Facies classification with different machine learning algorithm – An efficient artificial intelligence technique for improved classificationPartha Pratim MandalReza Rezaee
Facies classification using machine learning There has been much excitement recently about big data and the dire need for data scientists who possess the ability to extract meaning from it. Geoscienti... B Hall - 《Leading Edge》 被引量: 7发表: 2016年 Seismic facies analysis from well logs ...
A Machine Learning Benchmark for Facies Classification The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. As...
Machine Learning-Based Rock Facies Classification for Improved Reservoir Characterization in Niger Delta FUPRE Journal of Scientific & Industrial ResearchO., IBOYITIEC. W., OKOLOGUMEC., ONWUCHEKWAO. O., OMO-IRABOR
Using our proposed workflow, we observed that, the classification accuracy of a machine learning model trained on a small amount of training data can be enhanced considerably. Pseudo-labels also helped improve lateral continuity of facies. Furthermore, the proposed method outperformed conventional ...
Based on the net and non-net reservoir classification, a discrete parameter was then defined in the static model that more accurately visualized the 2D spatial connectivity of net-reservoir facies in final model (Fig. 8C and D). Download: Download high-res image (4MB) Download: Download ...
Thus, automation of the facies classification process using machine learning is a potentially intuitive and efficient way to facilitate facies interpretation based on large-volume data. It can also enable more adequate quantification of the uncertainty in facies classification by ensuring that possible ...
Machine learning methods including support‐vector‐machine and deep learning are applied to facies classification problems using elastic impedances acquired from a Paleocene oil discovery in the UK Central North Sea. Both of the supervised learning approaches showed similar accuracy when predicting facies...
In the context of machine learning, it is crucial to avoid these redundant and irrelevant attributes as they can result in overfitting, building unnecessary complex models, and prolonging computational time. The current study incorporates an attribute selection approach toseismic facies classification and...
Electrofacies classification of deeply buried carbonate strata using machine learning methods: A case study on ordovician paleokarst reservoirs in Tarim BasinPaleokarst reservoirsElectrofaciesPCAK-meansLDAThe paleokarst system is one of the main carbonate reservoirs, which can form important super-large ...