REAL-TIME ENSEMBLE-BASED WELL-LOG INTERPRETATION FOR GEOSTEERING

Abstract

The cost of drilling wells on the Norwegian Continen-tal 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 work-flows that can interpret well logs and capture uncertain-ties in real time are necessary for successful well place-ment. We present a formation evaluation workflow for geosteering that implements an iterative version of an ensemble-based method, namely the approximate Leven-berg Marquardt form of the Ensemble Randomized Max-imum Likelihood (LM-EnRML). The workflow jointly estimates the petrophysical and geological model param-eters and their uncertainties. In this paper the demon-strate 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., shallow-sensing nuclear density and shallow to extra-deep EM logs). Numerical experiments performed on a synthetic exam-ple verified that the iterative ensemble-based method can estimate multiple petrophysical parameters and decrease their uncertainties in a fraction of time compared to clas-sical Monte Carlo methods. Extra-deep EM measure-ments are known to provide the best reliable informa-tion for geosteering, and we show that they can be in-terpreted 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 es-timation 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 re-duction of the boundary positions ahead of bit, which is crucial for geosteering decisions and reservoir mapping.