Hydrocarbon reservoirs are often located in spatially complex and uncertain geological environments, where the associated costs of drilling wells for exploration and development are notoriously high. These costs may be reduced with an optimized well-placement strategy based on real-time geological information, known as well geosteering. To effectively place the well in an updated geomodel and support well geosteering decisions in real-time, we apply an iterative inversion approach based on the Levenberg-Marquardt form of the Ensemble Randomized Maximum Likelihood method. The method estimates geomodel properties together with their uncertainties by reducing the statistical misfit between the measurements acquired with well-logging tools and the predicted measurements from numerical simulations. Analyses of synthetic cases indicate that the method's reliability and computational speed depend on the distance from the logging tool to formation boundaries, the contrast of model properties, and the thickness of formation layers. The proposed method delivers reliable estimates of model properties with only 40 ensemble members and 2--10 iterations, hence it is approximately 10--125 times faster than Metropolis-Hastings Monte Carlo, which we use as a baseline condition given its proven track record. Likewise, the developed method is amenable to parallelization to further reduce computational times.Implementation of the method with a synthetic example inspired by a historical well geosteering operation yielded accurate formation evaluation and verified its accurate and reliable performance under complex geological conditions.

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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 employing as few as 40 ensemble members and 2-10 iterations. 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. The reliability and speed of well-log interpretation is normally sensitive to several parameters such as the distances between the formation boundaries and the logging tool, model parameter's contrast, formation layer thickness and well inclination. 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.