Search Results (72)

This study innovatively proposes a pipeline-type pneumatic lift sediment removal device for cleaning pollutants at the bottom of fish breeding tanks and conducts hydrodynamic characteristic analysis on its core component, the pneumatic lift pipeline structure, which consists of a horizontal circular tube with multiple micro-orifices at the bottom and an upward-inclined circular tube. The pipeline has an inner diameter of 20 mm and a vertical length of 1.2 m, with the orifice at one end of the horizontal tube connected to the gas supply line. During operation, compressed gas enters the horizontal tube, generating negative liquid pressure that draws solid–liquid mixtures from the tank bottom into the pipeline, while buoyant forces propel the gas–liquid–solid mixture upward for discharge through the outlet. Under a constant gas flow rate, numerical simulations investigated efficiency variations through three operational scenarios: ① different pipeline orifice diameters, ② varying orifice quantities and spacings, and ③ adjustable pipeline bottom clearance heights. The results indicate that in scenario ①, an orifice diameter of 4 mm demonstrated optimal efficiency; in scenario ②, the eight-orifice configuration achieved peak efficiency; and scenario ③ showed that the proper adjustment of the bottom clearance height enhances pneumatic efficiency, with maximum efficiency observed at a clearance of 10 mm between sediment suction pipe and tank bottom. Full article

Advanced gas turbine cooling technologies are required to bridge the gap between turbine inlet temperatures and component thermal limits. Transpiration cooling has emerged as a promising method, leveraging porous structures to enhance cooling effectiveness. Recent advancements in additive manufacturing (AM) enable precise fabrication of complex transpiration cooling architectures, such as triply periodic minimal surface (TPMS) and biomimetic designs. This review analyzes AM-enabled transpiration cooling for gas turbines, elucidating key parameters, heat transfer mechanisms, and flow characteristics of AM-fabricated designs through experimental and numerical studies. Previous research has concluded that well-designed transpiration cooling achieves cooling effectiveness up to five times higher than the traditional film cooling methods, minimizes jet lift-off, improves temperature uniformity, and reduces coolant requirements. Optimized coolant controls, graded porosity designs, complex topologies, and hybrid cooling architectures further enhance the flow uniformity and cooling effectiveness in AM transpiration cooling. However, challenges remain, including 4–77% porosity shrinkage in perforated transpiration cooling for 0.5–0.06 mm holes, 15% permeability loss from defects, and 10% strength reduction in AM models. Emerging solutions include experimental validations using advanced diagnostics, high-fidelity multiphysics simulations, AI-driven and topology optimizations, and novel AM techniques, which aim at revolutionizing transpiration cooling for next-generation gas turbines operating under extreme conditions. Full article

Proper determination of the bottomhole pressure in a gas lift well is essential to enhance production, tackle operating concerns, and use the least amount of gas. Mechanistic models, empirical correlation, and hybrid models are usually limited by the requirements for calibration, large amounts of inputs, or limited scope of work. Through this study, sixteen well-tested machine learning (ML) models, such as genetic programming-based symbolic regression and neural networks, are developed and studied to accurately predict flowing BHP at the perforation depth, using a dataset from 304 gas lift wells. The dataset covers a variety of parameters related to reservoirs, completions, and operations. After careful preprocessing and analysis of features, the models were prepared and tested with cross-validation, random sampling, and blind testing. Among all approaches, using the L-BFGS optimizer on the neural network gave the best predictions, with an R² of 0.97, low errors, and better accuracy than other ML methods. Upon using SHAP analysis, it was found that the injection point depth, tubing depth, and fluid flow rate are the main determining factors. Further using the model on 30 unseen additional wells confirmed its reliability and real-world utility. This study reveals that ML prediction for BHP is an effective alternative for traditional models and pressure gauges, as it is simpler, quicker, more accurate, and more economical. Full article

The global emphasis on clean energy has increased interest in producing hydrogen from petroleum reservoirs through in situ combustion-based processes. While field practices have demonstrated the feasibility of co-producing hydrogen and oil, the question of which offers greater economic potential, oil, or hydrogen, remains central to ongoing discussions, especially as researchers explore ways to produce hydrogen exclusively from petroleum reservoirs. This study presents the first integrated techno-economic model comparing oil and hydrogen production under varying injection strategies, using CMG STARS for reservoir simulations and GoldSim for economic modeling. Key technical factors, including injection compositions, well configurations, reservoir heterogeneity, and formation damage (issues not addressed in previous studies), were analyzed for their impact on hydrogen yield and profitability. The results indicate that CO₂-enriched injection strategies enhance hydrogen production but are economically constrained by the high costs of CO₂ procurement and recycling. In contrast, air injection, although less efficient in hydrogen yield, provides a more cost-effective alternative. Despite the technological promise of hydrogen, oil revenue remains the dominant economic driver, with hydrogen co-production facing significant economic challenges unless supported by policy incentives or advancements in gas lifting, separation, and storage technologies. This study highlights the economic trade-offs and strategic considerations crucial for integrating hydrogen production into conventional petroleum extraction, offering valuable insights for optimizing hydrogen co-production in the context of a sustainable energy transition. Additionally, while the present work focuses on oil reservoirs, future research should extend the approach to natural gas and gas condensate reservoirs, which may offer more favorable conditions for hydrogen generation. Full article

The anticipated growth of the aviation industry has driven regulators to establish stringent targets for achieving net-zero greenhouse gas emissions, which are challenging to meet with conventional aircraft configurations. The strut-braced wing configuration has emerged as a promising alternative for improving aerodynamic efficiency. This study investigates the aerodynamic performance of such a configuration using a high-fidelity computational fluid dynamics analysis, conducted in two phases. The first phase involves a parametric study examining the effects of two key parameters: the length and the tilt angle of the strut’s elbow. The reference configuration is based on the strut braced wing configuration from the Platform for Aircraft Drag Reduction Innovation workshop. Building on the insights from the parametric study, the second phase involves an optimization of the strut’s geometry, focusing on minimizing aerodynamic drag. The results demonstrate an 11.5% reduction in the drag coefficient while maintaining the same lift coefficient, primarily attributed to a decrease in shockwave interactions within the strut–wing gap. These findings, combined with the parametric study, provide valuable insights into the influence of strut geometric parameters on drag minimization, highlighting their potential role in advancing sustainable aircraft design. Full article

Heat pumps are widely regarded as a key technology for sustainable heating, offering a pathway to significantly reduce fossil fuel dependency and combat the climate crisis. However, replacing individual gas boilers with heat pumps in multi-unit residential buildings remains a substantial challenge despite its immense potential to lower urban greenhouse gas emissions. To address this, the following paper describes the development of a compact, modular heat pump system designed to replace conventional gas boilers, focusing on the building and testing of a prototype for such a modular heat pump system. The prototype supports multiple functionalities, including space heating, cooling, and domestic hot water production. The performance advantages of two different compressor technologies were exploited to optimize the efficiency of the complete system and the pressure lifts associated with applications for heating and domestic hot water production. Thus, measurements were conducted across a range of operating points, comparing different heat pump module types. In the case of the piston compressor module, the Carnot efficiency was in the range of 47.2% to 50.4%. The total isentropic efficiency for floor heating and domestic hot water production was above 0.45 for both piston and rotary compressors. Full article

The development of deep coalbed methane (CBM) wells faces challenges such as significant reservoir depth, low permeability, and severe liquid loading in the wellbore. Traditional drainage and gas recovery techniques struggle to meet the dynamic production demands. This study, using the deep CBM wells at the Jishen 15A platform as an example, proposes a “cyclic gas lift–wellhead compression-vent gas recovery” composite synergy technology. By selecting a critical liquid-carrying model, innovating equipment design, and dynamically regulating pressure, this approach enables efficient production from low-pressure, low-permeability gas wells. This research conducts a comparative analysis of different critical liquid-carrying velocity models and selects the Belfroid model, modified for well inclination angle effects, as the primary model to guide the matching of tubing production and annular gas injection parameters. A mobile vent gas rapid recovery unit was developed, utilizing a three-stage/two stage pressurization dual-process switching technology to achieve sealed vent gas recovery while optimizing pipeline frictional losses. By combining cyclic gas lift with wellhead compression, a dynamic wellbore pressure equilibrium system was established. Field tests show that after 140 days of implementation, the platform’s daily gas production increased to 11.32 × 10⁴ m³, representing a 35.8% rise. The average bottom-hole flow pressure decreased by 38%, liquid accumulation was reduced by 72%, and cumulative gas production increased by 370 × 10⁴ m³. This technology effectively addresses gas–liquid imbalance and liquid loading issues in the middle and late stages of deep CBM well production, providing a technical solution for the efficient development of low-permeability CBM reservoirs. Full article

This study optimized the constants of the RNG k-ε model using the Ensemble Kalman Filter (ENKF) data assimilation method to improve the accuracy of air-lift plunger gap flow predictions. For high Reynolds number turbulent flow, we conducted numerical simulations integrating experimental data with a library of predicted data generated via optimal Latin hypercube sampling. ENKF was employed to assimilate these data and adjust the model constants, significantly reducing prediction errors and enhancing the accuracy of plunger models. Specifically, mean square errors for rectangular and circular plungers decreased from 60.67 and 61.48 to 7.12 and 7.20, respectively. The study also revealed significant changes in vortex dynamics and flow distribution following data assimilation, providing insights for optimizing plunger design and improving system energy efficiency. These findings underscore the potential of data assimilation in advancing oil and gas production. Full article

Dynamic response characteristics of the passive heave compensator with auxiliary gas bottles are investigated in this paper. A mathematical model of the passive heave compensator is developed which includes mechanics, hydraulics and pneumatics. The key innovation of the proposed model is that the thermodynamic model of gas exchange between the piston accumulator and the gas bottles is derived and discussed. Meanwhile, a one-dimensional model of the pipeline resistance effect is established to calculate the pressure drop across the oil pipeline. The proposed model is used to evaluate the different design parameters of the passive heave compensator for heavy lifting cranes. A study was conducted to investigate the influence of the design parameters on the effectiveness of the passive compensator to reduce the payload displacement. The simulation results indicated that substantial improvement may be possible by careful design parameter selection and optimization. Full article

In this paper, the D3Q19 multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) for large eddy simulation (LES) was employed to optimize the shape of the vortex generator in a triangular vortex flowmeter. The optimization process focused on the vortex shedding frequency, lift force per unit area, and symmetry of the vortex street. The optimal shape of the vortex generator was determined to feature a 180° incoming flow surface, a concave arc side with a curvature radius of 25 mm, and a fillet radius of 4 mm at the end. Numerical simulations revealed that the optimized vortex generator achieves a 2.72~13.8% increase in vortex shedding frequency and a 17.2~53.9% reduction in pressure drop and can adapt to the flow conditions of productivity fluctuations (6.498 × 10⁵ ≤ Re ≤ 22.597 × 10⁵) in the gas well production. The results demonstrated significant advantages, including low pressure loss, minimal secondary vortex generation, high vortex shedding frequency, and substantial lift force. These findings underscore the robustness and efficiency of the LBM-LES method in simulating complex flow dynamics and optimizing vortex generator designs. Full article

This article considers the numerical solution of the direct and inverse problems of the gas lift process in oil production, described by a system of hyperbolic equations. The inverse problem is reduced to an optimal control problem, where the control is the initial velocity of the gas. To minimize the quadratic objective functional, the gradient method is used, in which the gradient is determined using the conjugate equation method. The latter involves constructing a conjugate problem based on the Lagrange identity and the duality principle. Solving the conjugate problem allows us to obtain an analytical expression for the gradient of the functional and effectively implements the Landweber iterative method. A numerical experiment was carried out that confirmed the effectiveness of the proposed method in optimizing the parameters of the gas lift process. Full article

Waste heat recovery is a critical strategy for optimizing energy consumption and reducing greenhouse gas emissions. In this context, the circular economy highlights the importance of this practice as a key tool to enhance energy efficiency, minimize waste, and decrease environmental impact. Artificial neural networks are particularly well-suited for managing nonlinearities and complex interactions among multiple variables, making them ideal for controlling a double-stage absorption heat transformer. This study aims to simultaneously optimize both user-defined parameters. Levenberg–Marquardt and scaled conjugated gradient algorithms were compared from five to twenty-five neurons to determine the optimal operating conditions while the coefficient of performance and the gross temperature lift were simultaneously maximized. The methodology includes R2024a MATLAB© programming, real-time data acquisition, visual engineering environment software, and flow control hardware. The results show that applying the Levenberg–Marquardt algorithm resulted in an increase in the correlation coefficient (R) at 20 neurons, improving the thermodynamic performance and enabling greater energy recovery from waste heat. Full article

Geothermal energy is a renewable energy source that is rich in reserves, widely distributed, stable and reliable. The development of geothermal energy needs to be carried out by drilling wells to exploit the underground thermal fluid, and air-lift reverse circulation drilling technology has the advantages of protecting the thermal reserves and reducing costs in the development of geothermal energy. In this paper, based on the working principle of air-lift reverse circulation drilling, combined with the single-phase liquid, liquid–solid, gas–liquid–solid three-phase fluid mechanics theory, the pressure model of air-lift reverse circulation in geothermal deep wells is established. The influence of the depth of dual-wall drilling rods on the lifting force and total friction loss pressure of air-lifting reverse circulation is analyzed, and it is proved that there is an optimal value of the depth of dual-wall drilling rods, which provides a theoretical basis for selecting a suitable depth of dual-wall drilling rods in the construction of air-lifting reverse circulation in geothermal deep wells. Full article

As the development of oil and gas resources becomes increasingly complex, the traditional oil and gas well management model faces many challenges. Continuous gas lift technology has become an important means to improve oil and gas well recovery due to its high efficiency and adaptability. Because of the multi-well continuous gas lift process on the platform, there is mutual interference between wells, and the constraints of the total gas production of each well need to be greater than the critical liquid-carrying flow rate. (Under production conditions, when gas–liquid two-phase flow occurs, the minimum gas flow rate required when the liquid phase can be completely carried out of the wellhead by the gas phase). To achieve the optimization goal of maximizing gas production, an optimized gas distribution based on a particle swarm optimization algorithm is proposed. This method achieves the overall optimal allocation of resources through dynamic optimization. Through actual engineering case analysis, the feasibility of this method is verified, which is of great significance for improving the gas lift efficiency and economic benefits of the platform. Full article

The electrical submersible pump (ESP) well system is widely used in the oil industry due to its advantages of high displacement and lift capability. However, it is associated with significant energy consumption. In order to conserve electrical energy and enhance the efficiency of petroleum companies, a deep learning-based energy consumption calculation method is proposed and utilized to optimize the most energy-efficient operating regime. The energy consumption of the ESP well system is precisely determined through the application of the Pearson correlation coefficient analysis method, which is utilized to examine the relationship between production parameters and energy usage. This process aids in identifying the input parameters of the model. Following this, an energy consumption prediction model is developed using the dual-stage attention-based recurrent neural network (DA-RNN) algorithm. To evaluate the accuracy of the DA-RNN model, a comparison of its errors is carried out in comparison to three other deep learning algorithms: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transform. Lastly, an orthogonal experiment is executed using the chosen model to pinpoint the most energy-efficient operating regime. Analysis of 325 ESP wells in the Bohai PL oil field indicated that ten parameters, including choke diameter, casing pressure, pump inlet pressure, pump outlet pressure, motor temperature, frequency, oil production, gas production, water production, and GOR significantly impact the energy consumption of the ESP well system. Consequently, these parameters were selected as input variables for the deep learning model. Due to the attention mechanisms employed in the encoding and decoding stages, the DA-RNN algorithm achieved the best performance during model evaluation and was chosen for constructing the energy consumption prediction model. Furthermore, the DA-RNN algorithm demonstrates better model generalization capabilities compared to the other three algorithms. Based on the energy consumption prediction model, the operating regime of the ESP system was optimized to save up to 12% of the maximum energy. The energy consumption of the ESP well system is affected by numerous parameters, and it is difficult to comprehensively evaluate and predict quantitatively. Thus, this work proposes a data-driven model based on the DA-RNN algorithm, which has a dual-stage attention mechanism to rapidly and accurately predict the energy consumption of the ESP well system. Optimization of production parameters using this model can effectively reduce energy consumption. Full article