Augmenting Well Production Analysis with Subsurface Data
The Montney Formation, located in the Western Canadian Sedimentary Basin, is developed in a multi-zone stack throughout the fairway. Unfortunately, these refined target zones are not captured in public data. For analysis, it is important to differentiate Montney Wells into multiple target zones because they vary significantly in reservoir properties both vertically and laterally.
Identifying the target zone based on sequence stratigraphy is a valuable process but can be time-consuming. In this blog we show a quick method to differentiate target zones using a depth-based approach that is helpful when you have limited time and resources. This workflow can be applied to any map-based data to derive a data set suitable for well production analysis.
For contour-based geologic data to be useful for well production analysis, we need a map-derived value for each individual well. To accomplish this, we started with a publicly available Geologic map of the Montney Top in Meters Subsea (BC OGC, 2012).
Figure 1: Digitized and interpolated Montney Top Subsea TVD (True Vertical Depth) Structure Map, (OGC, 2012).
The Montney Top Structure map contours were digitized (Figure 1), so that interpolated well-values could be derived. Using point-sampling, the Montney Top Depth was extracted at the intersection of the Bottom Hole locations. The benefit of this method is that you can derive values for any well within the map area and the point-sampling process can be re-run as new wells are drilled within the map area.
The resulting dataset consists of a Top Depth for every Well Bottom Hole Location and can then be used to calculate other attributes. In this example workflow, the Formation Top Depth and Well Bottom Hole Depth were used to calculate Depth Below Formation Top at Bottom Hole locations for a subset of Montney Horizontal wells in British Columbia.
The Formation Top Depth and Depth Below Formation Top were imported into VERDAZO as User Defined Attributes (UDA) and used to group wells into bins based on the Montney zone they were targeting (Figures 2 & 3).
Figure 2: Schematic diagrams showing the map view, dip section, and strike section of a pad targeting the Montney Formation.
To illustrate the targeted zones, Figure 2 shows the Depth Below Formation Top well groups. The target zone definitions will vary depending on geographic location within the Montney Fairway. In this specific area, we grouped 3 target zones based on the Depth Below Formation Top.
Grouping the wells based on Depth Below Formation Top allows us to quickly differentiate target zones and compare their production from multiple perspectives (Figure 3). The Type Well Curve Chart shows the average gas peak rate is highest in the shallowest target zone and decreases with depth. When comparing the First 12-Months Cumulative Gas Production per 100m Completed Length in a cumulative probability probit plot, it shows the gas production decreases with depth (Probit Mean: 250 to 112 mcf/day/100m). Additionally, the P10:P90 ratios indicate the Target Zone wells have similar statistical variability (P10/P90: 3.7 to 3.9).
Figure 3: Production comparison using depth-based Target Zones (IHS Datahub – VERDAZO, March 2019).
Without differentiating the target zones, we would not have been able to compare and contrast the production changes seen throughout the multi-zone stack. Bringing in a single piece of subsurface data, a Montney Top Structure map, substantially improved our production analysis and insights. This workflow can be used to incorporate any map-based geoscience data. Instead of overlaying production bubbles on maps to visually look for trends, extract well information from the map to provide a more detailed analysis.
Contact email@example.com if you would like to learn more about integrating subsurface data into your analyses.
Public Map: BC Oil & Gas Commission Open Data Portal, Mark Hayes, 2012, Structure Top Montney, ftp://ftp.bcogc.ca/outgoing/OGC_Data/Geology_and_Engineering/montney_play_atlas_maps/.
Production data: IHS Information Hub
Completion data: geoLogic WCFD
Production Analysis: VERDAZO
GIS Tool: QGIS