Development and technology status of energy storage in depleted gas reservoirs

Utilizing energy storage in depleted oil and gas reservoirs can improve productivity while reducing power costs and is one of the best ways to achieve synergistic development of"Carbon Peak–Carbon Neutral"and"Underground Resource Utiliza-tion".Starting from the development of Compressed Air Energy Storage(CAES)technology,the site selection of CAES in depleted gas and oil reservoirs,the evolution mechanism of reservoir dynamic sealing,and the high-flow CAES and injection technology are summarized.It focuses on analyzing the characteristics,key equipment,reservoir construction,application scenarios and cost analysis of CAES projects,and sorting out the technical key points and existing difficulties.The devel-opment trend of CAES technology is proposed,and the future development path is scrutinized to provide reference for the research of CAES projects in depleted oil and gas reservoirs.

Advanced oxidation via the synergy of C-defective/C-O band modified ultrathin porous g-C_(3)N_(4)and PMS for efficient photothermal degradation of bisphenol pollutants and lignin derivatives

This work uses thermal polymerization of urea nitrate,oxyacetic acid and urea as the raw material to prepare ultra-thin porous carbon nitride with carbon defects and C-O band(OA-UN-CN).Density functional theory(DFT)calculations showed OA-UN-CN had narrower band gap,faster electron transport and a new internal construction electric field.Additionally,the prepared OA-UN-CN significantly enhanced photocatalytic activation of peroxymonosulfate(PMS)due to enhanced light absorption performance and faster electron overflow.As the result,the OA-UN-CN/PMS could entirely degrade bisphenol A(BPA)within 30 min,where the photodegradation rate was 81.8 and 7.9 times higher than that of g-C_(3)N_(4)and OA-UN-CN,respectively.Beyond,the OA-UN-CN/PMS could likewise degrade other bisphenol pollutants and sodium lignosulfonate efficiently.We suggested possible photocatalytic degradation pathways accordingly and explored the toxicity of its degradation products.This work provides a new idea on the development of advanced photocatalytic oxidation processes for the treatment of bisphenol pollutants and lignin derivatives,via a metal-free photothermal-catalyst.

Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon market.Its economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible option.Despite the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection criteria.These distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of SCCS.Therefore,targeted and forward-looking scientific research on SCCS is imperative.This paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak shaving.It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific challenges.In this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon injection.By comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is demonstrated.Building on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on stability.Field countermeasures to ensure stability are proposed.Site selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume of abandoned salt caverns.This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters,such as the operating pressure,burial depth,and storage scale,and it offers essential guidance for implementing SCCS projects in China.

Harvesting Energy Via Water Movement and Surface Ionics in Microfibrous Ceramic Wools

Due to the push for carbon neutrality in various human activities,the development of methods for producing electricity without relying on chemical reaction processes or heat sources has become highly significant.Also,the challenge lies in achieving microwatt-scale outputs due to the inherent conductivity of the materials and diverting electric currents.To address this challenge,our research has concentrated on utilizing nonconductive mediums for water-based low-cost microfibrous ceramic wools in conjunction with a NaCl aqueous solution for power generation.The main source of electricity originates from the directed movement of water molecules and surface ions through densely packed microfibrous ceramic wools due to the effect of dynamic electric double layer.This occurrence bears resemblance to the natural water transpiration in plants,thereby presenting a fresh and straightforward approach for producing electricity in an ecofriendly manner.The generator module demonstrated in this study,measuring 12×6 cm^(2),exhibited a noteworthy open-circuit voltage of 0.35 V,coupled with a short-circuit current of 0.51 mA.Such low-cost ceramic wools are suitable for ubiquitous,permanent energy sources and hold potential for use as self-powered sensors and systems,eliminating the requirement for external energy sources such as sunlight or heat.

Stability of high-salinity-enhanced foam:Surface behavior and thin-film drainage

Cocamidopropyl hydroxyl sulfobetaine(CHSB)is one of the most promising foaming agents for high-salinity reservoirs because the salt in place facilitates its foam stability,even with salinity as high as 2×10^(5)mg/L.However,the synergistic effects between CHSB and salt have not been fully understood.This study utilized bulk foam tests and thin-film interferometry to comprehensively investigate the macroscopic and microscopic decay processes of CHSB foams with NaCl concentrations ranging from 2.3×10^(4)to 2.1×10^(5)mg/L.We focused on the dilatational viscoelasticity and dynamic thin-film thickness to elucidate the high-salinity-enhanced foam stability.The increase in dilatational viscoelasticity and supramolecular oscillating structural force(Π_(OS))with salinity dominated the superior stability of CHSB foam.With increasing salinity,more CHSB molecules accumulated on the surface with a lower diffusion rate,leading to high dilatational moduli and surface elasticity,thus decelerating coarsening and coalescence.Meanwhile,the number density of micelles in the thin film increased with salinity,resulting in increasedΠOS.Consequently,the energy barrier for stepwise thinning intensified,and the thin-film drainage slowed.This work conduces to understand the mechanisms behind the pronounced stability of betaine foam and can promote the widespread application of foam in harsh reservoirs.

The coupled deep neural networks for coupling of the Stokes and Darcy–Forchheimer problems

We present an efficient deep learning method called coupled deep neural networks(CDNNs) for coupling of the Stokes and Darcy–Forchheimer problems. Our method compiles the interface conditions of the coupled problems into the networks properly and can be served as an efficient alternative to the complex coupled problems. To impose energy conservation constraints, the CDNNs utilize simple fully connected layers and a custom loss function to perform the model training process as well as the physical property of the exact solution. The approach can be beneficial for the following reasons: Firstly, we sample randomly and only input spatial coordinates without being restricted by the nature of samples.Secondly, our method is meshfree, which makes it more efficient than the traditional methods. Finally, the method is parallel and can solve multiple variables independently at the same time. We present the theoretical results to guarantee the convergence of the loss function and the convergence of the neural networks to the exact solution. Some numerical experiments are performed and discussed to demonstrate performance of the proposed method.

A machine learning-based study of multifactor susceptibility and risk control of induced seismicity in unconventional reservoirs

A comprehensive dataset from 594 fracturing wells throughout the Duvernay Formation near Fox Creek, Alberta, is collected to quantify the influences of geological, geomechanical, and operational features on the distribution and magnitude of hydraulic fracturing-induced seismicity. An integrated machine learning-based investigation is conducted to systematically evaluate multiple factors that contribute to induced seismicity. Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors. Our seismicity prediction map matches the observed spatial seismicity, and the prediction model successfully guides the fracturing job size of a new well to reduce seismicity risks. This study can apply to mitigating potential seismicity risks in other seismicity-frequent regions.

Investigating the Performance of the Coagulation Process When Using a Combination of Zinc Oxide Nanoparticles and Ferric Polysulfate

In this article, a new type of coagulant material has been investigated and the performance of the coagulation process using this type of coagulant was evaluated. This new type is a combination of zinc oxide nanoparticles and polyferric sulfate (ZnOPFS). The structure of zinc oxide nanoparticles was determined by spectroscopic, X-ray and electron microscopy methods, and based on this, it was determined that ZnOPFS is a complex and mixed compound that is mainly composed of zinc oxide nanoparticles and ferric sulfate. The effects of Zn/Fe (Zn/Fe) molar ratio and aging (time) on acidity and zeta potential were also evaluated using a specific method. The obtained results showed that in the simultaneous deposition process, zinc ions can prevent the formation of polyferric acid coagulation and subsequently improve the stability of ZnOPFS.

Oxidative Desulfurization of Non-hydrotreated Kerosene Using Hydrogen Peroxide and Acetic Acid

The oxidative desulfurization of a real refinery feedstock (i.e.,non-hydrotreated kerosene with total sulfur mass content of 0.16%) with a mixture of hydrogen peroxide and acetic acid was studied.The influences of various operating parameters including reaction temperature (T),acid to sulfur molar ratio (nacid/nS),and oxidant to sulfur molar ratio (nO/nS) on the sulfur removal of kerosene were investigated.The results revealed that an increase in the reaction temperature (T) and nacid/nS enhances the sulfur removal.Moreover,there is an optimum nO/nS related to the reaction temperature and the best sulfur removal could be obtained at nO/nS=8 and 23 for the reaction temperatures of 25 and 60°C,respectively.The maximum observed sulfur removal in the present oxidative desulfurization system was 83.3%.

Progress in the Study on the Phase Equilibria of the CO2·H2O and CO2-HeO-NaCI Systems

To study the feasibility of CO2 geological sequestration,it is needed to understand the complicated mul- tiple-phase equilibrium and the densities of aqueous solution with CO2 and multi-ions under wide geological condi- tions(273.15—473.15K,0—60MPa),which are also essential for designing separation equipments in chemical or oil-related industries.For this purpose,studies on the relevant phase equilibria and densities are reviewed and ana- lyzed and the method to improve or modify the existing model is suggested in order to obtain more reliable pre- dictions in a wide temperature and pressure range.Besides,three different models(the electrolyte non random two-liquid(ELECNRTL),the electrolyte NRTL combining with Helgeson model(ENRTL-HG),Pitzer activity co- efficient model combining with Helgeson model(PITZ-HG))are used to calculate the vapor-liquid phase equilib- rium of CO2-H2O and CO2-H2O-NaCl systems.For CO2-H2O system,the calculation results agree with the experi- mental data very well at low and medium pressure(0—20MPa),but there are great discrepancies above 20MPa.For the water content at 473.15K,the calculated results agree with the experimental data quite well.For the CO2-H2O-NaCl system,the PITZ-HG model show better results than ELECNRTL and ENRTL-HG models at the NaCl concentration of 0.52mol·L -1 .Bur for the NaCl concentration of 3.997mol·L -1 ,using the ELECNRTL and ENRTL-HG models gives better results than using the PITZ-HG model.It is shown that available experimental data and the thermodynamic calculations can satisfy the needs of the calculation of the sequestration capacity in the temperature and pressure range for disposal of CO2 in deep saline aquifers.More experimental data and more accu- rate thermodynamic calculations are needed in high temperature and pressure ranges(above 398.15K and 31.5MPa).

Experimental study of enhanced oil recovery by CO_(2) huff-n-puff in shales and tight sandstones with fractures

The fractures and kerogen,which generally exist in the shale,are signifcant to the CO_(2) huf-n-puf in the shale reservoir.It is important to study the efects of fractures and kerogen on oil recovery during CO_(2) huf-n-puf operations in the fracture-matrix system.In this study,a modifed CO_(2) huf-n-puf experiment method is developed to estimate the recovery factors and the CO_(2) injectivity in the fractured organic-rich shales and tight sandstones.The efects of rock properties,injection pressure,and injection time on the recovery factors and CO_(2) usage efciency in shales and sandstones are discussed,respectively.The results show that although the CO_(2) injectivity in the shale is higher than that in the sandstone with the same porosity;besides,the recovery factors of two shale samples are much lower than that of two sandstone samples.This demonstrates that compared with the tight sandstone,more cycles are needed for the shale to reach a higher recovery factor.Furthermore,there are optimal injection pressures(close to the minimum miscible pressure)and CO_(2) injection volumes for CO_(2) huf-npuf in the shale.Since the optimal CO_(2) injection volume in the shale is higher than that in the sandstone,more injection time is needed to enhance the oil recovery in the shale.There is a reference sense for CO_(2) huf-n-puf in the fractured shale oil reservoir for enhanced oil recovery(EOR)purposes.

Effect of surfactants and their blend with silica nanoparticles on wax deposition in a Malaysian crude oil

The present study investigated the wax deposition tendencies of a light Malaysian crude oil（42.4° API）, and the wax inhibiting potential of some surfactants and their blends with nanoparticles. With the knowledge that the majority of the wax inhibition research revolved around polymeric wax inhibitors, which cause environmental issues, we highlighted the potential of surfactants and their blend with SiO2 nanoparticles as wax deposition inhibitors. Different surfactants including oil-based, silane-based, Gemini and bio-surfactants were considered as primary surfactants. The primary surfactants and their respective blends at a concentration of 400 ppm were screened as wax inhibitor candidates using cold finger apparatus. The screening results showed a significant influence on the paraffin inhibition efficiency on wax deposition by using 400 ppm of silane-based surfactant, which decreased the wax deposition up to 53.9% as compared to that of the untreated crude oil. The inhibition efficiency among the silane-based surfactant（highest） and bio-surfactant（lowest）revealed an appreciable difference up to 36.5%. Furthermore, the wax from the treated sample was found to deposit in a thin gel-like form, which adhered inadequately to the surface of the cold finger. A further investigation by blending the 400 ppm silane-based surfactant with a 400 ppm SiO2 nanoparticle suspension in a load ratio of 3：1 found that the wax inhibition decreased up to 81% as compared to the scenario when they were not added. However, we have shown that the synergy between the silane-based surfactant and the nanoparticles is influenced by the concentration and load ratio of surfactant and nanoparticles, residence time, differential temperature and rotation rate.

Theoretical and Experimental Study on the Adsorption and Desorption of Methane by Granular Activated Carbon at 25℃

A theoretical and experimental study was conducted to accurately determine the amount of adsorption and desorption of methane by various Granular Activated Carbon （GAC） under different physical conditions. To carry out the experiments, the volumetric method was used up to 500 psia at constant temperature of 25℃. In these experiments, adsorption as well as desorption capacities of four different GAC in the adsorption of methane, the major constituent of natural gas, at various equilibrium pressures and a constant temperature were studied. Also, various adsorption isotherm models were used to model the experimental data collected from the experiments. The accuracy of the results obtained from the adsorption isotherm models was compared and the values for the regressed parameters were reported. The results shows that the physical characteristics of activated carbons such as BET surface area, micropore volume, packing density, and pore size distribution play an important role in the amount of methane to be adsorbed and desorbed.

Experimental and Theoretical Study of the Effect of Moisture on Methane Adsorption and Desorption by Activated Carbon at 273.5 K

Adsorption and desorption of methane by activated carbon （AC） at constant temperature and at various pressures were investigated. The effect of moisture was also studied. A volumetric method was used, up to 40 bar, at a temperature of 273.5 K. Results of a dry AC sample were compared with those obtained from a moist sample and two different ACs with different physical and surface properties were used. As expected, the results showed that the existence of moisture, trapped in the AC pores, could lead to a decrease in the amount of methane adsorbed and a decrease in the amount of methane delivered during desorption. To model the experimental results, a large variety of adsorption isotherms were used. The regressed parameters for the adsorption isotherms were obtained using the experimental data generated in the present study. The accuracy of the results obtained from the different adsorption isotherms was favorably compared.

A three dimensional CFD simulation and optimization of direct DME synthesis in a fixed bed reactor

In this study, a comprehensive three-dimensional dynamic model was developed for simulating the flow behavior and catalytic coupling reactions for direct synthesis of dimethyl ether （DME） from syngas including CO2 in a fixed bed reactor at commercial scale under both adiabatic and isothermal conditions. For this purpose, a computational fluid dynamic （CFD） simulation was carried out through which the standard κ-ε model with 10% turbulence tolerations was implemented. At first, an adiabatic fixed bed reactor was simulated and the obtained results were compared with those of an equivalent commercial slurry reactor. Then the concentration and temperature profiles along the reactor were predicted. Consequently, the optimum temperature, pressure, hydrogen to carbon monoxide ratio in the feedstock and the reactor height under different operation conditions were determined. Finally, the results obtained from this three-dimensional dynamic model under appropriate industrial boundary conditions were compared with those of others available in literature to verify the model. Next, through changing the boundary conditions, the simulation was performed for an isothermal fixed bed reactor. Furthermore, it was revealed that, under isothermal conditions, the performed equilibrium simulations were done for a single phase system. Considering the simultaneous effects of temperature and pressure, the optimum operation conditions for the isothermal and adiabatic fixed bed reactors were investigated. The results of the H2＋CO conversions indicated that, under isothermal condition, higher conversion could be achieved, in compared with that under adiabatic conditions. Then, the effects of various operating parameters, including the pressure and temperature, of the reactor on the DME production were examined. Ultimately, the CFD modeling results generated in the present work showed reasonable agreement with previously obtained data available in the literature.

Kinetics and mechanisms of the catalytic thermal cracking of asphaltenes adsorbed on supported nanoparticles

The production of heavy and extra-heavy oil is challenging because of the rheological properties that crude oil presents due to its high asphaltene content.The upgrading and recovery processes of these unconventional oils are typically water and energy intensive,which makes such processes costly and environmentally unfriendly.Nanoparticle catalysts could be used to enhance the upgrading and recovery of heavy oil under both in situ and ex situ conditions.In this study,the effect of the Ni-Pd nanocatalysts supported on fumed silica nanoparticles on post-adsorption catalytic thermal cracking of n-C_7 asphaltenes was investigated using a thermogravimetric analyzer coupled with FTIR.The performance of catalytic thermal cracking of n-C_7asphaltenes in the presence of NiO and PdO supported on fumed silica nanoparticles was better than on the fumed silica support alone.For a fixed amount of adsorbed n-C_7asphaltenes（0.2 mg/m～2）,bimetallic nanoparticles showed better catalytic behavior than monometallic nanoparticles,confirming their synergistic effects.The corrected OzawaFlynn-Wall equation（OFW） was used to estimate the effective activation energies of the catalytic process.The mechanism function,kinetic parameters,and transition state thermodynamic functions for the thermal cracking process of n-C_7 asphaltenes in the presence and absence of nanoparticles are investigated.

Editorial for the Special Issue on Unconventional and Intelligent Oil and Gas Engineering

A key global challenge in the 21st century is how to secure sustainable access to energy for a growing global population—set to reach 10 billion by 2035—while coping with the threat of dangerous climate change.The oil and gas industry will still play an essential role in the energy transition by providing affordable and reliable energy to improve living conditions.Meanwhile,producing this energy with decreasing emissions supports a net-zero world.

Preliminary Studies on the Microbial Degradation of Plastic Waste Using <i>Aspergillus niger</i>and <i>Pseudomonas</i>sp.

The possibility of microbial degradation of plastic waste was investigated by isolating microorganisms present in dumpsite containing low-density polyethylene (LDP). Aspergillus niger (fungi) and Pseudomonas sp. (bacteria) were identified and subsequently used to biodegrade plastic waste. The medium was made up of 0.2 g of MgSO4, 1.0 g of KH2PO4, 1.0 g of K2HPO4, 1.0 g of NH4NO3, 0.02 g of CaCl2, 0.05 g of FeCl3 in 1000 ml water. 10 ml of the medium containing the bacteria and/or fungi was poured into test tubes and 0.1 g of the plastic sample (Pure water sachet) pre-treated with ethanol was introduced into the tubes. The pH of the medium was adjusted to 7.2, 5.4 and 6.0 for Pseudomonas sp., Aspergillus niger and the mixed culture respectively. Each experiment was carried out aerobically at room temperature and incubated on a rotary shaker at 120 rpm. The weight loss in each experiment was monitored at 10 days interval for 60 days. The total weight loss after 60 days was 7.2%, 12.4%, 15% for degradation with Pseudomonas sp., Aspergillus niger and the mixed culture respectively. From this study it can be inferred that Pseudomonas sp. and Aspergillus niger have the ability to degrade plastics. It can also be inferred that Aspergillus niger degraded plastics better than Pseudomonas sp. and there was synergy between the two microorganisms since the mixed culture gave a higher degradation.

Infrared thin layer drying of saffron(Crocus sativus L.) stigmas:Mass transfer parameters and quality assessment

Saffron is the most precious and expensive agricultural product. A dehydration treatment is necessary to convert Crocus sativus L. stigmas into saffron spice. To the best of our knowledge, no information on mass transfer parameters of saffron stigmas is available in the literature. This study aimed at investigating the moisture transfer parameters and quality attributes of saffron stigmas under infrared treatment at different temperatures（60,70, …, 110 ℃）. It was observed that the dehydration process of the samples occurred in a short accelerating rate period at the start followed by a falling rate period. The effective moisture diffusivity and convective mass transfer coefficient were determined by using the Dincer and Dost model. The diffusivity values varied from1.1103 × 10^-10m^2·s^-1to 4.1397 × 10^-10m^2·s^-1 and mass transfer coefficient varied in the range of 2.6433 × 10^-7–8.7203 × 10^-7m·s^-1. The activation energy was obtained to be 27.86 kJ·mol^-1. The quality assessment results showed that the total crocin content increased, when the temperature increased up to90 ℃ but, in higher temperatures, the amount of crocin decreased slightly. The total safranal content of the samples decreased slightly when drying temperature increased from 60 ℃ to 70 ℃ and then continuously increased up to 110 ℃. Also, the amount of picrocrocin increased from 83.1 to 93.3 as the drying temperature increased from 60 ℃ to 100 ℃.

A systematic machine learning method for reservoir identification and production prediction

Reservoir identification and production prediction are two of the most important tasks in petroleum exploration and development.Machine learning(ML)methods are used for petroleum-related studies,but have not been applied to reservoir identification and production prediction based on reservoir identification.Production forecasting studies are typically based on overall reservoir thickness and lack accuracy when reservoirs contain a water or dry layer without oil production.In this paper,a systematic ML method was developed using classification models for reservoir identification,and regression models for production prediction.The production models are based on the reservoir identification results.To realize the reservoir identification,seven optimized ML methods were used:four typical single ML methods and three ensemble ML methods.These methods classify the reservoir into five types of layers:water,dry and three levels of oil(I oil layer,II oil layer,III oil layer).The validation and test results of these seven optimized ML methods suggest the three ensemble methods perform better than the four single ML methods in reservoir identification.The XGBoost produced the model with the highest accuracy;up to 99%.The effective thickness of I and II oil layers determined during the reservoir identification was fed into the models for predicting production.Effective thickness considers the distribution of the water and the oil resulting in a more reasonable production prediction compared to predictions based on the overall reservoir thickness.To validate the superiority of the ML methods,reference models using overall reservoir thickness were built for comparison.The models based on effective thickness outperformed the reference models in every evaluation metric.The prediction accuracy of the ML models using effective thickness were 10%higher than that of reference model.Without the personal error or data distortion existing in traditional methods,this novel system realizes rapid analysis of data while reducing the time required to resolve reservoir classification and production prediction challenges.The ML models using the effective thickness obtained from reservoir identification were more accurate when predicting oil production compared to previous studies which use overall reservoir thickness.