ERM 2005 Morgantown, W.V. SPE Paper # Reservoir Characterization Using Intelligent Seismic Inversion

Transcription

1 ERM 2005 Morgantown, W.V. SPE Paper # Reservoir Characterization Using Intelligent Seismic Inversion Emre Artun, WVU Shahab D. Mohaghegh, WVU Jaime Toro, WVU Tom Wilson, WVU Alejandro Sanchez, Anadarko September 15, 2005

2 motivation > Reservoir Modeling Workflow Exploration: Seismic Surveys A structural model of the reservoir can be attained. Exploration Drilling Some data can be obtained from wells ( i.e. well logs, cores, well tests ) Reservoir Characterization Geostatistical variogram models can be developed with the available data to interpolate / extrapolate available well data to the entire field. Reservoir Simulation Flow in that 3D reservoir can be modeled with commercial reservoir simulators to predict reservoir performance. Field Development

3 motivation > Reservoir Characterization - Issues about the data and problems regarding data analysis must be considered carefully in reservoir characterization. - Geostatistical models become insufficient in dealing with issues like uncertainty, large variety of scales, immense size of data, etc. - As an alternate; our industry has realized the power of soft computing tools, which are capable of dealing with uncertainty, imprecision, and partial truth.

4 motivation > Reservoir Characterization SEISMIC Ten-feet Integrating all different types of data in an accurate and high-resolution reservoir model One of inches WELL LOGS Fraction of inches CORES

5 motivation > Reservoir Characterization - Due to its low resolution, seismic data is used only to attain a structural view of the reservoir. - However, its 3D coverage over a large area attracts engineers to merge it more detailed characterization studies. SEISMIC LOGS - Inverse modeling of reservoir properties from the seismic data is known as seismic inversion.

6 Statement of the Problem 1. Does a relationship exist between seismic data and reservoir characteristics, beyond the structural relationship? 2. If such a relationship exists, can it be extracted through the use of soft computing tools, such as artificial neural networks? 3. How that tool should be designed to develop the most reliable correlation models? i.e. neural network algorithm, number and type of seismic attributes that should be included... etc.

7 Previous Work Chawathe et. al (1997) Surface seismic neural network Cross-well seismic neural network Gamma ray logs Reeves et. al (2002) - In this study; vertical seismic profile (VSP) is incorporated into the study as the intermediate scale instead of cross-well seismic. Surface seismic neural network VSP neural network Well logs

8 Vertical Seismic Profile (VSP) - Signal receivers are located in the borehole instead of surface, both down-going and up-going signals are received. Well Source surface rock layer boundary Receivers (Geophones) VSP resolution 2 * Surface seismic resolution

9 Statement of the Problem - Using artificial neural networks is proposed to find a desirable correlation between well logs and seismic data. Generalized regression neural network (GRNN) algorithm is used. - Vertical seismic profile (VSP) is incorporated into the study as the intermediate scale data. - Another unique feature of this study was to develop and work on a synthetic model, before dealing with real data.

10 Two-step Correlation Methodology Two steps of correlation 1) Correlation of surface seismic with VSP 2) Correlation of VSP with well logs Step 1 Step 2 Surface Seismic VSP Well Logs Low frequency Medium frequency High frequency

11 Case 1 Synthetic Model

12 Description of the Model - The model represents the Pennsylvanian stratigraphy of the Buffalo Valley Field in New Mexico, including the gasproducing Atoka and Morrow formations. - The geological complexity increases with depth; sec. (6,600 9,000 ft) interval has been used. - Surface seismic and VSP responses have been computed through a synthetic seismic line of 100 traces.

13 Description of the Model A synthetic seismic line with 100 traces, having 3 traces 20, 50, and 80. Trace 20 Trace 50 ( VSP well ) Trace 80

14 Available Data 1. Density and acoustic velocity distributions. 2. Surface seismic and VSP responses in the form of the following seismic attributes: - Amplitude - Average energy - Envelope - Frequency - Hilbert transform - Paraphase - Phase

15 Seismic Amplitude Distribution

16 Case 1 Synthetic Model Step 1 Step 2 Correlation of surface seismic with VSP Correlation of VSP with well logs

17 Case 1 _ Step 1 (Surface seismic VSP) Trace 32 Trace 57

18 Case 1 _ Step 1( Surface seismic VSP) Neural network design: Inputs Output Time + 7 surface seismic attributes neural network Single VSP attribute

19 Case 1 _ Step 1 (Surface seismic VSP)

20 Correlation Map Step 1 Step 2 Surface Seismic Model found Seven Well separate prediction models Logs have have been developed for for seven VSP VSP attributes with with the the data data of of traces VSP Now, let s let s apply these models to to the the other traces to to have have the the predicted distributions. Surface Seismic Virtual VSP Virtual Well Logs

21 Case 1 _ Step 1 (Surface seismic VSP) FREQUENCY Actual Predicted

22 Case 1 _ Step 1 (Surface seismic VSP) PHASE Actual Predicted

23 Case 1 _ Step 1 (Surface seismic VSP) HILBERT TRANSFORM Actual Predicted

24 Case 1 _ Step 1 (Surface seismic VSP) ENVELOPE Actual Predicted

25 Correlation Map Step 1 Step 2 Surface Seismic VSP Well Logs Model found Step Step 1 --ACCOMPLISHED!..!.. Surface Seismic Virtual VSP Virtual Well Logs

26 Case 1 Synthetic Model Step 1 Step 2 Correlation of surface seismic with VSP Correlation of VSP with well logs

27 Case 1 _ Step 2 ( VSP Well Logs ) - Density log has been selected as the target log, and data of t-50 have been used in building network models. - Instead of using actual values, the problem was converted to a classification problem, because of observable averaged values of density log of t-50.

28 Case 1 _ Step 2 ( VSP Well Logs ) Class 1 ρ 1.9 g/cc Class 1 Class 2 Class 2 ρ 2.3 g/cc Class 3 ρ 2.65 g/cc Class 3

29 Case 1 _ Step 2 ( VSP Well Logs ) Neural network design: Inputs Outputs Time + 7 VSP attributes neural network Three Classes of Density

30 Case 1 _ Step 2 ( VSP Well Logs ) Class 1 r 2 = 0.82 ρ 1.9 g/cc Class 2 ρ 2.3 g/cc Class 3 ρ 2.65 g/cc

31 Case 1 _ Step 2 ( VSP Well Logs ) Class 4 ρ 2.09 g/cc Class 1 Class 2 Class 3 Class 4 r 2 = 0.94

32 Correlation Map Step 1 Step 2 The Surface The prediction model VSP Seismic for for density has has been developed with with the the data data of of trace Model found Now, we we can can generate the the cross-sectional density distribution. Model found Well Logs Surface Seismic Virtual VSP Virtual Well Logs

33 Case 1 _ Step 2 ( VSP Well Logs ) DENSITY Actual Predicted

34 Case 1 _ Step 2 ( VSP Well Logs )

35 Correlation Map Step 1 Step 2 Surface Seismic VSP Well Logs Model found Model found Step Step 2 --ACCOMPLISHED!..!.. Surface Seismic Virtual VSP Virtual Well Logs

36 Case 2 Real Case The Buffalo Valley Field

37 The Buffalo Valley Field, New Mexico

38 Available Data - Paper logs from around 40 wells within a 3D seismic survey area have been digitized. - Only one well had a VSP survey, i.e. it s the only well to build network models. - Seismic data were loaned by WesternGeco; a total of 27 seismic attributes were available.

39 Map of Wells and Seismic Survey Area VSP well

40 Seismic Amplitude Distribution Well #1 ( VSP well ) Well #2 Well #3 Well #4 Well #5

41 Case 2 Real Case: The B.Valley Field Step 1 Step 2 Correlation of surface seismic with VSP Correlation of VSP with well logs

42 Case 2 _ Step 1 (Surface seismic VSP)

43 Case 2 _ Step 1 (Surface seismic VSP)

44 Correlation Map Step 1 Step 2 Surface Seismic VSP Well Logs Model found Surface Seismic Virtual VSP Virtual Well Logs

45 Case 2 Real Case: The B. Valley Field Step 1 Step 2 Correlation of surface seismic with VSP Correlation of VSP with well logs

46 Case 2 _ Step 2 ( VSP Well Logs ) - After a quality check of available logs, gamma ray and neutron porosity logs were selected as target logs, considering their availability, and quality.

47 Case 2 _ Step 2 ( VSP Well Logs ) - Data from all available wells were used in developing the neural network models. - A Key Performance Indicators (KPI) study was conducted to see influences of each seismic attribute on the target log.

48 Key Performance Indicators (KPI) Intelligent Reservoir Characterization and Analysis (IRCA) software: - Most influent attributes were selected due to large number of available attributes.

49 Gamma Ray Log Well #1 r = 0.76 Well #2 r = 0.86 Well #3 r = 0.81 Well #4 r = 0.90 Well #5 r = 0.90

50 Gamma Ray Log

51 Neutron Porosity Log Well #1 r = 0.98 Well #2 r = 0.97

52 Neutron Porosity Log

53 Correlation Map Step 1 Step 2 Surface Seismic VSP Well Logs Model found Model found Step Step 2 --ACCOMPLISHED!..!.. Surface Seismic Virtual VSP Virtual Well Logs

54 Conclusions - The proposed two-scale-step, intelligent seismic inversion methodology has been successfully developed on a synthetic model. The same methodology has then been applied to real data of the Buffalo Valley Field in New Mexico. - Density logs for the synthetic model, and gamma ray logs for the field data have been produced from seismic data.

55 Conclusions - The complex and non-linear relationships have been extracted with the power of artificial neural networks with both classification and prediction. - A novel approach has been presented to solve an important data integration problem in reservoir characterization. - The same methodology can be applied to a 3D seismic block to obtain 3D distributions of reservoir properties.

56 ERM 2005 Morgantown, W.V. SPE Paper # Reservoir Characterization Using Intelligent Seismic Inversion Acknowledgements --This This study study was was supported by by the the U.S. U.S. Department of of Energy. Help Help and and support of of Mr. Mr. Thomas Mroz Mroz (project manager) is is appreciated. --Seismic Seismic data data were were used used with with the the courtesy of of WesternGeco. --Mrs. Mrs. Janaina Pereira s help help in in digitizing well well logs logs is is also also appreciated. September 15, 2005