Presentations
Kenex staff regularly present their work at conferences and workshops in New Zealand, Australia and Internationally. Have a look at our latest video presentations or scroll back through our archives below.
Predicting the wind: Wind Farm prospecting using GIS
Developing Wind and Mineral Exploration Models using GIS for Project Development in Argentina
Kenex in partnership with Emprendimientos Energeticos y Desarrollos S.A (EEDSA) have recently completed a number of strategic business development projects in Argentina to develop wind energy and mineral resources. A partnership was developed with EEDSA in 2010 to explore for and develop wind energy opportunities in Argentina using Kenex’s recently developed wind prospecting techniques. These techniques have been successfully used to map wind farm locations in New Zealand and rank each site according to its economic potential. After a year of data collection and modelling, which has successfully mapped potential wind farm sites in a number of provinces in Argentina, the partnership decided to expand into mineral exploration. Spatial Data Modelling techniques were used to map potential mineral exploration opportunities for gold, copper, base-metals, tin, tungsten and uranium at a regional scale in Argentina and Chile. Regional scale prospectivity models were developed for Argentina and Chile to identify prospective areas for a variety of metals and mineralisation styles. Fuzzy logic techniques were used to develop the wind prospectivity maps and Weights of Evidence modelling techniques were used to develop the mineral potential maps in Argentina and Chile. The models have successfully identified areas that are prospective for wind energy and gold, copper and silver and have also identified areas where new mineralised systems could be discovered with further exploration and development. Economic and risk factors will be included and target areas can then be sorted and mapped according to positive and negative exploration risk. A similar analysis will be carried out for the wind targets. This will lead to the development of an Argentina wide database of prioritised metal and wind energy targets for exploration and development. The prioritised targets will then be combined with social and logistical factors to highlight projects for acquisition. The regional targeting work for both wind energy and mineral resources has now been completed and the partnership is in the process of developing a number of business.
Using predictive modelling to aid planning in the mineral exploration and mining sector, a case study using the Powelliphanta Land Snail
The weights of evidence spatial data modelling technique has been used to create a predictive map that identifies possible locations of alpine Powelliphanta land snails in the South Island of New Zealand. This technique is commonly used in the mineral exploration industry to identify locations most likely to host mineralisation and is becoming more widely used in environmental fields as data becomes available in a digital form. Climatic, soil, topographic, and botanical data used in the model came from various organisations including NIWA and Landcare Research. The model uses the known locations of five Powelliphanta “taxa” that occur in high elevation, isolated alpine habitats to find other areas that may support similar Powelliphanta populations. The weights of evidence technique allows data to be assessed and weighted according to how great its influence is in relation to the current known locations of Powelliphanta snails. The most important variables identified from this spatial analysis were combined to produce a map showing the most likely places for Powelliphanta snails to be found. The resulting predictive model for snail habitat locations shows that mountain ranges in north-western part of the South Island have the highest probability of finding Powelliphanta land snails. It also shows that high altitude, low temperature and high rainfall condition are favoured by the snails. The model has been validated in the field and some areas not covered by the training points that were classified as highly probable by the model have recorded sightings of snails. Knowing the locations of species that will be affected, as well as knowing the potential relocation sites could help facilitate decision making during mineral exploration and mine planning.
Energy in the wind: integrating the transmission network for a better approach to wind farm prospecting
The availability of suitable transmission grid connectivity is quickly becoming a key factor for developing new wind farms globally. While wind speed remains the most important requirement for a successful wind farm along with other key parameters such as suitable terrain, current land use and distance from populated areas which are essential for site selection; a good grid connection is a significant factor in determining the economics pre-construction and future profitability of the wind farm. In the past three years, Kenex and Aurecon have developed advanced spatial modelling techniques that combine wind speed and direction, advanced terrain analysis, land use and social acceptability parameters to define the extent of potential wind farms at regional and country-wide scales. Our modelling has been successfully used by New Zealand developers to quickly and effectively target new wind farm opportunities and define the potential extent of an individual wind farm. Kenex and Aurecon have now refined the spatial modelling by integrating grid connection variables so as to identify not only the most suitable sites for a wind farm, but also which sites may potentially have the best available connections to the grid, and subsequently more preferable project economics.
Prospectivity Modelling of Seafloor Massive Sulphide (SMS) Deposits in the Kermadec Arc and Colville Ridge Regions
Prospectivity modeling of seafloor massive sulphide (SMS) deposits has been completed over the Kermadec Arc-Colville Ridge area using the GIS based weights of evidence and fuzzy logic modeling techniques. SMS deposits are the current equivalent of ancient onshore volcanogenic massive sulphide (VMS) ore deposits. These high-grade deposits are formed on the seafloor and commonly consist of a black smoker and metal rich sediment mound complex resulting from the discharge of hydrothermal fluids (up to 400°C) from fractures on the seafloor. Metal sulphides are continuously precipitated in response to mixing of high-temperature hydrothermal fluids with ambient seawater. Accumulation of metal sulphides has led to SMS deposits being potentially major sources of copper, zinc, lead and other metals such as gold, silver, which to date remain untapped. Modeling of SMS deposits was undertaken to illustrate the power of GIS modeling for seafloor resource evaluation and how it can be used to quickly identify and rank in terms of the most likely prospective areas of the seafloor where new SMS deposits might exist. The mineral deposit modeling was constrained by the mineral systems concept which defines those parts of a mineralisation system that are critical to the ore-forming process. The deposits are typically formed in extensional tectonic settings, including both submerged tectonic margins and sea-floor spreading. Volcanic vent systems and underlying dykes, stocks and sills are the sources of heat that are responsible for converting sea water drawn down through fractures in the oceanic crust into an ore-forming hydrothermal fluid. This fluid is then capable of leaching metals and elements from surrounding footwall rocks, which are then transported upwards via the convection of hydrothermal fluids. The ore materials are then precipitated within the black smoker field as massive sulphides due to the mixing of high-temperature (250-400°C), metal-rich hydrothermal fluids with cold (about 2°C) oxygen bearing seawater. Prospectivity modeling is done by compiling all the relevant data and integrating it in a way the matches the mineral system being modeled and combining them into a single mineral potential map. The commercial value of modeling from the exploration sense is that it enables more effective data management and data use, it aids decision making (focus of time, effort and expenditure) and it identifies where and what type of additional data should be collected. The modeling results can also aid government agencies from a planning perspective for areas such as mineral rights allocation, research funding direction, environmental planning and long term economic strategy regarding mineral development.
Genesis of the Chatham Rise Phosphorite; an interpretation from current literature
A synthesis of new ideas from papers relating to the genesis of the Chatham Rise phosphorite deposit is presented. Since the Sonne and Valdivia Cruises in the late 1970's and early 80's, little has been contributed to further define, quantify or explain the Chatham Rise phosphorite deposit. There have been, however, many advances in geochemistry, paleo-geography, paleo-oceanography and paleo-climatology which have contributed to understanding the genesis of phosphorite deposits worldwide. Recent oil and gas exploration in the Great South and Canterbury Basins has resulted in increased seismic coverage which has yielded in new insights into the deformation sequence on New Zealand's continental shelf marginal out in to the adjoining deep water basins. It is proposed that the Miocene southern ocean, open shelf, replacement type phosphorite deposits (which include the Chatham Rise phosphorite) were formed in response to tectonic movements, the subsequent erosion of the ancient super continent of Gondwana and the migration of ocean fronts in response to changing ocean topography. It follows that a reconstruction of paleo-geography and paleo-oceanography adjacent to the Gondwana supercontinent will provide insight into the development of this large phosphorite resource in time and space.
Resource assessment using GIS modelling of orogenic gold mineralisation potential in New Zealand
Prospectivity modelling of orogenic gold mineralisation has been completed over New Zealand using the GIS based weights of evidence modelling technique. New Zealand orogenic gold deposits are restricted to the South Island and lower North Island and are divided into two groups (Paleozoic and Mesozoic) based on their age and host rock association. Modelling of Paleozoic and Mesozoic orogenic gold deposits was undertaken to illustrate the power of GIS modelling in regional and nationwide resource evaluation and how it can be used to quickly identify and rank in terms of prospectivity areas of land where new orogenic gold deposits might exist. The mineral deposit modelling was constrained by the mineral systems concept which defines those parts of a mineralisation system that are critical to the ore-forming process. Both of the New Zealand gold models identified possible sources of metals in the region, structures that could be used for fluid migration, mineral trap zones ideally suited to host a mineral deposit, and outflow zones that may indicate a subsurface deposit. The models were validated against known areas of historical gold mining such as the Reefton deposits (Paleozoic) and Macraes Flat (Mesozoic). Two prospectivity maps showing areas favourable for Paleozoic and Mesozoic orogenic gold formation were produced. The prospectivity modelling successfully identified known areas for both types of orogenic gold mineralisation as well as several new localities not currently covered by existing tenements. The spatial modelling techniques used here can be applied elsewhere to evaluate resource potential, whether for gold, or any other land based resource, and can help planners and land owners manage future developments and their assets more effectively. Both models supersede those undertaken in 2002 by Crown Minerals and GNS Science under the purview of Dr Greg Partington (now Director of Kenex Ltd.). The new models were re-run due to the addition of new data and new modelling techniques and appear to have much better definition and are better for targeting at a prospect scale.
Predictive modelling for environmental management and mineral exploration – potential applications for the marine minerals industry
Deep sea mineral exploration has progressed significantly in the past few years, however it remains a nascent industry when compared to terrestrial mineral exploration and mining and the offshore petroleum industry. Given that in general marine exploration is more costly than terrestrial exploration the ability to focus exploration efforts and funds should be highly desirable to those companies involved. Similarly, the detailed understanding and distribution of species or habitats in the marine environment in many areas which coincide with prospective minerals deposits is often limited. Predictive modelling could therefore be a valuable tool for aiding the management of both facets of a marine minerals project. Although a GIS is a perfect way of visualising data and producing maps from that data, GIS also allows you to create new data through using statistically based gridding techniques or predictive maps using spatial data modelling techniques. This modelling is where businesses can really add value, using their data more effectively rather than just passively using it to generate maps and figures. Basic statistical gridding allows you to predict unknown values from within a single layer such as topography, bathymetry, geochemistry, vegetation, hydrology, water temperature or climate data. However the real power of GIS is when spatial modelling is applied to combine several layers to predict outcomes based on probability such as mineral prospectivity, agricultural sustainability, geotechnical risk, environmental risk, and onservation planning. Adapting the technique for locating or ranking prospective seafloor massive sulphide or manganese nodule targets or for aiding baseline and detailed environmental planning are some of the possible applications for predictive modelling for the marine minerals industry.
Exploration targeting using GIS: more than a digital light table
The use of computers in mineral exploration in the last twenty years has dramatically changed the way we carry out exploration targeting (e.g. Bonham-Carter, 1994; Bonham-Carter et al., 1988; Mihalasky, 2001; Rattenbury and Partington, 2003; Partington and Sale 2004; Partington 2009; Carranza, 2009). This is especially true in the last five years where computer and GPS technology has developed to the stage where it is possible to digitally locate, accurately store, visualise and manipulate geological data at the scale of a mineral system. These tasks are commonly carried out using a Geographic Information System (GIS), which has become as an important tool to a geologist as his hammer. The aim of this paper is to provide a brief review of the techniques available to explorers using GIS and discuss the advantages and problems associated with using GIS techniques for exploration targeting.
Resource assessment using GIS modelling of orogenic gold mineralisation and wind energy potential in Wellington, New Zealand
Prospectivity modelling of orogenic gold mineralisation and ideal locations for wind farm development has been completed over southwest Wellington in New Zealand. This modelling used a combination of the GIS based weights of evidence and fuzzy logic techniques. These models were undertaken to illustrate the power of GIS modelling in regional resource evaluation and how they can be used to quickly identify areas of land which should be considered for wind farm development or those where new gold deposits might exist. The mineral deposit modelling was constrained by the minerals systems concept which defines those parts of a mineralisation system that are critical to the ore-forming process. The Wellington gold model identified possible sources of metals in the region, structures that could be used for fluid migration, mineral trap zones ideally suited to host a mineral deposit, and outflow zones that may indicate a subsurface deposit. Similarly, the Wellington wind farm model identified ideal sites to develop a wind farm using elements critical for successful turbine placement such as wind speed, terrain, sources of air turbulence, access and land use. The models were validated against known areas of historical gold mining such as at Terawhiti and the turbine locations of Meridian Energy's new West Wind development. The modelling clearly shows that the resource potential in southwest Wellington is greater for wind energy especially after consideration of potential archaeological and environmental restrictions which may rule out key areas of possible orogenic gold mineralisation identified by the model. The spatial modelling techniques used here can be applied elsewhere in New Zealand to evaluate resource potential, whether for wind, gold, or any other land based resource, and can help planners and land owners manage future developments and their assets more effectively.