Antoine Bertoncello, Hongmei Li and Jef Caers

Transcription

1 Antoine Bertoncello, Hongmei Li and Jef Caers

2 model created by surface based model (Bertoncello and Caers, 2010)

3 A conditioning methodology have been developed and tested on a model with few parameters are influent East-Breaks data set (Bertoncello and Caers 2010) We want to test the conditioning methodology when a sensitivity analysis does not lead to a simplification of the model.

4 Geologic concept is integrated into reservoir models using rules and statistics (from process-based models or other analysis)

5 1% 70% Net-to-Gross

6 Parameters uncertainty (e.g. thickness statistics) Spatial uncertainty (placement)? Output variability Possible lobe locations for a same set of parameters Both inputs parameters and geobodies location can be optimized to match desired data (wells, seismic )

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8 Nparameters= Ngeneral_parameters + Nlobes X Nlobe_parameters 10 lobes 7+8*10 = 87 parameters A simplification of the problem is required

9 Identify the leading uncertainty Spatial or parameter? Sensitivity analysis on parameters Not necessary leads to a simplification o the problem Reformulation of the optimization problem

10 General parameters Initial Topography Lobe 1 Lobe 2 Lobe 3 Lobe

11 Optimization Step 1 General parameters predefined Depositional surface Lobe 1 Lobe 2 Lobe 3 Lobe

12 Optimized General parameters Step 2 Depositional surface Lobe Optimization Lobe 1 Lobe 2 predefined Lobe 3

13 Optimized General parameters Step 3 Optimization Depositional surface Optimized Lobe 1 Lobe 2 predefined Lobe 3 Lobe

14 Optimized General parameters Step 4 Depositional surface Lobe Optimized Lobe 1 Optimized Lobe 2 Optimization Lobe 3

15 For each step Initial population GA Best individual NM Optimized solution GA Genetic Algorithm NM Nelder-Mead (polytope) -Efficient for global search -Not based on gradient -Use a set of individual solutions modified after each iteration -Efficient for local search -More sensitive to gradient -Work on one single solutions (not a full population)

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17 9 wells data Source 2 Source 1 Deep water EOD

18 # parameters 2 sources locations 4 Flow direction 2 Number of lobes 2 Depositional model 3 Location of the lobes 2 Average thickness 1 Average width 1 Average length 1 Noise 3 10 lobes 8 * = 91 parameters

19 Parameters uncertainty Spatial uncertainty? Output variability Possible lobe locations for a same set of parameters σ= = 2.63 σ= = 2.57 No leading uncertainty

20 Define the most influencing inputs parameters -> interaction between parameters to consider -> Interference with spatial uncertainty No leading uncertainty 10 lobes 8 * = 91 parameters

21 - 11 parameters for step 1 (general parameters) - 8 parameters for the following steps (lobe specific parameters) 10 lobes 8 * = 91 parameters iterations per step: 400 for GA and 100 NM 10 lobes = 5500 iterations ~ 3 hours

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32 - Net-to-gross +

33 Define 5 ensembles of 100 realizations Each ensemble fit more or less the 8 wells For each realization, the mismatch with the 9 th well is computed (predictability)

34 Define 5 ensembles of 100 realizations Each ensemble fit more or less the 8 wells For each realization, the mismatch with the 9 th well is computed (predictability)

35 Use this well for cross validation Define 5 ensembles of 100 realizations Each ensemble fit more or less the 8 wells For each realization, the mismatch with the 9 th well is computed (predictability)

36 Quality of the Match

37 Conclusion The method match thickness and facies at data location The conditioning approach is iterative This approach is in two steps Optimization environmental input parameters Optimization the lobes features Further work are required to study the predictability of the model

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