Developing Fracture Measure as an Index of Fracture Impact on Well-Logs

Due to the three-dimensional nature of fractures, it is difficult to characterize them completely and accurately. In this paper, a novel fractured zone detection criterion, Fracture Measure (FM), is proposed. FM is a parameter calculated by aperture, fracture type, azimuth and apparent distance. These factors have not been incorporated in previous studies to detect fractured zones. This study attempts to estimate FM by Artificial Neural Network to see if there is any relation between FM and conventional logs and to check the generalization ability of FM. Two datasets were used for the investigation: a real carbonate reservoir of an oil field in Iran and a synthetic heterogeneous reservoir, here incalled SYN. Comparing outputs of heterogeneous and homogeneous conditions showed that the Classification Correctness Rate (CCR) of the model in the homogeneous state was approximately 97%, and in the heterogeneous condition, it was between 74% and approximately 92%. Generalization ability in the homogeneous state varied from 91% to 94%, and in the heterogeneous condition, varied from 52% to 86%. In the real dataset, ANN was able to estimate FM with an average accuracy of approximately 80%and Classification Correctness Rate (CCR) of approximately 100%, which shows that FM could be modeled through well-logs. It is noteworthy that FM is capable of providing a fuzzy measure for fracture study.