Rock-typing using the complete set of additive morphological descriptors

350 words maximum: (PLEASE TYPE) Declaration relating to disposition of project thesis/dissertation I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). Signature Witness 5 AUGUST 2014 Date The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research. FOR OFFICE USE ONLY Date of completion of requirements for Award: THIS SHEET IS TO BE GLUED TO THE INSIDE FRONT COVER OF THE THESIS The mechanical and transport properties of reservoir rocks depend on the morphology of microstructure, e.g. connectivity, size and shape of grains and of pores. Such information can be gained from digital core analysis, which is increasingly used to understand the internal fabric of heterogeneous rocks or for the analysis of samples not amenable to standard laboratory analysis. At the same time, big advances are made on the imaging hardware side including the development of ultra-high resolution CCDs and recording techniques like helical scanning, leading to a datasets of enormous dimensions and relatively large field of view. In this context, it is highly desirable to develop automatic coarse scale classification methods to e.g. recognize the occurrence and spatial structure of digital rock types within such tomographic images-or existing morphological trends within a rock type, as may lead to powerful characterization and data reduction techniques as well as upscaling methods. We use regional Minkowski measures to define fine-scale rock types using a multivariate Gaussian mixture model for classification. The discriminative power of this method is firstly demonstrated for a thin-bedded sandstone. The constituting layers of the reservoir rock sample are clearly recovered and the resulting classes of coarse grained, fine-grained, and transitional regions display distinct physical properties. Following this, the method is extended to a hierarchical mixture of two Boolean processes placing particles of different shapes, with the particle shape controlled by a Gaussian Random Field. In this example the classification method is extended to 3D. Again, a good separation of the two micro-structures is possible and the resulting characteristics of the classified regions agree with the characteristics of the generating Boolean grain processes. The novel pattern recognition approach to rock typing developed in this work will assist in incorporating small scale heterogeneity effects into reservoir simulations, increasing the efficiency of hydrocarbon production and/or the accuracy of reservoir performance prediction. ENGINEERING descriptors.