Oil field production forecasts rely heavily on an accurate characterization of the subsurface. Static geological models are computer-based three-dimensional representations of the subsurface that approximate the reservoir properties and are the basis for dynamic simulation and subsequent field development decisions. With the advent of new data acquisition technologies, the prompt availability of additional information can potentially impact the validity and reliability of existing geological models. This case study introduces accelerated verification methods to evaluate the scope of a static model rebuild to accommodate business decisions.
Recent development efforts in the Tengiz oil field have led to the acquisition of new data, including the drilling of new wells and well log acquisition, reprocessing of seismic data, and microseismicity. Drilling new wells has provided valuable information about the reservoir's petrophysical properties. Specifically, high-resolution well logs, including wellbore images, helped derive valuable information about lithology, porosity, and fluid saturation, leading not only to a more accurate petrophysical interpretation but also help to define local variations of reservoir quality, allowing for more precise reservoir characterization. Reprocessed seismic data has improved image quality and event positioning, enabling improved structural mapping and seismic mega-amplitude detection. New wells with drilling data, wireline and production logs also provided additional information about fracture orientations, aperture, and density.
A fast-track, fit-for-purpose geological model was built through effective communication and collaboration among multidisciplinary teams and using fit-for-purpose quantitative and qualitative techniques, including 2D methodologies, statistical analysis, and geostatistical modeling. The main seismic horizons were reinterpreted from newly reprocessed seismic. New wellbore data was prioritized based on available logs and the existing data coverage. Matrix porosity and water saturation were calculated using a multimineral inversion model; fracture orientations were picked from image logs and fracture intensity and porosity were quantified. Quality control included statistical and variogram analyses before porosity propagation in a 3D model, which allowed for assessing a change in matrix and fracture porosity and pore volume in the model. A standard data-driven dual-porosity dual-permeability property modeling workflow was leveraged with microseismicity data to define effective fracture regions for the discrete fracture network model. These fast-track modeling approaches, developed in a time-efficient period, enabled the maturation of techniques to be applied in a future next-generation static model and provided valuable insights for reservoir management and production optimization in the Tengiz oil field.
