Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

Classification and characterisation of crude oils based on distillation properties

Copyright © 2006 Elsevier B.V. All rights reserved.Peter Behrenbruch and Thivanka Dedigamahttp://www.elsevier.com/wps/find/journaldescription.cws_home/503345/description#descriptio

Wettability quantification - Prediction of wettability for Australian formations

Document ID: 15230-MSAs a rock-fluid interaction property, wettability is well recognized to influence the flow in multi-phase systems such as hydrocarbon reservoirs. In the laboratory, wettabilty measurements are made according to certain standard procedures and the results are expressed as indices for comparative purposes. The two most commonly used wettability indices are the USBM index, related to areas under capillary pressure curves, and the Amott-Harvey wettability index related to imbibition characteristics. If such measurements are not available, relative permeability curve characteristics may be used to quantify wettability. As is the case with most special core measurements, wettability tests are expensive and time consuming, with the consequence that the number of plugs subjected to wettability testing is usually limited, often resulting in a poor definition of reservoir wettability characteristics. One objective of the study presented is to introduce a mathematical expression, which may be used to gauge relative wettability, as an alternative to the above-mentioned indices. The model has been validated using data from Australian hydrocarbon basins. A genetic algorithm approach was utilised to tuning parameters in the wettability model presented. The model compares favourably with laboratory measurements and may be used to predict USBM indices if experimental values are not available. As such, the formulation presented may also be used in wettability classification. One of the relative permeability characteristics used to gauge wettability is the ratio of relative permeability end points. A second objective in the presented research is to predict this ratio, useful for the prediction of relative permeability characteristics. In considering possible analytical forms, the final derived formulae are extensions of the Carman-Kozeny equation. Copyright 2011, International Petroleum Technology Conference.Hussam Goda and Peter Behrenbruc

Using a modified brooks-corey model to study oil-water relative permeability for diverse pore structures

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Characterisation of clastic depositional environments and rock pore structures using the Carman-Kozeny equation: Australian sedimentary basins

P. Behrenbruch and S. Biniwal

Using a Modified Brooks-Corey Model to Study Oil-Water Relative Permeability for Diverse Pore Structures

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The development of an optimal artificial neural network model for estimating initial, irreducible water saturation - Australian reservoirs

Initial, irreductible water saturation, S wir is an important parameter that needs to be determined accurately when attempting to characterize hydrocarbon reservoirs. S wir is also one of the key parameters in relative permeability relationships. Furthermore, an unrepresentative value of S wir may lead to invalid residual oil saturation estimates when the latter is correlated with the former. S wi may have a dependence on several other parameters, including: absolute rock permeability, porosity, pore size distribution and capillary pressure. The above parameters are directly influenced by geological deposition and subsequent changes, such as diagenesis effects (for example clay-filled pores). It is a common practice to measure S wir utilizing representative core plugs by measuring capillary pressure with a centrifuge, at speeds equivalent to the maximum representative (reservoir) capillary pressure. However, a semi-empirical model that could estimate S wir to a good degree of accuracy would be of significant value. Over the last few years, artificial neural networks have found their application in petroleum engineering. In some cases such models have outperformed models employing conventional statistical and regression analysis. In this study, an Artificial Neural Network (ANN) model has been developed for the prediction of S wi (specifically irreducible saturation, S wir) using data from a number of onshore and offshore Australian hydrocarbon basins. The paper outlines a methodology for developing ANN models and the results obtained indicate that the ANN model developed is successful in predicating values of S wir over the range of data used for calibration. This neural network based model is believed to be unique for Australian reservoirs Copyright 2005, Society of Petroleum Engineers Inc