Explore the vast range of titles available.

During deep and ultra-deep well drilling operations, the throttling performance of the hydraulic-while-drilling jar is significantly affected by the combined influence of temperature-induced differential thermal expansion among components and changes in the rheological properties of hydraulic oil. These effects often lead to unstable jarring behavior or even complete failure to trigger jarring during stuck pipe events. Here, we propose a high-temperature degradation evaluation model for the throttling performance of the throttle valve in an HWD jar based on thermal expansion testing of individual components and high-temperature rheological experiments of hydraulic oil. By using the variation characteristics of the throttling passage geometry as a linkage, this model integrates the thermo-mechanical coupling of the valve body with flow field simulation. Numerical results reveal that fluid pressure decreases progressively along the flow path through the throttle valve, while flow velocity increases sharply at the channel entrance and exhibits mild fluctuations within the throttling region. Under fluid compression, the throttling areas of both the upper and lower valves expand to some extent, with their spatial distributions closely following the pressure gradient and decreasing gradually along the flow direction. Compared with ambient conditions, thermal expansion under elevated temperatures causes a more pronounced increase in throttling area. Additionally, as hydraulic oil viscosity decreases with increasing temperature, flow velocities and mass flow rates rise significantly, leading to a marked deterioration in the throttling performance of the drilling jar under high-temperature downhole conditions.

Due to water resource limitations and the environmental challenges associated with wastewater generated during oil and gas well drilling processes, the treatment and reuse of drilling wastewater have become essential. In Iran, most drilling wastewater treatment is conducted chemically using coagulant and flocculant agents, typically managed by on-site jar testing, which requires high technical expertise and can be time-consuming and prone to human error. Replacing this conventional approach with artificial intelligence techniques can significantly accelerate the process and reduce operational inaccuracies. In this study, data from 200 drilling waste management reports across various wells in the West Karun oilfields were collected, including input wastewater characteristics, dosages of polyaluminum chloride (coagulant) and polyacrylamide (flocculant), and the quality of the treated effluent. After conducting sensitivity analysis to select relevant input-output parameters, predictive models were developed using Recurrent Neural Networks (RNN), a hybrid PSO-RNN model, Extreme Learning Machines (ELMs), and Random Forest (RF). Each model was trained, tested, and validated, and their performance was evaluated using correlation coefficient (R) and root mean square error (RMSE). The validation results showed that for coagulant prediction, the RF model achieved the highest R value (0.89), while for flocculant prediction, the ELMs model outperformed others with an R value of 0.95. In terms of error, the ELMs model demonstrated the lowest RMSE values for both coagulant (0.13) and flocculant (0.10) predictions. ELM and Random Forest showed strong predictive performance ( R  ≈ 0.95, RMSE ≈ 0.10 g/m³), with high NSE (> 0.85) and low AIC (< 110), confirming model robustness and stability through cross-validation. Overall, Among the four models tested, the ELMs model demonstrated relatively strong predictive performance in both coagulant and flocculant estimation tasks, though limitations in capturing extreme values remain.