Modern open‑pit projects increasingly rely on an integrated digital workflow: Surpac for geological modelling and resource estimation, Whittle for pit optimisation and phase design, and MineSched for tactical and operational scheduling. Case studies across gold, copper and iron ore show that this combination improves reserve estimation, NPV, production stability and planning efficiency (Deressa et al., 2024; Sobko, Lozhnikov and Tretyakov, 2021; Kržanović et al., 2018; Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015; Marković et al., 2025).

The following sections outline how each software is implemented and how geostatistics, geological models, resource estimates, pit optimisation and mine scheduling fit together.

Geological Data Integration and Modelling in Surpac

Data preparation and geological interpretation

In most published applications, exploration drilling, assays, lithology and structural logs are first organised into a relational database and imported to GEOVIA Surpac (Deressa et al., 2024; Mbolela et al., 2022; Sdvyzhkova et al., 2022; Marković et al., 2025). Common steps include:

  • Validation and compositing of assays, with attention to sampling intervals, top‑cut values and mixed distributions before any geostatistical work (Deressa et al., 2024; Mbolela et al., 2022).
  • Construction of sectional interpretations and 3D solids representing ore zones, lithological units and weathering horizons, which then become estimation domains (Deressa et al., 2024; Akisa and Mireku-Gyimah, 2015; Sdvyzhkova et al., 2022).

For example, Deressa et al. generated a detailed geological model of a hydrothermal gold deposit in Surpac before any reserve estimation, emphasising that pit design quality depends on this upfront geological work (Deressa et al., 2024). Similar 3D models have been built for iron ore and deep copper deposits and then passed to geomechanical or risk models (Sobko, Lozhnikov and Tretyakov, 2021; Sdvyzhkova et al., 2022; Marković et al., 2025).

Role of the geological model

Across studies, the geological model:

  • Defines estimation domains (grade continuity, stationarity) for geostatistics.
  • Sets the physical limits within which resource and reserve estimates are meaningful.
  • Provides the structural context for later slope design in Whittle and mine design software (Deressa et al., 2024; Kržanović et al., 2025; Tyo and Zeitinova, 2023; Sdvyzhkova et al., 2022).

You can expand this section by detailing Surpac workflows (digitising strings, creating triangulated surfaces/solids, coding drillholes and block models).

Geostatistics and Resource Estimation with Surpac

Exploratory analysis and variography

Surpac is widely used for geostatistical analysis:

  • Exploratory data analysis to understand grade distributions, proportional effects and outliers.
  • Construction of experimental variograms and variogram models for key elements (Au, Cu, Fe) (Deressa et al., 2024; Mbolela et al., 2022; Marković et al., 2025).

Deressa et al. explicitly studied sampling intervals, proportional effects and mixed distributions to build “robust experimental variograms” before kriging reserves for a gold deposit (Deressa et al., 2024). Similar variographic work underpins Surpac‑based models used later in Whittle for economic optimisation (Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015; Marković et al., 2025).

Surpac Variogram
Surpac Variogram

Kriging and block modelling

Reserve estimation typically uses kriging or inverse‑distance methods implemented in Surpac:

  • Ordinary kriging or variants for grade estimation into 3D block models (Deressa et al., 2024; Mbolela et al., 2022; Marković et al., 2025).
  • Inverse distance methods (e.g., IDW) where data support or project objectives make them acceptable approximations (Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015).

Examples:

  • For a hydrothermal gold deposit, Surpac kriging and block modelling underpinned the estimate of 861.8 Mt of reserves, later used for pit design and NPV calculation (Deressa et al., 2024).
  • At Mpeasem Gold Project, Surpac IDW block modelling produced 22.79 Mt at 1.533 g/t Au prior to Whittle optimisation (Akisa and Mireku-Gyimah, 2015).
  • A copper deposit case used Surpac with inverse‑square distance and kriging to estimate 935,150 ore blocks (15×15×15 m), later analysed stochastically for risk (Marković et al., 2025).

Recoverable resources and cut‑off

Surpac block models, combined with cut‑off grade selection, provide in‑situ and recoverable resource estimates that feed Whittle:

  • Classification of blocks as ore or waste for different cut‑offs.
  • Calculation of tonnage, grade and metal content per block and per potential pit shell (Deressa et al., 2024; Kržanović et al., 2018; Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015; Marković et al., 2025).
Surpac Blockmodel Resource
Surpac Resources Block Model

These studies highlight that the same Surpac block model supports both deterministic NPV analysis and stochastic or risk‑based assessments where grade uncertainty and economic variability are considered (Deressa et al., 2024; Godoy, 2018; Mbolela et al., 2022; Marković et al., 2025).

From Resources to Reserves: Integrating Surpac and Whittle

Pit optimisation in Whittle

GEOVIA Whittle is then used to translate Surpac block models into economically optimal pit shells. Across multiple deposits:

  • Surpac‑based block models are imported into Whittle as the key geological and grade input (Deressa et al., 2024; Kržanović et al., 2018; Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015; Marković et al., 2025).
  • Economic parameters (prices, mining/processing costs, recoveries, discount rate) and physical constraints (slope angles, capacities) are specified (Deressa et al., 2024; Tyo and Zeitinova, 2023; Kržanović et al., 2018; Akisa and Mireku-Gyimah, 2015; Marković et al., 2025).
  • Whittle runs Lerchs–Grossmann or, more recently, the pseudoflow algorithm, which produces the same optimal pits as LG but faster, facilitating multiple scenarios (Whittle et al., 2017; Whittle et al., 2020; Tyo and Zeitinova, 2023).

Akisa and Mireku‑Gyimah used Whittle’s 3D Lerchs–Grossmann implementation to generate 82 nested pits for a gold project; pit 36, with 21.19 Mt at 1.557 g/t, maximised NPV and became the basis for detailed design (Akisa and Mireku-Gyimah, 2015). Similar Surpac–Whittle chains have been applied to hydrothermal gold (Deressa et al., 2024), copper (Kržanović et al., 2018; Mbolela et al., 2022; Marković et al., 2025)and iron ore (Malisa and Genc, 2019).

Sensitivity analysis and strategic design

Pit optimisation is typically coupled with sensitivity analysis:

  • Testing NPV sensitivity to metal price, mining cost and other parameters (Deressa et al., 2024; Malisa and Genc, 2019; Kržanović et al., 2018; Akisa and Mireku-Gyimah, 2015).
  • Grouping technically feasible Surpac pits into “project families” and then performing economic optimisation in Whittle to select the best long‑term project shell (Mbolela et al., 2022).
  • Analysing the impact of slope changes or landslides on optimal contours and NPV (Kržanović et al., 2025; Quansah, Anani and Adewuyi, 2024; Sdvyzhkova et al., 2022).

For example, Kržanović et al. showed that modifying the Whittle‑optimised pit to account for a landslide increased overburden and reduced project NPV, illustrating the tight coupling between geotechnical factors and economic optimisation (Kržanović et al., 2025). Another study used Whittle to identify slope angles that balance safety and economics, integrating geotechnical monitoring data into pit design (Quansah, Anani and Adewuyi, 2024).

Whittle
Whittle Pit Optimization Result

Risk‑based and stochastic optimisation

Traditional Whittle applications are deterministic, but some frameworks process multiple Surpac grade simulations in Whittle to produce distributions of possible NPVs and pit limits (Godoy, 2018; Goodfellow and Dimitrakopoulos, 2016; Morales et al., 2019; Jélvez et al., 2023). These works show that:

  • Running Whittle on multiple grade scenarios yields a distribution of NPV rather than a single value, improving risk assessment (Godoy, 2018; Goodfellow and Dimitrakopoulos, 2016; Morales et al., 2019).
  • Integrating geometallurgical attributes and geological uncertainty can materially change both pit limits and production schedules (Morales et al., 2019).
  • Multi‑stage frameworks use conditional simulation of the deposit and optimisation models for ultimate pits, pushbacks and schedules to propagate uncertainty through the entire planning chain (Jélvez et al., 2023).

You can extend this section by describing how simulated block models from Surpac can be batch‑processed through Whittle to obtain pit inclusion probabilities and risk‑adjusted designs.

Detailed Design Back in Surpac

In many workflows, the optimal pit shell from Whittle is returned to Surpac for engineering design:

  • Creation of detailed pit geometries, benches, berms and haul ramps, respecting geotechnical parameters (Deressa et al., 2024; Tyo and Zeitinova, 2023; Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015).
  • Coding of blocks by phase and design to support scheduling and reconciliation.
Pit Design
Pit Design

Deressa et al. reintegrated the Whittle‑optimised shell into Surpac for final pit design and reserve reporting for a hydrothermal gold deposit (Deressa et al., 2024). Akisa and Mireku‑Gyimah similarly exported the selected Whittle pit to Surpac and compared different ramp designs to maximise revenue and minimise stripping (Akisa and Mireku-Gyimah, 2015).

Mine Scheduling with Surpac and MineSched

Monthly and long‑term scheduling

GEOVIA MineSched is designed to take block models and phase designs and generate practical mining schedules. In iron‑ore operations, studies have used Surpac–MineSched integration to:

  • Build a geological block model in Surpac, including ore–waste delineation and quality attributes.
  • Import this model and pit design into MineSched for monthly mining plans, considering equipment productivity, bench advance and blending requirements (Sobko, Lozhnikov and Tretyakov, 2021).

Sobko et al. showed that moving from older methods to a Surpac–MineSched workflow:

  • Enabled monthly planning with uniform feed quality to the concentrator.
  • Improved control of ore quality and production in different pit contours.
  • Reduced time and cost of planning and allowed analysis of equipment productivity vs. mining system parameters (Sobko, Lozhnikov and Tretyakov, 2021).

Production constraints and blending

MineSched supports:

  • Capacity constraints for mining and processing, aligning with Whittle’s long‑term strategic limits.
  • Grade control and blending constraints to maintain feed quality within required ranges (Sobko, Lozhnikov and Tretyakov, 2021; Marković et al., 2025; Jélvez et al., 2023).

Marković et al. and Jélvez et al. emphasise that long‑term scheduling models must handle mining and processing capacities, grade targets and blending, often under uncertainty (Marković et al., 2025; Jélvez et al., 2023). While these works use general optimisation rather than specifically MineSched, the same concepts (maximum/minimum capacities, grade ranges, horizon length, discount rate) are applied in MineSched scheduling based on GEOVIA block models (Sobko, Lozhnikov and Tretyakov, 2021).

Economic and operational impact

Compared with manual or simpler approaches, Surpac–MineSched workflows:

  • Improve the accuracy of medium‑term plans and the ability to guarantee plant feed quality (Sobko, Lozhnikov and Tretyakov, 2021).
  • Reduce planning time and thus the cost of computing and engineering work (Sobko, Lozhnikov and Tretyakov, 2021).
  • Support evaluation of alternative sequences and equipment deployment strategies before implementation.

You can grow this section by explaining typical MineSched inputs (block attributes, phases, equipment fleets, constraints) and outputs (Gantt charts, period‑by‑period tonnage and grade, equipment utilisation).

Economic Analysis and Profitability with Whittle and MineSched

Several studies quantify how GEOVIA‑based workflows affect project economics:

  • For a large hydrothermal gold deposit, Surpac–Whittle analysis yielded a final pit with a stripping ratio around 4:1 and an NPV of USD 1.02 billion at 10% discount, demonstrating the impact of integrated reserve estimation and pit optimisation (Deressa et al., 2024).
  • A copper deposit at Cerovo Primarno–Drenova used Whittle to compare scenarios with 6.0 and 12.0 Mt/year processing capacities, evaluating NPV and excavation dynamics and using Milawa scheduling to balance ore and waste while maintaining constant plant feed (Kržanović et al., 2018).
  • MineSched and Surpac, applied to an iron quarry, enabled a new planning method that reduced production costs by improving feed quality and reducing planning time (Sobko, Lozhnikov and Tretyakov, 2021).
Geovia MineSched for Mine Schedulling
MineSched Mine Scheduling

These examples show that the Surpac–Whittle–MineSched chain can be used not only to define a single “optimal” plan but also to compare multiple strategic and tactical options.

Toward Uncertainty‑Aware Planning

More recent work, although not always tied directly to GEOVIA, highlights the value of incorporating ore‑grade uncertainty, economic variability and geometallurgy into pit optimisation and scheduling (Godoy, 2018; Goodfellow and Dimitrakopoulos, 2016; Morales et al., 2019; Marković et al., 2025; Jélvez et al., 2023). When combined with Surpac’s ability to generate multiple block‑model scenarios and Whittle’s capacity to process them:

  • Strategic planning moves from single‑scenario NPV to distributions of outcomes, aligning with risk‑management frameworks such as ISO 31000 (Godoy, 2018; Marković et al., 2025).
  • Ultimate pit limits, pushbacks and schedules can be designed to maximise expected NPV while managing downside risk and production deviations (Goodfellow and Dimitrakopoulos, 2016; Morales et al., 2019; Jélvez et al., 2023).

You can expand your article by describing how Surpac‑generated simulations, Whittle optimisation and MineSched scheduling can be integrated into such stochastic frameworks.

Conclusion

Published case studies confirm that:

  • Surpac supports robust geological models, geostatistics and resource estimation for a wide range of deposits (Deressa et al., 2024; Sobko, Lozhnikov and Tretyakov, 2021; Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015; Sdvyzhkova et al., 2022; Marković et al., 2025).
  • Whittle transforms these models into economically optimised pit shells and excavation dynamics, using efficient algorithms like pseudoflow and advanced scheduling methods such as Milawa (Whittle et al., 2017; Whittle et al., 2020; Tyo and Zeitinova, 2023; Kržanović et al., 2018; Mbolela et al., 2022; Akisa and Mireku-Gyimah, 2015).
  • MineSched, integrated with Surpac block models and pit designs, enables practical monthly and long‑term schedules that respect capacity and quality constraints and increase planning efficiency (Sobko, Lozhnikov and Tretyakov, 2021; Malisa and Genc, 2019; Kržanović et al., 2018; Marković et al., 2025; Jélvez et al., 2023).

References

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Sobko, B., Lozhnikov, O., & Tretyakov, V., 2021. RESEARCH OF THE EFFICIENCY OF USING «GEOVIA SURPAC» AND «MINESCHED» WHEN PLANNING MINING WORKS IN IRON QUARRY. SCIENTIFIC PAPERS OF DONNTU Series: “The Mining and Geology”. https://doi.org/10.31474/2073-9575-2021-1(25)-2(26)-7-15

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Quansah, E., Anani, A., & Adewuyi, S., 2024. Optimizing Overall Slope Angle and Net Present Value (NPV) Through the Utilization of Ground-Based Pit Wall Monitoring Data. 58th U.S. Rock Mechanics/Geomechanics Symposium. https://doi.org/10.56952/arma-2024-0509

Godoy, M., 2018. A Risk Analysis Based Framework for Strategic Mine Planning and Design—Method and Application. **, pp. 75-90. https://doi.org/10.1007/978-3-319-69320-0_7

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Tyo, S., & Zeitinova, S., 2023. Optimizing the contours of open pit mining with the use of mining and geological information systems and technologies. Kompleksnoe Ispolʹzovanie Mineralʹnogo syrʹâ/Complex Use of Mineral Resources/Mineraldik Shikisattardy Keshendi Paidalanu. https://doi.org/10.31643/2023/6445.39

Kržanović, D., Vušović, N., Ljubojev, M., & Rajković, R., 2018. Analysis the profitability of the copper ore exploitation on the Cerovo Primarno-Drenova deposit for the capacities of flotation ore processing of 6.0 and 12.0 million tons annually. Mining and Metallurgy Engineering Bor, pp. 9-18. https://doi.org/10.5937/mmeb1804009k

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Sdvyzhkova, O., Moldabayev, S., Bascetin, A., Babets, D., Kuldeyev, E., Sultanbekova, Z., Amankulov, M., & Issakov, B., 2022. Probabilistic assessment of slope stability at ore mining with steep layers in deep open pits. Mining of Mineral Deposits. https://doi.org/10.33271/mining16.04.011

Marković, P., Stevanović, D., Kolonja, B., Slavković, D., & Kržanović, D., 2025. A Hybrid Model for Risk-Based Strategic Planning in Open-Pit Mining: Integrating Deterministic, Stochastic, and ISO 31000 Approaches. Applied Sciences. https://doi.org/10.3390/app15052500

Jélvez, E., Ortiz, J., Varela, N., Askari-Nasab, H., & Nelis, G., 2023. A Multi-Stage Methodology for Long-Term Open-Pit Mine Production Planning under Ore Grade Uncertainty. Mathematics. https://doi.org/10.3390/math11183907