Please use this identifier to cite or link to this item:
|Title:||Characterisation of formation heterogeneity.|
|Authors:||Gonçalves, Carlos Augusto.|
|Presented at:||University of Leicester|
|Abstract:||The characterisation of formation heterogeneities requires a multidisciplinary study of data acquired using a large number of numerical geophysical and geological measurements and a rigorous evaluation of the precision and accuracy of the data. Another essential aspect of the appraisal of any measurement is the quality assessment and quality control of the data. In this work multivariate statistical techniques and an Artificial Neural Network (ANN) are used provide lithofacies characterisation and to identify heterogeneities in complex formations as well as to evaluate the boundaries they generate. The precision and accuracy of the data from different sources are very important and are considered here by using sample support in the integration of measurements at different scales. We use examples from two holes of the Ocean Drilling Program and two oilfield holes to show the differences in characterisation obtained with each technique. Multivariate Statistical Analysis are initially used to group the petrophysical, geophysical and geological parameters extracted from the downhole measurements into distinct geologically definable zones. This technique has the advantage of being quasi-independent of any pre-determined ideas we have about the whole dataset, and has proved very reliable in formation characterisation. Thus the result obtained here is used as a basis for comparison with that obtained from the Neural Network. Artificial Neural Network is used to characterise the different lithology sequences present in each well. Neural Networks are relatively new tools and have proved very useful in applications where conventional computing methods are inadequate. Another application is the possibility of determining quantitative petrophysical parameters from well logs and core data in uncored intervals. The results are presented as a comparison between the two techniques. We show that both methods are very encouraging. When comparing the ANN derived petrophysical parameter logs with actual core measurements and other petrophysical parameters prediction techniques we see a good match. Low quality petrophysical measurements can be determined by a mismatch between the responses.|
|Rights:||Copyright © the author. All rights reserved.|
|Appears in Collections:||Theses, Dept. of Geology|
Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.