Improved Fractured Reservoir Characterization Using Neural Networks, Geomechanics and 3-D Seismic (1995)

Improved Fractured Reservoir Characterization Using Neural Networks, Geomechanics and 3-D Seismic A.M. Zellou; A.M. Zellou New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar A. Ouenes; A. Ouenes New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar A.K. Banik A.K. Banik New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 1995. Paper Number: SPE-30722-MS https://doi.org/10.2118/30722-MS Published: October 22 1995 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Zellou, A.M., Ouenes, A., and A.K. Banik. "Improved Fractured Reservoir Characterization Using Neural Networks, Geomechanics and 3-D Seismic." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 1995. doi: https://doi.org/10.2118/30722-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractIn this paper, a new approach which uses seismic attributes in a quantitative manner to enhance the characterization of fractured reservoirs is presented. The new approach uses the seismic travel time to identify the reservoir structure and the thickness of the fractured producing formation. Using these data, a quantitative geomechanical model is constructed. When comparing the geomechanical models derived from seismic data and mapping methods, it becomes apparent that many structure details may be misrepresented and/or missed when interpolation methods are used for defining reservoir structure. Using a Neural Network and the available well data, the geomechanical model is correlated with the oil production. This model is compared to the seismic amplitude which appears to provide the best indication of fracture intensity in the case of the field studied. Given the seismic data, which are available over the entire reservoir, and the fracture model found by the neural network, the overall reservoir fracture network is predicted.IntroductionNaturally fractured reservoirs represent a significant percentage of oil reservoirs throughout the world. Because of their specificity and heterogeneity, naturally fractured reservoirs have been the subject of many studies. Basically, these studies deal with the prediction of the subsurface fracture network. Indeed, a good understanding of the fracture network, i.e., a good understanding of fracture connectivity, orientation, and location is the key point to fractured reservoir characterization.A new method in fractured reservoir characterization is presented in this paper. As a first step, this new method combines the curvature method and a neural network to describe the subsurface fracture intensity. As a second step, the subsurface fracture network is obtained from the fracture intensity map using the "weighting method." This new method provides a new tool.For the geologic interpretation of two types of fractures: fold-related fractures in the Young Deep Unit (YDU) and regional fracturesFor the simulation of fractured reservoirs described in a companion paperFor the exploration and the development of fractured reservoirs.Fracture MappingBackground.During the last few years, the characterization of naturally fractured reservoirs has been a challenging task for geologists and petroleum engineers. A good understanding of the fracture network in the subsurface and on the outcrop implies the knowledge of fracture genesis. A first classification of natural fracture systems proposed by Stearns and Friedman consisted of two major categories of fracturesregional orthogonal fracturesstructure-related fractures (tectonic fractures).The fracture classification was further systemized by Nelson and two other types of fractures were added: contraction or diagenetic fractures and surface-related fractures. This paper, and the companion paper focus on regional orthogonal fractures and structure-related fractures.Regional fractures are those that pervade over large areas with little or no change in orientation and are always perpendicular to the bedding surface. The constant orientation of regional fractures is due to a constant state of stress over a large area. Zoback et al. described the state of stress for the North American and parts of the Pacific plate. The description includes the Permian Basin, where the reservoirs addressed in this paper are located.Structure-related fractures, or tectonic fractures, are those related to a local tectonic event.P. 205 Keywords: neural network, amplitude, spe 30722, information, upstream oil & gas, geomechanics, machine learning, structure-related fracture, fracture, travel time Subjects: Reservoir Characterization, Unconventional and Complex Reservoirs, Faults and fracture characterization, Seismic processing and interpretation, Naturally-fractured reservoirs This content is only available via PDF. 1995. Society of Petroleum Engineers You can access this article if you purchase or spend a download.