Study of remote sensing based parameter uncertainty in Production Efficiency Models

The remote sensing based Production Efficiency Models (PEMs), springs from the concept of "Light Use Efficiency" and has been applied more and more in estimating terrestrial Net Primary Productivity (NPP) regionally and globally. However, global NPP estimates vary greatly among different models in different data sources and handling methods. Because direct observation or measurement of NPP is unavailable at global scale, the precision and reliability of the models cannot be guaranteed. Though, there are ways to improve the accuracy of the models from input parameters. In this study, five remote sensing based PEMs have been compared: CASA, GLO-PEM, TURC, SDBM and VPM. We divided input parameters into three categories, and analyzed the uncertainty of (1) vegetation distribution, (2) fraction of photosynthetically active radiation absorbed by the canopy (fPAR) and (3) light use efficiency (e). Ground measurements of Hulunbeier typical grassland and meteorology measurements were introduced for accuracy evaluation. Results show that a real-time, more accurate vegetation distribution could significantly affect the accuracy of the models, since it's applied directly or indirectly in all models and affects other parameters simultaneously. Higher spatial and spectral resolution remote sensing data may reduce uncertainty of fPAR up to 51.3%, which is essential to improve model accuracy.

面向CIMS的多数据源互操作与开放分布处理系统———CIMS-MIODP的设计与实现

多数据源互操作与开放分布处理系统ＣＩＭＳ－ＭＩＯＤＰ是采用分布对象互操作与代理技术，实现面向ＣＩＭＳ的基于ＲＰＣ的远程对象访问ＲＯＡ（ＲｅｍｏｔｅＯｂｊｅｃｔＡｃｅｓ）功能和基于ＳＱＬ３的远程数据库访问ＲＤＡ（ＲｅｍｏｔＤａｔａｂａｓｅＡｃｅｓ）功能的系统，为ＣＩＭＳ环境下的信息集成与分布处理提供了不同层次的支持功能。本文讨论了ＣＩＭＳ－ＭＩＯＤＰ系统的主要设计和实现问题，包括基本模型、扩展服务和协议、对ＳＱＬ３的支持、系统实现结构等。

裂缝型储层检测方法——P波方位各向异性技术

Nowadays, the exploration of fractured reservoir plays a vital role in the further development of petroleum industry through out the world. Fractured hydrocarbon reservoirs are widely distributed in China. Usually, S-wave technique prevails, but it also has its disadvantage, prohibitive expense in S-wave data acquisition and processing. So directly utilizing P-wave data to detect fractures, comes to our mind. We briefly introduce theoretical model (HTI) for fractured reservoir. Then study Ruger’s reflectivity method to recognize reflection P-wave reflection coefficient of the top and bottom interface of HTI layer respectively, and its azimuth anisotropy character. Base on that study, we gives a review and comparison of two seismic exploration technologies for fractures available in the industry-- P-wave AVO and AVA. They has shown great potential for application to the oil and gas prediction of fractured reservoir and the reservoir fine description．Every technique has its disadvantage, AVO limited to small reflection angle; and AVA just offering relatively results. So that, We can draw a conclusion that a better way to any particular field is using synthesis of multiple data sources including core、outcrop、well-test、image logs、3D VSPs, generally to improve the accuracy.

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.