Search Results (49)

During the process of oil and gas drilling, due to the existence of pores or micro-cracks, drilling fluid is prone to invade the formation. Under the action of hydration expansion of clay in the formation and liquid pressure, wellbore instability occurs. In order to reduce the wellbore instability caused by drilling fluid intrusion into the formation, this study proposed a method of forming a dynamic hydrogen bond cross-linked network weak gel structure with modified nano-silica and P(AM-AAC). The plugging performance of the drilling fluid and the performance of inhibiting the hydration of shale were evaluated through various experimental methods. The results show that the gel composite system (GCS) effectively optimizes the plugging performance of drilling fluid. The 1% GCS can reduce the linear expansion rate of cuttings to 14.8% and increase the recovery rate of cuttings to 96.7%, and its hydration inhibition effect is better than that of KCl and polyamines. The dynamic cross-linked network structure can significantly increase the viscosity of drilling fluid. Meanwhile, by taking advantage of the liquid-phase viscosity effect and the physical blocking effect, the loss of drilling fluid can be significantly reduced. Mechanism studies conducted using zeta potential measurement, SEM analysis, contact angle measurement and capillary force assessment have shown that modified nano-silica stabilizes the wellbore by physically blocking the nano-pores of shale and changing the wettability of the shale surface from hydrophilic to hydrophobic when the contact angle exceeds 60°, thereby reducing capillary force and surface free energy. Meanwhile, the dynamic cross-linked network can reduce the seepage of free water into the formation, thereby significantly lowering the fluid loss of the drilling fluid. This research provides new insights into improving the stability of the wellbore in drilling fluids. Full article

The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article

To elucidate the gas adsorption characteristics of medium-rank coal, this study collected samples from fresh mining faces in the Qinshui Basin. A series of experiments were conducted, including low-temperature carbon dioxide adsorption, low-temperature liquid nitrogen adsorption, mercury intrusion, and methane isothermal adsorption experiments, which clarify the pore structure characteristics of medium-rank coals, reveal the gas adsorption behavior in medium-rank coal, and identify the control mechanism. The results demonstrate that the modified Dubinin–Radushkevich (D-R) isothermal adsorption model accurately describes the gas adsorption in medium-rank coal, with fitting errors remaining below 1%. Comprehensive pore structure analysis reveals that the coal pore volume consists primarily of absorption pores (<2 nm), transitional pores (10–100 nm), and seepage pores (>100 nm), while the specific surface area is predominantly contributed by absorption pores (<2 nm). At low pressures, gas molecules form monolayer adsorption on absorption pore (<2 nm) and adsorption pore (2–10 nm) surfaces. With increasing pressure, multilayer adsorption dominates. As pore filling approaches the maximum capacity, the adsorption rate decreases progressively until reaching an equilibrium, at which point the adsorption capacity attains its saturation limit. The adsorption data of the gas in medium-rank coal can be explained by the improved D-R isothermal adsorption model. The priority of gas filling in pores is different, and the absorption pore is normally better than the adsorption pore. The results provide a new idea and understanding for the further study of the coalbed gas adsorption mechanism. Full article

Deep Neural Networks (DNNs) have been widely used to solve complex problems in natural language processing, image classification, and autonomous systems. The strength of DNNs is derived from their ability to model complex functions and to improve detection engines through deeper architecture. Despite the strengths of DNN engines, they present several crucial challenges, such as the number of hidden layers, the learning rate, and the neuron weight. These parameters are considered to play a crucial role in the ability of DNNs to detect anomalies. Optimizing these parameters could improve the detection engine and expand the utilization of DNNs for various areas of application. Bio-inspired optimization algorithms, especially Particle Swarm Intelligence (PSO) and the Gray Wolf Optimizer (GWO), have been widely used to optimize complex tasks because of their ability to explore the search space and their fast convergence. Despite the significant successes of PSO and GWO, there remains a gap in the literature regarding their hybridization and application in Intrusion Detection Systems (IDSs), such as Sunburst attack detection, especially using DNN. Therefore, in this paper, we introduce a hybrid detection model that investigates the ability to integrate PSO and GWO so as to improve the DNN architecture to detect the Sunburst attack. The PSO algorithm was used to optimize the learning rate and the number of hidden layers, while the GWO algorithm was used to optimize the neuron weight. The hybrid model was tested and evaluated based on open-source Sunburst attacks. The results demonstrate the effectiveness and robustness of the suggested hybrid DNN model. Furthermore, an extensive analysis was conducted by evaluating the suggested hybrid PSO–GWO along with other hybrid optimization techniques, namely Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The results demonstrate that the suggested hybrid model outperformed other optimization techniques in terms of accuracy, precision, recall, and F1-score. Full article

The extensive interconnection and intelligent collaboration of multi-source heterogeneous devices in the industrial Internet environment have significantly improved the efficiency of industrial production and resource utilization. However, at the same time, the deployment characteristics of open-network architecture and the promotion of the concept of deep integration of OT/IT have led to an exponential growth of attacks on the industrial Internet. At present, most of the detection methods for industrial internet attacks use deep learning. However, due to the black-box characteristics caused by the complex structure of deep learning models, the explainability of industrial internet detection results generated based on deep learning is low. Therefore, we proposed an industrial internet intrusion response method xIIRS based on explainable deep learning. Firstly, an explanation method was improved to enhance the explanation by approximating and sampling the historical input and calculating the dynamic weighting for the sparse group lasso based on the evaluation criteria for the importance of features between and within feature groups. Then, we determined the defense rule scope based on the obtained explanation results and generated more fine-grained defense rules to implement intrusion response in combination with security constraints. The proposed method was experimented on two public datasets, TON_IoT and Gas Pipeline. The experimental results show that the explanation effect of xIIRS is better than the baseline method while achieving an average malicious traffic blocking rate of about 95% and an average normal traffic passing rate of about 99%. Full article

Coalbed Methane (CBM) accounts for about 5% of China’s domestic gas supply, which has been regarded as one of the most promising energies for alleviating the energy supply–demand imbalance. Deep CBM reservoirs have the characteristics of low permeability, low porosity, and low water saturation, which easily experience reservoir damage during the drilling process, further affecting the gas productivity. Based on the analysis of coal mineral composition, pore structure distribution, and the surface micromorphology change in coal surface before and after hydration, a possible mechanism for CBM formation damage was revealed. It was found that the damage caused by drilling fluid intrusion can be divided into three stages: stripping, migration, and plugging. Based on the water-sensitive, acid-sensitive, and stress-sensitive evaluation tests, a novel anti-waterblocking agent with both wettability alteration and surface tension reduction was developed; then a reservoir protection drilling fluid for deep coal formation in Daning-Jixian block was constructed; then the reservoir protection performance of drilling fluid was evaluated. The results show that as the concentration of the anti-waterblocking agent FSS increases from 0% to 1%, the surface tension of the water phase is significantly reduced from 72.15 mN/m to 26.58 mN/m, while the maximum contact angle of water on the surface reaches 117°. This enhancement in wettability leads to an improvement in the permeability recovery rate from 56.6% to 80.0%, indicating a substantial reduction in waterblocking effects and better fluid mobility within the reservoir. These findings highlight the efficacy of FSS in mitigating formation damage and optimizing gas production in coalbed methane reservoirs. The drilling fluid has good wettability alteration, inhibition, and sealing performance, which is of great significance for protecting gas well productivity. Full article

The rapid desorption of gas in coal is an important cause of gas over-limit and outbursts. In order to explain the causes of coal and gas outbursts induced by vibration, this paper studies the gas desorption experiments of tectonic coal with different particle sizes and different adsorption equilibrium pressures under 0~50 Hz vibration. High-pressure mercury intrusion experiments were used to measure the changes in pore volume and specific surface area of tectonic coal before and after vibration, revealing the control of pore structure changes on the initial desorption capacity of gas. Additionally, from the perspective of energy transformation during coal and gas outbursts, the effect of vibration on the process of coal and gas outbursts in tectonic coal was analyzed. The results showed that tectonic coal has strong initial desorption capacity, desorbing 29.58% to 54.51% of the ultimate desorption volume within 10 min. Vibration with frequencies of 0~50 Hz increased both the gas desorption ratios and desorption volume as the frequency increased. The initial desorption rate also increased with the vibration frequency, and vibration can enhance the initial desorption capacity of tectonic coal and delay the attenuation of desorption rate. Vibration affected the changes in the initial gas desorption rate and desorption rate attenuation coefficient by increasing the pore volume and specific surface area, with the changes in macropores and mesopores primarily affecting the initial desorption rate and 0~10 min desorption ratios, while the changes in micropores and minipores mainly influenced the attenuation rate of the desorption rate. Vibration increased the free gas expansion energy of tectonic coal as the frequency increased. During the incubation and triggering processes of coal and gas outbursts, vibration has been observed to accelerate the fragmentation and destabilisation of the coal body, while simultaneously increasing the gas expansion energy to a point where it reaches the threshold energy necessary for coal transportation, thus inducing and triggering the coal and gas protrusion. The study results elucidate, from an energy perspective, the underlying mechanisms that facilitate the occurrence of coal and gas outbursts, providing theoretical guidance for coal and gas outburst prevention and mine safety production. Full article

In-depth understanding of the gas–water seepage law caused by different degrees of gas layer perforation and varying gas production rates is key to determining a reasonable development technology policy for vertical heterogeneous edge-water gas reservoirs. Based on core physical data from the entire section of the X2 well, a large-scale high-pressure positive-rhythm profile model that takes into account the influence of “discontinuous interlayer” was innovatively established. The water intrusion process of the gas layer profile under different gas production rates and degrees of gas layer perforation was simulated using an electrical resistivity scanning device. The experimental model has an area of 3000 cm², with a maximum pressure of 70 MPa and a maximum temperature resistance of 150 °C. It includes 456 evenly distributed fluid saturation test points to accurately monitor the gas–water distribution, addressing the issues of small bearing pressure and insufficient saturation monitoring points found in other large-scale models. The experimental results show that, in heterogeneous reservoirs, the high-permeability zone controls the invasion path of edge water, which is the main reason for the uneven invasion of edge water. For the positive-rhythm profile of the F layer, a higher gas production rate (1000 mL/min) shortens the water-free gas recovery period of the gas well and reduces the recovery rate. Perforating the upper two-thirds of the layer can inhibit edge-water breakthrough, prolong the water-free gas recovery period of the gas well, enable the gas–water interface to advance more uniformly, and enhance the recovery degree. The results of this study greatly enhance our understanding of the water invasion characteristics of positive-rhythm reservoirs under the influence of different gas production rates and varying degrees of gas layer perforation. Full article

The exploration and development of conventional oil and gas resources are becoming more difficult, and the proportion of low-permeability reservoirs in newly discovered reservoir resources has expanded to 45%. As the main focus of the oil industry, the global average recovery rate of low-permeability reservoir resources is only 20%, and most crude oil is still unavailable, so our understanding of such reservoirs needs to be deepened. The microscopic pore structure of low-permeability reservoir rocks exhibits significant complexity and variability; reservoir evaluation is more difficult. For elucidating the internal distribution of storage space and the mechanisms influencing seepage, we focus on the low-permeability sandstone reservoir of the Shahejie Formation, located on the northern slope of the Chenjiazhuang uplift, Bohai Bay. Employing a suite of advanced analytical techniques, including helium expansion, pressure pulse, high-pressure mercury intrusion (HPMI), and micro-computed tomography (micro-CT) scanning, we examined the main pore–throat size affecting reservoir storage and seepage in the reservoir at both the micrometer and nanometer scales. The results reveal that pores with diameters exceeding 40 μm are sparsely developed within the low-permeability reservoir rocks of the study area. However, pores ranging from 0 to 20 μm predominate, exhibiting an uneven distribution and a clustered structure in the three-dimensional pore structure model. The pore volume showed a unimodal and bimodal distribution, thus significantly contributing to the storage space. The main sizes of the reservoir in this study area are 40–80 μm and 200–400 μm. Micron-sized pores, while present, are not the primary determinants of the reservoir’s seepage capacity. Instead, coarser submicron and nano-pores exert a more substantial influence on the permeability of the rock. Additionally, the presence of micro-fractures is found to enhance the reservoir’s seepage capacity markedly. The critical pore–throat size range impacting the permeability of the reservoir in the study area is identified to be between 0.025 and 0.4 μm. Full article

This article discusses the use of distributed acoustic sensing (DAS) for monitoring gas–liquid two-phase slug flow in horizontal pipes, using standard telecommunication fiber optics connected to a DAS integrator for data acquisition. The experiments were performed in a 14 m long, 5 cm diameter transparent PVC pipe with a fiber cable helically wrapped around the pipe. Using mineral oil and compressed air, the system captured various flow rates and gas–oil ratios. New algorithms were developed to characterize slug flow using DAS data, including slug frequency, translational velocity, and the lengths of slug body, slug unit, and the liquid film region that had never been discussed previously. This study employed a high-speed camera next to the fiber cable sensing section for validation purposes and achieved a good correlation among the measurements under all conditions tested. Compared to traditional multiphase flow sensors, this technology is non-intrusive and offers continuous, real-time measurement across long distances and in harsh environments, such as subsurface or downhole conditions. It is cost-effective, particularly where multiple measurement points are required. Characterizing slug flow in real time is crucial to many industries that suffer slug-flow-related issues. This research demonstrated the DAS’s potential to characterize slug flow quantitively. It will offer the industry a more optimal solution for facility design and operation and ensure safer operational practices. Full article

Intrusion detection systems (IDSs) in wireless sensor networks (WSNs) rely heavily on effective feature selection (FS) for enhanced efficacy. This study proposes a novel approach called Genetic Sacrificial Whale Optimization (GSWO) to address the limitations of conventional methods. GSWO combines a genetic algorithm (GA) and whale optimization algorithms (WOA) modified by applying a new three-population division strategy with a proposed conditional inherited choice (CIC) to overcome premature convergence in WOA. The proposed approach achieves a balance between exploration and exploitation and enhances global search abilities. Additionally, the CatBoost model is employed for classification, effectively handling categorical data with complex patterns. A new technique for fine-tuning CatBoost’s hyperparameters is introduced, using effective quantization and the GSWO strategy. Extensive experimentation on various datasets demonstrates the superiority of GSWO-CatBoost, achieving higher accuracy rates on the WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017 datasets than the existing approaches. The comprehensive evaluations highlight the real-time applicability and accuracy of the proposed method across diverse data sources, including specialized WSN datasets and established benchmarks. Specifically, our GSWO-CatBoost method has an inference time nearly 100 times faster than deep learning methods while achieving high accuracy rates of 99.65%, 99.99%, 99.76%, and 99.74% for WSN-DS, WSNBFSF, NSL-KDD, and CICIDS2017, respectively. Full article

The wettability of coal is an important factor influencing hydraulic stimulation. Field-trial data has proven that high-pressure N₂ injection plays a positive role in increasing the coalbed methane (CBM) production rate. For the purpose of investigating the mechanism by which N₂ promotes the gas rate, multiple experiments were conducted sequentially on the wettability of anthracite under different N₂ pressures. Testing of the coal surface contact angle was conducted under 0.1–8 MPa nitrogen pressure using a newly built contact angle measuring device. The coal samples were collected from the Xinjing Coal Mine in the Qinshui Basin, China. The test results revealed that the contact angle increased with increasing N₂ pressure. That is, the contact angle was 77.9° at an N₂ pressure of 0.1 MPa and gradually increased to 101.4° at an infinite N₂ pressure. In contrast, the capillary pressure decreased with an increasing N₂ pressure, from 0.298 MPa to −0.281 MPa. The relationship between contact angle and N₂ pressure indicated that the wettability was reversed at a N₂ pressure of 5.26 MPa, with a contact angle of 90° and a capillary pressure of 0 MPa. The capillary pressure reversed to a negative value as the N₂ pressure increased. At the microlevel, a high N₂ pressure increases the surface roughness of coal, which improves the ability of the coal matrix to adsorb N₂, forming the gas barrier that hinders the intrusion of water into the pores of the coal matrix. The results of this study provide laboratory evidence that high-pressure N₂ injection can prevent water contamination and reduce the capillary pressure, thus benefiting coalbed methane production. Full article

During deepwater drilling, testing, production, or hydrate mining, the circulating medium in the wellbore may contain solid particles, such as rock chips and sand, in addition to drilling fluids, gas, and water. In the high-pressure, low-temperature conditions of deep water, gas intrusion can easily combine with free water in the drilling fluid to form hydrates, increasing the drilling risk. Therefore, understanding the formation patterns of hydrates in drilling fluids is of significant importance for the prevention and control of hydrates. This study utilized a small-scale high-pressure reactor to investigate the impact of the stirring rate, NaCl, and methanol additives, as well as the sand content on the hydrate formation process and gas consumption. The results indicate that the hydrate formation process can be divided into an induction stage, a rapid formation stage, and a slow formation stage. The induction stage and rapid formation stage durations are significantly reduced under stirring conditions. In NaCl and methanol solutions, hydrate formation is inhibited, with the induction stage duration increasing with higher concentrations of NaCl and methanol. There was no apparent rapid formation stage observed. The final gas consumption decreases substantially with increasing concentrations of NaCl and methanol, reaching no significant hydrate formation at a 20% concentration. The sand content has a significant impact on the slow formation stage, with the final gas consumption increasing within a certain range (in this work, at a sand content of 20%), and being notably higher than in the pure water system under the same conditions. Full article

The global shale gas resources are huge and have good development prospects, but shale is mainly composed of nanoscale pores, which have the characteristics of low porosity and low permeability. Horizontal drilling and volume fracturing techniques have become the effective means for developing the shale reservoirs. However, a large amount of mining data indicate that the fracturing fluid trapped in the reservoir will inevitably cause hydration interaction between water and rock. On the one hand, the intrusion of fracturing fluid into the formation causes cracks to expand, which is conducive to the formation of complex fracture networks; on the other hand, the intrusion of fracturing fluid into the formation causes the volume expansion of clay minerals, resulting in liquid-phase trap damage. At present, the determination of well closure time is mainly based on experience without theoretical guidance. Therefore, how to effectively play the positive role of shale hydration while minimizing its negative effects is the key to optimizing the well closure time after fracturing. This paper first analyzes the shale pore characteristics of organic pores, clay pores, and brittle mineral pores, and the multi-pore self-absorption model of shale is established. Then, combined with the distribution characteristics of shale hydraulic fracturing fluid in the reservoir, the calculation model of backflow rate and shut-in time is established. Finally, the model is validated and applied with an experiment and example well. The research results show that the self-imbibition rate increases with the increase in self-imbibition time, and the flowback rate decreases with the increase in self-imbibition time. The self-imbibition of slick water is the maximum, the self-imbibition of breaking fluid is the minimum, and the self-imbibition of mixed fluid is the middle, and the backflow rates of these three liquids are in reverse order. It is recommended the shut-in time of Longmaxi Formation shale is 17 days according to the hydration and infiltration model. Full article

The exploitation of ultradeep, fractured, and low-porosity gas reservoirs often encounters challenges from water invasion, exacerbated by the presence of faults and fractures. This is particularly evident in the Kelasu gas reservoir group, located in the Kuqa Depression of the Tarim Basin. The complexity of the water invasion patterns in these reservoirs demands a thorough investigation to devise effective water control measures. To elucidate the water invasion patterns, a combined approach of large-scale physical modeling and discrete fracture numerical simulations was adopted. These models allowed for the identification and categorization of water invasion behaviors in various gas reservoirs. Furthermore, production dynamic analysis was utilized to tailor water control strategies to specific invasion patterns. The large-scale physical simulation experiment revealed that water invasion in gas reservoirs is primarily influenced by high-permeability channels (faults + fractures), and that the gas production rate serves as the key factor governing gas reservoir development. The range of gas extraction rates spans from 3% to 5%. As the gas extraction rate increases, the extraction intensity diminishes and the stable production duration shortens. On the basis of the changes in the water breakthrough time and water production rate, a 2% gas extraction rate is determined as the optimal rate for the model. The embedded discrete fracture numerical simulation model further supports the findings of the physical simulation experiments and demonstrates that ① this type of gas reservoir exhibits typical nonuniform water invasion patterns, controlled by structural location, faults, and degree of crack development; ② the water invasion patterns of gas reservoirs can be categorized into three types, these being explosive water flooding and channeling along faults, uniform intrusion along fractures, and combined intrusion along faults and fractures; ③ drawing from the characteristics of water invasion in various gas reservoirs, combined with production well dynamics and structural location, a five-character water control strategy of “prevention, control, drainage, adjustment, and plugging” is formulated, with the implementation of differentiated, one-well, one-policy governance. The study concludes that a proactive approach, prioritizing prevention, is crucial for managing water-free gas reservoirs. For water-bearing reservoirs, a combination of three-dimensional water plugging and drainage strategies is recommended. These insights have significant implications for extending the productive lifespan of gas reservoirs, enhancing recovery rates, and contributing to the economic and efficient development of ultradeep, fractured, and low-porosity gas reservoirs. Full article