Precise reservoir characterization is of primary importance to reservoir evaluation and optimization. Effective integration of borehole and surface seismic data is the key to achieve this objective. However, such an integration is well known to be a complex task due to various difficulties, e.g. different resolutions between well data and seismic.
In this paper, we show an integrated workflow in which borehole data and surface seismic data are integrated for reservoir characterization. The workflow consists of three steps as follow:
Formation evaluation. Accurate petrophysical evaluation of well logs with other borehole data is carried out to build a petrophysical model that describes formation composition, lithology and fluid type.
Fluid substitution and AVO modeling. This establishes the link between the seismic responses and petrophysical parameters. Borehole seismic data provide the calibration for the sonic data, and also the input for the anisotropic and inelastic effects. Fluid substitution investigates the effects of pore fluids on the propagation speeds of waves. AVO modeling generates offset dependent seismic responses for the models of interest with known petrophysical parameters.
Seismic classification. Knowledge extrapolation is performed to populate the seismic volumes or sections with the calibrated AVO response obtained at the well location. A geostatistical and neural network analysis establishes the relationship between the seismic attributes and formation properties (lithofacies, porosity, permeability, pore fluids, etc) at the well location. The quantified knowledge between formation parameters and seismic response at the well location is exported to the rest of the seismic data (2D sections and/or 3D seismic volumes).
This workflow will reveal if there is enough acoustic difference between different lithofacies for them to be distinguished from seismic data. When there is sufficient acoustic difference between different lithofacies, the distribution and variation of these lithofacies can be tracked using the seismic data.
It can provide direct hydrocarbon indicators for the optimization of well locations. When the acoustic impedance difference between different fluids in the formation is large enough to be captured by AVO analysis, direct hydrocarbon detection is possible and the well locations can be optimized. This integrated approach adds tremendous value to the existing data and shows what is needed for future data acquisition.