Margaret A. Lessenger¹, Timothy A. Cross²

(1) Platte River Associates, Boulder, CO
(2) Colorado School of Mines, Golden, CO

ABSTRACT: Predicting Lithology and Fluid Properties with Geoscience Inversion

The new technology of stratigraphic inversion provides the means of quantitatively linking data, concepts, methods and interpretations among geoscience and geoengineering disciplines. The products of this linkage are increased accuracy of interpreting and predicting lithology and fluid properties, and reduction of uncertainty in those predictions. Inversion works with the logic that if the forward model is an adequate simulation of the behavior of the system under analysis, and if the observations being compared with model predictions are correctly measured, then matches of observations with predictions at random locations within a 3-D volume assure correct simulation at other locations.

Mathematical inverse methods have been applied routinely in seismic and borehole geophysics for several decades. In the past decade, they have been used to predict fluid flow paths through strata using information from fluids (e.g., pressure, head, composition). Most recently, they were applied to stratigraphic prediction and predictions were accurate in two case studies. Prior to the application of inverse methods to stratigraphic analysis, interpretation and prediction of lithology and fluid properties using inverse methods relied on analysis of a data from a single discipline. With the advent of stratigraphic inversion, we now have the means of coupling data types from multiple disciplines. This is because stratigraphic inversion provides the carrier to link data, concepts, methods and interpretations of fluid and geophysical inversion. Using simultaneous coupled inversions of more than one data type-for example inversions on stratigraphic and fluid data or inversions on seismic geometry, seismic attributes and stratigraphic data-a portion of the population of solutions provided by one data type is eliminated by the populationof solutions derived from the other data type. Consequently, the population of possible solutions is reduced, accuracy of predictions is increased, and uncertainties are reduced.

AAPG Search and Discovery Article #90906©2001 AAPG Annual Convention, Denver, Colorado