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AAPG GEO 2010 Middle East
Geoscience Conference & Exhibition
Innovative Geoscience Solutions – Meeting Hydrocarbon Demand in Changing Times
March 7-10, 2010 – Manama, Bahrain

A Novel Approach towards Semi-Automated Lithofacies Identification from Image Logs

Angeleena Thomas1; Malcolm Rider1; Andrew Curtis1; Alasdair MacArthur1

(1) School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom.

Visualization is an important aspect of modern hydrocarbon borehole geophysical measurements. Downhole tools are now able to acquire high-resolution 2D and 3D maps of the acoustic and electrical properties of the borehole wall and display them in real time as false-colour images of the formations encountered during drilling. These data now form a huge industry database. However, the interpretation of these images under-utilizes the data.

To date, the only regularly used quantitative methodology applied to image log interpretation is for the derivation of orientation data (dip and azimuth). Other, occasional quantitative methods use the resistivity measurements themselves, and not the images. However, from the images themselves, much additional information can be extracted, by using advanced object based image analysis software which is widely available and is successfully employed for analyzing digital images at all scales, from microscopic cell structures to satellite pictures.

We present a method for identifying lithofacies from image logs employing image analysis methods used in remote sensing and medical science. The new technique presented synthesizes expert knowledge and digital image analysis, to recognize physically and/or chemically consistent objects within an image and relate these to geologically meaningful groups, such as lithofacies. Filters are used to mark bed boundaries and are created from a derivative log extracted from neutron and density logs and from bed orientation calculated using automated sinusoid fitting at every pixel depth in image log within a formal uncertainty framework. The resultant lithofacies classification is then validated through the interpretation of cored intervals by a geologist.

The image interpretation calibrated to core ensures the accuracy in the result obtained and the good match between the two gives the confidence to extrapolate the automated image analysis result from areas with core control to areas with poor to no core recovery. The developed method can be quickly adapted to other wells or applied field wide by defining the lithofacies in each case and by appropriate sample selection for each lithofacies. In addition, the methodology is applicable to several kinds of borehole images, for example wireline electrical borehole wall images, core photographs and the more specialized LWD images.