Abstract
Abstract
Understanding the geological setting and architecture in which a well is drilled is key to achieving optimal well placement, enhancing reservoir production and for future reservoir exploitation with the planning of additional wells. The planning of production wells is accomplished using different data sets with different resolutions, but understanding the subsurface geology is key to linking the data sources. During drilling operations LWD tools, which have greater resolution than seismic, are deployed to aid in decision making and optimise well placement. Focusing on the data sources in isolation can lead to successful wells, but placing this data in a geological context allows for more sophisticated decision making and leads to greater reservoir understanding for improved reservoir exploitation.
Key to linking the near wellbore measurements with the geological models derived from seismic interpretation are ultra-deep electromagnetic (EM) tools. Applying geophysical inversion processes to the ultra-deep resistivity data generates models that enhance the reservoir interpretation. Formation boundary identification and definition of thin layers in the vertical plane can be achieved with 1D EM inversion. Combining these results with a Gauss-Newton-based 3D inversion provides better identification of the reservoir lateral variability. Recently the introduction of inverting the 3D EM inversion for anisotropy as well as resistivity, permits the identification of isotropic and anisotropic intervals allowing lithological and fluid identification at greater distances from the borehole. The geological models derived from the inversion data can provide a good representation of the subsurface but are more useful for decision making when correlated with other LWD data and azimuthal images, for example density and gamma ray. These tools have a much shallower range of detection but provide more detail which can be critical when placed in its geological context.
Combining all available technologies to improve reservoir understanding of different depositional environments is a more effective approach. Interpretation of the 1D, 3D and 3D anisotropy inversions both allows identification of complex oil water contacts which is vital for hydrocarbon reserves calculation and in certain environments, identification of intra-reservoir thin shale layers that can act as a baffle of fluid movement. Refining these models with the information available from density/neutron, gamma and deep EM data provides a greater level of detail which can also play an important role in the completion design process.
The improved reservoir understanding derived when combining the interpretation of these diverse methodologies can provide a better understanding of the geological scenarios and allows the identification of elements that play a role in well and field production. Identifying these trends during the drilling operations allows for both optimization of the well placement and completion installation. Further analysis post well allows improved reservoir exploitation and planning of new wells.