Abstract
As most of the obvious hydrocarbon exploration targets have already been explored and are being produced, unlocking additional resources requires considering higher risk play types. Due to several recent success stories around the world, fractured basements have attracted and increased interest from explorationists. Despite their potential, fractured basements present multiple challenges, therefore a thorough basement characterization must be performed before drilling any prospect.
When characterizing fractured basements, a key element is to properly describe the fracture network and quantify its main properties, such as fracture intensity and aperture distribution. Usually, this can be achieved from well data, using micro-resistivity image logs, seismic data, well test data, etc. This information is then used as an input parameter for modeling the fracture network in the area of interest and assess its connectivity, porosity, etc. New venture areas present additional challenges: most of the time there is no well data at all, so the only concrete data available in the area of interest is seismic data.
In the study presented in this paper, no well observations were available, and relevant information had to be compiled from analogues (bibliography), offset well and production data (direct measurement data from nearby field) and, in this case, outcrop study. The outcrop measurements used two methodologies: Scanline and Window Scan. From these measurements, fracture properties such as spacing, orientation, and length are acquired and compared with regional or macroscopic elements.
The main workflow of fracture network modeling consists of four main steps: fracture identification and density calculation, fracture driver characterization, discrete fracture network modeling and fracture upscaling. To properly identify and model the most relevant fracture drivers the local geological knowledge, derived from analogues, offset fields and outcrop data, is important. In this study, the retained drivers are related to (1) fault damage zones, (2) structural bending, and (3) local stress field perturbations in the vicinity of faults.
The fracture network model can then be converted to a set of effective grid properties, maps which are used for reserve estimation, flow simulation, or for finding the best potential drilling locations and orientations by creating maps highlighting the sweet spots which are expected to yield high fracture productivity
