What are the best practices and tools to manage and share your exploration data and knowledge?

Risk involves known probabilities and quantifiable factors. Uncertainty deals with unknown probabilities and unpredictable factors, like sudden regulatory changes or unforeseen geological challenges. Risk can be managed with models and forecasts, while uncertainty requires adaptive strategies and continuous information gathering. Mining risk examples: Commodity Price: Historical informs predictions, but market volatility adds uncertainty. Environmental Impact: Predictable, but new discoveries or policy changes bring uncertainty. Social Conflict: Unpredictable, depending on local dynamics. Natural Disasters: Uncertain but can be mitigated through risk management like insurance.

My understanding is that 'risk' has a specific impact or cost associated with it. For example, the 'risk' of a bet at a roulette table is the loss of your wager with a probability of 52.6%.

From a mineral resource classification perspective, uncertainty around reasonable prospects for economic extraction can be mitigated through systematic exploration, geological interpretation, and engineering and financial analysis. As a consequence, the risks associated with each progressively higher classification of resource are mitigated, with Inferred Resources carrying the highest risks, and Proven Reserves carrying the lowest.

Think of risk and uncertainty as two mischievous gremlins lurking in the geologist's toolkit. Risk, the brazen one, barges in with a loud thud, brandishing tangible threats like unstable terrain, environmental impact, and fluctuating commodity prices. Its presence is immediate, a sharp slap of reality that can drain even the most ambitious ventures of their vitality. Uncertainty, on the other hand, is a cunning, cloaked figure, its whispers slithering into the ears of exploration teams, sowing doubt about the very existence of the coveted mineral veins. It's the ever-present "what if," the gnawing sense that beneath the surface, disappointment might lie instead of glittering riches.

One common problem I observed in exploration, is that validation of field data is sometimes not backed with field supervision. For exploration data to be clean, one must be able to match logs in the field. I believe these risks can be transferred to different levels harming interpretations.

Optimism bias (Blue Sky) in PFS/BFS studies & valuations has been evident for over a decade, due to the short-termist nature of incentives linked to these valuations (agency theory). For example, Relying solely on NPV for mining asset valuation leads to consistently poor decisions. More accurate valuation methods exist. Real Options (RO) and Risk-Adjusted Discounted Cash Flow (RDCF), modern asset pricing (MAP). These methods adjust cash flows for their risks and discount them at a risk-free rate, giving a clearer value. Despite their benefits, they are rarely used due to their complexity.

When evaluating the risks associated with a project feasibility study, sensitivity analysis of key variables to a financial model like commodity price, discount rates (for discounted cash flows), capital costs, or operating costs, is often a good starting point for identifying key risks to one or more “what if” scenarios.

Project feasibility is a complex mix of a range of factors, including, but not limited to: financial, technical, social, and environmental. On often overlooked issue when assessing a feasibility study, pre-feasibility study, or provisional economic assessment, is that of the standard to which each study is held against. Whilst there is no internationally recognised ’standard’, there are groups out there who have written examples that are growing in use. Tied to a comprehensive capital investment system, these are power tools for the mining companies, and those who may be seeking to deploying capital to build an operation.

The integrity of the data is paramount. If one keeps the integrity of data intact, one will achieve the best true results. A skilled and experienced interpreter understands how the data has been gathered, what has happened to the data in processing, searches for patterns that indicate a solution, can verify the solution with information from other sciences, and knows how to display his conclusions.

Mining still relies on Excel. Those rigid spreadsheets that are designed to produce ‘moment in time’ data and cannot be adjusted or interrogated to allow for variable scenarios or market fluctuations. Mining corporations typically use bespoke in-house models, which are generally based on Excel spreadsheets to produce the models and require input from experts in many fields. Producing the financial models in this way is costly in terms of resources and finance. The models that are produced are static and can not be manipulated to reflect potential variables. C-suite executives, decision-makers and potential investors rely on these static models to assess a mining project’s potential and suitability for capital investment.

While data sharing is becoming widely popular in other industries, the mining industry remains relatively siloed in its approach because of the competitive and complex nature of mineral rights. Knowledge sharing is typically limited to internal stakeholders, often to each mining or exploration companies detriment, especially when even adjacent property owners could mutually benefit from knowledge sharing to foster a more holistic view of each asset.

To manage and share exploration data mining industry should integrate AI, machine learning, and visual workflows software. It's cheaper than hiring a consultant and easier to share with investors and other stakeholders.

Organizations like the US Geological Survey have made great strides in the last century at consolidating data and knowledge, much of which is made available to the broader public. Yet this library of information is often limited to that already in the public domain and represents only a small fraction of what exists elsewhere.

The Global Carbon Capture and Storage Institute also led in global knowledge and best practices sharing in the 2000s. Australia funded the Institute which advanced the CCS cause. Overall I think it achieved its objectives.