenozoic magmatic arc (KCMA). Thus, the singularity index of different hydrothermal alteration zones was produced. Comparing the obtained results with the field data showed that Kader, Serenu, Meiduk, and Abdar deposits were acceptably identified by alteration mapping. Also, it seems that the singularity index could not discriminate the hydrothermal alterations of argillic and phyllic. The same spectral signature of kaolinite and muscovite minerals is the main reason for misclassifications.