Abstract
We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum. Thus, we can achieve fine pore evaluation in fracture–vug reservoirs based on electric imaging logging data. We automatically identify and extract fracture–vug information from the electric imaging images by adopting a path morphological operator that remains flexible enough to fit rectilinear and slightly curved structures because they are independent of the structuring element shape. The Otsu method was used to extract fracture–vug information from the background noise caused by the matrix. To accommodate the differences in scale and form of the different target regions, including fracture and vug path, operators with different lengths were selected for their recognition and extraction at the corresponding scale. Polynomial and elliptic functions are used to fit the extracted fractures and vugs, respectively, and the fracture–vug parameters are deduced from the fitted edge. Finally, test examples of numerical simulation data and several measured well data have been provided for the verification of the effectiveness and adaptability of the path morphology method in the application of electric imaging logging data processing. This also provides algorithm support for the fine evaluation of fracture–vug reservoirs.
Highlights
Fractures and vugs are the main reservoir space and seepage channels in fracture–vug reservoirs (Yang et al 2011)
We present a path morphology method to separate total rock pore space into matrix, fractures and vugs and derive their pore structure spectrum
On the basis of the fracture–vug response characteristics of the electric imaging logging data, we have developed a novel fracture–vug identification and extraction method based on the path morphology
Summary
Fractures and vugs are the main reservoir space and seepage channels in fracture–vug reservoirs (Yang et al 2011). Electric imaging logging, which is characterized by its high vertical resolution and borehole coverage, can display fractures and vugs intuitively It has been a key step for interpreters to identify and describe fracture–vug information from conductivity images (Lai 2011). The main electrical imaging logging software, including GeoFrame from the Schlumberger corporation, eXpress from the Atlas corporation, LEAD from CNPC logging and Logview from GEOTECH, requires interpreters to extract the fracture or vug based on a human– computer interaction platform. With the limitation of the complex geologic environment and logging conditions, the methods above cannot automatically identify fracture–vug information completely and achieve the required quantitative interpretation accuracy. They cannot accurately describe irregular fractures and vugs. We apply the path morphology method to accomplish the automatic fracture–vug extraction from the electric imaging images by constructing the adjacency relation and determining the length of the path operator
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