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New Approach for the Generation of the Geological Conceptual Model with Limited Information, Understanding our Green Fields

Abstract

Generate a plausible geological model for a green-field as well as rank reservoirs with different level of associated uncertainty can be quite challenging. In this paper, we propose a new approach to generate predictive static models in the first stages of reservoir appraisal. At this stage, structural, sedimentary and petro physical information is very limited. The workflow proposed aim to use any available information whether coming from analogues reservoirs and/or field-data to reduce uncertainty. Initially the structural uncertainty is characterized and propagated generating an ensemble of plausible scenarios based on the perturbation of structural types. Subsequently, a novel formulation is presented to define a conceptual sedimentary (optimum number and percentage of the facies) and petro-physical model for the generation of the 3-D geological plausible scenarios. The method is based on the formulation and solution of a multiobjective-optimization problem to compute facies and hydraulic flow units from the available data (hard data or the analog-predicted) via Monte Carlo sampling and cluster theory. The coupling between facies and hydraulic flow units is done statistically allowing multiple facies to reside on the same flow unit and minimizing the number of flow units. The workflow has been tested on the Brugge field benchmark modified as green-field. The proposed approach respects the static and dynamic reservoir statistics using as measure OOIP and NPV respectively. The OOIP statistics for the 100 geological scenarios generated with the conceptual model (CM) well envelop the statistics of the literature full-field model (FFM) with a computed expectation with around 1% error. Similar results for the NPV. The dynamic behavior of the CM well reproduces the FFM with less than 5% error for the prediction of the P50 and shows larger uncertainty for the P10, P90 as expected. In addition, the sedimentary definition of the facies is well characterized with respect to the core interpretation from laboratory. The new workflow presented allows to generate plausible geological scenarios with limited amount of information, consistently quantifying and propagating the uncertainty. Noteworthy, this workflow provides important insight on the sensitivity of the geological model to several uncertain parameters and configurations which can be the base for a optimization/risk analysis problem or to quantify the value of additional information.