Modeling Ithaca’s natural hydraulic fractures

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Modeling,Simulat...

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Book reviewModeling,Simulation,and Control of Flexible Manufacturing Systems ±A Petri Net Approach;Meng Chu Zhou;Kurapati Venkatesh;Yushun Fan;World Scienti®c,Singapore,19991.IntroductionA ¯exible manufacturing system (FMS)is an automated,mid-volume,mid-va-riety,central computer-controlled manufacturing system.It can be used to produce a variety of products with virtually no time lost for changeover from one product to the next.FMS is a capital-investment intensive and complex system.In order to get the best economic bene®ts,the design,implementation and operation of FMS should be carefully made.A lot of researches have been done regarding the modeling,simulation,scheduling and control of FMS [1±6].From time to time,Petri net (PN)method has also been used as a tool by di erent researcher in studying the problems regarding the modeling,simulation,scheduling and control of FMS.A lot of papers and books have been published in this area [7±14].``Modeling,Simulation,and Control of Flexible Manufacturing Systems ±A PN Approach''is a new book written by Zhou and Venkatesh which is focused on studying FMS using PN as a systematic method and integrated tool.The book's contents can be classi®ed into four parts.The four parts are introduction part (Chapter 1to Chapter 4),PNs application part (Chapter 5to Chapter 8),new research results part (Chapter 9to Chapter 13),and future development trend part (Chapter 14).In the introduction part,the background,motivation and objectives of the book are described in Chapter 1.The brief history of manufacturing systems and PNs is also presented in Chapter 1.The basic de®nitions and problems in FMS design and implementation are introduced in Chapter 2.The authors divide FMS related problems into two major areas ±managerial and technical.In Chapter 4,basic de®nitions,properties,and analysis techniques of PNs are presented,Chapter 4can be used as the fundamentals of PNs for those who are not familiar with PN method.In Chapter 3,the authors presented their approach to studying FMS related prob-lems,the approach uses PNs as an integrated tool and methodology in FMS design and implementation.In Chapter 3,various applications in modeling,analysis,sim-ulation,performance evaluation,discrete event control,planning and scheduling of FMS using PNs are presented.Through reading the introduction part,the readers can obtain basic concepts and methods about FMS and PNs.The readers can also get a clear picture about the relationshipbetween FMS and PNs.Mechatronics 11(2001)947±9500957-4158/01/$-see front matter Ó2001Elsevier Science Ltd.All rights reserved.PII:S 0957-4158(00)00057-X948Book review/Mechatronics11(2001)947±950The second part of the book is about PNs applications.In this part,various applications of using PNs in solving FMS related problems are introduced.FMS modeling is the basis for simulation,analysis,planning and scheduling.In Chapter5, after introduction of several kinds of PNs,a general modeling method of FMS using PNs is given.The systematic bottom-up and top-down modeling method is pre-sented.The presented method is demonstrated by modeling a real FMS cell in New Jersey Institute of Technology.The application of PNs in FMS performance analysis is introduced in Chapter 6.The stochastic PNs and the time distributions are introduced in this Chapter. The analysis of a¯exible workstation performance using the PN tool called SPNP developed at Duke University is given in Section6.4.In Chapter7,the procedures and steps involved for discrete event simulation using PNs are discussed.The use of various modeling techniques such as queuing network models,state-transition models,high-level PNs,object-oriented models for simulations are brie¯y explained.A software package that is used to simulate PN models is introduced.Several CASE tools for PNs simulations are brie¯y intro-duced.In Chapter8,PNs application in studying the di erent e ects between push and pull paradigms is shown.The presented application method is useful for the selection of suitable management paradigm for manufacturing systems.A manufacturing system is modeled considering both push and pull paradigms in Section8.3which is used as a practical example.The general procedures for performance evaluation of FMS with pull paradigm are given in Section8.4.The third part of the book is mainly the research results of the authors in the area of PNs applications.In Chapter9,an augmented-timed PN is put forward. The proposed method is used to model the manufacturing systems with break-down handling.It is demonstrated using a¯exible assembly system in Section9.3. In Chapter10,a new class of PNs called Real-time PN is proposed.The pro-posed PN method is used to model and control the discrete event control sys-tems.The comparison of the proposed method and ladder logic diagrams is given in Chapter11.Due to the signi®cant advantages of Object-oriented method,it has been used in PNs to de®ne a new kind of PNs.In Chapter12,the authors propose an Object-oriented design methodology for the development of FMS control software.The OMT and PNs are integrated in order to developreusable, modi®able,and extendible control software.The proposed methodology is used in a FMS.The OMT is used to®nd the static relationshipamong di erent objects.The PN models are formulated to study the performance of the FMS.In Chapter12,the scheduling methods of FMS using PNs are introduced.Some examples are presented for automated manufacturing system and semiconductor test facility.In the last Chapter,the future research directions of PNs are pointed out.The contents include CASE tool environment,scheduling of large production system,su-pervisory control,multi-lifecycle engineering and benchmark studies.Book review/Mechatronics11(2001)947±950949 mentsAs a monograph in PNs and its applications in FMS,the book is abundant in contents.Besides the rich knowledge of PNs,the book covers almost every aspects regarding FMS design and analysis,such as modeling,simulation,performance evaluation,planning and scheduling,break down handling,real-time control,con-trol software development,etc.So,the reader can obtain much knowledge in PN, FMS,discrete event system control,system simulation,scheduling,as well as in software development.The book is a very good book in the combinations of PNs theory and prac-tical applications.Throughout the book,the integrated style is demonstrated.It is very well suited for the graduate students and beginners who are interested in using PN methods in studying their speci®c problems.The book is especially suited for the researchers working in the areas of FMS,CIMS,advanced man-ufacturing technologies.The feedback messages from our graduate students show that compared with other books about PNs,this book is more interested and easy to learn.It is easy to get a clear picture about what is PNs method and how it can be used in the FMS design and analysis.So,the book is a very good textbook for the graduate students whose majors are manufacturing systems, industrial engineering,factory automation,enterprise management,and computer applications.Both PNs and FMS are complex and research intensive areas.Due to the deep understanding for PNs,FMS,and the writing skills of the authors,the book has good advantages in describing complex problems and theories in a very easy read and understandable fashion.The easy understanding and abundant contents enable the book to be a good reference book both for the students and researchers. Through reading the book,the readers can also learn the new research results in PNs and its applications in FMS that do not contained in other books.Because the most new results given in the book are the study achievements of the authors,the readers can better know not only the results,but also the background,history,and research methodology of the related areas.This would helpthe researchers who are going to do the study to know the state-of-art of relevant areas,thus the researchers can begin the study in less preparing time and to get new results more earlier.As compared to other books,the organization of the book is very application oriented.The aims are to present new research results in FMS applications using PNs method,the organization of the book is cohesive to the topics.A lot of live examples have reinforced the presented methods.These advantages make the book to be a very good practical guide for the students and beginners to start their re-search in the related areas.The history and reference of related research given in this book provides the reader a good way to better know PNs methods and its applications in FMS.It is especially suited for the Ph.D.candidates who are determined to choose PNs as their thesis topics.950Book review/Mechatronics11(2001)947±9503.ConclusionsDue to the signi®cant importance of PNs and its applications,PNs have become a common background and basic method for the students and researchers to do re-search in modeling,planning and scheduling,performance analysis,discrete event system control,and shop-¯oor control software development.The book under re-view provides us a good approach to learn as well as to begin the research in PNs and its application in manufacturing systems.The integrated and application oriented style of book enables the book to be a very good book both for graduate students and researchers.The easy understanding and step-by-step deeper introduction of the contents makes it to be a good textbook for the graduate students.It is suited to the graduated students whose majors are manufacturing system,industrial engineering, enterprise management,computer application,and automation.References[1]Talavage J,Hannam RG.Flexible manufacturing systems in practice:application,design,andsimulation.New York:Marcel Dekker Inc.;1988.[2]Tetzla UAW.Optimal design of¯exible manufacturing systems.New York:Springer;1990.[3]Jha NK,editor.Handbook of¯exible manufacturing systems.San Diego:Academic Press,1991.[4]Carrie C.Simulation of manufacturing.New York:John Wiley&Sons;1988.[5]Gupta YP,Goyal S.Flexibility of manufacturing systems:concepts and measurements.EuropeanJournal of Operational Research1989;43:119±35.[6]Carter MF.Designing¯exibility into automated manufacturing systems.In:Stecke KE,Suri R,editors.Proceedings of the Second ORSA/TIMS Conference on FMS:Operations Research Models and Applications.New York:Elsevier;1986.p.107±18.[7]David R,Alla H.Petri nets and grafcet.New York:Prentice Hall;1992.[8]Zhou MC,DiCesare F.Petri net synthesis for discrete event control of manufacturing systems.Norwell,MA:Kluwer Academic Publishers;1993.[9]Desrochers AA,Al-Jaar RY.Applications of petri nets in manufacturing systems.New York:IEEEPress;1995.[10]Zhou MC,editor.Petri nets in¯exible and agile automation.Boston:Kluwer Academic Publishers,1995.[11]Lin C.Stochastic petri nets and system performance evaluations.Beijing:Tsinghua University Press;1999.[12]Peterson JL.Petri net theory and the modeling of systems.Englewood Cli s,NJ:Prentice-Hall;1981.[13]Resig W.Petri nets.New York:Springer;1985.[14]Jensen K.Coloured Petri Nets.Berlin:Springer;1992.Yushun FanDepartment of Automation,Tsinghua UniversityBeijing100084,People's Republic of ChinaE-mail address:*****************。

Modeling Ithaca’s Natural Hydraulic Fractures-ppt

Modeling Ithaca’s Natural Hydraulic Fractures-ppt

Sequence of fracturing
• Jointing in the siltstone occurs before jointing in the shale; horizontal stress is lower in the siltstone because the shale compacts more. • Joints act as conduits for the pore fluid. • Joints in the shale are in plane with the siltstone joints unless the stress field rotates, which leads to en-echelon crack patterns.
Watkins Glen, NY
Taughannock Falls 215 ft high
©
Evidence of Natural Hydraulic Fracturing
• Crack surface morphology indicates periodic crack growth and arrest. • Many closely spaced multiple cracks initiate and propagate from a single source. • Fractures initiate in the siltstone first, followed by fracturing of the shale. • Organic rich sediments (black shales) are capable of producing natural gas or fluids. • Presence of a paleo-pressure seal could induce abnormally high pressure. • Shear displacement of joint surfaces occurs without shear deformation surface features.

simulation modeling and analysis -回复

simulation modeling and analysis -回复

simulation modeling and analysis -回复Simulation modeling and analysis is a powerful tool used in various industries to understand complex systems, predict their behavior, and make informed decisions. In this article, we will explore what simulation modeling and analysis are, how they work, and why they are valuable in today's world.Simulation modeling is the process of creating a computer-based representation of a real system or process. It involves developing a mathematical model that captures the key components and interactions of the system. This model is then used to simulate the behavior and performance of the system under different scenarios and conditions.Simulation analysis, on the other hand, refers to the process of evaluating the output or results generated by the simulation model. It involves analyzing and interpreting the data produced during the simulation to gain insights into the system's behavior and performance.The first step in simulation modeling and analysis is defining the objectives and scope of the study. This includes identifying the keyvariables, parameters, and constraints that need to be included in the model. For example, in a manufacturing setting, variables such as production rate, inventory levels, and machine downtime may be of interest.Once the objectives and scope are defined, the next step is data collection. This involves gathering relevant data about the system or process under study. This data can come from a variety of sources, including historical records, surveys, and observations. In some cases, it may be necessary to create synthetic or hypothetical data to supplement the available information.After data collection, the model building phase begins. This involves constructing a mathematical representation of the system using specialized software or programming languages. The model should be able to capture the important characteristics and dynamics of the system, such as its inputs, outputs, and interactions.Next, the model needs to be verified and validated. Verification ensures that the model is free from errors and accurately represents the system. Validation, on the other hand, involvescomparing the output of the model with real-world data or expert knowledge to ensure that it accurately captures the system's behavior.Once the model is verified and validated, the simulation experiments can be conducted. These experiments involve running the model using different input values and scenario conditions to generate data on the system's behavior and performance. The output data can then be analyzed using statistical techniques to understand the effects of various factors on the system's performance.Simulation modeling and analysis provide several benefits. First, they allow decision-makers to experiment with different scenarios and conditions without having to disrupt or modify the real system. This can be particularly valuable in sensitive or high-risk environments, where the consequences of change can be costly or dangerous.Second, simulation modeling and analysis provide a level of detail and visibility that is difficult to achieve through other methods. They allow decision-makers to understand the complexinteractions and dependencies within a system, leading to more informed and effective decision-making.Additionally, simulation modeling and analysis can help optimize system performance. By running multiple simulations and analyzing the results, decision-makers can identify bottlenecks, inefficiencies, and areas of improvement. This can lead to cost savings, increased productivity, and enhanced customer satisfaction.In conclusion, simulation modeling and analysis are valuable tools that enable decision-makers to gain insights into complex systems and make informed decisions. By creating a computer-based representation of a system and running simulations,decision-makers can experiment with different scenarios and conditions to understand the system's behavior and optimize its performance. With the increasing complexity of modern systems, simulation modeling and analysis are becoming essential tools in various industries.。

surrogate model 解释

surrogate model 解释

英文回答:The proxy model is a mathematical model used to simulate the behaviour ofplex systems. It is designed to simplifyplex systems and make them easier to process. Proxy models are usually built on existing data or empirical knowledge, using mathematics, statistics or machine learning. It can replace the original system for forecasting, optimization or control, thus saving costs and time. Proxy models are widely used in modern science and engineering, for example in aircraft design using proxy modelsto simulate aerodynamics and in chemical engineering to optimize production processes.代理模型是一种数学模型,用于模拟复杂系统的行为。

其设计旨在简化复杂系统,使其易于处理。

代理模型通常基于已有数据或经验知识,利用数学、统计学或机器学习等方法建立起来。

它可以替代原系统进行预测、优化或控制,从而节约成本和时间。

代理模型在现代科学和工程领域有广泛应用,例如在飞行器设计中利用代理模型模拟空气动力学,在化工工程中利用代理模型优化生产过程。

The proxy model has many advantages. For example, it could simplify the originalplex system and make calculations more efficient. Some systems are inherentlyplex and requireconsiderableputing resources to analyse and optimize. But proxy models can extract key features and patterns and then solve problems with simple mathematical models, so they can produce results quickly. The proxy model could also supplement the original system data. Some systems may have little or no access to data, which makes analysis difficult. However, proxy models can predict missing data by using available data or empirical knowledge, thus making system—wide analysis moreplete.代理模型有很多好处。

挖掘机英语、工程机械英语(常用中英文对照)(挖掘机资料)

挖掘机英语、工程机械英语(常用中英文对照)(挖掘机资料)
1. 启动(Start):启动发动机,使挖掘机进入工作状态。
2. 行走(Travel):控制行走装置,使挖掘机在地面移动。
3. 回转(Swing):控制回转机构,使挖掘机旋转。
4. 工作装置操作(Working Device Operation):控制工作装置,进行挖掘、装载、卸载等作业。
5. 停机(Stop):关闭发动机,使挖掘机停止工作。
2. 租赁费用:根据挖掘机型号、租赁期限、作业量等因素确定。
3. 维护服务:提供定期检查、保养、维修等服务。
4. 故障处理:快速响应故障报告,及时排除故障。
5. 零部件供应:提供原厂零部件供应,确保维修质量。
6. 技术支持:提供技术咨询服务,解决操作难题。
十一、挖掘机市场分析与竞争策略
1. 市场需求:分析挖掘机在不同领域的需求量,预测市场趋势。
4. 公路养护(Road Maintenance):如道路修补、清理等。
5. 城市建设(Urban Construction):如市政工程、园林绿化等。
6. 灾后救援(Disaster Relief):如地震、洪水、泥石流等灾害后的救援工作。
挖掘机英语、工程机械英语(常用中英文对照)(挖掘机资料)
五、挖掘机的技术参数
挖掘机英语、工程机械英语(常用中英文对照)(挖掘机资料)
1. 挖掘机:Excavatorom
4. 斗杆:Stick
5. 驾驶室:Cabin
6. 发动机:Engine
7. 液压系统:Hydraulic system
8. 轮胎:Tire
9. 液压油:Hydraulic oil
5. 最大挖掘半径(Maximum Digging Radius):挖掘机在标准工况下能够达到的最大挖掘半径。

System Modeling and Simulation

System Modeling and Simulation

System Modeling and Simulation System modeling and simulation play a crucial role in various industries, including engineering, healthcare, finance, and many more. The process of system modeling involves creating a simplified representation of a real system, while simulation allows for the analysis of the system's behavior under different conditions. This powerful combination enables professionals to make informed decisions, optimize processes, and predict outcomes with a high degree of accuracy. From an engineering perspective, system modeling and simulation are essential for designing and testing complex systems such as aircraft, automobiles, andindustrial machinery. By creating virtual models of these systems, engineers can analyze their performance, identify potential issues, and make necessary adjustments before physical prototypes are built. This not only saves time and resources but also enhances the overall safety and reliability of the final products. In the healthcare industry, system modeling and simulation are used to improve patient care, optimize hospital operations, and advance medical research. For instance, simulation models can help healthcare providers better understand patient flow, resource allocation, and the impact of different treatment protocols. This can lead to more efficient healthcare delivery, reduced wait times, and ultimately, better patient outcomes. In the realm of finance, system modeling and simulation are employed to analyze market trends, assess risks, and develop investment strategies. Financial institutions rely on these tools to simulate various economic scenarios, stress test their portfolios, and make well-informed decisions in a rapidly changing market environment. Additionally, system modeling and simulation are integral to the development of predictive models for pricing derivatives, managing assets, and mitigating financial risks. Beyond thesespecific industries, system modeling and simulation have broader implications for society as a whole. For example, in the context of urban planning, these tools can be used to simulate traffic patterns, analyze the impact of infrastructureprojects, and optimize public transportation systems. This can lead to more sustainable and livable cities, with reduced congestion and improved accessibility for residents. Despite the numerous benefits of system modeling and simulation, there are challenges that need to be addressed. One such challenge is thecomplexity of creating accurate models that capture all relevant aspects of a system. This requires a deep understanding of the system's behavior, as well as the availability of reliable data for validation and calibration. Additionally, the computational resources required for running simulations of large-scale systems can be substantial, necessitating efficient algorithms and high-performance computing infrastructure. Furthermore, the interpretation of simulation results and the translation of findings into actionable insights can be a daunting task. It requires interdisciplinary collaboration between domain experts, data scientists, and simulation specialists to ensure that the outcomes are meaningful and applicable in real-world scenarios. Moreover, there is a need for continuous refinement and validation of simulation models to keep them relevant and accurate in dynamic environments. From a human perspective, the use of system modeling and simulation can evoke a sense of empowerment and confidence in decision-making. Professionals who leverage these tools are better equipped to anticipate challenges, explore innovative solutions, and make evidence-based choices. This can lead to a greater sense of control over complex systems and a reduced fear of the unknown, ultimately fostering a culture of continuous improvement and resilience. In conclusion, system modeling and simulation are indispensable tools that have far-reaching implications across various industries and societal domains. While they offer tremendous potential for innovation and progress, it is essential to acknowledge the challenges associated with their application and to work towards overcoming them through collaboration, innovation, and a commitment to excellence. As technology continues to advance, the future of system modeling and simulation holds great promise for shaping a more efficient, sustainable, and prosperous world.。

simulation modelling practice

simulation modelling practice

simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。

economic model定义

economic model定义

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1INRODUCTIONHydraulic fractures occur naturally in the Earth’s crust. These range in size from mm as small pres-sure partings to km as magmatic dike intrusions. Ithaca, New York, which lies in the heart of the Fin-ger Lakes region, is a great place to see intermedi-ate-scale natural hydraulic fractures. Glaciation and erosion have led to large outcrops of middle and up-per Devonian clastic rocks. These rocks have ex-perienced two major orogenic events in addition to uplift and erosion all of which have left a pattern of brittle fracture in the rocks as seen today. A brief geological history of the area is necessary to set the stage for the discussion of natural hydraulic frac-tures in these rocks.Outcrops within the Finger Lakes are part of the Devonian Catskill Delta Complex, a clastic wedge that is more than 2 km thick in the Finger Lakes re-gion. The complex was deposited on a continental shelf with an open sea to the south and consists of layers of black shales, gray shales, silstones and sandstones, and an occasional limestone. Uplift ac-companying the Acadian orogeny to the east pro-vided a large source of sediment for the clastic wedge that was prograding into the Appalachian Ba-sin. Deposition rates reached 170m per Ma. At the close of the Paleozoic, during the Pennsylvanian and Permian, the Alleghanian orogeny caused wide-spread folding, faulting and jointing in eastern North America. This was the last major orogeny to affect this part of the continent.Engelder and others (see Engelder et al. 2000) have studied the joints and other geologic features within the Catskill Delta Complex for many years. There is an abundance of evidence that indicates that many of these joints developed as natural hydraulic fractures caused by abnormally high fluid pressures. The Alleghanian orogeny also left a unique imprint as many joints show a clockwise sequence in re-sponse to the reorientation of stress as the African continent collided with North America.There is a significant body of literature that de-scribes the joints and tries to interpret their origin and formation (e.g. Pollard & Aydin 1988). How-ever, there have been few numerical simulations to illustrate the theorized processes. The aim of this paper is to examine various components of these joints using numerical simulations to determine whether current formation theories are reasonable or valid.2GEOLOGIC EVIDENCE OF NATURAL HYDRAULIC FRACTURES AND STRESS ROTATIONSThe current theories of joint formation in the Cats-kill Delta Complex depend upon visual inspection of many joints and joint sets (Engelder et al. 2000). The first bit of evidence comes from the surface morphology of many of the joint surfaces in the silt-stone.Modeling Ithaca’s natural hydraulic fracturesB.J. Carter & A.R. IngraffeaCornell University, Ithaca, NY, USAT. EngelderPenn State University, University Park, PA, USAABSTRACT: Joints within the Catskill Delta complex are believed to be natural hydraulic fractures and abundant indirect evidence supports this claim. The indirect evidence is based on many observations of joint surface morphology and joint spacing among other features. The Alleghanian orogeny left a unique imprint on the joint surfaces also, indicating a rotating horizontal compressive stress field. Numerical simulations il-lustrate that the formation and propagation of the joints can be described by hydraulic fracturing mechanisms. The numerical simulations include hydraulic fracture propagation from sedimentary structures at layer inter-faces and fracture twisting in response to the reorientation of the remote stress field after initial joint propaga-tion.Cracks tend to start at points of higher stress con-centration (McConaughy & Engelder 2000). In the case of sedimentary beds, the largest stress concen-trations are in the form of concretions, fossils, rip-ples, groove casts, and similar features. Figure 1 shows a joint surface in the Ithaca Formation silt-stone that starts at a groove cast, a location where the siltstone penetrates the shale layer, formed dur-ing erosion of the muddy substrate and deposition of the silt. The figure also shows the plumose surface morphology of the joint surface. The plumose struc-tures and the related arrest lines are well described by Bahat & Engelder (1984). Barbs, the darkened lines in Figure 1, radiate from the plume axis and indicate the direction of fracture growth. Arrest lines mark the termination of crack propagation in-crements. Natural hydraulic fractures do not have a steady supply of fluid (Lacazette & Engelder 1992). As the crack propagates, the pressure decays and the crack arrests until the pressure builds up again.Figure 1. Plumose joint surface starting from a groove cast.It can also be seen in Figure 1 that the fracture initially stays within the siltstone layer. It has been found that in sedimentary basins where the horizon-tal stress is less than the vertical, the shale layers have a higher horizontal stress than the siltstones (Evans et al. 1989, Warpinski 1989). Assuming that the pore pressure is the same in both the shale and siltstone layers, then the fractures would form in the siltstones first. This will be more evident in the next figure.The difference in horizontal stress leads to joint-ing in the siltstones followed by jointing in the shale. Joints in the shale beds usually start at the edges of existing joints in the siltstone; the joints act as con-duits for the pore fluid. If there is no rotation of the principal horizontal stresses, joints will develop in the shale in plane with those in the sandstone. How-ever, if the horizontal stress does rotate, then en-echelon or fringe cracks will develop (Pollard et al. 1982). Figure 2 shows a set of fringe cracks that start at the interface between the siltstone and shale. These fractures propagate downward. The numer-ous small cracks at the immediate interface reduce to a few large cracks as propagation continues.Figure 2. Multiple en-echelon cracks propagate down from the siltstone-shale interface and are rotated from the parent joint.There are many more features of the joints that offer indirect evidence of natural hydraulic fractures, including joint spacing, slip on open joints, crosscut-ting and abutting relationships, and evidence of a pressure seal that could have led to over-pressurization of the formation. However, it is be-yond the scope of this paper to discuss all of this evidence. Instead, numerical simulations are used to illustrate that natural hydraulic fracturing processes can explain some of the features described herein.3HYDRAULIC FRACTURING SOFTWARE The 3D numerical simulations that are described in the following sections are performed using HY-FRANC3D and accompanying software (Carter et al. 2000a,b). In addition, ANSYS (ANSYS 2000) and FRANC2D (CFG 2000) are used for some 2D analyses.The 3D software consists of OSM, FRANC3D, HYFRANC3D, and BES. OSM is used to create the initial geometric models. FRANC3D is a fracture analysis code with pre- and post-processing capabili-ties. HYFRANC3D builds upon FRANC3D by add-ing a fluid flow module that couples the elastic structural response with fluid flowing in the fracture. BES is a linear elastic boundary element program. BES provides an elastic influence matrix that re-lates the set of unit pressures (p) at the nodes on the crack surface to the elastic displacements (w). The fluid flow formulation is based on the elasticity, lu-brication and continuity equations. For a Newtonian fluid, the stress ahead of the crack front has a 1/3 singularity, as does the fluid pressure inside thecrack (Carter et al. 2000b). Physically, the fluid pressure cannot be singular, thus, a fluid lag must develop at the crack front (Figure 3). The finite ele-ment formulation in HYFRANC3D combines an analytical solution for the pressure and crack open-ing displacement at the crack front with a standard finite element formulation of the lubrication equa-tion for the bulk of the fracture. The analytical solu-tion at the crack front provides an elegant way to overcome the singular behavior and limits the re-quired number of elements at the crack front. Hy-draulic fracture simulation proceeds as a series of crack advance increments, requiring an elastic solu-tion and coupled flow analysis at each step.Figure 3. Crack front region with fluid lag; p is pressure, w is crack opening displacement, V is crack front velocity, σ3 is clo-sure stress and x is the local coordinate axis along the crack.4SIMULATION OF HYDRAULIC FRACTURE PROPAGATION FROM A GROOVE CASTCracks tend to start at pre-existing flaws and points of high stress concentration. The groove cast in Fig-ure 1 is modeled to evaluate the stress intensification and to determine the hydraulic fracture propagation behavior from this location. The intent of these simulations is to show that hydraulic fracture is a plausible mechanism; thus, realistic boundary condi-tions are necessary, but accurate values are not es-sential to provide qualitative results.The numerical model consists of the siltstone layer only. McConaughy & Engelder (2000) de-scribe the difficulties in determining and modeling the correct boundary conditions. They conclude that the interface between the shale and siltstone must al-low slip. To simplify the 3D model, the upper and lower shale layers are notmodeled; a vertical con-straint is applied to the upper surface. A far-field compressive stress is applied in the horizontal direc-tion only; the vertical stress is neglected. The com-pressive stress perpendicular to the crack surface is 50 MPa and the stress parallel to the crack plane is 100 MPa. The elastic modulus is set to 14 GPa and the Poisson’s ratio is set to 0.1.The initial plane strain ANSYS stress analysis clearly shows that the groove cast alters the horizon-tal stress (Fig. 4). A tensile stress is induced at the groove cast. An initial crack is nucleated in the 3D numerical model at this location and hydraulic frac-ture propagation is simulated.Figure 4. ANSYS contours of maximum principal stress around a groove cast in a siltstone layer; tension is positive and values are in MPa.Figure 5 shows the shape of the hydraulic fracture after 17 steps of propagation. The fluid boundary conditions consist of a crack mouth flow rate equal to 1.0e-6 m3/s and zero flow rate at the crack front. These boundary conditions do not match the pre-sumed conditions of periodic fluidpressure build-up, but it is assumed that this will only affect the fracturing time and the surface features. The shape of the fracture to this stage matches that seen in the field reasonably well, although the barbs and arrest lines cannot be reproduced. The simulation can be continued such that the fracture breaks through the upper surface of the silt layer, but this is not com-plete yet.Figure 5. Shape of a hydraulic fracture from a groove cast af-ter 17 steps of propagation.5SIMULATION OF HYDRAULIC FRACTURE IN A ROTATING STRESS FIELDHydraulic fractures in these brittle rocks tend to grow perpendicular to the direction of maximum tension (or minimum compression). If an initial crack is subjected to a rotated stress field, the crack will kink out of the original plane to align itself with the new stress field. The rotation of the stress field induces different modes of deformation on different parts of the crack front. The kinking behavior de-pends on the pressure distribution inside the crack, the geometry of the crack, and the stress and stiff-ness contrasts in the rock layers (Carter et al. 1999). As a simple example, an initial elongated radial crack (emanating from a horizontal borehole) is ori-ented 70°from the maximum horizontal compres-sive stress (Fig. 6). The crack is nucleated in a single layer model with an elastic modulus of 27.6 GPa and a Poisson’s ratio of 0.25. The vertical stress is 69 MPa, the maximum horizontal stress is 62 MPa, and the minimum horizontal stress is 48 MPa. The crack mouth flow rate is set to 0.0795 m3/s.Figure 6. The initial crack shape is 9 m long and 0.3 m deep; σHmax is aligned with the x-axis (not to scale).Hydraulic fracture is simulated and stress inten-sity factors (SIF’s) are computed. Figure 7 shows the distribution of SIF’s along the initial crack front. The mode I values are highest, but there is a signifi-cant mode III component; mode II is significant only at the ends of the crack front. Pollard et al. (1982) provide a relationship between the mode III/I ratio and the number and spacing of en-echelon cracks that form along the crack front. Germanovich (2000) is conducting experiments on glass and brittle plastic to further characterize these relationships.The combination of all three modes is studied less well. However, the HYFRANC3D software allows the crack to propagate while ignoring the mode III SIF. Under these circumstances, the crack front does not break up, although, it quickly shows that it should. Figure 8 shows that the ends of the crack turn to align with σHmax. Most of the crack front is not aligned with σHmax. Instead, “large” ripples can be seen. The ripples are a result of the numerical propagation procedure, which only accounts for modes I and II. As the fracture propagates, the mode II and mode III SIFs increase rather than decrease. Due to the elongated shape, more effort is required to turn the entire crack front as compared to a more circular shape crack (Carter et al, 1999). As the crack front does not break, the combination of modes I and II produces initially minor oscillations in the crack front. These escalate as the crack con-tinues to grow, leading to the ripples.The mode III SIF value in the middle of the crack front reaches 10 MPa⋅√m at the final stage (15th step of propagation) while the mode I value remains rela-tively constant for each step, ranging from 20 to 25 MPa⋅√m along the crack front. Rotation and break up of the crack front is seen in many locations in the Ithaca area; Figure 9 shows a typical example.Figure 7. Mode I, II, and III SIF’s along the crack front; values are in MPa⋅√m.Figure 8. Ignoring mode III SIF during propagation leads to “large” ripples in the crack front indicating it should break up. The ends of the crack turn to align with the stress field.Figure 9. Joint in a siltstone indicating a rotated stress field as the crack surface shows obvious breaks; this might be seen as a set of en-echelon cracks when looking from above.6SIMULATION OF MULTIPLE HYDRAULIC FRACTURESMultiple parallel cracks tend to reduce in number and increase in spacing as they propagate. It is often assumed that the length to spacing ratio should be about 1.0 for a stable system of parallel cracks (Hwang, 1999). Figure 2 shows an example of a large number of closely spaced fractures at the shale-siltstone interface. As the cracks propagate down into the shale, most of the cracks arrest very quickly. Only a few, relatively widely spaced cracks remain at the base of the shale layer. Based on this figure, the length to spacing ratio is significantly greater than 1.0. Hwang (1999) has studied systems of parallel cracks, although not for 3D hydraulic fractures. Both 3D hydraulic fracture simulations and 2D simulations will be performed here.A set of three hydraulic fractures is used to gain insight into the behavior of a system of many paral-lel hydraulic fractures. The initial model is shown in Figure 10. The model is symmetrically constrained. The initial cracks are 1 mm deep and 6.35 mm apart. The crack mouth flow rate is 1.0e-6 m3/s and the material properties are the same as in section 4. In reality, more cracks should be modeled, but the simulations become quite time consuming as more cracks are added; three cracks should provide ade-quate insight.HYFRAN3D computes pressure distributions and crack opening displacements in the 3D fractures along with SIF distributions along the crack front. The usual procedure for growing multiples cracks is to scale the crack growth for all cracks based on the relative values of the stress intensity factors. Using this technique, the 3D hydraulic fractures are propa-gated for 10 steps. Although the mode I SIF for the center crack is slightly less than that for the two outer cracks (14.5 versus 15.2 MPa⋅√m for the initial crack), it continues to propagate. At step 10, the crack length is 10.5 mm. The length to spacing ratio is 1.7 and the center crack shows no signs of arrest-ing based on the relative mode I SIF values, 3.4 ver-sus 3.5 MPa⋅√m for the center and outer cracks re-spectively. Based on Figure 2, one might expect that the length to spacing ratio could be much higher than this. To verify that the crack growth is still sta-ble, 2D simulations are performed.FRANC2D is able to compute SIF’s, energy re-lease rates, and rates of energy release rates. The lat-ter allows the program to decide whether crack growth is stable and to decide which of the cracks are arrested. Although FRANC2D does not simulate fluid flow, a static pressure distribution of arbitrary shape can be applied to the crack surfaces (edges). It was shown previously (Carter et al, 1999) that if the correct pressure distribution, based on the 3D fluid flow analysis in HYFRANC3D, is applied to the crack surfaces of the 2D model, the correct hydrau-lic fracture behavior is captured. Thus, the pressure profile through the middle of each 3D crack is ap-plied to the 2D cracks to evaluate the rates of energy release rates.Figure 10. Initial 3D model of multiple parallel cracks.The initial FRANC2D analysis predicts that the center crack has a slightly lower rate of energy re-lease rate than the two outer cracks. The values are presented in matrix form in Table 1. The off-diagonal terms define the interaction between neighboring cracks. The negative values indicatethat crack growth is stable for all cracks. The influ-ence of neighboring cracks is relatively small, indi-cating a weak interaction.Table 1. Matrix of rates of energy release rate for 1 mm cracks.Crack 1 Crack 2 Crack 3 Crack 1 -0.6537E-01 -0.8141E-02 -0.2088E-02 Crack 2 -0.8141E-02 -0.6184E-01 -0.8141E-02 Crack 3 -0.2088E-02 -0.8141E-02 -0.6537E-01FRANC2D simulations corresponding to steps 5 and 10 of the 3D model are performed to examine the change in the rates as the cracks grow. The pres-sure profiles from the corresponding 3D HF simula-tions are used in the 2D simulations. The rates of energy release rate are provided in Tables 2-3. It is evident that the rate for the center crack is much less than the rate for the outer cracks at step 10, almost an order of magnitude. Also, the interaction between neighboring cracks is much stronger than for the ini-tial configuration. However, the rate is still non-zero and crack growth is still stable. The simulations are being continued to determine the final length to spacing ratio at which the center crack arrests.Table 2. Matrix of rates of energy release rate at 3.7 mm.Crack 1 Crack 2 Crack 3 Crack 1 -0.2531E-01 -0.5444E-02 -0.2027E-02 Crack 2 -0.5444E-02 -0.1514E-01 -0.5443E-02 Crack 3 -0.2027E-02 -0.5443E-02 -0.2534E-01Table 3. Matrix of rates of energy release rate at 10.7 mm.Crack 1 Crack 2 Crack 3 Crack 1 -0.1474E-01 -0.7807E-02 -0.4911E-02 Crack 2 -0.7807E-02 -0.2951E-02 -0.7807E-02 Crack 3 -0.4911E-02 -0.7807E-02 -0.1473E-017SIMULATION OF EN-ECHELON CRACKSEn-echelon cracks are often the result of crack front break up due to high mode III stress intensity fac-tors. The model in section 5 shows that the mode III stress intensity factor should not be ignored during propagation. Crack front break up is difficult to model, however, and is still not well understood.A simplified approach is taken here to examine crack front break up under a mode III loading. The model is shown in Figure 11. A single edge crack in a unit cube is subjected to a shear force while the crack faces are prevented from overlapping by the inclusion of a static fluid pressure. The elastic modulus is 10 GPa and Poisson’s ratio is set to zero. The SIFs are shown in Figure 12, which indicates the relatively high mode II and mode III SIF.Figure 11. Single edge crack subjected to mode III shear.Figure 12. SIF’s for single edge crack under torsion. Pollard et al. (1982) provide the following rela-tionship between the mode III/I ratio and the en-echelon crack orientation:()21tan211>−=−IIIIIKKKνβ(1)The inequality ensures an open crack. The equa-tion defines the orientation of a plane in front of the parent crack that has zero shear stress and the maximum tensile stress. If the mode III SIF is zero, the crack grows in its own plane. Based on the val-ues in Figure 12, β is –21° in the middle portion of the crack front. The initial spacing of the en-echeloncracks is less well defined and Germanovich (2000) continues to work on this.The initial crack front break up is modeled by manually creating a set of three small en-echelon “petal” cracks at the crack front as described by Germanovich (2000). Figures 13 and 14 show the side and plan views of the crack configurations. They are spaced 0.05 units apart with a radius of 0.02 units.Figure 13. Side view of 3 en-echelon cracks simulating the crack front break up.Figure 14. Plan view of 3 en-echelon cracks simulating the crack front break up.The initial stress analysis was performed without computing the elastic influence matrix. The maxi-mum principal stress is concentrated at the intersec-tion of the main and “petal” cracks (Figure 15). Ef-forts to define the crack growth criterion and propagation behavior for this crack configuration are continuing and further results will be presented elsewhere.Figure 15. Maximum principal stress is concentrated at the in-tersection of the main and “petal” cracks.8CONCLUSIONSThe indirect evidence for natural hydraulic fractures is abundant in the Ithaca, NY region. The proof might depend upon the interpretation, however. Numerical hydraulic fracture simulations lend addi-tional insight to the natural processes. Hydraulic fracture simulation from a sedimentary interface is modeled to examine the influence of natural stress concentrations, the propagation of multiple frac-tures, and the re-orientation of fractures in a rotating stress field.The simulations are not complete yet, but they do indicate that hydraulic fracturing is a plausible mechanism for describing the joints seen in the rocks in the Ithaca, NY region. The simulations are continuing and the final results will be presented later.ACKNOWLEDGEMENTSThe authors would like to acknowledge the past and present financial support of the NSF (EIA-9972853; EIA-9726388; CMS-9625406). This research was conducted using the resources of the Cornell Theory Center, which receives funding from Cornell Uni-versity, New York State, federal agencies, and cor-porate partners. Penn State’s Seal Evaluation Con-sortium (SEC) supported the fieldwork.REFERENCESANSYS 2000. /.Bahat, D. & T. Engelder 1984. Surface morphology on cross-fold joints of the Appalachian Plateau, New York and Pennsylvania, Tectonophysics 104:299-313.Carter, B.J., X. Weng, J. Desroches & A.R. Ingraffea, 1999.Hydraulic Fracture Reorientation: Influence of 3D Geome-try, Hydraulic Fracture Workshop, 37th US Rock Mechan-ics Symposium, Vail, CO.Carter, B.J., P.A Wawrzynek & A.R. Ingraffea 2000a. Auto-mated 3D Crack Growth Simulation, Gallagher Special Is-sue Int. J. Num. Meth. Engng. 47:229-253.Carter, B.J., J. Desroches, A.R. Ingraffea & Wawrzynek, P.A.2000b. Simulating fully 3d hydraulic fracturing. In M.Zaman, G. Gioda & Booker, J. 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