Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering.

Data assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. When applying ensemble-based methods to assimilate big geophysical data, substantial computational resources are needed in order to compute and/or store certain quantities (e.g., the Kalman-gain-type matrix), given both big model and data sizes. In addition, uncertainty quantification of observational data, e.g., in terms of estimating the observation error covariance matrix, also becomes computationally challenging, if not infeasible. To tackle the aforementioned challenges in the presence of big data, in a previous study, the authors proposed a wavelet-based sparse representation procedure for 2D seismic data assimilation problems (also known as history matching problems in petroleum engineering). In the current study, we extend the sparse representation procedure to 3D problems, as this is an important step towards real field case studies. To demonstrate the efficiency of the extended sparse representation procedure, we apply an ensemble-based seismic history matching framework with the extended sparse representation procedure to a 3D benchmark case, the Brugge field. In this benchmark case study, the total number of seismic data is in the order of [Formula: see text]. We show that the wavelet-based sparse representation procedure is extremely efficient in reducing the size of seismic data, while preserving the salient features of seismic data. Moreover, even with a substantial data-size reduction through sparse representation, the ensemble-based seismic history matching framework can still achieve good estimation accuracy.

Tumors of the urinary bladder: an analysis of the occupations of 1,030 patients in Leeds, England.

The whole-life occupational histories of 1,030 patients (812 men and 218 women) with papilloma and carcinoma of the bladder were analyzed. Bladder tumor patients whose smoking habits were known were matched with surgical control patients (340 men and 50 women) and with other cancer patients (312 men and 39 women) for sex, age decade, habitat, and smoking habits. Numbers in the matched pairs in different occupations (Registrar General's Classification of Occupations, 1966) were compared for predominant occupation, occupied at any time, and for 20 years or more. Results were confirmed from the distribution of occupations among the unmatched bladder tumor patients (429 men and 161 women) and from a comparison of expected and observed numbers in different occupations for the patients living in the City of Leeds (519 men and 146 women). The results confirmed the risk to dye workers and revealed risks to medical workers (mainly nurses), to tailors, tailors' pressers, and some groups of engineers and textile workers (associated with long-term employment only), and possibly also to hairdressers and tailors' cutters. Tumor occurred at younger ages in men who had been employed as dye workers, tailors' cutters (P = < 0.025), or hairdressers (not significant), but not in the other suspect occupations. Over 20% of bladder tumors in men in this series could be occupational in origin.