Geobodies Detection
Geological bodies (reefs, channels, diapirs) form complex reservoirs whose morphology and internal structure determine the volume of reserves and the development strategy. The detection and interpretation of their boundaries is a time-consuming and subjective process that depends on the specialist's experience.
Their inaccurate localization is critical and leads to the following consequences:
Problematics
Financial losses
Misleading correct drilling with loosing on one well up to $3 mln
Misleading correct drilling with loosing on one well up to $3 mln
False rock properties
Leading to poor oil recovery factor from many wells
Leading to poor oil recovery factor from many wells
Simplified reservoir models
Resulting in errors of reserves estimation up to 50%
Resulting in errors of reserves estimation up to 50%
Geoplat AI technology automatically and accurately identifies the boundaries of geological bodies, creating a 3D model that reflects their true morphology and internal heterogeneity.
Transfer learning technology allows you to apply interpretator’s knowledge of the geology. Therefore, the solution is combining ML algorithms and human vision on the data.
Transfer learning technology allows you to apply interpretator’s knowledge of the geology. Therefore, the solution is combining ML algorithms and human vision on the data.
Approach
Calculation speed
From several hours to 2-3 days
From several hours to 2-3 days
Required parameter setting
1 or 2 parameters only per procedure
1 or 2 parameters only per procedure
Required input data
- Stacked seismic data of any quality;
- Manual geobody picks on 2–5 sections of the volume
Results
Exporting the boundaries
in a standard format for integration into interpretation process
in a standard format for integration into interpretation process
Adjusting the model details
for specific object type by manually marking some of their boundaries
for specific object type by manually marking some of their boundaries
Receiving quick information
on geobodies distribution across the entire volume of seismic data
on geobodies distribution across the entire volume of seismic data
Extracted geobodies boundaries as horizons
3D geobodies probability volume
Tools
Automatic detection and localization of geobodies with various origin
Detailed geological models under control of specialist for accurate reserve prediction
Extraction of geobodies boundaries into standard horizon formats
Geobodies classification
-
Terrigenous reservoirs- Paleo-channels
- Delta branches
- Underwater channels
- Cut-in valleys
-
Carbonate and biogenic reservoirs- Barrier reefs
- Atolls
- Carbonate platforms
-
Evaporite (salt) structures- Salt pillows
- Salt diapirs
- Salt domes
- Salt ramparts
-
Volcanogenic objects- Underwater volcanic structures
- Lava plateaus and streams
- Tuff bodies
-
Geohazards- Shallow Gas Accumulations
- Karst
- Scours
- Landslide Slip Planes
-
Cryogenic and glacial objectsGlacial valleys
Moraines (glacial-clastic bodies)
Fluvioglacial deposits
Reefs
Paleo-channels
Salt Bodies
Salt top as a horizon in 3D
Business Effect
Increased forecast accuracy
by 2 times due to the sedimentation model's detalization
by 2 times due to the sedimentation model's detalization
Increased work efficiency
by minimization routine picking geobody boundaries up to 90%
by minimization routine picking geobody boundaries up to 90%
Consideration of geological risks
to adjust well trajectories and reduce financial losses
to adjust well trajectories and reduce financial losses
Other AI Solutions
Fault interpretation is one of the most difficult tasks within a general structural interpretation workflow.
Fault interpretation is one of the most difficult tasks within a general structural interpretation workflow.
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