The right image meets the power of quantitative analysis.
Understanding the reservoir requires a combination of AVO-compliant seismic data and quantitative interpretive capabilities. This is the focus of FairfieldNodal’s Reservoir Services.
A unique partnership—and advantage.
Through an exclusive alliance with Ikon Science, an industry leader in geoprediction software services, FairfieldNodal offers an expanded technology portfolio for reservoir services. These unique, innovative workflows are designed to maximize production and improve drilling efficiencies.
Our quantitative interpretation (QI) methodologies integrate seismic, well, and geologic data to understand rock properties, lithology, fluid content, and flow characteristics (geomechanics) of the reservoir.
Gather conditioning for simultaneous inversion.
Quality of seismic data is a key component of 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.
Each workflow is dependent on the state of the seismic data. Some of the operations in the sequence include:
- Azimuthal Velocity Analysis
- High-Resolution Radon Demultiple
- Gather Flattening (Residual Moveout)
- Wavenumber Filter
- Amplitude Balancing
- Frequency Balancing
- Zero-Phase Analysis
- Angle-Stack Gathers
AVO attributes help with lithology and fluid identification. We can calculate two standard AVO attributes, intercept and gradient volumes. Also, to assess fidelity prior to any quantitative interpretation workflow, we compare the amplitudes of conditioned pre-stack seismic data to the amplitudes generated from a pre-stack synthetic gather.
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 enables them to do so. First, a zero-phase estimate must be determined and applied to the seismic 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
Pre-stack model-based inversion helps improve discrimination between lithology, porosity, and fluid effects by simultaneously solving for acoustic impedance (AI), shear impedance (SI), and density (ρ). From this simultaneous solution, 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. These angle stacked gathers are used to determine relative AI, SI, and ρ volumes.
The low-frequency model is designed to compensate for the low frequencies absent in the relative rock properties. Sonic, shear, and density well curves are used to calculate AI, SI, and Density well curves. These are then frequency filtered and interpolated through the survey area, guided by horizons.
The sum of each low-frequency model 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 the Ikon Science alliance, FairfieldNodal now provides its 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 fill in the 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.