综合多信息进行地震约束储层随机建模——以准噶尔盆地庄1井区J1S2^2砂组为例

针对地震约束储层随机建模中地震反演波阻抗数据体与岩相概率关系差的问题,提出综合多信息、逐级约束、多级建模的思路方法。首先以地震反演数据体约束建立地球物理特征曲线随机参数场,再以每一个地球物理特征曲线参数场为约束建立相模型,最后以优选的相模型和地球物理特征曲线参数场为约束进行储层参数建模。该方法在井距较大的开发前期评价阶段具有较好的应用效果。     

桂林洞穴滴水对应CaCO3沉积物的稳定同位素特征与环境意义

桂林地区降水及茅茅头大岩滴水滴速和现代碳酸盐的碳氧同位素研究表明:(1)滴水沉积的δ18O达到了同位素沉积平衡,洞穴滴水的δ18O与同期降雨的δ18O值呈正相关关系;(2)在旱季和湿季交替时,滴水δ18O值滞后于降雨δ18O,受活塞效应的影响;(3)δ13C难以达到同位素沉积平衡,主要受滴水动力学影响;(4)同一洞穴不同滴水对环境的响应不同,如果洞穴顶板覆盖较厚,水分在土壤和岩层中滞留时间较长,滴水则反映受到平滑作用的降雨,反之则波动较大.     

黄河口裸置管线对海床土影响范围研究

裸置管线在波浪和潮流等作用下,对海床土强度和整体固结水平产生一定的影响,而产生影响的范围还是一个悬而未决的问题。2005年8月和2006年9月,分别对黄河口地区106钻井平台附近的一条裸置管线以及该地区海床土强度进行了现场微贯测量,结果发现,在该地区水动力作用下,水平方向,管线使得其两侧2m以内海床土强度降低,均匀性变差,尤其是降低了0～40cm深度海床土的强度,并使得其非均匀化程度加剧,使海床土出现成层现象。该研究成果为波浪-管线-土相互作用研究提供了典型实例,并对工程实践提供了借鉴。     

Profiling Planctomycetales diversity with reference to anammox‐related bacteria in a South China Sea,deep‐sea sediment

A systematic assessment of Planctomycetales diversity in a South China Sea, deep‐sea sediment (1657 m) was conducted using the 16S rRNA gene analysis approach. PCR amplification of the samples from seven sediment layers (0.1, 1, 3, 5, 7, 9 and 11 m below the surface sediment) using the primer set Pla‐46‐F/1392‐R showed that the Planctomycetales existed within a limited range of sediment depths (≤ 5 m), and had a decreasing trend in diversity with increasing depth. The majority of the retrieved Pla‐46‐F/1392‐R sequences belonged to Pirellula‐related Planctomycetales, and two sequences retrieved from the 0.1‐m layer (GenBank accession numbers: DQ996944 and DQ996945 ) shared the same anammox‐related signature oligonucleotides and were closely related to commonly recognized anammox organisms. To identify new anammox‐related biomarkers, three primer sets were designed for amplifying the fragments of hydroxylamine oxidoreductase and S‐adenosylmethionine radical enzyme genes, but no related sequences were found. Our multiple 16S rRNA gene primer sets (Journal of Rapid Methods and Automation in Microbiology, 2008, in press) revealed even an higher diversity of Planctomycetales in the 0.1‐m layer of the sediment, especially at genus level. Our data profiled the distribution pattern of Planctomycetales diversity along sediment depths, and provided molecular evidence for the existence of anammox‐related bacteria in a new location, which broadens our understanding of Planctomycetales diversity in deep sea sediments.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Theoretical Model and Dynamic Analysis of Soft Yoke Mooring System

As a popular solution for mooring an FPSO （Floating Production, Storage and Offloading） permanently in shallow water, the soft yoke mooring system has been widely used in ocean oil production activities in the Bohai Bay of China. In order to simulate the interaction mechanism and conduct dynamic analysis of the soft yoke mooring system, a theoretical model with basic dynamic equations is established. A numerical iteration algorithm based on error estimation is developed to solve the equations and calculate the dynanfic response of the mooring system due to FPSO motions. Validation is conducted by wave basin experimentation. It is shown that the numerical simulation takes only a few iteration times and the final errors are small. Furthermore, the calculated results of both the static and dynamic responses agree well with those ones obtained by the model test. It indicates that the efficiency, the precision, the reliability and the validity of the developed numerical algorithm and program are rather good. It is proposed to develop a real-time monitoring system to further monitor the dynamic performance of the FPSO with a soft yoke mooring system under various real sea environments.