Parametric Study with GEOFRAC: a Three-Dimensional Stochastic Fracture Flow Model

Abstract

In deep geothermal energy projects naturally and artificially induced fractures in rock are used to circulate a fluid (usually water) to extract heat; this heat is then either used directly or converted to electric energy. MIT has developed a stochastic fracture pattern model GEOFRAC. This is based on statistical input on fracture patterns from the field. The statistical input is in form of the fracture intensity P32 (fracture area per volume) and the best estimate fracture size. P32 can be obtained from borehole spacing information on observations and outcrops. Best estimate fracture size can be obtained from fracture trace lengths on outcrops with suitable bias corrections. Distribution and estimates of fracture size can also be obtained subjectively. GEOFRAC has been applied and tested by estimating the fracture intensity and estimated fracture size from tunnel records and from borehole logs. In the research case here presented, GEOFRAC predictions were satisfactorily applied for geothermal basin characterization. Since its original development, GEOFRAC has been made more effective by basing it on Matlab and it has been expanded by including an intersection algorithm and, most recently, a flow model. GEOFRAC belongs to the category of Discrete-Fracture Network models. In this type of model the porous medium is not represented and all flow is restricted to the fractures. Fractures are represented by polygons in three dimensions. Both the fracture and flow model have been tested and a parametric study was conducted in order to check the sensitivity of the output results to the inputs.