Impact Damage Testing Study of Shanxi-Beijing Natural Gas Pipeline Based on Decision Tree Rotary Tiller Operation

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.

Effects of Different Concentrations of Sulfate Ions on Carbonate Crude Oil Desorption:Experimental Analysis and Molecular Simulation

Low salinity water containing sulfate ions can significantly alter the surface wettability of carbonate rocks.Nevertheless,the impact of sulfate concentration on the desorption of oil film on the surface of carbonate rock is still unknown.This study examines the variations in the wettability of the surface of carbonate rocks in solutions containing varying amounts of sodium sulfate and pure water.The problem is addressed in the framework of molecular dynamics simulation(Material Studio software)and experiments.The experiment’s findings demonstrate that sodium sulfate can increase the rate at which oil moisture is turned into water moisture.The final contact angle is smaller than that of pure water.The results of the simulations show that many water molecules travel down the water channel under the influence of several powerful forces,including the electrostatic force,the van der Waals force and hydrogen bond,crowding out the oil molecules on the calcite’s surface and causing the oil film to separate.The relative concentration curve of water and oil molecules indicates that the separation rate of the oil film on the surface of calcite increases with the number of sulfate ions.

Relation between structural evolution of the Longmenshan orogenic zone and sedimentation of its foreland basin

In order to determine the area for oil and gas exploration in China’s north Sichuan basin,we have divided the time during which the Longmenshan foreland basin was formed into five periods,based on the sedimentary response relationship of the foreland basin to structural evolution: 1) a late Triassic Noric period;2) an early-Middle Jurassic period;3) a late Jurassic to early Cretaceous period;4) a late Cretaceous to Paleogene-Neogene period and 5) the Quaternary period. As well,we analyzed the sedimentary environment and lithologic features of every basin-forming period. The results show that there are several favorable source-reservoir-cap assemblages in our study area,making it a major region for future oil and gas exploration in China’s northern Sichuan basin.

Impact of clay stabilizer on the methane desorption kinetics and isotherms of Longmaxi Shale,China

Knowing methane desorption characteristics is essential to define the contribution of adsorbed gas to gas well production.To evaluate the synthetic effect of a clay stabilizer solution on methane desorption kinetics and isotherms pertaining to Longmaxi shale,an experimental setup was designed based on the volumetric method.The objective was to conduct experiments on methane adsorption and desorption kinetics and isotherms before and after clay stabilizer treatments.The experimental data were a good fit for both the intraparticle diffusion model and the Freundlich isotherm model.We analyzed the effect of the clay stabilizer on desorption kinetics and isotherms.Results show that clay stabilizer can obviously improve the diffusion rate constant and reduce the methane adsorption amount.Moreover,we analyzed the desorption efficiency before and after treatment as well as the adsorbed methane content.The results show that a higher desorption efficiency after treatment can be observed when the pressure is higher than 6.84 MPa.Meanwhile,the adsorbed methane content before and after treatment all increase when the pressure decreases,and clay stabilizer can obviously promote the adsorbed methane to free gas when the pressure is lower than 19 MPa.This can also be applied to the optimization formulation of slickwater and the design of gas well production.

Analysis of Wellbore Flow in Shale Gas Horizontal Wells

Theflow behavior of shale gas horizontal wells is relatively complex,and this should be regarded as the main reason for which conventional pipeflow models are not suitable to describe the related dynamics.In this study,numerical simulations have been conducted to determine the gas-liquid distribution in these wells.In particular,using the measuredflow pressure data related to 97 groups of shale gas wells as a basis,9 distinct pipeflow models have been assessed,and the models displaying a high calculation accuracy for different water-gas ratio(WGR)ranges have been identified.The results show that:(1)The variation law of WGR in gas well satisfies a power function relation.(2)The well structure is the main factor affecting the gas-liquid distribution in the wellbore.(3)The Beggs&Brill,Hagedorn&Brown and Gray models exhibit a high calculation accuracy.

Log interpretation of carbonate rocks based on petrophysical facies constraints

The complex pore structure of carbonate reservoirs hinders the correlation between porosity and permeability.In view of the sedimentation,diagenesis,testing,and production characteristics of carbonate reservoirs in the study area,combined with the current trends and advances in well log interpretation techniques for carbonate reservoirs,a log interpretation technology route of“geological information constraint+deep learning”was developed.The principal component analysis(PCA)was employed to establish lithology identification criteria with an accuracy of 91%.The Bayesian stepwise discriminant method was used to construct a sedimentary microfacies identification method with an accuracy of 90.5%.Based on production data,the main lithologies and sedimentary microfacies of effective reservoirs were determined,and 10 petrophysical facies with effective reservoir characteristics were identified.Constrained by petrophysical facies,the mean interpretation error of porosity compared to core analysis results is 2.7%,and the ratio of interpreted permeability to core analysis is within one order of magnitude,averaging 3.6.The research results demonstrate that deep learning algorithms can uncover the correlation in carbonate reservoir well logging data.Integrating geological and production data and selecting appropriate machine learning algorithms can significantly improve the accuracy of well log interpretation for carbonate reservoirs.

A theoretical study on gaseous pollutant flushing of natural ventilation driven by buoyancy forces in industrial buildings

The acceleration of industrialization worsening indoor environments of industrial buildings has drawn more attention in recent years.Natural ventilation can improve indoor air quality(IAQ)and reduce carbon emissions.To evaluate gaseous pollutant levels in industrial buildings for the development of buoyancy-driven natural ventilation,two theoretical models of pollutant flushing(Model I and Model II)are developed based on the existing thermal stratification theory in combination with the mixing characteristics of lower pollutant.The results show that indoor pollutant flushing is mainly dependent on the pollution source intensity and effective ventilation area.The mixing characteristics of lower pollutant has an important effect on pollutant stratification and evolution during ventilation,but it does not change the prediction results at steady state.When the dimensionless pollution source intensity is larger than 1,the pollution source should be cleaned up or other ventilation methods should be used instead to improve IAQ.In addition,the comparisons between Model I and Model II on instantaneous pollutant concentration are significantly influenced by the pollution source intensity,and the actual pollutant concentration is more likely to be between the predicted values of Model I and Model II.To reduce pollutant concentration to a required level,the pollution source intensity should be in a certain range.The theoretical models as well as the necessary conditions for ventilation effectiveness obtained can be used for the ventilation optimization design of industrial buildings.

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.

Optimization of a High-pressure Water-Powder Mixing Prototype for Offshore Platforms

A new device is designed to promote the mixing of high-pressure water jets and powders in typical industrial applications.The water and powder mixing devices traditionally used on offshore platforms are detrimentally affected by the geometrical configuration of the water nozzle and the powder spraying pipe,which are parallel,resulting in small intersecting volumes of liquid and powder.By allowing the related jets to intersect,in the present work the optimal horizontal distance,vertical distance and intersection angle are determined through a parametric investigation.It is also shown that such values change if the number of water layers is increased from one to two.In such a case,the water and powder intersection volume can be greatly increased because the trajectory of the dry powder particles becomes more chaotic.

A hybrid machine learning optimization algorithm for multivariable pore pressure prediction

Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.

Solidification crack characteristics and sensitivity of peritectic steel

The characteristics and sensitivity of solidification cracks in peritectic steels were investigated using directional solidifi-cation technology.Interdendritic cracks were observed in both hypoperitectic steels(12CrlMoV,15CrMo)and hyper-peritectic steel(20CrMo)during solidification at growth velocities of 15,50,and 80 pm/s.At the dendritic boundaries,sulphide precipitates were found,promoting crack formation.Based on the statistical analysis of interdendritic cracks in peritectic steels,the area ratio(RA)of interdendritic cracks in a directional solidification structure was proposed to evaluate the crack sensitivity of peritectic steels.Furthermore,the crack sensitivities of peritectic steels(12CrlMoV,15CrMo,and 20CrMo)were tested,evaluated,and compared with the surface crack rates of three types of steels produced from a steel plant.The results demonstrated that RA was in good agreement with that of the steel plant,and the crack sensitivity of 12CrlMoV steel was the strongest,followed by that of 15CrMo and 20CrMo steels.Thus,RA can be used to evaluate the crack sensitivity of peritectic steel.

Study on Optimal Water ControlMethods for Horizontal Wells in Bottom Water Clastic Rock Reservoirs

The segmented water control technology for bottom water reservoirs can effectively delay the entry of bottom water and adjust the production profile.To clarify the impact of different methods on horizontal well production with different reservoir conditions and to provide theoretical support for the scientific selection of methods for bottom water reservoirs,a numerical simulation method is presented in this study,which is able to deal with wellbore reservoir coupling under screen tube,perforation,and ICD(Inflow Control Device)completion.Assuming the geological characteristics of the bottom-water conglomerate reservoir in the Triassic Formation of the Tahe Block 9 as a test case,the three aforementioned completion methods are tested to predict the transient production characteristics.The impact of completion parameters,reservoir permeability,bottom-water energy,and individual well control on the time to encounter water in horizontal wells(during a water-free production period)is discussed.A boundary chart for the selection of completion methods is introduced accordingly.The results show that the optimized ICD completion development effect for heterogeneous reservoirs is the best,followed by optimized perforation completion.Permeability is the main factor affecting the performances of completion methods,while bottom water energy and single well controlled reserves have a scarce impact.The average permeability of the reservoir is less than 500 mD,and ICD has the best water control effect.If the permeability is greater than 500 mD,the water control effect of perforation completion becomes a better option.

A New Heat Transfer Model for Multi-Gradient Drilling with Hollow Sphere Injection

Multi-gradient drilling is a new offshore drilling method.The accurate calculation of the related wellbore temperature is of great significance for the prediction of the gas hydrate formation area and the precise control of the wellbore pressure.In this study,a new heat transfer model is proposed by which the variable mass flow is properly taken into account.Using this model,the effects of the main factors influencing the wellbore temperature are analyzed.The results indicate that at the position where the separation injection device is installed,the temperature increase of the fluid in the drill pipe is mitigated due to the inflow/outflow of hollow spheres,and the temperature drop of the fluid in the annulus also decreases.In addition,a lower separation efficiency of the device,a shallower installation depth and a smaller circulating displacement tend to increase the temperature near the bottom of the annulus,thereby helping to reduce the hydrate generation area and playing a positive role in the prevention and control of hydrates in deepwater drilling.

Using polyvinylidene fluoride to improve ignition and combustion of micron-sized boron powder by fluorination reaction

Boron has a promising application in the field of propellants due to its high calorific value.However,the difficulty of ignition and the poor combustion efficiency of boron(B)have severely limited its efficient application.In response to this issue,this paper proposes to improve the ignition and combustion performance of micron-sized boron by the Polyvinylidene Fluoride(PVDF)coating.The effect of PVDF content on the B combustion performance was systematically studied using a Thermogravimetry-Differential Scanning Calorimetry(TG-DSC),a Transmission Electron Microscope(TEM),an X-Ray Diffractometer(XRD),a laser Particle Size Analyzer(PSA),and a high-speed camera.The results show that PVDF can significantly reduce the initial oxidation temperature of B powder and increase its reaction heat.When the PVDF content is 23wt%,the reaction heat and the combustion intensity of B powder reach the maximum and are significantly higher than those of the uncoated B powder.Moreover,the fluorination reaction that occurs during the combustion process not only can effectively shorten the combustion time of B powder,but also has a positive effect on its flame intensity and propagation speed,and it significantly reduces B particle agglomeration,which improves the combustion efficiency significantly.This study lays the foundation for the application of PVDF modified B in B-based solid propellants.

Application of ARIMA-RTS optimal smoothing algorithm in gas well production prediction

Gas field production forecast is an important basis for decision-making in the gas industry.How to accurately predict the dynamic production during gas field development is an important content of reservoir engineering research.Reservoir numerical simulation is the most common method for predicting oil and gas production.However,it requires a lot of data to build an accurate geological model which is tedious and time-consuming.At present,many scholars have used machine learning and data mining methods to predict oil and gas production,but they have not considered whether the use of increasing production measures will affect the predicted results.Thus,ARIMA-RTS optimal smooth algorithm is the first applied to establish the prediction model of gas well production.According to the historical production data,the model is processed,the production differential autoregressive integral moving average(ARIMA)model in time series is established,then ARIMA model is combined with RTS(Rauch Tung Striebel)smoothing,and the production prediction model is constructed.RTS smoothing algorithm is an enhanced version of Kalman filter.The measurements are firstly processed by the forward filter,and then,a separate backward smoothing pass is used for obtaining the smoothing solution.The correctness of ARIMA-RTS model was verified with the actual production data.The results show that the prediction based on ARIMA-RTS model can accurately reflect the production performance of gas well.This method can effectively reduce the error caused by stimulation when predicting.When using the ARIMA-RTS model and the ARIMA-Kalman model to predict the production of the same gas well,the prediction accuracy of ARIMA-RTS model is higher than that of ARIMA-Kalman model in production wells with stimulation.Compared with that of the ARIMA-Kalman model,the mean relative error fitted by the ARIMA-RTS model is reduced by 46.3%,and the relative mean square error is reduced by 56.48%.ARIMA-RTS optimal smooth algorithm improves the prediction accuracy of gas well that uses stimulation.We therefore conclude that the ARIMA-RTS optimal smooth algorithm can help us better forecast the forecasting gas well production with stimulation,as well as other fuels output.

Improvement of predictions of petrophysical transport behavior using three-dimensional finite volume element model with micro-CT images

Due to the intricate structure of porous media, the macroscopic petrophysical transport properties such as the permeability and the saturation used for the reservoir prediction also show a very complex nature and are difficult to obtain. Thus, a better understanding of the influence of the rock structure on the petrophysical transport properties is important. In this paper, we present a universal finite volume element modeling approach to reconstruct the three dimensional pore models from the micro-CT images based on the commercial software Mimics and ICEM, prior to the pore network model based on some basic assumptions. Moreover, tetra finite volume elements are piled up to realize the geometry reconstruction and the meshing process. Compared with the former methods, this process avoids the tremendously large storage requirement for the reconstructed porous geometry and the failures of meshing these complex polygon geometries, and at the same time improves the predictions of petrophysical transport behaviors. The model is tested on two Berea sandstones, four sandstone samples, two carbonate samples, and one Synthetic Silica. Single- and two phase flow simulations are conducted based on the Navier-Stokes equations in the Fluent software. Good agreements are obtained on both the network structures and predicted single- and two- phase transport properties against benchmark experimental data.

Forecasting oil production in unconventional reservoirs using long short term memory network coupled support vector regression method: A case study

Production prediction is crucial for the recovery of hydrocarbon resources.However,accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data.To address this problem,a novel model combining a long short-term memory network(LSTM)and support vector regression(SVR)was proposed to forecast tight oil production.Three variables,the tubing head pressure,nozzle size,and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters.The time-series response of tight oil production was the output and was predicted by the optimized LSTM model.An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy.Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield.Decline curve analysis(DCA)methods,LSTM and artificial neural network(ANN)models were also applied in this study and compared with the LSTM-SVR model to prove its superiority.It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance.The LSTM-SVR model of Well A produced the lowest prediction root mean square error(RMSE)of 5.42,while the RMSE of Arps,PLE Duong,ANN,and LSTM were 5.84,6.65,5.85,8.16,and 7.70,respectively.The RMSE of Well B of LSTM-SVR model is 0.94,while the RMSE of ANN,and LSTM were 1.48,and 2.32.