A ti Internal cross-sectional area of the tubing, m 2 A J Cross-sectional area of the coupling, m 2 c g Specific heat of water vapor, J/(kg•K) c o Specific heat of crude oil, J/(kg•K) C o Compressibility coefficient of the well fluid, Pa-1 c rf Viscous frictional resistance coefficient c rt Coulomb friction coefficient c u Curvature of the wellbore trajectory, m-1 c w Specific heat of

Under shear and non-uniform loads, the deformation of the section shape of a casing results in an irregular section, and the spatial continuity is poor. The change in the distance between the wall of the casing before and after stress is recorded to analyze the deformation of the casing, and the distance value is taken as the key characteristic of the casing. A large number of the key characteristic values of shale gas casing deformation can be obtained by using the circular traversal detection method. At the same time, this article focuses on the center deviation between the laser sensor axis and the pipe string axis, as well as on the disturbance problem during measurement. An eccentricity error correction algorithm is derived to correct the eccentricity error that occurs during the detection process, and then we use interpolation algorithms to draw cubic spline curves to improve detection accuracy. The test results show that the algorithm can effectively eliminate eccentricity errors in measurement and achieve the accurate measurement of deformation casing characteristic values, which can provide basic data for the study of a shale gas casing deformation mechanism.

Existing deep-well multi-node observation instrument hoisting and lowering systems suffer from poor compartment coupling adaptability, fuzzy anti-jamming judgment criteria, non-standard operation procedures, and lack of classified hoisting control strategies, which easily cause compartment jamming. Aiming at the above research gaps, this study proposes a standardized full-process hoisting/lowering technology tailored for deep-well multi-node observation instruments. This paper first summarizes and classifies mainstream instrument compartment coupling and hoisting schemes and quantitatively compares their application boundaries, operational risks and applicable borehole conditions. Then it optimizes the integral system layout, compartment installation mode and unified standardized operating workflow for downhole instrument deployment. Furthermore, the downhole jamming mechanism and rapid identification means of instrument compartments are clarified. Classified collaborative control strategies are also established to match different hoisting/lowering working conditions. The research results fill the gap in standardized anti-jamming operation specification for deep-well multi-node instrument deployment and can effectively reduce downhole fault handling time and observation operation interruption frequency, providing feasible technical support for the safe and efficient deployment of downhole observation equipment.

Aiming at the engineering challenges of low Rate of Penetration (ROP), high rock-breaking energy consumption, and the difficulty in realizing collaborative optimization of efficiency improvement and consumption reduction through traditional drilling parameter regulation in deep interbedded sandstone and mudstone formations of the Southwest Oil and Gas Field, a study on multi-objective optimization of drilling parameters is carried out. First, the traditional Teale Mechanical Specific Energy (MSE) model is modified, with the introduction of the effective energy utilization coefficient of the drill bit and the downhole torque calculation method, to establish an improved MSE model that fits the actual field drilling conditions. Second, a hybrid TCN-LSTM model fused with an additive attention mechanism is constructed to realize high-precision dynamic prediction of ROP. Finally, taking the on-site real-time controllable Weight on Bit (WOB) and rotary speed as decision variables, the synergy–conflict boundary between the dual objectives of ROP maximization and MSE minimization is clarified, a dual-objective coupled optimization model is established, and the genetic algorithm is used to complete the global optimization. Case verification is carried out based on the actual drilling data of 12 deep wells in the study block, and the results show that the Coefficient of Determination R2 of the established ROP prediction model on the test set reaches 0.91, and the prediction accuracy is significantly better than that of BP, CNN and single LSTM models; after optimization, the ROP of the target well interval is increased by 13.1% compared with the on-site actual drilling value, and the improved MSE is reduced by 23.5%, which simultaneously realizes drilling efficiency improvement and rock-breaking energy saving within the safe operation boundary. The stability of the model and the effectiveness of each module are verified through five-fold inter-well cross-validation and module ablation experiments. The research results can provide a theoretical basis and technical support for the precise regulation of drilling parameters in deep formations of the target block.