FieldOpt [Open Research Version] is a C++ programming framework for efficient prototyping and testing of optimization methodologies for problems involving large-scale numerical simulations.
FieldOpt serves as a multi-disciplinary knowledge repository for coupling optimization with reservoir simulation. Technology development is based on integration of efficient iterative procedures with expert domain parametrizations.
FieldOpt facilitates research and innovation through up-scaling of prototype methodology to realistic cases, coupling, integration and hybridization of optimization methodology and problem solutions, and cross-application of existing methods to new domains.
- Well placement optimization [1]
- Production optimization
- Optimization of inflow-control valve settings
- Well completion optimization and model-update while drilling
- Minimization of C02 emissions
- Compass Search (CS)
- Asynchronous Paralell Pattern Search (APPS)
- Derivative-Free Trust-Region Algorithm (DFTR) [2]
- Genetic Algorithm (GA)
- Particle Swarm Optimization (PSO)
- Covariance Matrix Adaption Evolutionary Strategy (CMA-ES)
- Bayesian Optimization (EGO)
- Simultaneous Perturbation Stochastic Approximation (SPSA)
- mPSO
- APPS/PSO + data-driven meta-optimization
- Joint optimization using embedded reduced-order sub-routines
- Multi-level joint optimization (concurrent, sequential, embedded)
- Automatic variable segregation for multi-level optimization
- Variable scaling
- Weighted function, Net Present Value
- Well cost
- Augmented terms: Geology & geophysics-based (SWCT)
- Schlumberger's E100/E300/IX
- Open Porous Media Flow
- Stanford's AD-GPRS
- Pre-/Post-processing
- E300 adjoint-gradient read-in
- Automatic well planner (AWP) [5]
- State-of-the-art well connection transmissibility factor calculation [6]
- Variable mapping onto multi-segmented well model (WELSEGS/COMPSEGS/WSEGVALV) [7]
- Method of Alternating Projections (MAP) [8]
- Length, inter-well distance, user-defined convex-polytope reservoir-boundary
- Topside facility model for CO2 emission calculation
- Expected cost function evaluation over realization set
- Reduced random sampling strategy[9]
- Algorithm-level parallelization of cost function evaluations (simulations) through MPI runtime library [10]
[1] Bellout, M.C.; Echeverria Ciaurri, D.; Durlofsky, L.J.; Foss, B.; Kleppe, J. (2012). Joint optimization of oil well placement and controls. Computational Geosciences, 16(4), pp.1061-1079. https://doi.org/10.1007/s10596-012-9303-5
[2] Silva, T.L.; Bellout, M.C.; Giuliani, C.; Camponogara, E.; Pavlov, A. (2020). A Derivative-Free Trust-Region Algorithm for Well Control Optimization. 17th European Conference on the Mathematics of Oil Recovery, 14th-17th September, Online Event. https://doi.org/10.3997/2214-4609.202035086
[3] Gill, P.E.; Murray, W.; Saunders, M.A. (2005). SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization. SIAM Review, 47(1), pp.99-131. http://dx.doi.org/10.1137/S0036144504446096
[4] Equinor. (2021). Ensemble based Reservoir Tool. https://github.com/equinor/ert
[5] Kristoffersen, B.S.; Silva, T.L.; Bellout, M.C.; Berg, C.F. (2020). An Automatic Well Planner for Efficient Well Placement Optimization Under Geological Uncertainty. 17th European Conference on the Mathematics of Oil Recovery, 14th-17th September, Online Event. https://doi.org/10.3997/2214-4609.202035211
[6] Ceetron Solutions AS; Equinor ASA. (2020). ResInsight. http://resinsight.org
[7] Schlumberger AS. (2012). Eclipse technical description. Chp.44: Multi-segment Wells. pp.683-703. https://www.software.slb.com/products/eclipse/simulators
[8] Bellout, M.C.; Volkov, O. (2018). Development of efficient constraint-handling approaches for well placement optimization. 16th European Conference on the Mathematics of Oil Recovery, 3rd-6th September, Barcelona, Spain. https://doi.org/10.3997/2214-4609.201802247
[9] Jesmani, M.; Jafarpour, B.; Bellout, M.C.; Foss, B. (2020). A reduced random sampling strategy for fast robust well placement optimization. Journal of Petroleum Science and Engineering, 184, pp.106414. https://doi.org/10.1016/j.petrol.2019.106414
[10] Baumann, E.J.M.; Dale, S.I.; Bellout, M.C. (2020). FieldOpt: A powerful and effective programming framework tailored for field development optimization. Computers & Geosciences, 135, pp.104379. https://doi.org/10.1016/j.cageo.2019.104379