TX 75083-3836 U.S.A., fax 01-972-952-9435. AbstractAn accurate description of the reservoir is necessary for predicting the reservoir performance. The flow unit model is most practical approach for reservoir simulation purposes. The flow units are defined according to geological and petrophysical properties that influence the flow of fluids in the reservoir. Identification of flow units requires permeability data. Predicting permeability distribution is a difficult problem in heterogeneous reservoirs due to insufficient permeability measurements. The geophysical well logs are the most abundant source of data in a reservoir. Therefore, a well logbased methodology for predicting permeability can enhance prediction of the flow unit distribution.In this study, in a complex reservoir was successfully characterized using flow unit modeling. Statistical techniques were employed to identify flow units based on limited data obtained from core analysis supplemented by minipermeameter measurements and geological interpretations. Permeability was then predicted from geophysical well log and preliminary flow units using artificial neural network (ANN). An innovative approach for training and testing of the ANN was developed which provided consistent and reliable predictions. The neural network predictions were then verified for the wells with core data. Well log data, available on substantial number of wells in the reservoir, were then utilized to predict the distribution of flow units, permeability, and porosity in the reservoir. This approach led to development of a reliable reservoir model. The accuracy of model was verified by successful simulation of the production performance. The methodology presented in this paper can serve as a new guideline for the characterization of reservoirs with limited core data.

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In this paper we propose a system that recognises gait and quadruped structure from a sparse set of tracked points. In this work the motion information is derived from dynamic wildlife film footage and is consequently extremely complex and noisy. The gait analysis is carried out on footage that contains quadrupeds, usually walking in profile to the camera, however, this part of the system is pose independent and useful for gait detection in general. The dominant motion is assumed to be generated by the background and its relationship to the camera motion, enabling its removal as an initial step. Along with frequency analysis, an eigengait model is used as a template to synchronise clusters of points and to established an underlying spatia-temporal structure. Given this synchronised structure, further tracking observdtions are used to deform the structure to better fit the overall motion. We demonstrate that the use of an eigengait model enables the spatio-temporal localisation of walking animals and assists in overcoming difficulties caused by occlusion, tracking failure and noisy measurements. INTRODUCTlONThe motivation for this paper is a system for automatically archiving and indexing large video databases of wildlife film footage. In particular the detection and classification of gaited animals is important for the efficient reuse of the video content in terms of creating new wildlife productions. The major difficulty in analysing such video data is the vast range of content and variability of low level image properties. The footage has been captured under many different conditions causing dramatic variations in texhlre and particularly colour. This is typical of our data and hence this paper concentrates purely on motion based recognition as a first step to higher level understanding of the video content.Previous ,work on gait analysis has tended to focus on the biometrics of human subjects. Standard approaches initially include motion analysis such as optical flow or condensation often in conjunction with background subtraction. Periodic features are then extracted and used for recognition [I, 2, 31 or are matched and filled to statistical models of motion [4, 51.Understanding how things move over time is a powerful cue to recognition and classification, this has been demonstrated by work on Moving Light Displays (MLDs). Much work has been carried out regarding MLDs indicating that, for humans at least, a small number of points moving in a particular way can infer a large amount of potentially useful information [6, 71. It is these concepts that inspire the work described by this paper. METHODThe video footage used in this work focuses on dynamic outdoor scenes of walking animals. The content consists of multiple motions derived from the movement of the animal, often in addition to the apparent motion generated by a panning camera. Scenes often contain large sky regions, which often afford very little or no texture information, as well as complex foliage such as long grass. Other factors contribute to the c...

Here we propose an approach to object recognition that facilitates the transition from recognition to understanding. The proposed approach begins by segmenting the images into regions using standard image processing approaches, which are subsequently classified using a discovered fuzzy Cartesian granule feature classifier. Understanding is made possible through the transparent and succinct nature of the discovered models. The recognition of roads in images is taken as an illustrative problem in the vision domain. The discoveredfuzzy models while providing high levels of accuracy (97%), also provide understanding of the problem domain through the transparency of the learnt models. The learning step in the proposed approach is compared with other techniques such as decision trees, naiLe Bayes and neural networks.

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