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.

贵州台江下、中寒武统界线层型候选剖面碳同位素研究

在扬子地台贵州台江八郎下、中寒武统界线剖面界线附近,碳酸盐岩和干酪根碳同位素组成有规律变化。δ13Cker (PDB) 值在23314 ‰和226. 5 ‰间漂移与δ13Ccarb (PDB) 值在2217 ‰和+ 311 ‰间变化,反映了当时海水的碳同位素组成。无机和有机碳同位素组成之差的Δδ值,沿剖面不断变小,指示剖面上部样品可能受到热扰动和成岩后期作用影响。碳同位素规律的变化,反映了最初的沉积信息,特别是有机质埋藏量的变化,这些变化与早2中寒武世环境变化和生物组合差异有关。

华南晚震旦世陡山沱组有机碳同位素地球化学研究

晚震旦世扬子地台陡山沱组地层出露较好，沉积环境多变，由浅水区的台地相向深水区的盆地相变化，是最重要的成磷期之一。地层中有著名的“帽”碳酸盐岩沉积和瓮安生物群产出。本研究展示了来自以下几个剖面沉积岩的碳酸盐和与之共生的有机质碳同位素组成：台地相区的贵州瓮安剖面，过渡相区的贵州松桃剖面和南明剖面，盆地相区的湖南岩屋滩剖面。主要是分析和探讨该时期扬子地台的生命演化过程和环境变化的关系。从南沱组到陡山沱组.