Analysis of Subsurface Data from the South Berwyndale Gas Field. / Vuruktu, Nanpan.

2017. 149 p.

Research output: ThesisDoctoral Thesis

Unpublished

Documents

  • Nanpan Vuruktu

Abstract

Sediments formed by fluvial processes are highly heterogeneous and often characterised by complex reservoir sequences. The hydrocarbon potential of these reservoirs is often associated with high degree of uncertainty due to the level of variability in properties and dimensions of the sandstone bodies. The variability in the properties and dimensions of these sandstone bodies is caused by fluvial controls during the deposition and these reservoirs are characterised by variable facies, structures, grain sizes, textures and, architectural style. Many of these sandstone bodies are characterised by sub-environment (genetic units) and predicting the lateral extent of these sandstone bodies critical in the exploration and production of hydrocarbon. There are many established approaches in the oil and gas industry that realistically predict the lateral extent of sandstone bodies using the width thickness relationship. This study documents the dataset generated from the subsurface analysis of 41 wells spread across the Berwyndale South Gas field of the Surat Basin of Australia. The dimensions of the seventeen sandstone bodies identified from four stratigraphic intervals show high level of variability regardless of the methodology. The dimensions (thickness, channel width and channel belt width) of these sandstone bodies range between 2 and 15m in thickness, between 35 to 200 m for channel width and between 62 to 4902m. These dimensions are typical for a meandering river deposits which can serve as analogue in the evaluation of fluvial hydrocarbon reservoirs of similar geological settings.
Original languageEnglish
QualificationMPhil
Awarding Institution
Supervisors/Advisors
Thesis sponsors
  • Petroleum Technology Development Fund
Award date1 Oct 2017
Publication statusUnpublished - 13 Sep 2017
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 28656512