A New Method for Hydrocarbon Detection Based on Multi-phase Theory

The hydrocarbon detection techniques used currently are generally based on the theory of single-phase medium, but hydrocarbon reservoir mostly is multi-phase medium, therefore, multisolutions and uncertainties are existed in the result of hydrocarbon detection. This paper presents a fast way to detect hydrocarbon in accordance with BOIT theory and laboratory data. The technique called DHAF technique has been applied to several survey area and obtained good result where the coincidence rate for hydrocarbon detection is higher than other similar techniques. The method shows a good prospect of the application in hydrocarbon detecting at exploration stage and in reservoir monitoring at production stage.

Hydrocarbon Detection Based on Phase Decomposition in Chaoshan Depression, Northern South China Sea

Located in the northern South China Sea,Chaoshan Depression is mainly a residual Mesozoic depression,with a construction of Meso-Cenozoic strata over 7000m thick and good hydrocarbon accumulation conditions.Amplitude attribute of-90°phase component derived by phase decomposition is employed to detect Hydrocarbon in the zone of interest(ZOI)in Chaoshan Depression.And it is found that there are evident amplitude anomalies occurring around ZOI.Phase decomposition is applied to forward modeling results of the ZOI,and high amplitudes occur on the-90°phase component more or less when ZOI is charged with hydrocarbon,which shows that the amplitude abnormality in ZOI is probably caused by oil and gas accumulation.

Hydrocarbon detections using multi-attributes based quantum neural networks in a tight sandstone gas reservoir in the Sichuan Basin, China

A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields.The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decomposition based seismic attenuation characteristics,relative wave impedance features of prestack seismic data as the selected multiple attributes for one tight sandstone gas reservoir and further employ principal component analysis combined with quantum neural networks for giving the distinguishing results of the weak responses of the gas reservoir,which is hard to detect by using the conventional technologies.For the seismic data from a tight sandstone gas reservoir in the Sichuan basin,China,we found that multiattributes based quantum neural networks can effectively capture the weak seismic responses features associated with gas saturation in the gas reservoir.This study is hoped to be useful as an aid for hydrocarbon detections for the gas reservoir with the characteristics of the weak seismic responses by the complement of the multiattributes based quantum neural networks.

Prediction study of hydrocarbon reservoir based on time-frequency domain electromagnetic technique taking Ili Basin as an example

The time-frequency domain electromagnetic(TFEM)sounding technique can directly detect oil and gas characteristics through anomalies in resistivity and polarizability.In recent years,it has made some breakthroughs in hydrocarbon detection.TFEM was applied to predict the petroliferous property of the Ili Basin.In accordance with the geological structure characteristics of the study area,a two-dimensional layered medium model was constructed and forward modeling was performed.We used the forward-modeling results to guide fi eld construction and ensure the quality of the fi eld data collection.We used the model inversion results to identify and distinguish the resolution of the geoelectric information and provide a reliable basis for data processing.On the basis of our results,key technologies such as 2D resistivity tomography imaging inversion and polarimetric constrained inversion were developed,and we obtained abundant geological and geophysical information.The characteristics of the TFEM anomalies of the hydrocarbon reservoirs in the Ili Basin were summarized through an analysis of the electrical logging data in the study area.Moreover,the oil-gas properties of the Permian and Triassic layers were predicted,and the next favorable exploration targets were optimized.

Porous Organic Polymer Nanoparticles for Sensing of Unsaturated Hydrocarbons

The design and synthesis of porous organic polymers for the potential application in chemical sensors remains a huge challenge nowadays. Herein, a porous organic polymer possessing tetrazole groups（TTZ-3） was synthe-sized via simple Schiff base chemical reaction. Thermogravimetric analysis（TGA）, Fourier transform infrared spec- trometer（FTIR）, solid-state 13C cross polarization/magic angle spinning nuclear magnetic resonance（CP/MAS NMR）, transmission electron microscopies（TEM） and field-scanning electron microscopies（FE-SEM） were adopted to cha- racterize the structure and morphology in detail. Significantly, the formed polymers exhibited special detection of unsaturated hydrocarbons through fluorescence enhancement based on photoactivatable 1,3-dipolar cycloaddition reactions. Furthermore, the reaction activity of different unsaturated hydrocarbons towards the polymers was investigated. This work highlights the great potential of porous organic polymers as chemical sensors in realizing environmental pollution monitoring and reducing the incidence of disease, such as chronic obstructive pulmonary disease.