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As global warming intensifies, carbon capture, utilization, and storage (CCUS) technology is widely used to reduce greenhouse gas emissions. CO2-enhanced oil recovery (CO2-EOR) technology has, once again, received attention, which can achieve the dual benefits of oil recovery and CO2 storage. However, flexibly and effectively predicting the CO2 flooding and storage capacity of potential reservoirs is a major problem. Traditional prediction methods often lack the ability to comprehensively integrate static and dynamic predictions and, thus, cannot fully understand CO2-EOR and storage capacity. This study proposes a comprehensive deep learning framework, named LightTrans, based on a lightweight gradient boosting machine (LightGBM) and Temporal Fusion Transformers, for dynamic and static prediction of CO2-EOR and storage capacity. The model predicts cumulative oil production, CO2 storage amount, and Net Present Value on a test set with an average R-square (R2) of 0.9482 and an average mean absolute percentage error (MAPE) of 0.0143. It shows great static prediction performance. In addition, its average R2 of dynamic prediction is 0.9998, and MAPE is 0.0025. It shows excellent dynamic prediction ability. The proposed model successfully captures the time-varying characteristics of CO2-EOR and storage systems. It is worth noting that our model is 105–106 times faster than traditional numerical simulators, which once again demonstrates the high-efficiency value of the LightTrans model. Our framework provides an efficient, reliable, and intelligent solution for the development and optimization of CO2 flooding and storage. [ABSTRACT FROM AUTHOR]

Introduction: Seeking effective multimodal analgesia and anesthetic regimen is the basis for the success of ERAS. Opioid-free anesthesia (OFA) is a multimodal anesthesia associating hypnotics, N-methyl-D-aspartate (NMDA) antagonists, local anesthetics, anti-inflammatory drugs and α-2 agonists. Although previous studies have confirmed that OFA is safe and feasible for VATS surgery, there is great heterogeneity in how to select and combine anti-harm drugs to replace opioids. We hypothesized that the reduced opioid use during and after surgery allowed by OFA compared with standard of care will be associated with a reduction of postoperative opioid-related adverse events and an improvement in the quality of rehabilitation of patients after partial VATS lung resection. Methods/Analysis: The TEDOFA Study is a prospective double-blind, randomized, controlled clinical trial with a concealed allocation of patients scheduled to undergo elective partial VATS pneumonectomy 1:1 to receive either a standard anesthesia protocol or an OFA. A total of 146 patients were recruited in the study. Primary endpoint was the 15-item recovery quality scale (QoR-15) at 24 hours after surgery. Ethics and Dissemination: This trial has been approved by the Institutional Review Board of Beijing Friendship Hospital of China Capital University. The TEDOFA trial study protocol was approved on 27 February 2023. The trial started recruiting patients after registered on the Chinese Clinical Trial Registry. Trial Registration Number: ChiCTR2300069210; Pre-results. [ABSTRACT FROM AUTHOR]