seismic Facies analysis
Seismic facies analysis is a key step in seismic interpretation and reservoir characterization. It involves dividing a seismic volume (or section) into areas or intervals that show similar reflection characteristics, which are then interpreted in terms of depositional environment, lithology, and fluid content.
Seismic facies analysis is the interpretation of groups of seismic reflections based on their geometry, amplitude, continuity, frequency, and configuration, to infer the geological meaning such as sedimentary environment or rock type.
Key Seismic Attributes Used
Seismic facies are typically recognized by patterns in:
- Amplitude – strength of reflection (indicates acoustic impedance contrasts).
- Continuity – degree of lateral persistence of reflectors (related to depositional uniformity).
- Frequency / bandwidth – sharpness or smoothness of reflections (related to layer thickness and lithologic variability).
- Configuration / geometry – reflection terminations (onlap, downlap, truncation) and shapes of reflectors (parallel, chaotic, hummocky, etc.).
- Phase – can indicate polarity or tuning effects.
Types of Seismic Facies Analysis
1. Qualitative (visual) analysis
- Done by the interpreter visually examining seismic sections or time slices.
- Based on classical criteria defined by Mitchum et al. (1977) in seismic stratigraphy.
2. Quantitative (attribute-based) analysis
- Uses seismic attributes (e.g., RMS amplitude, instantaneous frequency, similarity) or machine learning techniques (e.g., PCA, SOM, K-means) to classify seismic facies automatically.
- Provides facies maps that can be correlated with well data.
Workflow
Typical workflow for seismic facies analysis:
- Define objective (e.g., reservoir delineation, channel detection).
- Select seismic interval (horizon or volume).
- Compute relevant seismic attributes.
- Apply classification or clustering techniques (manual or automatic).
- Interpret facies in geological terms (e.g., channel sand, shale, reef, etc.).
- Validate with well logs, core data, or known geology.
Geological Significance
Seismic facies interpretation helps geoscientists to:
- Identify depositional environments (fluvial, deltaic, turbiditic, reefal, etc.).
- Map reservoir distribution and quality.
- Detect stratigraphic traps or unconformities.
- Support 3D reservoir modeling.
Principal Component Analysis (PCA)
PCA is a statistical technique that reduces a large set of correlated variables (attributes) into a smaller set of uncorrelated variables called principal components (PCs). Each principal component is a linear combination of the original attributes. The first few components usually explain most of the variability in the data.
In seismic studies, we may have dozens to hundreds of attributes (amplitude, frequency, coherence, curvature, etc.). Many are redundant (e.g., RMS amplitude, reflection strength, and envelope all measure signal energy). PCA helps by:
- Removing redundancy.
- Reducing dimensionality.
- Highlighting the most significant variations in the data.
- Creating composite attributes that better separate geological features.
How PCA Works (Step by Step)
1. Standardize the data
- Normalize each attribute so they have comparable scales.
- Example: RMS amplitude values might range in 100s, while coherence is between 0–1.
2. Compute the covariance matrix
- Measures how attributes vary with each other.
3. Calculate eigenvalues and eigenvectors
- Eigenvectors = direction of maximum variance (new attribute axes).
- Eigenvalues = amount of variance explained by each axis.
4. Rank the components
- PC1 (first principal component) explains the maximum variance.
- PC2 explains the next largest variance (orthogonal to PC1).
- PC3, PC4, etc., explain progressively less.
5. Project data into new space
- The seismic attributes are transformed into a smaller set of PCs.
Practical Example in Seismic
- Suppose you extract 10 seismic attributes around a channel system.
- PCA shows:
- PC1 = mostly amplitude-based attributes (RMS, reflection strength, envelope).
- PC2 = mostly geometric attributes (coherence, curvature).
- PC3 = mostly spectral attributes (dominant frequency, spectral energy).
- Instead of interpreting 10 maps, you now interpret 3 principal components that capture the essential information.
Advantages of PCA
- Reduces data size while preserving most information.
- Removes redundancy between correlated attributes.
- Helps identify hidden patterns in attribute space.
- Often used as a preprocessing step before clustering or machine learning.
Limitations
- PCA is linear → may miss non-linear relationships.
- PCs are mathematical combinations of attributes, not always directly interpretable geologically.
- Requires careful scaling and validation with well data.
PCA ANALYSIS on real data
PCA analysis base on 8 seismic attributes: instantaneous amp, average energy, reflection strength, instantaneous phase, instantaneous frequency, dominant frequency, spectral energy and semblance. PCi were sorted based on maximum correlation of Eigenvalues.