Llevando la experiencia “Seequent” a la estimación de recursos: una nueva era de flujos de trabajo de escritorio y en la nube

Resource estimation is a crucial aspect of the mining industry, influencing decisions that impact everything from early phase project evaluation to daily mine operations. It is a process that produces a multi-million-dollar digital asset used for important decision making. However, for decades, resource estimation software has been challenging to learn and use, and often disconnected from underlying geology, with the outputs from hours of painstaking work stored insecurely and without an audit trail on local and network drives. The process has heavily relied on siloed workflows, manual data handling, and disconnected systems that hinder efficiency and collaboration.

At Seequent, we believe that modern resource estimation should be intuitive, technically robust, and seamlessly integrated with the broader mining value chain. As the industry moves towards cloud-based collaboration and enhanced data governance, we are helping to redefine what best practice looks like, ensuring geoscientists, engineers, and decision-makers have the right tools, the right data, and the right workflows to build reliable, auditable, and accessible resource models.

Resource estimation workflows have historically been constrained by data silos and manual processes. A key challenge is ensuring the right data is readily available and easily integrated into the modelling workflow. Geoscientists spend a large amount of time locating and accessing data across local and shared files and moving data between software tools using formats like CSV, which introduces risks to data integrity, version control, and efficiency.

While innovation in some areas of mining operations, such as truck and drill automation, has been happening at pace, the geosciences have lagged behind. A number of factors have contributed to this lag: modelling software’s were originally developed from within the industry and now suffer serious legacy issues; recent moves in the software market have focussed on consolidating and profiting from existing technologies rather than fostering innovation; and the niche market size deters serious capital investment.

Seequent’s approach to innovation is different and its Leapfrog implicit modelling technology has already had a profound impact on the mining industry. Leapfrog Edge was developed with the intention of bringing the same workflow based, user-focused experience to resource estimation. Since its introduction in 2017, Edge has significantly grown in popularity as a tool for resource modelling, with users appreciating the modern intuitive interface, simple modelling workflows and the tight integration to underlying dynamically linked geological models. Seequent are continuing to invest heavily in Edge, with the focus being on improving the flexibility of well-designed workflows, while retaining the strong link to the visual scene. At the same time, Seequent recognises that there are limits to desktop solutions and is actively working to connect Leapfrog workflows to cloud-hosted geoscience data and the power of cloud compute within a connected platform.

To meet the evolving needs of the mining industry and resource estimation, innovative technology is needed. Seequent has recently announced Seequent Evo, a new platform dedicated to geoscience data and compute capabilities.

At the heart of Seequent Evo is a set of new open schemas for subsurface data that are designed specifically for efficient cloud storage and computation, covering all common types of sub-surface geoscience data – geology, geochemistry, geophysics, geotech and geostatistics. These schemas, which are open, documented, versioned, and accessible via API, will enable companies to:

While cloud hosting and management of data has value, the real potential that it provides is in the enablement of cloud computing.

Consider conditional simulation – a proven geostatistical method for quantifying resource uncertainty. Despite the technique being available for decades, adoption of conditional simulation to date has been slow because the method is computationally intensive, time consuming, and the available software implementations require specialist expertise. The Seequent Evo platform has allowed cloud processing of conditional simulation to be directly and seamlessly integrated into desktop software, putting a fast, solution focused conditional simulation workflow directly in the hands of resource geologists, without requiring any expert scripting.

At the same time, the open API’s that underpin Evo data mean that the same conditional simulation workflow can alternatively be accessed and run through Jupyter notebook scripting. Over time, a full suite of geostatistical tools will be available within a fully managed computational environment, where governance, security and versioning are taken care of.

Seequent ya ha desarrollado una cantidad de aplicaciones propias para Evo, como las siguientes:

These tools don’t just replace outdated methods, they are an evolution of the way resource models are built, analysed, and shared, creating a more efficient, data-driven approach to resource estimation.

The mining industry is rapidly moving toward cloud-based collaboration to improve governance, scalability, and data accessibility. As deposits become more complex and project timelines shorten, geoscientists need workflows that integrates traditional expertise with modern technology.

The future of resource estimation will rely on increased transparency, reproducibility and data driven decision making. As mining companies adapt to industry shifts, such as the growing demand for critical minerals and evolving reporting standards, combining estimation expertise with cloud solutions will ensure companies can meet these challenges.