--> Abstract: Accelerated Borehole Image Log Facies Interpretation, by G. Burmester and K. MacPherson, #90188 (2014)

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Accelerated Borehole Image Log Facies Interpretation

G. Burmester1 and K. MacPherson1

1Saudi Aramco


Manual interpretation of facies from borehole images has been applied to many reservoirs over the last twenty years. It is an established industry method for creating geologically-based facies utilizing borehole image logs (BHI) in conjunction with open hole logs and core calibration to extrapolate into uncored intervals. The methodology relies heavily on the experience of the BHI interpreter to correctly identify the image features, textures and orientation data. During this study, our goal was to assess whether the manual methodology could be replaced by an automatic or semi-automatic workflow. Six clastic reference wells with core, open hole and high quality image log data were selected and manually interpreted. The same data was independently used for image based rock typing (facies) using software-driven image-based petrophysics applications: the image data was calibrated, zoned and compensated for fluid phase changes prior to binning into image-based petrophysics facies using various thresholding techniques. Comparison of the results showed that the semi-automatic processes could not reliably match either the manual image or core-based interpretations without considerable manipulation. Fluid (oil and water) contact zones were particularly difficult to resolve, requiring many reiterations, whereas an experienced interpreter could compensate for the ambiguity faster and more reliably. Analysis of geological fabrics and textures, especially nonplanar image fabrics like cross-bedding, bio- or pedo-turbation, could not be reliably reproduced using automated processes. Tests with neural network applications suggest, however, that pre-binning of the facies, rock types and units, based upon the open hole log’s response and image resistivity, can effectively provide a faster and more repeatable standard gross lithological unit. We therefore suggest that, at present, the most effective methodology for rapid reservoir characterization is an image-based petrophysical interpretation followed by facies interpretation of the image texture/fabric by an experienced BHI interpreter.


AAPG Search and Discovery Article #90188 ©GEO-2014, 11th Middle East Geosciences Conference and Exhibition, 10-12 March 2014, Manama, Bahrain