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  1. Reservoir Characterization Workflows 3D Seismic Attributes for Prospect Identification and Reservoir Characterization Kurt J. Marfurt (The University of Oklahoma)

  2. Course Outline • Introduction • Complex Trace, Horizon, and Formation Attributes • Multiattribute Display • Spectral Decomposition • Geometric Attributes • Attribute Expression of Geology • Tectonic deformation • Clastic depositional environments • Carbonate deposition environments • Shallow stratigraphy and drilling hazards • Igneous and intrusive reservoirs and seals • Attribute expression of shale reservoirs and correlation to hydraulic fracturing • Impact of Acquisition and Processing on Attributes • Attribute Prediction of Fractures and Stress • Data Conditioning • Inversion for Acoustic and Elastic Impedance • Image Enhancement and Object Extraction • Unsupervised Multiattribute Classification • Supervised Multiattribute Classification • Statistical Attribute Analysis • Reservoir Characterization Workflows • 3D Texture Analysis

  3. Objectives • Describe value of pseudowells • Discuss alternative multiattribute workflows: • workflow #1: Constraining ant-tracking to map faults consistent with image log data • workflow #2 : Impedance inversion followed by geostatistics estimate of facies • workflow #3 : Multiattribute linear stepwise regression followed by geostatistics to predict porosity • workflow # 4: Neural net with a nonlinear search for best attribute combination • workflow #5 : Seismic waveform classification/clustering followed by forward modeling calibration • workflow #6 : Matching seismic attributes to attributes of well log synthetics • Recognize potential risks when using a small well population

  4. tight facies ss – low porosity ss –hi porosity ls –low porosity ls –hi porosity dol –low porosity dol –hi porosity The value of pseudowells (Joseph et al., 1999)

  5. The value of pseudowells (Joseph et al., 1999)

  6. The value of pseudowells Pseudo-well synthetics projected on first two planes of the seismic attribute space. (Joseph et al., 1999)

  7. The value of pseudowells Isolated anomalies (Can be fixed by geostatistics) Seismic facies map (Joseph et al., 1999)

  8. Workflow #2: Facies constrained geostatistics (RC2-IFP) • establish a correlation between facies with a certain seismic attribute. • construct facies geostatistical models using: • facies logs as hard data (honored at 100%), • 3-D seismic attribute model as soft data (honored at less than 100%), and • other geologic data such as preferred channel direction and global facies proportions as additional soft data. • model geologically constrained seismic facies by: • stochastic seismic inversion, • facies modeling by sequential indicator simulation with collocated cokriging, and • porosity modeling by facies dependent collocated cokriging. (Lo and Bashore, 1999)

  9. A) Petrophysically-constrained geostatistics S impedance,IS P impedance, IP Reservoir rocks Cross plot of P impedance vs. S impedance. Shale volume in color. (Barens and Biver, 2004)

  10. 1) Construct background model using wells and seismic velocities Background (Barens et al., 2003)

  11. 2) Classical seismic inversion for the ‘most likely’ impedance model at seismic resolution Seismic bandwidth Background Trace Inversion (Barens et al., 2003)

  12. 3) Geostatistical Inversion at the geologic model resolution using detailed well control seismic data variograms Uncertainties on Inversion Seismic bandwidth Geostatistical Modelling Background (Barens et al., 2003)

  13. Given a P impedance, IP,use the Prior Joint Distribution and Bayes’ rule to constrain the shear impedance, IS: P(IS|d)IP~ L(d|IS) · P(IS)IP. = P(IS) (IS Kriging)= P(data|IS) = P(IS|data) (Barens and Biver, 2004)

  14. For each (IP,IS) realization, forward model near and far AVO effects Seismic Synthetic Near Seismic Synthetic Far IP Aki & Richards approximation IS Pre-Stack Geostatistical InversionConstrained by:- Seismic data- Geostatistical Modelling (Barens and Biver, 2004) Probability Density Functions

  15. Result 1: mean impedances The mean impedances for a stratigraphic layer in the grid. P impedance S impedance (Barens et al., 2003)

  16. Result 2: variability of impedance estimates The standard deviation of impedances for a stratigraphic layer in the grid. P impedance S impedance (Barens et al., 2003)

  17. Result 3: multiple realizations of P- and S-impedances that honor both well and seismic data. (Barens and Biver, 2004)

  18. B) Facies-constrained inversion1) Data preparation Pick surfaces Estimate variograms Lateral (seismic) Vertical (wells) (Barens and Biver, 2004)

  19. 1) Define Geologic Facies by relating geologic interpretation and inversion results Vsh IP IS Facies Geologic Facies 50 ms Shale Debris Flows Lags Laminated Sands Coarse Sands (Barens and Biver, 2004)

  20. 2) Results of cross plotting - Facies estimation Shale IP/IS Ratio Shale Debris Flows Coarse sand Laminated sand Lags Laminated Sands Coarse Sands IP (with shale detrend) (Barens and Biver, 2004)

  21. 3) Generate a seismic facies realization at each trace For a given IP and IS pair, each voxel has a probability of being a given seismic facies. (Barens and Biver, 2004)

  22. 3) Realisation of Seismic Facies Realisation 26 Realisation 34 Realisation 12 SF Region 1: Shale SF Region 2: Laminated Sands SF Region 3: Coarse Sands (Barens and Biver, 2004)

  23. Workflow #3: Choosing the optimum attributes for inversion using supervised learning: Multiattribute linear stepwise regression • Correlate candidate attributes with a subset of the available well data. Russell et al (2000) and Cooke et al (1999) find that seismic inversion is much better correlated with porosity than original seismic amplitudes. • Add additional attributes to enhance the final well-seismic tie. This technique is called thereby obtaining a multiattribute transform (this vector of weights vector will be ‘cross correlated’ with the multiattribute vector at each trace) • Avoid over fitting the data by validating the transform with wells left outside the training step (cross validation step). • Apply geostatistics to honor both well data and the mulitattribute transform, with the well data being a hard constraint.  (Russell et al. 2001)

  24. porosity sonic seismic Multiattribute linear stepwise regression 75 CDPs 125 CDPs Well distribution map (Russell et al. 2001)

  25. Inst. freq. impedance inst. phase Integ. trace ANN system trained without target well (Validation error) Average error ANN system trained using all wells Number of attributes Average error for curve of the form (x,y)=w0+w1A1(x,y)+…+wmAm(x,y). Using 5 attributes ‘overtrains’ the neural net system to fit the control data. Multiattribute linear stepwise regression (Russell et al. 2001)

  26. Multiattribute linear stepwise regression  Porosity prediction from acoustic impedance only: Correlation =-0.65 Porosity prediction from linear combination of 3 attributes Correlation = +0.82 (Russell et al. 2001)

  27. Multiattribute linear stepwise regression  Porosity prediction after Kriging with external drift. Porosity at wells uses the same color scale as the seismic prediction (Russell et al. 2001)

  28. Workflow # 5: Stratigraphic Interpretation via Neural Net Driven Seismic Trace Shape Classification (a workflow used at Paradigm Geophysical) • Generate a seismic facies (similarity) map by correlating the modeled wave forms with the actual traces • Correlate synthetic seismic response to field seismic data. • Perform forward modelling at the well bore. • Perturb reservoir properties and interactively observe changes in synthetic seismic response. • Predict reservoir properties away from well location. • Use synthetic seismic response to improve understanding of neural network facies traces. • Use neural networks to re-classify data based on modeling to produce property maps. (Poupon et al., 1999)

  29. What’s inside Stratimagic (or what Marfurt has pieced together) 1. Window a suite of traces of length N samples using picked and/or phantom horizons and obtain djk(t) 2. Increase the frequency content by taking a time difference: 3. Normalize each windowed trace to have energy=1.0: 4. Select a subset of the data volume (e.g. every 10th line and 10th cdp) and cluster in N-dimensional ‘attribute’ space, where each time sample below the picked horizon is a separate attribute. Call the mean of each cluster the ‘model trace’ for the cluster. 5. Crosscorrelate every trace in the extracted volume with each and every modeled trace. Assign it to the cluster whose mean is closest.

  30. Seismic Facies Classification • Unsupervised Regional Seismic Facies Analysis Actual seismic traces are crosscorrelated with the the model traces obtained using a Kahonen Self Organized Map. A color is assigned corresponding to the nearest model trace. Interval of interest Data courtesy of CGG-USA Model Traces (cluster means) (Poupon et al., 1999)

  31. Seismic Facies Classification • Supervised Seismic Facies Analysis The set of model traces can be updated by inserting a seismic trace at well location. The classification process is repeated. Seismic Facies Map Model Traces Seismic response at Well location Data courtesy of CGG-USA (Poupon et al., 1999)

  32. Seismic Facies Classification The correlation of seismic trace shape to a particular seismic response (usually a producing well) allows one to identify prospect based on waveform. Correlation Map (in %) Between trace shape at well and seismic traces over the studied interval Data courtesy of CGG-USA (Poupon et al., 1999)

  33. Seismic classes Seismic line Facies Correlation to Synthetic Models Blocked Acoustic Impedance Well curves (Depth domain) Petro-acoustic model (Time domain) Petro-acoustic Modeling Optimizing Seismic Interval & Interpreting Classification maps (Poupon et al., 1999)

  34. Facies Modeling – Austral Basin, Argentina Max flooding surface S- N strat section Max flooding surface W-E strat section (Silva-Telles et al., 2003)

  35. Facies Modeling – Austral Basin, Argentina 40 ms analysis window along the maximum flooding surface 40 ms analysis window along the maximum flooding surface S- N seismic section W-E seismic section (Silva-Telles et al., 2003)

  36. Mix dip/isochron Average abs amp Shape class don’t drill drill don’t drill Only wells D and E were drilled when map was made (Silva-Telles et al., 2003)

  37. (Silva-Telles et al., 2003)

  38. Channel-fan complex (Winters Sands, California). Wells indicated by red dots. Note that the SOM differentiates the main gas producer (70 ft of net pay in Winters Sands classified as blue trace shape) from the shaled-out well to the east classified as yellow trace shape. These two wells could not be separated based on coherence and/or conventional amplitude analysis Coh SOM facies (Coleou et al, 2003)

  39. Comparison between an average absolute amplitude map (top figure) and a SOM facies map (central) over a meandering channel (Wolfcamp Sands, Permian, West Texas). Green dots correspond to producing wells. Note that the seismic facies map identifies a meandering channel (brown classes) not expressed in the interval amplitude map. Trace shapes and a section through the channel are shown at the bottom. (Coleou et al, 2003)

  40. Unsupervised classification of a fluvial channel Without PCA With PCA Natuna Sea, Indonesia. Note that the seismic facies map without PCA does not differentiate the channel facies from the flood plain deposits (same red class). It also tends to bias the interpretation toward a channel system not affected by the W-E strike-slip fault. On the other hand, the PCA seismic facies map differentiates the channel facies (blue class) and clearly highlights the effect of the W-E strike-slip fault in a relatively noisy area. Also, note the development of the overbank deposits to the east (yellow facies outlined by black dotted line). (Coleou et al, 2003)

  41. Clustering using AVO and impedance attributes Plots showing the classification results. (a) Amplitude versus fluid factor and (b) amplitude versus acoustic impedance. The square dots connected by the solid black lines show cluster nodes. (Linari et al, 2003)

  42. Clustering using AVO and impedance attributes Comparison between a horizon slice through the facies classification volume (a) and a conventional amplitude map (b). Both maps are overlaid by depth contours. Note that most producing wells are outside amplitude anomalies. Also note that the NE-SW trend penetrated by producing wells is not visible on the amplitude map. (Linari et al, 2003)

  43. San Luis Pass weather prediction exercise Exercise: flip 6 coins: Heads=sunny Tails=stormy Read out your correlation rate: 0/6 = -1.00 3/6 = -0.00 1/6 = -0.67 4/6 = +0.33 2/6 = -0.33 5/6=+0.67 6/6 = 1.00 August 24, 2005 – sunny August 25, 2005 - storms August 26, 2005 - sunny August 27, 2005 - sunny August 28, 2005 - sunny August 29, 2005 - storms tails heads

  44. San Luis Pass weather prediction exercise Which coins best predict the weather in San Luis Pass? Should Marfurt go fishing?

  45. Potential risks when using seismic attributes as predictors of reservoir properties • When the sample size is small, the uncertainty about the value of the true correlation can be large. • given 10 wells with a correlation of r=0.8, the 95% confidence level is [0.34,0.95] • given only 5 wells with a correlation of r=0.8, the 95% confidence level is [-0.28,0.99] ! (Kalkomey, 1997)

  46. Spurious Correlations A spurious correlation is a sample correlation that is large in absolute value purely by chance. (Kalkomey, 1997)

  47. The more attributes, the more spurious correlations! (Kalkomey, 1997)

  48. Risk = expected loss due to our uncertainty about the truth * cost of making a bad decision Cost of a Type I Error (using a seismic attribute to predict a reservoir property which is actually uncorrelated) is: • Inaccurate prediction biased by the attribute. • Inflated confidence in the inaccurate prediction — apparent prediction errors are small. Cost of a Type II Error (rejecting a seismic attribute for use in predicting a reservoir property when in fact they are truly correlated) is: • Less accurate prediction than if we’d used the seismic attribute. • Larger prediction errors than if we’d used the attribute. (Kalkomey, 1997)

  49. Validation of Attribute Anomalies • 1. Basic QC • is the well tie good? • are the interpreted horizons consistent and accurate? • are the correlations statistically meaningful? • is there a physical or well-documented reason for an attribute to correlate with the reservoir property to be predicted? • 2. Validation • does the prediction correlate to control not used in training? • does the prediction make geologic sense? • does the prediction fit production data? • can you validate the correlation through forward modeling? (Hart, 2002)

  50. Validation of Attribute Anomalies (Porosity prediction in lower Brushy Canyon) From probabilistic neural network. From multivariate linear regression Right map has higher statistical significance and is geologically more realistic (Hart, 2002)