Accurate identification of lithology is the basis and key process of fine logging interpretation and evaluation. However, reservoirs formed by different sedimentary environments and tectonic movements generally have the characteristics of complex and diverse lithology and strong heterogeneity, which brings great difficulty to the identification of reservoir lithology. This paper proposes an automatic identification technology for lithology logging based on the GWO-SVM algorithm model. The technology is actually applied, and the results are compared with the results of the support vector machine cross-validation optimization model, PNN (probabilistic neural network) model, and ELM (extreme learning machine) model. The results show that the GWO-SVM lithology logging recognition model can efficiently solve the lithology recognition and classification problems in complex reservoir analysis and has strong adaptability and higher recognition accuracy.
Aiming at the problem that the layered water injection cannot be accurately obtained in the process of oilfield development, this paper proposes an optical fiber vibration signal recognition and classification algorithm based on XGBoost integrated learning. Firstly, the optical fiber vibration signal collected by the optical fiber vibration sensor is denoised by variational mode decomposition and reconstruction. Then generate spectrograms corresponding to different water absorption layers at different times. A dataset containing 3000 spectral images is obtained. The data sets are imported into support vector machine, random forest classifier and XGBoost integrated classifier based on decision tree for recognition and classification. The parameters of the obtained model are optimized and the model is evaluated by cross validation. Finally, the obtained models are compared and tested. The experimental results show that the XGBoost ensemble learning algorithm with decision tree as the base classifier can effectively identify different vibration signals. This method has a certain significance for the identification of layered relative water absorption of water injection profile.
The identification and classification of petroliferous unit is the key to the success in the exploration for oil-gas fields with geophysical knowledge that are the main means and ways to reduce exploration risk and improve exploration efficiency. In this paper, it analyzes geophysical response characteristics under conditions of gas, oil and water through the establishment of horizontal layered medium model. Then it extracts and analyzes seismic abnormal characteristics of the data structure combining the method of grey system theory based on the experiment, which are considered as an important reference for further objectively identifying petroliferous unit and the exploration of oil-gas fields in the practical application with comparison and analysis.
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