关键词: 氦气, 氦源岩, 氦气成藏机理, 资源评价, 区带目标优选, 响应面法, 深度学习, 回归模型

Abstract:

China's helium industrial chain needs scientific and technological support, but there lack a systematic theoretical understanding of helium geology, along with the lacks of targeted helium resource evaluation methods and parameter selection standard, comprehensive accurate detection of helium content, helium prospecting methods, cost index system, as well as methodology for integrative evaluation of the entire helium industrial chain. To address these knowledge/technology gaps we developed a helium accumulation model and three key technologies for helium resource and asset evaluation, using interdisciplinary research methodology and experimental techniques involving geology, geochemistry, gravity-aeromagnetic-electrical-seismic, and investment and economics. Through detailed investigation of typical helium-rich gas reservoirs, combined with analysis of “helium-natural gas-water” phase equilibria and phase-potential coupling in underground fluids, we revealed three helium occurrences—water-dissolved, gas-dissolved, free-particle; three migration mechanisms—mass-flow, seepage, diffusion; and four dispersion-enrichment controlling factors—proximity to source, adjacent faults, low-pressure zone, high-location. We developed a theoretical framework for the understanding of helium geology, recognizing high-quality source, efficient migration, suitable gas-carrier are the three key controlling factors of effective helium accumulation. To overcome a series of challenges in helium detection, such as variable detection techniques, low accuracy, large discrepancies with foreign data, and no targeted resource evaluation methods, we developed a comprehensive, accurate detection technique for helium content, with helium source and content at the core, and established 10 resource evaluation methods under four categories, solving the technical bottleneck in helium resource classification and evaluation. A normalized gravity/magnetic downward extension scheme was created to address challenges in helium source-rock distribution, lithofacies identification, source-fault characterization, and reservoir evaluation. An intelligent identification technique for multi-scale faults based on deep learning and a simulation method for acoustic properties of gas reservoir under different helium contents were developed, laying the foundation of predicting source-rock distribution, characterizing source-faults, logging interpretation, and evaluating helium-bearing gas reservoirs. By establishing a multi-process helium control model for helium-rich gas zones and target optimization methodology, the problem of target optimization for helium-rich gas zones is solved. Facing the reality of helium deficiency in China, with the goal of promoting cost-effective investment in helium extraction equipment, we developed a methodology for integrative evaluation of the entire helium industrial chain by adopting response surface methodology to build a nonlinear regression model between optimization target and various main process parameters, which preliminarily addressed the technical demand for cost-effective helium extraction from natural gas. Results from this research provide effective support for China's long-term, safe, and large-scale utilization of its natural helium asset.

Key words: helium, helium source rock, helium accumulation mechanism, resource evaluation, zone target optimization, response surface, deep learning, regression models