QI workflow: from seismic to rock properties

Why QI is a pipeline

QI is not a single analysis. It is a sequence of six clearly defined stages, each one taking the output of the previous stage as its input and handing off its result to the next. The sequence is:

• Input data: pre-stack angle gathers (amplitude-preserving) + well logs (Vp, Vs, density).
• Rock-Physics Model (RPM): calibrate Gassmann (§5.3) + forward model; build a rock-physics template for each plausible rock + fluid combination.
• Pre-stack inversion: solve for 3D Ip, Is, ρ volumes from the angle gathers.
• Property transform: map elastic (Ip, Is, ρ) to rock property (porosity, lithology, saturation) using the RPM.
• Facies classification: probabilistic Bayesian classifier mapping voxel-by-voxel probability for each facies class.
• Reservoir model: the 3D output with rock properties + facies probabilities, ready for volumetrics, reservoir simulation, and drilling decisions.

Each stage needs DIFFERENT expertise (geophysicist, rock physicist, inversion specialist, petrophysicist, reservoir modeler), which is why modern QI teams are multi-disciplinary. But the pipeline is tight: the geophysicist and rock-physicist talk, the inversion specialist consumes their RPM, the petrophysicist shapes the property transform, the modeler assembles the final product.

Exercise — walk the pipeline

• The widget opens in Workflow overview — all six stages visible. Read them left to right, row by row: INPUT → ROCK PHYSICS → INVERSION in row 1, then PROPERTIES → FACIES → RESERVOIR in row 2. Arrows show the data flow.
• Switch to Stage 1 — Input data. This is where a QI project begins and ends: the data. The inputs are (a) AVO-preserving pre-stack gathers and (b) well logs. No gathers, no AVO; no wells, no calibration. Without both, a QI project is not viable.
• Switch to Stage 2 — Rock-physics model. The crossplot icon shows two colored clusters — imagine shale in one cluster and sand in the other. The RPM uses well data + Gassmann to predict where each rock + fluid combination will fall in elastic space. This calibration typically consumes 20-40% of project time and is the most common source of QI failure when done poorly.
• Switch to Stage 3 — Inversion. The three stacked tracks are the output volumes: Ip (P-impedance), Is (S-impedance), ρ (density). Inversion is a numerical optimization that solves for these three 3D cubes simultaneously from the AVO response at multiple angle stacks. It needs a LOW-FREQUENCY MODEL (from well logs interpolated along geological horizons) to supply the DC trend that seismic does not carry.
• Switch to Stage 4 — Properties. The transform takes the elastic volumes (Ip, Vp/Vs) and maps them to rock-property volumes (porosity φ, shale volume φsh). The transform is based on the RPM from stage 2: every voxel’s elastic signature is compared with the RPM to estimate the rock property.
• Switch to Stage 5 — Facies. The bands show three probabilistic classes (reservoir, marginal, non-reservoir). Modern QI outputs PROBABILITIES, not deterministic classes. A voxel might be 75% oil-sand, 20% brine-sand, 5% shale — and decisions are made accounting for all three possibilities.
• Switch to Stage 6 — Reservoir model. The 3D block shows the final product: a rock-property + facies volume suitable for volumetric calculation, well planning, and reservoir simulation. This is what the asset team uses to drill and produce.
• Finish in Flow mode — every stage bright, every arrow thick. This is how you should internalize the pipeline: as a single continuous flow rather than six separate analyses.

How QI integrates Parts 3–6

QI is not a new body of knowledge. It is a pipeline that WIRES TOGETHER the parts of the textbook you have already learned.

• Part 3 (Structural framework) delivers the GEOMETRY on which the QI volumes sit. The low-frequency model that inversion needs is built along mapped horizons (§3.4) and respects fault geometries (§3.2). Traps identified in §3.6 become QI targets.
• Part 4 (Stratigraphy) delivers the DEPOSITIONAL CONTEXT. The sequence stratigraphy from §4.2 provides the horizon framework; the depositional systems from §4.3 constrain the rock types expected at each location; seismic geomorphology (§4.6) validates the spatial pattern of QI outputs against expected depositional geometries.
• Part 5 (Rock physics + AVO) delivers the PHYSICS. The Gassmann fluid substitution (§5.3) is the calibration engine of the RPM. The AVO theory (§5.4–§5.5) is what pre-stack inversion inverts. The synthetic-seismogram loop (§5.6) ties all QI outputs back to the wells.
• Part 6 (Attributes) delivers the FEATURE ENGINEERING. Attribute volumes can be used as QI inputs (spectral decomposition to isolate tuning, coherence to mask faults). Attribute-based facies classification (§6.5) is a cousin of probabilistic QI classification.

Understanding this integration is the single biggest leap from interpreter-as-picker (Parts 3-4 era) to interpreter-as-modeler (QI era). You don’t need to abandon your picking skills; you need to extend them into a quantitative framework.

Where QI projects fail

QI looks clean on paper. In practice, projects fail for well-understood reasons:

• Bad input data. Non-amplitude-preserving gathers. Poor velocity model. Too few wells. Wells in the wrong facies. Garbage in, garbage out — no QI magic can rescue bad data.
• Wrong RPM. If the rock-physics template is wrong (e.g., Gassmann calibration doesn’t match observed log data because of anisotropy, micro-cracks, or exotic rock types), every downstream prediction is wrong. The RPM is the biggest project-risk stage.
• Bad low-frequency model. Inversion CAN NOT resolve the DC trend of impedance. Without a good low-frequency model (from well-log interpolation + stratigraphic framework), inversion output looks technically correct but doesn’t match well ties.
• Unpropagated uncertainty. Deterministic outputs presented as truth when the underlying data supports only probabilistic predictions. This is the scientific sin that burns careers and budgets.
• Over-claimed resolution. QI output resolution is limited by seismic bandwidth (¼-wavelength ≈ 8-40 m vertically). Presenting reservoir properties at sub-resolution detail as if they were known produces false precision.
• Weak integration with asset team. A QI product that sits in a folder but doesn’t drive drill-location selection has failed. QI must be plumbed into the decision process, not handed off as a deliverable.

Deliverables and deliverable formats

A typical QI project ends with the following set of deliverables, each produced by specific stages of the pipeline:

• 3D elastic volumes (from stage 3): Ip, Is, Vp/Vs, ρ. Standard SEG-Y or proprietary volume format, same geometry as input seismic.
• 3D rock-property volumes (from stage 4): porosity, shale volume, saturation. Same format.
• 3D facies probability volumes (from stage 5): one probability volume per facies class. Same format.
• Reservoir property maps (horizon-based): porosity-thickness maps, net-to-gross maps, hydrocarbon-saturated pore volume (HCPV) maps. These drive volumetrics.
• Uncertainty quantification: P10/P50/P90 maps of key reservoir properties. Risk maps integrating multiple uncertainty sources.
• Calibration report: well tie quality, RPM calibration match, synthetic-to-real seismic match, cross-validation with blind wells.
• Decision deliverable: prioritized well-location recommendations, drainage maps for field development, production-forecast inputs for reservoir simulation.

Every deliverable is traceable back to the stage that produced it, so the asset team can understand what uncertainty affects what prediction.