Daqing Oilfield General Hospital

In order to clarify the aging law of drilling fluid andthe formationmechanism of sealing properties, thereby providing a theoretical basisfor the design of drilling fluid sealing evaluation methods and fordetermining the necessity of secondary cementing in uncemented intervalsof oil wells, laboratory experiments were conducted in this study.The influencing factors of the coalescence stability and sedimentationstability of the drilling fluid, as well as the temporal and spatialevolution law of the macroscopic properties and microscopic morphologyof the aging drilling fluid, were systematically analyzed in turn.The results show that a high temperature of 120 °C and a calciumand magnesium ion concentration of 1000 mg/L significantly weakenthe coalescence stability of the drilling fluid. However, sedimentationstability can be effectively improved by increasing the viscosityand reducing the fluid loss, lowering the temperature, decreasingthe concentration of calcium and magnesium ions, or increasing theamount of barite. With the extension of the standing time, the settlementof the system exhibits a three-stage evolution characteristic of “stability–stabilitydeterioration–restability”. At 50 days, the densitydifference between the upper and lower layers reaches 2.27 g/cm3, and the gel strength steadily increases to a peak valueof 124 Pa. The annular fluid forms a sealing layer due to the denseaccumulation of a colloidal network and solid phases in the middleand lower layers.

Intelligent computing methods offer significant strengths in managing the intricate scheduling demands of contemporary energy infrastructures. This work introduces a fusion-centric optimization architecture specifically designed for oil and gas transmission systems, incorporating Improved Fuzzy Bayesian Reasoning (IFBR) alongside a Multi-Objective Differential Evolution algorithm. The proposed framework supports dynamic assimilation of heterogeneous data inputs and facilitates route optimization by integrating modeling strategies across both feature abstraction and decision-making layers. Compared to conventional methods such as Dempster–Shafer fusion (D-S) and Multi-View SVM (MV-SVM), the IFBR-based solution achieves a 5.63% improvement in forecasting accuracy, decreases RMSE to 0.085, and minimizes inference latency to 41.2 ms. Under three emulated real-world conditions, it shortens average route reconfiguration time by 37.8%, while keeping flow deviation under 3%, verifying its suitability for low-latency, high-frequency scheduling scenarios.

Fracture failure in supercritical CO2 transportation pipelines can arise from contaminants, third-party damage, or welding defects, primarily due to the unique phase properties of the conveying medium. This study presents a probabilistic fracture failure assessment of supercritical CO2 pipelines using a Dynamic Bayesian Network (DBN) approach. The DBN model captures the causal evolution of pipeline failure from a microscopic damage mechanism perspective, incorporating both ductile and brittle fracture behaviors. It explicitly accounts for the temporal dependencies among contributing factors of pipeline fracture failure. Based on the model, the dynamic failure probability of pipeline fracture is quantified. Additionally, a diagnostic analysis is performed to identify the most probable factors leading to CO2 pipeline fracture failure. The methodology is demonstrated through a case study, which can support risk management strategies for supercritical CO2 pipeline fracture failure.