1. 3-D Seismic Waveform Analysis for Reservoir Characterization
Good morning. This presentation is titled “3-D Seismic Waveform Analysis for Reservoir Characterization”. This is a case study using for our waveform analysis technology.

2. Outline Introduction Swan Hills Unit #1 Overview Statistical Learning
Application of Statistical Learning to Predict *H using 3-D Seismic Waveform
Conclusions
I will first cover the location, history, geology and reservoir of Swan Hills Unit #1. Then, I will discuss the inversion work that was done.
Next, I will talk about statistical learning and the application of it to predict H in the Swan Hills unit.
Finally I will present the conclusions.

3. Swan Hills, Alberta, Canada
North America
Alberta, Canada
Edmonton
Calgary
Miles 6
SWAN HILLS
VIRGINIA
HILLS
SWAN HILLS
UNIT No. 1
SOUTH
JUDY
CREEK
CARSON
Town of
Swan
Hills
Where is Swan Hills? The town of Swan Hills is located approximately 200 km (120 miles) northwest of Edmonton, Alberta, Canada, North America. Unit #1 is located by the town along a trend of several longstanding producing fields from the mid-Devonian.

4. Swan Hills Unit #1 : History
Discovered in 1957
Home Oil - Anderson Exploration the operator
Water flood started in 1965
Miscible flood (ethane-rich solvent) started in 1985
3-D geological modeling (485 wells) completed in 1992
First 3-D seismic in winter of
Second 3-D seismic, South extension, in
Waveform analysis done in 1995
The field was discovered in Home Oil now Devon Canada is the operator.
Water flood started in 1965 and miscible flood started in 1985
3-D geological modeling based on 485 wells was completed in 1992
First 3-D seismic in the winter of and the second one was shot in Our waveform analysis work was done in 1995.

5. Swan Hill, Unit #1 : Geology
Pool “A” produces from stromatoporoid limestone reef complex
Reef complex well developed: lagoonal, reef margin, foreslope facies
Reef complex is developed on a spatially extensive carbonate platform
Pool “B” produces from a thin porous coral zone within the lower platform
The producing zone is mid-Devonian Swan Hills reef and reefal platform at depths ranging from ft. ( m). There are 2 pools. Pool A produces from a stromatoporoid limestone reef complex. It is well developed with lagoonal, reef margin and foreslope facies. The complex is on a spatially extensive carbonate platform. Pool B produces from a thin porous coral zone within the lower platform.

6. Geological Model After Uffen,1994
This schematic geologic cross section illustrates the position of the reef edge between the two wells shown.
The reef edge position is critical in determining the volume of reserves left unswept by the downdip producing well.
Determining the actual position of the reef edge is vital in this reserve prediction exercise.
The porosity is preferentially developed in the reef although porosities up to 7% are possible in the platform. Some reefal layers possess “hot streaks” porosity in excess of 18%. In some areas, vertical impermeable barriers exist that disappear laterally. This heterogeneity complicates the design and implementation of water floods, miscible floods, and well workovers. The full reefal buildup is 69 m.
After Uffen,1994

7. Swan Hills, Unit #1 : Reservoir
Original oil in place : ,413 million bbls
Cumulative production: million bbls
Current oil production: ,100 bbls/day
Current water production: 220,000 bbls/day
Current gas production : mmcf/day
Average field water-cut : 91.5%
Decline of oil production: 15% /year
Here are some production statistics. The important numbers to look at are the average field watercut of 91% and the decline of oil production which was 15%/year. These are the reasons for delineating the reef edge to find clean unswept reef edge oil.

8. 3-D Seismic Inversion (see “Applications of 3-D Seismic Data to Exploration and Production”, p. 179)
Motivation:
Integrate wells and seismic
Impedance correlates to porosity
Results:
Seismic resolution with inversion: 10 m
Delineation of reef edge
3 successful wells
What else?
Is impedance the best attribute to predict porosity?
Home Oil, now Devon Canada, did a model-based 3-D inversion. The result was published in “Applications of 3-D Seismic Data to Exploration and Production”, p The motivation for 3-D seismic inversion was to integrate wells and seismic, and correlate impedance with porosity..
Results: The seismic resolution with the inversion was 10 m. The reef edge was delineated better than before and 3 successful wells were drilled.
What else?
Is impedance the best attribute to predict porosity? The answer will come in a couple of slides.

9. Isovelocity Map
The isovelocity map of the reef and platform is shown here. The geological zero reef edge is superimposed. As expected, the reef edge from the inversion is more detailed than the one from the geological modelling.
Now we will try to answer the question we raised a couple of slides back.

10. Waveform Analysis: Motivation
Seismic attributes:
are imbedded in the seismic waveform
the optimality of any given set of attributes is questioned
Statistical learning:
learns optimal attributes adapted to the rock property at the wells
predicts the rock property away from the wells
Attribute analysis in the past was done to help delineate petrophysical properties. One reason to do waveform analysis is that extractable attributes are imbedded in the seismic waveform plus others that are not extractable. The second reason is that the optimality of any given set of attributes is questioned.
Statistical learning requires 2 steps. First, it learns the optimal attributes from the waveform adapted to the rock property of interest at the well location. Second, it uses the learned attributes to predict this property away from the wells.

11. Waveform Analysis for RC
Feature Benefits
Small sample statistics Appraisal & 4-D applications
Prop. accurate clustering Consistent classification maps
Sup. Classification Qualitative characterization
Inference Quantitative characterization
Auto model building Consistent, optimal results
Waveform directly used Optimal attributes extracted
Here are several features of our waveform analysis technique for reservoir characterization.
Small sample statistics helps in appraisal and 4-D application because of the small number of wells available in appraisal and the small number of observation wells available in 4-D.
Our proprietary accurate clustering gives consistent classification maps because it is not sensitive to the initial picks of the class centres. We have options for qualitative or quantitative characterization using supervised classification or inference. An important feature is the auto-model building which in addition to consistency gives you optimal results. Oh, I forgot to tell you that we use the waveform directly and therefore we don’t leave any useful information out. A predetermined number of attributes may miss some important information.

12. Statistical Learning vs Wiener Filter=
Nonlinear Wiener
Filter
Training (filter design)
Seismic
Waveform
near Well
Location
Desired H
Value
Prediction (filter application)
Statistical Learning =
Nonlinear Wiener
Filter
The concept of statistical learning is similar to the Wiener filter we all know. At the design or training stage, the Wiener filter linearly maps an input wavelet into a desired wavelet. Statistical learning non-linearly maps the waveform at tie traces into the rock property of the corresponding well.
At the application or prediction stage, the Wiener filter deconvolves the seismic data. Statistical learning infers the rock property away from the wells.
Seismic
Waveform
Output H
Prediction

13. Statistical Learning Prediction of *H MapB C1 D1
Statistical Learning Prediction of *H Map
ThisH map of the reef shows more detailed reef edge than the previous inversion work (isovelocity map). All the wells drilled based on this map were predicted correctly.

14. *H Map- North 3-D A B
This is a close up examination of the previous map. Well A was drilled on N-S porosity trend away from the main reservoir.
Well B drilled another isolated anomaly. This well is close to the reef edge inferred by geologic modeling and outside the edge estimated by model based inversion.

15. A Vertical Well “A” Reef 16 m (prog. 15.5m)
B2A and B1 reef margin facies
Net pay: reef 10m, platf.14m, total 24m
*H : reef 3.2, platf. 1.0, total 4.2
Here is a detailed look at the well log of Well A. The porosity is shaded in black. The predicted H was about 4.5 around the well. The actual H was 4.2.

16. Vertical Well “B” B Reef 17m (prog. 15m) B2A and B1 foreslope facies
Net pay: reef 10m, paltf. 14m, total 24m
*H: reef 1.5, platf. 1.2, total 2.7
Flowing: 750 bbls/day of clean oil
Here is the log for well B. Again, the porosity is shaded in black. The predicted H was 3 while the actual was 2.7.

17. *H Map - North 3-D C C1
Well C1 is a dry hole with H = 0.7%. This inferred H map shows good reef porosity close to well C1. The well bore was reentered and a horizontal well was drilled along the indicated trajectory to well C. As predicted by the map, the drilling entered a porous zone, then a tight saddle followed by another porous zone. The porous zone was predicted within 0.5 m of the actual location. This horizontal well proved the existence of tight channels inside the reservoir as predicted.

18. Conclusion-1 *H maps used to: Adding value to 3-D seismic continues:
estimate volumetric reserves for economics
design horizontal well trajectories
Adding value to 3-D seismic continues:
identified undrained parts of the reservoir
drilled 4 successful wells; more locations
added reserves at attractive cost
Increased production by 12%
Several conclusions can be made. The H maps were very useful in estimating the volumetric reserves for economical purposes and to design horizontal well trajectories.
Our technique added value to the 3-D seismic. It identified undrained parts of the reservoir. 4 successful wells were drilled at the time. Other untapped reef edge porosities detected show potential for further improvement. Reserves were added at an attractive cost. In fact, after the 4 wells, production was increased by 12% instead of the annual 15% decline.

19. Conclusion-2
Waveform contains useful information about reservoir properties
A mathematical formula is not needed to successfully relate seismic waveform to reservoir parameter
Statistical learning prediction of *H, an accurate approach for:
differentiating geological facies
predicting spatial distribution of reservoir quality
This case study showed three things. First, the waveform contains useful information about the reservoir properties. Second, the mathematical formula is not need to successfully relate the seismic waveform to the reservoir parameter. Finally, the prediction of H using statistical learning is an accurate approach for differentiating geological facies and predicting the spatial distribution of the reservoir quality.

20. ACKNOWLEDGMENTS Home Oil / Anderson Exploration permission to release
Unit Partners - BP-Amoco, Exxon-Mobil, Gulf
I would like to acknowledge Home Oil, now Devon Canada, and the unit partners, BP-Amoco, Exxon-Mobil and Gulf for permission to present this case study.
Thank you.

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