Abstract

The Sinian Dengying karst dolomite formation is the main gas production interval of the Sichuan Basin. Mud loss occurs in the cave section, and vug always enriches in wells along platform margin. However, the gas test production result ranges from more than 100×104m3/day to 20×104m3/day in vug and fracture section. It suggests that the quantitative evaluation of vugs and fractures is the key point to a good karst dolomite reservoir.

However, due to the limited resolution of conventional logs and strong heterogeneity of carbonate reservoir, conventional open hole logs and seismic data have limitation to provide the details of secondary pore and fracture to evaluate them quantitatively. The electrical image logs have high resolution images with high borehole coverage, which can provide abundant information about secondary ore and fracture. The new technology is designed to extract effective vug, cave and fracture information from electrical images and calculate quantitative parameter curves of them.

In total, the new technology was applied in 40 vertical wells, and two wells were performed to validate the open flow capacity level in this case study. Core data were used to calibrate the appropriate cut-off range of the 40 vertical wells. Effective secondary pore and fracture on electrical image will be extracted, while filled secondary pore and fracture or invalid feature won’t be selected. The quantitative parameter PROP, CONNECT and SIZE are much more sensitive to different sizes of vugs, caves, fractures than conventional logs. Besides, this paper attempts to use reservoir effective parameter based on electrical image as input to predict open flow capacity levels by using neural network model.

The case study presents the new technology to quantitatively evaluate vug and fracture with different size based on electrical image logs. The quantitative parameter curves are not only used to learn the relationship with gas test production as input, but also can be used to calibrate seismic data to find out the dominant seismic facies.

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