I am an applied mathematician developing innovative ideas, algorithms and software for geological processes, basin and reservoir characterization and simulation. In addition to classical mathematical tools such as discretisation methods in space (finite differences, finite volumes, multiscale, etc.) and time, optimisation, inversion, homogeneisation, upscaling, I also use machine learning approaches in these modeling of partial differential equations. As application domains, I work on CO2 seqestration, enhanced oil recovery methods and water management problems for assessing, notably, uncertainties related to these processes.
- Rabbani, H. S., Seers, T. D., & Guerillot, D. (2019). Analytical Pore-Network Approach (APNA): A novel method for rapid prediction of capillary pressure-saturation relationship in porous media. ADVANCES IN WATER RESOURCES. 130, 147-156.
- Guerillot, D., & Bruyelle, J. (2019). Transmissibility Upscaling on Unstructured Grids for Highly Heterogeneous Reservoirs. Water (Switzerland). 11(12), 2647-2647.
- Guerillot, D. R. (2014). Neural networks and their derivatives for history matching and reservoir optimization problems.
- Guerillot, D. R. (2007). Inverse model to adjust some parameters associated to a diffusive geologic process.
- Guerillot, D., & Bruyelle, J. (2019). Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation. COMPUTATIONAL GEOSCIENCES.
- (2018). Reactive transport modeling for CO2 sequestration with a dual mesh method. 2018-November,
- Guerillot, D., & Bruyelle, J. (2017). Compositional dual mesh method for single phase flow in heterogeneous porous media-application to CO2 storage. COMPUTATIONAL GEOSCIENCES. 21(5-6), 949-961.
- Guérillot, D., & Bruyelle, J. (2017). Coupling fluid flow around wells with rock-fluid interactions equations for simulating acid stimulations with a dual mesh approach. 2017-December,