Evaluating Reservoir Property Estimation Potential
TraceSeis conducts comprehensive feasibility studies to determine whether reservoir and geomechanical properties can be reliably estimated from your seismic data. Our systematic four-stage workflow evaluates the potential for successful property estimation using well-calibrated seismic data.
Stage 1: Rock Physics Analysis with SeisRP
Our feasibility assessment begins with detailed rock physics modeling using well log data. The SeisRP software analyzes and estimates rock properties and seismic attributes at both well-log and seismic resolution for various reservoir conditions, including:
- Porosity variations
- Lithology changes
- Different pore fluid scenarios
This critical analysis determines whether the physical properties of your rocks will support accurate estimation of target reservoir properties.
Stage 2: Seismic Data Fitness Evaluation and Conditioning
For feasible quantitative reservoir property estimation, pre-stack seismic data must accurately represent the offset-dependent reflectivity of the subsurface. Our team evaluates and conditions your data through specialized processes including:
- Calibration of amplitude variation with offset
- Attenuation of residual multiples
- Residual alignment of events across offset
- Random noise attenuation
- Wavelet phase and amplitude equalization across offset
Stage 3: Seismic Attribute Evaluation
Based on the optimal parameters identified in Stage 1, we evaluate the most relevant seismic attributes (reflectivities or relative properties) that will best characterize your reservoir.
Stage 4: Property Estimation Feasibility with SeisCar
In the final stage, we assess the feasibility of estimating reservoir and geomechanical properties through linear combinations of the seismic attributes from Stage 3. Our SeisCar software calculates the coefficients of these linear fits using well and synthetic data to determine if reliable property estimation is possible with your real seismic data.
The results of our feasibility study provide a clear understanding of the potential for quantitative interpretation, with projected accuracy levels compared to actual properties measured by well logs.