Wei Zhou

Bio: Wei Zhou is an academic researcher from Wuhan University. The author has contributed to research in topics: Hydraulic fracturing & Rock mass classification. The author has an hindex of 4, co-authored 4 publications receiving 105 citations.

Seepage analysis of a diversion tunnel with high pressure in different periods: a case study

Abstract: The seepage field for a diversion tunnel with high pressure in the Coca Codo Sinclair hydroelectric project is investigated using finite difference algorithms during periods of construction complet...

Interaction between hydraulic fractures and natural fractures: current status and prospective directions

Abstract: Hydraulic fracturing treatment is one of the most efficient conventional matrix stimulation techniques currently utilized in the petroleum industry. However, due to the spatiotemporal complex nature of fracture propagation in a naturally- and often times systematically fractured media, the influence of natural fractures (NF) and in situ stresses on hydraulic fracture (HF) initiation and propagation within a reservoir during the hydrofracturing process remains an important issue. Over the past 50 years of advances in the understanding of HF–NF interactions, no comprehensive revision of the state of the knowledge exists. Here, we reviewed over 140 scientific articles on investigations of HF–NF interactions, published over the past 50 years. We highlight the most commonly observed HF–NF interactions and their implications for unconventional oil and gas production. Using observational and quantitative analyses, we find that numerical modeling and simulation is the most prominent method of approach, whereas there are less publications on the experimental approach, and analytical method is the least utilized approach. Further, we suggest how HF–NF interactions can be monitored in real time on the field during a pre-frac test. Lastly, based on the results of our literature review, we recommend promising areas of investigation that may provide more profound insights into HF–NF interactions in such a way that can be directly applied to the optimization of fracture-stimulation field operations.

Deep learning based classification of rock structure of tunnel face

Abstract: The automated interpretation of rock structure can improve the efficiency, accuracy, and consistency of the geological risk assessment of tunnel face. Because of the high uncertainties in the geological images as a result of different regional rock types, as well as in-situ conditions (e.g., temperature, humidity, and construction procedure), previous automated methods have limited performance in classification of rock structure of tunnel face during construction. This paper presents a framework for classifying multiple rock structures based on the geological images of tunnel face using convolutional neural networks (CNN), namely Inception-ResNet-V2 (IRV2). A prototype recognition system is implemented to classify 5 types of rock structures including mosaic, granular, layered, block, and fragmentation structures. The proposed IRV2 network is trained by over 35,000 out of 42,400 images extracted from over 150 sections of tunnel faces and tested by the remaining 7400 images. Furthermore, different hyperparameters of the CNN model are introduced to optimize the most efficient algorithm parameter. Among all the discussed models, i.e., ResNet-50, ResNet-101, and Inception-v4, Inception-ResNet-V2 exhibits the best performance in terms of various indicators, such as precision, recall, F-score, and testing time per image. Meanwhile, the model trained by a large database can obtain the object features more comprehensively, leading to higher accuracy. Compared with the original image classification method, the sub-image method is closer to the reality considering both the accuracy and the perspective of error divergence. The experimental results reveal that the proposed method is optimal and efficient for automated classification of rock structure using the geological images of the tunnel face.