Jahani, Nazanin (NORCE Norwegian Research Centre) | Ambía, Joaquín (The University of Texas at Austin) | Fossum, Kristian (NORCE Norwegian Research Centre) | Alyaev, Sergey (NORCE Norwegian Research Centre) | Suter, Erich (NORCE Norwegian Research Centre) | Torres-Verdín, Carlos (The University of Texas at Austin)
Abstract The cost of drilling wells on the Norwegian Continental Shelf are extremely high, and hydrocarbon reservoirs are often located in spatially complex rock formations. Optimized well placement with real-time geosteering is crucial to efficiently produce from such reservoirs and reduce exploration and development costs. Geosteering is commonly assisted by repeated formation evaluation based on the interpretation of well logs while drilling. Thus, reliable computationally efficient and robust workflows that can interpret well logs and capture uncertainties in real time are necessary for successful well placement. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Levenberg Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model parameters and their uncertainties. In this paper the demonstrate joint estimation of layer-by-layer water saturation, porosity, and layer-boundary locations and inference of layers’ resistivities and densities. The parameters are estimated by minimizing the statistical misfit between the simulated and the observed measurements for several logs on different scales simultaneously (i.e., shallowsensing nuclear density and shallow to extra-deep EM logs). Numerical experiments performed on a synthetic example verified that the iterative ensemble-based method can estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of time compared to classical Monte Carlo methods. Extra-deep EM measurements are known to provide the best reliable information for geosteering, and we show that they can be interpreted within the proposed workflow. However, we also observe that the parameter uncertainties noticeably decrease when deep-sensing EM logs are combined with shallow sensing nuclear density logs. Importantly the estimation quality increases not only in the proximity of the shallow tool but also extends to the look ahead of the extra-deep EM capabilities. We specifically quantify how shallow data can lead to significant uncertainty reduction of the boundary positions ahead of bit, which is crucial for geosteering decisions and reservoir mapping.