Petrophysical characterisation of the Neoproterozoic and Cambrian successions in the Officer Basin

Abstract

The Neoproterozoic–Paleozoic Officer Basin, located in South Australia and Western Australia, remains a frontier basin for energy exploration, with significant uncertainty due to a paucity of data. As part of Geoscience Australia’s Exploring for the Future (EFTF) program, the objective of this study is to derive the petrophysical properties and to characterise potential reservoirs in the Neoproterozoic–Cambrian sedimentary succession in the Officer Basin through laboratory testing and well log interpretation using both conventional and neural network methods. Laboratory measurements of 41 legacy core samples provide the relationships between gas permeability, Klinkenberg corrected permeability, and nano-scale permeability, as well as grain density, effective and total porosity for various rock types. Conventional log interpretation generates the volume fraction of shale, effective and total porosity from gamma ray and lithology logs. A self-organising map (SOM) was used to cluster the well log data to generate petrophysical group/class index and probability profiles for different classes. Neural network technology was employed to approximate porosity and permeability from logs, conventional interpretation results and class index from SOM modelling. The Neoproterozoic−Cambrian successions have the potential to host both conventional and tight hydrocarbon reservoirs. Neoproterozoic successions are demonstrated to host mainly tight reservoirs with the range in average porosity and geometric mean permeability of 4.77–6.39% and 0.00087–0.01307 mD, respectively, in the different sequences. The range in average porosity and geometric mean permeability of the potential Cambrian conventional reservoirs is 14.54−26.38% and 0.341−103.68 mD, respectively. The Neoproterozoic shales have favourable sealing capacities. This work updates the knowledge of rock properties to further the evaluation of the resource potential of the Officer Basin.

Petrophysical interpretation and reservoir characterisation on Proterozoic shales in National Drilling Initiative Carrara 1, Northern Territory

“…A SOM is a two-layer neural network, including both an input and Kohonan layer and using an unsupervised competitive learning technique (Kohonen 1982). The SOM technique has been applied in clustering well logs to derive petrophysical groups or classes (Wang et al 2022).…”

“…The APPEA Journal estimate the parameters where the relationships are complex and non-linear (Wong et al 1995), such as in the Proterozoic shales in NDI Carrara 1. As a widely used machine learning approach, the artificial neural networks (ANNs) estimator is a non-linear estimator using multiple layers of weights and biases, which have been applied to estimate hard data, such as laboratory measurements, from seismic attributes, well logs and conceptual geological descriptions in the petroleum domain (Raiche 1991;Wong et al 1995;Wang et al 1999Wang et al , 2021Wang et al , 2022.…”

“…Fig. 5 presents a schematic architecture of ANNs with input, output and three hidden layers (Roberts et al 2022;Wang et al 2022). Each layer has a different number of neurons.…”

Geomechanical rock properties of the Officer Basin

“…The results of the geomechanical and petrophysical analyses are detailed in Bailey et al (2021), with the geomechanical results discussed herein (Table 1). Discussion of the petrophysical results can be found in Wang et al (2022), where the authors discuss the results alongside well log interpretation using both conventional and neural network methods.…”

“…This work contributes to a larger-scale regional stratigraphic study, which also includes new chemostratigraphy, geochemistry, fluid inclusion stratigraphy, thin section petrography, conventional wireline log interpretation and neural network identification of petrophysical parameters (Carr et al 2022). Further detail on the petrophysical work is included in this volume (Wang et al 2022).…”