In this study we intend to provide an overview on fossilized tree resins (amber) commonly found in Southeast Asia, more particularly in the Indo-Australian Archipelago (IAA). These remains are often referred in literature as "Indonesian amber", "Borneo amber" or simply as "dammar". They are very common in the region and the Brunei Sultanate is no exception as most of its Neogene sedimentary successions contain amber-rich layers. Although amber is a common fossil in the country and in northern Borneo, to our knowledge it has not been studied in great detail so far. Here we present an account on the "Borneo Ambers" from Brunei, regarding their stratigraphic origin, basic physical properties, their interaction with the biosphere and their botanical origin using Fourier-transform infrared spectroscopy (FTIR). Additionally, a number of ambers and modern tree resins were analysed for their carbon isotope composition and a few were tested with gas chromatography. We discuss the results in a regional and global context, in comparison with available data from the IAA. The ambers come from four different lithostratigraphic units with an age range of 12 to 3 million years (middle Miocene to Pliocene). Recently reworked ambers from the coast, ambers from younger alluvial deposits, and several modern tree resins from Dipterocarpaceae and Araucariaceae (Agathis borneensis) were also included in the study. The more than 60 FTIR analyses of modern and fossil specimens suggest that all the Brunei ambers were produced by trees of Dipterocarpaceae. There is no indication of Agathis in the fossil record, in agreement with their lower abundance in the forests of Borneo. Modern and fossil dipterocarp resins were found to be different based on the following criteria: (1) Different reactions to solubility, hot needle and UV tests with faster reaction time and less fluorescence for the modern ones; (2) Clear distinction based on certain FTIR absorbance band ratios, mostly by those that represent carboxylic acids and esters (e.g., ~1700 and 1243 cm-1); (3) Modern resin yielded on an average 3 ‰ lower δ 13 C values, (4) Gas chromatography data reflect maturation differences among the samples. Although there is some overlap in the chemical results between the two groups, generally all these differences reflect different maturation stages of the resinous material and point towards loss of low δ 13 C components from the organic structure of the resin. The minor timewise decreasing trend in average δ 13 C from the late middle Miocene to late Miocene can be explained by (1) gradual changes in local environmental conditions, and/or (2) increased amount of less mature specimens among the younger samples. In contrast, the highest obtained δ 13 C values were found in the youngest Pliocene ambers. Instead of maturation bias this can be linked to environmental factors such as cooler-drier climate with increased seasonality, probably reflecting the onset of the northern hemisphere glaciation.

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Thermal maturity, organic richness and kerogen typing are very important parameters to be evaluated for source rock characterization. Due to the difficulties of high cost geochemical analyses and the unavailability of rock samples, it was necessary to examine and test many different method and techniques to help in the prediction of TOC values as well as other maturity indicators in case of missing or absence of geochemical data. Integrated study of machine learning techniques and well-log data has been applied on Cretaceous–Paleocene formations in the Taranaki Basin, New Zealand. A novel approach of maturity prediction using Tmax and vitrinite reflectance (VR%) is the first and preliminary objective of this research. Moreover, the organic richness or the total organic carbon (TOC) content has been predicted as well. Geochemical and well-log data collected from the Cretaceous Rakopi and North Cape formations and Paleocene Mangahewa Formation have been processed and prepared to apply the machine learning techniques. Five machine learning techniques, namely Bayesian regularization for feed-forward neural networks (BRNNs), random forest (RF), support vector machine (SVM) for regression, linear regression (LR) and Gaussian process regression (GPR), were employed for prediction of TOC, Tmax and VR, and their results have been compared. For TOC prediction, the best model achieved the coefficient of determination (R2) value of 0.964 using RF model. For Tmax prediction, BRNN with one hidden layer achieved the R2 value of 0.828. BRNN with two hidden layers produced the best model for VR prediction achieving R2 = 0.636. A comparison of five ML techniques showed that all of these techniques performed exceedingly well for TOC prediction with a value of R2 > 0.96. In contrast, BRNN with one hidden layer was the only ML technique able to achieve R2 > 0.8 for Tmax and BRNN with two hidden layers was the only ML technique able to achieve R2 > 0.6 for VR prediction. Therefore, this research provides a strong empirical evidence that ML techniques can capture the nonlinear relationship between the well-log data and TOC as well as the maturity indicators which may not be fully understood by existing linear models.

A reservoir characterization study, using petrophysical and petrographic analyses, has been made on the Paleocene Farewell Formation in the Taranaki Basin, New Zealand, based on five selected wells. Farewell Formation is largely a sandstone formation belonging to the Kapuni Group. The integrated study has shown that Farewell Formation is a good promising reservoir with average effective porosity of 17.7% and permeability of 415 mD. The petrographic study indicates the occurrence of abundant intergranular and secondary pores. It also proved that the Farewell Formation has been affected by several diagenetic features. Compaction, cementation and clay mineral authigenesis are the most common. Quartz and feldspar overgrowths have been recorded in many samples, and secondary porosity due to dissolution is also observed. In general, good reservoir quality features are dominant in the Farewell Formation and diagenesis has little effect on the reservoir quality. These findings are supported by well log interpretation results, which confirm good sand and net pay zones are available with very low average water saturation (24.9%).