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Collaborating Authors
Results
Results of the First Norne Field Case on History Matching and Recovery Optimization Using Production and 4D Seismic Data
Rwechungura, Richard (1Department of Petroleum Engineering and Applied Geophysics, IO center, NTNU, 7491, Trondheim, Norway) | Bhark, Eric (3Texas A&M University, College Station, Texas USA) | Miljeteig, Ola T. (1Department of Petroleum Engineering and Applied Geophysics, IO center, NTNU, 7491, Trondheim, Norway) | Suman, Amit (4Stanford University, USA) | Kourounis, Drosos (4Stanford University, USA) | Foss, Bjarne (2Department of Engineering Cybernetics, IO center, NTNU) | Hoier, Lars (5Statoil in Trondheim) | Kleppe, Jon (1Department of Petroleum Engineering and Applied Geophysics, IO center, NTNU, 7491, Trondheim, Norway)
Abstract In preparation for the SPE Applied Technology Workshop, "Use of 4D seismic and production data for history matching and optimization – application to Norne (Norway)" held in Trondheim 14-16 June 2011, a unique test case (Norne E-segment) study based on real field data of a brown field offshore Norway was organized to evaluate and compare mathematical methods for history matching as well as methods on optimal production strategy and/or enhanced oil recovery. The integrated data set provided an opportunity to discuss emerging and classical history matching and optimization methods after being tested using real field data. The participants of this comparative case study were expected to come up with a history matched model preferably using an integration of production and time-lapse seismic data and with an optimal production strategy for the remaining recoverable resources for the future period. Participants were allowed to suggest techniques to enhance recovery. Taking into account that the Norne benchmark case is a case study based on real data and no one exactly knows the true answer, participants and delegates were encouraged to discuss the methods, results and challenges during the course of the workshop, and thus in this case there are no winners or losers. Everyone who participated gained experience during the course of the exercise. Participants were asked to history match the model until the end of 2004 and optimally predict the production (oil, water and gas rates) performance until the end of 2008. Participants were from different universities in collaboration with other research organizations namely Stanford University in collaboration with IBM and Chevron, TU Delft in collaboration with TNO, Texas A&M University, and NTNU in collaboration with Sintef. This paper summarizes the presented results from these groups and the outcome of the discussion of the workshop delegates.
- North America > United States > Texas (1.00)
- Europe > Netherlands > South Holland > Delft (0.24)
- Europe > Norway > Trøndelag > Trondheim (0.24)
- Geology > Geological Subdiscipline > Geomechanics (0.93)
- Geology > Rock Type > Sedimentary Rock (0.67)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Tofte Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Not Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Ile Formation (0.99)
- (8 more...)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Four-dimensional and four-component seismic (1.00)
- (2 more...)
History matching provides to reservoir engineers an improved spatial distribution of physical properties to be used in forecasting the reservoir response for field management. The ill-posed character of the history-matching problem yields nonuniqueness and numerical instabilities that increase with the reservoir complexity. These features might cause local optimization methods to provide unpredictable results not being able to discriminate among the multiple models that fit the observed data (production history). Also, the high dimensionality of the inverse problem impedes estimation of uncertainties using classical Markov-chain Monte Carlo methods. We attenuated the ill-conditioned character of this history-matching inverse problem by reducing the model complexity using a spatial principal component basis and by combining as observables flow production measurements and time-lapse seismic crosswell tomographic images. Additionally the inverse problem was solved in a stochastic framework. For this purpose, we used a family of particle swarm optimization (PSO) optimizers that have been deduced from a physical analogy of the swarm system. For a synthetic sand-and-shale reservoir, we analyzed the performance of the different PSO optimizers, both in terms of exploration and convergence rate for two different reservoir models with different complexity and under the presence of different levels of white Gaussian noise added to the synthetic observed data. We demonstrated that PSO optimizers have a very good convergence rate for this example, and provide in addition, approximate measures of uncertainty around the optimum facies model. The PSO algorithms are robust in presence of noise, which is always the case for real data.
- Europe (0.93)
- North America > United States > California (0.46)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Geologic modeling (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)