Common Risk Segment Mapping for Cuttings Reinjection

INTRODUCTION

Drilling scenarios such as infill drilling, water flood optimization, cuttings reinjection and CO2 injection often require an in-depth study of a field to determine the optimal drilling location within a set of restrictions. Quantifying the decision making around the process requires the integration of dozens of diverse datasets, often containing dynamic information and spanning a range of realizations.
Within CoViz® 4D it is possible to create 3D Common Risk Segment (CRS) models using a variety of inputs defining the types and locations of geologic controls such as faults and surfaces, along with existing well locations, bounding polygons, etc., in conjunction with user-defined “risk” distances (e.g., drilling envelopes) from these controls. With such 3D CRS models in place it rapidly becomes very clear which areas of the reservoir have high, medium and low risk levels, heavily influencing any future drilling.

Use Case: Common Risk Segment Mapping for Cuttings Reinjection

A North Sea field team needed to determine the best location for a cuttings reinjection well. The group turned to the Common Risk Segment (CRS) Mapping workflow within CoViz 4D to easily and rapidly determine potential, data-supported, cuttings reinjection sites based on specific input criteria designed to minimize cost and risk.

The CRS process started with integration of the field’s simulation model, horizon surfaces, fault surfaces and well paths in the CoViz 4D visualization environment.

Figure 1. Data integration within CoViz 4D.

The CRS workflow uses a ternary green/yellow/red risk scheme to apply cutoffs to the input criteria. The following input criteria and thresholds were decided upon by the team and used to define the spatial relationships which underlie the risk analysis calculation:
Input
Fault Distance
Top Horizon
Bottom Horizon
Distance from Wells
Oil Saturation
Reach from Drill Center
Green/Yellow Cutoff
200
10
10
200
0.5
2000
Yellow/Red Cutoff
150
5
5
100
0.3
3500

Each of these criteria was added to the Common Risk Segment Model graphical user interface, which also allowed for additional inputs that the group was not considering at the time: fluid contacts, seismic attribute cutoffs, drill center location, and a drilling envelope.

Figure 2. The CRS Mapping graphical user interface showing details of the Fault Controls, Horizon Controls and Drilling Controls tabs.

Utilizing minimum distance gridding, the CRS workflow generated a separate 3D grid for each user entered criteria (e.g., distance from fault, distance from well, etc.). Every grid node in each grid was assigned a classification based on the tertiary scheme: green (low risk), yellow (moderate risk), and red (high risk).

Figure 3. 3D grid of distance from wells. Red areas are less than 100m which fail the input criterion; green areas represent distances greater than 200m which pass the input criterion.

A combined CRS grid was then created which included each of the individual grids’ risks to generate the final model. The amalgamated results indicated where a cuttings reinjection well could safely be placed and which areas needed to be avoided, making it immediately obvious which regions in the field met all the input criteria.

The Common Risk Segment Mapping workflow within CoViz 4D enabled the North Sea group to easily and rapidly assess multiple input criteria, and determine potential cuttings reinjection well locations. Furthermore, the CRS workflow could be customized to include additional criteria as the field conditions evolved over time.

Contact DGI to find out more about how CoViz 4D can simplify your Common Risk Segment Mapping workflow.

Data Sources/Credits:

Data used with permission of owner.

FURTHER READING

Common Risk Segment Mapping for Cuttings Reinjection

Drilling scenarios such as infill drilling, water flood optimization, cuttings reinjection and CO2 injection often require an in-depth study of a field to determine the optimal drilling location within a set of restrictions. Quantifying the decision making around the process requires the integration of dozens of diverse datasets, often containing dynamic information and spanning a range of realizations.

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