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. Thi...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 equa- tions 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 rela- tively well the PFE optimized by Tikhonov regularization in sand units of a gas reservoir. The obtained results indi- cate 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.展开更多
文摘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 equa- tions 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 rela- tively well the PFE optimized by Tikhonov regularization in sand units of a gas reservoir. The obtained results indi- cate 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.
