Tim Pritchard

Microbial Carbonates in Space and Time: Implications for Global Exploration and Production

Microbial carbonates (microbialites) are remarkable sedimentary deposits. They have the longest geological range of any type of biogenic limestones, form in the greatest range of different sedimentary environments, oxygenated the Earth’s atmosphere and produce and, furthermore, store large volumes of hydrocarbons. This Special Publication provides significant contributions at a pivotal time in our understanding of microbial carbonates when their economic importance has become established and the results of many research programmes are coming to fruition. It is the first book to focus on the economic aspects of microbialites and in particular the giant pre-salt discoveries offshore Brazil. The volume contains papers on the processes involved in the formation of both ancient and modern microbialites and the diversity of style in microbial carbonate build-ups. Structures and fabrics from both marine and non-marine settings are discussed from throughout the geological record.

Microbial carbonates in space and time: introduction

DAN BOSENCE1*, KATHRYN GIBBONS2, DANIEL P. LE HERON1, WILLIAM A. MORGAN3, TIM PRITCHARD4 & BERNARD A. VINING1,5 Department Earth Sciences, Royal Holloway University of London, Egham, Surrey, TW20 0EX, UK Nexen Petroleum UK Ltd., Prospect House, 97 Oxford Road, Uxbridge, Middlesex, UB8 1LU, UK Morgan Geoscience Consulting LLC, 132 W. Ellendale Estates Drive, Houma, LA 70360, USA bg Group, 100 Thames Valley Park Drive, Reading, Berkshire, RG6 1PT, UK Baker Hughes, Bentley Hall, Alton, Hampshire, GU34 4PU, UK

Automatic classification of carbonate rocks permeability from 1H NMR relaxation data

This work investigates the effectiveness of data mining analysis on NMR data.The goal is to accurately predict the permeability class of carbonate rocks.Our approach outperforms the traditional NMR models Timur-Coates and Kenyon.Traditional models ignore the singular relationship between T2 bins and pore throat.Data mining models capture the influence of each T2 bin over the permeability class. The accurate permeability mapping, even with the aid of modern borehole geophysics methods, is still a big challenge on the reservoir management framework. One concern within the petrophysics community is that rock permeability value predicted by well logging should not be considered as absolute, mainly for carbonates, but a relative index for identifying more permeable zones. Therefore, in this paper a permeability classification methodology, based exclusively on 1H NMR (Nuclear Magnetic Resonance) relaxation data, was evaluated for the first time as an alternative to the prediction of permeability as a continuous variable. To pursue this, a side-by-side comparison of different data mining techniques for the permeability classification task was performed using a petrophysical dataset with 78 rock samples from six different carbonate reservoirs. The effectiveness of six classification algorithms (k-NN, Naive Bayes, C4.5, SMO, Random Forest and Multilayer Perceptron) was evaluated to predict the rock permeability class according to the following ranges: low ( 100mD). Discretization and feature selection strategies were also employed as preprocessing steps in order to improve the classification accuracy. For the studied dataset, the results demonstrated that the Random Forest and SMO strategies delivered the best classification performance among the selected classifiers. The computational experiments also evidenced that our approach led to more accurate predictions when compared with two methods widely adopted by the petroleum industry (Kenyon and Timur-Coates models).

Carbonate Facies and Permeability Estimation using Rock Physics and Flow-Zone Facies

Summary Modeling permeability in heterogeneous carbonate reservoirs may be challenging due to often extreme spatial variations in pore geometry. This paper introduces a method to model permeability in terms of Flow-Zone Facies, and demonstrates this approach in a case study. The method is utilized to model facies and permeability across thirty wells in a field with limited data availability. Rock physics relationships and a modified Kozeny-Carman permeability equation are used to define facies and predict permeability. The results indicate that capturing complex variations in pore geometry within a rock is the key to the successful application of this method.