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

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

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

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)

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.

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

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.

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.

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.

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

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

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

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

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)

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)

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

Synthetic Basin Model – Base Case Accumulations

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

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

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

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)

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

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

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

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

Analyze Outputs - examples Pareto charts

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

Building the Bayesian Network (BN) Source sub-network

Building the Bayesian Network (BN) Reservoir sub-network

Building the Bayesian Network (BN) Trap sub-network

Bayesian Network

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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