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Tackling A Critical Challenge in Shale Development in the Delaware Basin: An Interdisciplinary Field Case Study on Subsurface Scale Diagnosis and control and Impact on Production Performance

Tackling A Critical Challenge in Shale Development in the De...

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2022 SPE/AAPG/SEG Unconventional Resources Technology conference, URTC 2022

作者： Wang, Wei DeVoe, Lauren Wei, Wei Dechongkit, Piya Fung, Megan Ostrowicki, Katrina S. Fusilier, Le'Kendra B. Alla, Ramez Hanna Luo, Qinmin Gallegos, Eddye Kostinovsky, Aygul Saneifar, Mehrnoosh Crousse, Luisa Yan, Chao Winslow, Gregory Chebli, Hussein Chevron Corporation United States

Objectives/Scope: Subsurface scales include scale deposits present at downhole, in or near perforations, in fractures, on fracture proppants/frac faces, in porous media near frac face. As fractures and frac faces provide key pathways for hydrocarbon production in shale & tight formation, assessing and controlling subsurface scale formation has become a critical task for operators in the shale sector. This paper presents a field case study on subsurface scale diagnosis/control and produced water source allocation for a shale asset development in the Delaware Basin. Methods/Procedures/process: Subsurface scales at shale horizontal producers are difficult to collect or visually observe and thus poses challenges to diagnose and mitigate. In this study, integrated analysis of time-lapse produced water chemistry/production surveillance data, frac water data, well history/intervention jobs/field observations, petrophysics/geological data, and analog well data were conducted to understand produced water sources and diagnose subsurface scale formation for two underperforming wells. Scale modeling/risk assessment was conducted to assess the composition of subsurface scale and provided an important basis for scale formation root cause analysis and proactive scale control for upcoming development wells. Results/Observations/Conclusions: Through collaborative efforts across different disciplines, this data-driven field case study has demonstrated that scale formation at the subsurface is a primary root cause for the dramatic production underperformance of an impacted well (Well A). Integrated analysis suggested presence of high Ba water from non-targeted formation and Barite scale formation at the subsurface due to the commingling of high SO4 water and high Ba water sources during the frac/shut in stages for this well. The Chevon conceptual model on subsurface scale management (SPE-200668) was applied and updated in explaining the findings and the well production performance. A

关键词： Porous materials

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Key Technologies for Dynamic Early Warning of BIM Power Monitoring data in Bridge Operation and Maintenance

Key Technologies for Dynamic Early Warning of BIM Power Moni...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), International conference on

作者： Yu Peng Tian Wang Chongqing College of Electronic Engineering Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

Bridge operation and maintenance is an important solution to how to ensure the safe operation of bridges and extend their service life. In this paper, a dynamic early warning technology of BIM (Building Information modeling) power monitoring data was proposed to supervise the safety of bridges. Through the layout of sensors and monitoring equipment, the power data of the bridge was obtained in real-time. The quality control and data correction of the collected power data were carried out, and data cleaning, data correlation and other technologies were used to process the power data and extract useful characteristic information. An early warning model was also constructed, and machine learning, data mining and other methods were used to analyze and model power data to realize dynamic early warning of bridge power systems. After experiments and analysis, it was concluded that the stability of the method in this paper was maintained at 89%-98%. Through the dynamic early warning of BIM power monitoring data, real-time monitoring and fault diagnosis of the bridge power system can be realized and abnormal conditions and potential problems can be found in a timely manner.

关键词： Bridges Power system dynamics Power system stability data models Stability analysis Real-time systems Maintenance Safety data mining Monitoring

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data-Driven Turbine modeling and Load Rejection analysis 5

Data-Driven Turbine Modeling and Load Rejection Analysis

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5th International conference on Renewable Energy and Power Engineering, REPE 2022

作者： Jiang, Xiaoping Liu, Xuan Wang, Ziting Xu, Zhenye Shi, Chao Zheng, Yueqiu Beijing China Tibet Huadong Hydropower Complete Plant Co. Ltd Tibet China

ISBN: (数字)9781665482066

ISBN: (纸本)9781665482066

The full characteristic model of hydraulic turbine must be considered in the research of control and transition process calculation of hydraulic turbine generator unit. In order to obtain the full characteristic model of hydraulic turbine, it is indispensable to reasonably extend the high-efficiency working condition characteristic area to the low-efficiency area according to the comprehensive characteristic curve. In this paper, BP(back propagation) neural network is used to process the comprehensive characteristic curve of hydraulic turbine to obtain the full characteristic model. In view of the defects of traditional BP neural network, such as slow convergence speed, long training time and easy oscillation in the learning process, BP neural network is improved. The improved BP neural network is used to train the model of flow and torque characteristics, and then the full characteristic model of hydraulic turbine is trained by using the value point to extend the flow and torque characteristic data. Experiments demonstrate that the full characteristic model established by the improved BP neural network algorithm has higher accuracy. Finally, the full characteristic model is used to calculate the load rejection transition process. The results demonstrate that the full characteristic model is suitable for the calculation of hydraulic turbine transition process and meets the requirements of engineering application. © 2022 IEEE.

关键词： Hydraulic turbines

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Closed-Loop Subspace Identification for MIMO LPV System with Minimum Euclidean Distance of State Sequence

Closed-Loop Subspace Identification for MIMO LPV System with...

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Industrial Automation, Robotics and control Engineering (IARCE), International conference on

作者： Liang Zhang Peng Deng Shuangjiang Du Tian Zhou Kaiming Yang Tao Huang State Key Laboratory of Mechanical Transmission & College of Mechanical and Vehicle Engineering Chongqing University Chongqing China Department of Mechanical Engineering Tsinghua University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The rigid-flexible coupled variable mechanical system, as a typical multi-input multi-output (MIMO) system, poses significant challenges in system modeling, control design, theoretical analysis, and practical engineering implementation due to its complex dynamic characteristics and the interactions among multiple variables. To address these challenges, this paper proposes a subspace closed-loop identification method for MIMO linear parameter-varying (LPV) systems based on the minimum Euclidean distance of the state sequence. First, through the subspace closed-loop identification algorithm, a non-unique form of the linear time-invariant state-space (L TI-SS) model with the minimum Euclidean distance state sequence is obtained. Then, using the least squares method, a similarity transformation matrix is computed to convert the non-unique form of the LTI-SS model into the standard model. Finally, the global linear parameter-varying state-space (LPV -SS) model is obtained through cubic spline interpolation. The proposed method is experimentally validated on a single-degree-of-freedom ball screw test rig and simulated on a two-degree-of-freedom motion system. Through simulation and experimentation, the calibration determination coefficient of the LPV MIMO model is above 0.98, demonstrating the superior accuracy of the model. Compared with traditional modal standard models and controllable standard models, the proposed method requires fewer data to achieve the specified model accuracy, improves the computational efficiency of the identification process, and reduces experimental and data collection costs. This provides an effective solution for modeling rigid-flexible coupled variable mechanical systems.

关键词： Interpolation Accuracy Computational modeling Euclidean distance Transforms Systems modeling data models Mechanical products Mechanical systems Standards

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A data-Mechanism Joint Driven Based Multi-Energy Flow Calculation Method for Electricity-Heat Interconnected System

A Data-Mechanism Joint Driven Based Multi-Energy Flow Calcul...

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Shi Zhang Ke Li Jianfei Chen Haiyang Wang Yuchen Mu Chenghui Zhang School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

With the rapid development of integrated energy system (IES), the combine operation of heterogeneous energy networks becomes richer and richer, and the nonlinear characteristics of the multi-energy flow (MEF) model become more and more significant, which poses a challenge to the analysis and optimization of system operation. In this paper, a joint driven MEF modeling method based on electricity-heat interconnected system is proposed, which utilizes a deep learning method to mine the internal relationship of MEF historical data and effectively avoids the stability problem caused by iterative. On this basis, the fusion mechanism-driven module is used as a buffer zone for the nonlinear regression process of MEF to reduce the error stacking. This model can achieve the organic unification of calculation accuracy and timeliness to a certain extent. Finally, different scales of electricity-heat interconnection systems are selected for example analysis to verify the accuracy and implementation of the proposed method.

关键词： Accuracy Uncertainty Network topology Computational modeling Systems operation Stacking Interconnected systems

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A Combined Subjective and Objective Evaluation Method for Intelligent Vehicles Using Choquet Integral

A Combined Subjective and Objective Evaluation Method for In...

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Chinese control conference (CCC)

作者： Yulei Wang Meiyu Lu Meng Li Hong Chen Department of Control Science and Engineering Tongji University Shanghai China

For the urgent requirement of a full-scenario adaptive testing and evaluation (T&E) for intelligent vehicles (IVs), multi-dimensional evaluation technologies for IVs are focused on, where a rapid and accurate identification of evaluation preference is essential to production development and marketing in T&E systems for IVs. In this paper, A combined subjective and objective evaluation method for IVs is proposed in the framework of Choquet integral for solve multicriteria decision making. Specifically, we deploy a new aggregation function, the Choquet integral, for effective modeling of the multi-dimensional evaluation process. About operational design domain (ODD), vehicle performance (VP) and interaction and risk control (IRC), a case study with two subjective evaluation methods (Delphi method and analytic hierarchy process) and four objective evaluation methods (principal components analysis, factor analysis method, data envelopment analysis and grey relational analysis) is used to verify the effectiveness of the proposed evaluation method.

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Trade Volume Prediction Using Exponential Smoothing

Trade Volume Prediction Using Exponential Smoothing

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International conference on Intelligent Computing in data Sciences (ICDS)

作者： Haidar Doha Mouatassim Salma Benabbou Rajaa Benhra Jamal GIL Department LARILE Laboratory OSIL Team ENSEM Hassan II University Casablanca Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Accurately forecasting financial indicators is challenging given their volatile nature, dependence on various market-related factors and impact on supply chains. Therefore, finding predictive models striking the balance between accuracy, simplicity of implementation and cost-effectiveness is of critical importance. In light of this context, many studies were conducted to design and evaluate high-performing models predicting financial indicators. This article is a preliminary study of the performance of parametric models in predicting volatile-prone time series. The adopted forecasting process enables the proper Exponential Smoothing (ETS or ES) model selection using three R functions, two horizon sets and different accuracy metrics. Since our data exhibits trend and seasonal patterns, we used the Holt-Winters (HW) Exponential Smoothing method to forecast the average monthly trading volume in a retail supply chain. The findings show that forecasting for shorter spans while updating the fit model selection (model building, choice of metrics) is recommended for better accuracy.

关键词： Measurement Solid modeling Accuracy Smoothing methods Statistical analysis Supply chains Buildings Time series analysis Predictive models Forecasting

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Bayesian analysis of Zero-Inflated Count data Using Beta-Lindley Distribution

Bayesian Analysis of Zero-Inflated Count Data Using Beta-Lin...

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Neural Networks and Neurotechnologies (NeuroNT), ii International conference on

作者： Atheer Ismael Fadhel Hossein Jabbari Khamnei University Mustansiriyah Iraq - Baghdad University Tabriz Iran - Tabriz

ISBN: (数字)9798350363739

ISBN: (纸本)9798350363746

The process of analyzing data of large numbers assumes the presence of a unique and large set of challenges due to extra zeros and excessive dispersion processes. Therefore, data types that rely on the traditional counting principle attempt to capture the characteristics of this data appropriately and work on it bypassing these problems. Hence, the Bayesian principle was proposed, which seeks To model inflated zero count data based on the beta Lindley distribution, as this distribution is known for its flexibility and ability to deal with excess zeros and excess dispersion simultaneously, through the use of Markov chain Monte Carlo (MCMC) techniques for parameter estimation and model inference, as this paper seeks. The research aims to clarify the effectiveness of the proposed method by studying simulations and applying them to real-world data sets. The results of the Beta-Lindley distribution provide a robust and flexible framework based on the principle of modeling zero-inflated count data. This approach will provide accurate parameter estimates and reliable model predictions and will therefore have multiple uses in a range of different activities, including epidemiology, some environmental studies, and even within the economic sciences, where data are common.

关键词： Economics Parameter estimation Monte Carlo methods Predictive models data models Bayes methods Reliability

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International conference on modeling, Simulation and Optimization, CoMSO 2021

International Conference on Modeling, Simulation and Optimiz...

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International conference on modeling, Simulation and Optimization, CoMSO 2021

ISBN: (纸本)9789811908354

The proceedings contain 50 papers. The special focus in this conference is on modeling, Simulation and Optimization. The topics include: Fast Implementation for Computational Method of Optimum Attacking Play in Rugby Sevens;AHP-GRA Integrated Methodology for Decision-Making in WEDM of Ti-6Al-4 V Alloy;defect Detection Using Correlation Approach for Frequency Modulated Thermal Wave Imaging;comparative Study of Aero and Non-aero Formula Student Type Race Car Using Optimum Lap;Simulation and Stabilization of a Custom-Made Quadcopter in Gazebo Using ArduPilot and QGroundcontrol;MNIST Image Classification Using Convolutional Neural Networks;A Contemporary Initiative to Uphold Cease COVID-19 Using Keras and Tensorflow;double Sampling Plans for Life Test Based on Marshall–Olkin Extended Exponential Distribution;modeling and Simulation of Electric Vehicle with Synchronous Reluctance Motor;effect of Suspension Parameter on Lateral Dynamics Study of High Speed Railway Vehicle;modeling Clusters in Streamflow Time Series Based on an Affine process;a Comparative Study of Various Traditional and Hybrid Cryptography Algorithm Models for data Security;Assessing and Predicting Urban Growth Patterns Using ANN-MLP and CA Model in Jammu Urban Agglomeration, India;Chaotic Lorenz Time Series Prediction via NLMS Algorithm with Fuzzy Adaptive Step Size;Channel Adaptive Equalizer Design Based on FIR Filter via FVSS-NLMS Algorithm;Direct Adaptive Inverse control Based on Nonlinear Volterra Model via Fractional LMS Algorithm;indirect Adaptive Inverse control Synthesis via Fractional Least Mean Square Algorithm;thermo-Economic analysis for the Feasibility Study of a Binary Geothermal Power Plant in India;V2G/G2V Bi-directional On-Board EV Charger Using Two Phase Interleaved DC-DC Converter;hydrodynamic Coupling Between Comoving Microrobots;preface.

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Mathematical study of low-temperature natural gas separation process

Mathematical study of low-temperature natural gas separation...

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Women in data Science at Prince Sultan University (WiDS PSU), International conference of

作者： Maria Podkina Sergey Abramkin Department of System Analysis and Control Saint-Petersburg Mining University Saint-Peterburg Russia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

The main focus of this study is the procedure for separating natural gas at low temperatures. The system consists of three heat exchangers and three stages for reducing gas. The composition and purpose of the correction column are explained. The study demonstrates how various parameters, with pressure being the most important, affect the rectification process. Additionally, the study explains how the inhibitor regeneration mechanism works. The system involves interconnected mass and heat exchange processes within the "Rectification" subsystem, which can be described by a dynamic nonlinear mathematical ***, the system also includes a nonlinear discrete-continuous model. The transient processes of mass and heat exchange are obtained and studied based on the discrete-continuous model, using the specified initial and boundary conditions.

关键词： Heating systems Solid modeling Inhibitors Electric breakdown Mathematical models Software Nonlinear dynamical systems

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