Gains:
- Ability to distinguish the cycle skip in automatic horizon tracking and the acquisition footprint in fault detection from the real structure and manually inspect it
- Understand that seismic facies classes are statistical and be able to make geological meaning with the knowledge of wells and depositional environments.
- Ability to test the risk of overfitting in feature combination with the blind well test and limit the number of features according to the number of wells
Processing is finished; You have a clean, migrated seismic volume. Now the real question begins: what does this image say about the underground? Where are the layer boundaries, in which direction do the faults extend, which area is sand, which is clay, where might there be a reservoir? Seismic interpretation is the process of extracting the structural and sedimentary (stratigraphic) model of the subsurface from migrated data. In this unit, we will see how artificial intelligence (AI) produces a "quick first draft" in horizon and fault tracking, seismic facies classification and attribute analysis, but who builds the geological meaning and final model.
Building blocks of interpretation
Let's clarify the terms. Horizon is the continuous reflective surface observed in the seismic section and generally corresponding to a geological layer boundary. A fault is a plane of discontinuity where layers break and shift; It appears in places where reflections are interrupted or shifted in the cross-section. Seismic facies are volume regions that have similar wave character (amplitude, continuity, texture) and generally reflect a similar sedimentary environment. Seismic attributes are measurements derived from traces that direct the eye to certain features (discontinuity, slope, amplitude, frequency); for example, the coherence/continuity attribute highlights faults and the spectral decomposition highlights channel geometries.
AI in horizon and fault tracking
Manual horizon tracking is when the interpreter traces a reflection section by section and marks thousands of points—accurate but slow. AI-based automatic trackers can start from a few seed points and spread across the entire volume; In fault detection, CNN-based models (image pattern learning networks) can mark discontinuities throughout the volume. This cuts what used to take weeks by hand into hours.
However, there are two traps. First, the automatic tracker can cycle skip a horizon—shifting from one wave crest to a neighboring crest; The result is a seemingly smooth but geologically inaccurate surface. Second, the fault detector may mistake processing-generated linear noise (e.g., acquisition footprint) as a fault. That's why every surface and fault network generated by AI is manually inspected at intersections (fault-horizon intersection) and well locations.
Tip: Visually monitor an automatically tracked horizon in several key sections by overlaying it on the data itself. The surface has cycle skipped where it leaves a strong reflection and jumps to a weak peak; Do not spread the volume without correcting these areas manually.
Seismic facies classification
Facies classification is the task of dividing the volume into regions according to wave character. In the supervised approach, you assign labels from known lithologies in the wells and train the model; In the unsupervised approach, the model itself clusters regions with similar characteristics and you give geological meaning later. AI provides great speed here, but classes are statistical similarity, not geological fact. The region that the model calls "facies 3" only gains a meaning like "channel sand" with the knowledge of the well and the depositional environment.
Attention: You provide the number of unsupervised clustering classes; Different numbers of classes produce completely different maps. Don't get locked into a single cluster; Change the number of classes and test whether the result remains geologically stable.
Pitfall in feature analysis: multivariate relationship
AI can combine dozens of features and build powerful classifiers. But trying to fit a large number of features to a small number of wells results in overfitting - the inability of the model to memorize training examples and generalize to new data: the map is perfect in wells and meaningless between wells. Rule: keep the number of features proportional to the control data (well) and test the map in an independent well (blind test).
three mini cases
Case 1 — Horizon tracking down to hours. The main reservoir horizon in a volume of 900 km² was marked manually in 9 days. The AI tracker spread the surface from 6 seed points in 3 hours; The reviewer manually corrected the 14 cycle skip area. Result: same accuracy, one day instead of days.
Case 2 — Mistaking a footprint for a fault. A fault detector marked regular north-south lines. The commentator noticed that these lines corresponded exactly to the acquisition line spacing: they were not faults, but acquisition footprints. The actual faults ran perpendicular to this regular pattern; When the footprint was eliminated, the fault map became meaningful.
Case 3 — Overfitting facies map. A classifier trained with 22 features and only 5 wells gave 98% accuracy on the wells but classified sandstone as claystone in the sixth (blind) well. When the number of features was reduced to 6 and retrained, blind well accuracy increased from 61% to 88%: the map was also reliable across wells.
Four copyable templates
1) Attribute selection consultation:
Your role: seismic interpretation expert. My target: [fluvial channel sand / carbonate reservoir]. Which seismic attributes (coherence, spectral decomposition, RMS amplitude, slope) and in what order should I use to highlight this goal? What does each attribute represent, what misleading pattern might it produce? Explain briefly.
2) Automatic horizon inspection:
I have an automatically tracked horizon. Give me a checklist for detecting cycle skip: in which sections, which symptoms should I look for (amplitude drop, sudden depth jump), how do I confirm with the well tie, and in what order do I make the correction?
3) Facies class number testing:
I'm doing unsupervised facies clustering. I tried the number of classes 4,6 and 8, the maps came out differently. What criteria (cluster size distribution, well compatibility, spatial consistency) should I look at to evaluate which cluster is more geologically balanced? Suggest a decision procedure.
4) Overfitting control:
I trained a facies classifier with [N] features and [M] wells. How do I measure the risk of overfitting? How do I set up a blind well test, how do I limit the number of attributes based on the number of wells? Give actionable steps.
Weak prompt / Strong prompt
Weak prompt:
Find the reservoir in this volume and map it.
“Find” expects one-step magic; AI produces a statistical map without geological context, it cannot be verified.
Powerful prompt:
Your role: reservoir-focused interpretation specialist. Context: fluvialsystem, target channel sand, lithology known in 3 wells. Task:(1) suggest combination of attributes that will highlight the channel geometry; (2) write the steps to extract candidate facies by unsupervised clustering; (3) Tell me how to test each candidate with 3 wells and a blind 4th well. Present the result in "candidate" language, not "geological fact" language.
Geological context, well control, and the demand for blind testing make the output defensible.
Interpretation tasks and the role of AI
Quest
AI contribution
Main risk
verification
Horizon tracking
high speed
Cycle skip
Well tie, visual section inspection
fault detection
Generating candidates
Don't mistake your footprint for a fault
Direction/cross data control
Facies classification
clustering
meaningless class
Well and depositional environment
Attribute merging
strong distinction
overfitting
blind well test
Common mistakes
- Automatic spreading of the horizon without visual inspection. Cycle skip produces smooth but inaccurate surface.
- Mistaking every linear pattern for a fault. The acquisition footprint produces regular lines, look at its direction.
- Attributing direct geological meaning to the facies class. Class is statistical; Make sense with the well.
- More attributes, fewer wells. Overfitting turns the map into garbage between wells.
- Skip the blind test. Measuring accuracy only in training wells is misleading.
In summary
AI in seismic interpretation; It reduces horizon/fault tracking to hours, quickly clusters facies, and combines attributes powerfully. But every speed gain has a trap: cycle skip, mistaking a footprint for a fault, meaningless facies, overfitting. AI produces a statistical outline; He is the interpreter who turns the draft into geological reality, makes sense of it with the knowledge of the well and the depositional environment, and tests it with a blind test. The ultimate underground model is always the one established and defended by man.
Application task
Select a target (channel sand, baking soda, etc.). Create an attribute plan with the "Attribute selection consultation" template. Then use the “Facies class count testing” template for unsupervised clustering and compare at least two different class counts; Write down which one is more geologically stable and which well test you will use to verify this.
checklist
- [ ] I visually inspected the automatic horizon in key sections and corrected the cycle skip.
- [ ] I separated the fault candidates from the footprint with direction and cross-over data.
- [ ] I interpreted the facies classes with the well and depositional environment.
- [ ] I limited the number of attributes based on the number of wells.
- [ ] I tested the result in a blind well.