本文選題：巖屑成分預(yù)測 + 砂礫巖��； 參考：《西安石油大學(xué)學(xué)報(自然科學(xué)版)》2017年05期

【摘要】：選擇凝灰?guī)r巖屑作為預(yù)測對象,對測井?dāng)?shù)據(jù)進行標(biāo)準(zhǔn)化處理,對砂礫巖儲層薄片鑒定結(jié)果和測井?dāng)?shù)據(jù)進行相關(guān)性分析,優(yōu)選對巖屑敏感的CNL、GR、RT、RI、SP測井參數(shù)作為訓(xùn)練學(xué)習(xí)的對象;分別利用SVM、BP神經(jīng)網(wǎng)絡(luò)、CART、BP神經(jīng)網(wǎng)絡(luò)-Bagging、CART-Bagging、隨機森林等機器學(xué)習(xí)方法建立巖屑預(yù)測模型,對西北緣X723井百口泉組巖屑成分進行預(yù)測、對比和分析。結(jié)果表明:單個機器學(xué)習(xí)方法預(yù)測效果不佳,而經(jīng)集成學(xué)習(xí)方法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)-Bagging、隨機森林取得較好的實驗結(jié)果,尤其是隨機森林的預(yù)測效果最好,平均相對誤差絕對值為17.17%,證實機器學(xué)習(xí)方法在本工區(qū)預(yù)測巖屑成分是有效的,可以進行推廣。
[Abstract]:Tuff cuttings are selected as prediction objects, log data are standardized processed, correlation analysis is carried out on the results of sheet identification and logging data of sandy gravel reservoir, and logging parameters are selected as the object of training and learning.The cuttings prediction model was established by using SVMS-BP neural network and Carton-BP neural network (Baggingling CART-Bagginging, random forest), respectively. The cuttings composition of Baikouquan formation in X723 well in northwest margin was predicted, compared and analyzed.The results show that the prediction effect of single machine learning method is not good, but the BP neural network (BP neural network), which is optimized by integrated learning method, obtains better experimental results, especially the prediction effect of random forest is the best.The absolute value of the average relative error is 17.17. It is proved that the machine learning method is effective in predicting cuttings in this area and can be popularized.
【作者單位】： 長江大學(xué)地球科學(xué)學(xué)院;中國石油新疆油田公司勘探開發(fā)研究院;
【基金】：國家科技重大專項(2016ZX05027)
【分類號】：P618.13;P631.81
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本文編號：1736582