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Collaborating Authors
Zhao, Yi
Ensemble empirical mode decomposition and stacking model for filtering borehole distributed acoustic sensing records
Zhao, Yi (Jilin University) | Zhong, Zhicheng (Jilin University, Southern Marine Science and Engineering Guangdong Laboratory) | Li, Yue (Jilin University) | Shao, Dan (Jilin University) | Wu, Yongpeng (Southern Marine Science and Engineering Guangdong Laboratory)
ABSTRACT We have evaluated the ensemble empirical mode decomposition (EEMD) and stacking model for borehole seismic-data denoising. The borehole records collected by distributed acoustic sensing (DAS) technology have multitype noise contamination, and it is difficult to attenuate these noises while recovering the seismic waves well. We first perform EEMD on the seismic data to obtain the signal-to-noise modal components, then extract the time and frequency information of the decomposed modes using six feature factors, and finally introduce an ensemble learning method to classify the acquired modal features effectively. Stacking is the ensemble learning technique we used in our study. This technique integrates several diverse basic ensemble models using the meta-learning strategy and constructs a highly integrated framework with superior performance and good generalization. In addition, the basic ensemble models consist of many decision tree classifiers following two different ideas of parallelization and serialization. The feature extraction process provides sufficient DAS feature data for the training process of the framework. Synthetic and real experimental results demonstrate that the stacking integration framework effectively separates the signal-to-noise modal features of the borehole DAS records. Furthermore, the EEMD-stacking method performs better than wavelet transform, intrinsic time-scale decomposition, robust principal component analysis, k-means singular value decomposition, and median filtering on the denoising task.
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Production logging (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Evaluation of Dynamic Reserves in Ultra-Deep Naturally Fractured Tight Sandstone Gas Reservoirs
Luo, Ruilan (RIPED, PetroChina) | Yu, Jichen (RIPED, PetroChina) | Wan, Yujin (RIPED, PetroChina) | Liu, Xiaohua (RIPED, PetroChina) | Zhang, Lin (RIPED, PetroChina) | Mei, Qingyan (PetroChina Southwest Oil& Gas Company) | Zhao, Yi (PetroChina Southwest Oil& Gas Company) | Chen, Yingli (PetroChina Southwest Oil& Gas Company)
Abstract Ultra-deep naturally fractured tight sandstone gas reservoirs have the characteristics of tight matrix, natural fractures development, strong heterogeneity and complex gas-water relations. There is strong uncertainty of gas reserves estimation in the early stage for such reservoirs, which brings big challenge to the development design of gas fields. Taking Keshen gas field in Tarim basin as example, during the early development stage, the dynamic reserves were much less than those of proven geologic reserves. As results, the actual production performances are obviously different from those of conceptual design. What are the reasons? How to adjust the development program of gas field? Based on special core analysis, production performance analysis, gas reservoir engineering method, and numerical simulations, influencing factors on evaluation of dynamic reserves for ultra-deep fractured tight sanstone gas reservoirs are analyzed. The results show that rock pore compressibility, recovery percent of gas reserves, gas supply capacity of matrix rock, water invasion are the major factors affecting the evaluation of dynamic reserves. On the basis of above analysis, some suggestions are given for the evaluation of dynamic reserves in Ultra-deep fractured tight sandstone gas reservoirs. For this kind of reservoirs, it is reasonable to determine the gas production scale based on dynamic reserves instead of proven geological reserves.