LMDT: A weakly-supervised large-margin-domain-transfer for handwritten digit recognition

Hosseinzadeh, Hamidreza; Razzazi, Farbod

doi:10.1016/j.engappai.2016.02.014

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…Furthermore, we can use the pretrained machine learning algorithm of mature blocks to quickly transfer the learning content to the new block through the combination of field data and synthetic data of the new block to accomplish the knowledge transfer cross shale formations/ blocks. In the early stage, transfer learning research focused more on classification tasks, including image recognition, 38 text classification, 39 number recognition, 40 outlier diagnosis, 41,42 etc. In recent years, with the development of transfer learning theory, the research on transfer learning of regression task is booming.…”

Section: Introductionmentioning

confidence: 99%

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Toward Production Forecasting for Shale Gas Wells Using Transfer Learning

et al. 2023

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Accurate prediction of shale gas well production and estimated ultimate recovery (EUR) is always a difficult and hot spot in shale gas development. In particular, the production and EUR prediction of shale gas wells in new production blocks are faced with the lack of field gas well data and the difficulty of model development. In view of the above problems, this study proposes a new deep transfer learning strategy, which uses transfer component analysis (TCA) and deep neural network (DNN) to achieve shale gas well production and EUR prediction across formations/blocks. The feature extractor based on TCA can narrow the input feature distribution of the source and the target domains. The neural network model can be used to establish a domainadaptive transfer learning model without the prediction performance degradation caused by distribution offset. Validity and accuracy of the model were analyzed using gas well data from Weiyuan and Luzhou blocks in Sichuan Basin, China. The results appear that the reasonable application of TCA can greatly improve the prediction performance of shale gas well transfer learning model. For data sets of the same size, compared with the transfer learning model developed by classical machine learning algorithms, the proposed neural network-based transfer learning model can significantly improve the accuracy of production prediction across formations/ blocks. In addition, the proposed model can also be extended to other types of oil and gas production prediction tasks cross formations/blocks.

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Section: Introductionmentioning

confidence: 99%

“…In the early stage, transfer learning research focused more on classification tasks, including image recognition, text classification, number recognition, outlier diagnosis, , etc. In recent years, with the development of transfer learning theory, the research on transfer learning of regression task is booming .…”

Section: Introductionmentioning

confidence: 99%