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Hong Cao

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3 papers

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EAAI Journal 2025 Journal Article

A search method for fractured-vuggy reservoir inter-well connectivity path based on multi-modal multi-agent

Wenbin Jiang
Dongmei Zhang
Hong Cao
Xiaofeng Wang

The complex geological structure of carbonate reservoirs and the intricate fracture-vuggy configurations obscure inter-well connectivity, making its evaluation challenging. Conventional studies primarily rely on seismic static data to delineate fracture-vuggy reservoirs, but the limited recognition accuracy hampers the precise characterization of inter-well connectivity and the spatial configuration of fractures and vugs. To address this, this study constructs a 3D (Three-Dimensional) search environment and use multi-modal static and dynamic data and proposes a multi-agent connected channel search model based on deep reinforcement learning. The model treats multiphase fluid as an agent and incorporates Swin Transformer (Shift Window Transformer) to extract large-scale fracture features from seismic data, providing global prior information for path search. A Graph Attention Network is established based on dynamic response relationships to extract spatial geological features, while a multi-head self-attention mechanism captures real-time fluid interactions in various directions. The model fuses multi-modal features, including seismic attributes and production data, to generate decisions and automatically search for inter-well connectivity channels. Experiments were conducted using the WE1 and WE5 well groups from the fault-controlled karst reservoirs in the Tahe oilfield, with results compared against tracer tests. The findings demonstrate that the proposed model's automatic search paths closely align with seismic data and tracer test results, effectively capturing the spatial distribution of fractures and vugs across different scales. This validates the model's effectiveness in evaluating inter-well connectivity in complex carbonate reservoirs.

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AAAI Conference 2016 Conference Paper

Cold-Start Heterogeneous-Device Wireless Localization

Vincent W. Zheng
Hong Cao
Shenghua Gao
Aditi Adhikari
Miao Lin
Kevin Chang

In this paper, we study a cold-start heterogeneous-device localization problem. This problem is challenging, because it results in an extreme inductive transfer learning setting, where there is only source domain data but no target domain data. This problem is also underexplored. As there is no target domain data for calibration, we aim to learn a robust feature representation only from the source domain. There is little previous work on such a robust feature learning task; besides, the existing robust feature representation proposals are both heuristic and inexpressive. As our contribution, we for the ﬁrst time provide a principled and expressive robust feature representation to solve the challenging cold-start heterogeneous-device localization problem. We evaluate our model on two public real-world data sets, and show that it signiﬁcantly outperforms the best baseline by 23. 1%–91. 3% across four pairs of heterogeneous devices.

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IJCAI Conference 2015 Conference Paper

Mobility Profiling for User Verification with Anonymized Location Data

Miao Lin
Hong Cao
Vincent Zheng
Kevin Chen-Chuan Chang
Shonali Krishnaswamy

Mobile user verification is to authenticate whether a given user is the legitimate user of a smartphone device. Unlike the current methods that commonly require users active cooperation, such as entering a short pin or a one-stroke draw pattern, we propose a new passive verification method that requires minimal imposition of users through modelling users subtle mobility patterns. Specifically, our method computes the statistical ambience features on WiFi and cell tower data from location anonymized data sets and then we customize Hidden Markov Model (HMM) to capture the spatialtemporal patterns of each user’s mobility behaviors. Our learned model is subsequently validated and applied to verify a test user in a time-evolving manner through sequential likelihood test. Experimentally, our method achieves 72% verification accuracy with less than a day’s data and a detection rate of 94% of illegitimate users with only 2 hours of selected data. As the first verification method that models users’ mobility pattern on locationanonymized smartphone data, our achieved result is significant showing the good possibility of leveraging such information for live user authentication.

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