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Summary Gas-oil two-phase flow usually occurs during gas influx into the wellbore annulus of deepwater drilling with oil-based drilling fluid or gas-oil production from the wellbore annulus. A flow pattern transition often is used in well control or in production parameter design. Related studies primarily have focused on gas-water two-phase flow, which is significantly different from gas-viscosity oil two-phase flow. We conducted experiments on a facility that included a flow section that was 13 000 mm long, with a 60-mm inner pipe and a 100-mm outer pipe in the annulus. A range of oil viscosities from 16 to 39 mPa∙s has been studied. The superficial gas and oil velocities varied from 0.55 to 17.077 m/s and from 0 to 0.414 m/s, respectively. A flow pattern was identified based on visualized analysis using electrical capacitance volume tomography (ECVT) and void faction wave analysis. Flow pattern maps of different viscosities were plotted based on measured data. Transition criteria based on Froude number and Archimedes number were established. The new model was compared with gas-water two-phase flow transition models and validated with experimental data. The effect of viscosity on flow pattern transition was revealed. The transition boundary of the bubble to slug flow and slug to churn flow both moved in the direction of smaller superficial gas velocity with an increase in oil viscosity. There may exist a critical viscosity value when churn flow transited to annular flow. When the viscosity was lower than this critical point, the above result was the same for churn to annular flow when the superficial oil velocity was low. With an increase in the superficial oil velocity, however, the boundary gradually changed to high superficial gas velocity as the oil viscosity increased. When the viscosity was larger than the critical point, the oil viscosity had a slight influence on the transition.

Adipose derived stem cells (ADSCs) have the potential to attenuate osteoarthritis (OA); however, complications such as immune rejection and tumour formation limit their application. Exosomes (Exos)-mediated acellular therapy is promising in alleviating OA. This study aims to confirm whether ADSC-exos derived from infrapatellar fat pad (IPFP, ExosIPFP) are more suitable for ameliorating OA than ADSC-exos derived from subcutaneous fat (ScAT, ExoScAT) in vitro and in DMM models. Then, we investigated the regulatory mechanism by which the two kinds of Exos inhibit extracellular matrix (ECM) degradation in OA. ADSCs were successfully isolated and Exos were then obtained. ExosIPFP exhibited better attenuated effects on osteoarthritic chondrocytes in vitro and in vivo than ExoScAT. Small RNA sequencing was performed and the results shown that miR-99b-3p was upregulated in ExosIPFP. In vitro experiments confirmed that ADAMTS4 is a direct downstream target of miR-99b-3p. Over-expression miR-99b-3p in ExosScAT (ExosScAT-99b-3p) indicated that miR-99b-3p serves a positive role for OA treatment by inhibiting ADAMTS4 expression both in vitro and in vivo. In addition, hydrogel microparticles (HMPs) system was prepared by microfluidic technology, and confirmed the beneficial results for long-term therapeutic by continuous release of Exos. Take together, these results suggest that the therapeutic effects of ADSC-Exos may vary according to differential expression of miRNAs. Exosomal miR-99b-3p may act as a promising therapeutic strategy for OA, in addition, the injectable HMPs act as a sustained local drug release system, therefore representing great potential for treating OA and other diseases.

Purpose This study aimed to find the safe zone of two-dimensional (2D) fluoroscopy images in open reduction and internal fixation (ORIF) with long screws for Ogawa type I coracoid process fractures through three-dimensional (3D) simulation operations performed. Preliminary verification was carried out in cadaveric bone and clinical operations. Methods Shoulder computed tomography data of 100 adult participants were collected and reconstructed into 3D models. Virtual screws were created and placed to simulate long screw fixation. 3D models were adjusted to the Y-view of the scapula to be observed for 2D fluoroscopy, and quadrants were established with the centre of the glenoid of the shoulder as the origin. The positions of the screw tips were recorded, and the screw lengths (L1 and L2) and angles (α1, α2, β1, and β2) were measured. A scatter diagram was used to record the position of the screw tips and screw positions. Then the scatter diagram was switched to a thermal diagram to find the safe zone. Verification was carried out in both cadaveric bone and clinical operation. Results A fan-liked arc was obtained in the Y-view of the scapula of the 3D simulation. Most of the screw tips were located in the inferior posterior quadrant. According to the density of screw tips in the quadrant, the safe zone for screw placement was obtained. The screw lengths L1 and L2 were 53.44 ± 5.37 mm and 40.74 ± 6.02 mm, and the angles α1, α2, β1 and β2 were 30.43°±8.04°, 42.43°±6.44°, 65.14°±14.07° and − 1.7°±26.41°, respectively. Sex-dependent differences were found in L1, L2, and β1, P 0.05. Excellent results were obtained both in cadaveric bone and clinical operation based on this safe zone. Conclusions In this study, the safety zone of long screws in Ogawa type I coracoid process fracture was obtained, helps reduce iatrogenic injuries caused by screw penetration. For the best placement of screws, personalised simulated placement of screws was recommended before surgery.

Dried tangerine peel is a Chinese medicine with high medicinal value. The storage age is an important indicator of its medicinal value, so it is very significant to accurately identify the storage age of dried tangerine peel. Traditional physical and chemical analysis methods can be used to achieve this goal, but these methods are limited by their operability and convenience. Near infrared (NIR) spectroscopy and machine learning have excellent performance in the rapid detection of food and pharmaceutical samples. This study investigated the novel application of integrating a hand-held NIR spectrometer combined with machine learning to rapidly and accurately identify the storage age of Xinhui dried tangerine peel. Savitzky–Golay convolution smoothing, standard normal variate (SNV), first derivative, and second derivative pretreatments were employed to preprocess spectral data. Principal component analysis (PCA) was used to reduce the spectral data dimensions and obtain the characteristic spectral variables of each sample. Support vector machine (SVM) and k-nearest neighbor were applied to establish the qualitative discriminant models. The SNV-PCA-SVM model discriminant accuracy was 99.60% in the validation set and was 96.50% in the test set, showing excellent generalization performance. The results indicated that the method of using a hand-held NIR spectrometer combined with machine learning could be applied to rapidly identify the storage age of Xinhui dried tangerine peel. This is a promising and economical hand-held NIR spectroscopic method for assuring the dried tangerine peel age on-site.

Summary The exploration and development of offshore oil and gas have greatly alleviated the tension of global oil and gas resources. However, the abnormal pressure of offshore reservoirs is more common compared with terrestrial oil and gas reservoirs, and the marine geological structure is complex, with the development of faults, fractures, and high and steep structures, which leads to the strong anisotropy of formation pore pressure distribution and uncertainty of pressure system change. In this paper, considering the corresponding characteristics of the randomness of the formation pressure prediction results in the Eaton equation for their respective variables, a formation pressure inversion method based on multisource information, such as predrilling data, bottomhole while drilling data, seabed measured data, and surface measured data, is established. On this basis, combined with the data of a well in the South China Sea, the variation law of the uncertainty of formation pressure prediction results under the conditions of predrilling data, measurement while drilling (MWD) data, and their mutual coupling is analyzed. The simulation results show that the uncertainty distribution of formation pressure prediction based solely on predrilling data shows linear accumulation trend with well depth, and the formation pressure inversion method based on multisource information can significantly curb the increasing trend of uncertainty when MWD data are introduced. Therefore, through the analysis of typical change patterns of monitoring parameters under normal/abnormal conditions during drilling, combined with the method of multisource information, the abnormal pressure information can be accurately predicted and inversed, which provides important support for wellbore pressure regulation under complex formation conditions.

Summary Gas entrapment is a typical phenomenon in gas-yield stress fluid two-phase flow, and most of the related research focuses on the entrapped condition of the single bubble. However, the amount of entrapped gas, which is more meaningful for engineering, is rarely involved. In this paper, a theoretical model for calculating the maximum gas entrapment concentration (MGEC) is established for the first time. The critical distance between horizontal and vertical entrapped bubbles was determined by the yielded region caused by the buoyancy and the coupled stress field around the multiple bubbles. The MGEC is the ratio of a single bubble volume to its domain volume, which is calculated from the distance between the vertical and the horizontal bubbles. By comparing with the experimental results, the average error of MGEC calculated by this model is 4.42%, and the maximum error is 7.32%. According to the prediction results of the model, an empirical equation that can be conveniently used for predicting MGEC is proposed.

At present, more than 90% of China’s oil production equipment comprises rod pump production systems. Indicator diagram analysis of the pumping unit is not only an effective method for monitoring the current working condition of a rod pump production system but also the main way to prevent, detect, and rectify various faults in the oil production process. However, the identification of the pumping unit indicator diagram mainly involves manual effort, and the identification accuracy depends on the experience of the monitoring personnel. Automatic and accurate identification and classification of the pumping unit indicator diagram using new computer technology has long been the research focus of studies for monitoring the pumping unit working condition. In this paper, the indicator diagram is briefly introduced, and the AlexNet model is presented to distinguish the indicator diagram of abnormal wells. The influence of the step size, convolution kernel size, and batch normalization (BN) layer on the accuracy of the model is analyzed. Finally, the AlexNet model is improved. The improved model reduces the calculation cost and parameters, accelerates the convergence, and improves the accuracy and speed of the calculation. In the experimental analysis of abnormal well diagnosis, the data are preprocessed via data deduplication, binary filling, random line distortion, random scaling and stretching, and random vertical horizontal displacement. In addition, the image is expanded by transforming several well indicator diagrams. Finally, data sets of 10 types of indicator diagrams are created for better adaptability and application in the analysis and classification of indicator diagrams, and the ideal application effect is achieved in actual working conditions. In summary, this technology not only improves the recognition accuracy but also saves manpower. Thus, it has good application prospects in the field of oil production.

Summary The cost of offshore drilling operations can be significantly reduced by discharging drilling cuttings into the seabed. However, this leads to accumulation of cuttings piles on the seabed near the drilling site. A certain thickness of cuttings piles changes the original trend of the seabed terrain undulation condition, thus bringing potential safety hazards to the underwater installation of production manifolds. Moreover, the interaction between cuttings and ocean currents near the cuttings piles causes the geometric shape of cuttings piles to evolve over time, which makes it more difficult to accurately predict their characteristics. On the basis of the force analysis of cuttings, considering the effects of cuttings properties (cutting size, density) and ocean-current velocity on the geometric characteristic evolution of the formed cuttings piles, a continuous model for describing the evolution of the returned cuttings piles is established in this study. This model can quantitatively characterize the functional relationship between characteristics of cuttings piles and relevant parameters (current velocity, cutting size, evolution time), and predict the location and geometry characteristics of the cuttings piles evolving into a stable state in ocean currents. Comparing the measured data in laboratory experiments and at an offshore drilling field, the relative error of the model amounts to less than 10%, which demonstrates its rationality. Simulation results show that there will be significant changes in the geometry of cuttings piles before and after the evolution, in which the intensity is correlated with current velocity and cuttings size, and cuttings piles might even split into several parts under certain conditions. The simulation and analysis of the transport and deposition of cuttings returned from the wellhead on the seabed is highly significant for the guide and optimal design of underwater production manifolds.

The 2020 Nobel Prize in Chemistry was awarded to the American scientist Jennifer A. Doudna and the French scientist Emmanuelle Charpentier, in recognition of their discovery in one of the greatest weapons in genetic technology: CRISPR-Cas9 gene scissors. The CRISPR-Cas system is a bacterial defense immune system against exogenous genetic material. Because the system can specifically recognize and cut DNA, this technology is widely used for precise editing of animal, plant, and microbial DNA. The discovery of CRISPR-Cas9 gene scissors enables the tedious and complicated cell gene editing work to be completed in a few weeks or even less, which has promoted the development of gene editing technology in various fields and brought revolutionary influence to the field of life sciences. At the same time, CRISPR gene editing technology has become one of the new therapies for tumors because of its large number of targets and relatively simple operation, and it also makes gene therapy possible. Although the technology still needs to solve technical problems such as off-target and promoter inefficiency, the CRISPR-Cas system will show its unique advantages in more fields with the continuous development of life science and basic medicine.2020年诺贝尔化学奖授予法国科学家艾曼纽尔·卡彭蒂耶 (Emmanuelle Charpentier)和美国科学家詹妮弗·杜德纳(Jennifer A. Doudna)，以表彰她们发现基因技术中最大的利器之一CRISPR-Cas9基因剪刀。CRISPR-Cas系统是一种细菌针对外源性遗传物质的防御免疫系统，该系统可以特异性识别并剪切DNA，因而被广泛应用于动物、植物和微生物DNA的精准编辑上。CRISPR-Cas9基因剪刀的发现使得原本繁琐复杂的细胞基因编辑工作能在几周甚至更短的时间内完成，推动了各个领域基因编辑技术的发展，并给生命科学领域带来革命性影响。同时CRISPR基因编辑技术因靶点数目多、操作较为简便，成为肿瘤的新型疗法之一，也使得基因治疗有可能成为现实。虽然该技术目前依旧需要解决脱靶、启动子低效等问题，但随着生命科学与基础医学的不断发展，相信CRISPR-Cas系统将会在更多的领域展现其独有的优势。.

The feasibility of identifying geographical origin and storage age of tangerine peel was explored by using a handheld near-infrared (NIR) spectrometer combined with machine learning. A handheld NIR spectrometer (900-1700 nm) was used to scan the outer surface of tangerine peel and collect the corresponding NIR diffuse reflectance spectra. Principal component analysis (PCA) combined with Mahalanobis distance were used to detect outliers. The accuracies of all models in the anomaly set were much lower than that in calibration set and test set, indicating that the outliers were effectively identified. After removing the outliers, in order to initially explore the clustering characteristics of tangerine peels, PCA was performed on tangerine peels from different origins and the same origin with different storage ages. The results showed that the tangerine peels from the same origin or the same storage age had the potential to cluster, indicating that the spectral data of the same origin or the same storage age had a certain similarity, which laid the foundation for subsequent modeling and identification. However, there were quite a few samples with different origins or different storage ages overlapped and could not be distinguished from each other. In order to achieve qualitative identification of origin and storage age, Savitzky-Golay convolution smoothing with first derivative (SGFD) and standard normal variate (SNV) were used to preprocess the raw spectra. Random forest (RF), K-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to establish the discriminant model. The results showed that SGFD-LDA could accurately distinguish the origin and storage age of tangerine peel at the same time. The origin identification accuracy was 96.99%. The storage age identification accuracy was 100% for Guangdong tangerine peel and 97.15% for Sichuan tangerine peel. This indicated that the near-infrared spectroscopy (NIRS) combine with machine learning can simultaneously and rapidly identify the origin and storage age of tangerine peel on site.

In the oil industry, the drilling fluid is yield stress fluid. The gas invading the wellbore during the drilling process is distributed in the wellbore in the form of bubbles. When the buoyancy of the bubble is less than the resistance of the yield stress, the bubble will be suspended in the drilling fluid, which will lead to wellbore pressure inaccurately predicting and overflow. In this paper, the prediction model of gas limit suspension concentration under different yield stresses of drilling fluids is obtained by experiments, and the calculation method of wellbore pressure considering the influence of gas suspension under shut-in conditions is established. Based on the calculation of the basic data of a case well, the distribution of gas in different yield stress drilling fluids and the influence of gas suspension on the wellbore pressure are analyzed. The results show that with the increase of yield stress, the volume of suspended single bubbles increases, the gas suspension concentration increases, and the height at which the gas can rise is reduced. When the yield stress of drilling fluid is 2 Pa, the increment of wellhead pressure decreases by 37.1% compared with that without considering gas suspension, and when the yield stress of drilling fluid is 10Pa, the increment of wellhead pressure can decrease by 78.6%, which shows that when the yield stress of drilling fluid is different, the final stable wellhead pressure is quite different. This is of great significance for the optimization design of field overflow and kill parameters, and for the accurate calculation of wellbore pressure by considering the suspension effect of drilling fluid on the invasion gas through the shut in wellhead pressure.

The hydraulic transport of solid materials is widely used in various industrial fields owing to its high efficiency, low cost, and environmental friendliness, and it has received extensive attention. However, the violent interaction between the liquid and solid phases during transportation makes the slurry flow strongly unsteady and heterogeneous, and it is difficult to use the existing mathematical models describing the motion characteristics in the hydraulic transport of slurry because of the limitations of a single theory or experimental data basis. In this study, considering the randomness and uncertainty in the transportation of solids, a spatio-temporal evolution model of the hydraulic transport characteristics of particulate solids was established. This model is suitable for hydraulic transport in pressure pipelines and open channels, and it can be used to analyze the influence of changes in the motion and property parameters of the liquid–solid phase on the characteristics of the temporal-spatial evolution of the slurry velocity and concentration distributions. The rationality of the model was verified through laboratory experiments. Through an interaction analysis of slurry components, this work explores the influence of the transport of solids on the slurry motion and property parameters, fills the gap in the evolution mechanism of the slurry velocity and concentration distributions in existing models, and overcomes the limitation that layer-based models can only be used in pressure pipelines. Therefore, it has important guiding significance for the engineering design of particulate solid hydraulic transport.

A key challenge in Exploring oil and gas resources in permafrost regions is to predict the thawing subsidence of frozen soil around the wellbore. At present, the traditional thermo-hydro-mechanic (THM) model calculates thawing subsidence without actually considering the spatial-temporal evolution of the thawed-frozen boundary. The Darcy-Stefan model is used to describe the internal mobile interface problems in the heat transfer of saturated porous media due to ice/water phase transitions. In this paper, we established the coupling model of the Darcy-Stefan model and the elastic theory of porous media, including boundary conditions of heat transfer and Darcy flow on the moving boundary. The new model was validated through a series of small-scale examples and a test well in the Arctic region, which demonstrated the predicted subsidence amount of ground surface around the wellhead was in good agreement with the field test data. Further research indicated that with the increase of the thawing zone, the accuracy of calculation for the thawing subsidence could be significantly improved by using the THM model and considering the internal moving boundary. The models and conclusions in this paper can help design more reliable wellbore in permafrost regions throughout the expected life.

Currently, studies on bubble velocity in yield stress fluids with low Reynolds numbers are lacking. In most engineering applications, such value is often calculated using predictive models developed for viscous fluids. However, these models are only applicable to yield stress fluids in limited conditions because the bubble rise velocity is affected by the yield stress in these fluids significantly. In this study, a velocity field model around a bubble is first developed by performing an infinitesimal analysis of the velocity field in the equatorial plane of the bubble and combining the continuity equation with the constitutive equation of yield stress fluids. Next, a new model is proposed for predicting bubble velocity in yield stress fluids at a low transport rate by integrating the model of velocity field around the bubble with energy conservation equations. Finally, experimental studies are performed to analyze the bubble transport behavior in yield stress fluids at low Reynolds numbers. The experimental results showed that under a low Reynolds number, the bubble rise velocity is dominated by the yield stress of the fluid. Hence, we further validated the proposed bubble velocity prediction model with experimental measurements, which match closely with each other. We conclude that for a Reynolds number less than 0.4, the model developed in this study can be used to predict the bubble velocity in yield stress fluids with high accuracy.

UAV equipped with tilt photogrammetry platform can obtain multiple angles images and establish 3D model of tilt photogrammetry, which has unique advantages in 3D intelligent construction. The purpose of this paper is to take the playground and library of the Department of Informatics of Wuhan University as the research area, obtain regional UAV images, and evaluate the residual in the spatial direction of the model by using the measured control points and model points collected in the field, so as to evaluate and analyze the accuracy of the 3D model. By analyzing the data, the root mean square error and maximum error of plane coordinates are 0.31 cm and 0.34 cm respectively, the root mean square error and maximum error of elevation are 0.07 cm and 0.11 cm respectively, and the field measured control point coordinates. Through the accuracy evaluation and analysis of the constructed three-dimensional model, it is concluded that the maximum error in X direction is 1.26 cm, the maximum error in Y direction is 1.39 cm, and the maximum error in Z direction is 1.60 cm. The results show that the accuracy of the three-dimensional model in the study area reaches centimeter-level and meets the requirements of relevant production specifications. It can provide reliable data support for construction and urban planning industries.

In order to solve the over-fitting problem in Convolutional Neural Networks (CNN), a new method to improve the performance of CNN with noise layer on the basis of the previous studies has been proposed. This method improves the generalization performance of the CNN model by adding corresponding noise to the feature image obtained after convolution operation. The constructed noise layer can be flexibly embedded in a certain position of the CNN structure, and with each iteration of training, the added noise is also constantly changing, which makes the interference to the CNN model more profound, thus the more essential features of the input image are obtained. The experimental results show that the improved CNN model based on the noise layer has better recognition effect on some test images than the CNN model without any improvement; for different CNN models, the position of the noise layer which can improve the recognition accuracy is different; as the number of layers deepens, to improve the generalization performance of the CNN model, the position of the noise layer needs to be moved back. The improved CNN model based on the noise layer proposed in this paper solves the overfitting problem to a certain extent, and it has a certain reference significance for studying how to improve the generalization performance of CNN.

Aiming at the subjectivity and poor environmental adaptability of traditional reservoir porosity prediction models, as well as the non-linear characteristics of reservoir parameters, the paper proposes a porosity prediction algorithm based on eXtreme Gradient Boosting (XGBoost). The hyperparameters of XGBoost are optimized by Grid_search(GS) and Genetic A lgorithm(GA), and then a highly accurate porosity prediction model is established based on the optimized parameters. By comparing with the MAE, MAPE and RMSE of the five models, it is found that the three performance indicators of the GS+GA-XGBoost model are 0.155880, 0.020500 and 0.527946, which are at least 4–6 times lower than the errors of the other five models. At the same time, the influence of multi-threaded on the XGBoost model is studied. The final experimental results show that on the test set, the prediction results of the GS+GA-XGBoost model are basically consistent with the original porosity, and 4 threads can improve the speed of model establishment and prediction. The model can effectively predict the porosity of the reservoirs in this area, and the model is very popular.

Drilling fluid is a yield stress fluid, which can trap invading gas in the wellbore. The entrapment of gas leads to the decrease of ECD and the inaccuracy of wellbore pressure calculation during gas kick. This study established a mathematic model for predicting the ultimate gas entrapment concentration (UGEC) by coupling the stress field between multiple bubbles. The prediction model for UGEC is related to drilling fluid yield stress, the mean bubble size and the density difference. The UGEC is linearly with the dimensionless drilling fluid yield stress Bi (=τy/(ρm-ρg)gRb) and there is no gas can entrapped in the drilling fluid for Bi less than 0.143. Based on the UGEC model, a new method for calculating the wellbore pressure is established. The results of model simulation show that with the increase of the drilling fluid yield stress, the height of the invaded gas front and the peak value of the wellbore pressure decrease. Thus, we proposed to inject the high yield stress slug in the well bottom to minimize the damage caused by the gas invasion under the shut-in situation.

For waterflooding oilfields, both the lab experiments and field practices have proved that the physical properties of water-flooding reservoirs would be altered due to the washing of injected water. This condition has not aroused enough attention. There is no proper approaches to describe the change of reservoir parameters against water flux in reservoir simulation. Most commonly used reservoir simulators (Eclipse, CMG, et al.) neglect the alteration of reservoir properties during simulation, and thus, lead to unreasonable history matching and production forecasting. A new parameter known as Pore Cross-sectional Area Flux (PCAF) is introduced to continuously characterize the time-variation of a property during simulation. This new method overcomes the disadvantage of the previous characterization approach known as Pore Volume (PV), which is strongly dependent on grid size. A new numerical simulation software in which reservoir properties are considered functions of PCAF is developed based on the black oil model, and the new simulator is validated against Eclipse. Furthermore, the effects of the time-variation of different parameters on the ultimate oil recovery are investigated. Finally, this simulator is applied to SZ-I oilfield in Bohai Bay to demonstrate how this simulator can achieve more accurate simulation.

During the period of avoiding typhoon in deep-water drilling, the gas intrusion in the wellbore brings higher risk to the well opening operation. It is very important to improve the accuracy of typhoon avoidance cycle prediction to improve the efficiency of drilling operation and reduce the risk of well control. According to the yield stress characteristics of drilling fluid, the gas migration experiment in the yield stress fluid is carried out in this paper. Based on the experimental results and stress analysis, a prediction model of bubble rising velocity suitable for the yield drilling fluid is established. Considering the rheological properties of drilling fluid and pore characteristics of formation, a prediction model of initial bubble size is established. Combined with the velocity prediction model and bubble size prediction model, the calculation method of typhoon avoidance period is proposed. Through the calculation and analysis, it is found that under the condition of low invasion rate, the formation porosity has little influence on the initial size of bubbles, and the yield stress value of drilling fluid has great influence on the initial size of bubbles. At the same time, the model in this paper makes up for the problem that the prediction value of safe typhoon avoidance period calculated by the previous model is large, and reduces the risk of well opening operation. According to our calculation results, the measures of injecting drilling fluid with low yield stress at the bottom of the well and injecting drilling fluid with high yield stress at the top are put forward, which can effectively increase the safe typhoon avoidance period.

The existence of droplets in an annular flow has a non-negligible influence on the mass transfer and heat transfer between phases, and a phase property, such as liquid viscosity, can dominate the droplet characteristics. However, the effects of liquid viscosity on droplets have not yet received sufficient attention. In this study, the distribution characteristics of droplets in a vertical annular flow with a liquid viscosity of 1–30 mPa•s are experimentally investigated using a particle dynamic analyzer. The results reveal that the diameter and momentum of the droplet undergo two different changes during the increase of liquid viscosity, μL. In the low-liquid-viscosity range (μL

Hydrates cause serious flow assurance problems during deepwater operations (e.g. well testing). In order to efficiently prevent wellbore/pipeline blockage, it is of great importance to have a good understanding of hydrate formation, deposition and blockage behavior in the flowline. In this work, a model is developed to describe the development of hydrate blockage in the wellbore. The results indicate a non-uniform hydrate layer is formed on the inner wall along the testing tubing during deepwater gas well testing. With the proposed model, the position where hydrate blockage is most likely to occur can be identified. The blockage severity in terms of dimensionless hydrate layer thickness can also be assessed. Based on the model, a method is developed to prevent hydrate blockage with lower inhibitor consumption. We recommend that testing operations should be run within the Hydrate Blockage Free Window (HBFW). The HBFW refers to the period from the beginning of testing operations to the moment when a significant pressure drop increase is encountered. Implementation procedure of the proposed method is developed and further illustrated through case studies. The inhibitor consumption is much lower compared with the current over-inhibition THI-based method. This work provides possible ways to overcome the shortcomings of the current over-inhibition THI-based method.

To overcome the deficiencies of current porosity prediction methods, the XGBoost algorithm is introduced to construct a model for porosity prediction, and the obtained model is optimized by the grid search method and genetic algorithm. First, the optimal values of the three integer hyperparameters and the ranges of optimal values for the five floating-point hyperparameters of the XGBoost algorithm are determined by the grid search method. Then, the optimal values of the five floating-point hyperparameters of the XGBoost algorithm are determined with the genetic algorithm based on the determined value ranges. In this way, the model for porosity prediction based on the XGBoost algorithm and optimized by the grid search method and genetic algorithm (GS-GA-XGBoost) is constructed, and it has eight hyperparameters with determined optimal values. Compared with other porosity prediction methods, our method solves the problems such as the strong subjectivity and poor generalizability of conventional logging interpretation methods and the insufficient generalization performance of machine learning methods in previous porosity prediction studies, and the accuracy of the constructed model for porosity prediction is also greatly improved. Specifically, the RMSE, MAE and MAPE generated by GS-GA-XGBoost on the test set are 0.527946, 0.155880 and 0.020500 respectively, while those generated by linear regression (LR), support vector regression (SVR), random forest (RF), the XGBoost algorithm with default parameters (XGBoost) and the XGBoost algorithm optimized only by the grid search method (GS-XGBoost) on the test set are 3.535521, 2.801047, 0.375713, and 2.695310, 2.002280, 0.283582, and 1.015801, 0.638878, 0.085942, and 2.781069, 1.860557, 0.293334, and 1.380065, 0.979419, 0.128486, respectively. Finally, the multithread technology is introduced to improve the computational speed of GS-GA-XGBoost. GS-GA-XGBoost provides some technical references for the construction of porosity prediction models for oil fields in Northern Shaanxi, China, and other regions.

The dynamic simulation software of water flooding is of great significance for observing the exploitation of crude oil and understanding the distribution of microscopic remaining oil in the underground reservoirs. Based on Unified Modeling Language (UML), the design of the dynamic simulation software of water flooding is completed. First, based on the results of demand analysis, the function module diagram of the software is established; then the use case diagram is designed to clarify the goals that the software needs to achieve; finally, the class diagram and the sequence diagram of the software are designed, which provides the basis for the specific coding implementation. The design of the dynamic simulation software of water flooding based on UML enriches and develops the theory, technology and method of exploitation geology, and promotes the further application of advanced computer technology in the petroleum industry.

Property graph representation learning is using the property features from the graph to build the embeddings over the nodes and edges. There are many graph application tasks are using the property graph representation learning as part of the process. However, existing methods on Property graph representation learning ignore either the property features or the global topological structure information. We propose the TPGL, which utilizes the topological data analysis with a bias property graph representation learning strategy. The topological data analysis could augment the global topological information to the embedding and significantly improve the embedding performance on node classification experiments. Moreover, the designed bias strategy aggregated the property features into node embedding by using GNN. Particularly, the proposed TPGL outperformed the start of the art methods including PGE in node classification tasks on public datasets.

In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution Neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution Neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution Neural Network models are used to predict test sets, and the results show that deeper Convolution Neural Networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.

Most of the oilfields in China have entered the stage of high water cut nowadays, and it is very difficult to enhance oil recovery in these oilfields. In this paper, combined with the image processing technology, the visualization technology of 3D data field and the seepage mechanics theory, a simulation model on microscopic remaining oil distribution has been constructed. After that, the remaining oil distribution of a high water cut oilfield in south China in three different microscopic pore throat networks, namely big pore and coarse throat, medium pore and medium throat and small pore and fine throat, was simulated. Through the simulation results, the conclusions are drawn: compared with the small pore and fine throat network, the development resistance of oil in the big pore and coarse throat network and the medium pore and medium throat network is smaller under the same water-flooding development conditions; compared with the big pore and coarse throat network, the development resistance of oil in the medium pore and medium throat network is larger, which leads to more remaining oil left. The simulation on microscopic remaining oil distribution is of great significance for enhancing oil recovery in high water cut oilfields of China.

In this study, a velocity field model around a Taylor bubble is established after the analysis of the disturbed region around the bubble. The model combines the continuity and yield stress fluid constitutive equations. Based on this velocity field model, an energy dissipation model for the work accomplished by each resistance of bubbles is proposed. In addition, based on an analysis of energy conservation, a new model for predicting the velocity of a Taylor bubble in a yield stress fluid is presented. The experiments of Taylor bubble through Carbopol solution migration in 50 and 60 mm diameter tubes were carried out. Results show that the yield stress has considerable influence on the rise speed of the bubble, improving the anti-disturbance ability of the fluid and further stabilizing the tail of the bubble. Combined with experimental data, the bubble velocity prediction model is verified, and the error value of the calculation result is found to be less than 6%. An energy analysis of the Taylor bubble rising process shows that the volume of the bubble increases, the proportion of work done by inertial force and surface tension decreases, and that done by viscosity force and yield stress increases. The work done by surface tension has little effect on the rise speed of bubbles, which can be ignored. The Taylor bubble velocity changes slightly with the volume, as the bubble volume increases, and the buoyancy work is consumed by the viscous and inertial forces in the film region.

Hydrate dissociation induced by artificial or environmental factors in submarine sediments may lead to drilling risks, submarine landslides, and the collapse of offshore platforms—all of which are associated with suddenly increased pore pressure. This study establishes a coupled model for the two-phase flow of fluids in porous media in which the pore pressure of water and gas are used as dependent variables and heat transfer and hydrate dissociation equations are combined. The simulation results of the model are consistent with Masuda’s experimental dataset. A quantifiable controlling hydrate dissociation specific reaction surface area (SRSA) model and an absolute permeability model are proposed to conduct numerical simulations of laboratory-scale hydrate cores, with the aim to investigate the mechanism of hydrate dissociation-induced geological hazard initiation and a law of their evolution in sediments. The results show that i) suddenly depressurized hydrate dissociation in porous media can be divided into three phases—pressure induction, transition, and temperature-controlled—and that the first two are much shorter than the third. Moreover, the lower initial permeability results in a shorter holding period for the first stage, and the transition phase becomes indistinguishable; conversely, the larger initial permeability makes the three stages more distinguishable; ii) hardly increasing the core permeability during the intermediate stage of hydrate dissociation makes the pore pressure rebound. However, the steep increase in SRSA does not result in a significant pore gas pressure rebound; conversely, a larger SRSA does facilitate pore pressure dissipation. This work provides theoretical and laboratory-scale model analyses for the safe development of natural gas hydrates bearing in marine sediments.

This paper reported a dynamic model of bubble expansion for predicting the geometry of the entrapped bubble by considering the elasto-vicsco-plastic rheology properties of the yield stress fluid and the resistance caused by the ambient yielded region. The yielded region caused by the bubble expansion or collapse is obtained by the Von Mises criterion and the yielded region caused by the buoyancy is obtained by the experiment data. The model can predict the volume and the geometry of the entrapped bubbles in various fluid rheology properties and surface tension. By comparing the bubble geometries obtained from the model and the experiment, the results show that the equivalent bubble radius error is less than 0.89%, the discrepancies of the maximum width and length for the bubble are less than 4.86% and 3.50%.

Natural gas hydrates may be the world's future resources, but extracting from the reservoirs involves accurately understanding the transport mechanism of the fluids caused by hydrate dissociation. In this paper, based on Darcy's unsaturated soil mechanics theory, we establish a coupling model of two-phase flow and heat transfer caused by hydrate dissociation, which considering methane gas compressibility and capillary force naturally. The new model is validated through Masuda's test dataset, and notably, the experimental phenomenon of the small peak in the gas pressure of the far-end boundary (Run 4) is reproduced for the first time. Additionally, an implicit function of pore pressure and time in the initial stage of hydrate dissociation is deduced to interpret the in-situ pore accumulation of the gas generated in dissociation, which leads to a more comprehensive description of the migration mechanism of hydrate reservoir fluids. Furthermore, the initial hydrate saturation of the Run 4 sample in Masuda's test is proved to be closer to 0.501 instead of 0.443 that the author reported, which can help Masuda's test dataset being a benchmark for the hydrate dissociation researching in future. Thus, this work provides further insight into the migration mechanism of the gases caused by hydrate dissociation and services practical support for efficiently and safely extracting methane gas from the hydrate reservoirs.

Gas hydrates can form during deepwater well testing, resulting in serious flow assurance problems. Once plugs form, remediation operations must be carried out to remove the plugs. We developed a method to quickly locate the plugging position and assess the severity of hydrate plugs. The proposed method was developed based on hydrate thermodynamics, kinetics, and hydrate particle transport & deposition dynamics. The application procedure was established. Flowrate, pressure and temperature data in well testing was collected and used as input data for hydrate plugging development analysis, from which the plugging location and severity could be quickly assessed. The plugging position and severity under various water depth, gas production rate and inhibitor concentration conditions were obtained. It indicates that hydrates generate and attach to the tubing wall and form a growing hydrate layer, which narrows the effective flow area of the tubing. As the hydrate layer becomes thicker, the plugging becomes more severe and bring a greater interference to well testing operations. A significant choking effect is encountered when the hydrate layer thickness increases to a critical value, resulting in distinct pressure drop increase in the testing tubing. It indicates the occurrence of conduit blockage. The plugging position is found at the site where the maximum subcooling is encountered, rather than where the lowest ambient temperature presents. For a typical deepwater gas well, the plugging position is at 150 m below the sea surface. The effects of gas production rate, inhibitor concentration and water depth on plugging position are studied. It is found that the plugging position becomes deeper as the water depth increases and/or the production rate decreases. More severe plugging can be encountered when the gas production rate is lower, the water is deeper, and/or the inhibitor concentration is lower. The proposed method can help operators locate the plugging position and assess the severity of hydrate plugs in a shorter time compared to traditional methods. This study provides key instructions and helps reduce uncertainties involved in removal work. Therefore the operational risk and duration can be significantly cut down.

Hydrate formation/blockage causes serious flow assurance issues during deepwater operations (e.g. well testing). Current dominant method is total prevention of hydrate formation with massive thermodynamics inhibitors (THIs), which render this method economically infeasible. Moreover, the required inhibitor storage and injection capacity may be far beyond the facility limits when the water production rate is high. In this work, aimed at addressing hydrate associated problems at reduced cost, a method is developed to prevent hydrate blockage with lower inhibitor consumption. Based on hydrate formation and deposition dynamics, a method is developed to prevent hydrate blockage during deepwater gas well testing. Implementation procedure of the proposed method is developed and further illustrated through case studies. We recommend that testing operations should be run within the Hydrate Blockage Free Window, HBFW. The HBFW refers to the period from the beginning of testing operations to the moment when a significant pressure drop increase is encountered. Case study work is conducted on a deepwater gas well in the South China Sea. The case study suggests that hydrate inhibitor can postpone the occurrence of conduit blockage. When the scheduled operation time (SOT) is shorter than the HBFW, the testing operation can be completed within the HBFW and no hydrate inhibitor is needed. If the SOT is longer than the HBFW, a small amount of inhibitor is required to extend the HBFW, thus the testing operation can be completed within the HBFW. The inhibitor consumption is much lower compared with the current over-inhibition method. With the proposed HBFW-based method, the inhibitor storage/injection capacity requirement can also be relaxed. The study suggests that with lower inhibitor consumption and inhibitor storage/injection capacity requirement, the HBFW-based method can address hydrate associated problems during deepwater gas well testing at reduced cost. In addition, the reduced inhibitor storage/injection capacity requirement makes this method more feasible especially when high water production is encountered during testing operations.

In this paper, an attribute clustering based collaborative filtering algorithm is applied for development well recommendation towards exploratory wells argumentation. The algorithm utilizes similarity characteristics of exploratory and development well related attributes, especially porosity, permeability and oil saturation, to filter redundant data by feature selection. Experiment use practical well data of the oil company for clustering. By integration of a scaled rating scheme on properties and the collaborative filtering philosophy to provide the recommend cluster. Such cluster contains the candidate development wells for recommendation. Finally, by calculating the similarity between exploratory and development wells, the scheme is able to provide the geologist the selected development wells for the decided exploratory in well argumentation.

In compare with the traditional Artificial Neural Network, PSO-BP neutral network has fast convergence and is immune to local minimum. This paper presents an application of PSO-BP neural network for dynamic predicting small layer reservoir parameters of fault block E1f11-1 in well ZHuang 2. By defining input and output neuron number, our method firstly realizes quantization of input neuron. Then we choose proper samples for training neural network in order to build a dynamic prediction model of reservoir parameters. Such model has been successfully tested and the model itself is appropriate for predicting unknown reservoir parameters. Testing result indicates that PSO-BP neural network is superior to the genetic algorithm optimized BP neural network and the pure neural network. Finally, PSO-BP neural network gained certain achievements for dynamically predicting reservoir parameters according as dynamic production information.

This paper proposes an OpenGL approach with the rendering of material and texture for plotting reservoir profile. Such approach realizes the plotting along X or Y axis and arbitrary direction. At last, the application example shows correctness and intuitiveness of plotting reservoir profile by illustrating sectional drawing from subzone sedimentary facies between S1 and S2 in 106 well of one oil field.