Technology

Written in Python, EVEREST provides a user-friendly interface for configuring optimization runs via YAML files, monitoring on-going processes, and exporting results for further analysis. Although EVEREST is presented as an independent tool, it shares much of its internal codebase with ERT, an open-source data assimilation tool developed by Equinor. ERT and EVEREST are developed in tandem and currently employ a shared code repository.

• For installation instructions see: https://pypi.org/project/ert
• For documentation see: https://everest.readthedocs.io/en/latest
• For contribution, feature requests and bug reports see: https://github.com/equinor/ert

At the core of EVEREST lies our state-of-the-art stochastic gradient method. This innovative approach makes robust optimization over multiple model scenarios not just possible, but computationally viable in practice. Traditional optimization methods often struggle with complex, real-world problems. Gradient-based methods are computationally efficient but typically require derivative information from simulation models, which is often unavailable or impractical to obtain. EVEREST overcomes this limitation with stochastic gradient-based techniques:

• We estimate approximate gradients using statistical perturbations.
• This approach offers flexibility and ease of implementation in model-based optimization workflows.
• Models and simulators are treated as a “black-box”, allowing for seamless integration with various simulation tools.
• Our method is particularly advantageous for robust optimization, efficiently leveraging the variability of model realizations and optimization
  variables.

In the complex world of large-scale applications, every decision problem is unique. While EVEREST excels at handling common field development decisions like well placement and drilling order, our true strength lies in our flexibility. Our modular structure allows you to introduce new optimization variables, addressing the specific challenges of your target asset. This adaptability extends to model integration – whether you’re working with different reservoir simulators, multi-physics tools, analytical models, or proxy surrogates, EVEREST seamlessly integrates into your existing workflows.

How to cite EVEREST

Hanea, R.G.,  Oliveira, R., Popa, T.,  Feldmann, Eide, Ø., Sortland, S., Van Der Heijden, T., Barros, E.G.D., Szklarz, S.P., Leeuwenburgh, O., Verveer, P.J., De Hoop, S., Bottero, S., and Hopstaken, K. (2025). EVEREST™ - An Open-Source Platform for Decision Making Under Uncertainties. Society of Petroleum Engineers - SPE Reservoir Simulation Conference 2025, RSC 2025. https://doi.org/10.2118/223850-MS

Fonseca, R.M., Chen, B., Jansen, J.D., and Reynolds, A.C. (2017). A stochastic simplex approximate gradient (StoSAG) for optimization under uncertainty. International Journal for Numerical Methods in Engineering, 109 (13), 1756-1776. https://doi.org/10.1002/nme.5342

Publications using EVEREST

Barros, E.G.D., Fonseca, R.M., and de Moraes, R.J. (2019). Production Optimisation Under Uncertainty with Automated Scenario Reduction: A Real-Field Case Application. Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, September 17-19. https://doi.org/10.2118/196637-MS

Barros, E.G.D., Van Aken, B.B., Burgers, A.R., Slooff-Hoek, L.H., and Fonseca, R.M. (2022). Multi-objective optimization of solar park design under climatic uncertainty. Solar Energy, 231, 958-969. https://doi.org/10.1016/j.solener.2021.12.026

Barros, E.G.D., Chitu, A.G., and Leeuwenburgh, O. (2020). Ensemble-based well trajectory and drilling schedule optimization – Application to the Olympus benchmark model. Computational Geosciences, 24, 2095-2109. https://doi.org/10.1007/s10596-020-09952-7

Barros, E.G.D., Szklarz, S.P., Khoshnevis Gargar, N., Hopman, J., Zirotti, G., Bascialla, G., Ramsay, T.S., and Fonseca, R.M. (2023). Field Development Optimization with Stochastic Gradient Method: Application to a Multi-Reservoir Carbonate Field in the Middle East. Paper SPE-212620-MS presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, January 25-26. https://doi.org/10.2118/212620-MS

Barros, E.G.D., Szklarz, S.P., Hopman, J., Hopstaken, K., Gonçalves da Silva, J.P., Bjørlykke, O.P., Rios, V., Videla, J., Oliveira, R., and Hanea, R.G. (2023). Well Swapping and Conversion Optimization Under Uncertainty Based on Extended Well Priority Parametrization. Paper OTC-32960-MS presented at the Offshore Technology Conference Brasil, Rio de Janeiro, Brazil, October 24-26. https://doi.org/10.4043/32960-MS

Barros E.G.D., Szklarz S.P., Khoshnevis Gargar N., Wollenweber J., and van Wees J.D. (2025). Optimization of Well Locations and Trajectories: Comparing Sub-Vertical, Sub-Horizontal and Multi-Lateral Well Concepts for Marginal Geothermal Reservoir in The Netherlands. Energies, 18(3), 627. https://doi.org/10.3390/en18030627

Fonseca, R.M., Leeuwenburgh, O., Della Rossa, E., Van den Hof, P.M.J., and Jansen, J.D. (2015). Ensemble-Based Multiobjective Optimization of On/Off Control Devices Under Geological Uncertainty. SPE Reservoir Evaluation & Engineering, 18 (4), 554-563. https://doi.org/10.2118/173268-PA

Leeuwenburgh, O., Chitu, A.G., Nair, R., Egberts, P.J.P., Ghazaryan, L., Feng, T., and Hustoft, L. (2016). Ensemble-Based Methods for Well Drilling Sequence and Time Optimization under Uncertainty. Paper presented at the 15th European Conference on the Mathematics of Oil Recovery, Amsterdam, The Netherlands, August 29 - September 1. https://doi.org/10.3997/2214-4609.201601871

Szklarz, S.P., Barros, E.G.D., Berawala, D., Hegstad, B.K. and Petvipusit, K.R. (2022). How Could Reservoir Engineers Harvest Wind Energy? Practical Parametrization Approaches For Wind Farm Layout Optimization. EAGE GET 2022, Nov 2022, pp. 1-5. https://doi.org/10.3997/2214-4609.202221059

Szklarz, S.P., Barros, E.G.D., Khoshnevis Gargar, N., Peeters, S.H.J., van Wees, J.D., van Pul-Verboom, V. (2024). Geothermal field development optimization under geomechanical constraints and geological uncertainty: Application to a reservoir with stacked formations. Geothermics. Volume 123. 103094. ISSN 0375-6505. https://doi.org/10.1016/j.geothermics.2024.103094