AI Inversion

Seismic inversion is a process that converts seismic reflection data into detailed subsurface models, such as acoustic impedance, elastic properties, or even direct estimates of rock and fluid characteristics. This technique transforms the complex seismic signals recorded during surveys into meaningful geological information, helping geoscientists to better understand the composition, structure, and properties of the subsurface. Seismic inversion is essential for accurate reservoir characterization, hydrocarbon exploration, and other geophysical applications, as it provides a more precise depiction of subsurface features than conventional seismic data alone.

Deterministic INVersion

Deterministic seismic inversion is a method used in geophysics to estimate subsurface rock properties, specifically acoustic impedance, from seismic data. It involves creating a single, "best-estimate" model of these properties based on the seismic data and prior information.

Constraint Sparse Spike Inversion

Definition:
It’s an inversion technique that attempts to recover the earth’s reflectivity series from band-limited seismic data by assuming the reflectivity is sparse meaning it consists of relatively few significant reflection coefficients (spikes) separated by intervals of zero or very small coefficients.

This method:

Assumes the earth’s reflectivity is sparse (only a few significant reflection coefficients).
Tries to recover high-frequency details lost in seismic bandwidth.
Produces a reflectivity series, which is integrated to get acoustic impedance.

Advantages

✅ Enhances vertical resolution by recovering high frequencies.
✅ Simple to run — no need for detailed low-frequency model (unlike model-based inversion).
✅ Good for thin-bed detection.

📌 Limitations

❌ Can be unstable if the wavelet estimate is poor.
❌ No low-frequency content — cannot predict long-wavelength trends.
❌ Non-uniqueness — sparse solution is not always unique.
❌ Sensitive to noise.

Model Based inversion

Definition:

Model-Based Inversion is a deterministic seismic inversion method that starts with a low-frequency model of the subsurface (typically derived from well log trends) and updates it with higher-frequency information extracted from the seismic data.

In other words:

· The wells provide the long-wavelength trend (which the seismic band is missing).

· The seismic provides the band-limited reflection detail.

· The inversion combines these to produce a full-bandwidth model of a rock property — usually acoustic impedance (AI).

Starts with a low-frequency model (from well logs) and uses seismic to fill in higher frequencies.
Typically, the workflow is:
1. Build low-frequency trend (from wells).
2. Use seismic to update higher-frequency content.

Common in commercial software (e.g., Hampson-Russell).

Seismic data alone cannot resolve very low frequencies (below ~10 Hz) or very high frequencies (above ~80 Hz). But rock property models (like impedance) need both:

· Low frequencies → the overall trend (geological layering, depth trend)

· Seismic → the details and contrasts at interfaces.

So, Model-Based Inversion fills this gap.

📌 Advantages

✅ Recovers missing low frequencies → realistic absolute impedance.
✅ Provides smoother, geologically plausible results.
✅ Well-suited for reservoir property prediction (porosity, lithology).
✅ Can be run on post-stack seismic.

📌 Limitations

❌ Needs good well control — sparse or poor-quality wells can limit accuracy.
❌ Relies on accurate wavelet estimation and good ties.
❌ Smoother than true reflectivity — can miss very thin beds if the seismic bandwidth is limited.
❌ Assumes the low-frequency model is correct — errors here propagate into the final result.

Colored inversion

Definition:
Colored Inversion is a fast, simple spectral shaping method that transforms a seismic trace into a relative impedance section by matching the seismic amplitude spectrum to that of a typical reflectivity series derived from well logs. It’s called colored because the process adjusts (colors) the frequency spectrum of the seismic data to make it look like the desired reflectivity spectrum.

This method is:

A fast, simplified method.
Adjusts the seismic spectrum to match the desired reflectivity spectrum.
No iterative inversion mainly used for initial interpretation.

The seismic wavelet and earth’s filtering limit the usable bandwidth.

· Seismic data has a band-limited, bell-shaped amplitude spectrum.

· The real reflectivity spectrum (from well logs) typically follows a power-law decay (about 6 dB per octave).

Colored inversion corrects this by shaping the seismic spectrum to match the target reflectivity spectrum without building an explicit model or doing iterative inversion.

📌 Advantages

✅ Very fast — suitable for large volumes.
✅ Requires minimal well control.
✅ Simple to run — robust, easy QC.
✅ Provides relative impedance with enhanced vertical resolution.

📌 Limitations

❌ Does not recover missing low frequencies— only gives relative impedance.
❌ Cannot directly estimate absolute impedance or absolute rock properties.
❌ Assumes the spectrum is stationary — local variations might be missed.
❌ Sensitive to noise if improperly filtered.

STOCHASTIC inversion

Stochastic inversion is a seismic inversion technique that generates multiple subsurface models to capture the uncertainty and variability in the seismic data. Unlike deterministic inversion, which produces a single best-fit model, stochastic inversion recognizes that several equally plausible models may explain the observed data. By creating an ensemble of possible subsurface models, stochastic inversion provides a probabilistic understanding of subsurface properties, offering insights into the range of potential scenarios rather than just one.

This approach is instrumental in complex geological settings or when data quality is poor, as it helps assess the risks and uncertainties associated with subsurface interpretation. The multiple realizations generated by stochastic inversion can be analyzed to determine the likelihood of different geological features or reservoir characteristics, aiding in decision-making for exploration, reservoir management, and risk assessment. Stochastic inversion is a powerful tool for geoscientists when dealing with uncertain or ambiguous seismic data, allowing for a more comprehensive and informed analysis of the subsurface.

Definition:
Stochastic (or geostatistical) seismic inversion is a probabilistic method that generates multiple equally probable realizations(models) of subsurface rock properties, such as acoustic impedance or facies, by combining:

· Seismic data (band-limited detail)

· Well data (absolute rock property control)

· Geostatistical simulation (spatial continuity, variability)

Unlike deterministic methods (which produce one best-fit model), stochastic inversion captures uncertainty and small-scale heterogeneity especially important for reservoir modeling and risk analysis.

Accounts for uncertainty.
Uses geostatistical simulation (like sequential Gaussian simulation) combined with seismic constraints.
Generates multiple equally probable models useful for risk assessment.
Examples: Pseudo wells, stochastic impedance cubes.

The method builds many equally probable realizations that:

Match the wells exactly (hard data)
Match the seismic statistically (soft constraint)
Preserve geostatistical continuity (facies or rock property trends)

📌 Typical Workflow

1. Well Tie & Wavelet (Extract wavelet, tie wells)

2. Variogram Analysis (Model spatial continuity of impedance or facies)

3. Simulate Realization (Create an initial rock property model)

4. Forward Model (Create synthetic seismic by convolving with the wavelet).

5. Check Misfit (Compare synthetic to real seismic)

6. Update (Accept/reject or adjust the model)

7. Repeat (Run many simulations (50–100 or more))

📌 Advantages

✔️ Captures uncertainty: you see the range, not just a single answer
✔️ Better for thin beds: small-scale heterogeneity is preserved
✔️ Reservoir connectivity: realistic detail for flow simulation
✔️ Facies distribution: not just rock property but discrete facies or lithology

📌 Limitations

❌ Computationally intensive: can require thousands of realizations
❌ Needs good variogram models — sensitive to continuity assumptions
❌ Needs reliable well control
❌ Results depend on assumptions about statistical distributions

Stochastic Inversion

Prestack (AVO) inversion

Pre-stack seismic inversion is a geophysical method used to estimate subsurface rock properties like P-wave and S-wave impedance, and density, by analyzing seismic data collected at different angles or offsets before stacking. This approach goes beyond post-stack inversion, which only analyzes stacked data, by leveraging information from Amplitude Variation with Offset (AVO) to provide a more detailed and accurate subsurface characterization.

Definition:
Prestack Inversion is a method that uses prestack seismic data(i.e., data sorted by incident angles or offsets) to estimate angle-dependent reflectivity and invert it to recover multiple elastic rock properties typically P-wave impedance (Ip), S-wave impedance (Is), and density (ρ).

It is the core workflow behind AVO (Amplitude Versus Offset) analysis, which looks at how reflection amplitudes change with incident angle or offset a key indicator of rock and fluid properties.

Goal: Uses angle-dependent reflectivity to derive elastic properties.

Works on prestack gathers or angle stacks.
Recovers P-impedance (Ip), S-impedance (Is), and density.
Basis for fluid and lithology prediction (AVO analysis).
Methods: Linearized AVO inversion (e.g., Shuey’s approximation).

When you only invert post-stack seismic, you recover acoustic impedance but you can’t separate the effects of lithology and fluid easily.

By working with prestack data, you:
✔️ Get both compressional and shearinformation (Ip, Is).
✔️ Sometimes get density, although this is harder.
✔️ Use AVO effects to discriminate between lithology and fluid changes (gas sands, oil vs. brine).

This is critical for:

· Identifying hydrocarbon indicators.

· Reservoir facies classification.

· Rock physics diagnostics (e.g., Lambda-Mu-Rho analysis).

📌 Typical Prestack Inversion Workflow

1️⃣ Angle Stacks (Sort prestack gathers into angle stacks (e.g., near, mid, far))

2️⃣ AVO Analysis (Compute intercept & gradient (fit Shuey or similar))

3️⃣ Rock Physics Link (Relate AVO behavior to Ip, Is, ρ)

4️⃣ Simultaneous Inversion (Invert multiple angle stacks together for Ip, Is, ρ)

5️⃣ QC

📌 Advantages

✅ Extracts more rock physics information than post-stack alone.
✅ Supports fluid vs lithologydiscrimination.
✅ Provides multiple elastic property volumes → crossplot for facies.
✅ Essential input for advanced rock physics and reservoir characterization.

📌 Limitations

❌ Needs high-quality, well-conditioned prestack gathers → good offset coverage and amplitudes preserved.
❌ Sensitive to noise, multiples, and poor AVO behavior.
❌ Density is hard to estimate accurately — it’s weakly constrained by seismic AVO alone.
❌ Requires good wavelet estimation for each angle stack.

AI Inversion

Deterministic INVersion

Constraint Sparse Spike Inversion

Advantages

📌 Limitations

Model Based inversion

📌 Advantages

📌 Limitations

Colored inversion

📌 Advantages

📌 Limitations

Constraint Sparse Spike Inversion

Model Based inversion

Colored inversion

STOCHASTIC inversion

📌 Advantages

📌 Limitations

Prestack (AVO) inversion

📌 Typical Prestack Inversion Workflow

📌 Advantages

📌 Limitations

This website uses cookies.