4D processing concepts & repeatability

4D seismic — more formally "time-lapse" seismic — is the practice of repeatedly imaging the same subsurface volume at different calendar times and comparing the images to track changes. The principal application is reservoir monitoring: fluid fronts migrating under injection, gas coming out of solution as reservoir pressure drops, compaction as a field depletes. A 4D dataset is two or more 3D surveys of the same target plus a processing chain designed to preserve the differences that reflect actual reservoir change while suppressing everything else.

1. What makes 4D hard

A reservoir pressure or saturation change typically perturbs the reflection coefficient at the reservoir horizon by 5–15 % of its baseline value. This is the 4D signal. Non-repeatability between baseline and monitor surveys — different shot positions, different tides, different source signatures, different noise — produces differences of the same order of magnitude or larger at every reflector in the volume. The signal is local to the reservoir; the noise is everywhere, including at the reservoir. If you subtract the two surveys directly, the reservoir change is typically invisible underneath the repeatability noise.

A successful 4D acquisition and processing flow drives the non-repeatability noise down to below the signal level, ideally at every reflector including the ones above and flanking the reservoir. That is what "repeatability" means in 4D.

2. The widget

Baseline has three reflectors; the deepest (1.5 s) is the reservoir with amplitude 0.12. Monitor has an amplitude change $\Delta R$ at the reservoir (the "4D signal") plus a time shift and amplitude scalar (the "non-repeatability"). The difference panel subtracts monitor minus baseline. Turn up $\Delta R$ with no non-repeatability and the difference shows a clean wavelet at 1.5 s only — the reservoir change. Now add a few milliseconds of time shift, or a 10 % amplitude scalar, and watch the difference fill up with energy at every reflector. The reservoir change has not moved, but it is now swamped by non-repeatability signal at the other reflectors.

The info strip reports overall NRMS and a signal-to-noise ratio: RMS of the difference at the reservoir window divided by RMS of the difference elsewhere. Above +6 dB the signal is detectable; near 0 dB it is marginal; below 0 dB it is swamped.

3. NRMS — the standard metric

The 200 rather than 100 is historical convention (matches AMPLITUDE factor-of-2 scaling). Interpretation bands:

• NRMS < 20 %: excellent repeatability. Small 4D changes (a few percent of baseline) are visible.
• NRMS 20–40 %: good repeatability. Medium 4D changes are visible; small ones are marginal.
• NRMS 40–60 %: marginal. Only large-scale 4D changes are detectable; local changes are lost.
• NRMS > 60 %: surveys are effectively uncomparable. Usually indicates acquisition geometry mismatch.

Modern dedicated 4D surveys routinely achieve NRMS of 8–15 % over the non-reservoir section. A legacy-to-modern comparison (baseline from 1998, monitor from 2020) typically sits at 40–70 % no matter what you do.

4. Sources of non-repeatability

• Acquisition geometry. Shot and receiver positions not exactly repeated between surveys. For streamer data, the tow direction, cable feathering, and streamer depth all vary.
• Tides and currents (marine). Water column thickness changes; so does its velocity. Even a 20 cm tidal difference between surveys shifts arrival times by a few ms.
• Weather and sea state. Wind and swell modulate the hydrophone depth; surface noise differs.
• Source signature. Air-gun bubble period depends on source pressure, water temperature, and array geometry. A 5 % pressure difference modulates bubble amplitude at a specific frequency.
• Near-surface changes (land and shallow-water). Groundwater table, freeze–thaw, vegetation growth. These change the shallow velocity and statics between surveys.
• Processing differences. If baseline and monitor are processed with different flows (different filter settings, different demultiple parameters), the differences show up as 4D "signal".
• Overburden pressure changes. Production itself compacts the reservoir and overburden, changing velocities above the target. The "true" 4D signal is therefore also partly due to overburden response.

5. Strategies to drive NRMS down

• Dedicated 4D acquisition. Plan both surveys with identical shot-receiver grids, streamer layouts, and source arrays. Pre-plan and maintain positions to within meters.
• Ocean-bottom nodes. Nodes sit on the seafloor permanently — exact repeatability by construction. The gold standard for 4D. Expensive but transformative.
• Joint processing (§8.4). Baseline and monitor go through the same processing chain with shared parameters, eliminating processing-induced differences.
• 4D binning and matching filters (§8.2). Post-acquisition, match traces from baseline and monitor by nearest-neighbour binning and apply a spectral / phase / amplitude matching filter.
• 4D noise attenuation. Coherent shot/swell noise that appears in one survey but not the other can be attenuated by specific filters designed in the difference domain.
• NRMS-aware QC (§8.3). Monitor NRMS through the processing chain; each processing step should either lower NRMS or leave it unchanged. A step that raises NRMS is removing 4D signal along with noise.

6. What 4D can measure

• Fluid front tracking. Oil-to-water contacts move as injection proceeds. 4D shows the water sweep.
• Gas out of solution. As reservoir pressure drops below the bubble point, dissolved gas comes out and reflectivity brightens dramatically.
• Pressure depletion. Effective stress changes alter rock frame stiffness; seismic velocity drops. The reservoir time shifts downward as the field depletes.
• Compartment identification. A reservoir with internal barriers shows 4D changes only in the compartments being produced; no-change zones mark the barriers.
• Compaction. Soft shales above the reservoir compact as pore pressure drops, causing time delays that propagate upward — the well-known "compaction signal" in 4D.

7. What 4D cannot (easily) do

• Quantify absolute saturation. 4D tells you that saturation changed; converting to absolute water saturation requires rock physics and is imprecise.
• Very small reservoirs. Below thin-bed tuning, 4D signal is ambiguous.
• Very deep reservoirs. Accumulated non-repeatability noise scales with travel time; deep reservoirs have less favourable SNR.
• Image what never reflected. If the reservoir did not produce a seismic reflection in the first place (sub-tuning thickness, low impedance contrast), 4D changes are invisible too.

Where this goes next

§8.2 goes into the post-acquisition tools that reduce NRMS: 4D binning (match traces by nearest source-receiver geometry), matching filters (align spectra, amplitudes, phases), and 4D-specific denoise. These are the workhorses that take a 40 % NRMS raw dataset down to 10–15 %.

References

• Yilmaz, Ö. (2001). Seismic Data Analysis (2 vols.). SEG.
• Sheriff, R. E., Geldart, L. P. (1995). Exploration Seismology (2nd ed.). Cambridge UP.
• Claerbout, J. F. (1976). Fundamentals of Geophysical Data Processing. McGraw-Hill.