| Abstract |
A statistical method was developed to test if the current model for targeting deep permeability (1,350m to 3,000 mbsl) in the Bulalo reservoir, based on mapped faults projected downwards at dips of 90° +3°, predicts permeable zones (PZ) with a success rate better than random. The performance measure PPZ is the “hit rate” percentage (i.e., number of PZs inside the fault zones divided by total number of PZs) divided by the percent targeted wellbore length (or percentage of wellbore below the top of reservoir that passed through the target zones). If the resulting PPZ ratio exceeds 1, the performance can be considered better than random. For example, if 40% of the wellbore length was inside the target zones, and 3 of the 5 PZs (60%) were encountered within those target zones, the PPZ would be 1.5, which could be considered 50% better than random. The relevant data set included a total of 44,246 m of drilled wellbore, and 122 PZs (as defined from Permeable-Temperature-Spinner data), from 59 wells. Overall results show that the production wells in Bulalo have encountered PZs in the targeted fault zones at a rate 13% better than random drilling would have. The method also enabled computation of the performance of each of the fault zones based on all the wells that intersected them. The most successful of the 11 well-established fault zones yielded a PPZ of 1.61, indicating that this zone is 61% richer in PZs than random. One other fault zone target demonstrated high rates of success, while others yielded PPZs no better than random or even worse than random. Overlapping fault zones were evaluated as separate targets, and, surprisingly, did not perform any better overall than the individual fault zones, but high PPZs were calculated for two of these multiple zones. A more conventional measure of success, i.e., number of PZs encountered per meter drilled, was also calculated for all the target zones. Results show that PZs were encountered once every 235 m in the best-performing fault zones, compared with an average interval of 470m for all the wells drilled in the deep reservoir. To determine if the high-performing fault targets also delivered high productivity, the average Productivity Index (PI) was calculated for all the PZs within each target fault zone. A cross plot showed a strong positive trend (55%) indicating that the fault zone targets richest in PZs also have the highest average PIs, which would tend to compound the advantage of targeting these zones. Also addressed was the “sweet-spot” issue, i.e. the high-performing fault zone targets coincide with a known area of high production, so maybe it’s that sweet spot that is driving the performance of those fault targets. But the high-performing fault zone targets show almost equally high PPZs outside the sweet spot, such that those fault zone targets can be considered innately productive. Possibly the sweet spot is actually due to a convergence of high-performing target zones. The same statistical methodology can be used to test almost any geometrically-defined targeting model, and work is ongoing to test alternative targeting models such as fault corridors, non-vertical faults, stratigraphic, and preferentially oriented fractures. The main limitation is data quantity and quality. Given Bulalo’s extensive history of drilling and production logging, it is hoped that future drilling can be guided by the quantitative results of this study. |