In the ever-evolving world of seismic interpretation, geoscientists constantly seek better tools to identify hydrocarbons and characterize reservoirs. The Distance and Quadrant trace method represents one such new tool in this effort, offering an alternative approach to visualize and analyze seismic data. By causing a seismic wiggle trace to emulate a porosity log, by enhancing the visual presentation of fluid contacts and by improving thin bed detection, the DQ method addresses several key challenges in modern seismic interpretation.
Using traditional seismic interpretation methods, geoscientists often struggle with reliably detecting hydrocarbons and characterizing reservoir properties. Common challenges include data quality issues, complex geology and the inherent uncertainties in seismic responses. While amplitude variation with offset analysis has been a standard tool for decades, it comes with limitations that can make interpretation challenging, particularly when dealing with thin beds or complex reservoirs. The industry has long sought ways to improve upon traditional AVO techniques, especially in areas with limited well control or complex stratigraphy. For a better understanding of near and far angle stacks, the AVO concept, and the processing of seismic amplitudes for AVO analysis, readers are encouraged to explore the Geophysical Corner articles published in the following issues: June 1999, April 2001, September 2002, July and August 2004, February 2010, January 2017, October 2019 and March and April 2023.
The foundation of the DQ process was developed in the early 2000s to support geologic mapping of porosity away from well control and to improve the quantitative predictions of hydrocarbon-pore-volume for reservoir simulation. Significant enhancements and additions to the DQ process occurred in 2023 and extended its functionality to exploration mapping of lithology and hydrocarbon detection. Unlike traditional AVO attributes that require prior knowledge of rock properties, wavelets or basin geology, the DQ trace works directly with seismic data to extract meaningful information. This makes it particularly valuable in early exploration phases when well data might be limited, allowing interpreters to make more confident assessments of reservoir properties and fluid content from seismic data alone.
So How Does It Work?
Through a systematic process that starts with standard seismic gathers, the DQ trace method transforms conventional seismic data (figure 1a) into a more visible and interpretable format shown in figures 1b and 1c, The auto-tracked horizons of figure 1a are lithologically nondescript, but the DQ traces (figure 1b) and the Theta PX (θpx) section (figure 1c) clearly block out the position, thickness and variable AVO attributes of two sand bodies; and even detect a third thin-sand body at letter A. This enhanced visibility happens because the DQ method works with two basic inputs: a partial near angle-stack (typically 0-10 degrees) and a partial far angle-stack (typically 20-30 degrees) that have been processed to preserve amplitude relationships. These angle ranges, optimized through extensive testing, provide the best balance of signal-to-noise ratio while maintaining critical AVO information.
The underlying mathematics use well established geophysical processing and AVO algorithms. The novelty of the DQ process involves the large number of numerical tests that are performed on each trace, and the unique order in which the calculations are performed. The method begins by auto-tracking every peak, trough and zero-crossing in the near angle-stack 3-D seismic volume, creating a framework that guides the analysis of 16 additional phase-shifted versions of the near- and far angle-stack data. These are then combined to produce two outputs: a DQ cube that measures the distance from the origin to each datapoint on an AVO crossplot (figure 2b, sector I, versus figure 1b), and a θpx cube that captures the polar angle relative to the x-axis (figure 2b, sector IV, versus figure 1c). As shown with synthetic data in figure 3, this transformation converts conventional seismic wiggle traces (figure 3c) into blocked patterns that better define reservoir boundaries (figures 3b and 3d). Further, note that figure 2b demonstrates how the DQ process enhances data separation in AVO crossplot space, pushing amplitudes further from the origin (changing the distance) and creating clearer distinctions between the different lithologies.
As mentioned, the first step in this transformation involves applying a series of phase-shift filters to both the near- and far angle-stacks. These filters help identify key points in the waveform – peaks, troughs and zero-crossings – and assign them to specific “quadrant #.s” (figure 2a). Think of this as similar to breaking down a sine wave into its different parts, but in a way that preserves amplitude information. This quadrant assignment is critical because it helps convert the typical wavy seismic trace into a “blocked” pattern that more closely resembles well log data, as illustrated in figure 4. This process dramatically improves the visualization of gas sands, wet sands, and fluid contacts compared to conventional seismic displays (figures 1and 3).
Practical Benefits for Exploration and Development
Unlike traditional AVO analysis, which often requires extensive preexisting knowledge about rock properties, background trends or basin characteristics, the DQ method works directly with the seismic data itself. By systematically examining phase relationships and amplitude patterns, it generates physically meaningful outputs that cut through the complexities of traditional interpretation techniques. The DQ trace method entails extensive automated interpretation steps before deciding what datapoints to display in the DQ or θpx traces.
The DQ trace method demonstrates a capability in differentiating reservoir types across varying geological contexts. It handles both gas and wet sands by maintaining strong amplitudes where expected (figure 3b) while simultaneously enhancing typically weak responses as in figure 1b. This characteristic proves particularly valuable when interpreting subtle features in complex reservoirs, where conventional seismic displays might obscure critical geological information. As demonstrated in figures 1 and 3, the method maintains visibility in areas where traditional stack data would typically show weak or indistinct responses, thereby providing geoscientists with a more nuanced approach to reservoir characterization.
Another aspect of the method is its relationship with porosity. As demonstrated in figure 4, the DQ trace shape mirrors patterns seen in porosity logs, allowing geoscientists to quickly estimate reservoir quality across an entire field. This correlation with porosity provides valuable insights for reservoir characterization without requiring additional well data or complex calculations. The histogram displays in figure 5 further demonstrate how the method effectively separates different lithologies and fluid types across all AVO classes, making interpretation more reliable.
Additionally, the method particularly excels at thin bed detection and characterization, addressing one of the most persistent challenges in seismic interpretation (figure 3d, trace 10). Where conventional seismic data might struggle to resolve thin reservoir units, the DQ trace method often provides clearer definition of these important features. This improved resolution can be critical for identifying bypassed pay zones or understanding reservoir compartmentalization. For example, letter A in figure 1 is the edge of another gas sand layer that extends beyond the plane of this section.
Future Applications
The DQ trace method provides a new approach to seismic interpretation that addresses several persistent challenges in quantitative analysis of seismic data. The method’s ability to transform conventional seismic data into a format that better correlates with reservoir properties, while requiring minimal a priori assumptions about rock properties or basin characteristics, represents a useful addition to existing interpretation workflows. The synthetic results shown in figures 1 to 5 demonstrate how this transformation can help delineate reservoir boundaries and fluid contacts across different geological settings and AVO classes, particularly in cases where conventional attributes may struggle with thin beds or complex stratigraphy.
Looking ahead, the DQ trace method’s systematic approach to seismic analysis suggests several potential applications worth investigating. The method’s two output volumes – DQ distance and Theta PX – provide meaningful attributes that could be integrated with machine learning workflows. The consistent nature of these outputs, combined with their demonstrated correlation to reservoir properties, makes them potentially valuable inputs for automated interpretation systems. The method’s computational efficiency also suggests possibilities for application to basin-scale datasets, where it might help identify subtle stratigraphic features or fluid contacts that are difficult to detect with conventional attributes.
The DQ trace method’s correlation with well log data, particularly porosity as shown in figure 4, suggests it may help bridge the scale gap between seismic and well data. This capability could prove especially useful in areas with limited well control or complex geology, where traditional methods of seismic-to-well correlation often face significant challenges. Further research and field testing will help determine the method’s full range of applications and limitations across different geological settings and data quality conditions.