A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.

Three-Dimensional Modelling of a Multi-Layer Sandstone Reservoir： the Sebei Gas Field, China

Multi-layer sandstone reservoirs occur globally and are currently in international production. The 3D characteristics of these reservoirs are too complicated to be accurately delineated by general structural-facies-reservoir modelling. In view of the special geological features, such as the vertical architecture of sandstone and mudstone interbeds, the lateral stable sedimentation and the strong heterogeneity of reservoir poroperm and fluid distribution, we developed a new three-stage and six-phase procedure for 3D characterization of multi-layer sandstone reservoirs. The procedure comprises two-phase structural modelling, two-phase facies modelling and modelling of two types of reservoir properties. Using this procedure, we established models of the formation structure, sand body structure and microfacies, reservoir facies and properties including porosity, permeability and gas saturation and provided a 3D fine-scale, systematic characterization of the Sebei multi-layer sandstone gas field, China. This new procedure, validated by the Sebei gas field, can be applied to characterize similar multi-layer sandstone reservoirs.

Multi-layer Tectonic Model for Intraplate Deformation and Plastic-Flow Network in the Asian Continental Lithosphere

In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

The role of polyurethane foam compressible layer in the mechanical behaviour of multi-layer yielding supports for deep soft rock tunnels

The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Enhancing Software Effort Estimation:A Hybrid Model Combining LSTM and Random Forest

Effort estimation plays a crucial role in software development projects,aiding in resource allocation,project planning,and risk management.Traditional estimation techniques often struggle to provide accurate estimates due to the complex nature of software projects.In recent years,machine learning approaches have shown promise in improving the accuracy of effort estimation models.This study proposes a hybrid model that combines Long Short-Term Memory(LSTM)and Random Forest(RF)algorithms to enhance software effort estimation.The proposed hybrid model takes advantage of the strengths of both LSTM and RF algorithms.To evaluate the performance of the hybrid model,an extensive set of software development projects is used as the experimental dataset.The experimental results demonstrate that the proposed hybrid model outperforms traditional estimation techniques in terms of accuracy and reliability.The integration of LSTM and RF enables the model to efficiently capture temporal dependencies and non-linear interactions in the software development data.The hybrid model enhances estimation accuracy,enabling project managers and stakeholders to make more precise predictions of effort needed for upcoming software projects.

Modeling Locking Angle of the Multi-layered Biaxial Weft Knitted Fabric in Shear Deformation

This paper introduces the construction of the multi-layered biaxial weft knitted fabric （MBWK fabric） and studies the locking angle of this kind of fabric. Moreover, a locking angle model of the MBWK fabric is established for the first time according to its unique construction. Two kinds of locking angles are considered under different restraint conditions: the locking angle θ1 controlled by the inserting yarns and the locking angle θ2 controlled by the stitch yarns. It is concluded that the ultimate value of the locking angle θ is the larger one of the two angles.

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.

NEW MODEL OF GAS FLOW PROBLEM IN MULTI-LAYERED GAS RESERVOIR AND APPLICATION

In this paper, the new model of the real gas filtration problem has been presented multi-layered gas reservoir, when a gas well output and wellbore storage may be variable, and have obtained the exact solutions of pressure distribution for each reservoir bed under three kinds of typical out-boundary conditions. As a special case, according to the new model have also obtained the qxact solutions of presssure distribution in homogeneous reservoir and is given important application in gas reservoir development.

Multi-layer Evacuation Model: A solution of Emergency Evacuation

The large-scale population accumulation in modem cities has become one of their important characteristics. With the development of urbanization in the world, the large-scale gathering activities are increasing, and the accidents caused by them are also rising. At the same time, the evacuation of visitors is faced with severe challenges in the event of an emergency such as terrorist attacks. The main problem for tourists is how to evacuate quickly and safely in an emergency. The Louvre is one of the largest and most visited art museums in the world.Visitors are large and come from all over the world, the volume of passengers varies greatly, and the interior architecture design is complicated, etc. These characteristics challenge the design of evacuation paths. Based on the consideration of these factors, we should develop the optimal evacuation scheme and minimize the accident risk and evacuation cost.

基于CNN-LSTM混合神经网络的高速铁路地震响应预测

为了更好地挖掘高速铁路在地震时的响应信息,提高光纤光栅监测的效率及预测精度,该文针对地震响应数据的时序性及非线性的特点,提出卷积神经网络(CNN)和长短期记忆(LSTM)网络的混合神经网络模型预测方法.通过在高速铁路简支梁桥上布设准分布式光纤光栅采集地震时轨道板、钢轨、底座板、箱梁的响应数据,在每根光纤上布置7个光栅,利用两边光栅的响应数据预测中间点的光栅响应,将采集位置、历史数据及地震波形等信息作为特征图输入.利用CNN提取特征,再将提前提取出来的特征数据以时序方式作为LSTM网络的输入数据,最后LSTM网络进行地震应变响应预测.实验结果表明,LSTM网络在3层时效果最好,CNN-LSTM方法具有较高的预测精度,根均平方误差(R_(RMSE))、平均绝对误差(R_(MAE))、决定系数(R^(2))分别达到了0.3753、0.2968、0.9371.

基于GRU和LSTM组合模型的车联网信道分配方法

针对车联网中高通信需求和高移动性造成的车对车链路(Vehicle to Vehicle,V2V)间的信道冲突及网络效用低下的问题,提出了一种基于并联门控循环单元(Gated Recurrent Unit,GRU)和长短期记忆网络(Long Short-Term Memory,LSTM)的组合模型的车联网信道分配算法。算法以降低V2V链路信道碰撞率和空闲率为目标,将信道分配问题建模为分布式深度强化学习问题,使每条V2V链路作为单个智能体,并通过最大化每回合平均奖励的方式进行集中训练、分布式执行。在训练过程中借助GRU训练周期短和LSTM拟合精度高的组合优势去拟合深度双重Q学习中Q函数,使V2V链路能快速地学习优化信道分配策略,合理地复用车对基础设施(Vehicle to Infrastructure,V2I)链路的信道资源,实现网络效用最大化。仿真结果表明,与单纯使用GRU或者LSTM网络模型的分配算法相比,该算法在收敛速度方面加快了5个训练回合,V2V链路间的信道碰撞率和空闲率降低了约27%,平均成功率提升了约10%。

Prophet-LSTM组合模型在运输航空征候预测中的应用

为准确预测中国运输航空征候万时率,提出了一种将时间序列模型和神经网络模型组合的预测方法。首先,利用2008年1月—2020年12月的运输航空征候万时率数据建立Prophet模型,使用RStudio软件进行模型拟合,获取运输航空征候万时率的线性部分;其次,利用长短期记忆网络(Long Short-Term Memory,LSTM)建模,获取运输航空征候万时率的非线性部分;最后,利用方差倒数法建立Prophet-LSTM组合模型,使用建立的组合模型对2021年1—12月运输航空征候万时率进行预测,将预测结果与实际值进行对比验证。结果表明,Prophet-LSTM组合模型的EMA、EMAP、ERMS分别为0.0973、16.1285%、0.1287。相较于已有的自回归移动平均(Auto Regression Integrated Moving Average,ARIMA)+反向传播神经网络(Back Propagation Neural Network,BPNN)组合模型和GM(1,1)+ARIMA+LSTM组合模型,Prophet-LSTM组合模型的EMA、EMAP、ERMS分别减小了0.0259、10.4874百分点、0.0143和0.0128、2.0599百分点、0.0086,验证了Prophet-LSTM组合模型的预测精度更高,性能更优良。

基于深度学习的LSTM-GRU复合模型矿井涌水量预测方法研究

为了解决矿井涌水预测问题,引入深度学习理论,将长短期记忆网络(LSTM)和门控循环单元(GRU)进行结合,选取矿井涌水量为研究对象,建立一种LSTM-GRU的矿井涌水预测模型。以陕西某矿的矿井涌水量为样本数据,采用7∶3的比例将数据集划分为训练集和测试集,选择模型训练效果较好的梯度下降算法确定网络模型参数和正则化参数,为了证明LSTM-GRU模型的预测精度,同时将结果分别与传统的ARIMA模型和LSTM模型预测矿井涌水所得到的预测结果进行对比。结果表明:LSTM-GRU复合模型的平均绝对百分比误差(RMSE)为70.51,均方根误差(MAE)为53.4,平均绝对误差(MAPE)为2.80%,可决系数(R^(2))为0.86,具有较高的预测精度和可靠性,预测效果优于传统的ARIMA模型和LSTM模型。

基于LSTM与Transformer的地面沉降智能预测方法研究——以上海市为例

受地面沉降严重威胁到生命财产安全的人口已达19%,开展地面沉降模拟预测对防灾减灾具有非常重要的现实意义。针对现有地面沉降预测在模型参数难以获取、单一深度学习方法在预测精度低等方面的局限性,本文提出了集成大模型核心技术的地面沉降预测方法。首先,从地面沉降模拟预测的顶层设计,提出了基于深度学习的地面沉降预测包括算力层、数据层、模型层、评估层与应用层的总体架构;其次,基于LSTM与Transformer提出了地面沉降预测的实用方法;最后,利用上海的地面沉降数据进行了实验研究。结果表明:深度学习技术可以在地面沉降模拟预测中取得较好的结果,多模型法对地面沉降变化不大、回弹、变化较大均可进行预测,iTransformer模型对地面沉降变化较小的情况预测效果较好;在微量地面沉降时代,利用大模型的核心技术Transformer可以取得较高的精度。

基于改进LSTM的蘑菇生长状态时空预测算法

密集蘑菇簇会严重影响蘑菇质量和自动采摘成功率。为避免形成超密集蘑菇簇,提出一种蘑菇生长状态时空预测算法,对蘑菇生长状态进行预测以指导提前疏蕾。该算法采用编码器-预测器框架,将历史序列图像转换为3D张量序列作为模型的输入;编码器网络中将卷积和长短时记忆(Long short term memory, LSTM)网络融合实现对蘑菇生长的时空相关性特征的提取;在预测网络中加入扩散模型以解决预测图像的模糊问题;此外,在损失函数中增加了蘑菇面积差异损失函数来进一步减小预测蘑菇与实际蘑菇的形状和位置偏差。实验结果表明,本文算法峰值信噪比可达35.611 dB、多层级结构相似性为0.927、蘑菇预测准确性高达0.93,有效提高了蘑菇生长状态图像预测质量和精度,为食用菌生长预测提供了一种新思路。

融合SEIR与LSTM模型的传染病预测研究

针对现有的传染病预测模型未充分考虑时间序列的复杂度且预测性能不稳定等问题,提出一种基于传染病动力学模型SEIR与长短时记忆网络(LSTM)的传染病组合预测模型.首先,通过计算Pearson相关系数分析气候因素与传染病新增人数之间的相关性;其次,通过FE(Fuzzy Entropy)算法提取序列的局部特征且保证序列的平稳性,降低时间序列的复杂度,提升时间序列的可预测性;最后,根据传染病特点,构建SEIR模型分析不同人群传播情况,并结合LSTM模型实现大幅度提升传染病预测精度.仿真结果表明,相较于传统的模型算法,本文提出的混合模型能保证预测的平稳性并实现更高的预测精度,同时,本文使用该混合模型在不同的干预策略下进行预测,表明了提早采取防控措施对遏制传染病传播的重要性.

融合GA-Attention-LSTM算法的温室樱桃环境参数预测与裂果预警

针对温室环境因素对樱桃的影响,设计一套大樱桃温室环境自动监测装置,用来采集温室内的环境参数值为樱桃裂果提供数字化预警支持及防治方案。基于采集的环境参数值,首先使用相关性分析得出与棚内裂果具有强相关性的环境参数特征;其次使用滑动窗口方法将输入的环境特征生成时间序列矩阵形式;随后提出一种融合GA-Attention-LSTM算法的预测模型,实现精准预测棚内的环境参数的功能;最后通过SPSS数据分析软件来分析不同大棚的环境参数和裂果率。所提的融合GA-Attention-LSTM算法的预测模型的平均绝对误差为0.112,均方误差为0.087,相比于LSTM网络模型高出12.80%和9.72%,对环境参数的预测精度更高,同时得出一套科学的樱桃环境参数值范围,为预测模型对樱桃裂果数字化预警提供有力支持。

基于LSTM-CNN的结构固有频率激励下正弦载荷识别方法研究

当外载荷频率达到或接近结构固有频率时,传统载荷识别方法(比如截断奇异值分解法)的识别精度会降低。为此,通过卷积网络的特征提取和长短期记忆网络的长时记忆功能建立LSTM-CNN载荷识别模型,提出一种基于LSTM-CNN模型的载荷识别方法,对GARTEUR飞机模型开展载荷时域波形识别研究。通过采集结构的响应数据和激励数据进行模型训练和载荷识别,并与截断奇异值分解(TSVD)方法、长短期记忆网络(LSTM)方法和深度卷积神经网络(DCNN)方法的识别结果进行对比分析。结果表明:基于LSTM-CNN模型的载荷识别方法可以有效应用于结构固有频率激励下正弦载荷识别问题,具有较高的识别精度和抗噪能力。