Search Results (80)

The current plunger gas lift production process still relies on the traditional ‘one-to-one’ control configuration, where one controller manages a single gas well. This approach does not fulfil platform requirements for centralized, efficient, and unified coordination and management of multiple wells. To increase production, improve efficiency, and mitigate safety risks, this article offers an intelligent optimization method for a collaborative plunger gas lift in multi-objective, multi-well platforms. The method integrates mechanistic modeling and data-driven approaches to develop a collaborative model for multiple wells on the platform, accounting for inter-well pressure interference and pipeline backpressure. A particle swarm optimization algorithm is implemented to solve the model, with a composite fitness function balancing maximum daily gas production and minimum production fluctuations. A case study on the XXX Platform shows that the method enhances total gas production, reduces production fluctuations, and lowers system backpressure compared to the current operating schedule. Implemented via a localized edge computing architecture, it supports real-time scheduling, providing technical references for shale gas development. Full article

Deep drilling and horizontal wells, as important means of unconventional oil and gas development, face problems with the high energy consumption but low removal efficiency of traditional well washing equipment, the uneven cleaning of horizontal well intervals, and an insufficient degree of automation. This paper proposes a novel hydraulic drive well washing device which consists of two main units. The wellbore cleaning unit comprises a hydraulic drive cutting–flushing module, a well cleaning mode-switching module, and a filter storage module. The unit uses hydraulic and mechanical forces to perform combined cleaning to prevent mud and sand from settling. By controlling the flow direction of the well washing fluid, it can directly switch between normal and reverse washing modes in the downhole area, and at the same time, it can control the working state of corresponding modules. The assembly control unit includes the chain lifting module and the arm assembly module, which can lift and move the device through the chain structure, allow for the rapid assembly of equipment through the use of a mechanical arm, and protect the reliability of equipment through the use of a centering structure. The device converts some of the hydraulic power into mechanical force, effectively improving cleaning and plugging removal efficiency, prolonging the downhole continuous working time of equipment, reducing manual operation requirements, and comprehensively improving cleaning efficiency and energy utilization efficiency. 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

Deep coalbed methane wells often encounter challenges such as inefficient sand transport and sand accumulation in the horizontal sections during drainage, which significantly impact the stability of gas production and the efficiency of the gas lift system. To investigate the sand-carrying mechanisms in the horizontal sections of deep coalbed methane wells, this study develops a theoretical model for critical sand-carrying velocity based on gravitational, buoyant, drag, and pressure gradient forces. Additionally, a visualized experimental system was constructed using a multiphase pipe flow platform. By varying parameters such as liquid flow rate, gas–liquid ratio, gravel particle size, and pipe inclination, the critical conditions for sand transport were examined, and the dominant factors influencing sand transport in horizontal wellbore sections were identified. The experimental results indicate that water flow rate and particle size are inversely correlated with the gas volume required for sand transport, whereas inclination angle is positively correlated. The proposed model was validated against experimental data, showing a prediction error within 15%, thereby confirming its accuracy and engineering applicability. These findings offer theoretical guidance and technical references for efficient drainage and stable gas production in horizontal wellbore sections of deep coalbed methane wells. 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

Shale gas development has made significant contributions to the increase in natural gas production capacity in recent years, particularly in promoting the transformation of the energy structure and enhancing energy autonomy. However, with the deepening of shale gas field exploitation, particularly in the later stages of development, low-pressure gas wells and liquid accumulation issues have become increasingly apparent, posing significant challenges to the normal production of gas wells. Traditional single gas lifting and pressurization techniques have disadvantages such as high equipment investment, high operating costs, and inflexibility in switching, which make it difficult to meet the long-term and stable production needs of shale gas fields. Therefore, to overcome these challenges, this study proposes an innovative integrated pressurization–gas lifting multifunctional compressor process, which achieves the “pressurization ↔ gas lifting ↔ pressurization–gas lifting synergy” multi-mode intelligent switching function through modular integration design, resulting in higher production flexibility and efficiency. Adaptability assessments were completed on two typical shale gas platforms, and field test results show that the equipment can achieve stable production increases across all three functional modes. The pressurization mode demonstrates good adaptability in gas processing, efficiently pressurizing and transporting natural gas produced from the platform’s wells, meeting the increasing demand for gas export. The gas lifting function of the equipment can effectively address gas wells affected by wellbore or bottom-hole liquid accumulation, improving production conditions. In the synergy mode, the equipment design enables the effective collaboration of pressurization and gas lifting functions. Driven by the same power source, the two functional modules work efficiently together, adapting to complex production conditions where both gas lifting and pressurization for gas export occur simultaneously. The innovative process paradigm developed by this study provides an engineering solution for the entire lifecycle of shale gas field development, characterized by equipment integration and intelligent operation, offering significant economic benefits and promotional value. 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

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

With the continuous development of oil and gas fields, the demand for corrosion-resistant tubing is increasing, which is important for the safe exploitation of oil and gas energy. Due to its excellent CO₂ corrosion resistance, 13Cr-110 martensitic stainless steel is widely used in sour gas-containing oil fields in western China. This paper describes a case of stress corrosion cracking (SCC) in a 13Cr-110 serviced in an ultra-deep gas well. The failure mode of the tubing is brittle along the lattice fracture, and the cracks are generated because of nitrogen gas-lift production-enhancement activities during the service of the tubing, leading to corrosion damage zones and cracks in the 13Cr-110 material under the synergistic effect of oxygen and chloric acid-containing environments. During subsequent production, the tubing is subjected to tensile stresses and cracks expanded at the 13Cr-110 lattice boundaries due to reduced structural strength in the corrosion region. To address the corrosion sensitivity of 13Cr-110 in an oxygen environment, it is recommended that the oxygen content in the wellbore be strictly controlled and that antioxidant corrosion inhibitors be added. 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

Polymetallic nodules and REE-rich mud under the seabed of 5500–5700 m water depth around Minamitorishima island are promising and attractive for exploration and development. Following our previous research, numerical analysis was used to investigate the unsteady flow characteristics and the lifting performance of a commercial production system using an air-lift pump for hybrid lifting, lifting both polymetallic nodules and REE-rich mud. Gas–liquid–solid three-phase flow and gas–liquid two-phase flow in the system were analyzed using the one-dimensional drift–flux model. First, the reliability of the schemes and program was verified by comparing the numerical results with the experimental ones. Next, numerical simulations were conducted, in which the model’s dimensions were related to a commercial production system operated in the deep sea around Minamitorishima island, and the conditions fit the expected production rate. The results revealed the unsteady flow characteristics under the operations, such as start-up, shut-down, feed of polymetallic nodules and REE-rich mud, and those associated with disturbances, such as feed rate fluctuations. We demonstrate that the program and the schemes can simulate the unsteady flow characteristics and the lifting performance of a commercial production system with an air-lift pump well, and they can derive useful information and know-how in advance for the safe and continuous operation of the system. 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