Geomechanical Data from Petrophysical Logs

Detecting borehole breakout intervals and identifying fractures zones | Estimated Length : 41 mins

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Course Content

This e-symposium will be introducing signal processing techniques as a means to maximize extracting geomechanical data from petrophysical logs.The focus will be on different signal processing techniques that can be used to mine data from log data.

Well logs are key input data to construct and verify geomechanical models and unquestionably the most important data when it gets to rock property modeling. In addition, they usually show correlations, in some extent, with down-hole information such as fractured intervals, enlarged hole sections, oil/water/gas bearing zones etc. Although there are many petrophysical approaches to extract highest amount of information from logs, there is still a lot more valuable information buried in different frequency levels of log signals which cannot be pull out with the common petrophysical methods.

This presentation shares the results of an attempt to apply some helpful signal processing techniques such as wavelet de-composition, de-noising, Parzen classification, Bayesian algorithm, and data fusion to mine extra information out of log data.

In this study, the capability of signal processing techniques was examined for two main objectives: detecting borehole breakout intervals and identifying fractures zones. A new multi-variable workflow was proposed to identify zones of interest in correlation with basic well logs.

The workflow was applied to actual logs from a shale gas and a carbonate reservoir to investigate its accuracy and applicability. Results confirmed that the workflow is able to identify breakout and fractured zones with a significant accuracy.


Who Should Attend

Geologists, petrophysicists, geophysicists, engineers, well log analysts who are currently working in areas where geomechanical properties are critical in making exploration, drilling, completions, and reserves decisions.

Estimated time

41 minutes of video lectures

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