Browsing by Author "Sun, Jian"

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    Chops studies - a modeling and simulation perspective
    (2012-08-23) Sun, Jian; Chen, Zhangxing (John)
    CHOPS refers to Cold Heavy Oil Production with Sand in Western Canada, with heavy oil and sand produced together. Being different from conventional primary recovery, wormhole propagation and foamy oil flow are observed to make CHOPS achieve considerable production. Differences between the conventional primary depletion and cold heavy oil production are discussed in terms of their drainage mechanisms, model formulation and modeling approaches. The goal of this study is to investigate the modeling techniques on wormhole propagation and foamy oil flow as well as their effects in simulation. This study demonstrates two kinds of wormhole modeling approaches: Modeling wormholes as high permeability channels resulting from the experimental observations in which new variables are set up to model the enhanced permeability channels and modeling wormholes as well segments resulting from the extension of wormholes that build more flow paths like wells. Also, a reaction - based pseudo bubble point model is applied in the modeling of foamy oil flow. The heavy oil solution gas drive is divided into several stages in order to capture the transfers between various gas partitions. The above different modeling approaches are implemented into a General Purpose Research Simulator. Numerical studies are conducted for pressure profiles, sand production characteristics in wormhole propagation, Gas-Oil Ratios (GOR) and Recovery Factors (RF) for foamy oil flow. The simulation results show the rationality of the proposed wormhole modeling approaches and indicate that pressure decline and a permeability increase rate are two main factors influencing CHOPS performance.
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    Computational and practical developments in single- and multi-component inverse scattering series internal multiple prediction
    (2018-05-23) Sun, Jian; Innanen, Kristopher A.; Malcolm, Alison E.; Krebes, Edward Stephen; Lines, Laurence R.; Trad, Daniel O.
    Prediction and removal of internal multiples, especially those caused by unknown generators and with insufficient subsurface information, remains a very high priority research problem in seismic data processing. Inverse scattering series internal multiple predictions are data-driven approaches to prediction in which lower-order reflected events are combined nonlinearly according to well-defined ordering relationships in vertical travel time or pseudo-depth. Implementations of instances of this algorithm in any one of the applicable transform domains encounter computational challenges and challenges caused by the practicalities of field data. In this thesis I systematically examine, develop and refine inverse scattering series internal multiple prediction algorithms and their computer implementations, introducing new ideas concerning calculation domain, search parameter optimization, artifact suppression, and computational cost reduction. A key step in my strategy is to formulate the computation in the horizontal slowness, plane-wave, domains, which is possible because of the clear relationship between horizontal slowness and wavenumber. Numerical and analytic arguments indicate that these domains, which tend to involve sparse representations input events (e.g., primary reflections), is able to proceed with a relatively stationary search parameter value, producing predictions with little numerical noise, suppression of some common high-angle prediction artifacts, and, importantly, at significantly lower computational cost. I next formulate multidimensional internal multiple prediction in 2D in the coupled plane wave domain, and examine its numerical behaviour using a benchmark synthetic dataset. In particular I show a detailed input data preparation workflow. The application of the algorithm to common-midpoint (CMP) gathers requires a modified version of the algorithm, and this is also examined. This is important for efficient prediction of internal multiples caused by dipping strata, because the so-called 1.5D formulation, nominally appropriate only for layered media, can be applied with surprising accuracy to CMP gathers over dipping interfaces. I demonstrate and provide a rationale for this observation. The most significant contribution of this thesis is to analyze and numerically implement the fully elastic form of the inverse scattering series internal multiple algorithm. Theory for this has been in existence since the 1990s, but to date neither implementation nor numerical analyses of any kind have been published. Here the ordering of input data events in pseudo-depth/vertical-traveltime and the relationships between these and the actual depths at which reflections took place is key to obtaining accurate multicomponent predictions. After a full analysis, a plane-wave formulation of the elastic multicomponent inverse scattering series internal multiple prediction algorithm is also introduced. Three candidate approaches are considered for input data preparation: pre-stack Stolt migration, vertical traveltime stretching, and incorporation of best-fit reference velocities. With numerical simulations and analysis, I conclude that: (1) best-fit reference velocities produce the best approximate solution obeying the ordering (travel-time monotonicity) requirement, but it requires a relative large search parameter to be chosen in practice; (2) a combination of vertical traveltime stretching and best-fit reference velocities allows the search parameter to be the chosen with a size comparable to those used in acoustic prediction, while correctly predicting all orders of internal multiples. The first numerical examples of multicomponent elastic internal multiple prediction are then presented.
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    ItemOpen Access
    Factors associated with positive and negative recommendations for cancer and non-cancer drugs for rare diseases in Canada
    (2019-06-07) Nagase, Fernanda N I; Stafinski, Tania; Sun, Jian; Jhangri, Gian; Menon, Devidas
    Abstract Background In Canada, reimbursement recommendations on drugs for common and rare diseases are overseen by the Canadian Agency for Drugs and Technologies in Health (CADTH) and made through the pan-Canadian Oncology Drug Review (pCODR) and the Common Drug Review (CDR). While the agency specifies information requirements for the review of drug submissions, how that information is used by each process to formulate final reimbursement recommendations, particularly on drugs for rare diseases (DRDs) in which per patient treatment costs are often high, is unclear. The purpose of this study was to determine which factors contribute to recommendation type for DRDs. Methods Information was extracted from CDR and pCODR recommendations on drugs for diseases with a prevalence < 1 in 2000 from January 2012 to April 2018. Data were tabulated and multiple logistic regression was applied to explore the association between recommendation type and the following factors: condition/review process (cancer vs non-cancer), year, prevalence, clinical effectiveness (improvements in surrogate, clinical and patient reported outcomes), safety, quality of evidence (availability of comparative data, consistency between population in trial and indication, and bias), clinical need, treatment cost, and incremental cost-effective ratio (ICER). Two-way interactions were also explored. Results A total of 103 recommendations were included. Eleven were resubmissions, all of which received a positive recommendation. Among new submissions (n = 92), DRDs that were safe or offered improvements in clinical or patient reported outcomes were more likely to receive positive reimbursement recommendations. No associations between recommendation type and daily treatment cost, cost-effectiveness, or condition (cancer or non-cancer) were found. Conclusions Clinical effectiveness, as opposed to economic considerations or whether the drug is indicated for cancer or non-cancer, determine the type of reimbursement recommendation.