Infill drilling aims to tap previously undrained reserves in a mature hydrocarbon field. By definition, infilling happens later in field life and hence involves the analysis of greater volumes and a greater diversity of a priori data. Furthermore, the economics of infill drilling can be marginal. Therefore, a rigorous quantitative decision-making process is necessary to justify the economic risk required to implement drilling and production plans.
Integrating and Filtering Data to Identify Infill Drilling Candidates
This interactive conditional filtering workflow was applied to the Volve oilfield in the Norwegian North Sea to find infill drilling locations. From the diverse datasets available from Volve, the following data were used:
- Geologic faults
- Seismic amplitude
- Reservoir fluid model
- Existing wellbores
Figure 2. 3D distances from faults; red areas are within 50m which was deemed close. For reference, the existing wellbores are also displayed.
Figure 3. Seismic amplitudes filtered to show values < – 2 Db which are likely reservoir sands.
Figure 4. Reservoir simulation model filtered to show Oil Saturation (SO) values > 50%.
Finally, it was decided that the infill drilling locations needed to be within a certain distance of existing wellbores to ensure accessibility via sidetracking from a previously drilled wellbore, providing a considerable cost savings over the drilling of a new borehole from the rig. Again, the 3D minimum distance tool was used to compute this from-wellbore distance. Then, to examine possible sidetrack opportunities, a visual filter was used to only highlight the volumes within 500 meters of any wellbore:
Figure 5. Animation showing dynamic filtering of distance to wellbores, which was calculated using 3D minimum distance gridding.
Figure 6. Integration of filtered volumes while powerful, can be challenging as input to quantitative analysis.
Figure 7. Result of resampling multiple attributes into a single model and applying compound filtering (>50% SO, <-2Db amplitude, <500m from any wellbore and >50m from any fault); these areas are the potential infill drilling candidates requiring further investigation.
Filtering Infill Drilling Candidates by Economic Viability
To achieve this, we used a module in the CoViz 4D toolkit to find areas which are physically connected and have enough volume for potential economic viability. The clustering criteria are:
- Grid cells must be connected either by a cell face or cell edge (do not include corner connections)
- There must be a minimum of 350 grid cells to form a viable cluster (cells are approximately 25m x 25m x 0.7m, or about 440m3, 350 cells would be about 15,000m3)
- The cluster must be a minimum of 12 meters thick
Figure 8. After running a cluster analysis, the best infill drilling location was identified based on specific user input criteria; the oil volume was estimated to be 1+ million barrels.
Furthermore, since any of the filtering and clustering criterion can be varied for testing and evaluating the viability of various reservoir conditions, any number of realizations can be generated programmatically to posit and examine possible outcomes. Because all of the CoViz 4D tools are modular, they can be scripted and run in Jupyter Notebooks, for example, to test many different variations of oil saturation and seismic amplitude as well as proximity to wellbores and faults.