Making Your Exploration Decisions Easier
An important quality control step for petroleum system evaluation is to compare the fetch areas with the general framework of sedimentary basins. This is commonly achieved by creating a numerical model that simulates nature through reality checks of each petroleum system element and process. The input elements must be supported by high-quality data about oil type and amount, reservoir existence and trapping conditions, which are transformed via a series of algorithms that simulate the natural process. The conceptual understanding of accumulation models is fundamental to the appropriate selection of the variables and ranges in the models. This is the usual numerical basin method used by BPS, where the basin’s stratigraphic and structural framework is defined through well and seismic data interpretation integrated with geochemical and reservoir evaluation datasets from the BPS Geodatabase (GBP) to support our clients’ exploration decisions.
Following this approach, the superposition of the basement’s structural map with the petroleum system elements and processes as diagnostic criteria, like source, fluid, trap or reservoir attributes on a well-by-well basis, is the quickest way to support the initial selection of the data to be used in basin modeling and exploration programs. The explorers using GDB may want to categorize the fetch areas as the first step in assessing the exploration potential on a basin-wide scale. The BPS basement maps allow them to infer the drainage pathways from the source-rock depocenters to the structural highs, constructing the model by merging a collection of maps developed by BPS from several previous projects. The resulting compilation leverages interpolation methods and data from wells that reached the basement, seismic mapping of the acoustic basement, and eventually the inversion from gravimetric and magnetometric regional maps. A low pass filter can then be applied to the grid, in depth domain, to remove the high frequency noise, with the resulting output being a smoother grid with definition of the intermediate plateaus and basement highs and lows. It is a regional representation of the main tendencies of the structural lows and highs that are fundamental to infer the hydrocarbon generation kitchens and fetch areas for fluid migration. The configuration of the basement represents the cumulative subsidence and uplift processes along the Brazilian offshore margin and gives subscribers the insights for maturation interpretation, sedimentary overburden, identification of oil drainage areas and paleogeographic interpretation. When overlaid with the oil, source rock and reservoir data, it provides an easily understood snapshot of the petroleum systems at work, from source to trap, even in areas where numerical modeling is absent.
The figure below is a basement map of the Sergipe-Alagoas Basin taken directly from the BPS Geodatabase. Note the position of the source rock and oil-well data in relation to the basement outline. Also note the oil slicks surrounding the structural lows and located above the highs, which indicates migration from the internal and external kitchens towards the central high and basin margins. With this quick snapshot, together with the screening of quantitative plots and numerical tables also delivered in GDB, it is easy to make important inferences about the petroleum system’s inner workings, the spatial correlation between accumulations, the basin framework and the attractiveness of areas for exploration. The main oil systems in Sergipe-Alagoas have been tested by several new wildcat wells, and we are now evaluating the conditions for charging, from source to trap, via the complete GDB dataset in order to support exploration decision making.
The next step for the BPS Geodatabase will be to provide a series of additional “Special” maps to spatialize the high-tech analytical data already available in the GDB, breaking down the barriers between petroleum system experts and those making exploration decisions. Maps of hydrocarbon cracking, %TOC, oil type, oil thermal maturity, oil quality, %sulfur in the oil, %CO2 content in the oil, API density and reservoir attributes, among others, will all be collected from previous projects or inferred from the individual samples obtained in the wells, using the principle that geology assessment is strongly visual and that maps and 3D views are fundamental to drive exploration decisions. The aim is to create value for key users, especially in situations that require the integration of the huge volumes of high-quality data from the BPS Geodatabase.
