Two-temperature modeling of lamellar cathode arc

A three-dimensional, two-temperature(2T) model of a lamellar cathode arc is constructed,drawing upon the conservation equations for mass, momentum, electron energy, and heavy particle energy, in addition to Maxwell's equations. The model aims to elucidate how the physical properties of electrons and heavy particles affect heat transfer and fluid flow in a lamellar cathode arc. This is achieved by solving and comparing the fields of electron temperature,heavy particle temperature, fluid flow, current density, and Lorentz force distribution under varying welding currents. The results show that the guiding effect of the lamellar cathode on current density, the inertial drag effect of moving arc, and the attraction effect of Lorentz force at the lamellar cathode tip primarily govern the distribution of the arc's physical fields. The guiding effect localizes the current density to the front end of the lamellar cathode, particularly where the discharge gap is minimal. Both the inertial drag effect and the attraction effect of Lorentz force direct arc flow toward its periphery. Under the influence of the aforementioned factors, the physical fields of the lamellar cathode arc undergo expansion and shift counter to the arc's direction of motion. A reduction in welding current substantially weakens the guiding effect,causing the arc's physical fields to deviate further in the direction opposite to the arc motion. In comparison with a cylindrical cathode arc, the physical fields of the lamellar cathode arc are markedly expanded, leading to a reduction in current density, electron temperature, heavy particle temperature, cathode jet flow velocity, and Lorentz force.

Investigations of methane adsorption characteristics on marine-continental transitional shales and gas storage capacity models considering pore evolution

Methane adsorption is a critical assessment of the gas storage capacity(GSC)of shales with geological conditions.Although the related research of marine shales has been well-illustrated,the methane adsorption of marine-continental transitional(MCT)shales is still ambiguous.In this study,a method of combining experimental data with analytical models was used to investigate the methane adsorption characteristics and GSC of MCT shales collected from the Qinshui Basin,China.The Ono-Kondo model was used to fit the adsorption data to obtain the adsorption parameters.Subsequently,the geological model of GSC based on pore evolution was constructed using a representative shale sample with a total organic carbon(TOC)content of 1.71%,and the effects of reservoir pressure coefficient and water saturation on GSC were explored.In experimental results,compared to the composition of the MCT shale,the pore structure dominates the methane adsorption,and meanwhile,the maturity mainly governs the pore structure.Besides,maturity in the middle-eastern region of the Qinshui Basin shows a strong positive correlation with burial depth.The two parameters,micropore pore volume and non-micropore surface area,induce a good fit for the adsorption capacity data of the shale.In simulation results,the depth,pressure coefficient,and water saturation of the shale all affect the GSC.It demonstrates a promising shale gas potential of the MCT shale in a deeper block,especially with low water saturation.Specifically,the economic feasibility of shale gas could be a major consideration for the shale with a depth of<800 m and/or water saturation>60%in the Yushe-Wuxiang area.This study provides a valuable reference for the reservoir evaluation and favorable block search of MCT shale gas.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.

Constitutive model of viscoelastic dynamic damage for the material of gas obturator in modular-charge howitzer

In order to investigate the mechanical response behavior of the gas obturator of the breech mechanism,made of polychloroprene rubber(PCR), uniaxial compression experiments were carried out by using a universal testing machine and a split Hopkinson pressure bar(SHPB), obtaining stress-strain responses at different temperatures and strain rates. The results revealed that, in comparison to other polymers, the gas obturator material exhibited inconspicuous strain softening and hardening effects;meanwhile, the mechanical response was more affected by the strain rate than by temperature. Subsequently, a succinct viscoelastic damage constitutive model was developed based on the ZWT model, including ten undetermined parameters, formulated with incorporating three parallel components to capture the viscoelastic response at high strain rate and further enhanced by integrating a three-parameter Weibull function to describe the damage. Compared to the ZWT model, the modified model could effectively describe the mechanical response behavior of the gas obturator material at high strain rates. This research laid a theoretical foundation for further investigation into the influence of chamber sealing issues on artillery firing.

A novel framework for predicting non-stationary production time series of shale gas based on BiLSTM-RF-MPA deep fusion model

Shale gas, as an environmentally friendly fossil energy resource, has gained significant commercial development and shows immense potential. However, accurately predicting shale gas production faces substantial challenges due to the complex law of decline, nonlinear and non-stationary features in production data, which greatly repair the robustness of current models in predicting shale gas production time series. To address these challenges and improve accuracy in production forecasting, this paper introduces a novel and innovative approach: a hybrid proxy model that combines the bidirectional long short-term memory(BiLSTM) neural network and random forest(RF) through deep learning. The BiLSTM neural network is adept at capturing long-term dependencies, making it suitable for understanding the intricate relationships between input and output variables in shale gas production.On the other hand, RF serves a dual purpose: reducing model variance and addressing the concept drift problem that arises in non-stationary time series predictions made by BiLSTM. By integrating these two models, the hybrid approach effectively captures the inherent dependencies present in long and nonstationary production time series, thereby reducing model uncertainty. Furthermore, the combination of BiLSTM and RF is optimized using the recently-proposed marine predators algorithm(MPA) to fine-tune hyperparameters and enhance the overall performance of the proxy model. The results demonstrate that the proposed BiLSTM-RF-MPA model achieves higher prediction accuracy and demonstrates stronger generalization capabilities by effectively handling the complex nonlinear and non-stationary characteristics of shale gas production time series. Compared to other models such as LSTM, BiLSTM, and RF, the proposed model exhibits superior fitting and prediction performance, with an average improvement in performance indicators exceeding 20%. This innovative framework provides valuable insights for forecasting the complex production performance of unconventional oil and gas reservoirs, which sheds light on the development of data-driven proxy models in the field of subsurface energy utilization.

Machine learning prediction of methane,ethane,and propane solubility in pure water and electrolyte solutions:Implications for stray gas migration modeling

Hydraulic fracturing is an effective technology for hydrocarbon extraction from unconventional shale and tight gas reservoirs.A potential risk of hydraulic fracturing is the upward migration of stray gas from the deep subsurface to shallow aquifers.The stray gas can dissolve in groundwater leading to chemical and biological reactions,which could negatively affect groundwater quality and contribute to atmospheric emissions.The knowledge oflight hydrocarbon solubility in the aqueous environment is essential for the numerical modelling offlow and transport in the subsurface.Herein,we compiled a database containing 2129experimental data of methane,ethane,and propane solubility in pure water and various electrolyte solutions over wide ranges of operating temperature and pressure.Two machine learning algorithms,namely regression tree(RT)and boosted regression tree(BRT)tuned with a Bayesian optimization algorithm(BO)were employed to determine the solubility of gases.The predictions were compared with the experimental data as well as four well-established thermodynamic models.Our analysis shows that the BRT-BO is sufficiently accurate,and the predicted values agree well with those obtained from the thermodynamic models.The coefficient of determination(R2)between experimental and predicted values is 0.99 and the mean squared error(MSE)is 9.97×10^(-8).The leverage statistical approach further confirmed the validity of the model developed.

Numerical investigation on the flow characteristics of a reverse-polarity plasma torch by two-temperature thermal non-equilibrium modelling

A two-temperature(2 T)thermal non-equilibrium model is developed to address the thermal nonequilibrium phenomenon that inevitably exists in the reverse-polarity plasma torch(RPT)and applied to numerically investigate the plasma flow characteristics inside and outside the RPT.Then,a detailed comparison of the results of the 2 T model with those of the local thermal equilibrium(LTE)model is presented.Furthermore,the temperature of the plasma jet generated by a RPT and the RPT’s voltage are experimentally measured to compare and validate the result obtained by different models.The differences of the measured excitation temperature and the arc voltage between the 2 T model and experimental measurement are less than 13%and 8%,respectively,in all operating cases,validating the effectiveness of the 2 T model.The LTE model overestimates the velocity and temperature distribution of the RPT and its plasma jet,showing that thermal non-equilibrium phenomena cannot be neglected in the numerical modelling of the RPT.Unlike other common hot cathode plasma torches,the thermal non-equilibrium phenomenon is found even in the arc core of the RPT,due to the strong cooling effect caused by the big gas flow rate.

The gas discharge visualization(GDV)order parameter model based on the principle of mastering both permanence and change

Objective To investigate the human body’s complex system,and classify and characterize the human body’s health states with“a comprehensive integrated method from qualitative to quantitative”.Methods This paper introduces the concept of“order parameters”and proposes a method for establishing an order parameter model of gas discharge visualization(GDV)based on the principle of“mastering both permanence and change(MBPC)”.The method involved the fol-lowing three steps.First,average luminous intensity(I)and average area(S)of the GDV im-ages were calculated to construct the phase space,and the score of the health questionnaire was calculated as the health deviation index(H).Second,the k-means++clustering method was employed to identify subclasses with the same health characteristics based on the data samples,and to statistically determine the symptom-specific frequencies of the subclasses.Third,the distance(d)between each sample and the“ideal health state”,which determined in the phase space of each subclass,was calculated as an order parameter describing the health imbalance,and a linear mapping was established between the d and the H.Further,the health implications of GDV signals were explored by analyzing subclass symptom profiles.We also compare the mean square error(MSE)with classification methods based on age,gen-der,and body mass index(BMI)indices to verify that the phase space possesses the ability to portray the health status of the human body.Results This study preliminarily tested the reliability of the order parameter model on data samples provided by 20 participants.Based on the discovered linear law,the current model can use d calculated by measuring the GDV signal to predict H(R^(2)>0.77).Combined with the symptom profiles of the subclasses,we explain the classification basis of the phase space based on the pattern identification.Compared with common classification methods based on age,gender,BMI,etc.,the MSE of phase space-based classification was reduced by an order of magnitude.Conclusion In this study,the GDV order parameter model based on MBPC can identify sub-classes and characterize individual health levels,and explore the TCM health meanings of the GDV signals by using subjective-objective methods,which holds significance for establishing mathematical models from TCM diagnosis principles to interpret human body signals.

Model Prediction and Optimal Control of Gas Oxygen Content for A Municipal Solid Waste Incineration Process

In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.

Multicomponent seismic forward modeling of gas hydrates beneath the seafloor

We investigated the effect of microscopic distribution modes of hydrates in porous sediments, and the saturation of hydrates and free gas on the elastic properties of saturated sediments. We simulated the propagation of seismic waves in gas hydrate-bearing sediments beneath the seafloor, and obtained the common receiver gathers of compressional waves（P-waves） and shear waves（S-waves）. The numerical results suggest that the interface between sediments containing gas hydrates and free gas produces a large-amplitude bottomsimulating reflector. The analysis of multicomponent common receiver data suggests that ocean-bottom seismometers receive the converted waves of upgoing P- and S-waves, which increases the complexity of the wavefield record.

1-D Controlled source electromagnetic forward modeling for marine gas hydrates studies

We discuss the feasibility of using controlled-source electromagnetic （CSEM） in the frequency domain for prospecting marine gas hydrates. Based on the Ocean Drilling Program （ODP） Leg 164 log data, we have established several 1-D resistivity models which have different gas hydrate concentrations. Meanwhile, we analyzed the electromagnetic response of marine gas hydrates in the frequency domain based on these models. We also studied the relationship between electrical field magnitude or phase and parameters such as receiver-transmitter distance and frequency. Our numerical modeling results provide us with a quantitative reference for exploration and resource evaluation of marine gas hydrates.

Model Selection of Gas Turbine for Large Scale Gas-Fired Combined Cycle Power Plant

This paper briefs the configuration and performance of large size gas turbines and their composed combined cycle power plants designed and produced by four large renown gas turbine manufacturing firms in the world, providing reference for the relevant sectors and enterprises in importing advanced gas turbines and technologies.

A mathematical model of gas flow during coal outburst initiation

A proposed concept of outburst initiation examines the release of a large amount of gas from coal seams resulted from disintegrating thermodynamically unstable coal organic matter(COM).A coal microstructure is assumed to getting unstable due to shear component appearance triggered by mining operations and tectonic activities considered as the primary factor while COM disintegration under the impact of weak electric fields can be defined as a secondary one.The energy of elastic deformations stored in the coal microstructure activates chemical reactions to tilt the energy balance in a“coal–gas”system.Based on this concept a mathematical model of a gas flow in the coal where porosity and permeability are changed due to chemical reactions has been developed.Using this model we calculated gas pressure changes in the pores initiated by gas release near the working face till satisfying force and energy criteria of outburst.The simulation results demonstrated forming overpressure zone in the area of intensive gas release with enhanced porosity and permeability.The calculated outburst parameters are well combined with those evaluated by field measurements.

Water modeling of molten steel flow in a multi-strand tundish with gas blowing

Fluid flow characteristics in a four-strand tundish with gas blowing were studied by water modeling experiments.It is found that gas blowing can greatly improve the flow characteristics in the tundish with a turbulence inhibitor.It dramatically increases the peak concentration time,and greatly decreases the dead volume,and reduces the minimum residence time.The gas blowing location,gas flow rate,and porous plug area greatly influence the flow characteristics in the tundish; the gas blowing location near the baffle,smaller gas flow rate,and smaller porous plug area are better for improving the fluid flow characteristics.Using gas blowing can reduce the difference of flows at the middle outlets and side outlets for the multi-strand tundish.Bubbles produced by gas blowing can absorb small inclusions and provide the condition for inclusion collision and aggregation.Therefore,introducing gas blowing into a tundish and combining the turbulence inhibitor can improve inclusion floating and removal,and the cleanness of molten steel can be advanced.

Forecasting China’s natural gas consumption based on a combination model

Ensuring a sufficient energy supply is essential to a country. Natural gas constitutes a vital part in energy supply and therefore forecasting natural gas consumption reliably and accurately is an essential part of a country＇s energy policy. Over the years, studies have shown that a combinative model gives better projected results compared to a single model. In this study, we used Polynomial Curve and Moving Average Combination Projection （PCMACP） model to estimate the future natural gas consumption in China from 2009 to 2015. The new proposed PCMACP model shows more reliable and accurate results： its Mean Absolute Percentage Error （MAPE） is less than those of any previous models within the investigated range. According to the PCMACP model, the average annual growth rate will increase for the next 7 years and the amount of natural gas consumption will reach 171600 million cubic meters in 2015 in China.

Heat-fluid-solid coupling model for gas-bearing coal seam and numerical modeling on gas drainage promotion by heat injection

Improving the absorbed gas to active desorption and seepage and delaying gas drainage attenuation are considered as key methods for increasing drainage efficiency and gas output.According to the solid mechanics theory,the nonlinear Darcy seepage theory and thermodynamics,the heat-fluid-solid coupling model for gassy coal has been improved.The numerical model was founded from the improved multi-field coupling model by COMSOL Multiphysics and gas drainage by borehole down the coal seam enhanced by heat injection was modelled.The results show that the heatfluid-solid model with adsorption effects for gassy coal was well simulated by the improved multi-field model.The mechanism of coal seam gas desorption seepage under the combined action of temperature,stress and adsorption can be well described.Gas desorption and seepage can be enhanced by heat injection into coal seams.The gas drainage rate was directly proportional to the temperature of injected heat in the scope of 30-150 ℃ and increasing in the whole modelleddrainage process (0-1000 d).The increased level was maximum in the initial drainage time and decreasing gradually along with drainage time.The increasing ratio of drainage rate was maximum when the temperature raised from 30 to 60 ℃.Although the drainage rate would increase along with increasing temperature,when exceeding 60 ℃,the increasing ratio of drainage rate with rising temperature would decrease.Gas drainage promotion was more effective in coal seams with lower permeability than with higher permeability.The coal seam temperature in a 5 m distance surrounding the heat injection borehole would rise to around 60 ℃ in 3 months.That was much less than the time of gas drainage in the coal mines in sites with low permeability coal seams.Therefore,it is valuable and feasible to inject heat into coal seams to promote gas drainage,and this has strong feasibility for coal seams with low permeability which are widespread in China.

Three Stage Equilibrium Model for Coal Gasification in Entrained Flow Gasifiers Based on Aspen Plus

A three stage equilibrium model is developed for coal gasification in the Texaco type coal gasifiersbased on Aspen Plus to calculate the composition of product gas, carbon conversion, and gasification teml^erature. The model is divided into three stages including pyrolysis and combustion stage, char gas reaction stage, and gas p.hase reaction stage. Part of the water produced in thepyrolysis and combust!on stag.e is assumed to be involved inthe second stage to react with the unburned carbon. Carbon conversion is then estimated in the second stage by steam participation ratio expressed as a function of temperature. And the gas product compositions are calculated from gas phase reactions in the third stage. The simulation results are consistent with published experimental data.

Natural gas hydrate shell model in gas-slurry pipeline flow

A hydrate shell model coupled with one-dimensional two-fluid pipe flow model was established to study the flow characteristics of gas-hydrate slurry flow system.The hydrate shell model was developed with kinetic limitations and mass transfer limitations,and it was solved by Euler method.The analysis of influence factors was performed.It was found that the diffusion coefficient was a key parameter in hydrate forming process.Considering the hydrate kinetics model and the contacting area between gas and water,the hydrate shell model was more close to its practical situations.

Three-Dimensional Modelling of a Multi-Layer Sandstone Reservoir： the Sebei Gas Field, China

Multi-layer sandstone reservoirs occur globally and are currently in international production. The 3D characteristics of these reservoirs are too complicated to be accurately delineated by general structural-facies-reservoir modelling. In view of the special geological features, such as the vertical architecture of sandstone and mudstone interbeds, the lateral stable sedimentation and the strong heterogeneity of reservoir poroperm and fluid distribution, we developed a new three-stage and six-phase procedure for 3D characterization of multi-layer sandstone reservoirs. The procedure comprises two-phase structural modelling, two-phase facies modelling and modelling of two types of reservoir properties. Using this procedure, we established models of the formation structure, sand body structure and microfacies, reservoir facies and properties including porosity, permeability and gas saturation and provided a 3D fine-scale, systematic characterization of the Sebei multi-layer sandstone gas field, China. This new procedure, validated by the Sebei gas field, can be applied to characterize similar multi-layer sandstone reservoirs.

Transport model for shale gas well leakage through the surrounding fractured zones of a longwall mine

The environmental risks associated with casing deformation in unconventional(shale)gas wells positioned in abutment pillars of longwall mines is a concern to many in the mining and gas well industry.With the recent interest in shale exploration and the proximity to longwall mining in Southwestern Pennsylvania,the risk to mine workers could be catastrophic as fractures in surrounding strata create pathways for transport of leaked gases.Hence,this research by the National Institute for Occupational Safety and Health(NIOSH)presents an analytical model of the gas transport through fractures in a low permeable stratum.The derived equations are used to conduct parametric studies of specific transport conditions to understand the influence of stratum geology,fracture lengths,and the leaked gas properties on subsurface transport.The results indicated that the prediction that the subsurface gas flux decreases with an increase in fracture length is specifically for a non-gassy stratum.The sub-transport trend could be significantly impacted by the stratum gas generation rate within specific fracture lengths,which emphasized the importance of the stratum geology.These findings provide new insights for improved understanding of subsurface gas transport to ensure mine safety.