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The migration of formation water plays a crucial role in hydrocarbon accumulation and preservation. The hydrodynamic field controls the content of various ions in formation water and is an important participant in hydrocarbon evolution. One potential high-yield gas field is the tight sandstone gas reservoirs in the northern Tianhuan Depression of the Ordos Basin, China. However, due to the complex gas-water relationship and limited water sample data, the development of gas reservoirs has encountered great difficulties; we thus analyzed the geochemical characteristics of a large scale of formation water acquired from the Permian in the Ordos Basin (60 water samples collected from 45 wells in the He8 Member). The results showed that formation water is the original sedimentary water in tight sandstone reservoirs, which represent a closed hydrological environment, which is conducive to gas accumulation. This is also related to the demonstrated strong water-rock reaction and diagenetic. We also developed a statistical model between these geochemical parameters and gas preservation based on machine learning algorithms (decision trees). Note that machine learning, as a data-driven artificial intelligence algorithm, generates massive correlation models that can learn from the structured training data sets to carry out predictions or evaluations in newly presented data. This algorithm can process large amounts of information data more quickly and can build more perfect correlation models through deep learning mechanisms than traditional statistical methods. The results suggest that the metamorphism coefficient has the best indication effect on the preservation of gas reservoirs. The hydrological environment with (Cl - -Na + )/Mg 2+ &gt; 50.066, Na + /Cl - ≤ 0.476, and Ma 2+ /Ca 2+ ≤ 0.102 is a good hydrocarbon accumulation area. This study can be applied, by analogy, to more comprehensively interpret the correlation between the geochemical characteristics of formation water and hydrocarbon storage and to improve the accuracy of predicting favorable hydrocarbon accumulation areas in tight sandstone gas reservoirs.

Indication of Formation Water Geochemistry for Hydrocarbon Preservation: New Applications of Machine Learning in Tight Sandstone Gas Reservoirs