An integrated tool for microarray data clustering and cluster validity assessment
Proceedings of the 2004 ACM symposium on Applied computing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Automatic extraction of clusters from hierarchical clustering representations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Automatic Cluster Selection Using Index Driven Search Strategy
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Clustering and classification techniques for blind predictions of reservoir facies
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Spot detection in images with noisy background
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Unsupervised and supervised learning in cascade for petroleum geology
Expert Systems with Applications: An International Journal
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In petroleum geology, exploration and production wells are often analyzed using image logs, because they provide a visual representation of the borehole surface and they are fundamental to retrieve information on bedding and rocks characteristics. In this paper, we present a novel approach for image log interpretation and extraction of the main features of the rock formation. This process led to the development of I2AM, a semiautomatic system that exploits image processing algorithms and artificial intelligence techniques to analyze and classify borehole images. I2AM analyzes log images using several image processing algorithms in order to extract numerical values for each characteristic and then performs a hierarchical clustering over the data obtained. Using three cluster evaluation indexes, possibly combined, I2AM can evaluate clustering results and, performing an automatic index-driven search, supplies a classification of the image logs. In this paper, we show I2AM application to the image logs from one well which were processed using the proposed method to identify different rock types and which were compared with those identified by the geologist. Main advantages of this approach are that interpretation time reduces from days to hours and subjectivity errors are avoided.