Deep- to ultradeep-strata carbonate reservoirs are a crucial strategic area for increasing oil and gas reserves and production in my country. These reservoirs are characterized by their great depth, strong heterogeneity, and complex pore structure. The high-temperature and high-pressure environment makes the seismic response mechanism unclear, posing a severe challenge to traditional rock physics models and seismic inversion methods. This paper systematically reviews the recent research progress in rock physics and seismic inversion methods for deep-strata carbonate reservoirs. In rock physics, the paper focuses on the achievements in high-temperature and high-pressure experiments, pore structure characterization, and theoretical model improvements. In seismic inversion, the paper reviews the current development status of pre-stack inversion, multi-information constraints, and artificial intelligence-assisted methods. Based on this, the paper points out the shortcomings of current research in terms of theoretical foundation and prediction accuracy, and proposes development trends and key research directions of deep-strata integration, refinement, and intelligence.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9