In-situ detection stimulation system for geochemical parameters of hydrates in abyssal deposits

Abstract

The invention belongs to the fields of in-situ measurement technology and applications of the geochemical parameters of ocean natural gas hydrates. An in-situ detection stimulation system for geochemical parameters of hydrates in abyssal deposits comprises a laser-Raman spectrum detection system (I), an abyssal environment stimulation system (II), a hydraulic system (III) and a real-time parameter monitoring processing system (IV), wherein the abyssal environment stimulation system (II) is electrically connected with the laser-Raman spectrum detection system (I) and the real-time parameter monitoring processing system (IV) respectively, and the hydraulic system (III) is arranged in the abyssal environment stimulation system (II). The in-situ detection stimulation system eliminates the nondeterminacy in sampling and ex-situ determination, is simple, real-time and efficient, and can safely acquire high-definition information in a high-pressure simulation cabin. In addition, the device can also be used for laboratory simulation and real-time monitoring of a hydrate exploring method.

Description

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AI Technical Summary

Benefits of technology

In this new way we developed an underwater simulator that has been tested on various types of rocks at sea level with varying levels of salinity or other factors like hydrogen sulfide (H2S) being present during deformation processes. By measuring these variables over time it becomes possible to monitor how well fluids are formed inside rock formations without causing damage from external influences.

Problems solved by technology

This patents describes various methods for detecting gas hydratases (gas bubbles) from sea floor sediments at different depth levels. One problem with current testing techniques involves difficulty ensuring accurate results when analyzing samples taken after drilling into deeper areas where there may have changed characteristics such as density and composition caused by factors like weather effects. Another issue being addressed is how to accurately monitor these changes without rebuilding previous data sets afterwards.

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