Automatic Deep Vector Learning Model Applied for Oil-Well-Testing Feature Mining, Purification and Classification

Abstract

Well-testing stage analysis is an important way for oilfield operation state decision-making and reservoir management. However, due to the variability and nonlinearity of the downhole data caused by the complex exploration activities and the differences of petroleum type and geological conditions, the classical methods are ineffective in feature extraction, learning network construction, and classifier optimization. In this work, we propose a new well-testing stage classification method based on a deep vector learning model (DVLM). The novelty of this study lies in the combination of multi-feature extraction, deep learning and feature vector mapping. The proposed method can overcome the problems of poor feature representation ability and poor classification model generalization ability in the existing machine learning methods, which mainly caused by the non-optimized training network structure and the unreasonable classifier design,. Firstly, the initial features are obtained by four classical methods. Then a five layers deep belief network embedded with the mutual information coefficient method is implemented for further feature extraction and purification. Finally, the optimized learning vector quantization classifier outputs the predicted tags. For model training and testing 572 field samples total of 4004 data streams are used. By considering the classification errors and accuracy metrics, the neurons number of deep learning network and the classifier are tuned, and an optimal and stable framework is obtained. Comparative experimental results with several classical integration models show that the proposed model achieves the highest classification accuracy of 98.065% as well as the least of features (nine). The results demonstrate that the proposed model has excellent performance in improving the classification accuracy and completing the feature compression. Moreover, the proposed model has very important practical significance for guiding the automatic analysis and processing oil and gas data. INDEX TERMS Well-testing stage analysis; Feature extraction; Deep belief network; Learning vector quantization.

Well Performance Classification and Prediction: Deep Learning and Machine Learning Long Term Regression Experiments on Oil, Gas, and Water Production

“…Lastly, the data are classified using the optimized learning vector quantization classifier, so the predicted tags are output. This will decrease the efforts of oil analysts and help to predict accurate sample labeling, with a total result of 98.065% stage classification accuracy [ 25 ].A study has been proposed that discusses anticipated deliverability. Researchers used a dataset from the US Energy Information Administration (EIA), which had 864, 432, and 216 records for the years 2017 to 2020, respectively.…”

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction

“…With the increasing popularity of artificial intelligence in various fields around the world, such as applications in natural language processing technology, intelligent computing chips, unmanned system driving, and other core technologies, the technological expansion is also becoming more mature. − While the oil and gas industry acts as the lifeline of national energy development, machine learning also gradually expands deeply into the exploitation of oil and gas resources. − For example, Ma et al attempted to correlate stochastic reservoir parameters with observable features in production time series data using artificial intelligence techniques . These techniques can be integrated into the modeling process as an aid to predicting recoverable reserves.…”