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.

Distribution of phytoplankton in the Three-Gorge Reservoir during rainy and dry seasons

After damming of the Yangtze River, in order to explore the impacts of the Three-Gorge Dam (TGD) on the aquatic ecosystem, phytoplankton composition, abundance and biomass spatial distribution were studied in the Three-Gorge Reservoir (TGR), and the closest upstream anabranch Xiangxi River, which is 38 kin away from the Three-Gorge Dam (TGD) during August (rainy season) 2004 and April (dry season) 2005. In surveys, 6 transects (2 downstream and 4 cross-stream) and 25 stations have been investigated and 314 samples were collected from the surface to the river bed with water samplers. In TGR, 63 taxa and 60 taxa were identified in the rainy and dry seasons, respectively. In the Xiangxi River, 39 taxa were observed in the rainy and dry seasons. Algal blooms occurred in the Xiangxi River and at the influx region of the Yangtze and Xiangxi in both seasons, but had not occurred prior to damming. In the rainy season, the dominant species was Chroomonas acuta with 1.84 x 10(7) cells l(-1), and in the dry season the dominant species were Asterionella formosa and Cryptomonas ovata with 1.34 x 10(7) cells l(-1) and 1.79 x 10(6) cells(.)l(-1), respectively. In the main channel of TGR, there were no significant correlations between phytoplankton abundance and the concentrations of the main soluble nutrients. In the Xiangxi River, significant negative correlations were observed between phytoplankton abundance and nitrate (Spearman, p < 0.01, n=21), phosphate (Spearman, p < 0.05, n=21) and silicate (Spearman, p < 0.01, n=21) in the rainy season, and similar correlations were also observed with nitrate (Spearman, p < 0.05, n=28) and silicate (Speannan, p < 0.01, n=28), but not with phosphate in the dry season. Since the damming of the Yangtze River, eutrophication in the anabranch within the backwater has occurred and become severe, and the frequency of algal bloom within TGR and anabranches is expected to increase. (c) 2006 Elsevier B.V. All rights reserved.

Polycyclic aromatic hydrocarbons in Ya-Er Lake (Hubei, China): sources and distribution

Sources and distribution of polycyclic aromatic hydrocarbons (PAH) in the Ya-Er Lake area (Hubei, China) sediment cores of 3 ponds in the shallow Ya-Er Lake were investigated for 16 PAH. Analytical procedure included extraction by ultrasonication, clean-up by gel-permeation and quantification by HPLC with fluorescence detection. The total PAH amount in sediment samples of the Ya-Er Lake ranged from 68 to 2242 mu g/kg. Concentrations decreased from pond 1 to pond 3 and from upper to lower sediment layers. In addition a soil sample from Ya-Er Lake area showed a total PAH amount of 58 mu g/kg. The PAH pattern in lower sediment layers were similar to that of the soil sample which indicates an atmospheric deposition into the sediments prior to 1970 only. The PAH profile of upper sediment samples, which differs completely from that of lower layers, may be explained by a gradually increasing input of mixed combustion and raw fuel sources since 1970. Therefore the origin of increased PAH contamination in Ya-Er Lake during the last 3 decades has been probably an industrial waste effluent in pond 1.