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Seismic Method Estimate velocity from seismic data.pptx

The document provides an extensive overview of seismic methods used in hydrocarbon exploration. It discusses the principles of seismic data acquisition, processing, and interpretation, focusing on how seismic waves are used to image subsurface structures and the importance of resolution and noise reduction in data processing. Key concepts include seismic theory, vertical and lateral resolution, and the steps involved in transforming raw data into usable geological insights.

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Md. Sakawat Hossain DGS, JU, BD Seismic Method Courtesy of ExxonMobil Mitchum et al., 1977b AAPG©1977 reprinted with permission of the AAPG whose permission is required for further use.
Courtesy of ExxonMobil Basic Exploration Workflow To D/P Drop Prospect Drill Wildcats Confirmation Well Identify Opportunities Process Seismic Data Capture Prime Areas Interpret Seismic Data Acquire Seismic Data Success Success Failure Uneconomic Economic Analysis Assess Prospects
• Seismic data shows edges of geologic units • Edge detection that responds to AI changes • AI = acoustic impedance = velocity*density Seismic is a risk-reducing technology. Oil is found by drilling, seismic reduces risk of - • Dry holes • Marginal producers • Reserve estimate errors General Concepts: Why seismic? The seismic method can provide best subsurface image in exploration.
Seismic Acquisition 2D or 3D seismic survey is designed based on: – Imaging Objectives: image area, target depth, dips, velocity, size/thickness of bodies to be imaged, etc. – Survey Parameters: survey area, fold, offsets, sampling, shooting direction, etc. – Balance between Data Quality & Cost of Survey Land Operations Vibrators Generate a Disturbance Geophones Detect Motion Marine Operations Air Guns Generate a Disturbance Hydrophones Detect Pressure
What is Seismology? Recall 3rd year The scientific discipline that is concerned with the study of earthquakes and of the propagation of seismic waves within the Earth. Application: Exploration and engineering seismology deals with the use of elastic waves to map the structure of the subsurface. It mainly applies to Hydrocarbon Exploration Reservoir characterization Geo-engineering Site characterization Contamination remediation Ore deposits Hydrogeology Archaeology CO2 storage monitoring Seismology
• Q: What is it that causes a seismic wave to reflect? – A: Contrasts in acoustic impedance (at vertical incidence) • Q: What happens as a seismic wave crosses a velocity interface? – A: Reflection and refraction (Snell’s Law) • Q: How do seismic waves lose energy in the ground? – A: • Geometrical spreading • Absorption • Partitioning at interfaces • Scattering • Earth is a low-pass filter Quick answer: Basic seismic theory
Seismic cartoon
Courtesy of ExxonMobil Listening Devices 0 s  An Explosion! 0 s Energy Source .1 s .2 s .3 s Some Energy is Reflected Most Energy is Transmitted .4 s .4 s .5 s Some Energy is Reflected Most Energy is Transmitted .6 s .7 s .8 s .8 s Seismic Methods
Device #1 Device #2 0.0 0.3 0.4 0.5 0.6 0.7 0.8 0.1 0.2 For the explosion we just considered ... Listening device #1 records a reflection starting at 0.4 seconds Listening device #2 records a reflection starting at 0.8 seconds To Image the Subsurface, We Use Many Shots (explosions) and Many Receivers (listening devices or Geophone) Arranged in Lines either on Land or Offshore Time Raw Seismic Data
• Half-space – Two-layer Earth – Boundary is the free surface • Direct arrivals – Air wave/Ground roll – Direct wave • Travel time is linear I. Seismic events: 1. Direct arrivals
• Half-space • Estimate velocity from seismic data – Plot of time vs distance (x-t space) – Velocity is slope of curve I. Seismic events: 1. Direct arrivals
I. Seismic events: 1. Direct arrivals
I. Seismic events:
• Beyond half-space, layer can introduce below the surface – In x-t space, reflection is hyperbolic curve – Hyperbolic moveout (or normal moveout) I. Seismic events: 2. Reflection
I. Seismic events: 2. Reflection •Horizontal reflector - Reflection point is midpoint on surface
• Dipping reflector - Reflection point is no longer midpoint on surface I. Seismic events: 2. Reflection
• Dipping reflector I. Seismic events: 2. Reflections
Layer Thickness 1 10 20 20 ms 30 base top top * Seismic Resolution Seismic resolution quantifies the level of precision, such as the finest size of subsurface objects (e.g., layers, structures) detectable by the seismic data. Since seismic data are bandlimited, seismic resolution is proportional to the frequency bandwidth of the data. If the bandwidth is too narrow, the resolution will be poor.
Seismic Resolution Frequency, ƒ = 1/T Unit of Frequency (ƒ), Hz Unit of Period (T), s The period (T) is the duration of time of one cycle in a repeating event, so the period is the reciprocal of the frequency. The frequency (ƒ) of a wave describes the number of complete cycles which are completed during a given period of time. The bandwidth describes simply the breadth of frequencies comprising a spectrum (e.g. 10-20 Hz)
Vertical Resolution • Resolution vs Detection • Thin Bed Response and Tuning Lateral Resolution • Fresnel Zone • Migration and Lateral Resolution Resolution Outline
II. Detection vs. Resolution - Analogy You are driving at night. You spot a light in the distance. Is it a car or a motorcycle??? Aha, it is a car!
Detection: Ability to identify that some feature exists • Detection limit is always smaller than the resolution limit • Detection limit depends upon Signal-to-Noise Resolution: Ability to distinguish two features (vertically or horizontally) from one another II. Detection vs. Resolution - Analogy
What is the minimum vertical distance between two subsurface features such that we can tell them apart seismically? Shale Sand Sd Gamma Ray Shale Baseline For Example: Based on seismic data, could you determine that there is a thin shale layer between the two sands? II. Vertical resolution Applying the concept of resolution to seismic wiggles, vertical resolution quantifies the resolvability of seismic waves along the direction of wave propagation, which is usually the vertical direction. Hence it is also called temporal resolution, depth resolution or 1D resolution, depending on the application.
The smallest change in input that will produce a detectable change in output. The ability to distinguish two points that are near each other. The ability to localize an event seen through a window, usually taken as the half width of the major lobe. Four cases of vertical resolution defined by the detectability of two events close together. (Lower) Richer’s resolution limit is the separation interval between inflection points of the seismic wavelet. II. Vertical resolution
Question: What is a thick bed? Impedance Composite Wavelet 1 R. C. Wavelet 2 NO Interference λ Wavelet 1 ends before Wavelet 2 begins Top of Bed Response Base of Bed Response Answer: A thick bed is one that has a TWT > λ (pulse duration or Dp) B A C Thick Bed Response Wavelet 1 ends before Wavelet 2 begins, no interference of wavelet
TWT thickness = 0.9 * Dp Impedance Composite Wavelet 1 R. C. Wavelet 2 Some Interference Wavelet 2 starts before Wavelet 1 ends Top of Bed Response Base of Bed Response 2nd half-cycle from Wavelet 1 and 1st half-cycle from Wavelet 2 form a trough doublet B A C Dp Partial Interference
TWT thickness = ½ Dp Impedance Composite Wavelet 1 R. C. Wavelet 2 Maximum Interference Wavelet 2 starts before Wavelet 1 ends Top of Bed Response Base of Bed Response 2nd half-cycle from Wavelet 1 and 1st half-cycle from Wavelet 2 are completely in phase resulting in 2x amplitude B A C Dp Maximum Interference - Tuning
• Beds with reflection coefficients of equal magnitude but opposite polarity II. Resolution: Vertical
• Beds thicker than ½ wavelength are interference free. Beds between ½ and ¼ wavelength overlap and interfere constructively if the reflection coefficients are of opposite sign • Thus, the bed thickness is greater than one quarter of a wavelength (λ/4), you have distinct reflections. Or, maximum constructive interference occurs at 1/4 wavelength (tuning thickness). II. Resolution: Vertical
• Amplitude decreases between 1/4 and 1/8 wavelength. • Below 1/8 wavelength waveform shape is indistinguishable from that produces by a single bed • At about 1/30 l interference destroys the reflection no matter how large the reflection coefficient II. Resolution: Vertical l/4 l/2 Single Reflector Wedge
Shallow Event (for example at 1 km depth) Velocity = 2000 Meters / sec Pulse: Center Frequency = 50 Hz Period = 1 / 50 = .020 sec Wavelength = .020 x 2000 = 40 Meters Limit of bed thickness to be detected (Tuning thickness) = 40 /4 = 10 Meters Deep Event (for example at 1.5 km depth) Velocity = 3000 Meters / sec Pulse: Center Frequency = 20 Hz Period = 1 / 20 = .050 sec Wavelength = .050 x 3000 = 150 Meters Limit of bed thickness to be detected (Tuning thickness) = 150 / 4 = 37.5 Meters II. Resolution: Vertical Period (ms) wavelength = period X velocity Pulse
What Is Lateral Resolution? Would we image the narrow horst? Would we image all three channel sands?
What is the minimum horizontal distance between two subsurface features such that we can tell them apart seismically? Reflections from Reflector with Gaps Neidell & Poggiaglioimi, 1977 AAPG©1977 reprinted with permission of the AAPG whose permission is required for further use. Lateral Resolution
• The portion of the reflector from which they add constructively is the Fresnel zone. • The size of the Fresnel zone depends upon the wavelength of the pulse and the depth of the reflector. The Freshnel Zone
Seismic Data Processing
I. Introduction: Aims of Data Processing (1) • We don’t only record energy from our target reflectors • What else is contained within a trace? • Boosting the signal- to-noise ratio is a major aspect of processing.
Idealised schematic subsurface… Potentially prospective subsurface… I. Introduction (cont…)
• Seismic datasets are recorded in two-way travel-time • Surely knowing the depth to a target horizon would be more useful? Position Two-Way Travel Time I. Introduction (cont…)
• We would like this in depth • Fortunately: – Speed = distance/time • Velocity analysis: – Provides the information needed for depth conversion – 90% processor’s time involves velocity analysis Position Two-Way Travel Time I. Introduction (cont…)
• An accurate images of subsurface structures, allowing an accurate geological interpretation. • To generate a true subsurface image from seismic, three aspects are … – High signal-to-noise ratio in a dataset. – Faithful geometric representation in data. – Accurate depth conversions. I. Introduction: Seismic processing output
Receiver cable – ‘streamer’ Source – ‘airgun’ Offset (m) two-way travel time reflection hyperbola Shot Gather Shot gather
Common Mid Point (CMP)
Common Mid Point (CMP) CMP or common mid point methods are the standard method for acquiring seismic reflection data. CMP gathers generate symmetric hyperbola, which have major conveniences in the processing of the data. An essential factor for the success of CMP methods is sufficient high fold, which has been facilitated with sources.
We wish to make good pictures… But we are usually hampered by noise Common Mid Point (CMP)
Any single measurement is poor Common Mid Point (CMP)
So we average multiple measurements… Common Mid Point (CMP)
Up to our maximum Fold ….But it is still not the same as the perfect Common Mid Point (CMP)
One single shot gather I. Introduction: real data shot gathers
• Shot gathers are an example of ‘multi-offset’ acquisition. • Multi-offset data permit velocity analysis and horizontal stacking. I. Introduction: why acquire shot gathers?
For flat layers, CMP gathers look like Shot Gathers! I. Introduction: CMP gathers
Data Acquisition Pre-Processing (boosts signal level) Main Processing (velocity analysis – provides a starting velocity model and boosts signal level) Post Processing (migration focuses an image – improved match to real structure; Improved depth conversion) Phases of Seismic Data Processing
I. Cleaning up seismic data 1. Noise 2. Geometry set-up 3. Amplitude Recovery 4. Trace Editing (muting, kills, reversals) 5. Trace Balancing II. Filtering signal in Pre-Processing 1. Frequency Filtering 2. Frequency-Wavenumber Filtering 3. Deconvolution 4. Tau-P (Radon) Filtering Processing outline
– Geometry set-up – Amplitude Recovery – Trace Editing (muting, kills, reversals) – Trace Balancing – Frequency Filtering – Frequency-Wavenumber Filtering – Deconvolution – Tau-P (Radon) Filtering Pre-processing algorithms
• Primary reflection energy is regarded as our signal; – Noise is any other energy type that obscures signals. • Noise can be classified as: 1. Coherent and random 2. Source-generated and ambient • In general: – Ambient noise may be regarded as random, whereas coherent noise is typically source generated. – However, some ambient noise may be coherent. I. Cleaning: Noise
Airblast -Coherent -330 m/s, high frequency Ground-roll - Coherent - slow, low frequency Cultural – random or coherent Multiples -Coherent - short-path (e.g. ghost, intrabed) - long path Weather - random I. Cleaning: Noise in land surveys
groundroll Multiple? airblast I. Cleaning: Noise in land surveys
Ocean-bottom cables - coherent Drilling platforms - coherent, some random? Propellor noise - ambient, but coherent Swell noise - ambient, but coherent Multiples: Surface ghost, Long path etc - Source generated, coherent Bubble pulse - Source generated, coherent I. Cleaning: Noise in marine surveys
First arrivals are refractions reflections I. Cleaning: Noise
• Noise can have different characteristics to primary energy – It can have different velocity (e.g. airblast and ground-roll) – It can be lower or higher frequency (ground-roll vs air-blast) – It can come from different directions (e.g. ground-roll, scattered energy, swell and propellor noise) – It can have a different moveout pattern (ground-roll vs reflections) I. Cleaning: Noise characteristics • Noise can have similar characteristics to primary energy, but – It can follow at some constant lag (multiples) – It can be predicted (multiples, bubble pulse) • All of these facts together help us to suppress noise in pre-processing. Most processing algorithms exploit one or more of these characteristics.
• Processing software must know the location of all shots and receivers; – it can then work out the position of reflection midpoints, for converting data to CMP gathers. • In a land survey, – All sources and receivers are fixed at any one time. • Marine surveys, – Have extensive navigation reports that must be loaded because sources and receivers are always moving. • All streamers are fitted with many compasses – Such that the streamer orientation can be accurately described. – This limits the impact of cable feathering. I. Cleaning: Geometry
• Generically, amplitude recovery is called ‘gain’. Seismic amplitudes decay with increasing travel-time. – What processes decay the amplitude? – Ans: Geometrical spreading, attenuation, partitioning at interfaces… • For geometrical spreading, amplitude decays as 1/r, where r is the distance travelled. We attempt to boost amplitudes to correct for distance. I. Cleaning: Amplitude recovery
I. Cleaning: Amplitude recovery
• The spread of energy is controlled by velocity… • Assume a velocity model for initial geometrical spreading corrections. • Apply Automatic Gain Control (AGC). – AGC is a non-physical scaling function, – Tries to balance amplitudes down a trace. - homogeneous earth; energy spreads linearly. - velocity increasing with depth; energy is spread non-linearly I. Cleaning: Geometrical Spreading
I. Cleaning: Geometrical Spreading
• Sometimes, traces are so heavily contaminated with noise that they are deemed ‘unprocessable’. • If it is decided that they will contribute only noise to the final image, they are ‘killed’ in early stages of processing. • Alternatively, part of the trace may be muted, if at least some of it is useful. • Other edits include reversals, where a trace is recorded with the wrong polarity. Simply, multiply all amplitudes in it by -1. I. Cleaning: Trace Editing
Traces to be killed… I. Cleaning: Trace Editing
Top mutes in Shot Gathers without top mute with top mute I. Cleaning: Trace Editing
Bottom mutes can be used to kill excessive ground-roll. I. Cleaning: Trace Editing
Surgical Mute • Note here the surgical mute is combined with a top mute, and a trace kill. • Surgical mutes may be applied to suppress air-blasts, although other better means are available… • Spot the dead trace in the ‘after mute’ panel? I. Cleaning: Trace Editing
– Demultiplexing and geometry set-up – Amplitude Recovery – Trace Editing (muting, kills, reversals) – Trace Balancing – Frequency Filtering – Frequency-Wavenumber Filtering – Deconvolution – Tau-P (Radon) Filtering Pre-processing Algorithms
• Trace muting is all very well and good – But it could be considered wasteful – Think how much data the bottom mute destroyed. • Frequency filtering is a more elegant means of distinguishing signal and noise based on their frequency characteristics. • Time domain data are converted to the frequency domain via Fourier transformation. Thus, frequency filtering is performed in Fourier domain. II. Filtering: Frequency Filtering
• Seismic traces are normally displayed in the time domain – Amplitude is plotted against time. • The Fourier (frequency) domain expresses the power of certain frequencies within a seismic trace. – Amplitude is plotted against frequency. II. Filtering: Fourier Domain
• Frequency filters are typically applied: – To suppress air-blast (higher frequency than signal) and ground-roll (lower frequency than signal). – For the marine case, they are also effective at removing swell and propeller noise. II. Filtering: Aims of Frequency Filtering
Time (ms) 1 2 3 4 amplitude Ground-roll; typically lower frequency than signal Air-blast; typically higher frequency than signal Refractions have similar frequency to signal Frequency (Hz) ampltidue 50 Hz 500 Hz 20 Hz 1 2 3 4 II. Filtering: Refractions, ground-roll, air-blast
• High-pass filter permits energy higher than some chosen frequency (for example here it is approximately 30 Hz). • Low-pass filter permits energy lower than some chosen frequency (for example here it is approximately 70 Hz). • Bandpass filter is a combined high- and low-pass filter (for example here it is approximately 30-70 Hz).). ampltidue 50 Hz 500 Hz 20 Hz 1 2 3 4 II. Filtering: High, Low- and Band-pass Filters
No filter 0-20 Hz 20-30 Hz 30-50 Hz 50-80 Hz 80-120 Hz 120-160 Hz 160-200 Hz II. Filtering: High, Low- and Band-pass Filters
No filter 0-20 Hz 20-30 Hz 30-50 Hz 50-80 Hz 80-120 Hz 120-160 Hz 160-200 Hz What should be the band-pass from this dataset?
The maths behind ‘decon’ is pretty complex, but the idea is simple. – Basically, if we know the waveform that was put into the ground, we can filter that waveform and simplify the data. Deconvolution or inverse filtering (Kanasewich 1981) is a process that counteracts a previous convolution (or filtering) action. We consider the convolution operation given in following equation (2.5) y(t) = g(t) * f(t) y(t) is the filtered output derived by passing the input waveform g(t) through a filter of impulse response f(t). II. Filtering: Deconvolution (inverse filtering)
Chirp Earth response Recorded Trace ‘Deconvolved Trace’ Simpler II. Filtering: Vibroseis Deconvolution • If we know the input wavelet, or the processes it underwent, we can filter it out. • This serves to increase the bandwidth of the recorded trace, improving the resolution of the source wavelet.
Outline I. Velocity analysis II. Stacking III. Static corrections Main Processing
Velocity Analysis routine – CMP Binning – Analysis Methods • Constant Velocity Panels • Semblance – NMO Correction – Stacking • Boosting Signal to Noise Ratio • Reducing Multiple Energy – Residual Statics I. Velocity Analysis: Main Processing
• Before starting any velocity analysis – We must understand exactly what velocity we are analysing – It’s not just as simple as ‘seismic velocity’ I. Velocity Analysis: But….
• Stacking velocity is also called the ‘normal moveout velocity’. • The following equation we use in velocity analysis is • Where VST is used to approximate interval velocity, we actually use the term ‘interval stacking velocity’, VIS. I. Velocity Analysis: Stacking Velocity
offset (m) 0 x With Dix’s Equation, we use two stacking velocities to estimate the interval velocity in the material between the two reflections. I. Velocity Analysis: Interval Velocity
• If we allocate the correct velocity to a reflection event, we can flatten its curvature across the offset range. • This flattening is called a ‘normal moveout (NMO) correction’, and is a very powerful processing tool. I. Velocity Analysis: Correct Velocity?
offset Two-way travel time offset Two-way travel time t0 offset Two-way travel time t0 t-t0 normal moveout (NMO) correction reflection moveout
I. Velocity Analysis: NMO Correction…
• In general, seismic velocities increase with depth. Since multiples spend more of their time in the near-surface, they express a slower stacking velocity than deeper events. • A multiple and a primary may appear in the CMP gather at the same t0, but the multiple should have a lower velocity. We can see this in CMPs and semblance… vINT1 vINT2 I. Velocity Analysis: Velocity of Multiples
• Notice the criss-crossing events; these are multiples arriving at the same time as primaries. • When NMO corrected, primaries are flattened and multiples have residual curvature because their velocities are different. • Further proof…? before NMO correction after NMO correction time (s) I. Velocity Analysis: Multiples in CMP gathers
• There are places in the semblance panel where two picks could be made at any one travel-time. • This occurs where there are obvious multiples in the CMP gather. • The slower semblance peak corresponds to the multiple. 3 2 1 4 Primary reflection Multiple reflection I. Velocity Analysis: Multiples in semblance panel
• Application of NMO corrections introduces NMO stretch. offset offset time time When NMO corrected… NMO stretch is more severe at far offsets I. Velocity Analysis: NMO stretch
• We shift all traces to some datum, before carrying out velocity analysis. • Sometimes, and particularly in land surveying, this shifting is not sufficient to properly account for all the near-surface structural variation. • Residual statics are applied to shift traces to a datum by changing the velocity model. This effectively ‘heals up’ gaps in reflectors that we expect to be coherent. I. Velocity Analysis: Residual statics
Without residual statics With residual statics I. Velocity Analysis: Residual statics
• After application of residual statics, the whole velocity analysis may be repeated, to further refine the velocity model. • This will be followed by more residual statics then more velocity analysis then more residual statics • The whole process of velocity analysis is therefore iterative, hence why it is so time consuming! • However, careful velocity analysis and residual statics produces data with much-improved signal-to-noise ratio. I. Velocity Analysis: Iterative Analysis
• In a CMP gather, the curvature of a reflection event is controlled by velocity. With knowledge of velocity, we can remove the curvature from reflection events, such that they appear flat in the gather. • We can then sum the traces of the CMP gather together, to boost signal energy. • Velocity analysis is therefore required to estimate the curvature of reflections in the dataset. II. Stacking: relationship with velocity?
• Imagine that traces are contaminated with random noise. If we could take a group of similar traces, with the same signal in them, we could add them together and boost signal energy. time II. Stacking: relationship with velocity?
• Summed traces then produce a ‘stacked section’ • The summing process is called ‘stacking’. II. Stacking: summed traces
Datum corrections, such that seismic data can be referenced to some common level. These impact the accuracy of velocity models, so are worth talking about here. III. Statics:
• All seismic processing reports should quote a datum from which all depths are to be referenced. – Marine datums include the sea bed or mean sea level, or some common source-receiver level. – Land datums include mean sea level, surface topography, or some arbitrary elevation. • To correct to any datum, ‘static time shifts’ are applied to traces. • Once a datum has been specified, extra datum corrections may be applied to allow two datasets to share the same datum. III. Statics: Datums in seismic processing
• A flat reflector appears distorted because of surface topography. • Seismic energy has to travel further in areas of high-elevation; leads to distortions in time. III. Statics: Visualising a static correction
offset time III. Statics: Distortion of reflections in gathers
zwater zsource zrec zsource-rec sea bed • Marine static corrections are fairly straight forward. • The seismic velocity in water is fairly constant (what is it?). If we know the distance between our observation point and the datum, we can easily calculate the static correction… • Simply, static shift = distance ÷ velocity III. Statics: corrections in marine surveying
• …are a bit more complicated than marine ones. • Remember that seismic sources may be buried beneath a weathering layer? This means you get two velocities to work with! One for the weathered material, one for the solid media in which the source is buried. • Visualise the problem… III. Statics: Statics in land survey
Weathering layer has lower velocity than underlying material; static times are composed of two vel’s. For a buried source, the static correction can be measured directly… For surface sources, and for geophones, static corrections may be derived from seismic refraction surveys. These measure the velocity and topography of the weathering layer. ‘uphole time’ III. Statics: Statics in land survey
without refraction statics with refraction statics
Outline: Main Processing I. Post-processing II. Migration III. Depth conversion DIP DIP
Overview: Consider a Dipping Horizon Remember: • NMO stack assumes source and receiver are at the same position • Also, subsurface has a constant velocity here… …is imaged here here… …is imaged here DIP What do you notice about the imaged horizon, compared to the real one? 1. Vertical position…? 2. Horizontal position…? 3. Dip angle…? appears deeper moved down-dip shallower dip
• Migration uses velocity information to ‘zip up’ diffraction hyperbolae, focussing them at their apex. • Because of the restoration of hyperbolae to their apex, migrated stacked sections are visibly more focussed than unmigrated sections. • Migration also repositions energy reflected from dipping layers. Overview: Effect of Migration
Yilmaz, 2001, SEG
But what are these…? “Deeper data, which are probably multiples, out-of-the-plane diffractions and other types of events, are not migrated properly.” Migration assumes that data contain ONLY primary reflections
I. Post-processing: noise suppression • Post-processing > after stacking (post-stack) • Migration doesn’t work if there are residual multiples present, and ‘other types’ of noise, in a dataset. • Post-stack processing can serve to suppress such noise, to improve the performance of migration.
I. Post-processing: long-path multiples • Stacking • Radon filtering • Here’s another few…
II. Migration • There are numerous algorithms by which seismic data can be migrated. • Certain methods work better where dip angles are shallow, or velocity contrasts are small. • Migration algorithms can be very expensive to apply, so the choice of algorithm is often a financial one. Different types of migration used in industry are - • Kirchhoff (diffraction stack) • Wave Equation Migration • F-k (Stolt) migration • Reverse Time Migration – a major extension to Wave Equation Migration. • Pre-Stack Migration • Post-Stack Migration
II. Migration: Characteristics of Migration Methods • Kirchhoff migration: – Good for steep dips but doesn’t do well with lateral velocity variations. Easily visualized. • Wave Equation migration: – Works well for low signal-to-noise ratio data, and for lateral velocity structure. Expensive, but can handle overturned rays (e.g. sub-salt imaging). • F-k migration: – Automatically handles all dips; good for moderate, but not for strong vertical velocity variations. And fast! And cheap(ish)! • Regardless of the algorithm, applying migration gives a better image than no migration at all!
TGS advert from First Break magazine
II. Migration: Dip vs. Strike Imaging Dip lines at least provide an image of a dipping reflector – for this reason, single profiles are acquired in the dip direction. In an acquisition line in the strike direction, a dipping reflector has constant two-way travel time… it appears flat!
II. Migration: Out-of-Plane Reflections • Consider a strike line over some dipping structure… position travel-time Both reflections appear flat in the stacked section; the out- of-plane one is called ‘sideswipe’.
II. Migration: None 2D problem? • The migration must consider both strike and dip lines; these are provided by a 3-D approach to acquisition and processing. • The key advantage with 3-D grids is that such data can be migrated in 3-D. • Only with 3-D migration can out-of-plane reflections be correctly returned to their true place of origin.
II. Migration: 2D vs. 3D • During 2-D migration, amplitudes were migrated along a 2-D diffraction hyperbola. • In 3-D, amplitudes are migrated along a 3-D hyperboloid…
2D vs. 3D seismic
II. Migration: Other benefits of 3-D Migration • 3-D migration focuses reflections from structures that have 3-D structure. • Although 3-D acquisition and processing is more expensive (we need to sample the wavefield with sufficient density to avoid spatial aliasing…), the potential improvement is such that we don’t need as high a fold-of-cover. • In other words, we can reduce the number of traces in a CMP gather because migration gives superior data quality to stacking. • Also – time slices can be made from 3-D grids.
II. Migration: Characteristics of Pre-Stack • Pre-stack migration provides significant improvement over post-stack migration. • It is expensive since every trace in every CMP gather (rather than just the stacked traces) has to be migrated. • 3-D pre-stack migration, however, is standard as more surveys are acquired in complex areas, and as computer power improves.
II. Migration: Comparison Post-stack Pre-stack
II. Migration: Depth Migration • Everything we’ve discussed so far is strictly termed ‘time migration’. Depth migration is much more accurate. • Time migration does not handle refraction of wavelets very well – therefore bad for complex velocity models. • Depth migration handles this, and automatically depth converts for us!
Data from Fugro
II. Migration: Finally, the cutting edge... • 3D  Pre-Stack  Reverse Time  Depth Migration  Although no migration yet considers shear waves...

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Seismic Method Estimate velocity from seismic data.pptx

  • 1.
    Md. Sakawat Hossain DGS,JU, BD Seismic Method Courtesy of ExxonMobil Mitchum et al., 1977b AAPG©1977 reprinted with permission of the AAPG whose permission is required for further use.
  • 4.
    Courtesy of ExxonMobil BasicExploration Workflow To D/P Drop Prospect Drill Wildcats Confirmation Well Identify Opportunities Process Seismic Data Capture Prime Areas Interpret Seismic Data Acquire Seismic Data Success Success Failure Uneconomic Economic Analysis Assess Prospects
  • 5.
    • Seismic datashows edges of geologic units • Edge detection that responds to AI changes • AI = acoustic impedance = velocity*density Seismic is a risk-reducing technology. Oil is found by drilling, seismic reduces risk of - • Dry holes • Marginal producers • Reserve estimate errors General Concepts: Why seismic? The seismic method can provide best subsurface image in exploration.
  • 6.
    Seismic Acquisition 2D or3D seismic survey is designed based on: – Imaging Objectives: image area, target depth, dips, velocity, size/thickness of bodies to be imaged, etc. – Survey Parameters: survey area, fold, offsets, sampling, shooting direction, etc. – Balance between Data Quality & Cost of Survey Land Operations Vibrators Generate a Disturbance Geophones Detect Motion Marine Operations Air Guns Generate a Disturbance Hydrophones Detect Pressure
  • 7.
    What is Seismology? Recall3rd year The scientific discipline that is concerned with the study of earthquakes and of the propagation of seismic waves within the Earth. Application: Exploration and engineering seismology deals with the use of elastic waves to map the structure of the subsurface. It mainly applies to Hydrocarbon Exploration Reservoir characterization Geo-engineering Site characterization Contamination remediation Ore deposits Hydrogeology Archaeology CO2 storage monitoring Seismology
  • 8.
    • Q: Whatis it that causes a seismic wave to reflect? – A: Contrasts in acoustic impedance (at vertical incidence) • Q: What happens as a seismic wave crosses a velocity interface? – A: Reflection and refraction (Snell’s Law) • Q: How do seismic waves lose energy in the ground? – A: • Geometrical spreading • Absorption • Partitioning at interfaces • Scattering • Earth is a low-pass filter Quick answer: Basic seismic theory
  • 9.
  • 10.
    Courtesy of ExxonMobil ListeningDevices 0 s  An Explosion! 0 s Energy Source .1 s .2 s .3 s Some Energy is Reflected Most Energy is Transmitted .4 s .4 s .5 s Some Energy is Reflected Most Energy is Transmitted .6 s .7 s .8 s .8 s Seismic Methods
  • 11.
    Device #1 Device #2 0.0 0.3 0.4 0.5 0.6 0.7 0.8 0.1 0.2 For the explosionwe just considered ... Listening device #1 records a reflection starting at 0.4 seconds Listening device #2 records a reflection starting at 0.8 seconds To Image the Subsurface, We Use Many Shots (explosions) and Many Receivers (listening devices or Geophone) Arranged in Lines either on Land or Offshore Time Raw Seismic Data
  • 12.
    • Half-space – Two-layerEarth – Boundary is the free surface • Direct arrivals – Air wave/Ground roll – Direct wave • Travel time is linear I. Seismic events: 1. Direct arrivals
  • 13.
    • Half-space • Estimatevelocity from seismic data – Plot of time vs distance (x-t space) – Velocity is slope of curve I. Seismic events: 1. Direct arrivals
  • 14.
    I. Seismic events: 1.Direct arrivals
  • 15.
  • 16.
    • Beyond half-space,layer can introduce below the surface – In x-t space, reflection is hyperbolic curve – Hyperbolic moveout (or normal moveout) I. Seismic events: 2. Reflection
  • 17.
    I. Seismic events: 2.Reflection •Horizontal reflector - Reflection point is midpoint on surface
  • 18.
    • Dipping reflector- Reflection point is no longer midpoint on surface I. Seismic events: 2. Reflection
  • 19.
    • Dipping reflector I.Seismic events: 2. Reflections
  • 20.
    Layer Thickness 1 1020 20 ms 30 base top top * Seismic Resolution Seismic resolution quantifies the level of precision, such as the finest size of subsurface objects (e.g., layers, structures) detectable by the seismic data. Since seismic data are bandlimited, seismic resolution is proportional to the frequency bandwidth of the data. If the bandwidth is too narrow, the resolution will be poor.
  • 21.
    Seismic Resolution Frequency, ƒ= 1/T Unit of Frequency (ƒ), Hz Unit of Period (T), s The period (T) is the duration of time of one cycle in a repeating event, so the period is the reciprocal of the frequency. The frequency (ƒ) of a wave describes the number of complete cycles which are completed during a given period of time. The bandwidth describes simply the breadth of frequencies comprising a spectrum (e.g. 10-20 Hz)
  • 22.
    Vertical Resolution • Resolutionvs Detection • Thin Bed Response and Tuning Lateral Resolution • Fresnel Zone • Migration and Lateral Resolution Resolution Outline
  • 23.
    II. Detection vs.Resolution - Analogy You are driving at night. You spot a light in the distance. Is it a car or a motorcycle??? Aha, it is a car!
  • 24.
    Detection: Ability toidentify that some feature exists • Detection limit is always smaller than the resolution limit • Detection limit depends upon Signal-to-Noise Resolution: Ability to distinguish two features (vertically or horizontally) from one another II. Detection vs. Resolution - Analogy
  • 25.
    What is theminimum vertical distance between two subsurface features such that we can tell them apart seismically? Shale Sand Sd Gamma Ray Shale Baseline For Example: Based on seismic data, could you determine that there is a thin shale layer between the two sands? II. Vertical resolution Applying the concept of resolution to seismic wiggles, vertical resolution quantifies the resolvability of seismic waves along the direction of wave propagation, which is usually the vertical direction. Hence it is also called temporal resolution, depth resolution or 1D resolution, depending on the application.
  • 26.
    The smallest changein input that will produce a detectable change in output. The ability to distinguish two points that are near each other. The ability to localize an event seen through a window, usually taken as the half width of the major lobe. Four cases of vertical resolution defined by the detectability of two events close together. (Lower) Richer’s resolution limit is the separation interval between inflection points of the seismic wavelet. II. Vertical resolution
  • 27.
    Question: What isa thick bed? Impedance Composite Wavelet 1 R. C. Wavelet 2 NO Interference λ Wavelet 1 ends before Wavelet 2 begins Top of Bed Response Base of Bed Response Answer: A thick bed is one that has a TWT > λ (pulse duration or Dp) B A C Thick Bed Response Wavelet 1 ends before Wavelet 2 begins, no interference of wavelet
  • 28.
    TWT thickness =0.9 * Dp Impedance Composite Wavelet 1 R. C. Wavelet 2 Some Interference Wavelet 2 starts before Wavelet 1 ends Top of Bed Response Base of Bed Response 2nd half-cycle from Wavelet 1 and 1st half-cycle from Wavelet 2 form a trough doublet B A C Dp Partial Interference
  • 29.
    TWT thickness =½ Dp Impedance Composite Wavelet 1 R. C. Wavelet 2 Maximum Interference Wavelet 2 starts before Wavelet 1 ends Top of Bed Response Base of Bed Response 2nd half-cycle from Wavelet 1 and 1st half-cycle from Wavelet 2 are completely in phase resulting in 2x amplitude B A C Dp Maximum Interference - Tuning
  • 30.
    • Beds withreflection coefficients of equal magnitude but opposite polarity II. Resolution: Vertical
  • 31.
    • Beds thickerthan ½ wavelength are interference free. Beds between ½ and ¼ wavelength overlap and interfere constructively if the reflection coefficients are of opposite sign • Thus, the bed thickness is greater than one quarter of a wavelength (λ/4), you have distinct reflections. Or, maximum constructive interference occurs at 1/4 wavelength (tuning thickness). II. Resolution: Vertical
  • 32.
    • Amplitude decreasesbetween 1/4 and 1/8 wavelength. • Below 1/8 wavelength waveform shape is indistinguishable from that produces by a single bed • At about 1/30 l interference destroys the reflection no matter how large the reflection coefficient II. Resolution: Vertical l/4 l/2 Single Reflector Wedge
  • 33.
    Shallow Event (forexample at 1 km depth) Velocity = 2000 Meters / sec Pulse: Center Frequency = 50 Hz Period = 1 / 50 = .020 sec Wavelength = .020 x 2000 = 40 Meters Limit of bed thickness to be detected (Tuning thickness) = 40 /4 = 10 Meters Deep Event (for example at 1.5 km depth) Velocity = 3000 Meters / sec Pulse: Center Frequency = 20 Hz Period = 1 / 20 = .050 sec Wavelength = .050 x 3000 = 150 Meters Limit of bed thickness to be detected (Tuning thickness) = 150 / 4 = 37.5 Meters II. Resolution: Vertical Period (ms) wavelength = period X velocity Pulse
  • 34.
    What Is LateralResolution? Would we image the narrow horst? Would we image all three channel sands?
  • 35.
    What is theminimum horizontal distance between two subsurface features such that we can tell them apart seismically? Reflections from Reflector with Gaps Neidell & Poggiaglioimi, 1977 AAPG©1977 reprinted with permission of the AAPG whose permission is required for further use. Lateral Resolution
  • 36.
    • The portionof the reflector from which they add constructively is the Fresnel zone. • The size of the Fresnel zone depends upon the wavelength of the pulse and the depth of the reflector. The Freshnel Zone
  • 37.
  • 38.
    I. Introduction: Aimsof Data Processing (1) • We don’t only record energy from our target reflectors • What else is contained within a trace? • Boosting the signal- to-noise ratio is a major aspect of processing.
  • 39.
    Idealised schematic subsurface… Potentiallyprospective subsurface… I. Introduction (cont…)
  • 40.
    • Seismic datasetsare recorded in two-way travel-time • Surely knowing the depth to a target horizon would be more useful? Position Two-Way Travel Time I. Introduction (cont…)
  • 41.
    • We wouldlike this in depth • Fortunately: – Speed = distance/time • Velocity analysis: – Provides the information needed for depth conversion – 90% processor’s time involves velocity analysis Position Two-Way Travel Time I. Introduction (cont…)
  • 42.
    • An accurateimages of subsurface structures, allowing an accurate geological interpretation. • To generate a true subsurface image from seismic, three aspects are … – High signal-to-noise ratio in a dataset. – Faithful geometric representation in data. – Accurate depth conversions. I. Introduction: Seismic processing output
  • 43.
    Receiver cable – ‘streamer’ Source– ‘airgun’ Offset (m) two-way travel time reflection hyperbola Shot Gather Shot gather
  • 44.
  • 45.
    Common Mid Point(CMP) CMP or common mid point methods are the standard method for acquiring seismic reflection data. CMP gathers generate symmetric hyperbola, which have major conveniences in the processing of the data. An essential factor for the success of CMP methods is sufficient high fold, which has been facilitated with sources.
  • 46.
    We wish tomake good pictures… But we are usually hampered by noise Common Mid Point (CMP)
  • 47.
    Any single measurementis poor Common Mid Point (CMP)
  • 48.
    So we averagemultiple measurements… Common Mid Point (CMP)
  • 49.
    Up to ourmaximum Fold ….But it is still not the same as the perfect Common Mid Point (CMP)
  • 50.
    One single shot gather I.Introduction: real data shot gathers
  • 51.
    • Shot gathersare an example of ‘multi-offset’ acquisition. • Multi-offset data permit velocity analysis and horizontal stacking. I. Introduction: why acquire shot gathers?
  • 52.
    For flat layers,CMP gathers look like Shot Gathers! I. Introduction: CMP gathers
  • 53.
    Data Acquisition Pre-Processing (boosts signallevel) Main Processing (velocity analysis – provides a starting velocity model and boosts signal level) Post Processing (migration focuses an image – improved match to real structure; Improved depth conversion) Phases of Seismic Data Processing
  • 54.
    I. Cleaning upseismic data 1. Noise 2. Geometry set-up 3. Amplitude Recovery 4. Trace Editing (muting, kills, reversals) 5. Trace Balancing II. Filtering signal in Pre-Processing 1. Frequency Filtering 2. Frequency-Wavenumber Filtering 3. Deconvolution 4. Tau-P (Radon) Filtering Processing outline
  • 55.
    – Geometry set-up –Amplitude Recovery – Trace Editing (muting, kills, reversals) – Trace Balancing – Frequency Filtering – Frequency-Wavenumber Filtering – Deconvolution – Tau-P (Radon) Filtering Pre-processing algorithms
  • 56.
    • Primary reflectionenergy is regarded as our signal; – Noise is any other energy type that obscures signals. • Noise can be classified as: 1. Coherent and random 2. Source-generated and ambient • In general: – Ambient noise may be regarded as random, whereas coherent noise is typically source generated. – However, some ambient noise may be coherent. I. Cleaning: Noise
  • 57.
    Airblast -Coherent -330 m/s, high frequency Ground-roll -Coherent - slow, low frequency Cultural – random or coherent Multiples -Coherent - short-path (e.g. ghost, intrabed) - long path Weather - random I. Cleaning: Noise in land surveys
  • 58.
  • 59.
    Ocean-bottom cables - coherent Drillingplatforms - coherent, some random? Propellor noise - ambient, but coherent Swell noise - ambient, but coherent Multiples: Surface ghost, Long path etc - Source generated, coherent Bubble pulse - Source generated, coherent I. Cleaning: Noise in marine surveys
  • 60.
    First arrivals arerefractions reflections I. Cleaning: Noise
  • 61.
    • Noise canhave different characteristics to primary energy – It can have different velocity (e.g. airblast and ground-roll) – It can be lower or higher frequency (ground-roll vs air-blast) – It can come from different directions (e.g. ground-roll, scattered energy, swell and propellor noise) – It can have a different moveout pattern (ground-roll vs reflections) I. Cleaning: Noise characteristics • Noise can have similar characteristics to primary energy, but – It can follow at some constant lag (multiples) – It can be predicted (multiples, bubble pulse) • All of these facts together help us to suppress noise in pre-processing. Most processing algorithms exploit one or more of these characteristics.
  • 62.
    • Processing softwaremust know the location of all shots and receivers; – it can then work out the position of reflection midpoints, for converting data to CMP gathers. • In a land survey, – All sources and receivers are fixed at any one time. • Marine surveys, – Have extensive navigation reports that must be loaded because sources and receivers are always moving. • All streamers are fitted with many compasses – Such that the streamer orientation can be accurately described. – This limits the impact of cable feathering. I. Cleaning: Geometry
  • 63.
    • Generically, amplituderecovery is called ‘gain’. Seismic amplitudes decay with increasing travel-time. – What processes decay the amplitude? – Ans: Geometrical spreading, attenuation, partitioning at interfaces… • For geometrical spreading, amplitude decays as 1/r, where r is the distance travelled. We attempt to boost amplitudes to correct for distance. I. Cleaning: Amplitude recovery
  • 64.
  • 65.
    • The spreadof energy is controlled by velocity… • Assume a velocity model for initial geometrical spreading corrections. • Apply Automatic Gain Control (AGC). – AGC is a non-physical scaling function, – Tries to balance amplitudes down a trace. - homogeneous earth; energy spreads linearly. - velocity increasing with depth; energy is spread non-linearly I. Cleaning: Geometrical Spreading
  • 66.
  • 67.
    • Sometimes, tracesare so heavily contaminated with noise that they are deemed ‘unprocessable’. • If it is decided that they will contribute only noise to the final image, they are ‘killed’ in early stages of processing. • Alternatively, part of the trace may be muted, if at least some of it is useful. • Other edits include reversals, where a trace is recorded with the wrong polarity. Simply, multiply all amplitudes in it by -1. I. Cleaning: Trace Editing
  • 68.
    Traces to bekilled… I. Cleaning: Trace Editing
  • 69.
    Top mutes inShot Gathers without top mute with top mute I. Cleaning: Trace Editing
  • 70.
    Bottom mutes canbe used to kill excessive ground-roll. I. Cleaning: Trace Editing
  • 71.
    Surgical Mute • Notehere the surgical mute is combined with a top mute, and a trace kill. • Surgical mutes may be applied to suppress air-blasts, although other better means are available… • Spot the dead trace in the ‘after mute’ panel? I. Cleaning: Trace Editing
  • 72.
    – Demultiplexing andgeometry set-up – Amplitude Recovery – Trace Editing (muting, kills, reversals) – Trace Balancing – Frequency Filtering – Frequency-Wavenumber Filtering – Deconvolution – Tau-P (Radon) Filtering Pre-processing Algorithms
  • 73.
    • Trace mutingis all very well and good – But it could be considered wasteful – Think how much data the bottom mute destroyed. • Frequency filtering is a more elegant means of distinguishing signal and noise based on their frequency characteristics. • Time domain data are converted to the frequency domain via Fourier transformation. Thus, frequency filtering is performed in Fourier domain. II. Filtering: Frequency Filtering
  • 74.
    • Seismic tracesare normally displayed in the time domain – Amplitude is plotted against time. • The Fourier (frequency) domain expresses the power of certain frequencies within a seismic trace. – Amplitude is plotted against frequency. II. Filtering: Fourier Domain
  • 75.
    • Frequency filtersare typically applied: – To suppress air-blast (higher frequency than signal) and ground-roll (lower frequency than signal). – For the marine case, they are also effective at removing swell and propeller noise. II. Filtering: Aims of Frequency Filtering
  • 76.
    Time (ms) 1 2 3 4 amplitude Ground-roll;typically lower frequency than signal Air-blast; typically higher frequency than signal Refractions have similar frequency to signal Frequency (Hz) ampltidue 50 Hz 500 Hz 20 Hz 1 2 3 4 II. Filtering: Refractions, ground-roll, air-blast
  • 77.
    • High-pass filterpermits energy higher than some chosen frequency (for example here it is approximately 30 Hz). • Low-pass filter permits energy lower than some chosen frequency (for example here it is approximately 70 Hz). • Bandpass filter is a combined high- and low-pass filter (for example here it is approximately 30-70 Hz).). ampltidue 50 Hz 500 Hz 20 Hz 1 2 3 4 II. Filtering: High, Low- and Band-pass Filters
  • 78.
    No filter 0-20Hz 20-30 Hz 30-50 Hz 50-80 Hz 80-120 Hz 120-160 Hz 160-200 Hz II. Filtering: High, Low- and Band-pass Filters
  • 79.
    No filter 0-20Hz 20-30 Hz 30-50 Hz 50-80 Hz 80-120 Hz 120-160 Hz 160-200 Hz What should be the band-pass from this dataset?
  • 80.
    The maths behind‘decon’ is pretty complex, but the idea is simple. – Basically, if we know the waveform that was put into the ground, we can filter that waveform and simplify the data. Deconvolution or inverse filtering (Kanasewich 1981) is a process that counteracts a previous convolution (or filtering) action. We consider the convolution operation given in following equation (2.5) y(t) = g(t) * f(t) y(t) is the filtered output derived by passing the input waveform g(t) through a filter of impulse response f(t). II. Filtering: Deconvolution (inverse filtering)
  • 81.
    Chirp Earth response Recorded Trace ‘DeconvolvedTrace’ Simpler II. Filtering: Vibroseis Deconvolution • If we know the input wavelet, or the processes it underwent, we can filter it out. • This serves to increase the bandwidth of the recorded trace, improving the resolution of the source wavelet.
  • 82.
    Outline I. Velocity analysis II.Stacking III. Static corrections Main Processing
  • 83.
    Velocity Analysis routine –CMP Binning – Analysis Methods • Constant Velocity Panels • Semblance – NMO Correction – Stacking • Boosting Signal to Noise Ratio • Reducing Multiple Energy – Residual Statics I. Velocity Analysis: Main Processing
  • 84.
    • Before startingany velocity analysis – We must understand exactly what velocity we are analysing – It’s not just as simple as ‘seismic velocity’ I. Velocity Analysis: But….
  • 85.
    • Stacking velocityis also called the ‘normal moveout velocity’. • The following equation we use in velocity analysis is • Where VST is used to approximate interval velocity, we actually use the term ‘interval stacking velocity’, VIS. I. Velocity Analysis: Stacking Velocity
  • 86.
    offset (m) 0 x WithDix’s Equation, we use two stacking velocities to estimate the interval velocity in the material between the two reflections. I. Velocity Analysis: Interval Velocity
  • 87.
    • If weallocate the correct velocity to a reflection event, we can flatten its curvature across the offset range. • This flattening is called a ‘normal moveout (NMO) correction’, and is a very powerful processing tool. I. Velocity Analysis: Correct Velocity?
  • 88.
  • 89.
    I. Velocity Analysis:NMO Correction…
  • 90.
    • In general,seismic velocities increase with depth. Since multiples spend more of their time in the near-surface, they express a slower stacking velocity than deeper events. • A multiple and a primary may appear in the CMP gather at the same t0, but the multiple should have a lower velocity. We can see this in CMPs and semblance… vINT1 vINT2 I. Velocity Analysis: Velocity of Multiples
  • 91.
    • Notice thecriss-crossing events; these are multiples arriving at the same time as primaries. • When NMO corrected, primaries are flattened and multiples have residual curvature because their velocities are different. • Further proof…? before NMO correction after NMO correction time (s) I. Velocity Analysis: Multiples in CMP gathers
  • 92.
    • There areplaces in the semblance panel where two picks could be made at any one travel-time. • This occurs where there are obvious multiples in the CMP gather. • The slower semblance peak corresponds to the multiple. 3 2 1 4 Primary reflection Multiple reflection I. Velocity Analysis: Multiples in semblance panel
  • 93.
    • Application ofNMO corrections introduces NMO stretch. offset offset time time When NMO corrected… NMO stretch is more severe at far offsets I. Velocity Analysis: NMO stretch
  • 94.
    • We shiftall traces to some datum, before carrying out velocity analysis. • Sometimes, and particularly in land surveying, this shifting is not sufficient to properly account for all the near-surface structural variation. • Residual statics are applied to shift traces to a datum by changing the velocity model. This effectively ‘heals up’ gaps in reflectors that we expect to be coherent. I. Velocity Analysis: Residual statics
  • 95.
    Without residual statics Withresidual statics I. Velocity Analysis: Residual statics
  • 96.
    • After applicationof residual statics, the whole velocity analysis may be repeated, to further refine the velocity model. • This will be followed by more residual statics then more velocity analysis then more residual statics • The whole process of velocity analysis is therefore iterative, hence why it is so time consuming! • However, careful velocity analysis and residual statics produces data with much-improved signal-to-noise ratio. I. Velocity Analysis: Iterative Analysis
  • 97.
    • In aCMP gather, the curvature of a reflection event is controlled by velocity. With knowledge of velocity, we can remove the curvature from reflection events, such that they appear flat in the gather. • We can then sum the traces of the CMP gather together, to boost signal energy. • Velocity analysis is therefore required to estimate the curvature of reflections in the dataset. II. Stacking: relationship with velocity?
  • 98.
    • Imagine thattraces are contaminated with random noise. If we could take a group of similar traces, with the same signal in them, we could add them together and boost signal energy. time II. Stacking: relationship with velocity?
  • 99.
    • Summed tracesthen produce a ‘stacked section’ • The summing process is called ‘stacking’. II. Stacking: summed traces
  • 100.
    Datum corrections, suchthat seismic data can be referenced to some common level. These impact the accuracy of velocity models, so are worth talking about here. III. Statics:
  • 101.
    • All seismicprocessing reports should quote a datum from which all depths are to be referenced. – Marine datums include the sea bed or mean sea level, or some common source-receiver level. – Land datums include mean sea level, surface topography, or some arbitrary elevation. • To correct to any datum, ‘static time shifts’ are applied to traces. • Once a datum has been specified, extra datum corrections may be applied to allow two datasets to share the same datum. III. Statics: Datums in seismic processing
  • 102.
    • A flatreflector appears distorted because of surface topography. • Seismic energy has to travel further in areas of high-elevation; leads to distortions in time. III. Statics: Visualising a static correction
  • 103.
    offset time III. Statics: Distortionof reflections in gathers
  • 104.
    zwater zsource zrec zsource-rec sea bed •Marine static corrections are fairly straight forward. • The seismic velocity in water is fairly constant (what is it?). If we know the distance between our observation point and the datum, we can easily calculate the static correction… • Simply, static shift = distance ÷ velocity III. Statics: corrections in marine surveying
  • 105.
    • …are abit more complicated than marine ones. • Remember that seismic sources may be buried beneath a weathering layer? This means you get two velocities to work with! One for the weathered material, one for the solid media in which the source is buried. • Visualise the problem… III. Statics: Statics in land survey
  • 106.
    Weathering layer has lowervelocity than underlying material; static times are composed of two vel’s. For a buried source, the static correction can be measured directly… For surface sources, and for geophones, static corrections may be derived from seismic refraction surveys. These measure the velocity and topography of the weathering layer. ‘uphole time’ III. Statics: Statics in land survey
  • 107.
  • 108.
    Outline: Main Processing I.Post-processing II. Migration III. Depth conversion DIP DIP
  • 109.
    Overview: Consider aDipping Horizon Remember: • NMO stack assumes source and receiver are at the same position • Also, subsurface has a constant velocity here… …is imaged here here… …is imaged here DIP What do you notice about the imaged horizon, compared to the real one? 1. Vertical position…? 2. Horizontal position…? 3. Dip angle…? appears deeper moved down-dip shallower dip
  • 110.
    • Migration usesvelocity information to ‘zip up’ diffraction hyperbolae, focussing them at their apex. • Because of the restoration of hyperbolae to their apex, migrated stacked sections are visibly more focussed than unmigrated sections. • Migration also repositions energy reflected from dipping layers. Overview: Effect of Migration
  • 111.
  • 112.
    But what arethese…? “Deeper data, which are probably multiples, out-of-the-plane diffractions and other types of events, are not migrated properly.” Migration assumes that data contain ONLY primary reflections
  • 113.
    I. Post-processing: noisesuppression • Post-processing > after stacking (post-stack) • Migration doesn’t work if there are residual multiples present, and ‘other types’ of noise, in a dataset. • Post-stack processing can serve to suppress such noise, to improve the performance of migration.
  • 114.
    I. Post-processing: long-pathmultiples • Stacking • Radon filtering • Here’s another few…
  • 115.
    II. Migration • Thereare numerous algorithms by which seismic data can be migrated. • Certain methods work better where dip angles are shallow, or velocity contrasts are small. • Migration algorithms can be very expensive to apply, so the choice of algorithm is often a financial one. Different types of migration used in industry are - • Kirchhoff (diffraction stack) • Wave Equation Migration • F-k (Stolt) migration • Reverse Time Migration – a major extension to Wave Equation Migration. • Pre-Stack Migration • Post-Stack Migration
  • 116.
    II. Migration: Characteristicsof Migration Methods • Kirchhoff migration: – Good for steep dips but doesn’t do well with lateral velocity variations. Easily visualized. • Wave Equation migration: – Works well for low signal-to-noise ratio data, and for lateral velocity structure. Expensive, but can handle overturned rays (e.g. sub-salt imaging). • F-k migration: – Automatically handles all dips; good for moderate, but not for strong vertical velocity variations. And fast! And cheap(ish)! • Regardless of the algorithm, applying migration gives a better image than no migration at all!
  • 117.
    TGS advert fromFirst Break magazine
  • 118.
    II. Migration: Dipvs. Strike Imaging Dip lines at least provide an image of a dipping reflector – for this reason, single profiles are acquired in the dip direction. In an acquisition line in the strike direction, a dipping reflector has constant two-way travel time… it appears flat!
  • 119.
    II. Migration: Out-of-PlaneReflections • Consider a strike line over some dipping structure… position travel-time Both reflections appear flat in the stacked section; the out- of-plane one is called ‘sideswipe’.
  • 120.
    II. Migration: None2D problem? • The migration must consider both strike and dip lines; these are provided by a 3-D approach to acquisition and processing. • The key advantage with 3-D grids is that such data can be migrated in 3-D. • Only with 3-D migration can out-of-plane reflections be correctly returned to their true place of origin.
  • 121.
    II. Migration: 2Dvs. 3D • During 2-D migration, amplitudes were migrated along a 2-D diffraction hyperbola. • In 3-D, amplitudes are migrated along a 3-D hyperboloid…
  • 122.
    2D vs. 3Dseismic
  • 123.
    II. Migration: Otherbenefits of 3-D Migration • 3-D migration focuses reflections from structures that have 3-D structure. • Although 3-D acquisition and processing is more expensive (we need to sample the wavefield with sufficient density to avoid spatial aliasing…), the potential improvement is such that we don’t need as high a fold-of-cover. • In other words, we can reduce the number of traces in a CMP gather because migration gives superior data quality to stacking. • Also – time slices can be made from 3-D grids.
  • 124.
    II. Migration: Characteristicsof Pre-Stack • Pre-stack migration provides significant improvement over post-stack migration. • It is expensive since every trace in every CMP gather (rather than just the stacked traces) has to be migrated. • 3-D pre-stack migration, however, is standard as more surveys are acquired in complex areas, and as computer power improves.
  • 125.
  • 126.
    II. Migration: DepthMigration • Everything we’ve discussed so far is strictly termed ‘time migration’. Depth migration is much more accurate. • Time migration does not handle refraction of wavelets very well – therefore bad for complex velocity models. • Depth migration handles this, and automatically depth converts for us!
  • 127.
  • 128.
    II. Migration: Finally,the cutting edge... • 3D  Pre-Stack  Reverse Time  Depth Migration  Although no migration yet considers shear waves...