Development Of Novel Gas-Logging Detection Methods Based On Raman Spectroscopy

With the development of unconventional oil and gas reservoirs such as shale gas,gas logging technology is desired to quantitatively detect both hydrocarbon gases and non-hydrocarbon gas in seconds for oil exploration.The information on these two kinds of gases was combined to discriminate the fine stratification of oil,gas,and water.However,the traditional gas logging technology can hardly meet the new requirement in oil exploration due to the long detection cycle,limited gas types,and a large number of auxiliary equipment.Therefore,it is urgent to develop new efficient gas logging detection technologies.Raman spectroscopy represents a new trend in the gas logging field due to its high speed,high throughput,and other significant advantages.In this research,the Raman spectral data collected during gas logging was taking as the object.The analysis of Raman spectral data and the discrimination of reservoir fluid properties were studied,respectively.The specific contents are as follows:1.Developing dynamic modeling techniques to construct segmented quantitative analysis models adaptively based on logging gas concentration differences.In this research,the 80 sets of mixed alkane gas(CH₄,C₂H₆,C₃H₈,i C₄H₁₀,n C₄H₁₀,i C₅H₁₂,n C₅H₁₂)with a large difference in high and low concentration was prepared.Through an in-depth discussion of Raman spectral characteristics and linear quantitative range of these samples,a segmented quantitative analysis model of high and low gas concentrations was constructed.Compared with the single quantitative model,dynamic modeling technology significantly improves the prediction accuracy of alkane gas,and its correlation coefficients of each component were better than0.981.In the stability test,the standard deviation is less than 0.058,and the range is less than 0.298.The prediction results of this model were basically consistent with the chromatographic analysis results,but the analysis time of this method was shortened to 6 s or even 1 s.The models can effectively meet the needs of the exploration and detection of complex oil and gas reservoirs.2.Developing oil-gas-water stratification model based on the data-driven to improve the detection rate of weak oil and gas layers.In this research,3000 logging data of 6 reservoir types generated by phase quantitative analysis model were used as the sample set,and the data structure was discussed.The model of oil,gas and water stratification based on the data-driven technology to realize the qualitative measurement of reservoir fluid characteristics.Compared with the actual results,the coincidence rate of logging interpretation reaches more than 92.1%,and four thin gas layers which are not found by the traditional reservoir evaluation methods are detected,which efficiently meet the requirements of the actual gas logging analysis accuracy.The model of oil,gas and water stratification not only realized online prediction of reservoir fluid information,but also improves the detection rate of thin oil and gas layers significantly.Above all,this paper tries to promote the application of Raman gas logging technology,and tests several oil and gas wells for 5 months in Mianyang,Sichuan and Tahe,Xinjiang.The satisfactory results illustrates that the Raman gas logging technology is a promising tool for oil and gas exploration.