首页|Reports Summarize Machine Learning Study Results from China University of Petrol eum (East China) (Base On Temporal Convolution and Spatial Convolution Transform er for Fluid Prediction Through Well Logging Data)
Reports Summarize Machine Learning Study Results from China University of Petrol eum (East China) (Base On Temporal Convolution and Spatial Convolution Transform er for Fluid Prediction Through Well Logging Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating in Shandong, People's Republi c of China, by NewsRx journalists, research stated, "Fluid prediction is importa nt in exploration work, helping to determine the location of exploration targets and the reserve potential of the estimated area. Machine learning methods can b etter adapt to different data distributions and nonlinear relationships through model training, resulting in better learning of these complex relationships." The news reporters obtained a quote from the research from the China University of Petroleum (East China), "We started by using the convolution operation to pro cess the log data, which includes temporal convolution and spatial convolution. Temporal convolution is specifically designed to capture time series relationshi ps in time series data. In well log data, time information is often critical for understanding fluid changes and other important details. Temporal convolution l earns trends and cyclical changes in the data. The spatial convolution operation makes the model more sensitive to the local features in the logging data throug h the design of the local receptive field and improves the sensitivity to fluid changes. Spatial convolution helps capture spatial correlations at different dep ths or locations. This can help the model understand the change of fluid in the vertical direction and identify the spatial relationship between different fluid s. Then, we use the transformer module to predict the fluid. The transformer mod ule uses a self-attention mechanism that allows the model to focus on informatio n with different weights at different locations in the sequence. In the well log data, this helps the model to better capture the formation characteristics at d ifferent depths or time points and improves the modeling ability of time series information. The fully connected structure in the transformer module enables eac h position to interact directly with other locations in the sequence. By applyin g it to the data of Tarim Oilfield, the experimental results show that the convo lutional transformer model proposed in this paper has better results than other machine learning models."
ShandongPeople's Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine LearningChina University of Petrol eum (East China)
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