Self-assembly of cinnamic acid-capped gold nanoparticles

In this work, a new capping agent, cinnamic acid ( CA) was used to synthesize Au nanoparticles (NPs) under ambient conditions. The size of the NPs can be controlled by adjusting the concentration of reductant ( in our experiment sodium borohydride was used) or CA. The CA-stabilized Au NPs can self-assemble into 'nanowire-like' or 'pearl-necklace-like' nanostructures by adjusting the molar ratio of CA to HAuCl4 or by tuning the pH value of the Au colloidal solution. The process of Au NPs self-assembly was investigated by UV - vis spectroscopy and transmission electron microscopy. The results reveal that the induced dipole - dipole interaction is the driving force of Au NP linear assemblies.

基于独立分量分析的地震属性分析方法研究

Conventional seismic attribute analysis is not only time consuming, but also has several possible results. Therefore, seismic attribute optimization and multi-attribute analysis are needed. In this paper, Fuyu oil layer in Daqing oil field is our main studying object. And there is much difference between seismic attributes and well logs. So under this condition, Independent Component Analysis (ICA) and Kohonen neural net are introduced to seismic attribute optimization and multi-attribute analysis. The main contents are as follows: (1) Now the method of seismic attribute compression is mainly principal component analysis (PCA). In this article, independent component analysis (ICA), which is superficially related to PCA, but much more powerful, is used to seismic reservoir characterizeation. The fundamental, algorithms and applications of ICA are surveyed. And comparation of ICA with PCA is stydied. On basis of the ne-entropy measurement of independence, the FastICA algorithm is implemented. (2) Two parts of ICA application are included in this article: First, ICA is used directly to identify sedimentary characters. Combined with geology and well data, ICA results can be used to predict sedimentary characters. Second, ICA treats many attributes as multi-dimension random vectors. Through ICA transform, a few good new attributes can be got from a lot of seismic attributes. Attributes got from ICA optimization are independent. (3) In this paper, Kohonen self-organizing neural network is studied. First, the characteristics of neural network’s structure and algorithm is analyzed in detail, and the traditional algorithm is achieved which has been used in seism. From experimental results, we know that the Kohonen self-organizing neural network converges fast and classifies accurately. Second, the self-organizing feature map algorithm needs to be improved because the result of classification is not very exact, the boundary is not quite clear and the velocity is not fast enough, and so on. Here frequency sensitive principle is introduced. Combine it with the self-organizing feature map algorithm, then get frequency sensitive self-organizing feature map algorithm. Experimental results show that it is really better. (4) Kohonen self-organizing neural network is used to classify seismic attributes. And it can be avoided drawing confusing conclusions because the algorithm’s characteristics integrate many kinds of seismic features. The result can be used in the division of sand group’s seismic faces, and so on. And when attributes are extracted from seismic data, some useful information is lost because of difference and deriveative. But multiattributes can make this lost information compensated in a certain degree.

孤北洼陷沙三-沙四段隐蔽油藏形成机制及其预测

Based on the study of sequence stratigraphy, modern sedimentary, basin analysis, and petroleum system in Gubei depression, this paper builds high resolution sequence stratigraphic structure, sedimentary system, sandbody distribution, the effect of tectonic in sequence and sedimentary system evolution and model of tectonic-lithofacies. The pool formation mechanism of subtle trap is developed. There are some conclusions and views as follows. 1.With the synthetic sequence analysis of drilling, seismic, and well log, the highly resolution sequence structure is build in Gubei depression. They are divided two secondary sequences and seven three-order sequences in Shahejie formation. They are include 4 kinds of system traces and 7 kinds of sedimentary systems which are alluvial fan, under water fan, alluvial fan and fan-delta, fan-delta, lacustrine-fan, fluvial-delta-turbidite, lakeshore beach and bar, and deep lake system. Sandbody distribution is show base on third order sequence. 2.Based on a lot of experiment and well log, it is point out that there are many types of pore in reservoir with the styles of corrosion pore, weak cementing, matrix cementing, impure filling, and 7 kinds of diagenetic facies. These reservoirs are evaluated by lateral and profile characteristics of diagenetic facies and reservoir properties. 3.The effect of simultaneous faulting on sediment process is analyzed from abrupt slope, gentle slope, and hollow zone. The 4 kinds of tectonic lithofacies models are developed in several periods in Gubei depression; the regional distribution of subtle trap is predicted by hydro accumulation characteristics of different tectonic lithofacies. 4.There are 4 types of compacting process, which are normal compaction, abnormal high pressure, abnormal low pressure and complex abnormal pressure. The domain type is normal compaction that locates any area of depression, but normal high pressure is located only deep hollow zone (depth more than 3000m), abnormal low pressures are located gentle slope and faulted abrupt slope (depth between 1200~2500m). 5.Two types dynamic systems of pool formation (enclosed and partly enclosed system) are recognized. They are composed by which source rocks are from Es3 and Es4, cap rocks are deep lacustrine shale of Esl and Es3, and sandstone reservoirs are 7 kinds of sedimentary system in Es3 and Es4. According to theory of petroleum system, two petroleum systems are divided in Es3 and Es4 of Gubei depression, which are high or normal pressure self-source system and normal or low pressure external-source system. 6.There are 3 kinds of combination model of pool formation, the first is litholgical pool of inner depression (high or normal pressure self-source type), the second is fault block or fault nose pool in marginal of depression (normal type), the third is fault block-lithological pool of central low lifted block (high or normal pressure type). The lithological pool is located central of depression, other pool are located gentle or abrupt slope that are controlled by lithological, faulting, unconfirmed. 7.This paper raise a new technique and process of exploration subtle trap which include geological modeling, coring description and logging recognition, and well log constrained inversion. These are composed to method and theory of predicting subtle trap. Application these methods and techniques, 6 hydro objects are predicted in three zone of depression.

油气藏自然电位异常与油气赋存的相关性研究

The Stack Spontaneous Potential (SSP) is a direct hydrocarbon location technology and a new hydrocarbon detection method with independent intellectual property. A subsurface hydrocarbon accumulation associated with the upward hydrocarbon micro seepage induces a relatively strong negative potential abnormal zone, of which the anomaly can be measured on the surface with specially designed instruments through careful field measuring procedures. With special software programmed according to a unique geochemical and geophysical model, the original data are analyzed, processed and interpreted on the computer, and then on a series of resulting anomaly distribution maps and/or profiles, the favorable surface locations of the hydrocarbon accumulations can be easily identified. The study of the SSP has been started since 1989, and especially from 1996 to 1997, both profile and area tests were conducted in the Daqing Oilfield. On the testing line of 15kms, there are 6 wells in total, among which some are oil-producing wells, and some are water-producing wells. The final matching ratio of the favorable oil well locations and the possible water well locations predicted by the SSP to those of known wells was up to 83 percent. In the area test, of which the acreage is 800 km2, the matching ratio compared with the existing wells was 87 percent; furthermore, regarding to wells subsequently drilled after the test, the matching ratio was 85 percent. The matching ratio in the development area is more about 10 percent than those of in exploration area. The reason is that, comparing the exploration area, the development area acreage is less and the container rocks are more simplex. In development area there is not so much interference of SSP also. Since 1997 the SSP has been tested and applied all over China to a number of hydrocarbon bearing basins and known oil fields, including the Daqing, Jiangsu, Changqing, Shengli, Nanyang, Jianghan and Zhongyuan Oilfields, only to name a few. The SSP surveys in total areas of over 10,000km2 in more than 30 regions in China so far have been completed in various exploration and development stages, the satisfactory outcomes of which have further evidenced that the dependence between the SP anomaly and abundance of hydrocarbon. Up to date, a substantial amount of successful tests and actual surveys finished in exploration and development practices have evidenced that the SSP is significantly more reliable in comparison with any other similar direct hydrocarbon indication technique generally known to the oil industry, such as the Redox. The SSP can be applied to search for almost all kinds of hydrocarbon accumulations, regardless of the type of traps, such as structural, stratigraphic, buried hill traps, and so on; however, it is interesting to be noted that the SSP seems to be particularly effective in detecting the stratigraphic oil traps according to our practices. On the other hand, there is virtually no surface geographical constrains in terms of field data acquisition, except for those water covered areas, because of the inherent characteristics of the technology itself. Furthermore, utilizing the SSP requires no special considerations to subsurface geological conditions in regard to formation resistivity, since the SSP measurements will not be influenced by either overly high or overly low resistivity of formations lying above the hydrocarbon accumulations. There are two kind of theory, of which, as we know one is called hypbyssal theory such as "Redox"[61 the other is call plutonic theory such as cracking of hydrocarbon [8][9] and natural polarization [3], to describe the mechanism of SP anomaly of oil reservoir and to indicate that the dependence between the SP abnormality and abundance of hydrocarbon has be existed theoretically/The quantitative dependence, which has not been founded due to the complicity of container rocks, be discovered during the exploration and development practices is the crux to the quantitative analysis of SP Anomaly processing. Based on the thorough study of the complex of collector rocks, every kind of thickness of collector rock can be conversed to be a standard effective thickness; the thickness is called apparent effective thickness (AET). The conversation coefficient (ai, 1=1,2,3) could be determined by the variety of every collector rock storability (CRS). The discoveration of quantitative: dependence between AET and the amplitude of SSP, in the practices of exploration and development, is a promotion for the SSP supplied in the oil exploration, and make the data analysis forward to the quantitative stage.