Petrophysical multimineral analysis using genetic optimization to solve complex mineral composition in unconventional reservoirs

Petrophysical modeling in unconventional reservoirs requires tools that take into account their complex mineral composition and lack of log information necessary to resolve this complexity in detail. We pose the estimation of properties of mineral constituents of the rock as a stochastic nonlinear optimization problem where a genetic algorithm (a type of algorithm in the artificial intelligence spectrum) replaces the time-consuming, manual trial-and-error process of adjusting properties and fitting the input logs in conventional multimineral analysis. The method requires interpretative inputs based on prior knowledge and experience, but such inputs are provided in the form of ranges instead of single property values, facilitating the work of the analyst. By testing adaptively thousands of solutions and considerably reducing the time needed to fit the input logs with a consistent set of properties, it becomes then possible to test other scenarios of input data and constituents, quantify the uncertainty and non-uniqueness of individual parameters, and shed light upon higher-level petrophysical questions such as spatial variations in kerogen maturity, water resistivity, or clay composition. We illustrate the use of the methodology to estimate fractions of constituents for the mineralogically complex Bakken Formation and to estimate variations of thermal maturity with depth in the Marcellus, shale gas Formation.