0003

3.1 Introduction

Overview

This chapter reviews aspects of seismic data acquisition and processing that are relevant to a seismic interpreter. These topics are covered extensively by other authors and, because the focus of this text is on interpretation, the interested reader is referred elsewhere (see below) for in-depth treatment. The primary purposes of this chapter are to:

1. Briefl y explain how seismic data are acquired and processed, including a short discussion of aspects of 2-D and 3-D seismic survey design.

2. Ensure the reader understands that seismic data acquisition and processing parameters will have a signifi cant impact on the interpretability of the data (i.e. the appearance of structural and stratigraphic features).

The chapter ends with a discussion of coherency (also referred to as semblance and other names) processing, a processing step that is, at least in the petroleum industry, almost routinely applied to 3-D seismic data in order to enhance faults and stratigraphic features.

There is a consensus that a seismic interpretation begins at the survey design phase. As described below, the target depth and dimensions, structural dip, rate at which high frequencies are attenuated with depth, and other factors will infl uence survey design. If these parameters are improperly known or planned for, the data acquisition effort is unlikely to yield useful results. Similarly, although data processing may appear to have a cookbook character (“follow the recipe for predictable results every time”), it is in fact full of interpretive choices made by the data processors. It is possible to process seismic data such that they are optimized for some types of analyses but not others. Although there may be cases for which processing can minimize problems caused by survey design fl aws, no amount of processing can make data interpretable if the survey design is too fl awed, or the acquisition is poorly executed.

Formerly it was common for seismic acquisition, processing and interpretation to be undertaken

An Introduction to Seismic Interpretation

Chapter 3: Acquisition and Processing

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An Introduction to Seismic Interpretation • Chapter Three— Acquisition and Processing

by separate groups, with little communication between those groups1. For example, the people processing the seismic data may not have known what the data were to be used for, and the interpreters may have had little knowledge of how the data were acquired or processed. This is not a recipe for success. Seismic acquisition, processing and interpretation groups need to work closely together in order to ensure that: 1) the data quality is optimized for the interpretation task at hand, and 2) the interpreters understand the limitations of the data being analyzed.

As discussed below, seismic data collected in the fi eld need to be processed to generate images that come close to representing the subsurface stratigraphy and structure. Seismic processing has several objectives that include:

• Enhancing signal-to-noise ratios• Deriving subsurface velocity information• Deriving geometrically accurate images of the subsurface• Enhancing resolution

The seismic processing canon is Yilmaz (2001), a two-volume publication that presents an extensive, mathematically focused treatment of the topic but also shows many real-data examples of the effects of different processing steps on the appearance of seismic data. Duncan’s (1992) summary is much shorter and somewhat dated, but still a useful starting point. General aspects of seismic data acquisition were covered by Evans (1997) and, for marine surveying, Dessler (1992). Further discussions of acquisition and processing were presented by Sheriff and Geldart (1995), Henry (1997), Gadallah and Fisher (2005), Veeken (2007) and others. Aspects of data acquisition and processing particular to 3-D seismic data were presented by Hardage (1997), Cordsen et al. (2000), Galbraith (2001), Vermeer (2002) and Liner (2004). Sheriff (2002) presented defi nitions and short explanations of many terms used in seismic data acquisition and processing. Mosher and Simpkin (1999) reviewed sources and techniques for the acquisition of high-resolution marine seismic profi les. Steeples and Miller (1998), Burger et al. (2006) and Schuck and Lange (2008) discussed aspects of shallow land-based refl ection profi ling.

Stacking

We generally need or want to increase the signal-to-noise ratio of our seismic data in order to better defi ne structural and stratigraphic features, and rock properties. Recall from CHAPTER 2 how the seismic method works. Very simplistically, we make a bang at the surface, the sound travels down through the subsurface, refl ects off various horizons, and we record the energy that was refl ected to the surface (FIGURE 2.1). Those refl ections can be weak, depending on the strength of the seismic source and the distance traveled by the sound. Furthermore, in addition to recording the refl ections, we also record noise. For example, at sea we will record acoustic energy generated by breaking waves and the engines of the survey ship, in addition to the refl ected energy. On land, our receivers might record acoustic energy generated by vehicles driving by our survey site or electrical noise generated by power lines passing overhead. Whatever its source, noise will be present in our data and the noise can be quite strong compared to the refl ections that come from horizons several kilometers below the surface. The ratio of the amplitude (strength) of the refl ections to the

__________________________________________________________________

1 This is, unfortunately, still common practice in some organizations.


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amplitude of the noise is known as the signal-to-noise ratio.

One way of increasing the signal-to-noise ratio is to make more than one recording, and then to combine those recordings together through a procedure called stacking. FIGURE 3.1 illustrates how stacking works. At the far left we have a signal, perhaps a refl ection from the top of a sandstone bed. There is no noise in this trace. The middle of that fi gure shows the same refl ection on several traces, each of which has some random noise added. The noise consists of a random series of positive and negative values on each trace. For this example, the strength of the noise is approximately equal to the strength of the signal, and the location of the top of the sandstone is not readily apparent. However, good things happen if we stack (add) several of these traces together. As shown at right, the positive amplitudes associated with the refl ection combine together to form a stronger signal that stands out above the noise. If the noise above and below the level of that refl ection is truly random on each pre-stack recording, it should tend to cancel out as more and more traces are stacked together.

The traces shown at right illustrate what our result might be if we stack 5, 10 or 20 traces together. The number of traces we stack together is known as the stacking fold, also known simply as the fold or (less simply) as the multiplicity. Our refl ection in FIGURE 3.1 is diffi cult to distinguish from noise when the stacking fold is 5, but it begins to be visible above the noise when the fold is 10, and the refl ection is readily apparent when we stack 20 traces together. The stacking process clearly increases the signal-to-noise ratio, and theoretically the signal-to-noise ratio increase is given by fN where Nf is the stacking fold (FIGURE 3.2).

Although stacking increases the signal-to-noise ratio, note that the relationship is not linear. The greatest increase in data quality for an increase in stacking fold (i.e. the steepest part of the curve in FIGURE 3.2) is at relatively low fold. The slope of the curve becomes lower as the fold increases. Furthermore, the relationship between signal-to-noise ratio and data interpretability (evaluated subjectively) is similar to the relationship between stacking fold and signal-to-noise ratio. As a consequence, there are diminishing returns as we increase the stacking fold. This relationship is important because, from a practical perspective, increasing

FIGURE 3.1:

Stacking to improve signal-to-noise ratio

Figure 3.1: Stacking to improve signal-to-noise ratio

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The trace at left is a noise-free signal, perhaps the refl ection from the top of a bed. In the middle, the signal is contaminated by random noise on each of the traces, with the strength of the noise being approximately equal to the strength of the signal. It is diffi cult to distinguish that there is indeed a signal. The traces at right show the eff ect of stacking (adding) diff erent numbers of noisy traces together. The signal is still diffi cult to see when the stacking fold is fi ve, but it becomes clearer for stacking folds of 10 or higher. The more traces that are stacked together, the more the random noise will tend to cancel itself out, making the signal easier to detect.

FIGURE 3.2:Theoretical increases in signal-to-noise ratio as the stacking fold is increased

Figure 3.2: Theoretical increases in signal-to-noise

ratio as the stacking fold is increased

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FIGURE 3.3:Refl ections from a marine seismic shot

Figure 3.3: Refl ections from a marine seismic shot

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We are interested in primary refl ections (black line)that travel directly from the source, to an impedance

boundary, and back up to a receiver. Other raypathsare possible and will be recorded by receivers at the

surface, including short-period multiples (red andblue dotted lines) and long-period multiples (e.g.,dashed green line). Eliminating multiples will be oneof the goals of seismic processing.


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the stacking fold will entail increasing survey cost.

Another problem with our simplistic presentation of the seismic method from CHAPTER 2 is that the energy does not always follow simple paths from the source to the top of a refl ecting horizon and back to the receiver, as might be implied by inspection of the fi gures in that chapter. FIGURE 3.3shows various raypaths that energy might take as it travels from a source (at left) to receivers (at right). We are interested in the shortest (black) raypath that travels from the surface down to the top of our horizon and is refl ected back up to the surface. This is known as a primary refl ection. Other raypaths have the sound taking more complicated paths, bouncing back up and down more than once. These refl ections are known as multiples. In some cases multiples can be quite strong and they can obscure the primary refl ections that we are interested in. Removing multiples will be another objective of seismic processing.

3.2 Sources and Receivers

Sources

The type of source used to collect seismic data will depend fi rst on whether the data are being collected offshore or on land. Additional considerations (Evans, 1997) include:

• Required penetration. All else being equal, the deeper the imaging target, the more energy will be needed to image it.

• Required bandwidth. Calculations such as those presented in SECTION 2.2 can be used to determine what range of frequencies will be needed in order to detect or resolve targets of a particular thickness, or to defi ne the Fresnel Zone. Commonly there is a trade off between penetration and frequency – higher energy sources (that would provide better penetration) tend to generate lower frequencies than lower energy (i.e., smaller) sources.

• Signal-to-noise characteristics. Surface and noise characteristics vary from place to place, and a particular type of source may be needed to account for problems. For example, a vibroseis source (see below) may produce a weak signal in an area of loose sand because the vibrations are poorly transferred through the loose sand into the underlying rock. Dynamite may be preferred in these cases, especially if the dynamite charges can be placed down a shot hole at a level below the loose sand.

• Environmental conditions. Environmental or safety requirements may prevent a particular type of source from being used. For example, dynamite commonly cannot be used in populated areas. Conversely, vibroseis trucks might do damage to fragile vegetation and so a dynamite source could be preferred in that area. In marine areas environmental assessment reports will need to be undertaken prior to surveying in order to minimize potential damage to marine fauna.

• Availability and cost. Seismic crews or particular types of equipment may not always be available. This can be particularly true of areas far removed from traditional petroleum exploration and development activities. Some seasons are preferred for seismic data acquisition. For example, seismic data in northern Canada is traditionally collected in the winter, when frozen ground makes these “muskeg” areas accessible to vehicular traffi c. Crews may wish to avoid marine seismic acquisition


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during traditionally stormy seasons offshore.

An ideal seismic source should be able to generate a repeatable pulse of known frequency, phase, and other characteristics.

Most land sources can be included in one of three categories:• Weight drops. These include sledgehammers striking a metal plate on the ground,

and weights (e.g., leather bags fi lled with birdshot) that are dropped from a height of 2 m or more. These types of sources are generally used in shallow seismic work. Burger et al. (2006) suggested that under optimal conditions sledgehammer sources can be used to detect the contact between overburden and bedrock at depths up to 50 m. Gendzwill et al. (1994) used a sledgehammer source in a mine dug into a granitic batholith and were able to image fractures and other features over 200 m below the mine fl oor.

• Explosive sources include dynamite and fi rearms. Dynamite charges are commonly used onshore in the petroleum industry. They need to be buried, both for safety reasons and to ensure that the blast energy is effectively transmitted to the ground, rather than dissipated as an air blast. Shotguns (FIGURE 3.4A) and other fi rearms are sometimes used in shallow seismic work (e.g., engineering and hydrogeology studies), and they provide more energy than sledgehammer sources. Seeber and Steeples (1986) described the use of a .50-caliber machine gun as a seismic source that produced frequencies in the range of 30 to 170 Hz. Even automobile spark plugs have been tested as seismic sources for very shallow surveying; they generate high frequencies but low energy levels (Don Steeples, personal communication, 2008).

• Vibratory sources include Vibroseis technology (commonly for petroleum industry applications) and a Mini-Sosie, a “rammer” type device similar to devices used on building sites to compact the earth. Vibroseis technology puts a signal with a known range of frequencies into the earth. The refl ected energy is recorded at the surface and the input signal is mathematically removed to leave a trace that ideally shows the earth’s refl ectivity (e.g., Sheriff and Geldart, 1995; Gadallah and Fisher, 2005). The signal is generated by a truck-mounted mass that can weigh a few tens of tonnes. The mass is made to vibrate up and down on a base plate, thereby transferring the energy to the ground. The period over which the mass vibrates is known as the sweep. The rate at which the mass vibrates up and down, i.e. the frequency of the source signal, is carefully controlled and changes over the length of the sweep. A typical range of frequencies might be 10 – 120 Hz, and the sweep length might be 10 – 20 seconds. The length of the sweep, the range of frequencies generated, and other variables are all optimized for the project at hand. As a general rule, the signal-to-noise ratio of the stacked section can be improved by increasing the force applied (F; i.e. the weight of the truck), the length of the sweep (L) and the bandwidth of the signal (W), and the improvement is proportional to FLW (Evans, 1997). More than one vibroseis truck might be used at the same time, as a source array (FIGURE 3.4B), in order to increase the applied force. By vibrating up and down, most vibroseis trucks are designed to generate P-waves. Shear waves can be generated by vibroseis trucks that are designed to vibrate laterally but, for a variety of reasons, these sources are not commonly used.


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Weight drops and explosive sources are known as “impulse” sources, wherein the energy is generated and dissipated quickly. The wavelet they generate is approximately minimum phase. The wavelet generated by vibroseis trucks over a 10 – 20 second sweep is clearly not an impulse (i.e. minimum phase).

Airguns, and their relatives known as sleeve guns, are the most commonly used seismic source in marine surveying for the petroleum industry. An airgun consists of a metal cylinder into which high-pressure air is injected(FIGURE 3.4C). On a signal from the ship, a piston moves in the cylinder thereby allowing the pressurized air to explode into the water column through ports on the side of the device. This outward expansion of the air generates a pressure pulse. The amount of energy and the range of frequencies generated depend on the size of the airgun (generally described in cubic inches, with commonly used sizes varying from 5 to 300 cubic inches) and the pressure used. Sleeve guns work on similar principles but have a somewhat different design (Evans, 1997). Usually, at least in the petroleum industry, more than one airgun is deployed and fi red at the same time, forming an airgun array. The number of guns used, the tow depth (usually a few meters below the water surface), and other variables are selected in order to provide the needed energy and frequency content to provide the best image. The wavelet generated by an airgun is approximately minimum phase.

Other types of marine sources include boomers (the pressure pulse is generated mechanically) and sparkers (the pressure pulse is generated by an electrical current that is discharged into the water, generating a bubble), both of which are impulsive sources. Chirp systems are vibratory sources, the marine equivalent of vibroseis sources although they are not as energetic as land-based vibrators (i.e. not as much penetration). Boomers, sparkers and chirp systems are most commonly used in shallow-marine surveying, and generate frequencies in the 100s to 1000s of Hertz range. Because of their high frequency content, boomers, sparkers and chirp systems provide much better resolution than petroleum industry airgun seismic sources (sometimes at the decimeter scale) but their relatively low energy levels usually restricts their use to imaging unconsolidated deposits. Evans (1997) and Mosher and Simpkin (1999) describe these and other marine sources.

FIGURE 3.4:

Selected seismic sources

FIGURE 3.5:Schematic illustrations of seismic receivers

Figure 3.4: Selected seismic sources

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A) Shotgun shell source (“Betsy gun”)

B) Vibroseis trucks in an array (photo courtesyFrancois Gauthier)

C) A small (10 cubic inch) airgun

Figure 3.5: Schematic illustrations of seismic receivers

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A) A P-wave geophone. Vertical

ground motion caused by up-

going P-wave refl ections cause a

coil to move through a magnetic

fi eld, thereby generating an

electrical current that is sent to a

recording truck.

B) A hydrophone. P-wave

refl ections traveling through the

water act as pressure pulses that

squeeze a piezoelectric crystal,

thereby generating an electrical

signal that is relayed to the ship.

ANIMATION 5:Various types of equipment and activities associated with both marine and land-based seismic acquisition

Animation 5: Various types of equipment and activities associated

with both marine and land-based seismic acquisition

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Courtesy Global Geophysical Services


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ANIMATION 5 (courtesy Global Geophysical Services) shows various types of equipment and activities associated with both marine and land-based seismic acquisition. It shows seismic crews engaged in various types of activities, including drilling shot holes (for dynamite), collecting airgun seismic data with relatively small vessels, having safety meetings, surveying the locations of geophones and shotpoints for land acquisition, and generating seismic waves with vibroseis trucks.

Receivers

Like the choice of sources, the type of receiver used depends on whether the seismic data is being collected on land or at sea.

On land, the primary type of receiver is the geophone. A geophone is a motion detector, and a schematic geophone design is shown in FIGURE 3.5A. The spike of a geophone is planted into the ground to ensure good coupling between the ground motion and the device itself. Inside the geophone is a hollow magnet that is attached to the body of the geophone. Refl ected P-waves cause the ground surface, and therefore the geophone and the magnet, to vibrate up and down. A wire coil is suspended within the opening of the magnet. Because of inertia, the wire coil will tend not to move at the same rate as the geophone housing. This differential movement causes the coil to pass through the magnetic fi eld generated by the magnet and therefore, in accordance with Faraday’s Law, generating an electrical current in the coil. This electrical signal is the recorded seismic trace, which is then transmitted to the recording truck.

At least in most petroleum applications, more than one geophone is planted at a receiver location, forming what is known as a geophone array. Designing the geophone array (e.g., should they be arranged in a line, in a cross or some other shape?) can be important. The location of each geophone group needs to be accurately known through surveying. The recordings of each geophone array are stacked prior to recording in an effort to enhance the signal-to-noise ratio. With newly designed Q-Technology (by WesternGeco) all active geophones or hydrophones (perhaps more than 30,000 for a single shot on land) are recorded separately. This acquisition effort allows all sorts of to noise be more effectively removed, preserves signal fi delity and high frequencies in the pre-stack data. Shabrawi et al (2005) described the fi rst application of this technology to improve seismic imaging of a carbonate reservoir in Kuwait.

When the objective is to record shear waves, the geophone needs to be designed to detect horizontal ground motions. FIGURE 3.6 compares the vertical motions recorded by P-wave geophones to the horizontal motions (in two mutually orthogonal directions) measured by S-wave geophones. Three-component geophones have two S-wave detectors arranged at right angles and a P-wave detector. New geophones have been designed that capture the full wavefi eld motion generated as the refl ected energy returns to the surface. Subsequent processing is used to decompose this energy into the constituent P-wave and S-wave signals.

During normal land-based operations, the geophones are planted into the ground and left there for most of the acquisition program. Only the source is moved, although the receivers can be selectively turned on or off depending on whether they are needed to record the refl ections coming from below. Manually planting and removing the geophones can be a time-consuming, and therefore costly, process. One innovative design, used to date in shallow geophysical work, involves a “land


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streamer” that is towed behind a vehicle (e.g., van der Veen and Green, 1998; FIGURE 3.7).The geophones in this case have fl at bottoms and are dragged along the ground as the truck moves, rather than being planted in the ground via spikes. Gimbals within the streamer allow the geophones to maintain a vertical orientation. This setup allows several kilometers of data to be acquired in a day, rather than a few hundred meters.

Hydrophones are traditionally used to record seismic refl ections at sea. A hydrophone is a pressure sensor. The refl ected energy generates a pressure pulse as it travels through the water column. A piezoelectric crystal converts that pressure into an electrical signal the voltage of which is proportional to the pressure. FIGURE 3.5B shows a schematic representation of a hydrophone.

Hydrophones are enclosed in a streamer, a neutrally buoyant cable that is towed behind the ship. The depth at which the streamer is towed (typically 10 m), the length of the streamer, and the number of hydrophone groups are all selected on a survey-by-survey basis to optimize the fi nal image. The location of the streamer needs to be monitored during survey operations because waves and ocean currents can push the streamer sideways, a problem known as cable feathering. Monitoring the cable location can be a technological challenge because some streamers are over 10 km in length. Gadallah and Fisher (2005) discussed how the streamer location is monitored during marine seismic profi ling.

In some cases it might be more useful to have the receivers directly on the seafl oor. This might be the case when collecting S-wave seismic (CHAPTER 8), in areas where production platforms obstruct survey operations, in water too shallow for ship traffi c, or if rough seas will cause too many problems for streamer deployment. Two options are possible.

• Ocean-bottom cables (OBC) are laid out on the seafl oor during survey operations. Receivers, which may comprise P-wave and two S-wave geophones as well as a hydrophone (4-component surveying), are located at fi xed distances along the cable. The cable is attached to a ship that records the refl ected energy. Like land-based

FIGURE 3.6:

Comparison of the vertical motions recorded by P-wave geophones to the horizontal motions (in two mutually orthogonal directions) measured by S-wave geophones

FIGURE 3.7:A small vibroseis truck towing a land streamer, a series of geophones in a cable that is dragged along the ground surface

Figure 3.6: Comparison of the vertical motions recorded by P-wave

geophones to the horizontal motions (in two mutually

orthogonal directions) measured by S-wave geophones

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A) P-wave refl ections cause vertical ground motions that are detectedby conventional geophones. Shear waves travel as horizontal motions that are sensed by geophones that detect mutually orthogonal horizontal ground motions (B and C).

Figure 3.7: A small vibroseis truck towing a land streamer, a series of

geophones in a cable that is dragged along the ground surface

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From Cummings and Russell, 2007.


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surveying, the location of each receiver group (x, y and z coordinates) needs to be known precisely.

• Ocean-bottom seismometers (OBS) are self-contained devices that are placed on the seafl oor to record refl ected seismic energy. They may contain P-wave and S-wave sensors and recordings are stored in solid-state memory within the device. The recordings are downloaded from the device when it is retrieved. The cost of deploying and retrieving OBS systems is high, and therefore they tend to be used to solve special problems that warrant the cost.

3.3 2-D Acquisition – The Common Midpoint Method

Data Acquisition, Normal Moveout and Stacking

Recall from SECTION 3.1 that we typically want to record refl ections from a horizon more than once, and then stack those recordings, in order to improve the signal-to-noise ratio. In principle we could have a single source and a single receiver, install them at a particular location and make multiple recordings at that location. We could then move our source and receiver and repeat the exercise at many locations in order to generate a 2-D seismic line or a 3-D seismic profi le. This would be an ineffective (and therefore expensive) way of collecting seismic data.

A simplifi ed 2-D seismic acquisition geometry (on land) is shown in FIGURE 3.8. The receivers (blue spikes) are strung out in a line away from the source location (red dot). After making the bang, the acoustic energy spreads out away from the source location in all directions. Raypaths are shown for energy refl ecting from a horizon at the base of the image, and traveling back up to four receiver locations. The horizontal distance between the source and each of the receiver locations is known as the offset. The nearest offset in the highly simplifi ed geometry shown in the fi gure is 60 m, and the farthest is 240 m. The distance between the nearest and farthest offset is known as the spread length. Only four receiver groups are shown in this simple example, but in a typical petroleum-industry seismic survey many more receiver locations (commonly over 100) would be used.

In FIGURE 3.8, both the ground surface and the horizon generating the refl ection are planar and horizontal. In this case, the raypaths give refl ections from a location which, if projected up to the surface, would be mid-way between the source and the associated receiver, i.e. a midpoint. The spacing between receivers is constant (60 m). The distance between midpoints (30 m in this example) will be one half the distance between our receivers.

The farthest offset should be at least equal to the depth of the primary target in order for subsequent processing steps to perform adequately. For example, if the primary target is at 5 km depth, we need to ensure that the farthest offset is at least 5 km (i.e. we need a spread length of at least that magnitude). There are limits to the longest offset that can be used. For example, if the offset is too long, the angle of incidence for rays to travel from source to receiver can exceed the critical angle and the rays are refracted rather than refl ected (CHAPTER 2). Note that the area of midpoint coverage is one half the length of the spread (i.e. if the farthest offset is 2 km, the farthest midpoint will be 1 km from the source).

The simple geometry shown in FIGURE 3.8 is known as an end-on geometry, where refl ected energy is recorded by a line of receivers located to one side of the source location. This geometry


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can be used on land, and nearly all marine 2-D seismic data is acquired this way, with a ship pulling an airgun array that is followed by a streamer. Another geometry used on land is known as a split spread. In this case receivers are placed on both sides of the source. FIGURE 3.9 compares the end-on and split-spread geometries. Whereas the locations of geophones and shotpoints are fi xed during land acquisition, at sea the source and receivers (at least for streamers) are attached to a ship that is moving constantly. Good navigation, i.e. accurately knowing the locations of sources and receivers, is vital.

Returning to the example of FIGURE 3.8, we now move the source and receiver locations along the line, and shoot again2. FIGURE 3.10 shows four such shots, with the source and receiver locations moved by a constant distance for each shot. Four shots, each with four receivers, will generate 16 recordings. Inspection of FIGURE 3.10 shows that four of the source-receiver combinations, each from a different shot, generated a refl ection from the same spot, i.e. they have a common midpoint. We now seek to isolate those recordings from the others and stack them, as shown in FIGURE 3.1, to produce one trace that shows what the geology looks like at that midpoint location. However, there is a problem. Although they provide refl ections from the same subsurface location, the offset for each of the four recordings is different. As such, the time it takes for the sound to travel from the source to the refl ection point and up to the receiver will vary. The longer the offset, the longer it will take for the sound to make the journey. Placing those recordings side by side (FIGURE 3.11A) generates a common midpoint gather (CMP gather). It can be seen that the refl ections do not line up, and so they cannot yet be stacked.

The increase in travel time with increase in offset is known as normal moveout (NMO). The word “normal” indicates that this is the anticipated response for a horizon that does not dip, and “moveout” refers to the increase in travel time. A plot of arrival times versus offset can be approximated by a hyperbola (Figures 3.11A, 3.12). The shape of the hyperbola is a function of the velocity between the surface and the horizon that generated the refl ection, and is given

FIGURE 3.8:

Schematic illustration of raypaths generated by a shot from a source point into a line of receivers during 2-D seismic acquisition

FIGURE 3.9:Comparison of an end-on 2-D seismic acquisition geometry and a split-spread 2-D seismic acquisition geometry

FIGURE 3.10:The common midpoint method

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2 Recall however from SECTION 3.2 that the receiver groups are usually not physically moved between shots on land. Instead all the receivers may be laid out prior to surveying and different combinations of receiver groups are activated for each shot.

Figure 3.8: Schematic illustration of raypaths generated

by a shot from a source point into a line of

receivers during 2-D seismic acquisition

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All the energy is assumed to travel down and up in a plane (light blue) beneath the receivers.

See text for further description.

Redrawn with modifi cations from Ray (1995).

A

adpb

Sd

R

mR

Figure 3.9: Comparison of an end-on 2-D seismic

acquisition geometry (top) and a split-spread

2-D seismic acquisition geometry (below)

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Figure 3.10: The common midpoint method

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Four shots are shown at top, each with a geometry similar to that shown in FIGURE 3.8 but the source and receiver

locations are moved for each shot. This geometry will produce 16 fi eld recordings (4 shots x 4 recordings per shot). Four of the 16 source-receiver

combinations have a common midpoint. Those four pre-stack traces are collected to form a

common midpoint gather, as shown in FIGURE 3.11, and will be stacked to improve the signal-to-noise ratio, as illustrated in FIGURE 3.1.


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by the following equation (Dix, 1955):

NMOVhtt 2

22

0 [3.1]

where t is the traveltime at offset h, t0 is the zero-offset or “normal incidence” traveltime (i.e. for sound going straight down and coming straight back up), and VNMO is the normal moveout velocity. One of the key seismic processing steps in the common midpoint method attempts to fi nd the velocity that can be used to level out the hyperbola generated by a refl ection, and adjust the traces in the CMP gather accordingly. This velocity is known as a stacking velocity, and the corrections are known as NMO corrections. Following the NMO corrections, the refl ections are aligned horizontally and the traces can be stacked (FIGURE 3.11B).

EQUATION 3.1 is, very simply, designed to fi nd the velocity that gives the best stack (i.e. aligns refl ections the best). It is an approximation that works best for relatively small offsets. For various types of advanced analyses and imaging applications (e.g., amplitude-variation with offset; CHAPTER 8) non-hyperbolic moveout (perhaps caused by velocity anisotropy, e.g. different velocities parallel and perpendicular to bedding) may need to be accounted for.

An obvious simplifi cation in our discussion so far is that there is only one horizon at depth that is producing a refl ection. In reality there will potentially be many horizons that generate refl ections and, because velocity typically changes with depth, we will need to derive different velocities to correct for NMO at various depths (FIGURES 3.12, 3.13). In this way, we gain information about how velocity changes with depth at each point in the seismic survey where a velocity analysis is completed. This process is known as velocity picking. The distance between velocity control points along our 2-D line will depend on factors such as our perception of how laterally variable the velocity fi eld is, and the time and money available for processors to undertake many velocity analyses. FIGURE 3.14 shows a 1980s vintage 2-D seismic line that was plotted on paper. At top are a number of velocity panels that show where stacking velocities were derived. In this case velocities were calculated approximately every 7 or 8 km, even though the line is

FIGURE 3.11:

Schematic representation of the common midpoint gather derived in Figure 3.10 before and after normal moveout (NMO) corrections

FIGURE 3.12:Hyperbolae approximating arrival times versus offset

FIGURE 3.13:A common midpoint gather before, and after velocity analyses and NMO corrections

Figure 3.11: Schematic representation of the common midpoint

gather derived in Figure 3.10 before (left) and after

(right) normal moveout (NMO) corrections

Back to Chapter

Although the refl ections all come from the same midpoint, the distances traveled by the raypaths are diff erent because of the diff erences in distance (off set) between the sources and receivers. NMO corrections level out the refl ection, making it possible to stack the traces to produce one trace that represents the geology at the CMP location.

Figure 3.12: Hyperbolae approximating arrival times versus off set

Back to Chapter

Normal moveout in synthetic seismic

data. A) Geologic model showing

raypaths from source locations to

receiver locations for a hypothetical

common midpoint (CMP) gather.

Refl ections are generated at three

interfaces in this four-layer model.

Note bending of raypaths at

interfaces. B) CMP gather showing

normal moveout (NMO) for the three

refl ections. C) Hyperbolae have

been fi t to the three refl ections by

substituting the values at right into

Equation 3.1. The optimum values

have been estimated visually for

this example. Note that the VNMO

corresponds to a type of average

velocity between the surface and the

refl ection-generating interface.

Figure 3.13: A common midpoint gather A) before, and B) after

velocity analyses and NMO corrections

Back to Chapter

Primary refl ections form hyperbolae (red lines) in part A that are leveled out after NMO corrections.

All traces in part B will subsequently be stacked to generate

a single trace in a 2-D seismic line or 3-D seismic volume. From Duncan (1992).


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nearly 150 km long and has nearly 2500 CMP locations. Picking velocities at each CMP location in this type of survey would not be economically feasible because of the time (computer processing and human) required to do so.

The stacking velocities derived using EQUATION 3.1 represent the average velocity from the surface down to the level that generated the refl ection. Stacking velocities may be derived for many levels at a common midpoint location. Dix (1955) presented a way of calculating the interval velocities between layers defi ned this way:

12

211

222

int ttVtVtV rmsrms

[3.2]

where Vint is the interval velocity between layers 1 and 2, t1 and t2 are the zero-offset times calculated for layers 1 and 2, and Vrms1 and Vrms2 are the root-mean-square velocities (approximated by the stacking velocities fromEQUATION 3.1) calculated for layers 1 and 2.FIGURE 3.15 shows an expanded view of one of the velocity panels from FIGURE 3.14. The three columns represent, from left to right, the zero-offset time, the stacking velocity at that time, and the interval velocities between two layers. The interested reader is encouraged to use EQUATION 3.2 and the data provided in the table to recalculate the interval velocities shown in the table. Note that EQUATION 3.2 was derived for situations where Vrms increases with depth. As such, it is inappropriate for areas which have velocity reversals.

Recall from CHAPTER 2 that raypaths bend at interfaces where there is a change in velocity. In the simplest case, where all horizons are planar and horizontal, and there are no lateral velocity variations within layers, refl ections from all depths still line up at the midpoint (FIGURE 3.16).

Originally the seismic data acquisition technique we are describing was termed the Common Depth Point (CDP) method, and geophysicists referred to CDPs rather than CMPs. This was because seismic data were commonly collected to identify structure at one stratigraphic or structural level (depth). Now, multiple targets at multiple depths are the norm, and common usage generally refers to common midpoints (CMP) rather than common depth points (CDP).

FIGURE 3.14:

Sample 1980s vintage 2-D seismic profi le showing locations of velocity analyses used for stacking

FIGURE 3.15:Application of the Dix equation (Equation 3.2) to calculate interval velocities

FIGURE 3.16:Refl ections from all depths where all horizons are planar and horizontal, and there are no lateral velocity variations within layers, still line up at the midpoint

Figure 3.14: Sample 1980s vintage 2-D seismic profi le showing

locations of velocity analyses used for stacking

Back to Chapter

Velocity analyses are not performed at each CMP location. Seismic data courtesy of Canada – Nova Scotia Off shore Petroleum Board.

Figure 3.15: Application of the dix equation (Equation 3.2)

to calculate interval velocities

Back to Chapter

A) Enlarged view of one of the

velocity panels from FIGURE 3.14.

Upper row indicates the common

depth point number (CDP) and

corresponding shot point number

(SPN) for the velocity analysis.

The left-hand column shows the

zero-off set TWT (in milliseconds

– labeled MSEC) and the middle

panel shows the stacking velocity

(an approximation of the root-

mean-squared velocity – VRMS

- from the surface down to the

level generating the refl ection;

see EQUATION 3.1) in meters

per second (labeled MT/SEC).

The right-hand column shows

the interval velocity (meters

per second – labeled MT/SEC)

between successive refl ections

calculated using EQUATION 3.2.

B) Schematic representation of

root-mean-squared velocities

down to two levels indicated in

red box of part A.

C) Application of EQUATION 3.2 to

derive interval velocity.

Figure 3.16: Refl ections from all depths where all horizons are planar

and horizontal, and there are no lateral velocity

variations within layers, still line up at the midpoint

Back to Chapter

Previous images (e.g., FIGURE 3.8) showed only one refl ecting horizon,

but midpoints should line up vertically for diff erent levels if the stratigraphy does not dip and there are no lateral velocity variations.


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FIGURE 3.13 shows a common midpoint gather before and after NMO corrections. Each trace in this gather corresponds to the recording made by a single source-receiver combination. These traces will be stacked together to produce a single trace in the 2-D seismic line that, ideally, illustrates what the geology would look like seismically if the source and receiver were both located at the CMP location (vertical incidence). Note the shape of the hyperbolae (marked with red lines) associated with specifi c refl ections in the original CMP gather and how those refl ections are leveled out after NMO corrections. If incorrect velocities are picked, the refl ections will not line up and data quality will be degraded. FIGURE 3.17 shows the importance of picking the correct velocities when stacking the data. Obviously, picking the correct stacking velocities greatly improves the image quality.

If the stratigraphy is dipping signifi cantly, then NMO corrections will not be able to line up the refl ections from a horizon. Dip moveout (DMO) corrections, described by Yilmaz (2001), Liner (2004) and others, may need to be applied in order to improve the quality of the stacked image in these cases.

As might be expected from inspection of FIGURE 3.1, the data quality of a stacked seismic image will depend on the number of traces added together in the common midpoint gather. FIGURE 3.18 shows the results of a decimation experiment to test the impact of stacking fold on data quality. These 2-D data were collected with a target stacking fold of 60, and a portion of the stacked data generated by stacking all 60 source-receiver combinations for every CMP is shown at right. The middle image shows the same data, but only using every other source-receiver combination to artifi cially produce a stacking fold of 30. The image at left was generated using only every fourth source-receiver combination in the stack to produce an image with a stacking fold of 15. Everything else in the three images is identical – only the stacking fold is different. Notice the improvement in refl ection continuity in the 60-fold version of the data. Clearly this version of the data is better for defi ning stratigraphic features, and it would be better for defi ning structural features (if they exist) or for quantitative prediction of rock properties. These images demonstrate that, all else being equal, higher fold data will produce better quality images, as predicted by FIGURE 3.2.

FIGURE 3.17:

Importance of correctly picking stacking velocities on the quality of the stacked seismic image

FIGURE 3.18:Decimation test to illustrate the importance of stacking fold on image quality

Figure 3.17: Importance of correctly picking stacking velocities

on the quality of the stacked seismic image

Back to Chapter

Correct stacking velocities are used in part A, but velocities in part B have been deliberately modifi ed by approximately 10% to show how the image would be degraded. From Henry (1997a). Reproduced with permission from Editions Technip.

Figure 3.18: Decimation test to illustrate the importance

of stacking fold on image quality

Back to Chapter

The 2-D seismic image at right was

generated using a stacking fold

of 60. The image in the middle

was generated from the same

input data, but was decimated

before stacking so that only every

2nd source-receiver combination

was used, thereby generating a

stacking fold of 30. The image

at left was generated using

only every 4th source-receiver

combination, thereby generating

an image with a stacking fold of

15. Everything else has remained

constant. Clearly, the higher the

stacking fold the better the image

quality. From Leetaru (1995).


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The stacking fold needed to generate a “useable” or “good” seismic image varies from basin to basin, and also varies according to parameters such as source energy, etc. A stacking fold of 30 might generate an acceptable image in one basin whereas a stacking fold of 120 might be needed somewhere else to generate an equivalent image. Experience, gained from examination of existing data, can help guide acquisition efforts. The stacking fold for a 3-D seismic survey does not have to be as high as the fold in a 2-D survey to generate a comparable image. Krey (1987) suggested that to obtain comparable data quality, the stacking fold of the 3-D data Nf3 can be calculated using:

10023fNN ff [3.3]

where Nf2 is the 2-D stacking fold and f is the frequency of interest. Lansley (2004) suggested however that for 3-D seismic data the important quantity is not the stacking fold but rather the trace density, defi ned as the traces per square kilometer.

In addition to improvements to signal to noise ratio, another benefi t of the NMO correction and stacking combination is that it tends to remove “long-period” multiples. Multiples take different paths through the subsurface than primary refl ections (FIGURE 3.3). As such, NMO corrections that cause primaries to line up horizontally do not cause multiples to line up. The energy from multiples does not stack constructively and therefore the multiples tend to be attenuated in the stacked 2-D data.

One disadvantage of the stacking process is that it reduces the frequency content of the seismic data somewhat. Refl ections that are imperfectly lined up will stack together to generate a broader, i.e. lower frequency, stacked trace (FIGURE 3.19).

In some cases the sources and receivers cannot be laid out in a straight line, perhaps because the seismic acquisition crew needs to follow a bending road. These types of 2-D acquisition geometries are known as crooked line geometries and a variety of problems can be encountered when processing and interpreting these data. Wu (1992) illustrated these problems using data acquired from a mining camp, but indicated that similar issues would be encountered when collecting seismic data in any type of environment. Similar problems can be encountered during 2-D marine surveying if cross currents cause receiver cable drift, such that the cable does not truly form a straight line behind the ship (a problem known as cable feathering). Nedimović et al. (2003) studied this latter problem and proposed a way for imaging 3-D structure in these cases.

Multi-Channel and Single-Channel Acquisition

Seismic data collected using multiple receiver locations to record refl ections from the subsurface are called multi-channel seismic (MCS) data. In some applications, especially in shallow marine profi ling, a single receiver group is used and the data are referred to as single-channel seismic (SCS) data. This type of data is not stacked, and commonly a printout of the data is made in real time. This instant gratifi cation can be satisfying. Furthermore, because they are not stacked, SCS data can have higher frequencies than MCS data. A disadvantage of SCS is that no subsurface velocity information is gained.


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3.4 3-D Acquisition

When collecting 2-D seismic data, we arrange the source and receivers in a line and anticipate that the sound will travel in a plane that extends beneath that line(FIGURE 3.8). However, in reality sound expands out in three dimensions beneath the ground surface. As such, if receivers are placed around the shot point we can generate and record refl ections from an area around the source.

FIGURE 3.20 shows a simple land acquisition geometry for 3-D seismic data. In this case the source lines might be arranged in an east-west direction and the receiver lines are arranged at right angles to that (i.e. north-south in this example). For each shot, receivers in a rectangular pattern around the shotpoint are turned on, forming a recording patch. The sound travels from the source and generates a grid of midpoints, rather than a line of midpoints such as that generated in 2-D work. Some simple geometric rules apply. The distance between midpoints in the source direction will be ½ the distance between source locations at the surface, and the distance between midpoints in the receiver direction will be ½ the distance between receiver groups.

Following the fi rst shot, the source is then moved to a different shotpoint location and a different recording patch of receivers is activated. This shot will generate a new set of midpoints, some of which will be the same as those generated by the previous shot. However, the offset (distance between source and receiver) and the azimuth (the compass direction traveled by the sound on its way from source to receiver) will vary from shot to shot. One goal of modern 3-D survey design is to ensure that a broad range of azimuths and offsets contributes to each midpoint.

In principle, and like 2-D seismic data, the CMP gathers are stacked to produce a single trace that (ideally) portrays the geology at that midpoint location. However, with 3-D seismic data we seek to portray the data as being laterally continuous, i.e. no gaps between adjacent traces, especially when viewing horizontal slices through the data (time slices, horizon slices, etc.; see CHAPTER 4). Accordingly, each CMP gather is treated as if it represents an area known as a bin (FIGURE 3.21) rather than a single point. The bin dimensions are the same as the spacing between CMP locations. For example if the CMP spacing

FIGURE 3.19:

The high-frequency content of the seismic data is reduced if refl ections are imperfectly aligned prior to stacking

FIGURE 3.20:Schematic illustration of a simple 3-D acquisition geometry on land

FIGURE 3.21:Plan-view image of midpoints (blue dots) from a 3-D seismic survey

Figure 3.19: The high-frequency content of the seismic data is reduced

if refl ections are imperfectly aligned prior to stacking

Back to Chapter

Figure 3.20: Schematic illustration of a simple 3-D acquisition geometry on land

Back to Chapter

Source lines (red dots) are

arranged at right angles to

receiver lines. Receivers will be

active all around the source for

each shot, thereby generating

a grid of midpoints for each

shot, rather than a single line

(compare with FIGURE 3.8).

Redrawn with modifi cations

from Ray (1995).

Figure 3.21: Plan-view image of midpoints (blue dots) from a 3-D

seismic survey such as the one shown in Figure 3.20

Back to Chapter

The seismic trace at each midpoint location will be used to represent an area known as a bin,

the x-y dimensions of which are equal to the trace spacing. Binning the data in this way gives the appearance of spatial continuity when viewing 3-D seismic data (CHAPTER 4).


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is 30 x 30 m (as per FIGURE 3.20), the bin size will be 30 x 30 m. Square bins are desirable from a software/visualization perspective, but rectangular bins (e.g. 30 x 45 m) are also possible.

Through the years, different types of acquisition geometries have been proposed (e.g., Galbraith, 2001; Gadallah and Fisher, 2005) in response to different geological and geophysical concerns. Liner (2004) discussed some of the important aspects of 3-D seismic survey design that need to be considered. Vermeer (2002) also discussed different 3-D seismic survey designs, and presented and compared images from two different 3-D volumes that were generated using two different designs (FIGURE 3.22). Comparing the advantages and disadvantages of different acquisition geometries is beyond the scope of this volume, but it should be clear to the reader that survey design will have an impact on the seismic image.

Marine 3-D seismic data acquisition differs from land-based acquisition. In early programs, a ship towed an airgun array and two streamers. Each shot generated two lines of midpoints, one for each streamer. As the ship sailed to the new shotpoint location, new shots would generate new midpoints, some of which would be the same as for previous shots. Unlike land-based acquisition where geophones commonly need to be planted manually and dynamite or vibroseis trucks moved from shotpoint to shotpoint, vessels designed for 3-D seismic acquisition continuously sail from shotpoint to shotpoint and move the source and receivers as they sail. As such marine seismic acquisition costs are lower than land-based surveying and, as a consequence, trace spacing in marine surveys is typically much closer than in land-based 3-D surveys.

Currently, two airgun arrays and two or more streamers (perhaps as many as 12) are usually deployed behind a ship during 3-D acquisition at sea. Liner (2004) discussed the advantages of these recording geometries. Marine-wide azimuth seismic data are proving to be particularly benefi cial for problems such as sub-salt imaging surveys.

A parallel geometry is the most commonly used marine 3-D acquisition design. The survey ship sails a series of parallel lines. In some cases it may be desirable to use two ships, one pulling the airgun array(s) and the other towing

FIGURE 3.22:

Horizontal slices through two different 3-D seismic cubes collected over the same area but with different acquisition geometries

FIGURE 3.23:Highly simplifi ed traditional seismic processing fl ow showing the order in which selected processing steps are completed

Figure 3.22: Horizontal slices through two diff erent 3-D seismic cubes collected over the same area but with diff erent acquisition geometries

Back to Chapter

High amplitudes in orange/red and low amplitudes in blue. Curvilinear blue areas/lines show the locations of faults. The acquisition geometry used for the survey on the right allowed the faults to be more sharply defi ned (compare arrowed faults between the two images) and is therefore the preferred acquisition geometry in this setting.

Modifi ed from Vermeer (2002) and reproduced with permission.

Figure 3.23: Highly simplifi ed traditional seismic processing fl ow showing

the order in which selected processing steps are completed

Back to Chapter

The inset quote reminds us of the diffi culty associated with simplifying complex workfl ows.

The inset quo

the diffi culty asimplifying co


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the streamers. In this way it can be possible to generate midpoints below surface obstacles such as production platforms3. Other recording geometries are possible (e.g., Vermeer, 2002; Gadallah and Fisher, 2005).

3.5 Other Processing Steps

Normal moveout corrections and stacking are important parts of a seismic processing workfl ow, but other processing steps are needed to generate seismic images that come close to representing the subsurface geology. A highly simplifi ed traditional processing fl ow is shown in FIGURE 3.23. In this section we examine some additional processing steps that have an impact on seismic data interpretability.

Amplitude Recovery

The acoustic pulse loses energy as it spreads away from the source (i.e. the farther one is from a source of sound, the quieter the sound will seem). As a result, horizons in the shallow part of the section will appear to have strong amplitudes and refl ections from deeper horizons will be weaker. Spherical divergence is a term that refers to the apparent loss of energy due to geometric spreading of a wavefront. The energy at a point on an expanding wavefront decreases with the square of the distance from the energy source. We need to compensate for this loss of signal amplitude as it travels through the earth because we want the strength of a refl ection to be proportional to the acoustic impedance change, and not be affected by the depth of the refl ector.

Automatic gain control (AGC) is one type of process, used in many electronics applications, that compensates for loss of signal strength with distance. AGC is an adaptive system that attempts to maintain a constant signal level. In the seismic processing realm it will produce a seismic image in which refl ection amplitudes will remain relatively constant with depth. Although this type of processing can help enhance the appearance of structural and stratigraphic features deep in the section, information about seismic amplitudes that is associated with acoustic impedance changes is lost. As such, AGC is no longer in favor in seismic processing situations where amplitude preservation is critical.

Other approaches exist to recover amplitudes, including application of a correction for spherical divergence. FIGURE 3.24 shows a shot gather before and after amplitude recovery. Note how the amplitude recovery enhances the appearance of deep refl ections. These events will be visible in the stacked data following the amplitude recovery, but would not be visible otherwise.

True-amplitude recovery is a processing approach that attempts to compensate for attenuation, spherical divergence and other effects. The objective is to produce seismic images in which the refl ection amplitudes are directly proportional to changes in acoustic impedance (i.e. refl ectivity) at all levels of the seismic data (both before and after stacking). Compensating for energy losses due to spherical divergence is relatively straightforward, but other factors are more problematic. Recall

__________________________________________________________________

3 This technique is known as undershooting. Undershooting is also used in land-based acquisition to collect databeneath surface obstructions such as small lakes or land held by uncooperative landowners.Instead all the receivers may be laid out prior to surveying and different combinations of receiver groups are activated for each shot.


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from CHAPTER 2 that energy loss (attenuation) because of absorption is related to a physical property called Q (the quality factor). If that property can be measured (perhaps via analysis of vertical seismic profi les – see CHAPTER 5)or estimated (sometimes estimated as 3% of the velocity expressed in m/s; Sheriff, 2002) then attenuation compensation (sometimes referred to as an inverse Q fi lter) can be helpful for amplitude recovery.

Static Corrections

Seismic data record the two-way traveltime from the surface to a refl ecting horizon and back up to the surface. By collecting a 2-D profi le or a 3-D volume we seek to image the structure at depth, taking into account velocity problems like those illustrated in FIGURE 2.1. However we will have an imaging problem if the surface over which we move our sources and receivers is not horizontal.

FIGURE 3.25 illustrates this problem in a simple conceptual example. In the illustration, the surface over which the seismic data are to be collected has a hill, and the target horizon at depth is planar and horizontal. If all we know is the two-way traveltime, our seismic image will show a false structure, because it takes longer for the sound to reach the horizon beneath the hill. Clearly we have to account for changes in surface elevation along the length (or area) of our seismic survey. In order to do so, we need to specify an elevation, known as the seismic reference datum, which corresponds to 0 s two-way traveltime in our seismic image. At sea, or in low-lying coastal areas, mean sea level is the obvious choice. In areas on land which are considerably above sea level, it is necessary to arbitrarily pick a seismic reference datum.

Changes in ground elevation are but one type of problem (FIGURE 3.26). In some areas unconsolidated materials in the shallow subsurface (e.g., marsh deposits, glacial till, sand dunes) can have velocities that are much lower than the underlying rocks. In some cases, high-porosity, uncemented sediments in the vadose zone (not all pore space fi lled with water) can have velocities of nearly 100 m/s, less than the velocity of sound in air (Schuck and Lange, 2008). This surface layer is sometimes referred to as the low-velocity layer or the weathered layer. Changes in thickness of this layer, if not properly accounted for, can cause imaging problems for the underlying strata.

FIGURE 3.24:

Comparison of a source gather before and after amplitude recovery

FIGURE 3.25:Illustration of imaging problem that occurs when the surface over which we move our sources and receivers is not horizontal

FIGURE 3.26:Statics Corrections

Figure 3.24: Comparison of a source gather before (left)

and after (right) amplitude recovery

Back to Chapter

Notice the improved imaging of refl ections lower in the section. From Duncan (1992).

Figure 3.25: Illustration of imaging problem that occurs when the surface over

which we move our sources and receivers is not horizontal

Back to Chapter

Seismic raypaths below

a hill will take diff erent

times to refl ect back

up to the surface from

a horizontal bed (top).

If changes in ground

surface elevation are

not accounted for, the

result will be a false

structure in the seismic

data (below).

Figure 3.26: Statics corrections

Back to Chapter

Changes in ground-surface elevation and changes in thickness of the low-velocity layer need to be accounted for in order to adequately

image the subsurface seismically. An arbitrary elevation, the seismic

reference datum (SRD), is chosen to represent 0 seconds TWT. All of these corrections are known as statics corrections.

Short-period statics occur within the length of the recording spread,

whereas long-period statics represent changes over distances longer than the spread length.


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The corrections necessary to solve both of these issues, changes in elevation and changes in thickness of the low velocity layer (if present), are known as statics corrections. Several different approaches can be used to deal with these problems (refraction statics, surface-consistent statics, residual statics, etc.). If not properly accounted for, statics problems can leave false structures in the data and degrade the seismic image quality (Figures 3.27, 3.28).

Although it might seem that no statics corrections are necessary when processing marine data, this is not necessarily the case. Changes in sea-surface elevation due to changes in tide levels may need to be accounted for. The depth of the streamer and the depth of the airguns also need to be considered. Finally, seafl oor relief needs to be accounted for when working with ocean-bottom cables or seismometers.

Deconvolution

Deconvolution is a processing operation, generally applied before stacking, that attempts to:

• Shorten the wavelet that is embedded in the seismic data

• Attenuate reverberations and short-period multiples

• Produce a wavelet of known phase (usually zero phase)

Different approaches to deconvolution are possible and a full discussion of these methods is beyond the scope of this text. Interested readers are referred to Yilmaz (2001), Gadallah and Fisher (2005) and other sources. What is important here is that deconvolution is the processing step that attempts to produce a seismic image containing a broad-bandwidth, zero-phase wavelet when the source wavelet was not zero phase.

FIGURE 3.29 compares two versions of a seismic image. The image in part A has been processed without deconvolution whereas the image in part B shows the same data but with deconvolution applied prior to stacking. There are somewhat subtle, but important, differences between the two versions of the data. In particular, look at the middle of the image at about 2 seconds down into the data. Three prominent peaks are visible at this level in

FIGURE 3.27:

Common midpoint gather before and after statics corrections, and prior to any NMO corrections

FIGURE 3.28:Stacked seismic image without and with statics corrections

FIGURE 3.29:Comparison of stacked seismic section generated without and with deconvolution prior to stacking

Figure 3.27: Common midpoint gather A) before, and B) after

statics corrections, and prior to any NMO corrections

Back to Chapter

In the upper image, refl ections from the left-central part of the gather appear to be pushed down, because those geophones were located above a low-velocity surface feature (e.g., swamp). NMO corrections will not adequately align these refl ections, and the quality of the stacked seismic image will be reduced. After the statics corrections (below) the refl ections more closely fall along a hyperbola that can be adjusted via NMO corrections. They will stack better, producing a better-quality seismic image. From Duncan (1992).

Figure 3.28: Stacked seismic image A) without and B) with statics corrections

Back to Chapter

Note how statics corrections

improve data quality and

remove false structures.

From Yilmaz (2001).

Figure 3.29: Comparison of stacked seismic section generated

A) without and B) with deconvolution prior to stacking

Back to Chapter

Stratigraphic features are more sharply defi ned in the lower image. From

Yilmaz (2001).


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the version of the data without deconvolution, whereas only two are visible when deconvolution has been applied. One of the peaks was probably either a reverberation or a short-period multiple and deconvolution has removed it. The resulting image more truly represents changes in amplitude that are due to changes in acoustic impedance (i.e. the stratigraphy) in the image. Similar changes are visible in other parts of the image.

Henry (2001) divided most deconvolution methods into two categories: deterministic deconvolution and statistical deconvolution. Deterministic deconvolution can be applied when the source wavelet is known, either because it is measured or it can be modeled. In this case, the deconvolution can produce a seismic section that truly is zero phase. Generally, the source wavelet is an unknown and so a statistical approach is taken. Two key assumptions that need to be made when working with statistical deconvolution are that: a) the refl ectivity series is random (sometimes referred to as having a “white spectrum”), and b) that the input wavelet was minimum phase. Neither of these assumptions is usually true, and so statistical deconvolution will not produce a truly zero-phase wavelet. Although in CHAPTER 2 we noted that the wavelets generated by airgun and dynamite sources are approximately minimum phase, the differences between the actual character and an ideal minimum phase wavelet are signifi cant enough to affect the deconvolution result.

FIGURE 3.30 compares the results of two different deconvolution methods applied to the same dataset. In part A, a statistical deconvolution has been applied that resulted in a mixed-phase wavelet being embedded in the seismic data. In part B, deterministic deconvolution was applied to the same dataset for purposes of comparison (the source wavelet was known). The wavelet embedded in the statistical deconvolution result is not zero phase, whereas the wavelet embedded in the deterministic deconvolution result is truly zero phase. The deterministic deconvolution (i.e. zero phase) image has better defi nition of structural and stratigraphic features.

A consequence of the shortcomings of statistical deconvolution methods is that we are never sure of the seismic data phase even if, in principle, the data were processed to zero phase. We need to determine the phase of the seismic data independently, and methods for doing so are described in CHAPTER 5 when discussing synthetic seismograms.

Deconvolution is commonly not applied in shallow seismic work (Don Steeples, Personal Communication, 2008). Reasons include:

• The refl ectivity profi le is not random. Commonly only 3 or 4 refl ections may be real, and the statistical basis of some deconvolution methods is not valid in these cases.

• Low signal-to-noise ratios. Deconvolution does not work properly in these cases.• Non-stationary wavelets. High attenuation in unconsolidated deposits causes the

wavelet to change shape rapidly with depth.

Migration/Seismic Imaging

In the simple cases examined so far, horizons in the subsurface have been planar and horizontal. In this case, and assuming that there are no statics-related problems at the surface, refl ections are from the midpoint between a source and a receiver. We now consider a scenario where a seismic transect is collected above a dipping horizon.


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Recall from CHAPTER 2 (FIGURE 2.24B) that the angle of incidence will be equal to the angle of refl ection (θ1 = θ2). A source and receiver combination located at Points A and C (respectively) in FIGURE 3.31 will therefore record a refl ection from Point B on the dipping surface. Note that Point B, if projected to the surface, does not correspond to the midpoint between Points A and C. However, and because we do not know the true subsurface structure, we need to make the assumption that the refl ection is coming from a position below the midpoint. The refl ection will be displayed in the seismic image as if it was generated at point B’. In other words, the refl ection will be improperly located in space.

Let us now consider a surface that has a slightly greater degree of complexity. Perhaps the concave-up feature on the surface shown in FIGURE 3.32A represents a buried syncline or a buried channel. That image shows a source location and a series of receiver locations. Because the surface is not planar, and because the acoustic energy expands out in all directions from the source, there are at least three points along the surface that are located and oriented in such a way that they generate a refl ection that will be recorded by each of the receivers at the surface. In other words, each source-receiver combination will record three refl ections from the one surface, and potentially none of those refl ections is generated at a midpoint. However, the common midpoint method assumes that the refl ections are coming from a location mid-way between the source and receiver pair. A 2-D seismic transect collected above the surface, using multiple source and receiver locations, would generate (after stacking) the image shown in FIGURE 3.32B. The crossing series of refl ections seen in that image are known as a bow-tie. Clearly this seismic image does not represent the true subsurface geometry.

Another type of seismic imaging problem that needs to be accounted for is related to discontinuities in the subsurface. Consider a point refl ector (perhaps a buried boulder) such as the feature shown in FIGURE 3.33A. Downgoing energy that hits the feature will be scattered and return to the surface along many different raypaths. If we have receivers at many surface locations, the seismic expression of the point refl ector will be a hyperbolic series of refl ections known as a diffraction. Clearly the diffraction does not show the true shape of the buried object. In

FIGURE 3.30:

Comparison of two different deconvolution results on the same data

FIGURE 3.31:A source and receiver combination located at Points A and C recording a refl ection from Point B on the dipping surface

FIGURE 3.32:Surface with a concave-up feature showing a source location and a series of receiver locations

Figure 3.30: Comparison of two diff erent deconvolution

results on the same data

Back to Chapter

Seismic image on left was generated using a statistical deconvolution approach that left a mixed-phase wavelet in the data. Seismic image on the right was generated using a deterministic deconvolution that generated a truly zero-phase version of the data. Arrows point to improvements in image quality in the zero-phase version of the data. From Henry (2001).

Figure 3.31: A source and receiver combination located at Points A and C

recording a refl ection from Point B on the dipping surface

Back to Chapter

When beds dip the refl ection points are not located at midpoints because the angle of incidence (Θ

1) is equal to the angle of refl ection (Θ

2) for the dipping bed. However, and in the absence of

any other knowledge of subsurface structure, the refl ection is still assumed to come from the midpoint location. In other words, the refl ection will be misplaced spatially from its true location.

Figure 3.32: Surface with a concave-up feature showing a

source location and a series of receiver locations

Back to Chapter

A) Raypaths from a source

location (red dot) to receivers (blue) above

a concave-up feature (perhaps a buried channel or a syncline). Each receiver will record three refl ections from the

surface, and generally the refl ections do not come from midpoint locations.

B) Acquisition of a 2-Dseismic profi le will generate a seismic feature, known as a bow tie, that

clearly does not portray the true subsurface geometry. The refl ections

are misplaced spatially, and need to be moved to their true positions using a processing step known as migration.


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addition to point refl ectors, diffractions will be generated at any abrupt change in physical properties such as a bed termination at a fault or beneath an unconformity. The apex of the diffraction shows the true location of the feature generating the refl ection.

Because of these and related problems, after stacking a seismic image has various distortions:

• Dips of refl ections are decreased and their spatial extent is increased compared to reality

• Concave-up features (synclines, etc.) appear as bow-ties

• Anticlines appear to be broader• Diffractions are present at abrupt

terminations

A seismic processing step known as migration is used to correct for the imaging problems just discussed. Properly executed, migration achieves three goals:

• Repositioning refl ected energy to its true subsurface location in the two- or three-dimensional space (x,time or x,y,time respectively) of the seismic data

• Shrinking the Fresnel zone. After migration, and if the migration is properly done, the

Fresnel zone diameter will be 4

(but see below for a comparison of 2-D and 3-D migration.)

• Collapsing diffractions.

The result should be a much more geologically realistic image of the subsurface. Compared to a seismic image produced without migration, the migrated image is an improvement in that it allows the true geometries of anticlines and synclines to be observed, dips are steeper, and diffractions are eliminated. Figures 3.34 and 3.35 show the difference between migrated and unmigrated seismic images.

A variety of mathematical methods exist for migrating seismic data (e.g., Kirchhoff migration, fi nite-difference migration, wave-equation migration, reverse time

FIGURE 3.33:

A single point refl ector generates a diffraction in a stacked seismic image

FIGURE 3.34:Comparison of stacked, unmigrated seismic image with migrated version of the same data

FIGURE 3.35:Comparison of an unmigrated and migrated seismic image of a fault

Figure 3.33: A single point refl ector generates a

diff raction in a stacked seismic image

Back to Chapter

In this schematic example, the source location is directly above the buried body and diff racted energy is received by receivers on both sides.

In this sch

example, location isabove theand diff rareceived b

both sides

Figure 3.34: Comparison of stacked, unmigrated seismic image

(A) with migrated version of the same data (B)

Back to Chapter

Note the improvement in imaging of the synclines (note bow ties in unmigrated version) and anticlines due to migration. From Duncan (1992).

Figure 3.35: Comparison of A) an unmigrated and

B) migrated seismic image of a fault

Back to Chapter

Note how the migration has moved the fault back to its true location and improved the

imaging of adjacent refl ections. Reproduced with permission from

Henry (1997a).


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migration), and a discussion of these methods is beyond the scope of this book. Interested readers are referred to the texts listed in the Introduction to this chapter for more details on migration. Here, and following Sheriff (2002), Liner (2004) and others, we distinguish between migration approaches according to two principal factors:

• Is the migration performed before or after stacking the data (pre- or post-stack migration respectively)?

• Does the migration properly handle lateral velocity variations and account for ray bending (depth migration) or does it not (time migration)?

All migration methods require some sort of velocity model in order to undertake the mathematical operation of migration. Velocity information needed to build that model can be obtained in a variety of ways. The derivation of stacking velocities was discussed in SECTION 3.3. Checkshot surveys, vertical seismic profi ling and sonic logs are described in CHAPTER 5. Seismic traveltime tomography uses seismic recordings to construct a velocity model that minimizes the error between the measured and theoretical traveltimes. Different approaches are possible to tomography, and Rawlinson et al. (2003), Accaino et al. (2005), and Lehmann (2007) discuss various applications ranging from engineering to lithospheric imaging.

Bednar (2005) discussed the history of seismic migration. Originally, i.e. before digital recording, migration was performed manually (on paper). Even once digital recording became commonplace (e.g., 1960s and 1970s) much seismic data was processed and displayed without being migrated. Subsequently, migration was applied after stacking, and stacking velocities, usually derived from NMO corrections, were used as a fi rst guess. These velocities were varied by perhaps ± 5 or 10% to examine the effects of changing the migration velocity on the seismic image. If the migration velocities are too low, the data are said to be under-migrated and concave-up patterns known as migration smiles are apparent. Migrated noise in the deep part of seismic images can generate prominent migration smiles. If the migration velocities are too high, i.e. the data are over-migrated, concave-down patterns known as frowns may be visible. The velocity fi eld that provided the sharpest image and best removed the diffractions and other problems was selected as the appropriate velocity model for migration. This approach is known as post-stack time migration (Post-STM) because the data are migrated after stacking with the vertical axis in time.

Post-stack time migration is an acceptable, and relatively cheap, way of migrating seismic data when velocities vary vertically in a more-or-less uniform manner, and lateral velocity variations are minimal or non-existent (e.g., areas with a structurally undisturbed layer-cake stratigraphy). However this type of migration is inadequate in the presence of abrupt lateral and vertical velocity variations such as those that might be present adjacent to salt diapirs or in thrusted areas (e.g., Paleozoic carbonates thrust over Tertiary clastics). In these cases, raypaths (or, more correctly, wavefronts) can follow complicated paths as they are refracted (according to Snell’s law, which is not honored in time imaging) at dipping interfaces having high velocity contrasts (e.g., Figures 2.24,2.26). Refl ections do not necessarily come from common midpoints in these areas (Figures 3.36, 3.37), and stacking then migrating these refl ections will not produce a clear or geometrically accurate image.

In cases of steep dips and signifi cant vertical and lateral velocity variations the data need to be


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migrated before stacking in order to defi ne common refl ection points (CRP; also referred to as common image points – CIP). Common refl ection point gathers represent combinations of sources and receivers that all image the same subsurface point. To identify common image points it is necessary to track rays or wavefronts as they bend through the subsurface according to Snell’s Law. Pre-stack depth migration (Pre-SDM) is used in these cases. Two classic settings where PSDM is critical are:

• Imaging adjacent to, or beneath, salt domes, where the Vp of the salt is signifi cantly different from that of the adjacent sedimentary deposits.

• Imaging in overthrust areas, especially where high-velocity strata are thrust over rocks with lower velocities.

Depth migration bends rays through the subsurface, thereby repositioning refl ections to their correct locations in space. Although it can be applied after stacking, the best results are obtained when the data are migrated before stacking.

An accurate velocity model is needed in order to properly characterize how rays bend. The velocity models are constructed in a variety of ways, such as tomography. In all cases there is a need for integration and interaction between structural geologists (who are hopefully in a position to make intelligent guesses about the likely subsurface structure) and data processors (who build the velocity models and migrate the data). Albertin et al. (2001) and Abriel et al. (2004) illustrate depth imaging of 3-D seismic data from the Gulf of Mexico. FIGURE 3.38 compares a time-migrated image (part A) with a depth-migrated version of the same seismic data (part B). Note the considerable improvement in imaging the base and fl anks of the salt body and the sub-salt area that is visible in the depth-migrated seismic image.

Although it is beyond the scope of this text to provide a detailed discussion or comparison of the algorithms employed to migrate seismic data, it is worth highlighting that different migration results can be useful for different purposes. For example, FIGURE 3.39 compares three different pre-stack depth migration results. The controlled-beam migration result (FIGURE 3.39C) provides better

FIGURE 3.36:

Source-receiver combinations with a common midpoint

FIGURE 3.37:Refl ections below a salt body do not come from a common midpoint

FIGURE 3.38:Comparison of post-stack time-migrated seismic image and pre-stack depth migrated seismic image of a salt body in the Gulf of Mexico

Figure 3.36: Source-receiver combinations with a common midpoint

Back to Chapter

Although these two source-receiver combinations share the same midpoint, the refl ections do not come from the same subsurface locations because of the dipping bed. (Raypaths bend according to Snell’s Law and the angle of incidence is equal to the angle of refl ection). Stacking these two traces will not help to generate a good-quality, or geometrically accurate, seismic image.

Figure 3.37: Refl ections below a salt body do not come from a

common midpoint

Back to Chapter

Raypaths for refl ections from a single horizon for a common midpoint gather in the presence of a salt body and associated structural deformation. Note that refl ection points do not coincide. Inset at lower right shows CMP gather. Refl ections from this one interface do not fall along a simple hyperbola (e.g., FIGURE 3.12) and NMO corrections followed by migration will not be adequate to generate an acceptable image. Pre-stack depth migration is needed in this case. From Abriel et al. (2004).

Figure 3.38: Comparison of A) post-stack time-migrated seismic image and B) pre-stack depth migrated seismic image of a salt body in the Gulf of Mexico

Back to Chapter

Note how the pre-stack depth migration has improved the seismic imaging adjacent to and below the salt. Images courtesy of TGS-Nopec.


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structural imaging of the sub-salt area, but information about lateral amplitude variations is suppressed with respect to the other two images. Potentially, either Figure 3.39A or 3.39B might be more useful for mapping of stratigraphic features (CHAPTER 7) or detecting hydrocarbons using amplitudes (CHAPTER 8).

Because post-stack migration methods work with seismic data after stacking, they involve working with less data than pre-stack methods and are therefore cheaper (cost is proportional to the computer time used, and the computer time used is proportional to the amount of data manipulated). Consider a hypothetical 60-fold 2-D seismic line that is 100 traces long. Post-stack migration would involve migrating 100 traces whereas pre-stack migration of the same dataset would include migrating 60 x 100 = 6000 traces. Depth migration methods more accurately capture the physics than time migration, meaning more calculations and further costs. It should be noted that small discrepancies in a velocity model used for post-stack migration will not signifi cantly change the migrated seismic image, however depth migration is very sensitive to errors in interval velocities because the method performs exact calculations of ray paths or wavefronts.

Pre-stack depth migration of large petroleum-industry 3-D seismic volumes is usually undertaken on supercomputers or Linux clusters. Post-stack migration can be handled by workstations or, for small datasets, even personal computers. Additional costs include the time spent constructing and validating velocity models. Because of these and other considerations, pre-stack depth migration is not yet used ubiquitously. Although it is common for time-migrated seismic data to be plotted with the vertical axis in time and depth-migrated seismic data to be plotted with the vertical axis in depth, in principle both methods can be used to plot seismic data with either time or depth as the vertical axis.

In cases where velocity anisotropy (CHAPTER 2) is pronounced, even Pre-SDM can be inadequate. Conventional PSDM corrects for lateral velocity heterogeneity, but anisotropic PSDM also corrects for velocity changes with direction (i.e., perpendicular or parallel to bedding in shale) in the anisotropic strata. Where pronounced, and the difference in velocity parallel

FIGURE 3.39:

Comparison of three different pre-stack migration algorithms: Kirchoff migration, wave-equation migration, and controlled-beam migration

FIGURE 3.40:Schematic illustration showing how sideswipe is generated

FIGURE 3.41:Modeling experiment undertaken by Fred Hilterman in the early 1970s to illustrate the appearance of sideswipe from a carbonate buildup on 2-D seismic profi les

Figure 3.39: Comparison of three diff erent pre-stack migration

algorithms: A) Kirchoff migration B) wave-equation

migration, C) controlled-beam migration

Back to Chapter

The wave-equation result in part B provides better sub-salt imaging than the Kirchoff result in part A. The result in part C provides better structural imaging (more continuous refl ections, sharper defi ntion of faults) than either of the other results, but lateral changes in amplitude that might be related to hydrocarbons or other factors are suppressed in this controlled-beam migration image. Images courtesy CGGVeritas.

Figure 3.40: Schematic illustration showing how sideswipe is generated

Back to Chapter

Although acoustic energy is assumed

to travel down and back up to the

surface along a raypath (red) in a

vertical plane that underlies the

source (red oval) and receiver (blue

spike), features off to the side of

that plane can generate a refl ection

(sideswipe) that is also recorded by

the receiver.

Figure 3.41: Modeling experiment undertaken by Fred Hilterman in

the early 1970s to illustrate the appearance of sideswipe

from a carbonate buildup on 2-D seismic profi les

Back to Chapter

Image at top shows the outline of an

oval-shaped carbonate buildup in map view. Immediately below is a cross-section view through the buildup showing the simple stratigraphy used in the modeling. At bottom are the

seismic model images corresponding to transects collected at various distances from the center of the buildup. The

carbonate buildup appears to be as wide in the transect collected 500’ from the center of the buildup as it is in the center, even though it is narrower at that location. The buildup appears

in the seismic data at 1000’ and 1500’ from the center of the buildup, even though neither of those lines crosses

it. These problems are related to sideswipe. No migration has been applied to these data (migration of any kind was not routine in the early 1970s). 2-D migration could improve

the imaging of the fl anks of the buildup but could not remove the sideswipe. (Jackson and Hilterman, 1979; cited by

Crawley Stewart, 1995. Reproduced with permission from Hilterman).


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to the bedding and perpendicular to the bedding can be as high as 30% (Lawton et al., 2001) the assumption of an isotropic velocity fi eld can lead to misplaced and poorly imaged refl ections.

3-D Migration

One of the biggest pitfalls of 2-D seismic methods is that acoustic energy does not simply follow 2-D raypaths such as those shown previously. Sound expands outwards from the source as a 3-D wavefront in the subsurface. As such, geological bodies located off to the side of the line can generate refl ections that are recorded by receivers on the line (FIGURE 3.40). These out-of-plane refl ections are known as sideswipe. Sideswipe, or seeing off to the side on a 2-D profi le, needs to be acknowledged as a potential problem when working with that type of data. Conventional 2-D migration cannot account for this problem. Instead, and as recognized at least in the 1970s (e.g., French, 1974), 3-D migration can be used to eliminate sideswipe.

The image shown in FIGURE 3.41 illustrates this point. The upper part of the fi gure shows a map view (top) and transect (below) through a geological model of a carbonate buildup. The locations of six modeled 2-D seismic lines are also shown. The lower images show these modeled seismic profi les. Note that the carbonate buildup is imaged even on lines that do not cross the feature (e.g., 1500’). That is because the sound expands out in three dimensions away from the seismic source. Some of that sound may be refl ected from points located below the line of receivers, but some of the sound may travel out away from the line, then bounce off a surface back into the line of geophones. There is commonly no way of knowing whether a feature on a 2-D profi le lies directly under the surface survey line or off to the side. Note that no migration was applied to the modeled seismic profi les shown in that fi gure because it was not common to apply even 2-D migration in the early 1970s. As such, the fl anks of the buildup are poorly imaged in the seismic profi les. Although it would improve the seismic defi nition of the fl anks of the buildups, 2-D seismic migration would not solve the sideswipe problem illustrated here.

The modeling shown in FIGURE 3.41 was designed to illustrate the effects of sideswipe on the exploration for carbonate buildups in the Michigan Basin, but has proven to be useful elsewhere. For example, Crawley Stewart (1995) described problems encountered by companies exploring for Pennsylvanian algal mounds in southeast Utah using 2-D seismic-based mapping. There would be times that the seismic profi les indicated the presence of a buildup and drilling would confi rm the seismic prognosis. However, there would be times that a buildup would be clearly observed on a 2-D seismic profi le, but drilling did not fi nd one. Only 3-D seismic, and the application of 3-D migration, could reliably indicate the presence or absence of productive mounds. This particular exploration problem is illustrated in FIGURE 3.42.

FIGURE 3.43 illustrates how 3-D migration eliminates sideswipe. Part A of that fi gure shows the same image as shown in FIGURE 3.39, but this time with the Fresnel zone illustrated. The diameter of the Fresnel zone is given by EQUATION 2.19. FIGURE 3.43B shows how 2-D migration

shrinks the Fresnel zone. The diameter is shrunk to 4

but only in the direction of the 2-D line. The Fresnel zone diameter is unchanged perpendicular to the line, and so sideswipe problems remain. Because of the way that sources and receivers image midpoints from a variety of azimuths with a

3-D acquisition geometry, the Fresnel zone is shrunk to 4

in all directions following 3-D migration


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and so sideswipe is eliminated (FIGURE 3.43C).

FIGURE 3.44 clearly illustrates the sideswipe problem with another real data example. The images here are both from the same 3-D seismic survey. However, the version of the data in the image on the left (part A) was processed as a 2-D seismic line (i.e. 2-D migration) whereas the version of the data in the image on the right (part B) was processed with 3-D migration. Note the prominent “bump” seen near the center of the image in the 2-D migration version. Perhaps this bump represents a carbonate buildup or some other type of potential drilling target. The bump disappears when the 3-D migration is applied. These images demonstrate that the feature is an out-of-plane refl ection (sideswipe) similar to the one shown schematically in Figure 3.40 or 3.43B. It is impossible to tell from inspection of FIGURE 3.44A that the bump is actually due to sideswipe.

All types of migration discussed previously, including pre-stack depth migration and post-stack time migration, can be applied to migrate 3-D seismic data.

Processing Summary

In an ideal world, the seismic data represent geometrically accurate images of the subsurface after processing. The seismic traces simulate data that would have been collected using waves that traveled vertically up and down from the surface to the refl ecting horizons and back up at the common-midpoint location. Stacking, migration and statics corrections are all processing steps that are intended to produce this type of image. Multiples and other sources of noise have been removed through deconvolution, stacking and potentially other processing steps for noise or multiple suppression that were discussed by Yilmaz (2001) and Gadallah and Fisher (2005). We have a true image of the geology, within the resolution limits of the seismic data. Unfortunately, in practice most seismic datasets have varying degrees of imaging problems and various types of noise, and an experienced seismic interpreter needs to be able to be aware of these limitations.

3.6 Survey Design

A simple conceptual framework for acquiring seismic data is shown in FIGURE 3.45. Geologic knowledge,

FIGURE 3.42:

2-D seismic transect and corresponding transect from a 3-D seismic volume showing the appearance of a sideswipe

FIGURE 3.43:3-D migration removes sideswipe

FIGURE 3.44:Comparison of seismic transect with 2-D migration with the same transect through a 3-D volume

Figure 3.42: 2-D seismic transect (top) and corresponding transect from a 3-D

seismic volume (below) showing the appearance of a sideswipe

Back to Chapter

A) The 2-D image appears toshow a carbonate buildup that is represented by a dim-out of the Upper Ismay refl ection (a strong peak).

B) This arbitrary line from a 3-D survey corresponds to the 2-D seismic transect shown in part A. The Upper Ismay refl ection does not dim here, indicating that no carbonate buildup is present at this location. Reproduced with permission from Crawley-Stewart (1995).

Figure 3.43: 3-D migration removes sideswipe

Back to Chapter

A) The original circular Fresnel zone (blue) for a 2-D

seismic line images the dipping surface to the

left, and sideswipe is recorded.

B) Migrating the 2-D seismic data shrinks the Fresnel

zone in the direction of the 2-D line, but cannot

reduce the diameter perpendicular to the line

and sideswipe remains a problem. The Fresnel

zone is elliptical.

C) The acquisition geometry of 3-D seismic methods

allows 3-D migration that shrinks the Fresnel

zone to a small circle, eliminating sideswipe.

Figure 3.44: Comparison of seismic transect with 2-D migration (A)

with the same transect through a 3-D volume (B)

Back to Chapter

The 2-D line was in fact extracted from the 3-D acquisition program and processed using 2-D migration that did not remove sideswipe. Notice the presence of the

“bump” in part A (red arrow) that is not seen in the image with the 3-D migration. That feature is due to sideswipe on the 2-D image. Data courtesy Western Geco.


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geophysical knowledge and logistical issues are all considered when designing a seismic program, either 2-D or 3-D. Geologic knowledge includes factors such as the depth to the primary target, the thickness of the units that need to be detected and mapped, and structural dip. Geophysical knowledge includes understanding how the seismic energy will be attenuated with depth, how best to design source or receiver arrays to increase the signal-to-noise ratio, the ideal stacking fold, data input requirements for certain processing or interpretation steps, and other factors. Logistical issues include defi ning the availability of seismic crews or equipment, obtaining permission to collect data in the desired area (“permitting”), and, inevitably, cost considerations. Survey design almost always involves making compromises between what should be done and what can be done given restrictions on available time, money and expertise.

Some of the parameters that need to be defi ned when designing a 2-D or 3-D seismic survey include:

• Trace spacing in 2-D seismic data. The trace spacing helps to defi ne the lateral resolution and can be a concern if a problem called spatial aliasing is to be avoided. Spatial aliasing arises when the trace spacing is not close enough to unambiguously defi ne true refl ection dip (FIGURE 3.46). Liner (2004) illustrated examples of spatial aliasing on real seismic data. The minimum trace spacing needed to avoid spatial aliasing problems can be calculated using:

DFVXsin4

[3.4]

where X is the trace spacing, V is the velocity, F is the maximum unaliased frequency (Hz) and D is the structural dip (degrees). Consider the following example. In an area of thrusted Paleozoic carbonates, the velocity is 5000 m/s, the expected maximum frequency is 50 Hz and the structural dip is 30°. After substituting these values into EQUATION 3.4, we conclude that the minimum trace spacing needed to avoid spatial aliasing is 50 m, indicating that our receiver groups should not be less than 100 m apart.

FIGURE 3.45:

Conceptual diagram for seismic acquisition

FIGURE 3.46:Spatial aliasing

Figure 3.45: Conceptual diagram for seismic acquisition

Back to Chapter

The inset quote reminds us of the diffi culties associated with simplifying complex workfl ows.

Figure 3.46: Spatial aliasing

Back to Chapter

A) When seismic traces are located closely enough together,

defi nition of refl ection dip is unambiguous.

B) If traces are too widely separated, other dip interpretations

are possible, i.e. the data are spatially aliased.


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• Bin size in 3-D seismic data. As per the discussion of 2-D seismic trace spacing, the bin size of a 3-D survey needs to be small enough to avoid spatial aliasing problems. The bin size also helps to defi ne the lateral resolution of a 3-D seismic survey. One rule of thumb is that the bin size should be small enough so that three bins will fall into the narrowest feature that needs to be resolved. For example, if an interpretation team needs to be able to consistently map channels that are 50 m wide, then the desired maximum bin size would be

305 ≈ 18 m. In reality, the true lateral resolution

of a 3-D survey is defi ned by whatever is greatest, 3 times the bin size or the post-migration Fresnel zone. Consider this example: A 3-D survey has a bin size of 15 m, a dominant frequency of 50 Hz, and an interval velocity of 2400 m/s at the level of a particular horizon. The wavelength is determined to be 48 m (using

EQUATION 2.4) and therefore the post-migration Fresnel zone ( 4

) will be 12 m. The width of three bins (45 m) is greater than the diameter of the post-migration Fresnel zone (12 m), and therefore the bin size determines the lateral resolution. In some cases, particularly when imaging deep targets and bin sizes are small (i.e. marine surveys), the post-migration Fresnel zone can be larger than three bin widths.

• Size of survey. The survey should be designed so that the entire area of the target is covered by full-fold data (FIGURE 3.47). This area is sometimes referred to as the “sweet spot” and, in the petroleum industry, it should be big enough for the structural or stratigraphic limits of a hydrocarbon reservoir to be defi ned. At the end of a 2-D seismic line or around the margins of a 3-D survey will be an area that has midpoints but the fold and range of offsets will not be adequate to properly stack or migrate the data. This poorly imaged area, commonly trimmed off by processors and sometimes referred to as the migration fringe, combines with the sweet spot to constitute the image area, i.e. the area for which seismic traces are recorded. Outside of the image area is the acquisition fringe, an area at the surface where receivers (and possibly sources) need to be located but for which no CMP locations (i.e. no traces) will be generated4. The dimensions of the acquisition area are a function of the depth to the target and the structural dip.

Once data collection begins, it is advisable to monitor the acquisition effort in order to ensure that hardware and crews are properly located and performing properly. Field records need to be monitored to ensure that the data quality will be adequate. If not, acquisition might need to be terminated.

3.7 Reprocessing and Post-Stack Processing

Sometimes an interpreter is asked to work on data (2-D or 3-D) that was acquired and processed several or more years previously, and the data quality is not good. The interpreter has three options:

1. Work with the data “as is”. This might be the only option if the data are paper copies and the original tapes are not accessible.

__________________________________________________________________

4 Recall from FIGURE 3.8 that the area of midpoint coverage does not extend to include the entire length of the spread.


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2. Reprocess the original data. This approach seeks to take advantage of improvements in processing capabilities with time. Perhaps processing algorithms are available now that were not when the data were originally processed. This option will incur a time delay, while the data are being reprocessed, and additional costs.

3. Attempt some post-stack processing on the existing seismic line(s) or volume(s).

Having the data reprocessed is the preferred option. Improvements in processing algorithms between the original processing and today could lead to improved imaging. Alternatively, the data might have had a “generic” processing workfl ow applied and special attention needs to be paid to a particular stratigraphic horizon or structural feature. Figures 3.48 and 3.49 show examples of how reprocessing was used to improve seismic image quality. Seismic reprocessing is helping to open new hydrocarbon exploration opportunities in many parts of the world (e.g., Roberts et al., 2008).

In some cases the time, or money, may be missing to send data out for reprocessing. Some workstation-based interpretation software packages provide an interpreter with the ability to implement certain processing steps on “fully processed” (stacked and migrated) seismic data and then see the results in near-real time. For example, an interpreter might judge that a certain data set has signifi cant high-frequency noise that obscures stratigraphic or structural features. Filtering out those high frequencies could make the low-frequency information more readily interpretable. At other times, the interpreter might attempt to attenuate coherent noise that is related to acquisition and processing (Marfurt et al., 1998).

Post-stack processing allows the interpreter to undertake various types of processing on the data, typically with the intent of enhancing the interpretability of the data. Various manipulations can be applied to the seismic data (e.g., bandpass fi ltering, deconvolution, amplitude balancing, dip fi ltering, trace averaging). Ogiesoba and Hart (2009) described how post-stack processing was used to improve fault defi nition in a relatively poor quality 3-D seismic

FIGURE 3.47:

Illustration of the sweet spot for 3-D seismic imaging

FIGURE 3.48:Examples of how reprocessing was used to improve seismic image quality

FIGURE 3.49:Timeslice through some marine seismic data showing strong acquisition footprint and corresponding timeslice through a reprocessed version of the data

Figure 3.47: Illustration of the sweet spot for 3-D seismic imaging

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The entire seismic target should be covered by a full-fold area known as the sweet spot. The image area includes the sweet spot and the adjacent, poorly imaged areas that cannot be properly stacked or migrated because not enough traces are available. The acquisition area includes areas outside of the image area where receivers will be located, but for which no data will be collected.

Figure 3.48: Examples of how reprocessing was used to improve seismic image quality

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Reprocessing data can improve seismic image quality.

A) Transect through a 3-Dseismic cube processed in 1991.

B) Corresponding transectthrough a cube that was reprocessed in 1998. Imaging of structural and stratigraphic features is much better in the lower image, at least in the shallow part of the section. The main diff erences were the application of dip moveout (DMO) corrections and removal of some high frequencies thought to be associated with noise in 1998. From Hart (1999).

Figure 3.49: Timeslice through some marine seismic data showing

strong acquisition footprint (A) and corresponding

timeslice through a reprocessed version of the data (B)

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Note the improvement in seismic imaging because of reprocessing.

Images courtesy TGS-NOPEC.


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survey. Masaferro et al. (2004) illustrate the application of structure-oriented fi ltering to improve image quality while preserving faults. The interpreter needs to realize that generally these operations are a double-edge sword and need to be used with caution and appropriate judgment. While they may enhance data interpretability, i.e. make the data appear more continuous and “smoother”, these methods can also remove important information (e.g., small-scale structures, lateral amplitude variations) or, worse, introduce artifacts that can be mistaken for geology.

3.8 Coherency (Semblance) Processing

Bahorich and Farmer (1995) introduced coherency processing on 3-D seismic cubes as a way to enhance faults and stratigraphic features. Since then, other processing and software companies have developed similar attributes that go by different names (e.g., “semblance”5) but, simplistically, they are measures of the lateral variability in trace shape – i.e. how similar or dissimilar the traces are. Hill et al. (2006) described and compared the principal algorithms. In brief, portions of the data where refl ections are continuous have high coherency, whereas abrupt changes in refl ection shape, such refl ection offsets associated with faults, have low coherency(FIGURE 3.50). Abrupt lateral facies changes, such as at reef or channel margins, can also generate coherency features.

FIGURE 3.51 shows two versions of a vertical transect through a 3-D seismic cube. The original amplitude version (part A) shows offsets of refl ections that correspond to faults (see CHAPTER 6). The coherency version shows that the areas corresponding to refl ection offsets correspond to low coherency (black). FIGURE 3.52 shows a coherency cube. The black lineations show the locations of faults in both vertical and horizontal planes through the cube.

The images shown in FIGURE 3.53 should help to demonstrate the usefulness of these attributes. Part A shows a timeslice through a 3-D seismic cube from the Western Desert of Egypt. After some inspection, it might be possible to recognize curvilinear trends (refl ection

FIGURE 3.50:

Factors affecting refl ection coherency across a fault

FIGURE 3.51:Comparison of seismic transect through a 3-D seismic amplitude volume, and corresponding transect through a coherency (semblance) volume

FIGURE 3.52:Sample coherency cube showing the locations of faults (black)

Figure 3.50: Factors aff ecting refl ection coherency across a fault

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Factors aff ecting seismic coherency across a vertical fault include:A) changes in refl ection amplitude,B) changes in refl ection phase,C) changes in refl ection dip,D) changes in noise level,E) refl ection off sets.

Based on an idea presented by Marfurt et al. (1998).

Figure 3.51: Comparison of A) seismic transect through a 3-D

seismic amplitude volume, and B) corresponding

transect through a coherency (semblance) volume

Back to Chapter

Original seismic data shown as variable area wiggle display to illustrate trace shape. Note how lateral changes in trace shape correspond to low coherency areas (black) that show the locations of two faults.

Figure 3.53: Comparison of A) amplitude timeslice and B) coherency (semblance)

timeslice for a structurally complex area

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The locations of the faults are much more clearly seen in the coherency timeslice.

From Hart (2007).

__________________________________________________________________

5 For simplicity, I will use the term “coherency” to refer to these processing products, even though there are differencesin the way the attributes are derived mathematically.


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truncations, changes in amplitude, etc.) running from upper left (NW) to lower right (SE) that indicate the locations of faults. Part B of the fi gure shows exactly the same timeslice, but this time through a version of the data that has been processed to show coherency. Discontinuities, i.e., areas of low coherency, are shown in black, and it is clear that this attribute is clearly delineating the location of the NW-SE striking faults. The reader should compare the number of faults visible in the coherency display with the number that might be interpreted in the amplitude timeslice of the amplitude version at top. It is clear that the coherency timeslice will be more useful for mapping faults than the amplitude version of the same data.

To generate a coherency volume, the interpreter must specify a minimum of three things:

• Choice of algorithm (coherence, semblance, etc.). In practice this choice is commonly dictated by the software available to the interpreter or by the capabilities of the company doing the processing.

• Choice of time window. Comparing amplitudes from trace to trace at a single TWT is not an adequate way of capturing changes in trace shape. As such, a sliding window of fi xed length (in ms) is used for this purpose. A larger window will be less sensitive to noise, but will tend to smooth out subtle structures more than a shorter time window.

• Search pattern. The trace must be compared to its neighbors, but the choice of how many neighbors, and their locations, is up to the user to decide. For example, a trace might be compared to two, four or all eight of its immediate neighbors.

Additionally, coherency calculations are generally able to account for dipping refl ections. Removal of acquisition footprint prior to generating the coherency volume by post-stack processing is recommended (Marfurt et al., 1998; Chopra and Marfurt, 2008).

Coherency is a type of seismic attribute (CHAPTER 4) that is commonly used in structural (CHAPTER 6) and

FIGURE 3.53:

Comparison of A) amplitude timeslice and B) coherency (semblance) timeslice for a structurally complex area

FIGURE 3.54:Comparison of timeslices through coherency volumes before and after post-stack processing using principal component fi ltering

Figure 3.53: Comparison of A) amplitude timeslice and B) coherency (semblance)

timeslice for a structurally complex area

Back to Chapter

The locations of the faults are much more clearly seen in the coherency timeslice.

From Hart (2007).

Figure 3.54: Comparison of timeslices through coherency volumes before (left)

and after (right) post-stack processing using principal component

fi ltering, a technique designed to attenuate random noise

Back to Chapter

Note the improved “crispness” of the

faults in the image on the right. Reproduced with permission from

Chopra and Marfurt (2008).


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stratigraphic (CHAPTER 7) interpretations. Its derivation is presented here because, at least in the petroleum industry, 3-D seismic cubes are routinely processed to generate coherency volumes. Those volumes then become available to the interpreter for use at all stages of the interpretation.

3.9 References

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Bahorich, M., and S. Farmer, 1995, 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube: The Leading Edge, v. 14, p. 1053-1058.

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Gadallah, M.R., and R.L. Fisher, 2005, Applied seismology. PennWell, 473 p.

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Ogiesoba O.C., and Hart B.S., 2009, Fault imaging in hydrothermal dolomite reservoirs: A case study. Geophysics, v. 74 , p. B71-B82.


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Ray, R.R., 1995, Utilizing seismic for higher defi nition of geologic prospects: 2-D, 2-D swath, and 3-D seismic in High-defi nition seismic 2-D, 2-D swath and 3-D case histories (R.R. Ray, ed.): Rocky Mountain Association of Geologists Guidebook, p. 1-6.

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