This website uses cookies to improve your experience. If you continue without changing your settings, you consent to our use of cookies in accordance with our cookie policy. You can disable cookies at any time.

From 7–12 Jun 2023, the SEG Library will undergo maintenance that may prevent users from logging into their accounts.
Please contact us if you are unable to access your content during this time.

Risking seismic amplitude anomaly prospects based on database trends


Many oil companies routinely evaluate prospects for their drilling portfolio and seismic amplitude anomalies play an important role in this process. When these anomalies occur at a potential reservoir level, they are often called DHIs or direct hydrocarbon indicators, which are changes in reflection response that may be related to oil and/or gas accumulations. Examples of DHIs include bright spots, flat spots, dim spots, character/phase change at a projected oil or gas/water contact, and an amplitude variation with offset. Many uncertainties should be considered and analyzed in the process of assigning a probability of success and resource estimate range before including a seismic amplitude anomaly prospect in an oil company's prospect portfolio.

Many oil companies routinely evaluate prospects for their drilling portfolio and seismic amplitude anomalies play an important role in this process. When these anomalies occur at a potential reservoir level, they are often called DHIs or direct hydrocarbon indicators, which are changes in reflection response that may be related to oil and/or gas accumulations. Examples of DHIs include bright spots, flat spots, dim spots, character/phase change at a projected oil or gas/water contact, and an amplitude variation with offset. Many uncertainties should be considered and analyzed in the process of assigning a probability of success and resource estimate range before including a seismic amplitude anomaly prospect in an oil company's prospect portfolio.

DHI anomalies are caused by changes in rock physics properties (VP, VS, and density) of the hydrocarbon-filled reservoir and the encasing rock generally without regard to a specific basin or play. Geology variables that may affect the seismic response include the lithology of the reservoir (generally a sand, but can be a carbonate), reservoir porosity (10–30%), sand morphology such as a blocky or laminated sand, and the properties of the overlying and underlying seal (often a shale that can have varying elastic properties).

Of course, the hydrocarbons can be gas or oil (as compared to water) but there is uncertainty. For example, seismic response can be affected by low-saturation gas because 5–10% free gas in a reservoir can have similar reflectivity as 80% saturation commercial gas (essentially the same interval velocity for low- and commercial-saturation gas but the density will be lower for high gas saturation). Also, low-gravity oil will have less affect on seismic reflectivity than high GOR (gas/oil ratio) light oil.

It is well known that seismic amplitude anomalies can be caused by factors other than commercial hydrocarbons, resulting in:

A low-saturation gas sand interpreted as a commercial gas sand.

A clean blocky wet sand interpreted as a commercial gas or oil sand.

A low-velocity shale or marl interpreted as a commercial oil or gas sand.

In addition, a low-porosity gas sand can be interpreted as a high-porosity oil sand.

As with any estimating task, a systematic and consistent work process is necessary to interpret and risk seismic amplitude anomalies. In another words, a process is needed to understand the uncertainties before adding prospects to an oil company's exploration portfolio for drilling. The first step in the process is classifying the AVO class of an amplitude anomaly: either AVO class 1, 2, or 3 as defined by Rutherford and Williams in 1989 or class 4 as defined by Castagna and Swan in 1997 (Figure 1). These AVO classifications are related to the rock physics of a gas-filled sand (or a high GOR oil) and overlying seal and are an indication of the geologic setting of the prospect.

Figure 1.

Figure 1. The four AVO classes related to specific geologic settings.

First understand the geology

The regional and prospect geology should be thoroughly reviewed before starting a detailed amplitude anomaly interpretation and risk analysis. Figure 2 lists the generally accepted geologic risk factors in a prospect analysis. Each factor has ranges of uncertainty (5–100%) that should be discussed with peer groups and management. It is sometimes difficult to separate closure risk from containment (seal) risk. Structural or stratigraphic closure is usually demonstrated on a horizon map whereas seal is usually related to potential vertical or lateral leakage.

Figure 2.

Figure 2. The five geologic risk factors that can be used to estimate the probability of geologic success (Pg) independent of the seismic amplitude anomaly.

A good risk analysis process to use is the product of these five risk factors as an estimate of Initial Pg based on geology alone, independent of the amplitude anomaly, to avoid problems with circular logic. Thus, a prospect in a frontier area or a pure stratigraphic trap will have a low probability of Initial Pg (that is, in the range of 5–15%) and a good structural prospect in a proven basin may have a Geology Pg of 30–50%. The uncertainty of Initial Pg can be reduced by analysis of the seismic amplitude anomaly if observed on the prospect.

Seismic and rock physics data quality uncertainty

The quality of the seismic and rock physics data must be carefully considered in risking seismic amplitude anomalies. For example, using 3D seismic rather than 2D seismic can dramatically reduce the uncertainty concerning the structural map and especially the fault interpretation. Preservation of the relative true-amplitude data throughout the processing sequence is an important but sometimes elusive goal. Prestack time- or prestack depth-migrated seismic data, especially if specifically processed at the seismic amplitude anomaly level, usually reduces uncertainty compared to so-called “spec” data processing. The phase of the seismic data should be well understood so impedance contrasts can be properly interpreted.

Prestack seismic data, used for data quality control and AVO studies, refers to seismic gathers (near-to-far offset common depth point traces that are included in a stack trace) and near- and far-offset or angle stacks. Figure 3 shows the results of a recent industry study for 175 prospects. AVO class 3 prospects had a success ratio of essentially 50% with or without prestack data where AVO class 2 prospects with prestack data had 67% success with zero successes without prestack data (admittedly a small database). Class 3 prospects can have varying AVO response, including no amplitude change with offset, but still be successful if the prospects have several other positive anomaly characteristics.

Figure 3.

Figure 3. Results from a database with 175 drilled prospects with known outcomes show prestack data are much more important for AVO class 2 anomalies than class 3 anomalies. Class 3 prospects are essentially 50% successful with or without prestack data whereas class 2 anomalies (admittedly a small sample) have 67% success with prestack data and zero success without prestack data.

This is in contrast to AVO class 2 prospects where prestack data are considered essential because interpreting stacked data alone for these types of prospects can be difficult.

Rock physics data, including sonic and density logs, are required for seismic forward-modeling studies (acoustic impedance and AVO measurements) to help predict gas, oil, or water that may be associated with a seismic amplitude anomaly. An uncertainty exists because the stratigraphy, including sand quality, used in the models may not be representative of the stratigraphy of the prospect. In frontier basins where well control is distant or unavailable, data from analog basins may be used but the large uncertainty in this approach must be recognized when risking an amplitude anomaly prospect.

Seismic amplitude anomaly characteristics

Multiple seismic anomaly characteristics should be used to reduce the uncertainty in estimating the probability of geologic success (Pg). The most critical characteristics (other than the strength of the amplitude itself) are:

Consistency of the amplitude in the target area (on full stack or far-offset data)

Downdip conformance on a depth structure map (possible G/O/water contact)

Signature match (polarity and confidence in phase of the seismic data)

DHIs proven nearby (analog successes in the same area)

AVO studies (observations, comparison between prospect and offstructure)

Flat spots (possible hydrocarbon-fluid contact)

Phase/amplitude change at edge of anomaly (possible fluid contact)

Few prospects will have all these characteristics, but high confidence in many of the above anomaly characteristics decreases the uncertainty in Pg in comparison to focusing on only one or two of the characteristics. For example, overemphasizing the value of a flat spot can create uncertainty because a flat spot can also be caused by a paleo-water contact in a breached trap or the base of a channel sand.

Generally, an AVO anomaly by itself is not a good indicator of a high Pg prospect. But a recent study of a database of 175 prospects shows an AVO anomaly in combination with other good DHI characteristics can increase the probability of success and reduce the uncertainty of failure of AVO class 3 prospects and especially AVO class 2 prospects (Figure 4). The interpreter should also review the acoustic impedance and AVO effects on the seismic event that represents the downdip “water leg” of a possible oil or gas pay.

Figure 4.

Figure 4. Percentage of successful wells associated with good AVO anomaly characteristics.

Seismic amplitude anomaly pitfalls

A major uncertainty in the interpretation and risk analysis of seismic amplitude anomalies is the presence of a pitfall. An anomaly can be caused by:

Reflectivity from the top of a clean, high-porosity wet sand

A low-saturation gas sand (can have positive AVO and downdip conformance similar to a commercial gas sand)

No reservoir (low-impedance shale, a gas-filled silt, or a marl)

Tight sands

Figure 5 shows the percentages of these major reasons for failure from a recent database study. Other pitfalls, some a subset of the above list, which the interpreter should be aware of are:

Top hard pressures

Seismic processing artifacts

Salt, volcanics, carbonates (polarity issue)


Tuning effect, especially AVO tuning

Lateral lithology or thickness change

Figure 5.

Figure 5. High-porosity wet sands and low-saturation gas sands are the most common reasons for failure on DHI prospects.

Low-saturation gas is often the result of seal failure. A valid trap may have existed at one time with high-saturation gas but subsequent faulting or structural tilting resulted in the loss of seal and most of the gas leaked out of the trap. The remaining residual gas usually has 5–10% free gas. In cases where the reservoir is low permeability, low-saturation gas may be present in a closure with a poor seal.

An important technical issue sometimes overlooked by interpreters is AVO tuning where far-offset amplitude increases because of lower-frequency data (Figure 6). Frequency-balancing methods can be used to mitigate this problem.

Figure 6.

Figure 6. AVO tuning can cause an increase in amplitude with offset distance/angle and thus can be incorrectly interpreted as a positive AVO characteristic. (From Xu and Chopra, 2007)

Uncertainty in the probability of geologic success

One approach to estimating Final Pg for a seismic amplitude anomaly prospect is to add the Initial Pg, based on the geology risk factors independent of the amplitude anomaly, to a DHI Index, which is based solely on the consideration of multiple anomaly characteristics modified by an interpretation of the seismic data quality and rock physics data. The predrill Final Pg for a number of prospects plotted against success/failure has the potential to calibrate Pg estimates on a statistical basis.

This systematic and consistent work process can be used to interpret and risk amplitude anomaly prospects with a final probability of success (Final Pg) in the range of 15–80% (important to use a wide range of probabilities). Even with a systematic and consistent work process, the uncertainty of Final Pg will often be ±10% but, if this is an unbiased estimate, it can be used to properly value order prospects.

Numerous high-quality anomaly characteristics reduce much of the uncertainty associated with the geologic chance factors on exploration prospects. For example, several good anomaly characteristics such as conformance to downdip closure, consistency of the anomalous amplitude within the target area, strong AVO response for hydrocarbons, and successful nearby analogs may reduce the risks of source, migration, reservoir, timing/migration, and containment to such a degree that the Final Pg should be relatively high. This can be called a high-end threshold effect which can vary from 40 to 65% depending on the methodology of the risk analysis work process. In many cases, Final Pg can be credibly increased when calibrated to a large database of drilled prospects.

Similarly, there is a tendency for low Pg estimates to not be low enough. At the low end of the risking spectrum, data quality, especially rock physics data, or lack of data is critical and usually these prospects have few or no DHI characteristics. In fact, because of the lack of dependable and positive information for these types of prospects, interpreters really do not know if the true risk is 15 or 1.5%. In other words, the risks of individual geologic chance factors are so unknown that the accuracy in determining high-risk (low Pg) prospects is poor and the uncertainty is high.

Prospect resource estimates

The uncertainty in resource estimates is associated with the area of the amplitude anomaly, the average thickness of pay and the hydrocarbon yield or recovery factor (bo/acre foot and/or MCF gas/acre foot). A high Pg seismic amplitude anomaly prospect will often have low variance in area due to the presence of sharply defined amplitude areas but moderately high resource variance due to uncertainty in pay thickness. The hydrocarbon yield or recovery is not addressed here.

Estimated pay thickness is acoustically related to seismic resolution which is related to the seismic tuning thickness. For a sand encased in a shale (the underlying and overlying shales having the same impedance), tuning thickness produces the highest composite amplitude at 1/4 wavelength of the dominant frequency for that zone. This high-amplitude event at tuning is a result of constructive interference from the top and bottom of the bed. Knowledge of the vertical resolution and whether prospect thickness estimates are above and/or below tuning is critical for proper thickness determinations.

A work process can be used to select the P90 (90% or better chance case) and P10 (10% or better chance case) net pay using the methodolgy shown in Figures 7 and 8. The mean is selected to be the most probable case. For thicknesses less than 1/4 wavelength, the seismic composite amplitude strength is related to bed thickness. For beds greater than 1/4 wavelength, the thickness is a function of the time separation from the top and bottom reflectors of the bed. A net-to-gross factor must be considered for thicknesses above tuning. In these cases, wave shape can be used to judge bed thickness and a gas/oil water level may be observed as a flat spot on seismic data. With statistical well control for calibration, acoustic impedance inversion and statistical methods may be employed for thickness determination.

Figure 7.

Figure 7. Work process to determine the net pay thickness from a seismic amplitude anomaly.

Figure 8.

Figure 8. The most important step to estimate thickness from a seismic amplitude anomaly is to make a detail isochron map, the time separation of the top and bottom of an anomaly.

In reality, seismic resolution is usually much more complex than this simple process as stratigraphy can have rapid variations across short distances. Also, reflections observed on a seismic line are often a result of multiple convolutions of bed reflection coefficients and the seismic wavelet. Spectral decomposition is now being used to separate the seismic wavelet into discrete frequency bands which can be useful in mapping thin beds as well as understanding varying sand thicknesses on a prospect.

A simple comparison of the seismic data with sand thickness interpreted from nearby wells can be useful. For example, the geology interpretation from nearby well logs may suggest a 200-ft sand thickness but the seismic tuning thickness analysis indicates the sand thickness cannot be thicker than 100 ft. Similarly, the geology interpretation may suggest thin sands, thickness less than 40 ft, but the seismic may have evidence for a flat spot that may indicate sand thickness of approximately 100 ft or greater. As indicated on Figures 7 and 8, the interpreter should measure the time thickness of an anomaly and make detail isochron maps.

In many oil and gas discoveries, the uncertainty in the proved reserves in a reservoir decreases as additional wells are drilled and geology/engineering studies are initiated. Complicating factors can include thin-bed or laminated turbidite sands that are depicted on logs as low-resistivity pay and one on more well bores drilled into a reservoir may not be representative of the overall sand quality and thickness.


Interpretation and risking seismic anomaly prospects require a systematic, objective and consistent work process to reduce the uncertainties in the probability of geologic success and the range of resource estimates. The critical steps are to indentify the AVO anomaly class, understand the geologic risk factors, review the seismic and rock physics data quality, and thoroughly analyze numerous seismic anomaly characteristics. This process can decrease the uncertainty so management is comfortable with including the prospect in a company's exploration portfolio for drilling a wildcat well.


  • Castagna, J. and H. Swan, 1997, Principles of AVO crossplotting: The Leading Edge, 16, 337–344.AbstractGoogle Scholar
  • Rutherford, S. E. and R. H. Williams, 1989, Amplitude versus offset variation in gas sands: GEOPHYSICS, 54, 680–688.AbstractWeb of ScienceGoogle Scholar
  • Roden, R., M. Forrest, and R. Holeywell, 2005, The impact of seismic amplitudes on prospect risk analysis: The Leading Edge, 24, 706–711.AbstractGoogle Scholar
  • Roden, R., M. Forrest, and R. Holeywell, 2009, DHI threshold effect in prospect risking: OTC Expanded Abstracts, paper 19983–MS.Google Scholar
  • Xu, Y. and S. Chopra, 2007, Benefiting from 3D AVO by using adaptive supergathers: The Leading Edge, 26, 1544–47.AbstractGoogle Scholar