Spectral Decomposition and AVF Analysis: A Solution for AGC-Processed Seismic Data

Amplitude Variation with Frequency (AVF) analysis offers a significant advantage in seismic interpretation, especially for data that has undergone automatic gain control (AGC) processing. One common issue with AGC is its impact on amplitude-driven Direct Hydrocarbon Indicators (DHI), such as bright spots. In this article, we explore how AVF analysis can bypass these limitations, providing reliable hydrocarbon detection through frequency-based methods.

Challenges of AGC for Bright Spot DHI Analysis

AGC is a common seismic processing technique used to balance the amplitude levels across the dataset. By normalizing amplitudes to a similar root-mean-square (RMS) level, AGC ensures consistent visualization. However, this process often diminishes or even masks amplitude anomalies, such as bright spots, which are traditionally used as DHIs. The bright amplitudes, which might indicate hydrocarbon presence, appear dimmer or disappear entirely after AGC, reducing the reliability of bright spot analysis.

This limitation arises because AGC operates in the amplitude domain, equalizing the amplitude variations that make DHIs recognizable. As a result, amplitude-driven hydrocarbon detection methods become ineffective for AGC-processed seismic data.

Why AVF Analysis Works for AGC-Processed Data

Unlike amplitude-based methods, AVF analysis focuses on frequency-dependent variations in seismic data, such as attenuation patterns. Since AGC primarily affects amplitude and has minimal impact on frequency content, AVF remains effective even after AGC processing. Key attributes like the dominant frequency, bandwidth, and frequency gradients remain relatively unchanged, allowing AVF to detect hydrocarbon-related anomalies.

Analyzing Frequency Content for Hydrocarbon Detection

AVF analysis involves studying the frequency gather at each seismic trace location. By comparing attributes like the dominant frequency or AVF regression gradients, we can identify areas with potential hydrocarbon presence. The gradient of the AVF curve, in particular, is closely linked to attenuation in seismic data. Intrinsic attenuation, which is often related to rock and fluid interactions, serves as a strong indicator of hydrocarbon or permeability properties.

When interpreting AVF results, it is crucial to account for trends in the seismic spectrum, which typically shows decreasing high-frequency content and a shifting dominant frequency with depth. Hydrocarbon-bearing zones usually exhibit reduced high-frequency content compared to their surroundings. To ensure accuracy, AVF analysis should be conducted within intervals that share similar geological trends, such as formations or packages.

Avoiding Common Pitfalls in Frequency-Based Interpretation

One common mistake in spectral decomposition and AVF interpretation is associating low frequencies with hydrocarbons without considering depth. For example, 15 Hz might be interpreted as a hydrocarbon indicator in shallow layers but is not necessarily low at greater depths. Therefore, interpretations must account for depth-dependent frequency trends.

AVF analysis offers flexibility in computing regression across specific frequency ranges or starting from the dominant frequency. This adaptability helps refine interpretations and reduces the risk of errors.

AVF Gradient Analysis in AGC-Processed Data

To validate AVF's reliability, we can compare AVF gradient sections from seismic data with and without AGC processing. The results often reveal comparable anomalies, confirming AVF's robustness against AGC's amplitude normalization. However, it's important to apply appropriate scaling to make the gradient sections comparable.

In addition, visualizing the dominant frequency and frequency bandwidth from AGC-processed data shows that these attributes remain stable, even though the amplitudes appear altered. This stability highlights why AVF analysis is a dependable tool for hydrocarbon detection, even in challenging scenarios where amplitude-based methods fail.

Conclusion

AVF analysis bridges the gap left by amplitude-driven techniques in AGC-processed seismic data. By leveraging frequency-dependent attributes, AVF provides a reliable alternative for detecting hydrocarbons, reducing interpretation risks, and ensuring robust results. This makes it a valuable complement to traditional amplitude-based methods, especially in complex seismic datasets.