Print Email Facebook Twitter Determination of lower and upper bounds of predicted production from history-matched models Title Determination of lower and upper bounds of predicted production from history-matched models Author van Essen, G. M. (Shell Global Solutions International B.V.) Kahrobaei, S.S. (TU Delft Reservoir Engineering) van Oeveren, H. van den Hof, P.M.J. (Eindhoven University of Technology) Jansen, J.D. (TU Delft Civil Engineering & Geosciences; TU Delft Geoscience and Engineering) Faculty Civil Engineering & Geosciences Department Geoscience and Engineering Date 2016-10-01 Abstract We present a method to determine lower and upper bounds to the predicted production or any other economic objective from history-matched reservoir models. The method consists of two steps: 1) performing a traditional computer-assisted history match of a reservoir model with the objective to minimize the mismatch between predicted and observed production data through adjusting the grid block permeability values of the model. 2) performing two optimization exercises to minimize and maximize an economic objective over the remaining field life, for a fixed production strategy, by manipulating the same grid block permeabilities, however without significantly changing the mismatch obtained under step 1. This is accomplished through a hierarchical optimization procedure that limits the solution space of a secondary optimization problem to the (approximate) null space of the primary optimization problem. We applied this procedure to two different reservoir models. We performed a history match based on synthetic data, starting from a uniform prior and using a gradient-based minimization procedure. After history matching, minimization and maximization of the net present value (NPV), using a fixed control strategy, were executed as secondary optimization problems by changing the model parameters while staying close to the null space of the primary optimization problem. In other words, we optimized the secondary objective functions, while requiring that optimality of the primary objective (a good history match) was preserved. This method therefore provides a way to quantify the economic consequences of the well-known problem that history matching is a strongly ill-posed problem. We also investigated how this method can be used as a means to assess the cost-effectiveness of acquiring different data types to reduce the uncertainty in the expected NPV. Subject Computer-assisted history matchingHierarchical optimizationMulti-objective optimizationUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:49a93c3d-c97a-4c05-9f37-dc794b14e231 DOI https://doi.org/10.1007/s10596-016-9576-1 ISSN 1420-0597 Source Computational Geosciences: modeling, simulation and data analysis, 20 (5), 1061-1073 Part of collection Institutional Repository Document type journal article Rights © 2016 G. M. van Essen, S.S. Kahrobaei, H. van Oeveren, P.M.J. van den Hof, J.D. Jansen Files PDF 10.1007_s10596_016_9576_1.pdf 2.42 MB Close viewer /islandora/object/uuid:49a93c3d-c97a-4c05-9f37-dc794b14e231/datastream/OBJ/view