Attributes for reservoir characterization: putting it together

The reservoir characterization workflow (in five steps)

• Frame the question. What is the target? A specific stratigraphic interval (e.g. "the Rotliegend sandstone at ~1700 ms in the F3 area")? A trap-bounded prospect? A field already in production where you're looking for missed pay? The interpretation pipeline is shaped by what you're trying to find.
• Pick the structural framework. Map the key horizons (§2.3, §2.4) and faults (§2.5). The structural picture is where everything else gets attached. Coherence and curvature (§6.4, §6.3) help find the structures that horizon picking then commits to.
• Compute attributes at the target depth. RMS amplitude, envelope, spectral content — either as full-volume attributes (§6.1, §6.2) and then slice through them, or as horizon-gated maps (this section's widget) extracted at the target horizon.
• Look for direct evidence of hydrocarbons. The four classical DHIs (bright spot, dim spot, flat spot, polarity reversal) each have a multi-attribute signature. RGB blends (§6.5) make several DHIs visible in one picture.
• Tie to well control. Calibrate attribute responses against any wells that penetrate the target. Without wells, the interpretation is qualitative; with even one well, you can start quantifying. The seismic-to-well tie is where rock physics (Part 5) re-enters the workflow.

This is a loop, not a sequence. You revisit each step as the interpretation evolves — a wider gate reveals an unexpected anomaly, which prompts re-examining the structure, which re-positions the next attribute extraction.

Volume attributes vs horizon-gated maps

So far we have been displaying attributes as slices through 3D volumes — inlines, crosslines, time slices. That works for exploration and orientation but not for reservoir delivery. The deliverable that goes into the reservoir model and the prospect ranking is a map: a single 2D image showing the property of interest at the target horizon, top-down, ready to overlay on a base map alongside leases, wells, and infrastructure.

To get a map, you extract the attribute over a depth gate centred on the horizon of interest and average over the gate:

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The gate width matters:

• Narrow gate (±10–20 ms) maps the immediate target reflector. Use when you have a sharp, well-defined horizon and you want to characterize its acoustic response specifically.
• Wide gate (±50–100 ms) maps an interval. Use for stratigraphic plays where the reservoir is a package of beds rather than a single reflector, or when horizon picking is uncertain.

In production interpretation, the gate is usually defined RELATIVE to a picked horizon (e.g. "from 10 ms above the Top Reservoir to 30 ms below"), so the gate follows the geology even where it dips and folds. The widget below uses a constant-time gate (centred on a chosen TWT) for simplicity, but the principle is the same.

Exercise — Map a target interval on F3

• The widget starts on RMS amplitude with a ±30 ms gate centred near the middle of the F3 volume. The map you see is the canonical "amplitude map" of reservoir interpretation. Each pixel's brightness is the average RMS over the depth gate at that (IL, XL) location.
• Slide the gate centre to ~1100 ms. F3 has prominent chalk-related amplitude features in this depth range; you should see the map pattern change as you move the gate to capture different intervals.
• Widen the half-gate to ±60 ms and slide back to 1100 ms. The map smooths — you're now averaging across more reflectors. Compare to the narrow-gate version: the wider map is better for showing broad regional trends; the narrower map for picking out specific reflector features.
• Switch the Attribute to Coherence. Now the map shows where the rock is laterally continuous (bright) versus where it is broken (dark). Fault networks should be visible as dark lineaments. Compared to the time-slice coherence view from §6.4: the gate-averaged version is more stable (averages out per-slice noise) and is what you'd typically deliver as a "fault map" for the target zone.
• Switch to Spectral power 30 Hz. Now the map shows where 30 Hz energy is concentrated within the gate. Compare with Spectral power 60 Hz — the maps are different, because different bed thicknesses tune at different frequencies. Together these maps tell you about the lithological character of the gated interval, not just its position.

The four classical DHIs (Direct Hydrocarbon Indicators)

Hydrocarbons (especially gas) change the acoustic properties of rock dramatically: lower density, lower P-wave velocity, often stronger amplitude contrast. These changes leave detectable seismic signatures called Direct Hydrocarbon Indicators. None alone is conclusive; multi-attribute evidence is what builds confidence.

• Bright spot. A sand reservoir charged with gas has lower acoustic impedance than the surrounding shale, producing a stronger negative reflection at the top. Bright spots show as anomalously high amplitudes that conform to the reservoir geometry. Multi-attribute evidence: localized RMS anomaly (§6.1) PLUS amplitude conformance to a structural high or stratigraphic trap PLUS often a flat spot beneath. Dangerous false positive: thin-bed tuning (§1.7) can also produce bright amplitudes without any hydrocarbons.
• Dim spot. The opposite case: a gas-charged carbonate or stiff sand may have HIGHER impedance than the shale above, weakening the reflection. Dim spots are easy to miss because "no anomaly" is the visual clue. Multi-attribute evidence: anomalously low amplitude in a region where you'd expect normal reflectivity, plus the same conformance and structural-trap reasoning. Spectral attributes sometimes help — dim spots may show distinctive frequency shifts.
• Flat spot. A horizontal reflector cutting through dipping geology, marking a fluid contact (gas–water, gas–oil, or oil–water). The contact is horizontal because hydrostatic equilibrium dominates; the surrounding rock is dipping because of structure. Flat spots are the strongest single DHI when present — there's no other geological process that produces horizontal reflectors cross-cutting structure. Look for them on inline sections through suspected reservoirs.
• Polarity reversal. A reflector that switches polarity laterally as you cross from a brine-filled to a hydrocarbon-filled section of the same reservoir. The brine response is one polarity; the hydrocarbon response (with different impedance contrast) is opposite. Subtle and easy to confuse with phase-rotation artefacts; cross-check against polarity convention (§2.2) and seismic-to-well ties.

An RGB blend (§6.5) of (RMS amplitude / spectral 15 Hz / spectral 30 Hz) is a powerful DHI screening tool. Bright zones with anomalous low-frequency content (red/yellow) flag candidate brights; structural conformance can be checked by comparing with a coherence/curvature blend.

Amplitude conformance: the most important visual check

An amplitude anomaly that doesn't conform to a structural or stratigraphic trap is suspicious. Conformance means the bright (or dim) zone's outline coincides with the closure of a trap — the boundary of the structural high, the edge of a fault block, the geomorphic outline of a channel. If the amplitude follows the trap boundary, it suggests the trap is filled with something acoustically distinctive (often hydrocarbons). If the amplitude crosses the trap boundary or sits in a region with no obvious trap, it's probably a depositional or processing artefact, not a hydrocarbon indicator.

Conformance is essentially a question of whether two maps look the same. Make the amplitude map (this section's widget). Make the structural map (a horizon-pick TWT contour map, or a coherence map outlining faults). Overlay them. If the amplitude follows the structure, you have a candidate prospect. If it doesn't, something else is causing the amplitude.

Tuning and reservoir thickness

Section 1.7's tuning curve told us that reflector amplitude depends on bed thickness in a non-monotonic way: as a bed thins from large thickness toward $\lambda/4$ (the tuning thickness), amplitude INCREASES due to constructive interference between top and base reflections. Below tuning, amplitude rapidly decreases as thickness goes to zero.

For reservoir characterization this is both a help and a hazard:

• Help. Below tuning thickness, the amplitude depends on thickness, so amplitude maps are also (calibrated against well control) reservoir-thickness maps. This is one of the most-used quantitative interpretation results: net pay thickness derived from amplitude.
• Hazard. A bright spot caused by tuning of a sub-resolution shale-sand-shale package can look indistinguishable from a bright spot caused by hydrocarbons in a thicker reservoir. Spectral decomposition (§6.2) helps disambiguate — a tuned response shows specific frequency enhancement; a hydrocarbon response shifts the whole spectrum.

The practical rule: never quote reservoir thickness from amplitude alone unless you have well control to calibrate the relationship for THIS reservoir in THIS data.

Beyond this section: AVO and inversion

Two large bodies of techniques for quantitative reservoir characterization are out of scope for this teaching app but worth naming so you know they exist:

• AVO (Amplitude vs Offset). Measures how reflection amplitude changes with the angle of incidence of the seismic wave. Different fluid types, lithologies, and porosities have different AVO signatures. An "AVO Class III" response is the classic gas-sand signature. Requires PRE-stack data (we work with stacked data here); needs the offset-dependent amplitudes preserved through processing. Standard tool in modern E&P; supported by every major interpretation package.
• Seismic inversion. Mathematically inverts the seismic for the underlying acoustic-impedance volume (or, with AVO, separately for V_p, V_s, density). The result is a property volume that no longer requires interpretation of wavelet conventions — high-impedance regions correspond directly to specific lithologies. Inversion-derived attributes (acoustic impedance maps, lambda-rho/mu-rho crossplots, etc.) feed directly into reservoir-characterization workflows. Inversion is a major sub-discipline of geophysics with its own textbooks.

Both AVO and inversion sit on top of the post-stack attribute toolbox we have built here. Once you understand amplitude, frequency, geometry, coherence, and how to combine them into maps and blends, the AVO and inversion workflows become natural extensions — they add quantitative rigour without changing the underlying interpretive logic.

Common reservoir-characterization pitfalls

• Cherry-picking attributes. The interpreter tries every attribute until they find one that "shows" the desired anomaly. With dozens of attributes, you can almost always find SOMETHING that matches your hypothesis. Discipline: pick the attributes you'll use BEFORE you look at the result, based on the geological hypothesis. Document the choice.
• Confirmation bias. An interpretation that found one DHI tends to find more, because the interpreter is now looking for support. Counter: actively search for evidence against the hypothesis. What would FALSIFY this interpretation? Look for that.
• Confusing tuning with hydrocarbons. The single most common mistake. A tuned thin bed produces amplitude effects that mimic gas. Always check thickness via spectral decomposition or, ideally, well control.
• Trusting attributes over wells. A bright spot that disagrees with a nearby well is almost always wrong (the well is much closer to ground truth than the seismic). The exception is when the well is far enough away that lateral facies changes can plausibly explain the discrepancy. Be honest about the well distance.
• Acquisition footprint masquerading as geology. Stripey amplitude or coherence patterns aligned with the acquisition shoot direction are almost certainly footprint, not geology. Always check this before reporting an anomaly.
• Ignoring uncertainty. Every attribute has a confidence; every map has zones where the data is good and zones where it isn't. Communicating uncertainty (e.g. confidence intervals on net pay, or "high-risk vs low-risk parts of the reservoir") is what distinguishes mature interpretation from naive overconfidence.

Closing Part 6

You now have the complete attribute toolkit:

• Amplitude attributes (§6.1) for "how loud is the reflectivity here"
• Frequency attributes (§6.2) for "what spectral colour is the wavelet"
• Geometric attributes (§6.3) for "how is the reflector tilted and bending"
• Coherence (§6.4) for "how similar to neighbours"
• Multi-attribute combination (§6.5) for integrating three at once into a colour image
• Horizon-gated maps (this section) for converting volumes into reservoir-delivery maps

Combined with the structural interpretation tools from Part 2 (horizons, faults, QC), this closes the foundational loop of post-stack quantitative interpretation. From here, the next stages of an interpreter's training go in two directions:

• Quantitative depth: rock physics (Part 5), AVO, pre-stack inversion, geomechanics. These extend the interpretive framework to predict ROCK properties from SEISMIC properties.
• Geological breadth: stratigraphic interpretation (Part 4), structural geology in depth (Part 3), basin analysis. These extend the interpretive framework to predict where the rock came from and how it ended up where it is.

Part 6 is one foundational block in the larger interpretive curriculum. Whatever you build next, you have what you need to read what a seismic volume is telling you.