... Abstract In reservoir management, the ensemble-based history matching is applied to quantify and update uncertainty in reservoir characterization with the main objective to support high quality decisions. However, the ensemble-based history matching could suffer from statistical problems...

... Abstract The assimilation of time-lapse (4D) seismic data is challenging with ensemble-based methods because of the massive number of data points. This situation requires an excessive computational time and memory usage during the model updating step. We addressed this problem using a deep...

.... The parameters are adjusted in a fully Bayesian manner to generate an ensemble of models and a probabilistic forecast. The use of a DAE distinguishes the approach from normal time-series analysis, where an ARIMA model or state space model is used, and is normally only reliable for short term forecasting...

.... We include information at different scales in the UQ workflow which allows for a large reduction in the computational time while converging to the high-fidelity full ensemble statistics. Ultimately, it allows us to make accurate predictions on geothermal energy production in highly heterogeneous...

... Abstract The Ensemble Smoother with Multiple Data Assimilation (ES-MDA) method has been popular for petroleum reservoir history matching. However, the increasing inclusion of automatic differentiation in reservoir models opens the possibility to history-match models using gradient-based...

... Abstract We present a deep learning architecture for efficient reduced-order implementation of ensemble data assimilation. Specifically, deep learning is used to improve two important aspects of data assimilation workflows: (i) low-rank representation of complex reservoir property distributions...

... matching at the well locations and constrains the model space by adding a priori information about the sub-surface structure using a seismic impedance volume. The new framework improves predictive performance and the geological realism of the assimilated ensemble through an efficient parameter estimation...

... Abstract An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross...

... Simulation random path ensemble hard data machine learning Upstream Oil & Gas information facies type facies simulation probability map information fusion integration probability realization facies model Introduction Strategic Integration of Facies Probabilities The results from...

... into a practical workflow that can efficiently generate upscaled EDFMs from stochastic realizations of thousands of geologically realistic natural fractures for ensemble applications. We first consider a simple waterflood example to illustrate our fracture upscaling to obtain coarse (pDPDK and CCM) models...

... Abstract This paper describes the application and testing of innovative dual porosity flow diagnostics to quantitatively rank large ensembles of fractured reservoir models. Flow diagnostics can approximate the dynamic response of multi-million cell models in seconds on standard hardware...

... characterization results are elaborated in section 3; finally, major conclusions of this study are illustrated in section 4. Methods Construction of Homogeneous SAGD Model enhanced recovery thermal method SAGD Artificial Intelligence Upstream Oil & Gas workflow ensemble shale barrier...

... trivial, could be achieved arguably with the current technology and uncertainty modeling methods. Current Study Approach For this specific study, a synthetic case of a new field discovery is presented and an equally probable set of 100 models are generated. The ensemble of models honors the initial...

... Ensemble Smoother robust Levenberg- Marquardt (ES-rLM) method to generate plausible model realizations that replicate the reservoir energy. Representative models are further improved in a sensitivity-based local inversion step to match multiphase production data at the well level. An approach alternative...

.... This representation then is used to estimate an identifiable reduced set of parameters using an ensemble Kalman filter (EnKF) strategy. This approach is attractive for the parameter estimation of permeabilities because it increases the ability to represent channelized structures in the updates resulting...

.... Ensemble methods (ES-MDA and StoSAG) are used for the history matching and optimization. The results presented are obtained by applying the proposed Drill and Learn workflow on a realistic synthetic case. Sensitivities to the amount of information obtained before a closed loop exercise is performed...

... Abstract An ensemble-based production optimization technique is applied to a simulation model of OMERS Energy's Chigwell Viking ‘I’ Pool in order to determine optimal CO 2 -WAG cycle length, injection rates and production bottom hole pressures (BHPs). An ensemble-based approximate gradient...

... in a more computationally efficient coarse-scale simulation grid. The Stochastic Simplex Approximate Gradient (StoSAG) method, a stochastic gradient technique, inspired from Ensemble Optimization (EnOpt), is employed to compute the gradient of the objective function. Stochastic methods have recently been...

... Abstract This work presents a systematic and rigorous approach of reservoir decomposition combined with the ensemble Kalman smoother to overcome the complexity and computational burden associated with history-matching field-scale reservoirs in the Middle East. The results demonstrate...

... Abstract Many recent developments in generating history matched reservoir models that approximately characterize subsurface uncertainty are associated with the ensemble smoother (ES) method. It is much better suited for practical history matching applications because it does not require...