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Utilizing Chemostratigraphic Techniques to Improve Lacustrine Carbonate Reservoir Characterization


A major challenge facing the understanding of lacustrine carbonates is the lateral prediction of reservoir properties away from the well-bore. Heterogeneities in the South Atlantic Lower Cretaceous lacustrine carbonates are difficult to extrapolate laterally given the current limitations on seismic and lack of pre-salt rock data. This in turn affects the ability to construct adequately-defined depositional models and predict reservoir presence and quality within a field, impacting optimal well placement. A relatively low-cost inorganic chemostratigraphic-based approach utilizing existing rock material (RSWC & conventional core) can help provide further insight into these complications. Specific geochemical techniques can therefore enhance the spatial and temporal understanding of ambiguous reservoir characteristics. While traditional rock evaluation techniques often provide an adequate analysis of well-bore Geology, reservoir modeling requires a geologically-underpinned dataset in undrilled areas to support its calculations. Certain geochemical procedures can infer larger scale depositional patterns and environments that are driven by primary processes, such as regional changes in base level or chemical conditions. The first approach uses elemental geochemistry (XRF or equivalent) that enables the analysis of over 50 elements and compounds. A second approach uses stable isotope geochemistry (d13C and d18O). The third approach uses strontium isotope ratios (87Sr/86Sr). Each technique can be utilized to address specific questions, and are most beneficial when information from one can be corroborated with another. Preliminary results reveal the validity and applicability in lacustrine carbonates of the south Atlantic. Complemented by core descriptions and well log data, these methods can be utilized to add significant value to subsurface products such as depositional models and reservoir quality distributions. The final reservoir model is then expected to be more geologically robust, thereby assisting with well placement planning for future production purposes.