To address the challenges of low signal-to-noise ratio （SNR） and the lack of clean ground truth labels for distributed acoustic sensing （DAS） and distributed temperature sensing （DTS） data during hydraulic fracturing，a physics-informed self-supervised blind denoising method is proposed. Instead of relying on clean reference data，this method constructs self-supervised training samples using a checkerboard alternating sampling strategy on time-depth sub-blocks and enables the denoising network to learn noise distributions adaptively. Furthermore，physical constraints such as thermal conduction smoothness，edge preservation，and wavefield coherence are introduced for DTS and DAS characteristics，respectively. A spatially weighted loss function based on perforation cluster positions is also established to prioritize the preservation of key response features near clusters. It is demonstrated that the proposed method outperforms traditional filtering methods in terms of SNR and structural similarity （SSIM）. The denoised data significantly enhances the clarity of cooling fronts and acoustic energy bands at fluid entry points，which effectively improves the accuracy of fracturing event recognition and cluster efficiency evaluation.

Given the current situation that some operating parameters of the gas extraction system in Hutubi gas storage deviate from the optimal value to cause low energy-saving efficiency，the energy efficiency of the gas extraction system is analyzed through pinch analysis and exergy analysis methods. A simulation study is performed based on HYSYS software，giving rise to the scheme of energy conservation and consumption reduction. The scheme mainly includes the selection of the gas-gas heat exchanger，the optimization of the glycol-rich liquid inlet temperature，the optimization of the reflux ratio of the regenerator，and the optimization of the phase-change furnace. The calculation model between the inlet pressure and J-T valve back pressure and the theoretical KA value of the gas-gas heat exchanger is established to optimize the selection of the heat exchanger. The optimum inlet temperature of the glycol-rich liquid is determined to be 67 ℃，and the reflux ratio of the regenerator is 0.06. During the operation of the top condenser of the regenerator，the consumption of glycol can be reduced by 84.6%. The energy consumption of the system can be reduced by 9.0% by using the waste heat of the exhaust gas from the regenerator to preheat the external transmission gas. This optimization scheme effectively reduces the total energy consumption of the gas extraction system with minimum alteration.

As a key link for the production and operation of oilfield enterprises,crude oil transportation and distribution system is complex and variable,covering a vast area and involving many stakeholders,through which commercial oil is exported and marketed. It is the primary goal for oil scheduling to optimize oil transportation and marketing allocation patterns,and maximize marketing profit under the premise of ensuring pipeline transportation safety. In this paper,the oil transportation and distribution system of Xinjiang Oilfield is studied,and oil production,supply,marketing and storage are integrated together. By setting up an optimization and allocation system for regional resources and determining performance evaluation factors,simulation model for long-distance oil transportation pipeline operation,mathematical model for oil density and viscosity calculations,and mathematical model for the optimized allocation of multiple stations and storages were built to optimize the flow direction for oil marketing and allocation,oil blending ratio,and the operation patterns of oil transportation stations and pipelines. After optimization,the annual oil marketing benefit has increased by hundreds of millions yuan.