[First Hit]

Datapages, Inc.Print this page

Study of Hydrocarbon Detection Methods in the Offshore Deepwater Sediments, Gulf of Guinea

Zuo, Guoping *1; Lu, Fuliang 1; Fan, Guozhang 1; Shao, Dali 1
(1) Offshore Research Center, Petrochina Hangzhou Research Institute of Geology, Hangzhou, China.

Offshore oil and gas exploration has become the main field in the exploration at now and in the future, however, the offshore oil and gas exploration is mainly based on seismic data, thus, it is significance to detect hydrocarbon in offshore deepwater exploration using seismic data.

Using seismic data to detect hydrocarbon is a process of inversion which are ambiguity and uncertainty. This paper illustrated five methods to detect hydrocarbon in order to reduce the uncertainty, seismic Previous HitamplitudeNext Hit attribute, frequency attribute, spectrum decomposition method, waveform classification and cross plot analysis.

(1) When reservoir contains hydrocarbon, it often has strong Previous HitamplitudeNext Hit, the Previous HitRMSNext Hit seismic attribute and Max seismic attribute are effective to distinguish hydrocarbon. The result indicates that the hydrocarbon boundary is close to the strong Previous HitamplitudeNext Hit on attribute maps extracted along horizon; (2) Frequency of reservoir decreases in terms of the gas and oil accumulated in the reservoir. Thus, the changes of frequency in the lateral can be used to detect hydrocarbon. The instantaneous frequency attribute achieved good effect in study area; (3) Seismic data was converted into six cubes from 10Hz to 60Hz as the interval of 10Hz. It is noticed that the gas layer shows strong Previous HitamplitudeNext Hit on the 10Hz and 20Hz profiles, however, water layer has not strong Previous HitamplitudeNext Hit, the Previous HitamplitudeNext Hit decreases fast from 40Hz to 60Hz, and there is no strong Previous HitamplitudeTop on the 50Hz and 60Hz profile s. The study indicates that the phenomenon of resonance in low frequency and attenuation in high frequency occurs in the oil and gas layers; (4) Neural network algorithm is used in the waveform classification to depict the form of seismic traces quantitatively. Waveform is divided into 10 types in the study area. The result shows that some types are sensitive to hydrocarbon, and the boundaries of them are coincided with known hydrocarbon distributions and drilled wells; (5) Different types of AVO are located at different areas on the cross plot. Oil and gas also have distinguished features on the cross plot.

The combined application of those methods is greatly reduced the ambiguity and uncertainty. According to this research, the results of hydrocarbon detection coincide with drilled wells and the known hydrocarbon distributions. It achieved better effect in study area, and formed a series of methods of hydrocarbon detection in offshore deepwater oil and gas exploration.

 

AAPG Search and Discovery Article #90142 © 2012 AAPG Annual Convention and Exhibition, April 22-25, 2012, Long Beach, California