基于机器学习的川中地区雷口坡组三段二亚段泥质灰岩储层分布预测

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Abstract：Recently，an industrial gas flow hasbeen obtained from thesecond sub-memberofthe third memberof the Leikoupo Formation （Lei32） at Well CT1 in the central Sichuan Basin， indicating favorable exploration prospects for marly limestone reservoirs.However，research onwel-logging response mechanisms and predictive modeling for such reservoirs remains limited.This study integratescore descriptions，thin-section petrography，anda well-log response analysis to characterize the lithological features of the Lei32 sub-member.A long short-term memory （LSTM） machine learning model was established using well-log data for lithofacies identification of marly limestone.By integrating a single-factor mineral content analysis and sedimentary facies characteristics，a multi-scale predictive framework （point-line-surface） was constructed to forecast marly limestone reservoirs.The results show that the Lei32 （20 sub-member is primarily composed of limestone，marly limestone，dolomite，and gypsum salt rocks.The marly limestonereservoir ischaracterizedbynanoscale to micrometer-scalepores and microfractures，indicatinga typical low-porosity， low-permeability system， with reservoir thickness ranging from 40 to 130m . Compared with CIFLog software calculations and petrographic identifications，the LSTM model achieved a prediction accuracy of （87.3± （204号 0.5）% . Spatial prediction results indicate that the reservoir's thickness ranges from 80 to 120m in the Xichong， Nanchong，and Yilong areas，which are favorable for exploration.Incontrast， Zhongjiang，Ziyang，Anyue，and Hechuan exhibit reservoir thicknesses of 60 to 80m ，suggesting potential exploration targets. This study provides useful a reference for the hydrocarbon exploration of marly limestone reservoirs in the Lei32 sub-member of the central Sichuan Basin.

Key words： central Sichuan area; Lei32 ； marly limestone reservoir； machine learning； prediction model

四川盆地中三叠统雷口坡组具有丰富的天然气资源量，是埋藏最浅的碳酸盐岩产层（尹宏等，2024），发育常规和非常规2类天然气资源（谢武仁等，2024）。（剩余23867字）