Molecular dynamics simulation on the displacement behaviour of crude oil by CO2/CH4 mixtures on a silica surface.

Produced gas re-injection is an effective and eco-friendly approach for enhancing oil recovery from shale oil reservoirs. However, the interactions between different gas phase components, and the oil phase and rocks are still unclear during the re-injection process. This study aims to investigate the potential of produced gas re-injection, particularly focusing on the effects of methane (CH4) content in the produced gas on shale oil displacement. Molecular dynamics simulations were employed to analyze the interactions between gas, oil, and matrix phases with different CH4 proportions (0%, 25%, 50%, and 100%), alkanes and under various burial depth. Results show that a 25% CH4 content in the produced gas achieves almost the same displacement effect as pure carbon dioxide (CO2) injection. However, when the CH4 content increases to 50% and 100%, the interaction between gas and quartz becomes insufficient to effectively isolate oil from quartz, causing only expansion and slight dispersion. Interestingly, the presence of CH4 has a synergistic effect on CO2, facilitating the diffusion of CO2 into the oil film. During the gas stripping process, CO2 is the main factor separating oil from quartz, while CH4 mainly contributes to oil expansion. In addition, for crude oil containing a large amount of light alkanes, extracting light components through mixed gas may be more effective than pure CO2. This study offers valuable insights for applications of produced gas re-injection to promote shale oil recovery.

Deep Survival Analysis With Latent Clustering and Contrastive Learning.

Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in survival analysis tasks. Although some progress has been made on this issue recently, the present methods generally treat the instances as separate ones while ignoring their potential correlations, thus rendering unsatisfactory performance. In this study, we propose a novel Deep Survival Analysis model with latent Clustering and Contrastive learning (DSACC). Specifically, we jointly optimize representation learning, latent clustering and survival prediction in a unified framework. In this way, the clusters distribution structure in latent representation space is revealed, and meanwhile the structure of the clusters is well incorporated to improve the ability of survival prediction. Besides, by virtue of the learned clusters, we further propose a contrastive loss function, where the uncensored data in each cluster are set as anchors, and the censored data are treated as positive/negative sample pairs according to whether they belong to the same cluster or not. This design enables the censored data to make full use of the supervision information of the uncensored samples. Through extensive experiments on four popular clinical datasets, we demonstrate that our proposed DSACC achieves advanced performance in terms of both C-index (0.6722, 0.6793, 0.6350, and 0.7943) and Integrated Brier Score (IBS) (0.1616, 0.1826, 0.2028, and 0.1120).

Surgical evidence-based comparison of [68Ga]Ga-FAPI-04 PET and MRI-DWI for assisting debulking surgery in ovarian cancer patients.

PURPOSE: Imaging assessment of abdominopelvic tumor burden is crucial for debulking surgery decision in ovarian cancer patients. This study aims to compare the efficiency of [68Ga]Ga-FAPI-04 FAPI PET and MRI-DWI in the preoperative evaluation and its potential impact to debulking surgery decision. METHODS: Thirty-six patients with suspected/confirmed ovarian cancer were enrolled and underwent integrated [68Ga]Ga-FAPI-04 PET/MRI. Nineteen patients (15 stage III-IV and 4 I-II stage) who underwent debulking surgery were involved in the diagnostic efficiency analysis. The images of [68Ga]Ga-FAPI-04 PET and MRI-DWI were visually analyzed respectively. Immunohistochemistry on FAP was performed in metastatic lesions to investigate the radiological missing of [68Ga]Ga-FAPI-04 PET as well as its different performance in primary debulking surgery (PDS) and interval debulking surgery (IDS) patients. Potential imaging impact on management was also studied in 35 confirmed ovarian cancer patients. RESULTS: [68Ga]Ga-FAPI-04 PET displayed higher sensitivity (76.8% vs.59.9%), higher accuracy (84.9% vs. 80.7%), and lower missing rate (23.2% vs. 40.1%) than MRI-DWI in detecting abdominopelvic metastasis. The diagnostic superiority of [68Ga]Ga-FAPI-04 PET is more obvious in PDS patients but diminished in IDS patients. [68Ga]Ga-FAPI-04 PET outperformed MRI-DWI in 70.8% abdominopelvic regions (17/24), which contained seven key regions that impact the resectability and surgical complexity. MRI-DWI hold advantage in the peritoneal surface of the bladder and the central tendon of the diaphragm. Of the contradictory judgments between the two modalities (14.9%), [68Ga]Ga-FAPI-04 PET correctly identified more lesions, particularly in PDS patients (73.8%). In addition, FAP expression was independent of lesion size and decreased in IDS patients. [68Ga]Ga-FAPI-04 PET changed 42% of surgical planning that was previously based on MRI-DWI. CONCLUSION: [68Ga]Ga-FAPI-04 PET is more efficient in assisting debulking surgery in ovarian cancer patients than MRI-DWI. Integrated [68Ga]Ga-FAPI-04 PET/MR imaging is a potential method for planning debulking surgery in ovarian cancer patients.