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Signal Estimation Technology Inc. 3-D Seismic Waveform Analysis for Reservoir Characterization

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Signal Estimation Technology Inc. Outline Introduction Introduction Swan Hills Unit #1 Overview Swan Hills Unit #1 Overview Statistical Learning Statistical Learning Application of Statistical Learning to Predict *H using 3-D Seismic Waveform Application of Statistical Learning to Predict *H using 3-D Seismic Waveform Conclusions Conclusions

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Signal Estimation Technology Inc. Swan Hills, Alberta, Canada North America Alberta, Canada Edmonton Calgary Miles 0 6 SWAN HILLS VIRGINIA HILLS SWAN HILLS UNIT No. 1 SOUTH SWAN HILLS JUDY CREEK CARSON CREEK Town of Swan Hills

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Signal Estimation Technology Inc. Swan Hills Unit #1 : History Discovered in 1957 Discovered in 1957 Home Oil - Anderson Exploration the operator Home Oil - Anderson Exploration the operator Water flood started in 1965 Water flood started in 1965 Miscible flood (ethane-rich solvent) started in 1985 Miscible flood (ethane-rich solvent) started in D geological modeling (485 wells) completed in D geological modeling (485 wells) completed in 1992 First 3-D seismic in winter of First 3-D seismic in winter of Second 3-D seismic, South extension, in Second 3-D seismic, South extension, in Waveform analysis done in 1995 Waveform analysis done in 1995

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Signal Estimation Technology Inc. Swan Hill, Unit #1 : Geology Pool “A” produces from stromatoporoid limestone reef complex Pool “A” produces from stromatoporoid limestone reef complex Reef complex well developed: lagoonal, reef margin, foreslope facies Reef complex well developed: lagoonal, reef margin, foreslope facies Reef complex is developed on a spatially extensive carbonate platform Reef complex is developed on a spatially extensive carbonate platform Pool “B” produces from a thin porous coral zone within the lower platform Pool “B” produces from a thin porous coral zone within the lower platform

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Signal Estimation Technology Inc. Geological Model After Uffen,1994

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Signal Estimation Technology Inc. Swan Hills, Unit #1 : Reservoir Original oil in place : 1,413 million bbls Original oil in place : 1,413 million bbls Cumulative production: 550 million bbls Cumulative production: 550 million bbls Current oil production: 20,100 bbls/day Current oil production: 20,100 bbls/day Current water production: 220,000 bbls/day Current water production: 220,000 bbls/day Current gas production : 50 mmcf/day Current gas production : 50 mmcf/day Average field water-cut :91.5% Average field water-cut :91.5% Decline of oil production: 15% /year Decline of oil production: 15% /year

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Signal Estimation Technology Inc. 3-D Seismic Inversion (see “Applications of 3-D Seismic Data to Exploration and Production”, p. 179) Motivation: Motivation: Integrate wells and seismic Integrate wells and seismic Impedance correlates to porosity Impedance correlates to porosity Results: Results: Seismic resolution with inversion: 10 m Seismic resolution with inversion: 10 m Delineation of reef edge Delineation of reef edge 3 successful wells 3 successful wells What else? What else? Is impedance the best attribute to predict porosity? Is impedance the best attribute to predict porosity?

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Signal Estimation Technology Inc. Isovelocity Map

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Signal Estimation Technology Inc. Waveform Analysis: Motivation Seismic attributes: Seismic attributes: are imbedded in the seismic waveform are imbedded in the seismic waveform the optimality of any given set of attributes is questioned the optimality of any given set of attributes is questioned Statistical learning: Statistical learning: learns optimal attributes adapted to the rock property at the wells learns optimal attributes adapted to the rock property at the wells predicts the rock property away from the wells predicts the rock property away from the wells

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Signal Estimation Technology Inc. Waveform Analysis for RC ë Small sample statistics Appraisal & 4-D applications ë Prop. accurate clustering Consistent classification maps ë Sup. Classification Qualitative characterization ë Inference Quantitative characterization ë Auto model building Consistent, optimal results ë Waveform directly used Optimal attributes extracted FeatureBenefits

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Signal Estimation Technology Inc. Statistical Learning vs Wiener Filter Desired H Value Seismic Waveform near Well Location Statistical Learning = Nonlinear Wiener Filter Training (filter design) Output H Prediction Seismic Waveform Prediction (filter application) Statistical Learning = Nonlinear Wiener Filter Statistical Learning = Nonlinear Wiener Filter

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Signal Estimation Technology Inc. A C D B C1C1 D1D1 Statistical Learning Prediction of *H Map

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Signal Estimation Technology Inc. *H Map- North 3-D A B

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Signal Estimation Technology Inc. l Reef 16 m (prog. 15.5m) l B2A and B1 reef margin facies l Net pay: reef 10m, platf.14m, total 24m l *H : reef 3.2, platf. 1.0, total 4.2 A Vertical Well “A”

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Signal Estimation Technology Inc. l Reef 17m (prog. 15m) l B2A and B1 foreslope facies l Net pay: reef 10m, paltf. 14m, total 24m l *H: reef 1.5, platf. 1.2, total 2.7 l Flowing: 750 bbls/day of clean oil B Vertical Well “B”

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Signal Estimation Technology Inc. *H Map - North 3-D C C1C1

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Signal Estimation Technology Inc. Conclusion-1 *H maps used to: *H maps used to: estimate volumetric reserves for economics estimate volumetric reserves for economics design horizontal well trajectories design horizontal well trajectories Adding value to 3-D seismic continues: Adding value to 3-D seismic continues: identified undrained parts of the reservoir identified undrained parts of the reservoir drilled 4 successful wells; more locations drilled 4 successful wells; more locations added reserves at attractive cost added reserves at attractive cost Increased production by 12% Increased production by 12%

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Signal Estimation Technology Inc. Conclusion-2 Waveform contains useful information about reservoir properties Waveform contains useful information about reservoir properties A mathematical formula is not needed to successfully relate seismic waveform to reservoir parameter A mathematical formula is not needed to successfully relate seismic waveform to reservoir parameter Statistical learning prediction of *H, an accurate approach for: Statistical learning prediction of *H, an accurate approach for: differentiating geological facies differentiating geological facies predicting spatial distribution of reservoir quality predicting spatial distribution of reservoir quality

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Signal Estimation Technology Inc. ACKNOWLEDGMENTS l l Home Oil / Anderson Exploration – – permission to release l l Unit Partners - BP-Amoco, Exxon-Mobil, Gulf – – permission to release

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Signal Estimation Technology Inc. Powerful Scientific Tools for all Phases of the Life Cycle of your Assets

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