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**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.

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**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.

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**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.

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**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.

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**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.

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**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

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**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.

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**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.

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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.

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**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.

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**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.

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**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

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**Statistical Learning Prediction of *H Map**

B C1 D1 Statistical Learning Prediction of *H Map ThisH 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.

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*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.

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**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.

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**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.

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*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.

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**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.

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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.

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**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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