Study on Wellbore Stability of Multilateral Wells under Seepage-Stress Coupling Condition Based on Finite Element Simulation

The use of multilateral wells is an important method to effectively develop complex oil reservoirs, and wellbore stability research of multilateral wells is of great importance. In the present study, the effects of formation fluids and rock damage were not taken into account by the wellbore stability model. Therefore, finite element analysis (FEA) software was used to establish a three-dimensional (3D) seepage-stress FEA model for the multilateral junctions. The model was used to analyze the wellbore stability of multilateral wells and study influences of wellbore parameters and drilling fluid density on wellbore stability at multilateral junctions. Simulation results show that the wellbore diameter insignificantly affects wellbore stability. When the angle between the main wellbore and branches enlarges to 45°, the equivalent plastic strain decreases by 0.0726, and the wellbores become more stable; when the angle is larger than or equal to 45°, the region prone to wellbore instability transfers from the multilateral junctions to the inner of multilateral wellbores. When the azimuth of wellbores is along the direction of the minimum horizontal principal stress, the equivalent plastic strain decreases by 78.2% and the wellbores are most stable. Moreover, appropriately increasing the drilling fluid density can effectively reduce the risk of wellbore instability at the multilateral junctions. A model has been developed that allows analysis of multilateral wellbore stability under seepage-stress coupling condition

Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism

Preoperative prediction of microvascular invasion (MVI) is essential for management decision in hepatocellular carcinoma (HCC). Deep learning-based prediction models of MVI are numerous but lack clinical interpretation due to their “black-box” nature. Consequently, we aimed to use an attention-guided feature fusion network, including intra- and inter-attention modules, to solve this problem. This retrospective study recruited 210 HCC patients who underwent gadoxetate-enhanced MRI examination before surgery. The MRIs on pre-contrast, arterial, portal, and hepatobiliary phases (hepatobiliary phase: HBP) were used to develop single-phase and multi-phase models. Attention weights provided by attention modules were used to obtain visual explanations of predictive decisions. The four-phase fusion model achieved the highest area under the curve (AUC) of 0.92 (95% CI: 0.84–1.00), and the other models proposed AUCs of 0.75–0.91. Attention heatmaps of collaborative-attention layers revealed that tumor margins in all phases and peritumoral areas in the arterial phase and HBP were salient regions for MVI prediction. Heatmaps of weights in fully connected layers showed that the HBP contributed the most to MVI prediction. Our study firstly implemented self-attention and collaborative-attention to reveal the relationship between deep features and MVI, improving the clinical interpretation of prediction models. The clinical interpretability offers radiologists and clinicians more confidence to apply deep learning models in clinical practice, helping HCC patients formulate personalized therapies

Self-Standing Polypyrrole/Black Phosphorus Laminated Film: Promising Electrode for Flexible Supercapacitor with Enhanced Capacitance and Cycling Stability

With the rapid development of portable electronics, solid-state flexible supercapacitors (SCs) are considered as one of the promising energy devices in powering electronics because of their intrinsic advantages. Polypyrrole (PPy) is an ideal electrode material in constructing flexible SCs owing to its high electrochemical activity and inherent flexibility, although its relatively low capacitance and poor cycling stability are still worthy of improvement. Herein, through the innovative introduction of black phosphorus (BP) nanosheets, we developed a laminated PPy/BP self-standing film with enhanced capacitance and cycling stability via a facile one-step electrochemical deposition method. The film exhibits a high capacitance of 497.5 F g^–1 (551.7 F cm^–3) and outstanding cycling stability of 10 000 charging/discharging cycles, thanks to BP nanosheets inducing laminated assembly which hinder dense and disordered stacking of PPy during electrodeposition, consequently providing a precise pathway for ion diffusion and electron transport together with alleviation of the structural deterioration during charge/discharge. The flexible SC fabricated by laminated films delivers a high capacitance of 452.8 F g^–1 (7.7 F cm^–3) besides its remarkable mechanical flexibility and cycling stability. Our facile strategy paves the way to improve the electrochemical performance of PPy-based SC that could serve as promising flexible energy device for portable electronics