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In 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. Introduction Naturally 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 Mapping Background. 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

In fractured reservoirs, data directly related to fractures are scarce and uni-dimensional (i.e. cores and image logs). Other types of data are better distributed and have proved to be related to fracturing but only indirectly (e.g. lithology or large scale structure). In such reservoirs, however, one has to understand fracture distribution and behavior at the field scale. A methodology has been developed within TotalFinaElf to define the relationships of all sources of data to fracturing and to integrate them and compared to another independent published method. To that end, a systematic work flow which goes from 1D to 2D and from static to dynamic data has been defined and various technologies tested. A field case in North Africa is taken to illustrate this methodology. In this field, fracture data from image logs have been related to: production data; 3D seismic attributes (coherency, amplitude, structural curvature) and fault interpretation and strain; log data such as porosity, thickness and lithology index. The former type of data is used to understand the contribution of each fracture set to flow. The latter two types of data are used to better map fracture distribution at the field scale. Ultimately, this mapping is calibrated with the production data of the other wells where fracturing data are not available and is then used to validate the specific role of fracturing in this field. A better reservoir simulation and infill well planning can be subsequently achieved. Introduction Fractured reservoirs are by nature highly heterogeneous. In such reservoirs, fracture systems control permeability and can also control porosity. Fracture modeling is therefore a key development issue and requires an integrated approach from geology to reservoir simulation and well planning. The geometry (i.e. static model) of the fracture network is generally defined from well data (i.e. cores or image logs) using conventional structural geology techniques. Then, fracture permeability can be assessed by relating the fracture aperture to the fracture excess conductivity measured on electrical image logs1 and/or to critically stressed fractures within the present day stress field2. However, it is the authors' opinion that such approaches can only give, in the best case, a relative estimate of the fracture permeability. A quantitative modeling of fracture flow behavior is therefore required (i.e. dynamic model). At the well scale, this can be done by constructing Discrete Fracture Networks (DFN)3–4 through which flow is modeled and which are matched to well test data5. Ultimately, these models can help in determining the fracture parameters required in dual porosity / dual permeability reservoir flow simulation6,7. However, if these DFN models are appropriate for reservoir sector models, their application to full field simulation is somewhat difficult since their extrapolation outside the well scale can be limited by the heterogeneous vertical and lateral distribution of the fracture networks. The modeling of the spatial distribution of fracturing at the scale of the entire field and its calibration to well data is the purpose of this paper.

Two categories of natural fractures are generally recognized: regional orthogonal fractures and structure-related or tectonickactures. Over the past eight years or so, we have developed a methodology using neural networks andfuzzy logic to assist with the characterization of naturally fractured reservoir rocks. Conventional methods use only one or two geological parameters to characterize a naturallykactured reservoir However; an integrated approach that utilizes all the information available (including lithology, thickness, state of stress and fault patterns) is required. Our approach makes use of fuzzy logic to quantifi and rank the importance of each geological parameter on fracturing; a neural network is used to find the complex, non-linear relationship between these geological parameters and the fracture index. Three case studies are described in this paper to illustrate the use of this methodology. They report respectively on a faulted limestone oil reservoir in North Africa which is deformed by fault-related fractures; a carbonate oil reservoir in New Mexico which is characterized by fold-related fractures; and a sandstone gas reservoir in NW New Mexico which has regional orthogonal fractures.These three case studies illustrate fracture identijkation and prediction using static information (i.e. image logs) and/or dynamic information (i.e. well performance) as a fracture index. The results of these studies are described as they relate to infill drilling, understanding the fractured reservoir and improved reservoir simulation.

TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractNaturally Fractured Reservoir (NFR) characterization represents an increased focus for oil and gas companies as it becomes more and more admitted that they represent a substantial part of their portfolio. However the complexity of the understanding of fractured reservoirs, in terms of fracturing mechanism, fracture density, orientation, and the complexity of their management issues (i.e. infill drilling, water production, steam injection, to list few of these issues) pushed several service and integrated companies to tackle the fractured reservoir characterization challenge. Moreover the use of integrated approaches with the help of 3D seismic and new technologies are started to show successful results. This paper will present two technologies where 3D seismic attributes along with geologic and engineering data are being used to characterize fractured reservoirs. The first technology will show how the use of post-stack seismic in an integrated approach, involving high resolution seismic inversion, spectral imaging and static geological modeling, provides an accurate fracture reservoir model that can be applied in the reservoir simulation and development stage. The second technology will highlight the use of pre-stack seismic to actually image the fracture distribution. Application of these technologies is presented on two different fields.