Infill Drilling Placement using Conditional Seismic Attribute Filtering

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.

Within the DGI CoViz 4D software platform it is possible to perform conditional attribute filtering using a variety of data inputs defining the types and locations of geologic controls such as faults, structural surfaces and reservoir properties, along with existing infrastructure such as wellbores, completions and facility locations. Using these inputs in conjunction with user-defined criteria for thresholding of each attribute, clustering analysis and volumetric calculations can be performed to further refine and identify potential infill targets. As a result, explorationists can quickly develop ranked and risked lists of infill drilling targets with likely economic outcomes. The simplicity and speed of this analysis, alongside the rapid, interactive quantification of results, is what sets CoViz 4D apart when it comes to this type of workflow.

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 1. Bathymetry, seismic amplitude, wellbores, and structural surfaces of the Volve Field, Norwegian North Sea, as depicted in CoViz 4D. Volve Field data courtesy of Equinor and former partners ExxonMobil Exploration & Production Norway AS and Bayerngas Norge AS. June, 2018. https://data.equinor.com.dataset.volve.
For each attribute, a criterion was set to define acceptable versus unacceptable regions of potential infill in the subsurface; and after applying a joint filtering and data fusion protocol, a clustering and volumetrics analyses produced the final results.
There were 56 interpreted and defined fault surfaces in the Volve field model. The 3D minimum distance gridding tool in the CoViz 4D software was used to calculate 3D distances from fault surfaces. With this grid displayed, the results were visually and interactively filtered, quickly showing areas unsuitable for infill drilling (red) that are too close (within 50 meters) of a fault surface (yellow).

Figure 2. 3D distances from faults; red areas are within 50m which was deemed close. For reference, the existing wellbores are also displayed.

Next, the depth-converted seismic amplitude data were considered. Based on rock physics analysis and comparisons to borehole data it was concluded that seismic samples with amplitudes less than -2 Db were likely to indicate reservoir sand. Hence all other samples were filtered out of the analysis. The result shows the complex nature of the reservoir with regards to sand bodies of varying quality, making anything but conditional filtering very difficult and inconsistent.

Figure 3. Seismic amplitudes filtered to show values < – 2 Db which are likely reservoir sands.

The dynamic reservoir simulation grid was then examined. A 50% threshold was agreed upon, requiring the pore space to have a modeled 50% or greater oil fraction to be consider a candidate for the infill drilling program.

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.

Bringing the filtered volumes together within the CoViz 4D viewer, all four threshold-constrained attributes were then visualized in the same geospatial area of interest. However, although this data fusion can be powerful in a qualitative sense, using this visual product alone made it nearly impossible to discern which areas in the reservoir meet all the conditional criteria.

Figure 6. Integration of filtered volumes while powerful, can be challenging as input to quantitative analysis.

To address this, we quantitatively sampled the distance to faults, distance to wellbores, and seismic amplitude into the reservoir model as new attributes. The powerful tools in CoVIz 4D allowed us to perform this geometrically diverse resampling in a rapid and robust way. The visual filtering is then easily achieved in CoViz 4D to depict areas of the reservoir which are >50% SO, <-2Db amplitude, <500m from any wellbore and >50m from any fault. These are our infill drilling candidates meeting all the unswept oil-in-place conditions.

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

We now wanted to quantify these results and see which of these locations, if any, are large enough to be potentially economically viable. Specifically, contiguous, fluid-connected clusters of samples are necessary to all meet the criteria; clusters that can thus be drained by a single well.

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
After the clustering analysis was run, the result was a single cluster of 631 contiguous cells which meet all the filtering and cluster analysis criteria. Again, using a module from the CoViz 4D toolkit, we computed the volume of the cluster which was just over 371 thousand cubic meters. Since the filtering had required an oil saturation of 50% or higher, depending on porosity, this could potentially be a million barrels or more within easy reach of an existing wellbore. Thus, within a relatively short space of time, infill drilling candidates were identified for further review and study has been quantified.

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.

In conclusion, using a conditional filtering, clustering, and volumetric analysis workflow in CoViz 4D we rapidly identified viable, if perhaps economically marginal, infill drilling targets. The workflow is interactive, robust, and can easily be captured for transparency and repeatability.
Contact DGI to learn more about how CoViz 4D can simplify the identification of infill drilling targets.
Data credit for all images: Volve Field data courtesy of Equinor and former partners ExxonMobil Exploration & Production Norway AS and Bayerngas Norge AS. June, 2018. https://data.equinor.com.dataset.volve.

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