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Exploration-Scale Pre-Drill Reservoir Quality Prediction Strategies for Gulf of Mexico Basin Sandstones and Carbonates



Consultant, Richardson, TX


Predrill estimates of reservoir quality (RQ; porosity, permeability, and net thickness) aid prospect risk assessment. Four approaches can be used to predict sandstone and carbonate RQ: seismic detection (not discussed further), analog/statistical prediction, petrological analysis, and numerical modeling.

Statistical prediction methods are based on databases that incorporate RQ uncertainty with predictive variables such as depth or burial history. Porosity is usually the RQ of interest, but permeability may be predicted from porosity and fabric where properly calibrated. RQ risk is estimated from cumulative probability curves. Analog/statistical predictions are only as good as the analogs; where analogs are poorly chosen, results are poor.

Petrological approaches mainly characterize diagenetic patterns and aid prediction of permeability from porosity. Depositional and diagenetic controls on RQ can only be identified by this approach. Once RQ controls are identified, process and statistical models can extrapolate these results to other locations.

Numerical RQ process modeling is used where no good analogs are available, such as deep, rank-wildcat, wells. The most successful model is the EXEMPLAR model for quartzose-sandstone porosity prediction. Some burial diagenetic models do a moderately successful job of predicting average carbonate porosity. Available numerical models for permeability prediction are complex and unreliable, reflecting the many controls on permeability evolution.

The real strength of RQ predictive technology comes from convergence of different approaches to the same answer. Two examples (a Cretaceous limestone and a Miocene shelf sandstone) are examined to illustrate how these RQ prediction techniques can be used.