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Vapor bubbles in cryogenic fluids may collapse violently under subcooled and pressurized conditions. Despite important implications for engineering applications such as cavitation erosion in liquid propellant rocket engines, these intense phenomena are still largely unexplored. In this paper, we systematically investigate the ambient conditions leading to the occurrence of violent collapses in liquid nitrogen and analyze their thermodynamic characteristics. Using Brenner’s time ratio χ, the regime of violent collapse is identified in the ambient pressure–temperature parameter space. Complete numerical simulations further refine the prediction and illustrate two classes of collapses. At 1 10 does the collapse show more inertial features. A mechanism analysis pinpoints a critical time when the surrounding liquid enters supercritical state. The ultimate collapse intensity is shown to be closely associated with the dynamics at this moment. Our study provides a fresh perspective to the treatment of cavitation in cryogenic fluids. The findings can be instrumental in engineering design to mitigate adverse effects arising from intense cavitational activities.

A fracture propagation model of radial well fracturing is established based on the finite element-meshless method. The model considers the coupling effect of fracturing fluid flow and rock matrix deformation. The fracture geometries of radial well fracturing are simulated, the induction effect of radial well on the fracture is quantitatively characterized, and the influences of azimuth, horizontal principle stress difference, and reservoir matrix permeability on the fracture geometries are revealed. The radial wells can induce the fractures to extend parallel to their axes when two radial wells in the same layer are fractured. When the radial wells are symmetrically distributed along the direction of the minimum horizontal principle stress with the azimuth greater than 15°, the extrusion effect reduces the fracture length of radial wells. When the radial wells are symmetrically distributed along the direction of the maximum horizontal principal stress, the extrusion increases the fracture length of the radial wells. The fracture geometries are controlled by the rectification of radial borehole, the extrusion between radial wells in the same layer, and the deflection of the maximum horizontal principal stress. When the radial wells are distributed along the minimum horizontal principal stress symmetrically, the fracture length induced by the radial well decreases with the increase of azimuth; in contrast, when the radial wells are distributed along the maximum horizontal principal stress symmetrically, the fracture length induced by the radial well first decreases and then increases with the increase of azimuth. The fracture length induced by the radial well decreases with the increase of horizontal principal stress difference. The increase of rock matrix permeability and pore pressure of the matrix around radial wells makes the inducing effect of the radial well on fractures increase.

The application of artificial intelligence (AI) has become inevitable in the petroleum industry. In drilling and completion engineering, AI is regarded as a transformative technology that can lower costs and significantly improve drilling efficiency (DE). In recent years, numerous studies have focused on intelligent algorithms and their application. Advanced technologies, such as digital twins and physics-guided neural networks, are expected to play roles in drilling and completion engineering. However, many challenges remain to be addressed, such as the automatic processing of multi-source and multi-scale data. Additionally, in intelligent drilling and completion, methods for the fusion of data-driven and physics-based models, few-sample learning, uncertainty modeling, and the interpretability and transferability of intelligent algorithms are research frontiers. Based on intelligent application scenarios, this study comprehensively reviews the research status of intelligent drilling and completion and discusses key research areas in the future. This study aims to enhance the berthing of AI techniques in drilling and completion engineering.

Natural Gas Hydrate (NGH) and Hydrate-bearing Sediments (HBS) are emerging as an important potential energy resource. Radial Jet Drilling (RJD) technology, turning sharply in the casing and drilling laterals by using water jet, is a valid approach to solve problems of high cost, low efficiency during the exploitation of NGHs. The performance of water jet drilling remains unclear, and traditional finite element methods cannot accurately depict the water jet drilling ability due to mesh distortion. This paper analyzes the water jet erosion process of NGH and HBS. Experiments on the erosion of reconstituted gas hydrates are conducted and visualized in both submerged and submerged confining pressure conditions. Subsequently, two coupled nozzle–target models are solved by Arbitrary Lagrangian Eulerian (ALE) and Smooth Particle Hydrodynamics (SPH) methods. The flow field, the deformation and erosion of the hydrates induced by water jet are simulated. The experimental results show that there are specific shapes of cylindrical erosion pits for NGH and HBS. The numerical results are consistent with the experiments, which proves the effectiveness of water jet exploring hydrate resources. The submerged condition and the confining pressure condition will hinder the erosion efficiency, and the critical erosion velocities for both HBS and NGH are obtained. ALE method has superior accuracy in modeling the damaged area and erosion pit characteristics; while SPH method, has advantages in showing the motion state of the single particles and unstable and discontinuous flow field. This paper provides a good guidance for understanding the water jet drilling performance and selecting the appropriate simulation method in NGH reservoirs development.

In the sonochemical degradation of nonvolatile compounds, the free radicals must be delivered into the aqueous solution from the cavitation bubble to initiate reduction–oxidation reactions. The penetration depth in the liquid becomes an important parameter that influences the radical delivery efficiency and eventual treatment performance. However, the transport of radicals in the liquid phase is not well understood yet. In this paper, we focus on the most reactive OH radical and numerically simulate its penetration behavior. This is realized by solving the coupled equations of bubble dynamics, intracavity chemistry, and radical dispersion in the aqueous phase. The results present both the local and global penetration patterns for the OH radicals. By performing simulations over a wide range of acoustic parameters, we find an undesirable phenomenon that the penetration can be adversely suppressed when strengthening the radical production. A mechanistic analysis attributes this to the excessively vigorous recombination reactions associated with high radical concentrations near the bubble interface. In this circumstance, the radicals are massively consumed and converted into molecular species before they can appreciably diffuse away. Our study sheds light on the interplay between radical production inside the bubble and dispersion in the outside liquid. The derived conclusions provide guides for sonochemical applications from a new perspective.

Natural gas hydrates occupy an important position in the development of clean energy around the world in the 21st century. It is of great significance to research the mechanical properties of methane hydrate-bearing sediment (MHBS). In this paper, gas-saturated MHBS were synthesized based on the self-developed triaxial compressor apparatus. The triaxial shear tests were performed at temperatures of 2 °C, 3 °C, and 5 °C and confining pressures of 7.5 MPa, 10 MPa, and 15 MPa. Results indicate that the axial strain process can be divided into three stages: initial elastic deformation, initial yield deformation, and strain softening. When confining pressure is increased, the shear strength of MHBS increases at a lower confining pressure. In contrast, shear strength appears to decrease with increasing confining pressure at a higher confining pressure. There is a negative correlation between temperature and shear strength of MHBS. The initial yield strain of MHBS increases in condition due to the increase in confining pressure and the decrease in temperature. The change in strength degradation is kept within 2 MPa. Using test data, the Duncan-Chang model was modified to describe the strength behaviors of gas-saturated MHBS. The accuracy of the model was verified by comparing calculated values with test data.

A sound driven air bubble can be transformed into an argon bubble emitting light pulses stably. The very foundation to investigate the sonoluminescing bubble is to accurately determine the ambient radius and gas composition in the interior. The conventional approach is to model the air-to-argon transformation process through a large number of bubble dynamics simulations to obtain the physical parameters of the ultimate argon bubble. In this paper, we propose a highly efficient method to pinpoint this information in a phase diagram. The method is based on the diffusive equilibrium for each species inside the bubble and derives the ambient radius and composition inversely. To calculate the former parameter, the bisection algorithm is employed to consecutively narrow down the searching range until the equilibria is approached. Afterward, several cycles of full dynamics simulations are conducted to refine the composition. The method is validated using published experimental data. The calculated ambient radii deviate from the test results by less than 1 μm, which falls within the margin of measurement error. The advantages of this method over the semi-analytical approach reported by Hilgenfeldt et al. [J. Fluid Mech. 365 (1998)] are also discussed. Our study provides a standard procedure to calculate the ambient radius and composition and is beneficial for the numerical simulation of sonoluminescing bubbles.

It is a common phenomenon during CBM drilling and production that reservoir damage is not eliminated completely. In view of this, a technical idea of composite pulsating hydraulic fracturing of radial horizontal wells which is conducive to blockage removing and stimulation was put forward in this paper. Speaking of the hydraulic jetting in a multi-branch radial well, it is to conduct pulsating hydraulic fracturing moderately through a high-diversion radial hole, so as to crush and break coal beds near the main hole to the uttermost. Thus, an extensive pressure relief and permeability increase area where high-diversion pathways are combined with fracture networks is formed. Then, to verify its technical principles, laboratory tests on pulsating hydraulic fracturing of radial wells were designed and carried out. Besides, the relationships of the features of acoustic emission (AE) response during the formation of fractures by composite fracturing of radial horizontal wells vs. coal breaking degree and macro fracture morphology were experimentally studied by using a pulse servo fatigue testing machine and an acoustic emission detector. And the following research results were obtained. First, under experimental conditions, fractures initially occur when the pressure of composite pulsating hydraulic fracturing of radial horizontal wells is 1/3–1/4 of the peak pressure of conventional fracturing, and the amount of its AE events is 1.38–7.07 times that of conventional fracturing. Second, when composite pulsating hydraulic fracturing of radial horizontal wells is conducted, AE emission signals respond strongly, the peak pressure for macro fracturing is lower and a larger fracture network can be generated more easily under the same condition. Third, radial laterals amount, borehole length, dynamic loading frequency and amplitude are the important factors affecting the effect of composite pulsating hydraulic fracturing of radial horizontal wells. In conclusion, composite pulsating hydraulic fracturing of radial horizontal wells provides a new idea of removing the blockages in CBM reservoirs and developing CBM efficiently, realizing effective blockage removing and stimulation of CBM wells. Keywords: Coalbed methane, Reservoir damage, Stimulation, Radial horizontal well, Composite pulsating hydraulic fracturing, Blockage removing, Laboratory

Sand production is one of the main problems restricting the safe, efficient and sustainable exploitation of marine natural gas hydrate. To explore the sand-control effects of gravel packing, experiments that simulate hydrate extraction in the water-rich environment were conducted with designed hydrate synthesis and exploitation devices. Three sand control completion methods, including 120 mesh sand screen, 400 mesh sand screen, 120 mesh sand screen combined with gravel packing, are adopted. Sand and gas production rates were compared under different well types and sand control completion methods. Results show that the gas production modes of radial wells and vertical wells are almost the same at the same time due to the small experimental scale and high permeability. The sand production of the vertical well with gravel packing combined with a sand-control screen is 50% lower than that of the vertical well with sand-control screens only. Radial well with gravel packing combined with sand-control screens produced 87% less sand than screen mesh alone. The cumulative gas production and recovery rates of a radial well with the composite sand control method are better than those without gravel packing in the same development time.

Abstract Metal‐containing polymer networks are ubiquitous in biological systems, and their unique structures enable a variety of fascinating biological behaviors. Cuticle of mussel byssal threads, containing Fe‐catecholate complexes, shows remarkably high hardness, high extensibility, and self‐healing capability. Understanding strengthening and self‐healing mechanisms is essential for elucidating animal behaviors and rationally designing mussel‐inspired materials. Here, direct evidence of Fe3+ and Fe2+ gradient distribution across the cuticle thickness is demonstrated, which shows more Fe2+ inside the inner cuticle, to support the hypothesis that the cuticle is a functionally graded material with high stiffness, extensibility, and self‐healing capacity. The mechanical tests of the mussel threads show that both strength and extensibility of the threads decrease with increasing oxygen contents, but this property degradation can be restored upon removing the oxygen. The first‐principles calculations explain the change in iron coordination, which plays a key role in strengthening, degradation, and self‐healing of the polymer networks. The oxygen absorbs on metal ions, weakening the iron‐catecholate bonds in the cuticle and collagen core, but this process can be reversed by sea water. These findings can have important implications in the design of next‐generation bioinspired robust, highly extensible materials, and catalysis.

Based on bubble dynamics theory, a mathematic model describing the cavitation bubble size variation in the flow field of self-resonating cavitating jet was developed considering the pressure field and mass and heat exchange between cavitation bubble and ambient fluid. With this model, the influence factors on the cavitation intensity are investigated. The results show that the destructiveness of cavitating jet in breaking rocks depends on the bubble's first collapse, with decreasing intensity in the subsequent collapses. The self-resonating effect significantly enhances the cavitation intensity by promoting the collapse pressure and elongating its duration. Hydraulic parameters are proven to be the dominating factors influencing cavitation intensity: while collapse intensity monotonously increases with jet velocity, there exists an optimum ambient pressure where highest collapse intensity can be achieved. Conversely, the fluid properties show minor influences: cavitation intensity only slightly decreases with the increasing of fluid's density and barely changes with the variation of viscosity and surface tension. The results from this investigation help to uncover the mechanism of the enhanced erosion potential of self-resonating cavitating jet. The conclusions can be used to further improve the performance of self-resonating cavitating jet in field applications. Key words: self-resonating cavitating jet, cavitating bubble, collapse intensity, hydraulic parameters, fluid properties

Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.

Apparent permeability is an important input parameter in the simulation of shale gas production. Most apparent permeability models assume a single pore size. In this study, we develop a theoretical model for quantifying the effect of pore size distribution on shale apparent permeability. The model accounts for the nonuniform distribution of pore sizes, the rarefaction effect, and gas characteristics. The model is validated against available experimental data. Theoretical calculations show that the larger the pore radius, the larger the apparent permeability. Moreover, the apparent permeability increases with an increase in the width of pore size distribution, with this effect being much more pronounced at low pressure than at high pressure.

Acid fracturing in deep carbonate reservoirs is challenged by deep well stimulation with high temperature (>120 °C), high fracture pressure (>2.0 MPa/m), high flow friction, and strong reservoir heterogeneity. To meet these challenges, a new stimulation method, called the hydraulic jet acid fracturing technique, was developed. According to the mechanisms of hydraulic jet acid fracturing, the authors self-design the downhole injector and pipe strings used in multistage hydraulic jet acid fracturing and provide optimization standards for the nozzle number and diameter combination, abrasive perforating parameter, and pumping program. The technique realizes multistage acid fracturing by hydraulic separation and features simple downhole tools, high temperature resistance (160 °C), low cost and risk. In addition, hydraulic acid injection can extend effective acid corrosion distance nearby well and enhance the acidification effect. The optimal jet phasing is 60 degrees with spiral arrangement to lower formation fracture pressure. A relationship chart between optimal flow rate and wellhead pressure is established, which helps to increase flow rate as far as possible under wellhead assembly capacity and to determine nozzle diameter and number. Results from field tests show that this method can work at a maximum depth of 6 400.53 m, with a total acid volume of up to 618 m3. It is effective in creating acid fractures in ultra-deep horizontal wells. Key words: horizontal well, deep formation, water jet, multistage acid-fracturing, field test

CO2 injection into oil reservoirs is expected to achieve enhanced oil recovery along with the benefit of carbon storage, while the application potential of this strategy for shale reservoirs is unclear. In this work, a numerical model for multiphase flow in shale oil reservoirs is developed to investigate the impacts of nano-confinement and oil composition on shale oil recovery and CO2 storage efficiency. Two shale oils with different maturity levels are selected, with the higher-maturity shale oil containing lighter components. The results indicate that the saturation pressure of the lower-maturity shale oil continues to increase with increasing CO2 injection, while that of the higher-maturity shale oil continues to decrease. The recovery factor and CO2 storage rate for higher-maturity shale oil after CO2 huff-n-puff are 12.02% and 44.76%, respectively, while for lower-maturity shale oil, these are 4.41% and 69.33%, respectively. These data confirm the potential of enhanced oil recovery in conjunction with carbon storage in shale oil reservoirs. Under the nanoconfinement impact, a decrease in the oil saturation in the matrix during production is reduced, which leads to a significant increase in oil production and a significant decrease in gas production. The oil production of the two kinds of shale oil is comparable, but the gas production of higher-maturity shale oil is significantly higher. Nano-confinement shows a greater impact on the bubble point pressure of higher-maturity shale oil and a more pronounced impact on the production of lower-maturity shale oil. [ABSTRACT FROM AUTHOR]

Digital rock analysis is a promising approach for visualizing geological microstructures and understanding transport mechanisms for underground geo-energy resources exploitation. Accurate image reconstruction methods are vital for capturing the diverse features and variability in digital rock samples. Stable diffusion, a cutting-edge artificial intelligence model, has revolutionized computer vision by creating realistic images. However, its application in digital rock analysis is still emerging. This study explores the applications of stable diffusion in digital rock analysis, including enhancing image resolution, improving quality with denoising and deblurring, segmenting images, filling missing sections, extending images with outpainting, and reconstructing three-dimensional rocks from twodimensional images. The powerful image generation capability of diffusion models shed light on digital rock analysis, showing potential in filling missing parts of rock images, lithologic discrimination, and generating network parameters. In addition, limitations in existing stable diffusion models are also discussed, including the lack of real digital rock images, and not being able to fully understand the mechanisms behind physical processes. Therefore, it is suggested to develop new models tailored to digital rock images for further progress. In sum, the integration of stable diffusion into digital core analysis presents immense research opportunities and holds the potential to transform the field, ushering in groundbreaking advances. [ABSTRACT FROM AUTHOR]

This study, based on static data from over a thousand fracturing wells, employs data governance, data mining, and machine learning regression uncover principal controlling factors for production in the fracturing context. Preprocessing methods, including outlier identification, missing value imputation, and label encoding, address the field data challenges. Correlations among geological, engineering, and production parameters are analyzed using Pearson coefficient, grey correlation, and maximum mutual information. The AutoGluon framework and SHAP post-explanation method compute feature importance. Utilizing multiple evaluation methods, the entropy weight method comprehensively scores and ranks the principal controlling factors. A machine learning production prediction model is established for validation. Results show that DBSCAN achieves better accuracy in identifying field anomaly data. [ABSTRACT FROM AUTHOR]

In the realm of unconventional reservoir hydraulic fracturing design, the conventional optimization of mechanistic model parameters is a time-consuming process that impedes its responsiveness to the swift demands of on-site development. This study, rooted in Xinjiang oilfield data, delves into the utilization of machine learning methods for extensive field data. The research systematically elucidates the training and optimization procedures of a production forecasting model, achieving effective optimization of hydraulic fracturing design parameters. By employing polynomial feature cross-construction generate composite features, feature filtering is performed using the maximal information coefficient. Subsequently, wrapper-style feature selection techniques, including ridge regression and decision trees, are applied to ascertain the optimal combinations of model input parameters. The integration of stacking during model training enhances performance, while stratified K-fold cross-validation is implemented to mitigate the risk of overfitting. The ultimate optimization of hydraulic fracturing design parameters is realized through a competitive learning particle swarm algorithm. Results indicate that the accuracy of the data-driven production forecasting model can reach 85%. This model proficiently learns patterns from mature blocks and effectively applies them to optimize new blocks. Furthermore, expert validation confirms that the optimization results align closely with actual field conditions. [ABSTRACT FROM AUTHOR]