Predicting Fractures Using Cathodoluminescence

Stephen E. Laubach1


Ongoing research is aimed at enhancing exploration and production in fractured reservoirs by development and integration of emerging and existing technologies for the observation, prediction, and fluid-flow modeling of natural fractures. Macroscopic fractures produce the largest impact on fluid flow through fractured rock, although they are orders of magnitude less abundant than microscopic fractures. Macrofractures in subsurface reservoirs are typically poorly represented by data acquired by using conventional techniques. Because of the abundance of microfractures, they can be well studied even in small samples from the subsurface. We are exploring the hypotheses that micro- and macrofractures are different-size fractions of the same fracture sets and that microfractures can be used to predict critical characteristics (in terms of fluid flow) of associated macrofractures.

Previously invisible microfractures are readily observed and characterized when their cathodoluminescence is imaged by using scanning electron microscopy. This process facilitates determination of the orientations, timing (relative to diagenetic events), and sizes of the numerous microfractures typically present in prospective fractured reservoirs. These observations may be made systematically on a bed-by-bed basis. Orientations and timing of microfractures commonly compare favorably with those of associated conductive macrofractures.

Microfractures are sufficiently abundant in the numerous fractured units we have studied that the size distributions can be readily quantified. Under special circumstances, the sizes (i.e., mechanical apertures and/or lengths) of both micro- and macrofractures can be reliably measured in the same fractured rock volume. The spatial frequency of fractures, as a function of fracture size, follows power-law distributions over at least four to five orders of magnitude in these cases. This fact confirms that microfracture sizes can be used quantitatively to predict spatial frequencies of associated macrofractures.

The orientations, time of formation relative to diagenetic minerals, and sizes of macrofractures are the most important factors for understanding fluid flow in fractured rock. Although discrete-fracture modeling provides the most realistic portrayal of fluid flow through fractured rock, this technique is computationally infeasible for simulation of the large volumes in a fractured hydrocarbon field. Dual-porosity simulation is therefore the dominant technique. We are developing a blended approach to simulation that is both cost effective and grounded in local fracture observation.

On a bed-by-bed basis, microfracture observations are used to make statistical predictions of key macrofracture attributes. From these predictions, multiple discrete-fracture models are generated for each bed, representing volumes that are comparable to those of the cells in dual-porosity simulations. Fluid-flow simulations in the discrete-fracture models provide us with a quantitative, observation-based understanding of the fluid-flow characteristics and spatial heterogeneity for each bed. These results can then be used to construct a dual-porosity simulation for large regions in the subsurface. Quantitative testing of this approach using subsurface flow data is ongoing.

Examples of this method’s application will be provided to oil and gas fields in carbonates and sandstones.

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