  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.
