Predictive coherence

Abstract

Detection and interpretation of fault systems and stratigraphic features and the relationship between them are crucial for seismic interpretation and reservoir characterization. To provide better interpretation insight and to be able to extract overlooked features out of seismic data volumes, we have developed a new attribute that detects faults and other discontinuities while handling local nonstationary variations across them. First, we used predictive painting to form a structural prediction of seismic events from neighboring traces (left and right neighboring traces in 2D and neighboring traces in all directions around a reference trace in 3D) according to the local structural slopes. Then, we computed prediction residuals by subtracting each prediction from the original data, and we found the smallest prediction-error interval for each point that best represented discontinuity information at that point. The extracted fault information changed with location (spatially and temporally), and it was nonstationary. Conventional coherence measures operate on a spatial window of neighboring traces and a temporal (vertical) analysis window of samples above and below the analysis point, and they can hardly cope with nonstationarity in fault information. In contrast, in our method, neither temporal nor spatial windows were involved in coherence computation, which allowed us to honor nonstationary changes of fault information and to achieve high resolution in the vertical and lateral directions. To assess the performance of the proposed attribute, we compared it with the conventional coherence attribute over the same data set. The comparison demonstrated the effectiveness of discontinuity detection using predictive coherence and showed its value in extracting additional information from seismic data.

Image processing of seismic attributes for automatic fault extraction

“…Semblance (Marfurt et al, 1998), eigenstructure (Gersztenkorn and Marfurt, 1999), gradient structure tensor (Bakker, 2002), and energy-ratio similarity (Chopra and Marfurt, 2007) are subsequent coherence algorithms that operate on a spatial window of five or more neighboring traces. Other variations of coherence algorithms, including local structure entropy (Cohen and Coifman, 2002), variance (Van Bemmel and Pepper, 2000), gradient magnitude (Aqrawi and Boe, 2011), automated fault extraction (Dorn et al, 2012), fault likelihood (Hale, 2013), predictive painting (Karimi et al, 2015), directional structure-tensor coherence (Wu, 2017), and generalized tensor-based coherence (Alaudah and AlRegib, 2017) provide similar results. Some of these algorithms are sensitive to lateral change in the amplitude (such as the Sobel filter), whereas other are sensitive to lateral change in the waveform (such as eigenstructure and GST "chaos") (Marfurt and Alves, 2015).…”

Oriented extrapolation of common‐midpoint gathers in the absence of near‐offset data using predictive painting

“…Burnett & Fomel, 2009; Casasanta & Fomel, 2011; Khoshnavaz et al ., 2016b; Raeisdana et al ., 2021), seismic interpretation (e.g. Karimi, 2015; Karimi et al ., 2015) and seismic interpolation (Khoshnavaz et al ., 2018). The main advantage of slope‐based workflows is associated with their considerable time‐efficiency over the conventional workflows (Fomel, 2007).…”

Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

“…Based on this observation, numerous fault detection methods compute a set of additional attribute images such as semblance (Marfurt et al., 1998), coherency (Karimi et al., 2015; Li & Lu, 2014; Marfurt et al., 1999), variance (Van Bemmel & Pepper, 2000), curvature (Al‐Dossary & Marfurt, 2006; Di & Gao, 2016; Roberts, 2001), or fault likelihood (Hale, 2013), where fault features are enhanced while other structural and stratigraphic features are suppressed. In calculating these fault attributes, we often need to estimate reflection slopes and measure reflection discontinuities along seismic horizons (Gersztenkorn & Marfurt, 1999; Hale, 2009, 2013; Karimi et al., 2015). However, the computed attributes are typically sensitive to noisy or stratigraphic features that are apparent to be discontinuities, which causes the necessity of using extra processing such as ant tracking (Pedersen et al., 2003) and optimal surface voting (X. Wu & Fomel, 2018a) to further highlight fault‐related features.…”