Evelyn June Hill

Core analysts principally study the storage, flow and saturation properties of porous rocks and sediments. Some of the derived parameters are specific to hydrocarbon production but many have commonality with other subsurface disciplines such as hydrology and soil science. Traditional core analysis involves direct physical experimentation on core plugs to derive a range of parameters used as calibration for conventional well logs, and to predict hydrocarbon reserves and recovery. The mechanisms and processes for obtaining such data have evolved significantly during the last century, from the manual instruments of the mid-twentieth century to the accredited digital data collection and recording of the 1990s onwards. X-ray micro- and nano-scale computed tomography (CT) imaging led to the development of the digital rock physics subdiscipline in the early 2000s. This has subsequently allowed direct visualization of fluid flow at the pore scale, imaging the wetting phase and multiphase fluid mobility. Multiscale imaging workflows are being developed to overcome issues around heterogeneous rock and the limited field of view associated with the highest resolution X-ray CT images. Hybrid workflows, which combine digital rock physics with traditional core analysis, are becoming increasingly common to meet the challenges associated with some of the most difficult to constrain properties, such as relative permeability. At a larger scale, the recent development of multisensor core logging (MSCL) tools has allowed the cost-effective acquisition of essentially continuous high-resolution 1D, 2D and 3D datasets from both slabbed and unslabbed whole core. Often aided by artificial intelligence to manage and interpret these large physical and chemical datasets, both new and legacy core can be rapidly screened to allow representative subsampling for detailed laboratory experimentation. The context and data provided by the MSCL then allows effective upscaling of these time- and cost-intensive point-source measurements. In the last decade, extended reality (XR) has resulted in a step change in the ability to visualize and integrate core and core-derived information with other subsurface datasets. A very wide range of scales can be managed effectively, from micrometre- to centimetre-scale petrographical and core analysis data, to metre-scale well logs and up to kilometre-scale 3D and 4D seismic. These tools allow stakeholders to work and meet from any location in a common workspace, and efficiently scale and interrogate data in a virtual 3D environment. The various advances in core analysis and associated technologies during the early twenty-first century mean that the study of porous media to help enable the energy transition looks assured. During the coming decades, applications as diverse as carbon capture, utilization and storage (CCUS), hydrogen storage, geothermal energy generation, mining for critical minerals, palaeoclimate studies, radioactive waste management, and site surveys for windfarms will all continue to benefit from the data and understanding derived from core analysis.

Modelling of 3D domain boundaries using information from drill holes is a standard procedure in mineral exploration and mining. Manual logging of drill holes can be difficult to exploit as the results may not be comparable between holes due to the subjective nature of geological logging. Exploration and mining companies commonly collect geochemical or mineralogical data from diamond drill core or drill chips; however, manual interpretation of multivariate data can be slow and challenging; therefore, automation of any of the steps in the interpretation process would be valuable. Hyperspectral analysis of drill chips provides a relatively inexpensive method of collecting very detailed information rapidly and consistently. However, the challenge of such data is the high dimensionality of the data’s variables in comparison to the number of samples. Hyperspectral data is usually processed to produce mineral abundances generally involving a range of assumptions. This paper presents the results of testing a new fast and objective methodology to identify the lithological boundaries from high dimensional hyperspectral data. This method applies a quadrant scan analysis to recurrence plots. The results, applied to nickel laterite deposits from New Caledonia, demonstrate that this method can identify transitions in the downhole data. These are interpreted as reflecting mineralogical changes that can be used as an aid in geological logging to improve boundary detection.

Geochemical data are frequently collected from mineral exploration drill-hole samples to more accurately define and characterise the geological units intersected by the drill hole. However, large multi-element data sets are slow and challenging to interpret without using some form of automated analysis, such as mathematical, statistical or machine learning techniques. Automated analysis techniques also have the advantage in that they are repeatable and can provide consistent results, even for very large data sets. In this paper, an automated litho-geochemical interpretation workflow is demonstrated, which includes data exploration and data preparation using appropriate compositional data-analysis techniques. Multiscale analysis using a modified wavelet tessellation has been applied to the data to provide coherent geological domains. Unsupervised machine learning (clustering) has been used to provide a first-pass classification. The results are compared with the detailed geologist’s logs. The comparison shows how the integration of automated analysis of geochemical data can be used to enhance traditional geological logging and demonstrates the identification of new geological units from the automated litho-geochemical logging that were not apparent from visual logging but are geochemically distinct.

The chemistry of hydrothermal monazite from the Carrapateena and Prominent Hill iron oxide-copper-gold (IOCG) deposits in the IOCG-rich Gawler Craton, South Australia, is used here to define geochemical criteria for IOCG exploration in the Gawler Craton as follows: Monazite associated with IOCG mineralisation: La + Ce > 63 wt% (where La > 22.5 wt% and Ce > 37 wt%), Y and/or Th < 1 wt% and Nd < 12.5 wt%; Intermediate composition monazite (between background and ore-related compositions): 45 wt% < La + Ce < 63 wt%, Y and/or Th < 1 wt%. Intermediate monazite compositions preserving Nd > 12.5 wt% are considered indicative of Carrapateena-style mineralisation; Background compositions: La + Ce < 45 wt% or Y or Th > 1 wt%. Mineralisation-related monazite compositions are recognised within monazite hosted within cover sequence materials that directly overly IOCG mineralisation at Carrapateena. Similar observations have been made at Prominent Hill. Recognition of these signatures within cover sequence materials demonstrates that the geochemical signatures can survive processes of weathering, erosion, transport and redeposition into younger cover sequence materials that overlie older, mineralised basement rocks. The monazite geochemical signatures therefore have the potential to be dispersed within the cover sequence, effectively increasing the geochemical footprint of mineralisation.

Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, without reference to spatial information, typically result in numerous small-scale units. These small-scale units result not only from the presence of very small rock units, which may be below the scale of interest, but also from misclassification. A novel method is proposed that uses the continuous wavelet transform to identify geological boundaries and uses wavelet coefficients to indicate boundary strength. The wavelet coefficient is a useful measure of boundary strength because it reflects both wavelength and amplitude of features in the signal. This means that boundary strength is an indicator of the apparent thickness of geological units and the amount of change occurring at each geological boundary. For multivariate data, boundaries from multiple variables are combined and multiscale domains are calculated using the combined boundary strengths. The method is demonstrated using multi-element geochemical data from mineral exploration drill holes. The method is fast, reduces misclassification, provides a choice of scales of interpretation and results in hierarchical classification for large scales where domains may contain more than one rock type.

An important step in mineral resource estimation process is the grouping of drill hole samples into domains that reflect zones of homogeneous properties for accurate grade estimation and practical exploitation purposes. In practice, this challenging task is performed through a subjective, time-consuming manual interpretation of the mineral deposit. Therefore, various interpretations are possible. The definition of domains can be viewed as a clustering problem consisting of grouping samples into clusters, herein called domains, so that samples belonging to the same cluster are more similar than those in different clusters. Several methods exist for this purpose; however, groups of samples created through traditional clustering tend to show poor spatial contiguity. Alternatively, spatially contiguous clusters can be obtained through geostatistical clustering where the spatial dependency between samples is considered. This paper is devoted to the application of geostatistical clustering to support domaining of an iron ore deposit located in Western Australia.