WOODY in Practice: Improving Mining Project Evaluation with CAPEX and OPEX Benchmarking
In this presentation, Costmine’s Director of Costing and Engineering, Gordon Sobering, PE and QP, examines how machine learning is changing the way mining projects are evaluated, estimated, and compared. As projects become more complex and data-driven, traditional approaches to cost estimation are increasingly difficult to maintain, often relying on static models that quickly become outdated.
Mining cost models depend on complex interactions between geology, mineral reserves, engineering design, and operations, while external factors such as commodity prices, labor, and supply costs continue to shift. This creates a nonlinear environment where static models struggle to keep pace. Machine learning offers a more dynamic approach, allowing cost models to be updated continuously as new data becomes available and helping users better understand how uncertainty impacts project outcomes.
The presentation highlights how Costmine supports this approach through an integrated set of tools. Mining Cost Service provides detailed cost data and industry-specific indices, while SHERPA applies engineering and geological inputs to generate CAPEX and OPEX estimates across the mine life cycle. WOODY brings these components together in a benchmarking platform that allows users to compare projects using publicly available technical reports and standardized cost categories.
A key focus is the contrast between traditional workflows and data-driven benchmarking. Conventional project evaluation requires manually collecting and normalizing data, building complex spreadsheets, and iterating through scenarios, often with inconsistent results. With WOODY, projects are already structured within a centralized database and can be filtered by key parameters such as commodity, stage of development, and geography. Users can quickly generate independent CAPEX and OPEX estimates, cost curves, and identify outliers without rebuilding models from scratch.
The session also includes comparisons of operating and capital costs across multiple mining projects. By applying consistent cost categories and inputs, WOODY highlights where estimates align and where gaps exist, helping users identify areas that may require further review.
Throughout the presentation, Gordon emphasizes the importance of transparency, consistency, and traceability in modern project evaluation. With reproducible models and standardized inputs, mining professionals can improve both the speed and reliability of their analysis while maintaining visibility into how costs are built.
For analysts, investors, and technical teams, this approach enables more efficient project comparison, supports better decision-making, and helps identify risks earlier in the evaluation process.
