Background: Altered cerebral blood flow (CBF) and amplitude of low-frequency fluctuation (ALFF) have been reported in hemodialysis patients. However, neurovascular coupling impairments, which provide a novel insight into the human brain, have not been reported in hemodialysis patients. Methods: We combined arterial spin labeling (ASL) and blood oxygen level dependent (BOLD) techniques to investigate neurovascular coupling alterations and its relationships with demographic and clinical data in 46 hemodialysis patients and 47 healthy controls. To explore regional neuronal activity, ALFF was obtained from resting-state functional MRI. To measure cerebral vascular response, CBF was calculated from ASL. The across-voxel CBF–ALFF correlations for global neurovascular coupling and CBF/ALFF ratio for regional neurovascular coupling were compared between hemodialysis patients and healthy controls. Two-sample t-tests were used to compare the intergroup differences in CBF and ALFF. Multiple comparisons were corrected using a voxel-wise false discovery rate (FDR) method (P < 0.05). Results: All hemodialysis patients and healthy controls showed significant across-voxel correlations between CBF and ALFF. Hemodialysis patients showed a significantly reduced global CBF–ALFF coupling (P = 0.0011) compared to healthy controls at the voxel-level. Of note, decreased CBF/ALFF ratio was exclusively located in the bilateral amygdala involved in emotional regulation and cognitive processing in hemodialysis patients. In hemodialysis patients, the decreased CBF (right olfactory cortex, anterior cingulate gyrus and bilateral insula) and ALFF (bilateral precuneus and superior frontal gyrus) were mainly located in the default mode network and salience network-related regions as well as increased CBF in the bilateral thalamus. Conclusions: These novel findings reveal that disrupted neurovascular coupling may be a potential neural mechanism in hemodialysis patients.

The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model’s time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.

Cement bond quality is critical to ensuring the long-term safety and structural integrity of oil and gas wells. However, due to the complex interdependencies among geological conditions, operational parameters, and fluid properties, accurately predicting cement bond quality remains a considerable challenge. To improve the accuracy and practical applicability of cement bond prediction, this study develops an intelligent prediction model. A Wide and Deep neural network architecture is adopted, into which two key parameters of the cement slurry’s power-law rheological model—the consistency coefficient and the flow behavior index—are embedded. A temperature correction mechanism is incorporated by integrating the correction equations directly into the network structure, allowing for a more realistic representation of the cement slurry’s behavior under downhole conditions. The proposed model is designed to simultaneously predict the bonding quality at both the casing–cement sheath and cement sheath–formation interfaces. It is trained on a field dataset comprising 30,000 samples from eight wells in an oilfield in western China. On the test set, the model achieved prediction accuracies of 87.29% and 87.49% at the two interfaces, respectively. Furthermore, field testing conducted during a third-stage cementing operation of a well demonstrated a prediction accuracy of approximately 90%, indicating strong adaptability to real-world engineering conditions. The results demonstrate that the temperature-corrected neural network effectively captures the flow characteristics of the cement slurry. The proposed model meets engineering application requirements and serves as a reliable, data-driven tool for optimizing cementing operations and enhancing well integrity.

Summary: Accurate cementing quality diagnosis is frequently impeded by the inherent “black-box” nature of conventional models and the fragmented semantic reasoning in multisource engineering data. To address these limitations, we propose a Causal-Aware Rule-Enhanced (CARE) Graph Neural Network (GNN)(CARE-GNN) framework, which specifically aims to enhance the representation and reasoning performance of domain-specific knowledge graphs (KGs) by integrating causal reasoning with the rule-based physical constraints. The core innovation features a dual-channel encoder that mathematically disentangles data-driven topological features from rule-based constraints via an attention gating unit, enabling an adaptive balance between statistical patterns and engineering logic. Unlike parallel multitask paradigms, a sequential causal chain is implemented to model the intrinsic dependencies among root cause diagnosis, quality prediction, and optimization recommendation, ensuring that identified physical precursors directly inform downstream decision-making. Integration of a physics-informed regularization loss further constrains the reasoning space to honor fundamental engineering boundaries. Comparative evaluations yield a quality prediction accuracy of 91.07% and a 6.63% improvement in the multitask hit rate (QRM@1) over state-of-the-art baselines. Field validation on Well [Bozi-X] confirms the system’s capability to provide interpretable, expert-aligned diagnostic insights, offering a theoretically grounded solution for intelligent cementing engineering.