A 3D Model of the Unconventional Play in the Goldwyer Formation: An Integrated Shale Rock Characterisation over the Broome Platform, Canning Basin.
- Lukman Johnson (Curtin University) | Gregory Smith (Curtin University) | Reza Rezaee (Curtin University) | Ali Kadkhodaie (University of Tabriz)
- Document ID
- Unconventional Resources Technology Conference
- SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, 18-19 November, Brisbane, Australia
- Publication Date
- Document Type
- Conference Paper
- 2019, Unconventional Resources Technology Conference (URTeC)
- Uplift and Erosion, 3D Geochemical Property Model, Burial History Model, Canning Basin, Kerogen Kinetics
- 3 in the last 30 days
- 89 since 2007
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In underexplored sedimentary basins such as the Canning Basin, understanding the geochemical property distribution is critical for successful exploration of unconventional hydrocarbon resources. This study utilizes an integrated approach to characterize the organic rich sections of the Ordovician Goldwyer Formation, in terms of their potential for shale oil and gas, on the Broome Platform of the Canning Basin. Core and cuttings samples from a large number of wells were analysed by pyrolysis of the organic matter (Rock-Eval 6 and kinetic studies), while additional geochemical data were collated from the Western Australia Department of Mines and Petroleum (WAPIMS) online database. A Machine Learning method was used to predict continuous geochemical logs in wells with limited or no geochemical based on available information from other wells with good downhole geochemical data and logs within in the Goldwyer Formation. The optimised geochemical logs in all wells were then used to create 3D petrophysical property models to predict the geochemical property distribution of this interval across the study area using the Kriging method in Petrel (Schlumberger software).
Burial and thermal history models were constructed in Petromod (Schlumberger software) for five selected well locations to assess the evolution through time of the kerogen maturity and transformation in the Goldwyer Formation. The pyrolysis and kinetic results indicate that the Goldwyer III shale unit shows fair to good hydrocarbon generative potential across the study area and is mostly within the early to peak mature stage generation at present day.
The average geochemical property distribution maps showed that the distribution of kerogen type (HI), total organic carbon (TOC), free hydrocarbons (S1) and yield potential (S2) are higher in the central to south-eastern part of the study area, while relatively lower values occur in the north-western part. The burial history models indicate that kerogen transformation in the Goldwyer III shale unit increases gradually from the north-western part of the study area to the south-eastern area where the kerogen transformation is highest. However, the maturation history is complicated because the region has experienced at least two episodes of burial with exposure to higher temperatures and pressures.
The best organic rich shales in the Goldwyer III unit for shale oil and gas occur in the central to the south-eastern part of the study area. This conclusion is based on an integrated study of their organic geochemical properties, kerogen transformation kinetics and thermal maturity. The timing of the generative episodes relative to trap formation remains an issue for successful conventional petroleum exploration. However, this is not such a major impediment to economic production for unconventional prospects.
|File Size||1 MB||Number of Pages||12|
Brown, S.; Boserio, I.; Jackson, K. & Spence, K. The geological evolution of the Canning Basin—implications for petroleum exploration. The Canning Basin, WA: Proceedings of the Geological Society of Australia and the Petroleum Exploration Society of Australia, Canning Basin Symposium, Perth Western Australia, 1984. 85–96,
Espitalié, J.; Ungerer, P.; Irwin, I. & Marquis, F. 1988. Primary cracking of kerogens. Experimenting and modelling C1, C2–C5, C6–C15 and C15+ classes of hydrocarbons formed. Organic Geochemistry, 13, 893–899. https://doi.org/10.1016/0146-6380(88)90243-4
Huang, Z. & Williamson, M. A. 1996. Artificial neural network modelling as an aid to source rock characterization. Marine and Petroleum Geology, 13, 277–290. http://dx.doi.org/10.1016/0264-8172(95)00062-3
Kadkhodaie-Ilkhchi, A.; Rezaee, M. R. & Rahimpour-Bonab, H. 2009. A committee neural network for prediction of normalized oil content from well log data: An example from South Pars Gas Field, Persian Gulf. Journal of Petroleum Science and Engineering, 65, 23–32. http://dx.doi.org/10.1016/j.petrol.2008.12.012
Kamali, M. R. & Allah Mirshady, A. 2004. Total organic carbon content determined from well logs using ?LogR and Neuro Fuzzy techniques. Journal of Petroleum Science and Engineering, 45, 141–148. http://dx.doi.org/10.1016/j.petrol.2004.08.005
Passey, Q. R.; Creaney, S.; Kulla, J. B.; Moretti, F. J. & Stroud, J. D. 1990. Practical model for organic richness from porosity and resistivity logs. American Association of Petroleum Geologists Bulletin, 74, 1777–1794, http://www.scopus.com/inward/record.url?eid=2-s2.0-0025570519&partnerID=40&md5=914afe78a548c8dae35f18be5e56c670
Poelchau, H. S.; Baker, D. R.; Hantschel, T.; Horsfield, B. & Wygrala, B. 1997. Basin Simulation and the Design of the Conceptual Basin Model. In: Welte D. H., Horsfield B. & Baker D. R. (eds.) Petroleum and Basin Evolution: Insights from Petroleum Geochemistry, Geology and Basin Modeling. Berlin, Heidelberg: Springer Berlin Heidelberg.