Subjects: Nuclear Science and Technology >> Other Disciplines of Nuclear Science submitted time 2024-04-25
Abstract： Abstract [Background]: Complex lithology well sections require high precision in density logging data, and traditional computational models cannot adequately meet the high precision requirements in these cases. [Purpose]: This study aims to improve the precision of density logging curves using machine learning regression prediction models. [Method]: Firstly, SSA algorithm was used to improve XGBoost, leading to the development of the SSA-XGBoost density prediction model. Then, Monte Carlo N-Particle transport code（MCNP） of dual-detector density logging tool instrument was used to obtain stratigraphic data of different densities to validate the predictive effectiveness of the model. By optimizing the parameters of SVR, RFR, and LSTM using the SSA, the SSA-SVR, SSA-RFR, and SSA-LSTM models were constructed to predict the simulated formation density. The predictive performance of each model was compared and analyzed using quantitative evaluation metrics and Taylor diagram models. Finally, the performance of different prediction models on actual density logging data was analyzed. [Result]: In the comparative analysis and processing of actual well density logging data with various models, the SSA-XGBoost model showed smaller errors between predicted and actual density, demonstrating high density accuracy and validating the precision of the method. [Conclusion]: The SSA-XGBoost model demonstrates higher predictive accuracy than traditional spine-ribs plot, showing great potential for applications in the processing of actual density logging data.
Subjects: Materials Science >> Materials Science (General) submitted time 2016-11-04 Cooperative journals: 《金属学报》
Abstract：利用透射电子显微镜研究了一种低合金钢中马氏体的微结构. 研究发现, 淬火态的马氏体由铁素体基体和弥散于基体中的高密度纳米小颗粒组成, 这种小颗粒的晶体结构可标定为六角结构的?相. ?相与铁素体基体(α-Fe)的取向关系为:[113]α//[2113]ω，(110)α//(1101)ω 和(211)α//(0110)ω, 点阵常数aω =1.414aα, cω =0.866aα. 马氏体中的碳原子呈非均匀分布, 铁素体基体具有很低的碳含量, 合金中的碳原子主要富集在ω相中.
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