Abstract

Quantitative representation of fracture intensity or its inverse, fracture spacing, in a static fracture model often comes from several data sources (core, borehole image logs, outcrop, and measures at the bit). These necessary constraints give inherently different quantitative assessments, due to differences in resolution and interpretative confidence. This is often imposed on an evaluation program by differing data types acquired during different stages of our exploration and development history of a reservoir due to changes in well path and borehole design. Our challenge is how to merge or meld differently acquired fracture quantifications to populate a quantitative static conceptual fracture model.

This manuscript shows (1) the varying interpreted fracture intensity numbers derived from differing types of datasets, (2) the need for dataset calibration early in the development history of a fractured reservoir, and (3) the plan for laying out an appropriate data acquisition program to effectively utilize a multi-faceted fracture database in model construction and quantification from core, outcrop, and various borehole image logs.

While there is great need to quantify fracture intensity to constrain of static fracture models for reservoir simulation and prediction, often we must have to merge disparate data types of inherently different resolution and error. This can be accomplished effectively if we gather appropriate calibration datasets. However, many of our calibration datasets fall short in doing the job in a quantitative sense. Proper calibration procedures are detailed.