Linking Geostatistics with Basin and Petroleum System Modeling:
Assessment of Structural Uncertainties
www.ies.de
Bin Jia and Tapan MukerjiStanford University
SCRF 22nd Affiliate MeetingMay 1, 2009
Outline
Basin and Petroleum System Modeling (BPSM)Assessment of structural uncertaintiesPreliminary resultsFuture works
Spatial scales of petroleum investigation
Sedimentary Basin investigations emphasize the stratigraphic sequence and structural style of sedimentary rock
Petroleum System studies the interplay between the main elements of a petroleum system.
Play investigations describe the present-day geologic similarity of a series of present-day hydrocarbon traps
Prospect studies describe the individual present-day trap to determine whether they have economic value and are exploitable with available technology and tools
Magoon and Dow, 1994
Source rockReservoir rockSeal rockOverburden rock
Trap FormationGeneration-Migration-Accumulation
Four essential elements and Two processesand all genetically related petroleum that originated from one pod of active source rock
Source
Reservoir
Seal
MigrationPath
Proc
esse
sEs
sent
ial
Elem
ents
What is Petroleum System?
Magoon and Dow, 1994
Objectives of BPSM
Basic Tasks: GenerationHave hydrocarbons been generated?Resource assessment studies and initial charge.
Where were hydrocarbons generated?If hydrocarbons were generated, we can define their locations quite accurately.
When were hydrocarbons generated?There are many clear examples of where basins/plays/prospects have failed due to timing problems.
Advanced Tasks: MigrationCould they have migrated to my prospect?Modeling of the dynamic process of generation, expulsion and migration makes it possible to determine if the oil and gas charge could reach the trap.
What are the properties of the hydrocarbons?Modeling of the phase behaviour of the hydrocarbons during migration, accumulation and loss makes it possible to determine oil vs. gas probabilities and even predict properties such as API gravities and GORs.
Wygrala, 2008
Importance of BPSM
Cum
ulat
ive
Dis
cove
ry (B
BO
E)
WorstDrillingSequence
Rand
om D
rillin
g
Rand
om D
rillin
g
Best Drilling SequenceSe
ism
ic Da
ta
Seis
mic
Data
Petro
leum
Sys
tem
Mod
elin
g
Petro
leum
Sys
tem
Mod
elin
g
6400
5600
4800
4000
3200
2400
1600
800
40 80 120 160 200
Drilling Sequence
100%
63%
28%
ForecastingEfficiency
165 Wildcat Wells(120 dry holes, 45 discoveries)
0%
Murris (1984)
GEOMETRY ANDSTRATIGRAPHYSeismic and well log
interpretation
PreprocessorTIMING OF UNITS,TECTONIC EVENTSDECOMPACTION
Rock/fluid properties
Forward modeling
TEMPERATURE FIELD
PORE PRESSURE FIELD
CHEMICAL REACTIONS
FLUID FLOW
GEOCHEMICAL
•Organic richness•Thermal maturity
•Kerogen type, Kinetics
OUTPUT
BOUNDARY CONDITIONS
•Heat flow history•Surface temperature•Paleo-water depth
CALIBRATIONS
BPSM Workflow
Peters, 2009
1-D Modeling Results
1.2%Ro
0.6%Ro
Dobbins source rock
Scheirer, 2008
3-D Modeling of Alaska North Slope Basin
Shubliksource rock
Vitrinite Reflectance, %
Prudhoe
Pt. Barrow
Kuparuk
Peters, 2009
A Parallel Universe ...
ProductionTechnology: Reservoir Modeling/Simulation
Tasks: simulate hydrocarbon fluid flow processes in hydrocarbon accumulations to understand and predict flow and optimize production
Data Models: 3D geological models constructed e.g. with Petrel and other tools
Model Scaling: meter to kilometer, months to years
Model Geometry: static, i.e. does not change during simulation
Software Product: EclipseEclipse
ExplorationTechnology: Basin/Petroleum System Modeling
Tasks: simulate hydrocarbon generation and migration processes in basins to understand generation potential, assess charge risks and predict properties
Data Models: 1D/2D/3D geological models constructed with various tools, including Petrel, GoCad and others
Model Scaling: tens to hundreds of kilometers, millions of years!
Model Geometry: dynamic, i.e. changes during simulation
Software Product: PetroModPetroMod
Wygrala, 2008
Uncertainty assessment in BPSM
Input Uncertainties
Heat flow
HI
StratigraphyGeometry
Lithology
Kinetics PWD
TOC …
Output Uncertainties
Structural modeling uncertainties
Structural uncertainties from seismic data– Time-to-depth conversion– Interpretation– Migration– Acquisition– Preprocessing– Stacking
Thore etc, 2002
Time-to-depth conversion with Bayesian Kriging
Bridge between simple kriging and kriging with trendStable when there are few well observationsSimilar to universal kriging results for large amount of well observations
Bayesian Kriging vs. Kriging with Trend
Abrahamsen 1993
Representation of Structural Model
Dubrule 2003
Impact of source rock structures
Workflow - uncertainties quantification
Data Gathering- Seismic Time- Well Data
Generate multiple structure realizations
PetroMod Simulation
Match calibration
data?
“NO” - Continue with next realization
Enough realizations?
YES
Uncertainty Assessment
YES
“NO” - Continue with next realization
Future work
Complex structuralFacies modeling3-D modeling
