基于MRF模型的油气储层属性随机建模方法研究

Stochastic reservoir modeling is a technique used in reservoir describing. Through this technique, multiple data sources with different scales can be integrated into the reservoir model and its uncertainty can be conveyed to researchers and supervisors. Stochastic reservoir modeling, for its digital models, its changeable scales, its honoring known information and data and its conveying uncertainty in models, provides a mathematical framework or platform for researchers to integrate multiple data sources and information with different scales into their prediction models. As a fresher method, stochastic reservoir modeling is on the upswing. Based on related works, this paper, starting with Markov property in reservoir, illustrates how to constitute spatial models for catalogued variables and continuum variables by use of Markov random fields. In order to explore reservoir properties, researchers should study the properties of rocks embedded in reservoirs. Apart from methods used in laboratories, geophysical means and subsequent interpretations may be the main sources for information and data used in petroleum exploration and exploitation. How to build a model for flow simulations based on incomplete information is to predict the spatial distributions of different reservoir variables. Considering data source, digital extent and methods, reservoir modeling can be catalogued into four sorts: reservoir sedimentology based method, reservoir seismic prediction, kriging and stochastic reservoir modeling. The application of Markov chain models in the analogue of sedimentary strata is introduced in the third of the paper. The concept of Markov chain model, N-step transition probability matrix, stationary distribution, the estimation of transition probability matrix, the testing of Markov property, 2 means for organizing sections-method based on equal intervals and based on rock facies, embedded Markov matrix, semi-Markov chain model, hidden Markov chain model, etc, are presented in this part. Based on 1-D Markov chain model, conditional 1-D Markov chain model is discussed in the fourth part. By extending 1-D Markov chain model to 2-D, 3-D situations, conditional 2-D, 3-D Markov chain models are presented. This part also discusses the estimation of vertical transition probability, lateral transition probability and the initialization of the top boundary. Corresponding digital models are used to specify, or testify related discussions. The fifth part, based on the fourth part and the application of MRF in image analysis, discusses MRF based method to simulate the spatial distribution of catalogued reservoir variables. In the part, the probability of a special catalogued variable mass, the definition of energy function for catalogued variable mass as a Markov random field, Strauss model, estimation of components in energy function are presented. Corresponding digital models are used to specify, or testify, related discussions. As for the simulation of the spatial distribution of continuum reservoir variables, the sixth part mainly explores 2 methods. The first is pure GMRF based method. Related contents include GMRF model and its neighborhood, parameters estimation, and MCMC iteration method. A digital example illustrates the corresponding method. The second is two-stage models method. Based on the results of catalogued variables distribution simulation, this method, taking GMRF as the prior distribution for continuum variables, taking the relationship between catalogued variables such as rock facies, continuum variables such as porosity, permeability, fluid saturation, can bring a series of stochastic images for the spatial distribution of continuum variables. Integrating multiple data sources into the reservoir model is one of the merits of stochastic reservoir modeling. After discussing how to model spatial distributions of catalogued reservoir variables, continuum reservoir variables, the paper explores how to combine conceptual depositional models, well logs, cores, seismic attributes production history.

员工的组织支持感和工作压力对工作结果变量的作用——中国民航管制员研究

Both perceived organizational support and job stresses have impact on employees’ work outcomes. Great progresses have been made in past researches. However, there are many disputes about the impact of perceived organizational support (POS) on job performance (especially, safety performance), the impact of job stresses on job performance (especially, safety performance) and job attitudes, as well as the interaction of subordinates’ POS and job stresses, and the impact of supervisor on subordinates’ POS et al.. Thus, the aim of the study is to explore the impact of supervisors’ POS, leader-member exchange(LMX) on subordinates’ POS, the direct impact of subordinates’ POS and job stressors from task and rewards on work attitudes(job satisfaction, turnover intention) and safety behaviors(safety compliance and safety participation), and the interaction of subordinates’ POS and job stresses. Analyses are based on the data from interviewing of 20 staff, posts of a Chinese civil aviation Bulletin Board System (BBS) and surveys of 216 subordinates and 42 supervisors from two Chinese civil aviation Air traffic control centers (ATC). The major findings are listed as follows: Firstly, the exchange relationship between supervisors and members has impact on subordinates’ POS by the fully mediating role of subordinates’ perceived supervisor support (PSS). But supervisors’ POS have no impact on subordinates’ POS. Secondly, subordinates’ POS has a direct and positive impact on their job satisfaction and safety behaviors, and a negative impact on turnover intention. Specifically, the higher the employees’ perceived organizational support, the higher job satisfaction and safety behaviors, as well as the lower turnover intention they have. Moreover, POS has stronger relationship with safety participation behaviors than that of safety compliance behaviors. Thirdly, task-related stressor has no significant impact on job satisfaction, turnover intention and safety behaviors. And compensation-related stressor has significant and positive impact on turnover intention and safety behaviors, which means that with the compensation-related stress increases, turnover intention increases, safety behaviors including safety compliance and safety participation also increases. Fourthly, POS and task-related stressor, POS and compensation-related stressor have significant interaction, respectively. Specifically, POS moderates the relationship between task-related stressor and job satisfaction, and between task-related stressor and turnover intention. Moreover, POS also moderates the relationship between compensation-related stressor and safety compliance behaviors.

人格特征对销售员绩效的预测

This research aims to discuss it is the complexity of interpersonal association and job autonomy that influence the predictive validity of personality for job performance. In addition, for service profession, incumbents' personality can predict not only contextual performance, but also task performance. Salesclerks in shopping center and life insurance agents are selected as subjects. The job performance rating scale is produced by using Critical Incidents Technique. The research method is measuring NEO-PI and collecting direct supervisors' rating of salespeople's job performance. The research results are as follows: 1. The factor analysis result of job performance is different from the west. That is to say, the support for organizations which belongs to contextual performance in the west can not be distinguished from task performance. Therefore, in China, or to say in the shopping center selected, task performance includes both technical proficiency and the support for organizations, and contextual performance includes job dedication and interpersonal facilitation. 2. For salespeople, personality can be the antecedent of contextual performance and task performance as well. However, the predictive validity for task performance is very low. 3. The more complexity of interpersonal association, the stronger relationship between personality and job performance. 4a. The correlation between job performance and facets of Big Five is higher than the one between job performance and factors of Big Five, such as Agreeableness, whose facets have different impacts on job performance, some positive and others negative. 4b. The correlation between personality and the items of job performance rating scale is higher than the one between personality and the factors of job performance. 4. Working experience is the moderator of the relationship between personality and job performance. For salesclerks, only if the working experience of subjects is less than 3 years, achievement striving-one facet of conscientiousness-is significantly correlated with the ratio of finished sales volume at 0.01 level.