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Interpretation 23.12.13
Seismic interpretation involves correlating seismic data features with geological elements to understand the subsurface. The goal is to map reservoirs, including depth, thickness, and properties. This involves processing data, well calibration, horizon and fault tracking, and attribute analysis. Direct hydrocarbon indicators on seismic can help identify potential reservoirs, but require validation with amplitude versus offset analysis due to limitations and need for a geological model.
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Interpretation 23.12.13
- 1. Oil & NaturalGas Corporation Ltd. Seismic Data Interpretation
- 2. What is seismicinterpretation? Interpretation is telling the geologic story contained in seismic data. It is correlating the features we see in seismic data with elements of geology as we know them. Depositional environments and depositional history Structure Anticline, syncline Faults Stratigraphy unconformity Pinch-out Channels, reefs, salt domes
- 3. Objective Prospect generationand identification of suitable locations for drilling by interpreting subsurface geo-data Petroleum system Reservoir rock Porous and permeable sandstone, limestone Any other rock type forming trap, e.g., fractured shales Source rock Shale/carbonates Cap rock Shale/carbonates Reservoir characterization Estimation of reservoir parameters Area, thickness, porosity, saturation etc. The primary goal of seismic interpretation is to make maps that provide geologic information (reservoir depth structure, thickness, porosity, etc.).
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- 6. Interpretation functionalities Project anddata management Data conditioning (interpretive processing) Scaling, filtering, wavelet processing etc. Integration of various types of data (seismic, well etc.) Calibration (well to seismic tie) Synthetic seismogram generation and correlation with seismic Visualization Horizon and fault Correlation Generation of time maps Depth conversion and generation of depth maps Special studies Seismic Attributes Direct hydrocarbon indicators (DHI) and AVO Seismic Inversion Time lapse reservoir monitoring Integration of structure, attributes, impedance, geologic model etc. Prospect generation and location identification Reports/proposals
- 7. Pre-requisite of interpretation Knowledgeof geology and geophysical processes Type of data and type of information which can be extracted Objective of interpretation and amenability of seismic data Elements of seismic trace data Modes of display Amplitude, time and frequency Role of colours and colour bar Contrasting and gradational colour scheme Polarity and phase conventions Resolution and detectability Seismic to well tie Character based matching between synthetic and seismic Depth to time conversion (T-D curves) Check shots, VSP, Synthetics
- 8. A seismic sectionshowing colour convention and other display elements Elements of display Positive amplitude Blue Negative amplitude Red
- 9. Elements of display -max + max Variable area wiggle display +/- zero crossing -/+ zero crossing
- 10. 3-D Cube of seismicdata Elements of display Time slice In-linecross-line
- 11. Successive time slices depictthe anticlinal closures Time 2430 Time 2390 Elements of display Vertical section and time slices
- 12. Top and bottomof a gas reservoir (low impedance zone) in (a) American polarity and (b) European polarity Polarity Convention American polarity is described as: An increase in impedance yields positive amplitude normally displayed in blue. A decrease in impedance yields negative amplitude normally displayed in red. European (or Australian) polarity is described as the reverse, namely: An increase in impedance yields negative amplitude normally displayed in red. A decrease in impedance yields positive amplitude normally displayed in blue. American European
- 13. Phase The task oftying seismic data and well data together and hence identifying seismic horizons is often oversimplified. The task requires knowledge of velocity, phase, polarity and tuning effects. Zero phase data makes all aspects of interpretation easier and everyone knows that zero phase is desirable. However, zero phase is difficult to accomplish and often it is not achieved in processing. Hence interpreters always need to check the phase and polarity of their data.
- 14. Resolution The ability toseparate two features that are close together The minimum separation of two bodies before their individual identities are lost on resultant map or cross-section The resolving power of seismic data is always measured in terms of seismic wavelength (λ=V/F) Limit of separability = λ/4 The predominant frequency decreases with depth because the higher frequencies in the seismic signal are more quickly attenuated. Wavelength increases with depth. Resolution decreases with depth For thinner intervals amplitude is progressively attenuated until Limit of visibility=λ/25 is reached when reflection signal becomes obscured by the background noise
- 15. Limit of Separability Ageof rocks Very young young medium old Very old Depth Very shallow shallow medium deep Very deep Velocity (m/s) 1600 2000 3500 5000 6000 Predominant frequency 70 50 35 25 20 Wavelength 23 40 100 200 300 Separability 6 10 25 50 75
- 16. Limit of visibility Factors affecting the visibility Impedance contrast of the geologic layer of interest relative to the embedding material Random and systematic noise in the data Phase of the data or shape of seismic wavelet It may be less than 1 m to more than 40 m Limit of visibility S/N Example Limit Poor Water sand poor data λ/8 Moderate Wayer or oil sand fairly good data λ/12 High Gas sand good data λ/20 Outstanding Gas sand excellent data λ/30
- 17. STRUCTURAL INTERPRETATION Calibration (Well-to-seismic-tie) Correlation (horizonsand faults) Map generation (gridding and contouring)
- 18. For geologic informationfrom seismic data, nearby wells are correlated to seismic reflectors. Synthetic seismograms (synthetics) provide this link by converting rock properties from well logs to a synthetic trace. Synthetics make “rocks look like wiggles,” using the convolution model (T = RC * W), which states that traces (T) are the result of convolving (*) the reflection coefficient series (RC) with the wavelet (W). When seismic data are acquired, a source wavelet is sent into the earth, reflected back (convolved) to the surface at geologic boundaries (RC), and recorded as a trace (T). RC is calculated from velocity and density logs Well to seismic tie Synthetic Seismograms
- 19. RC for normalincidence is RC = (r1v1- r2v2) /(r1v1+ r2v2) where v1, v2 are P-wave velocities (sonic log) and r1, r2 are densities (density log) in the layer above (1) and below (2) the reflecting boundary. The normal incidence assumption is generally valid, except where velocity and density contrasts are very large (gas sands, coal, hard streaks, etc.). When these exceptions are critical to the interpretation, RC needs to be calculated as a function of angle (AVA) from more complex equations and a third parameter, S- wave velocity (shear log). Well to seismic tie Synthetic Seismograms (1) r1,v1 (2) r2,v2
- 20. Synthetic to seismicmatching before starting the interpretation. Understand geologic elements (model) and their geophysical responses (Trace). DT RHOB IMP GR LLD RC Black traces Seismic Red traces synthetic
- 21. Seismic signatures ofpay sands in B12-11. Sandstone pays are marked by troughs. Pay1 and Pay3 high negative amplitudes Overlay of synthetic and logs on seismic sections. Here synthetic is shown by yellow trace Well to seismic tie Synthetic Seismograms
- 22. Horizon Correlation Identificationof sequence boundaries Reflection configuration and termination pattern, Onlap, Top lap and Down Lap Tracking Auto and manual mode Auto dip and correlation 3D tracking Fault correlation Picking on vertical and horizontal sections Fault plane correlation Computing throw of faults
- 23. Map generation Basemap generation Depicting seismic Well location Cultural data Block boundaries Scale, coordinates and legends Griding Various method with faults and without faults Incorporating heaves and throw Contouring Over lay of attributes Map analysis
- 24. Time map generatedfrom horizon and fault correlation
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- 26. Seismic attributes Attributesare derivatives of basic seismic measurements/Information Seismic attributes extract information from seismic data that is otherwise hidden in the data These information can be used for predicting, characterizing, and monitoring hydrocarbon reservoirs Basic information Time Amplitude Frequency Attenuation Phase Most attributes are derived from normal stacked and migrated data volume Can be derived from Pre-stack data (AVO)
- 27. Seismic attributes Attribute Information Time-derivedStructural information Amplitude-derived Stratigraphic and reservoir Frequency-derived Stratigraphic and reservoir Attenuation Permeability
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- 29. DHI Bright spot Water sand has lower impedance than embedding medium and impedance of gas sand is further reduced. Top and base reflections show natural pairing If sand is thick enough, flat spot or fluid contact reflection should be visible between gas sand and water sand Flat spot will have opposite polarity than bright spot at top More common in shallower sandstone reservoirs ( Mio- Plio) Peak on synthetic seismogram
- 30. DHI Dim spot Water sand has higher impedance than embedding medium and water is replaced by water impedance is reduced Contrast is reduced at upper and lower boundaries and reservoir is seen as dim spot Flat spot can be expected at the point where the dimming occurs. More common in deeper sandstone reservoirs where shale impedance is lower than sandstone impedance
- 31. DHI Polarity reversal Water sand is of higher impedance than enclosing medium and gas sand impedance is lower than enclosing medium The polarity of reflections from water-sand-shale interface and gas-sand- shale interface have opposite sign and thus polarity reversal Flat spot from GWC may show bright amplitude . More common in medium depth range sandstone reservoirs
- 32. Validation of DHI AVO In many practical cases gas sands show an increase of amplitude with offset Many difficulties of theoretical and practical nature Data is pre-stack hence lower S/N
- 33. Pitfalls in DHI Explorationprospects based on a sound geologic model and supported by seismic amplitude anomalies are highly prospective and are usually assigned a high probability of success. However, a fraction of such prospects, perhaps 10-30%, result in dry holes. Postdrill appraisal can usually assign these results to one or more of the following factors: • Unusually strong lithologic variations • Fizz water and low gas saturation • Superposition of seismic reflections • Contamination of the seismic signal by multiples or other undesired energy
- 34. In seismic gatherreflection coefficient at an incidence angle θ is given by: R(θ ) = Ro + BSin2θ Where: Ro: RC at zero offset and R (θ) : RC at angle θ B is a gradient term which produces the AVO effect. It is dependent on changes in density, ρ, P-wave velocity, VP, and S- wave velocity, Vs. Principle of AVO Analysis
- 35. Principle of AVOAnalysis The AVO response is dependent on the properties of P-wave velocity (VP), S-wave velocity (VS), and density (ρ) in a porous reservoir rock. This involves the matrix material, the porosity, and the fluids filling the pores. Poisson’s Ratio K = the bulk modulus, or the reciprocal of compressibility. μ = The shear modulus, modulus of rigidity
- 36. Increase of amplitude with offset Principleof AVO Analysis Stack Gather
- 37. Principle of AVOAnalysis S-wave P-wave Water Saturation Velocity(m/s) With increase in gas saturation, P- wave velocity drops dramatically, but S-wave velocity only increases slightly.
- 38. •Rutherford and Williams(1989) derived the following classification scheme for AVO anomalies, with further modifications by Ross and Kinman (1995) and Castagna (1997): • Class 1: High impedance gas sand with decreasing AVO • Class 2: Near-zero impedance contrast • Class 2p: Same as 2, with polarity change • Class 3: Low impedance gas sand with increasing AVO • Class 4: Low impedance sand with decreasing AVO Rutherford/Williams Classification
- 39. The generic AVOcurves at the top of the gas sand
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- 41. Inversion Inversion isthe process of extracting, from the seismic data, the underlying geology which gave rise to that seismic. Traditionally, inversion has been applied to post- stack seismic data, with the aim of extracting acoustic impedance volumes. Recently, inversion has been extended to pre-stack seismic data, with the aim of extracting both acoustic and shear impedance volumes. This allows the calculation of pore fluids. Another recent development is to use inversion results to directly predict lithologic parameters such as porosity and water saturation.
- 42. Inversion Non-Uniqueness Allinversion algorithms suffer from “non- uniqueness”. There is more than one possible geological model consistent with the seismic data. The only way to decidebetween the possibilities is to use other information, not present in the seismic data. This other information is usually provided in two ways: The initial guess model constraints on how far the final result may deviate from the initial guess The final result always depends on the “other information” as well as the seismic data
- 43. Acoustic Impedance The definitionof the zero-offset reflection coefficient, shown in the figure above. R0 , the reflection coefficient, is the amplitude of the seismic peak shown and represents relative impedance contrast. 1122 1122 0 VV VV R Seismic raypath Interface at depth = d 1 V1 2 V2 t Reflection at time t = 2d/V1 Geology Seismic Surface Seismic Wavelet Shale Gas Sand
- 44. Impedance Reflectivity Wavelet Seismic trace Thecommon forward model for all inversions Inversion
- 45. ImpedanceReflectivity Inverse Wavelet Seismic Inversion tries toreverse the forward model Inverse Model
- 46. QC plot showingaccuracy of inversion process. Error between synthetic generated from real and inverted impedance is insignificant
- 47. Differentiating different lithologytypes through acoustic impedance inversion
- 48. Impedance profile distinguishingbetween different rock types Limestone Sandstone Basement
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