- journal_title：AAPG Bulletin
- Contributor：Arnaud G. Lange
- Publisher：American Association of Petroleum Geologists (AAPG)
- Date：2009-11-01
- Format：text/html
- Language：en
- Identifier：10.1306/08040909050
- journal_abbrev：AAPG Bulletin
- issn：0149-1423
- volume：93
- issue：11
- firstpage：1609
- section：REGULAR ARTICLES
Methodologies and numerical tools are available (1) to construct geologically realistic models of fracture networks and (2) to turn these models into simplified conceptual models usable for field-scale simulations of multiphase production methods. A critical step remains however, that of characterizing the flow properties of the geological fracture network. The multiscale nature of fracture networks and the associated modeling cost impose a scale-dependent characterization: (1) multiscale fractures that may be characterized in local dynamic test areas, e.g., drainage areas involved in well tests, through the calibration of geologically realistic discrete fracture network (DFN) models and accurate local flow-test simulations; and (2) large-scale faults that are characterized through reservoir-scale production history simulations that involve upscaled flow models with an explicit fault representation. However, field data are commonly insufficient to fully characterize the multiscale fracture properties. Therefore, efficient inversion methodologies are necessary to sample wide ranges of property values and to characterize a variety of solutions, i.e., fracture models that are consistent with dynamic data. This article presents an inversion methodology to facilitate the characterization of fracture properties from well-test data. A genetic optimization algorithm has been developed and coupled with a three-dimensional DFN flow simulator to perform the simultaneous calibration of well-test data. As a first step, the calibration data result from interpreted well tests, i.e., data are equivalent transmissivities. Applications are presented on a geologically realistic fractured reservoir model having three facies, two fracture sets, and three wells. The characterized fracture properties are mean length, mean conductivity, orientation dispersion factors, and facies-dependent properties such as fracture density. The effectiveness of this inversion methodology to characterize physically meaningful and data-consistent fracture properties is discussed.