Reservoir Characterisation and Scenario-Based Modelling to Optimise Development Planning of the Jurassic Plover Formation in the Ichthys Field, North West Shelf of Australia
- Kazuyuki Yamamoto (INPEX) | Shuji Yamamoto (INPEX) | Toby Jones (INPEX) | Ryo Iizuka (INPEX) | Satoshi Maki (INPEX) | Takashi Yamatani (INPEX) | Takahito Banno (INPEX) | Kapil Seth (INPEX)
- Document ID
- Society of Petroleum Engineers
- SPE Asia Pacific Oil & Gas Conference and Exhibition, 17-19 November, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 5 Reservoir Desciption & Dynamics, 5.1.7 Seismic Processing and Interpretation, 5.6.1 Open hole/cased hole log analysis, 5.1.3 Sedimentology, 1.6.9 Coring, Fishing, 5.5.8 History Matching, 5.6 Formation Evaluation & Management, 5.5 Reservoir Simulation, 5.5.3 Scaling Methods, 4.3.4 Scale, 1.6 Drilling Operations, 5.1 Reservoir Characterisation
- Scenario-based modelling, Plover Formation, Ichthys Field, Reservoir characterisation
- 21 in the last 30 days
- 21 since 2007
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The Jurassic Plover Formation is one of two reservoirs in the Ichthys Field, North West Shelf of Australia. It consists of fluvial to shallow-marine sandstones, shales and igneous rocks. The objective of this study is to build multiple scenario-based models to optimise development planning in preparation for the upcoming production phase.
We have integrated data and interpretations of thin sections, cores, well logs and seismic data to create multiple geological concepts for the field and to identify key geological uncertainties. As the reservoir is geologically complex and many uncertainties were initially identified, it is essential to single out those uncertainties which have a significant impact on the development planning. We have established the key uncertainties and optimal model design for practical use through multi-disciplinary discussions and by running sensitivity models to check the production performance.
A rock type (RT) scheme has been devised based on detailed petrographic observations and justified in terms of sedimentology and diagenesis. Using the scheme, a wide range of permeability variations in the sandstones has been captured and modelled. Environments of deposition (EOD) are firstly interpreted at core and well-log scales, then upscaled to the model zone scale. The EOD interpretations are laterally extended using lithology (sandstone, shale and igneous rock) probability maps derived from quantitative seismic interpretation (QSI). Multiple EOD scenarios are generated to capture the possible range of reservoir distributions. Each EOD is characterised by a unique net-sand porosity and RT proportion based on the well data. These values are used to define multiple possible porosity trends and RT proportions, guided by the EOD maps. The distribution and quality of the reservoir sandstones have been identified as key uncertainties. Another key uncertainty is reservoir compartmentalisation, thought to be mainly caused by sheet-like igneous intrusions. Subtle seismic lineaments are regarded as possible indications of such igneous intrusions, and multiple compartmentalisation scenarios have been prepared based on our understanding of igneous activity across the field. Reservoir structure and water saturation are also recognised as key uncertainties. Integrating the key uncertainties, we have established a practical modelling workflow and built multiple scenario-based models to cover a sufficient range of geological uncertainty. The workflow is also adaptive for future history matching, enabling us to flexibly edit the model properties under geological constraints.
A decision tree for development planning, which defines a series of decisions for the well sequence depending on the well results, will be prepared based on the multiple scenario-based models delivered in this study. This will enable us to prepare for any potential decision-making in advance. The development planning will be continuously optimised throughout the production phase by simply selecting the scenario-based models most in line with the well results.
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