Quantitative textural analysis of sedimentary grains and basin subsidence modelling

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dc.contributor.advisor Mulchrone, Kieran F. en
dc.contributor.advisor Meere, Patrick A. en
dc.contributor.author Tunwal, Mohit
dc.date.accessioned 2018-11-14T12:54:39Z
dc.date.available 2018-11-14T12:54:39Z
dc.date.issued 2018
dc.date.submitted 2018
dc.identifier.citation Tunwal, M. 2018. Quantitative textural analysis of sedimentary grains and basin subsidence modelling. PhD Thesis, University College Cork. en
dc.identifier.endpage 140 en
dc.identifier.uri http://hdl.handle.net/10468/7114
dc.description.abstract Part 1: Quantitative textural analysis Shape analysis can provide important information regarding the origin, transport and deposition history of grains. Particle shape measurement has been an active area of research for sedimentologists since the 20th century. However, there is a lack of standardised methodology for quantitative characterisation of grain shapes. The main objective of this work is to develop methodologies that can be used by sedimentologists for quantitative textural analysis of grains such that the results obtained are comparable. A modular suite of code written in the Mathematica environment for the quantitative characterisation of sedimentary grains in 2- dimensions is presented. This image analysis package can be used to analyse consolidated as well as loose sediment samples. Using newly implemented image analysis methods, 20 loose sediment samples from four known depositional environments (beach, aeolian, glacial and fluvial) were analysed. This research aims to identify the most useful shape parameters for textural characterisation of populations of grains and determine the relative importance of the parameters. A key aspect of this study is to determine whether, in a particular sedimentary environment, textural maturity of the samples can be ranked based on their grain shape data. Furthermore, discrimination of sedimentary depositional environments is explored on the basis of grain shape. The available shape parameters suffer from a common shortcoming that particles, which are visually distinct, are not differentiated. To address this issue, the Inverse Radius of Curvature (IRC) plot which can be used to identify corners and measure their sharpness is introduced. Using the IRC plot, four shape parameters are proposed: number of corners, cumulative angularity, sharpest corner and straight fraction. This methodology is applied to a 4000 sand grain dataset. The textural analysis software package developed here allow users to quantitatively characterise large set of grains with a fast, cheap and robust methodology. This study indicate that textural maturity is readily categorised using automated grain shape parameter analysis. However, it is not possible to absolutely discriminate between different depositional environments on the basis of shape parameters alone. The four new shape parameters proposed here based on the IRC plot can be collectively used to quantitatively describe grains shape which correlates closely with visual perceptions. This work opens up the possibility of using detailed quantitative textural dataset of sediment grains along with other standard analyses (mineralogy, bulk composition, isotopic analysis, etc) for diverse sedimentary studies. Part 2: Basin modelling Subsidence modelling is an important part of basin analysis to better understand the tectonic evolution of sedimentary basins. The McKenzie model has been widely applied for subsidence modelling and stretching factor estimation for sedimentary basins formed in an extensional tectonic environment. In this contribution, a numerical model is presented that takes into account the effect of sedimentary cover on stretching factor estimation. Subsidence modelling requires values of physical parameters (crustal thickness, lithospheric thickness, stretching factor, etc.) which may not be always available. With a given subsidence history of a basin estimated using a stratigraphic backstripping method, these parameters can be estimated by quantitatively comparing the known subsidence curve with modelled subsidence curves. In this contribution, a method to compare known and modelled subsidence curves is presented aiming to constrain valid combinations of stretching factor, crustal thickness and lithospheric thickness of a basin. The parameter fitting method presented here is first applied to synthetically generated subsidence curves. Next, a case study using a known subsidence curve from the Campos Basin, offshore Brazil is considered. The range of stretching factors estimated for the Campos basin from this study is in accordance with previous work, with an additional estimate of corresponding lithospheric thickness. This study provides insights into the dependence of subsidence modelling methods on assumptions about input parameters as well as allowing for the estimation of valid combinations of physical lithospheric parameters, where the subsidence history is known. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2018, Mohit Tunwal. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Texture en
dc.subject Roundness en
dc.subject Angularity en
dc.subject Sedimentary depositional environment en
dc.subject Thermal history modelling en
dc.subject Subsidence modelling en
dc.subject Grain shape en
dc.subject Textural maturity en
dc.subject Basin modelling en
dc.title Quantitative textural analysis of sedimentary grains and basin subsidence modelling en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en
dc.check.info Not applicable en
dc.description.version Accepted Version
dc.contributor.funder Petroleum Infrastructure Programme, Ireland en
dc.contributor.funder Atlantic Petroleum, Ireland en
dc.contributor.funder Cairn Energy, United Kingdom en
dc.contributor.funder Chrysaor E&P, Ireland en
dc.contributor.funder Chevron North Sea, United Kingdom en
dc.contributor.funder ENI, Ireland en
dc.contributor.funder Europa Oil & Gas, Ireland en
dc.contributor.funder ExxonMobil E&P (Offshore), Ireland en
dc.contributor.funder Kosmos Energy en
dc.contributor.funder Maersk Oil North Sea, United Kingdom en
dc.contributor.funder Department of Communications en
dc.contributor.funder Providence Resources, Ireland en
dc.contributor.funder Repsol Exploración, South Africa en
dc.contributor.funder San Leon Energy, Ireland en
dc.contributor.funder Serica Energy, United Kingdom en
dc.contributor.funder Shell E&P, Ireland en
dc.contributor.funder Sosina Exploration, United Kingdom en
dc.contributor.funder Statoil, United Kingdom en
dc.contributor.funder Tullow Oil, United Kingdom en
dc.contributor.funder Woodside Energy, Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Biological, Earth and Environmental Sciences en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
ucc.workflow.supervisor k.mulchrone@ucc.ie
dc.internal.conferring Autumn 2018 en


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