Study on Deep Well Dewatering Optimization Design in Deep Foundation Pit and Engineering Application

Based on analyses of the theories of groundwater unsteady flow in deep well dewatering in the deep foundation pit, Theis equations are chosen to calculate and analyze the relationship between water level drawdown of confined aquifer and dewatering duration. In order to reduce engineering cost and diminish detrimental effect on ambient surrounding, optimization design target function based on the control of confined water drawdown and four restriction requisitions based on the control of safe water level, resistance to throwing up from the bottom of foundation pit, avoiding excessively great subsidence and unequal surface subsidence are proposed. A deep well dewatering project in the deep foundation pit is optimally designed. The calculated results including confined water level drawdown and surface subsidence are in close agreement with the measured results, and the optimization design can effectively control both surface subsidence outside foundation pit and unequal subsidence as a result of dewatering.

Characteristics of helical flow in slim holes and calculation of hydraulics for ultra-deep wells

Due to the slim hole at the lower part of the ultra-deep and deep wells, the eccentricity and rotation of drill string and drilling fluid properties have great effects on the annular pressure drop. This leads to the fact that conventional computational models for predicting circulating pressure drop are inapplicable to hydraulics design of deep wells. With the adoption of helical flow theory and H-B rheological model, a computational model of velocity and pressure drop of non-Newtonian fluid flow in the eccentric annulus was established for the cases where the drill string rotates. The effects of eccentricity, rotation of the drill string and the dimensions of annulus on pressure drop in the annulus were analyzed. Drilling hydraulics was given for an ultra-deep well. The results show that the annular pressure drop decreases with an increase in eccentricity and rotary speed, and increases with a decrease in annular flow area. There is a great difference between static mud density and equivalent circulating density during deep well drilling.

Uncertainty Analysis of the Residual Strength of Non-Uniformly Loaded Casingsin Deep Wells

An uncertainty analysis method is proposed for the assessment of the residual strength of a casing subjected to wear and non-uniform load in a deep well.The influence of casing residual stress,out-of-roundness and non-uniform load is considered.The distribution of multi-source parameters related to the residual anti extrusion strength and residual anti internal pressure strength of the casing after wear are determined using the probability theory.Considering the technical casing of X101 well in Xinjiang Oilfield as an example,it is shown that the randomness of casing wear depth,formation elastic modulus and formation Poisson’s ratio are the main factors that affect the uncertainty of residual strength.The wider the confidence interval is,the greater the uncertainty range is.Compared with the calculations resulting from the proposed uncertainty analysis method,the residual strength obtained by means of traditional single value calculation method is either larger or smaller,which leads to the conclusion that the residual strength should be considered in terms of a range of probabilities rather than a single value.

CAN-based Control System Design on Minor-caliber Deep Well Rescue Robot

By introdming a small-caliber deep well rescue robot, a hold-hug pattern rescue mechanism was brought forward. In order to reduce the volmne, the trader-well rescue imclmnism is modularizing designed. At the same tirae, the audio and video systyems, the illumination system and the ventilation system are expatiated. The rescuing robot can rescue the falling person in the deep well, it can save much manateral resources and time. It＇s really an ideal rescue device for the small-caliber fall.

Research on Temperature Distribution in Wellbore of Deep Well Drilling

The wellbore temperature has an important effect on design and drilling of deep well. Based on energy conservation equations and actual drilling data of one deep well, the wellbore temperature distribution was simulated and the influence of different parameters on the wellbore temperature was revealed using the software of Hydraulics Analysis System. The results show that, while drilling, the mud temperature in wellbore gradually decreases from the formation temperature to the stable temperature, and it is higher than the mud inlet temperature on ground, the annular temperature is higher than the temperature in drill string, and the bottom hole temperature is higher than the ground temperature. The effect of geothermal gradient on wellbore temperature is great, while the mud density is negligible. The bottom hole temperature increases with the increase of mud inlet temperature, geothermal gradient, mud thermal conductivity and decrease of mud flow rate, mud specific heat and mud density.

Reasons resulting in the collapsed tubing near wellhead in high pressure and high temperature deep well during well testing and measures to prevent the collapsing

Because various reasons, the tubing near wellhead was collapsed during well testing in high pressure and high temperature deep well when the outer pressure was less than collapsing strength. To find the reasons in the abnormally collapse and countermeasures, first the quality of the tubing was checked. It was founded that the collapse was not resulted from the defect of the tubing. Then, force and stress exerted in the tubing was analyzed taking XS2 well as an example. The analysis results were concluded as follows. The collapsing strength of tubing decreased due to the axial tensile, which is seriously at the upper tubing especially. During injecting, the additional axial force that was caused by the temperature effect increased the tubing near wellhead to suffer axial tensile and further reduced the collapsing strength of tubing near wellhead. Reinforcing defect, prohibiting defect tubing to trip in hole, according to the calculation to impose appropriate annular pressure, selecting size nozzle to reverse pumping and controlling the reverse pumping speed and pressure, prohibiting to be opened flow and reducing or releasing the annular pressure can prevent the well testing tubing down-hole being collapsed at the wellhead.

Stability control of gate groups in deep wells

In order to study stability control methods for a deep gate group under complex stresses,we conducted field investigations and analyses of reasons for damage in the Xuzhou mining district.Three reasons are proposed:deep high stress,improper roadway layout and support technology.The stability control countermeasures of the gate group consist of an intensive design technology and responding bolt-mesh-anchor truss support technology.Our research method has been applied at the -1000 m level gate group in Qishan Coal Mine.Suitable countermeasures have been tested by field monitoring.

Mechanism and prevention method of drill string uplift during shut-in after overflow in an ultra-deep well

Drill string will sustain large uplift force during the shut-in period after gas overflow in an ultra-deep well, and in serious case, it will run out of the wellhead. A calculation model of uplift force was established to analyze dynamic change characteristics of the uplift force of drill string during the shut-in period, and then a management procedure for the uplift risk during the shut-in period after gas overflow in the ultra-deep well was formed. Cross section method and pressure area method were used to analyze the force on drill string after shut-in of well, it was found that the source of uplift force was the "fictitious force" caused by the hydrostatic pressure in the well. When the fictitious force is in the opposite direction to the gravity, it is the uplift force. By adopting the theory of annular multiphase flow, considering the effects of wellbore afterflow and gas slippage, the dynamic change of the pressure and fluid in the wellbore and the uplift force of drill string during the shut-in period were analyzed. The magnitude and direction of uplift force are related to the length of drill string in the wellbore and shut-in time, and there is the risk of uplift of drill string when the length of drill string in the wellbore is smaller than the critical drill string length or the shut in time exceeds the critical shut in time. A set of treatment method and process to prevent the uplift of drill string is advanced during the shut-in period after overflow in the ultra-deep well, which makes the risk management of the drill string uplift in the ultra-deep well more rigorous and scientific.

Extreme massive hydraulic fracturing in deep coalbed methane horizontal wells:A case study of the Linxing Block,eastern Ordos Basin,NW China

Deep coal seams show low permeability,low elastic modulus,high Poisson’s ratio,strong plasticity,high fracture initiation pressure,difficulty in fracture extension,and difficulty in proppants addition.We proposed the concept of large-scale stimulation by fracture network,balanced propagation and effective support of fracture network in fracturing design and developed the extreme massive hydraulic fracturing technique for deep coalbed methane(CBM)horizontal wells.This technique involves massive injection with high pumping rate+high-intensity proppant injection+perforation with equal apertures and limited flow+temporary plugging and diverting fractures+slick water with integrated variable viscosity+graded proppants with multiple sizes.The technique was applied in the pioneering test of a multi-stage fracturing horizontal well in deep CBM of Linxing Block,eastern margin of the Ordos Basin.The injection flow rate is 18 m^(3)/min,proppant intensity is 2.1 m^(3)/m,and fracturing fluid intensity is 16.5 m^(3)/m.After fracturing,a complex fracture network was formed,with an average fracture length of 205 m.The stimulated reservoir volume was 1987×10^(4)m^(3),and the peak gas production rate reached 6.0×10^(4)m^(3)/d,which achieved efficient development of deep CBM.

Automatic depth matching method of well log based on deep reinforcement learning

In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep reinforcement learning(MARL)method to automate the depth matching of multi-well logs.This method defines multiple top-down dual sliding windows based on the convolutional neural network(CNN)to extract and capture similar feature sequences on well logs,and it establishes an interaction mechanism between agents and the environment to control the depth matching process.Specifically,the agent selects an action to translate or scale the feature sequence based on the double deep Q-network(DDQN).Through the feedback of the reward signal,it evaluates the effectiveness of each action,aiming to obtain the optimal strategy and improve the accuracy of the matching task.Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells,and reduce manual intervention.In the application to the oil field,a comparative analysis of dynamic time warping(DTW),deep Q-learning network(DQN),and DDQN methods revealed that the DDQN algorithm,with its dual-network evaluation mechanism,significantly improves performance by identifying and aligning more details in the well log feature sequences,thus achieving higher depth matching accuracy.

Well-defined high entropy-metal nanoparticles:Detection of the multi-element particles by deep learning

Characterizing and control the chemical compositions of multi-element particles as single metal nanoparticles(mNPs) on the surfaces of catalytic metal oxide supports is challenging.This can be attributed to the heterogeneity and large size at the nanoscale,the poorly defined catalyst nanostructure,and thermodynamic immiscibility of the strongly repelling metallic elements.To address these challenges,an ultrasonic-assisted coincident electro-oxidation-reduction-precipitation(U-SEO-P) is presented to fabricate ultra-stable PtRuAgCoCuP NPs,which produces numerous active intermediates and induces strong metal-support interactions.To sort the active high-entropy mNPs,individual NPs are described on the support surface and the role of deep learning in understanding/predicting the features of PtRuAgCoCu@TiO_(x) catalysts is explained.Notably,this deep learning approach required minimal to no human input.The as-prepared PtRuAgCoCu@TiO_(x) catalysts can be used to catalyze various important chemical reactions,such as a high reduction conversion(100% in 30 s),with no loss of catalytic activity even after 20 cycles of nitroarene and ketone/aldehyde,which is several times higher than commercial Pt@TiO_(x) owing to individual PtRuAgCoCuP NPs on TiO_(x) surface.In this study,we present the "Totally Defined Catalysis" concept,which has enormous potential for the advancement of high-activity catalysts in the reduction of organic compounds.

Evaluation and Application of Flowback Effect in Deep Shale Gas Wells

The pivotal areas for the extensive and effective exploitation of shale gas in the Southern Sichuan Basin have recently transitioned from mid-deep layers to deep layers.Given challenges such as intricate data analysis,absence of effective assessment methodologies,real-time control strategies,and scarce knowledge of the factors influencing deep gas wells in the so-called flowback stage,a comprehensive study was undertaken on over 160 deep gas wells in Luzhou block utilizing linear flow models and advanced big data analytics techniques.The research results show that:(1)The flowback stage of a deep gas well presents the characteristics of late gas channeling,high flowback rate after gas channeling,low 30-day flowback rate,and high flowback rate corresponding to peak production;(2)The comprehensive parameter AcmKm1/2 in the flowback stage exhibits a strong correlation with the Estimated Ultimate Recovery(EUR),allowing for the establishment of a standardized chart to evaluate EUR classification in typical shale gas wells during this stage.This enables quantitative assessment of gas well EUR,providing valuable insights into production potential and performance;(3)The spacing range and the initial productivity of gas wells have a significant impact on the overall effectiveness of gas wells.Therefore,it is crucial to further explore rational well patterns and spacing,as well as optimize initial drainage and production technical strategies in order to improve their performance.

Optimizing the Diameter of Plugging Balls in Deep Shale Gas Wells

Deep shale gas reserves that have been fractured typically have many relatively close perforation holes. Due to theproximity of each fracture during the formation of the fracture network, there is significant stress interference,which results in uneven fracture propagation. It is common practice to use “balls” to temporarily plug fractureopenings in order to lessen liquid intake and achieve uniform propagation in each cluster. In this study, a diameteroptimization model is introduced for these plugging balls based on a multi-cluster fracture propagationmodel and a perforation dynamic abrasion model. This approach relies on proper consideration of the multiphasenature of the considered problem and the interaction force between the involved fluid and solid phases. Accordingly,it can take into account the behavior of the gradually changing hole diameter due to proppant continuousperforation erosion. Moreover, it can provide useful information about the fluid-dynamic behavior of the consideredsystem before and after plugging. It is shown that when the diameter of the temporary plugging ball is1.2 times that of the perforation hole, the perforation holes of each cluster can be effectively blocked.

Deep sea mineral resources and underground space as well as infrastructure for sustainable and liveable cities

This issue covers the papers on two special themes:(1)Mineral resources from deep sea—Science and Engineering and(2)Planning and development of underground space and infrastructure for sustainable and liveable cities.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Deep eutectic solvents for separation and purification applications in critical metal metallurgy:Recent advances and perspectives

Solvent extraction,a separation and purification technology,is crucial in critical metal metallurgy.Organic solvents commonly used in solvent extraction exhibit disadvantages,such as high volatility,high toxicity,and flammability,causing a spectrum of hazards to human health and environmental safety.Neoteric solvents have been recognized as potential alternatives to these harmful organic solvents.In the past two decades,several neoteric solvents have been proposed,including ionic liquids(ILs)and deep eutectic solvents(DESs).DESs have gradually become the focus of green solvents owing to several advantages,namely,low toxicity,degradability,and low cost.In this critical review,their classification,formation mechanisms,preparation methods,characterization technologies,and special physicochemical properties based on the most recent advancements in research have been systematically described.Subsequently,the major separation and purification applications of DESs in critical metal metallurgy were comprehensively summarized.Finally,future opportunities and challenges of DESs were explored in the current research area.In conclusion,this review provides valuable insights for improving our overall understanding of DESs,and it holds important potential for expanding separation and purification applications in critical metal metallurgy.

Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology

Pore size analysis plays a pivotal role in unraveling reservoir behavior and its intricate relationship with confined fluids.Traditional methods for predicting pore size distribution(PSD),relying on drilling cores or thin sections,face limitations associated with depth specificity.In this study,we introduce an innovative framework that leverages nuclear magnetic resonance(NMR)log data,encompassing clay-bound water(CBW),bound volume irreducible(BVI),and free fluid volume(FFV),to determine three PSDs(micropores,mesopores,and macropores).Moreover,we establish a robust pore size classification(PSC)system utilizing ternary plots,derived from the PSDs.Within the three studied wells,NMR log data is exclusive to one well(well-A),while conventional well logs are accessible for all three wells(well-A,well-B,and well-C).This distinction enables PSD predictions for the remaining two wells(B and C).To prognosticate NMR outputs(CBW,BVI,FFV)for these wells,a two-step deep learning(DL)algorithm is implemented.Initially,three feature selection algorithms(f-classif,f-regression,and mutual-info-regression)identify the conventional well logs most correlated to NMR outputs in well-A.The three feature selection algorithms utilize statistical computations.These algorithms are utilized to systematically identify and optimize pertinent input features,thereby augmenting model interpretability and predictive efficacy within intricate data-driven endeavors.So,all three feature selection algorithms introduced the number of 4 logs as the most optimal number of inputs to the DL algorithm with different combinations of logs for each of the three desired outputs.Subsequently,the CUDA Deep Neural Network Long Short-Term Memory algorithm(CUDNNLSTM),belonging to the category of DL algorithms and harnessing the computational power of GPUs,is employed for the prediction of CBW,BVI,and FFV logs.This prediction leverages the optimal logs identified in the preceding step.Estimation of NMR outputs was done first in well-A(80%of data as training and 20%as testing).The correlation coefficient(CC)between the actual and estimated data for the three outputs CBW,BVI and FFV are 95%,94%,and 97%,respectively,as well as root mean square error(RMSE)was obtained 0.0081,0.098,and 0.0089,respectively.To assess the effectiveness of the proposed algorithm,we compared it with two traditional methods for log estimation:multiple regression and multi-resolution graph-based clustering methods.The results demonstrate the superior accuracy of our algorithm in comparison to these conventional approaches.This DL-driven approach facilitates PSD prediction grounded in fluid saturation for wells B and C.Ternary plots are then employed for PSCs.Seven distinct PSCs within well-A employing actual NMR logs(CBW,BVI,FFV),in conjunction with an equivalent count within wells B and C utilizing three predicted logs,are harmoniously categorized leading to the identification of seven distinct pore size classification facies(PSCF).this research introduces an advanced approach to pore size classification and prediction,fusing NMR logs with deep learning techniques and extending their application to nearby wells without NMR log.The resulting PSCFs offer valuable insights into generating precise and detailed reservoir 3D models.

The effect of P^+ deep well doping on SET pulse propagation

The change of P+ deep well doping will affect the charge collection of the active and passive devices in nano-technology,thus affecting the propagated single event transient(SET) pulsewidths in circuits.The propagated SET pulsewidths can be quenched by reducing the doping of P+ deep well in the appropriate range.The study shows that the doping of P+ deep well mainly affects the bipolar amplification component of SET current,and that changing the P+ deep well doping has little effect on NMOS but great effect on PMOS.

Study on prediction model of maximum hydraulic extension depth for ultra deep wells considering effects of temperature

How long the ultra deep well can extend and what is the ultra deep well's maximum hydraulic extension depth are always concerned and studied by drilling engineers. The well's maximum hydraulic extension depth can be predicted by the maximum hydraulic extension depth prediction model. To overcome the disadvantage that previous prediction model did not consider the effects of temperature and only applies to horizontal wells, a prediction model of maximum hydraulic extension depth for ultra deep wells considering effects of temperature is established. Considering the effects of temperature coupled with the constraints of drilling pump rated pressure and rated power, the prediction result of ultra deep well's maximum hydraulic extension depth is modified. An ultra deep well developed by Sinopec in Shunbei oilfield, China, is analyzed, and its wellbore temperature profile and maximum hydraulic extension depth are analyzed and predicted. Results show that the maximum hydraulic extension depth with considering temperature is larger than that without considering temperature. With the identical depth, the higher inlet temperature and the greater geothermal gradient mean the higher drilling fluid temperatures in the drill string and annulus as well as the larger maximum hydraulic extension depth. Besides, the maximum depth decreases with the increase in drilling fluid flow rate and density, while it increases with the increase in drilling pump rated pressure and rated power. To ensure the designed depth can be reached, there exists the maximum drilling fluid flow rate and density, as well as the minimum drilling pump rated pressure and rated power. This study is important for accurately predicting the ultra deep well's maximum depth within the limit capacity of drilling pump. In addition, it also plays a major role in avoiding drilling hazards.

Predicting Initial Formation Temperature for Deep Well Engineering with a New Method

With the progress of science and technology, human beings explore the energy underground with thousands of meters. As a thermophysical parameter, initial formation temperature （IFT） plays an essential role in deep well engineering. However, it is not easy to predict the IFT accurately before drilling. This work uses a new method to analyze the effect factors of the underground temperature field, and assumes an artificial surface to eliminate the disturbance of the human errors and equipment errors on the surface temperature and thermal conductivity. Considering different distributions of the formation thermal conductivity and the rock radiogenic heat production, an optimized model was established. With this model, the paper predicted the bottom temperature of the main hole of the Chinese Continental Scientific Drilling （CCSD） as 132.80 ℃ at 4 725 m depth with 0.5% error. When the thermal conduction is dominant in the formation, this simple method can predict the IFT distribution effectively for deep well in the exploration stage. However, it is almost impossible to avoid aquifers in the formation of drilling deep well, an existing drillhole including groundwater is needed to predict for testing the model＇s accuracy.