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.
Highlights
During oil well exploitation, the accurate classification of the well-testing stage plays an important role in real-time early warning of the operating platform, the rational revision of the production flow and the scientific management of theThe associate editor coordinating the review of this manuscript and approving it for publication was Kathiravan Srinivasan .reservoirs [1]
We focus on using the classical methods to classify the complex and nonlinear downhole geological parameters, which are mainly reflected in two parts: mining the data characteristics and improving the generalization ability of the classifier
EXPERIMENTAL RESULTS The proposed model is obtained on the basis of learning and training the sample set with given stage tags, and Fourth, input the purified features to the learning vector quantization (LVQ) classifier
Summary
The accurate classification of the well-testing stage plays an important role in real-time early warning of the operating platform, the rational revision of the production flow and the scientific management of theThe associate editor coordinating the review of this manuscript and approving it for publication was Kathiravan Srinivasan .reservoirs [1]. The accurate classification of the well-testing stage plays an important role in real-time early warning of the operating platform, the rational revision of the production flow and the scientific management of the. The off-line well-testing stage classification can improve the well-testing operation process, provide qualitative explanations to the data without assignment of the exploration category and working stage, and complete the accurate evaluation of complex oil reservoirs. The online well-testing stage classification can determine whether an operation stage is finished or terminated in advance, reduce production accidents, and provide guidance for real-time. The high nonlinearity of geological parameters and the high complexity of exploration processes caused by the continuous development of oil exploration in depth and width make it difficult for the existing classical mathematical method to extract a large amount of effective information in the exploration data
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