Stochastic Inversion of Borehole Ultra-Deep Azimuthal Resistivity Measurements Using Fast Multi-grid Simulated Annealing (Nazanin Jahani, NORCE)

The presentation “Stochastic Inversion of Borehole Ultra-Deep Azimuthal Resistivity Measurements Using Fast Multi-grid Simulated Annealing” will be given by Nazanin Jahani, from NORCE.


The growing demand for precise and efficient wellbore placement within heterogenous 3D geological structures, has propelled the adoption of three-dimensional (3D) well geosteering. This approach necessitates the real-time inversion of borehole electromagnetic or UDAR measurements to estimate subsurface resistivities around and ahead of well trajectory. However, in cases involving heterogeneous, and/or anisotropic geological formations with multidimensional physical properties, the nonlinear nature of the inversion introduces challenges concerning efficiency, reliability, non-uniqueness, and uncertainty. Stochastic inversion techniques provide an effective strategy for exploring global minima and quantifying uncertainty, but standard approaches often suffer from computational. inefficiencies. To estimate formation properties in real time, it is imperative to develop a computationally efficient stochastic inversion method for UDAR measurements. Adaptive simulated annealing has verified its robustness in tackling similarly challenging problems in the inversion of 3D seismic amplitude data. However, its application to UDAR inversion remains unexplored to date. We propose a hierarchical-multi grid and adaptive simulated annealing inversion method for formation parameter estimation and its associated uncertainty quantification. We validate our approach through testing in two geological scenarios characterized by high-contrast features and 2D structures, including faults and electrical anisotropy. Our approach offers a significantly computationally efficient and stable solution compared to classical simulated annealing. The method proves robust in handling complex geological scenarios, and its uncertainty quantification capability guide the need for 2D or 3D inversion and enhances informed decision-making in geosteering.


Nazanin Jahani received her PhD degree in Mechanical Engineering from the Norwegian University of Science and Technology (NTNU) in Trondheim in 2015. Since 2017 she has been a research/senior research scientist at NORCE Norwegian Research Centre in Bergen. In 2021, she received Fulbright scholar grants and was visiting research fellow at the University of Texas at Austin, the formation evaluation research consortium.

Her current research is real-time 3D forward modeling and inversion of LWD measurements including UDAR measurements to be applied in well geosteering. Currently, she is heading up a research initiative focused on 3D geological interpretation for well geosteering, supported by the Research Council of Norway and industry partners.