Ensemble-Based Well Log Interpretation and Uncertainty Quantification for Geosteering

Abstract: The costs for drilling offshore wells are high and hydrocarbons are often located in complex reservoir formations. To effectively produce from such reservoirs and reduce costs, optimized well placement in real-time (geosteering) is crucial. Geosteering is usually assisted by an updated formation evaluation obtained by well-log interpretation while drilling. A reliable, computationally efficient, and robust workflow to interpret well logs and capture uncertainties in real-time is necessary for this application. An iterative ensemble-based method, namely the approximate Levenberg Marquardt form of the Ensemble Randomized Maximum Likelihood (LM-EnRML) is integrated in our formation evaluation workflow. We estimate model parameters, resistivity and density in addition to boundary locations, and related uncertainties by reducing the statistical misfit between the measurements from the well logging tools and the theoretical measurements from the forward tool simulators. The results of analyzing several synthetic cases with several types of logs verified that the proposed method can give good estimate of model parameters. By comparing the CPU time, we conclude that the proposed method has at least about 10--125 times lower computational time compare to a common statistical method, such as Metropolis-Hastings Monte Carlo. In addition, the ensemble-based method can run in parallel on multiple CPUs. Testing the method on a case inspired from a real field also yielded accurate formation evaluation. Thus, the proposed ensemble-based method has been proven robust and computationally efficient to estimate petrophysical formation properties, layer boundaries and their uncertainties, indicating that it is suitable for geosteering.