Abstract
Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.
Highlights
Research scientists have worked for many years to develop viable methods to calibrate complex reservoir models
To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities
We demonstrate the efficiency of using the non-linear DR techniques t-distributed stochastic neighbor embedding (t-SNE) [11] and Gaussian process latent variable model (GPLVM) [23,24] along with clustering K-means to select effective reservoir models and save computational time without simulating and assimilating the entire initial ensemble
Summary
Research scientists have worked for many years to develop viable methods to calibrate complex reservoir models. The uncertainty associated with reservoir models is highly significant, introducing considerable errors in the modeling process. One is the conditioning of reservoir parameters to observed production data, a process referred to as inverse problem or history matching (HM). The second step is to select the production data, which must be sensitive to the parameters needed to be history matched. The sensitivity becomes more complex, in cases using reservoirs with multiphase flow. In these cases, the cross-covariance of production data to model variables is used instead, its main advantage being that it is generally smoother and can show a more global relationship between data and variables, since it is a product of sensitivities and covariances
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