Enhanced characterization of reservoir hydrocarbon components using electromagnetic data attributes

Abstract

Advances in electromagnetic imaging techniques have led to the growing utilization of this technology for reservoir monitoring and exploration. These exploit the strong conductivity contrast between the hydrocarbon and water phases and have been used for mapping water front propagation in hydrocarbon reservoirs and enhancing the characterization of the reservoir formation. The conventional approach for the integration of electromagnetic data is to invert the data for saturation properties and then subsequently use the inverted properties as constraints in the history matching process. The non-uniqueness and measurement errors may however make this electromagnetic inversion problem strongly ill-posed, leading to potentially inaccurate saturation profiles. Another limitation of this approach is the uncertainty of Archie's parameters in relating rock conductivity to water saturation, which may vary in the reservoir and are generally poorly known. We present an Ensemble Kalman Filter framework for efficiently integrating electromagnetic data into the history matching process and for simultaneously estimating the Archie's parameters and the variance of the observation error of the electromagnetic data. We apply the proposed framework to a compositional reservoir model. We aim at assessing the relevance of EM data for estimating the different hydrocarbon components of the reservoir. The experimental results demonstrate that the individual hydrocarbon components are generally well matched, with nitrogen exhibiting the strongest improvement. The estimated observation error standard deviations are also within expected levels (between 5 and 10%), significantly contributing to the robustness of the proposed EM history matching framework. Archie's parameter estimates approximate well the reference profile and assist in the accurate description of the electrical conductivity properties of the reservoir formation, hence leading to estimation accuracy improvements of around 15%.