Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis
نویسندگان
چکیده
منابع مشابه
Parallelization of Ensemble Kalman Filter (EnKF) for Oil Reservoirs
This thesis describes the design and implementation of a parallel algorithm for data assimilation with ensemble Kalman filter (EnKF) for oil reservoir management. The implemented application works on large number of observations from time-lapse seismic, which lead to a large turnaround time for the analysis step, in addition to the time consuming simulations of the realizations. Provided that p...
متن کاملAnalysis Scheme in the Ensemble Kalman Filter
This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the ensemble Kalman filter. It is shown that the observations must be treated as random variables at the analysis steps. That is, one should add random perturbations with the correct statistics to the observations and generate an ensemble of observations that then is used in updati...
متن کاملCharacterization of resting versus stimulated saliva fingerprints using Middle-Infrared Spectroscopy assisted by Principal Component Analysis
Fourier Transform Mid-Infrared spectroscopy with Attenuated Reflection combined with multivariate data Principal Component Analysis have been applied for the discrimination of 12 couples of resting and stimulated saliva obtained from healthy volunteers. The resting saliva samples were collected before eating, while stimulated saliva, after chewing stimulation with parafin and preserved in Natri...
متن کاملMulti-dimensional, paraunitary principal component filter banks
z In this paper, the one-dimensional principal component lter banks (PCFB's) derived in 17] are generalized to higher dimensions. As presented in 17], PCFB's minimize the mean-squared error (MSE) when only Q out of P subbands are retained. Previously, 2D PCFB's were proposed in 16]. The work in 16] was limited to 2D signals and separable resampling operators. The formulation presented here is g...
متن کاملCompression of Breast Cancer Images By Principal Component Analysis
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Ass...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Petroleum Science
سال: 2019
ISSN: 1672-5107,1995-8226
DOI: 10.1007/s12182-019-00362-8