1. SCRF - Multidisciplinary
Close collaborations with other research programs
SCRF
BPSM
Stanford Center for
Reservoir Forecasting
Basin and Petroleum System
Modeling

2. Basin Modeling and Decision Analysis
SCRF - Multidisciplinary
Basin Modeling and Decision Analysis

3. SCRF - Multidisciplinary
Basin modeling inputs to decision analyses
using Bayesian networks
collaboration with Eidsvik, Martinelli (NTNU)
2010

4. Basin Modeling and Decision Analysis
Building Bayesian networks from basin modeling scenarios for improved geological decision making
Martinelli, Tviberg, Eidsvik, Sinding-Larsen & Mukerji
In press Petroleum Geosciences (2013)

5. Summary
Integration between quantitative BPSM methodologies and decision analysis; quantify uncertainties, and how uncertainty affect decisions
Use of BPSM multiple runs to train probabilistic decision tool (Bayesian network)
Provide inputs for aiding Value of Information (VOI) based decision techniques with BPSM.

6. Basin Modeling and Decision Analysis
Building Bayesian networks from
basin modeling scenarios for improved geological decision making

7. Bayesian Networks
Bayesian networks (or Influence diagrams) are mathematical tools allowing fast computations of conditional probabilities
Used in decision analysis
Prof. Ronald Howard , James Matheson, Prof. Shachter, Koller,
developed, refined and popularized influence diagrams.

8. Bayesian Networks (BN)
Bayesian belief networks are probabilistic models where a graph structure is used to represent a set of random variables and their conditional dependencies.

9. Network Models - Arrows
Arrows represent relationships or influence between the nodes. A B
A influences B means that knowing A would directly affect our belief or expectation about the value of B. It does not necessarily imply a causal relation, or a flow of data or money.
Evidential dependence, not necessarily causal dependence.

10. Network Models Network structure -nodes representing variables
-links representing dependencies between nodes
P(A) A B C D
P(B)
P(C|A)
P(C|B)
P(D|C)
Probability distribution of variables at each node
given the state of its parent node

11. Bayesian Networks - examples
Representation of the relation between uncertainties and decisions
Example of Influence Diagram
Bhattacharjya & Mukerji, 2006

12. Expanding the Nodes Example of Influence Diagram
Bhattacharjya & Mukerji, 2006

13. Full Network Model for Monitoring
Example of Influence Diagram
Bhattacharjya & Mukerji, 2006

14. Network Models
Help to factorize large multivariate joint PDFs into more manageable smaller PDFs using the network to find conditional independencies
PDF: probability distribution functions

15. Evaluating Network Models
Network diagrams useful for representing decision problems.
But not merely pictorial depictions
Also computational tools to solve decision problems efficiently

16. Bayesian Networks in Decision Analysis
Representation of the relation between
uncertainties and decisions
as well as
computation of optimal decisions
impact of evidence
value of information (VOI)

17. Bayesian Networks in BPSM Decision Analysis
Bayesian networks (BN) need to encode dependencies in a geological system
- source rock,
- reservoir rock,
- trap

18. Conditioning the Network model
Important to note that for a Bayesian network model
to be effective the prior conditional probabilities should
be case specific:
based on the opinion of experts in that field

19. Bayesian Networks in BPSM Decision Analysis
Van Wees et al., 2008
Martinelli et al., 2010
Rasheva & Bratvold, 2011
Source
Source
Source
Martinelli et al., 2010
Source

20. Conditioning the Network model
Important to note that for a Bayesian network model
to be effective the prior conditional probabilities should
be case specific:
based on the opinion of experts in that field
from quantitative basin modeling runs based on data specific to that field

21. Basin and Petroleum System Modeling
Four essential elements and two processes
Source rock
Reservoir rock
Seal rock
Overburden rock
Trap Formation
Generation-Migration- Accumulation
Essential
Elements
Source
Reservoir
Seal
Migration
Path
The essential elements and processes as well as all genetically related hydrocarbons that occur in petroleum shows, seeps, and accumulations whose provenance is a single pod of active source rock.
Source rock characterization is key to model petroleum generation, Geochemistry is the key to study source rock properties, which controls the petroleum generation. Source rock richness like Total Organic Carbon and Hydrogen Index are quantified for the source rock. Overburden rock is also important in basin modeling, it provides the overburden necessary to thermally mature the source rock and impacts on the geometry of the underlying migration path and trap.
The timing of trap formation is important, we might see HC was generated, and trap was formed but late, then the generated HC will not accumulate but vanish.
By doing so, valuable inferences can be made about such matters as hydrocarbon generation and timing, maturity of potential source rocks and migration paths of expelled hydrocarbons.
Processes
Leslie B. Magoon, Wallace G. Dow, The Petroleum System, AAPG Memoir 60 23 23

22. Key Modeling Factors Burial History (deformation, compaction)
Thermal History (heat flow, thermal conductivities)
Source Rock Geochemistry ( chemical kinetics)
Fluid Migration (multiphase Darcy flow, streamlines)
Geohistory analysis aims at producing a curve for the subsidence and sediment accumulation rate through time.
Paleo-temperatures are controlled basal heat flow history of the basin, and also soe internal factors like thermal conductivities, heat generation from radioactive sources in the continental crust, regional water flow, and surface temperature are also important factor to the temperature modeling. Heat flow is intimately linked to the tectonic processes driving basin subsidence, where surface temperature is a function of long-term climatic and latitudinal variations.
Applied organic geochemistry provides the information needed to make maps of the richness, type and thermal maturity of a source rock. They are based on geochemical analysis of rock samples from outcrops and wells that are displayed on logs. The typical logs are 1)Rock-eval pyrolysis, total organic carbon, vitrinite reflectance and other rapid screening methods. The logs define 1)potential, effective and spent petroleum source rock. 2) The thermal maturation gradient, including immature, mature, and postmature zones and 3) in situ and migrated petroleum shows.
Decision on the migration method is one of the core decision when running your simulation. PetroMod provides several methods including Darcy flow, flowpath, and Hybrid which is basically Darcy plus Flowpath. Darcy flow is accurate but time consuming, accumulation will not be simulated. Flowpath simulates buoyancy driven migration including accumulation, simplified but effective way of simulation. Hybrid simulates both, most time consuming. 24 24

23. Coupled, non-linear, PDEs with moving boundaries
Basin Modeling
Coupled, non-linear, PDEs with moving boundaries
Numerical solution (finite elements) 25

24. Hydrocarbon flow and accumulations
Key outputs
Burial history
Thermal history
Geochemical history
Hydrocarbon flow and accumulations 26

25. Bayesian Networks in BPSM Decision Analysis
How can we train the network
using BPSM simulations?
Constructing Bayesian networks to address
decision problems requires key inputs from
basin modeling simulations

26. Bayesian Networks and BPSM Decision Analysis
How can we train the network
using BPSM simulations?
Synthetic example and workflow

27. Synthetic Basin Model Example – Base Case
Plausible petroleum system scenario and boundary conditions, 55Ma ago to present
100 x 100 km
9 layers
Source (Mlf)
Source (Eek)

28. Synthetic Basin Model Example – Base Case
Plausible petroleum system scenario and boundary conditions, 55Ma ago to present
100 x 100 km
Reservoir (Mmd, top)
Reservoir (Ou, bottom)

29. Synthetic Basin Model Example – Base Case
Plausible petroleum system scenario and boundary conditions, 55Ma ago to present
100 x 100 km
Traps:
Anticline (East)
Fault (West)
4 possible prospects:
Top, East (TE)
Bottom, East (BE)
Top, West (TW)
Bottom, West (BW)
East
West

30. Synthetic Basin Model – Base Case
Accumulations

31. Synthetic Basin Model – Base Case
Migration pathways
Anticline (East)
Fault (West)

32. Synthetic Basin Model – Uncertain factors
Experimental design, full factorial design with 4 factors
Porosity, Heat Flow, Fault 3 and TOC
2 x 3 x 2 x 2 = 24 total levels

33. Porosity – 2 compaction depth trends
Depth (m)
Porosity
Porosity
(High)
(low)

34. Synthetic Basin Model – Uncertain factors
Experimental design, full factorial design with 4 factors
Porosity, Heat Flow, Fault 3 and TOC
2 x 3 x 2 x 2 = 24 total levels
Responses: generation (size, oil, gas)
accumulation (size, oil, gas)

35. Analyze Outputs - examples
HC Generation Total (MMBOE)
HF TOC porosity Fault3

36. Analyze Outputs - examples
Generation Eek Oil (MMBOE)
HF TOC porosity Fault3

37. Analyze Outputs - examples
Generation Eek Gas (MMBOE)
HF TOC porosity Fault3

38. Analyze Outputs - examples
Accumulation Ou Gas (MMBOE)
HF TOC porosity Fault3

39. Analyze Outputs - examples
Pareto charts

40. Building the Bayesian Network (BN)
Decision Model
Building the Bayesian Network (BN)
Source nodes
Trap nodes
Reservoir nodes
Accumulation nodes

41. Building the Bayesian Network (BN)
Source sub-network

42. Building the Bayesian Network (BN)
Reservoir sub-network

43. Building the Bayesian Network (BN)
Trap sub-network

44. Bayesian Network

45. Training the Bayesian Network (BN) (Learning the network)
Decision Model
Training the Bayesian Network (BN)
(Learning the network)
Conditional Probability Tables (CPT) associated with nodes
Obtained by clustering output responses
generation (low, medium, high)
accumulation (low, high)
CPTs estimated by ratio of corresponding counts

46. Training the Bayesian Network (BN)
Clustering outputs:
Accumulation
2 levels
Accumulation total
Top
Bottom
Gas
Oil
Gas
Oil

47. Training the Bayesian Network (BN) (Learning the network)
Decision Model
Training the Bayesian Network (BN)
(Learning the network)
Conditional Probability Tables (CPT) associated with nodes
Obtained by clustering output responses
generation (low, medium, high)
accumulation (low, high)
CPTs estimated by ratio of corresponding counts

48. Using BN in Value of Information (VOI) calculations
VOI = Value with evidence – Prior value
max { }
Prior Value =
f: recovery factor
C: cost
Value with evidence =
Network model evaluates evidence based updating of probabilities

49. Evaluating the Bayesian Network
Evidence based update of probabilities
P(BE oil)
Evidence
about TE oil

50. Evidence based update of probabilities
P(BE oil)
Probability density
Volume oil (MMBOE)

51. Evidence based update of probabilities
P(BE oil|TE oil low)
P(BE oil)
Probability density
Volume oil (MMBOE)

52. Evidence based update of probabilities
P(BE oil)
Probability density
P(BE oil|TE oil high)
Volume oil (MMBOE)

53. Evaluating the Bayesian Network
Evidence based update of probabilities
Evidence
about BW oil
P(BE oil)

54. Evidence based update of probabilities
P(BE oil)
Probability density
Volume oil (MMBOE)

55. Evidence based update of probabilities
P(BE oil)
P(BE oil|BW oil high)
Probability density
Volume oil (MMBOE)

56. Conclusion
Workflow to study how the uncertainty of some critical parameters in BPSM impact decisions
Provide inputs to aid Value of Information (VOI) based decision techniques with BPSM simulations.
Integration between quantitative geology methodologies (BPSM) and decision analysis

57. When network models useful?
The real value of Bayesian network models in decision analysis is when observables are
conflicting,
inter-related,
uncertain,
and the decisions are not so straightforward.

58. Conditioning the Network model
Important to note that for a Bayesian network model
to be effective the prior conditional probabilities should
be case specific:
based on the opinion of experts in that field
from BPSM runs based on data specific to that field

59. Basin Modeling and Decision Analysis
Building Bayesian networks from basin modeling scenarios for improved geological decision making
Martinelli, Tviberg, Eidsvik, Sinding-Larsen & Mukerji
Acknowledgements:
SCRF - Stanford
BPSM - Stanford
SFI2 - Oslo