Climate Modelling Laboratory

Abstract Carbonate reservoirs, which contain a significant portion of the world's oil reserves, present unique challenges due to their complex pore structures and oil-wet characteristics, which hinder efficient oil extraction. The utilization of nanoparticles in improved and enhanced oil recovery (IOR/EOR) has emerged as a promising approach to address these challenges. Additionally, the modeling of nanoparticle behavior in carbonate rocks remains an area with significant gaps in understanding. This paper aims to develop and validate an advanced fluid modeling framework that integrates nanoparticle transport and interaction mechanisms in carbonate rock reservoirs under reservoir conditions. The primary objective is to identify and quantify critical parameters such as adsorption, wettability alteration, and transport dynamics—that influence the efficiency of nanoparticle-assisted oil recovery. The study involves complex mechanisms to understand these parameters and unlock the potential of nanoparticles in improving oil recovery. In this study, a comprehensive experimental and numerical modeling approach was employed to evaluate the performance of nanoparticles in coreflooding experiments. Laboratory tests involved fluid and rock characterization, adsorption rate, the injection of nanoparticle-enhanced fluids into carbonate core samples, with measurements taken for differential pressure, flow rates, effluent composition, adsorption. The experimental setup involved injecting the fluids into carbonate core samples under reservoir conditions, while monitoring pressure, flow rates, and effluent volume. The developed model incorporates key physical and physic-chemical processes, including wettability and adsorption measurements. The experimental coreflooding tests, which included rocks-fluid characterization and nanofluid injection, were successfully validated through a robust modeling framework. The results demonstrate that adsorption plays a crucial role in the efficiency of the oil displacement process. Adsorption of nanofluids onto the rock surface was investigated to significantly alter the wettability of the carbonate matrix, enhancing oil displacement and recovery. The integration of experimental insights with predictive modeling enables a deeper understanding of nanoparticle transport and interaction within complex carbonate matrices. The findings provide valuable insights into the design of next-generation IOR technologies, presenting a predictive model for a cost-effective and environmentally friendly solution to maximize hydrocarbon recovery and ultimately contributing to the sustainable development of carbonate oil fields.

Extreme rainfall in Queensland during December 2010 and January 2011 resulted in catastrophic flooding, causing loss of life, extensive property damage and major disruption of economic activity. Official medium-term rainfall forecasts failed to warn of the impending heavy rainfall. Since the flooding, the Australian Bureau of Meteorology has changed its method of forecast from an empirical statistical scheme to the application of a general circulation model (GCM), the Predictive Ocean and Atmospheric Model for Australia (POAMA). Our previous studies demonstrated that more skilful monthly rainfall forecasts can be achieved using artificial neural networks (ANNs). This study extends those previous investigations focussing on the capacity of the forecast methodology to differentiate between extreme rainfall events and more average conditions, up to one year in advance. Sites within two geographical regions of Queensland are examined: (i) coastal Queensland using rainfall observations from Bingera, Plane Creek and Victoria Mill; (ii) a region of south-east Queensland, using rainfall observations from 54 weather stations, extending approximately 300 km northward along the Queensland coast, from the Gold Coast to Bundaberg, and approximately 200 km inland. For both regions, the capacity to differentiate between average conditions and impending extreme rainfall events up to one year in advance is demonstrated.

For three decades there has been a significant global effort to improve El Nino-Southern Oscillation (ENSO) forecasts with the focus on using fully physical ocean-atmospheric coupled general circulation models (GCMs). Despite increasing sophistication of these models and the computational power of the computers that drive them, their predictive skill remains comparable with relatively simple statistical models. In this study, an artificial neural network (ANN) is used to forecast four indices that describe ENSO, namely Nino 1 + 2, 3, 3.4 and 4. The skill of the forecast for Nino 3.4 is compared with forecasts from GCMs and found to be more accurate particularly for forecasts with longer-lead times, and with no evidence of a Spring Predictability Barrier. The forecast values for Nino 1 + 2, 3, 3.4 and 4 were subsequently used as input to an ANN to forecast rainfall for Nebo, a locality in the Bowen Basin, a major coal-mining region of Queensland.

Accurate medium-term rainfall forecasts are a significant constraint to dry land cropping. In Australia, official monthly forecasts for the Western Australian wheat-belt are currently based on output from the Bureau of Meteorology’s general circulation model, the Predictive Ocean Atmosphere Model for Australia (POAMA). These forecasts are provided in a two-category format (above or below median rainfall) up to three months in advance for large grid areas, and are not considered reliable. An alternative approach is presented here for the three locations of Narrogin, Merredin and Southern Cross using artificial neural networks (ANNs) to forecast monthly rainfall up to 18 months in advance. Skilful monthly rainfall forecasts can be achieved at all lead times measured in terms of root mean square error (RMSE) and mean absolute error (MAE). This approach is of practical benefit to wheat growers in this region, with potential application to other locations with long historical temperature and rainfall records.