Interpretive analysis in overdrive

Understanding the reservoir requires a combination of AVO-compliant seismic data and rock properties predicition capabilities. Here, Fairfield Geotechnologies truly delivers.

Unique Geopredictive Technologies

Through an exclusive alliance with Ikon Science, an industry leader in GeoPrediction software services, we provide an expanded technology portfolio for reservoir services. iInnovative technology and workflows are designed to maximize production and improve drilling efficiencies.

Our geoprediction methodologies integrate seismic, well and geologic data to understand rock properties, lithology, fluid content, and flow characteristics (geomechanics) of the reservoir.

Gather Conditioning, Simultaneous Inversion

Seismicdata quality is key to any quantitative interpretation project. The goal of post-migration gather conditioning is to optimize both imaging and signal-to-noise ratio of pre-stack seismic data before it’s used in a quantitative interpretation workflow.

Every project is different and each workflow is dependent on the state of the seismic data. Some of the operations in the processing sequence may include:

  • Azimuthal Velocity Analysis
  • High-Resolution Radon Demultiple
  • Gather Flattening (Residual Moveout)
  • Wavenumber Filtering
  • Amplitude Balancing
  • Frequency Balancing
  • Zero-Phase Analysis
  • Angle-Stacks

AVO Analysis

To assist with lithology and fluid identification, we can calculate two standard AVO attributes—intercept and gradient volumes. We also compare the amplitudes of conditioned pre-stack seismic data to those generated from a pre-stack synthetic gather to assess amplitude fidelity prior to any quantitative interpretation workflow.

Post-Stack Model-Based Inversion

Most geophysicists find it more intuitive to interpret seismic data in terms of impedance rather than reflectivity. Post-stack model-based inversion makes this possible. First, a zero-phase estimate is determined and applied to the data. Then, a low-frequency trend based on available well data is interpolated through the survey area according to an appropriate structural interpretation. That low-frequency trend is added to the relative acoustic impedance volume, which is derived from the seismic stack, to arrive at a volume of absolute acoustic impedance.

Pre-Stack Model-Based Inversion

To help discriminate between lithology, porosity, and fluid effects, this method simultaneously solves for acoustic impedance (AI), shear impedance (SI) and density (ρ). From this, a suite of additional rock properties can be calculated including Vp/Vs Ratio, Poisson’s Ratio, Young’s Modulus, Bulk Modulus, λρ, µρ, and brittleness.

Once the gathers are conditioned and a zero-phase estimate is applied, a series of partial stacks are created based on angle ranges. Wavelets are extracted for each angle range to compensate for offset dependent phase and bandwidth. This angle stacked gathers are used to determine relative AI, SI, and ρ volumes.

To compensate for the low frequencies, absent from the relative rock properties, low-frequency models are designed using available well logs. These are interpolated through the survey area, guided by horizons .

The low-frequency models, with its associated relative rock property, produces broadband AI, SI, and ρ volumes (in the case of a three-term inversion), from which additional rock properties can be calculated.

Ji-Fi® (Joint Impedance and Facies Inversion)

With our Ikon Science alliance, Fairfield Geotechnologies now provides clients with JiFi, an award-winning Bayesian pre-stack inversion with four unique benefits:

  • Estimation of facies and impedance properties in a single step
  • No conventional low-frequency model required
  • Greater use of rock physics information from well logs
  • An apparent increase in the resolution

Other seismic inversion approaches require a low-frequency model to provide information missing from the seismic bandwidth. Constructing these models is time-consuming and vulnerable to bias in the final inversion results. Ji-Fi avoids these issues by calculating the low-frequency model from a set of rock-physics trends that describe the elastic properties of individual facies.

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