초록 ▼

A method and apparatus are disclosed for modeling a system to estimate values and associated uncertainties for a first set of variables describing the system. A second set of system variables is selected, where the second set is directly or indirectly causally related to the first set of variables.

A method and apparatus are disclosed for modeling a system to estimate values and associated uncertainties for a first set of variables describing the system. A second set of system variables is selected, where the second set is directly or indirectly causally related to the first set of variables. Data is obtained or estimated for each variable in the second set and the quality of selected data is appraised. A network is formed with nodes including both sets of variables and the quality appraisals, having directional links connecting interdependent nodes, the directional links honoring known causality relationships. A Bayesian Network algorithm is used with the data and quality information to solve the network for the first set of variables and their associated uncertainties.

대표청구항 ▼

What is claimed is: 1. A method for modeling a geologic or geophysical system to estimate values and associated uncertainties for a first set of geologic or geophysical variables describing said geologic or geophysical system, said method comprising: selecting a second set of geologic or geophysica

What is claimed is: 1. A method for modeling a geologic or geophysical system to estimate values and associated uncertainties for a first set of geologic or geophysical variables describing said geologic or geophysical system, said method comprising: selecting a second set of geologic or geophysical system variables, said second set being directly or indirectly causally related to said first set of geologic or geophysical variables, wherein at least one of the second set of geologic or geophysical variables represents observed values of one of the first set of geologic or geophysical variables; obtaining or estimating data for each geologic or geophysical variable in the second set; appraising the quality of the geologic or geophysical data from the second set; forming a network with nodes comprising both sets of geologic or geophysical variables and said quality appraisals, having directional links connecting interdependent nodes, wherein network forming includes forming one or more risking triads, each risking triad having a first node representing an observed value of one of the first set of geologic or geophysical variables, a second node representing an actual value of the variable of the first node, and a third node representing the quality of the observed value of the variable of the first node, each of the second and third nodes each being connected to the first node by a link indicating that the second and third nodes cause the first node; and using a Bayesian Network algorithm with said geologic or geophysical data and quality information to solve the network for said first set of geologic or geophysical variables and their associated uncertainties. 2. The method of claim 1, wherein risking triad forming includes forming the risking triad such that: the second node represents a probability distribution of the actual values of the variables of the first node. 3. The method of claim 1, wherein risking triad forming includes forming the risking triad such that: the third node represents a probability distribution of the quality of the observed values of the variables of the first node. 4. The method of claim 1, wherein risking triad forming includes forming the risking triad such that: the first node represents a probability distribution of the observed values of the variables of the first node. 5. The method of claim 1, wherein risking triad forming includes forming the risking triad such that: the second node represents a probability distribution of the actual values of the variables of the first node; the third node represents a probability distribution of the quality of the observed values of the variables of the first node; the first node represents a probability distribution of the observed values of the variables of the first node; and the probability distribution of the first node is related to the probability distribution of the second node and the probability distribution of the third node by Bayes Rule. 6. The method of claim 5, wherein risking triad forming includes forming the risking triad such that: the probability distribution of the first node has a dimension related to the probability distribution of the second node; and the probability distribution of the first node has a dimension related to the probability distribution of the third node. 7. The method of claim 5, wherein risking triad forming includes forming the risking triad such that: the probability distribution of the first node is a discrete probability distribution; the probability distribution of the second node is a discrete probability distribution; and the probability distribution of the third node is a discrete probability distribution. 8. The method of claim 5 wherein risking triad forming includes forming the risking triad such that: the variable of the first node has states; the states are mutually exclusive; and the states are exhaustive. 9. The method of claim 1, where the system has a behavior, the method further comprising: selecting the first set of variables and the second set of variables so that together they are sufficiently complete to account for the behavior of the system. 10. The method of claim 1 wherein none of the data are selected for quality appraisal. 11. The method of claim 1 wherein: the geologic or geophysical system to be modeled is a system governing sand composition and sand texture; the first set of geologic or geophysical variables includes sand composition and sand texture; and the second set of geologic or geophysical variables includes hinterland geology, hinterland weathering and transport, and basin transport and deposition. 12. The method of claim 1 wherein: the geologic or geophysical system to be modeled is a system governing reservoir quality; the first set of geologic or geophysical variables includes reservoir quality; and the second set of geologic or geophysical variables includes compositional control, over-pressure effect, burial depth, compaction, mean grain size, early clay coating, active surface area, max. temperature, time at max. T, cementation, sorting, matrix proportion, and initial RQ. 13. The method of claim 1 wherein: the geologic or geophysical system to be modeled is a system necessary to identify seismic bright spots; the first set of geologic or geophysical variables includes seismic tuning, net, gross, pore fluid; and the second set of geologic or geophysical variables includes kerogen type, maturation, charge, trap, seal, depo. model, seismic resolution, seismic amplitude response, net/gross, and resvr. thickness. 14. The method of claim 1 wherein: the geologic or geophysical system to be modeled is a system governing carbonate cement in clastic reservoirs; the first set of geologic or geophysical variables includes calcite cement susceptibility and local calcite volume; and the second set of geologic or geophysical variables includes depositional environment, aridity while exposed, systems tract, reactive calcite, initial CaCO3, Ca-rich volcanics, plagioclases, additional cement, flow properties, sequence setting, and concretions. 15. The method of claim 1 wherein: the geologic or geophysical system to be modeled is the system governing direct-hydrocarbon-indicator-based drilling decisions; the first set of geologic or geophysical variables includes do-full-DHI-analysis (decision) and drill (decision); and the second set of geologic or geophysical variables includes cost-of-full-DHI-analysis (utility), cost to drill (utility), hydrocarbons present, prelim. DHI indicates hydrocarbons, and full-study DHI indicates hydrocarbons. 16. A Bayesian Network representing a geologic or geophysical system, comprising: a risking triad having a first node representing an observed value of a geologic or geophysical variable, a second node representing an actual value of the geologic or geophysical variable, and a third node representing a reliability of the observed value of the geologic or geophysical variable, said second node being connected to the first node by a link indicating that the second node causes the first node, and the third node being connected to the first node by a link indicating that the third node causes the first node. 17. The Bayesian Network of claim 16, wherein: the second node represents a probability distribution of the actual value of the variable. 18. The Bayesian Network of claim 16, wherein: the third node represents a probability distribution of the reliability of the observed value of the variable. 19. The Bayesian Network of claim 16, wherein: the first node represents a probability distribution of the observed values of the variable. 20. The Bayesian Network of claim 16, wherein: the second node represents a probability distribution regarding the variable; the third node represents a probability distribution regarding the reliability of the observed value of the variable; the first node represents a probability distribution regarding the observed value of the variable; and the probability distribution of the first node is related to the probability distribution of the second node and the probability distribution of the third node by Bayes Rule. 21. The Bayesian Network of claim 20, wherein: the probability distribution of the first node has a dimension related to the probability distribution of the second node; and the probability distribution of the first node has a dimension related to the probability distribution of the third node. 22. The Bayesian Network of claim 20, wherein: the probability distribution of the first node is a discrete probability distribution; the probability distribution of the second node is a discrete probability distribution; and the probability distribution of the third node is a discrete probability distribution. 23. The Bayesian Network of claim 20 wherein: the variable of the first node has states; the states are disjoint; and the states are exhaustive. 24. A method for constructing a Bayesian Network that represents a geologic or geophysical system, comprising: creating a first node representing an observed value of a first geologic or geophysical variable; creating a second node representing an actual value of the geologic or geophysical variable; creating a third node representing a reliability of the observed value of the geologic or geophysical variable; and establishing a risking triad using the first node, the second node, and the third node such that said second node is connected to the first node by a link indicating that the second node causes the first node, and the third node is connected to the first node by a link indicating that the third node causes the first node. 25. The method of claim 24, wherein creating the second node includes: identifying a probability distribution of the actual value of the variable. 26. The method of claim 24, wherein creating the third node includes: identifying a probability distribution of the reliability of the observed value of the variable. 27. The method of claim 24, wherein creating the first node includes: identifying a probability distribution of the observed value of the variable. 28. The method of claim 24, wherein: creating the second node includes identifying a probability distribution regarding the actual value of the variable; creating the third node includes identifying a probability distribution regarding the reliability of the observed value of the variable; creating the first node includes identifying a probability distribution regarding the observed value of the variable; and relating by Bayes Rule the probability distribution of the first node to the probability distribution of the second node and the probability distribution of the third node. 29. The method of claim 28, wherein: identifying the probability distribution of the first node includes identifying a first dimension related to the probability distribution of the second node; and identifying the probability distribution of the first node includes identifying a second dimension related to the probability distribution of the third node. 30. The method of claim 28, wherein: identifying the probability distribution of the first node includes identifying a discrete probability distribution for the first node; identifying the probability distribution of the second node includes identifying a discrete probability distribution for the second node; and identifying the probability distribution of the third node includes identifying a discrete probability distribution for the third node. 31. The method of claim 28 wherein creating the first node includes: identifying disjoint and exhaustive states for the variable of the first node.