Gains:
- Understand how seismic reflection data is collected, the role of AI in horizon and fault interpretation, and the polysemy of inversion.
- Ability to locate fault detection, horizon tracking and facies mapping with seismic attributes and machine learning
- Ability to test seismic AI output with well tie, geological plausibility and uncertainty
The eye of oil and gas exploration is the seismic reflection method. A controlled energy source on the surface (vibrating truck on land, air gun at sea) sends a sound wave underground; These waves are reflected from the boundaries of different rock layers and returned to receivers (geophone/hydrophone) on the surface. We deduce the cross-section of the underground from the arrival time and amplitude of the reflections. The result is a huge volume of 3D data that “shows” kilometers of depth. But this image is not directly a photograph; It is a signal that is indirect, noisy and open to interpretation. In this unit, we use artificial intelligence in three parts of seismic interpretation: horizon (reflection surface) tracking, fault (fracture) detection, and facies/fluid extraction from seismic attributes. And let's reiterate the most important concept of this unit: seismic inversion makes a lot of sense — multiple subsurface models can explain the same data; so AI output is not accurate without connecting with well data.
Let's Get to Know Seismic Data
A few basic terms:
- Horizon: A traceable reflection surface corresponding to the boundary of a single geological layer in the seismic volume. By "following" these surfaces, the interpreter infers the shape of the structure.
- Fault: The plane where the rock is broken and shifted. Appears as discontinuity/shift in reflections; may be a hydrocarbon trap or escape route.
- Seismic attribute: Measurements derived from raw amplitude (amplitude, continuity, coherence, spectral separation, slope/dip). It is used as a fault and facies indicator.
- Inversion: The process of switching from the reflection amplitude to the acoustic impedance of the rock (density × velocity); It is associated with lithology and porosity.
- Well tie: Linking the actual depth/velocity data in the well to the seismic time axis; Anchors seismic interpretation to ground truth.
Hint: Seismic is on the time axis (round trip time of the wave), wells are on the depth axis. No seismic interpretation is reliable without a well tie connecting the two; AI output should also sit on this bond.
Where is AI Strong in Seismic Interpretation?
Fault detection. Faults with coherence and similar qualities appear as discontinuities; machine learning (especially convolutional neural networks) quickly flags these patterns in large volumes. Fault mapping, which takes days by hand, is reduced to hours.
Horizon tracking. The model can automatically track the reflection in a 3D volume from a starting point; It produces a draft that the commentator only has to correct.
Facies and fluid extraction. From multiple attributes (attribute fusion) the depositional environment or possible gas/water separation (e.g. “bright spot”—high amplitude caused by gas) can be classified.
Common ground: AI produces a draft comment; The commentator corrects it with geological plausibility and connects it with the well.
Polysemy and the Well Link
The fundamental limit of seismic inversion is non-uniqueness: different impedance/lithology/fluid combinations can produce the same amplitude data. A "bright spot" could be gas, but it could also be a coal layer or just a change in lithology. AI produces a “best fit” solution to the data; But what fits best is not what is right.
Solution: well constraints. Log and production data of wells drilled in the field are independent facts that narrow the seismic interpretation. Has a gas zone marked by AI actually produced gas in a well passing through that point? Is the fault consistent with the well correlation? Seismic AI output without well tie is a hypothesis.
Caution: Seismic resolution is limited; typically cannot separate layers below a few tens of meters ("separation limit"). If the AI shows a thin reservoir layer as "clear", there may be an over-interpretation below this resolution. Question the model's claim that it resolves thin layers reliably.
Three Mini Cases: By the Numbers
Case 1 — Fault mapping speed. Fault interpretation in a 3D volume (approximately 1,200 km²) was three weeks' work by hand. A model trained on the coherence attribute marked the main fault system in one day; The reviewer approved 85% of it, had to eliminate 15% (spurious discontinuities caused by noise). The net gain was large, but human elimination was essential.
Case 2 — Bright dot trap. The model found a strong bright spot in a structure and flagged it as "possible gas." The team evaluated this with enthusiasm; However, a previously drilled well in the area showed coal shale at exactly that level. The bright spot came not from the gas, but from the low impedance of the coal. Well bonding prevented an expensive mislocation decision.
Case 3 — Resolution limit. AI reported an 8-meter “quality reservoir” layer from the inversion. The separation limit at the seismic dominant frequency was ~15 meters; this thickness could not be resolved with confidence by seismic. The commentator relabeled the layer as “possible, sub-resolution, well confirmation required.” Excessive comments caught.
Weak Prompt / Strong Prompt
Weak prompt:
Find faults and gas zones of this seismic nature.[data summary]
Powerful prompt:
Generate a structural interpretation DRAFT from the seismic attribute/interpretation summary below. Rules:- List fault candidates as "possible faults"; For each, write down which attribute (coherence/slope) support you extracted from. Mark possible noise sources separately. - Present bright spot/amplitude anomalies with double probability "could be gas BUT could also be coal/lithology"; specify well tie requirement.- Consider seismic separation boundary; Mark sub-resolution thin layer claims as "sub-resolution, confirmation required". - Do not present any interpretation as "conclusive"; Write which well data to narrow down with.Data: [attribute/comment summary]
Four Copiable Templates
1) Fault candidate scanning:
List the fault candidates from the discontinuity/coherence summary below. For each candidate: orientation, continuity, possible noise source, confidence level (low/medium/high).What additional control is needed to separate true fault from artifact? Data: [summary]
2) Amplitude anomaly double-probability:
List all plausible explanations for the following amplitude anomaly (gas, coal, lithology, compression). Write the expected impedance signature for each and indicate which one will decompose with the well data. Don't focus on one explanation. Data: [anomaly]
3) Well tie checklist:
Generate checklist for connecting seismic interpretation to well: velocity model, syntheticseismogram matching, phase/polarity check, horizon-marker matching. What does it mean if there is incompatibility at every step?
4) Excessive comment filter:
Check the claims in that seismic interpretation for resolution. Estimate the separation limit from the dominant frequency and speed; Mark the thickness claims below this as "sub-resolution". Comment: [summary], frequency/speed: [value]
Seismic Evidence and Verification
item
what gives
border
verification
raw amplitude
reflection structure
Noise, resolution
Processing quality
Quality (coherence/slope)
fault, discontinuity
May produce artifact
human elimination
Inverse solution (impedance)
Lithology/porosity clue
very meaningful
well tie
AI classification
quick draft
Depends on training data
Geology + well
Well log/test
ground truth
spot measurement
Anchor (most reliable)
Common mistakes
- Bypassing the well tie. Mistaking seismic interpretation for real without time-depth/well tie.
- Focusing on a single explanation. Quickly declaring the bright spot "gas" and omitting the possibility of coal/lithology.
- Ignoring the resolution limit. To "clearly" report the thin layer that cannot be resolved by seismic.
- Mistaking the artifact for a fault. Taking noise and processing traces as real structure.
- Relying on the model's confidence score. Mistaking a high score for geological accuracy.
In summary
- Seismic is an indirect and noisy view of the subsurface; The inverse solution makes a lot of sense.
- AI produces powerful blueprints in fault detection, horizon tracking, and attribute fusion; but human elimination and geological plausibility are essential.
- Each seismic AI output is a hypothesis without being narrowed down by well tie and well data.
- Do not lock amplitude anomalies into a single explanation; Consider double/multiple probability.
- Claims below the seismic resolution limit are overinterpretation; Mark it with the "confirmation required" tag.
Application task
Take a seismic interpretation scenario (representation): an amplitude anomaly and several fault candidates. Have a draft structural comment produced with a powerful prompt. Then: (1) write at least two non-gas explanations for the anomaly, (2) indicate which well data it would discriminate against, (3) indicate which of the fault candidates could be noise. If you give dominant frequency and speed, have the model check the assertion of a sub-resolution layer.
checklist
- [ ] I understand that seismic data is indirect, noisy, and resolution limited.
- [ ] I produce fault/horizon/attribute drafts with AI and correct them with human elimination.
- [ ] Knowing the polysemy of the inverse solution, I narrow the interpretation to the well bond and well data.
- [ ] I consider amplitude anomalies with multiple possibilities without locking them into a single explanation.
- [ ] I flag sub-resolution claims as over-interpretation.