POROSITY PREDICTION FROM SEISMIC DATA USING MULTIATTRIBUTE
TRANSFORMATIONS, N SAND, AUGER FIELD, GULF OF MEXICO
Open Access
Author:
Valenti, Joseph Christian Adam Frank
Graduate Program:
Geosciences
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
None
Committee Members:
Charles James Ammon, Thesis Advisor/Co-Advisor Charles James Ammon, Thesis Advisor/Co-Advisor
Keywords:
neural network porosity seismic
Abstract:
We compare two methods of predicting well-log porosity from seismic data. The data consist of a suite of well logs and a full stack 3D seismic survey over Auger Field in the deepwater Gulf of Mexico. The 3D seismic is transformed into a number of attribute volumes. These attributes are combined in a nonlinear manner, via an Artificial Neural Network (ANN), or in a linear manner, via multilinear regression analysis, in order to predict the target porosity logs from the available suite of field data.
A feed-forward back propagation ANN is trained using the seismic attributes as an input set and with the porosity logs as the output set. The linear mode uses the same training data, but derives a series of weights which when applied to the input set minimize the differences in a least-squares sense between the target and predicted outputs.
In order to measure the accuracy of the attribute to porosity transformation, cross-well validation was performed. In this procedure one well is removed from the training set and the transformation is re-derived. The accuracy of the transformation in predicting the log from the removed well is then measured. This is done to every well in the training set so that we may determine a reasonable expectation for the performance of the transformation.
We see a marked improvement of the performance of the ANN over that of the multilinear regression. These results are evident not only in the training data but more importantly also in the testing data.
