Automated System for the Study of Environmental Loads Applied to Production Risers Dustin M. Brandt 1, Celso K. Morooka 2, Ivan R.

Transcription

1 EngOpt International Conferene on Engineering Optimization Rio de Janeiro, Brazil, June Automated System for the Study of Environmental Loads Applied to Prodution Risers Dustin M. Brandt 1, Celso K. Morooka 2, Ivan R. Guilherme 3 1 Department of Petroleum Engineering, State University of Campinas, Brazil, dustin@dep.fem.uniamp.br 2 Department of Petroleum Engineering, State University of Campinas, Brazil, morooka@dep.fem.uniamp.br 3 Department of Computer Siene, Sao Paulo State University - Rio Claro Campus, Brazil, ivan@r.unesp.br Abstrat Reent disoveries of petroleum reservoirs under deep waters have presented many new tehnial hallenges involved with the design of the offshore prodution systems used. Optimization of these systems is a ritial part of the design stage to insure their safety and eonomial viability. This paper fouses on the study of petroleum prodution risers. A riser is essentially a long tube or set of tubes that allow the transfer of fluids and tooling to and from the platform and undersea well. Due to non-linearities involved in the analysis of these systems, numerial methods are used to analyze riser dynamis. Evaluation of the proposed system under a wider variety of environmental loads is a ruial step in the overall design proess. This paper presents the use of modern omputational resoures to streamline the evaluation of a wide variety of environmental loads with the goal of optimizing the riser design by the generation, proessing and visualization of parametri studies. A self-optimizing system that proesses and exeutes numerial simulations using individual omputers and lusters will also be presented. Keywords: Offshore Petroleum System, Parametri Studies, Distributed Computing 1. Introdution The searh for more petroleum to satisfy the growing global demand for energy has driven petroleum operators and ontrators to operate in deeper and deeper waters. Oil fields in deep and ultra-deep water (greater than 1500 meters water depth) that were one onsidered eonomially infeasible are now being explored and exploited. There have also been a growing number of large reservoirs disovered offshore in deep waters. This trend of oil prodution in ever deeper water presents many tehnial hallenges for the petroleum E&P (exploration and prodution) industry. This trend to deeper waters ours off the ost of Brazil in basins suh as the Campos Basin and Santos Basin among others therefore it is of speial interest to the Brazilian petroleum market. One tehnial hallenge is the riser system. The riser system is omprised of one or more pipes that allow the transfer of tools and fluids between the undersea well and the floating prodution faility. This system is exposed to environmental loads inluding fores aused by waves and urrents. The understanding of the dynamis of the riser system is ritial to its proper design. Any failure ould ost millions of dollars, damage the environment and even result in loss of life. Evaluation of the dynamis is arried out through several methods inluding tests with redued sale models, observation of dynamis of similar systems under similar onditions and numerial models. This paper fouses on the use of numerial models, in partiular, finite element methods utilizing semi-empirial hydrodynami fore models. Riser dynamis an be highly non-linear thus are resolved numerially in the time or linearized and solved in the frequeny domain [7]. To better understand the influene of a partiular system harateristi or environmental load on the dynamis of the system, parametri studies are often undertaken. A parametri study is a olletion of possible onfigurations generated by inrementing one or more simulation variables. These studies an involve hundreds to thousands of individual ases. These studies determine the effet of the variane of a parameter on the system s dynamis or an also be used to define an aeptable mesh for the partiular riser system analysis. These ases must be proessed and results must be retrieved and analyzed whih an be an extremely time onsuming ativities. This paper presents the beginnings of a system that optimizes the use of loal omputer resoures and automates the reation of simulation inputs, exeution of numerial solvers and retrieval of results of these studies. This system employs tehniques inspired from multi-agent and distributed omputer systems. This system is omprised of four basi parts; a parametri study generator, server software, lient software and ode that allows the integration of the system with a pre-existing graphial user interfae presented by Morooka et. al [6]. The lient software an be installed on remote omputers onneted to a loal area network or a omputer luster. This lient software is responsible for proessing simulations utilizing the bag-of-tasks approah desribed by Costa. et. al [1]. Desired numerial program are installed on the lient omputer and then used by the system to alulate the dynami response of the riser system. The lient software thus reeives riser onfigurations from the server and exeutes the numerial programs desired. This approah is appropriate due to the fat that separate ases belonging to the same parametri study are not interdependent. The server software is installed on one omputer and monitors the lient software. The server software is also responsible for ating as an interfae between the distributed omputer system and the graphial user interfae. It is onneted to a database whih stores valuable information onerning past simulation ases and studies. The server also manages user aess rights allowing users to reate projets ontaining parametri studies or general simulation ases and allow aess only to desired users. It also autions off proesses to the lient software. The parametri study generator is ontained within the graphial user interfae. One a proposed riser system is modeled, parametri studies an be generated. The generator allows the user to selet up to three variables to vary inrementally in a user defined range. The user an then further refine the study by deseleting ombinations that are either infeasible or of no value. The parametri study generator thus reates the desired input files related to the numerial software being utilized. The last omponent of the software system is the software integrated in the graphial user interfae that allows users to submit parametri studies and isolated simulation ases and retrieves results. The software allows a user to login to the server and view, manage, delete, share and analyze results.

2 2. Bakground 2.1 Prodution Risers As mentioned above, a riser is a pipe or group of pipes that onnet the floating prodution system to the submarine well or manifold. A prodution riser system is used in the prodution phase of an offshore oil field. There are many types of onfigurations used for prodution riser systems. Every type of riser onfiguration varies in its design omplexities and has its own set of advantages and disadvantages. This paper briefly desribes just a few of the possible onfigurations whih are graphially represented in Figure 1. Steel Catenary Risers (SCR) are made up of steel tubing that forms a atenary shape in the water and runs along the seafloor for a distane in order to absorb heave motion imposed by the platform. Fatigue indued by VIV (Vortex Indued Vibrations) and heave (vertial motion) is a serious onern in the design of this system among others. Top Tensioned Risers (TTR) onsists of steel tubing tensioned by the platform or an undersea buoy forming a nearly vertial tensioned tube. This onfiguration has the advantage of low osts but is very sensitive to heave. Another alternative is riser systems utilizing flexible riser pipe whih is omprised of layers of varying materials that reate a flexible, fatigue resistant riser. Flexible riser s drawbak inludes its relatively high osts and lak of suffiient suppliers in the global market. The flexible riser system is used extensively in Brazil. Another onfiguration is a hybrid solution denoted as the Self-Standing Hybrid Riser (SSHR). This onfiguration is omprised of a rigid vertial riser tensioned by an undersea floater with a flexible riser used to omplete the onnetion with the platform. This system takes advantage of the relatively low ost of a rigid riser with the flexibility and damping of vertial motion harateristi of a atenary system made of flexible riser pipe. The analysis of a riser system needs to inlude a areful onsideration of the dynamis of the system under a wide range of environmental and operational senarios that ould possibly our in the field. Riser systems suh as SCRs and flexible risers are often highly sensitive to the diretion of the environmental loads in relation to the atenary s plane. The motion of the prodution platform is another extremely important onsideration due some riser onfigurations sensitivity to heave motion. Figure 1. Diagram of the three prodution riser onfigurations briefly disussed 2.2 Distributed Computing Systems The field of distributed omputing is extremely broad. For this reason, this setion is not intended to be an introdution to this area, but merely a omplement to the desription of the presented system in hapter three. A distributed omputing system an be defined as a system whose omponents are installed on omputers onneted together by a network and ommuniate by passing messages to omplete tasks [2]. They an be used for numerous purposes from sientifi proessing, information storage and retrieval, deision making tools and more. These types of systems are used to harness resoures from many different omputers to omplete a task or group of tasks. A prime example of a distributed omputing system is the internet. This omplex system allows users to aess material and servies through various appliation layer protools like, HTTP (HyperText Transfer Protool), FTP (File Transfer Protool), POP3 ( Post Offie Protool version 3) among others from onneted omputers. Aording to Nadiminti et. al[8] the main features of a distributed omputing systems are; funtion separation, inherent distribution, reliability, salable, and eonomy. Funtion separation means that omponents are divided by the types of servies that they are apable of providing. Inherent distribution means that information an be proessed and stored in various omputers that may or may not know of the existene of other omponents in the system. Reliability of a distributed system is ritial and is ahieved by storing proessed information in various loations and insuring the system an tolerate the abrupt losure of any omponent. The importane of salability of a distributed system is paramount. The system should be able to expand the number of servies offered and number of onneted omputers without drastially hanging its original arhiteture. One of the great advantages of a distributed omputing system is that it an aomplish the same task as an expensive luster by harnessing resoures of pre-existing omputers. Using already existing omputer resoures is an eonomial advantage ompared to investing in a luster solution. Distributed Computing shemes are employed in sientifi endeavors in order to proess large amounts of data. There are many urrent projets in various areas utilizing distributed omputing systems. A notable projet is SETI@home whih is used to proess signals from outer spae in the searh for extraterrestrial intelligene [9]. This projet uses a distributed omputing platform alled BOINC (Berkeley Open Infrastruture for Network Computing) whih is a non-ommerial system espeially reated for volunteer omputing. Volunteer omputing enables anyone to install software that will use unused omputer yles to further projets suh as Seti@home. Another example is distributed.net whih was the first distributed omputing system intended to take advantage of donated unused omputer yles to solve omplex problems in the field of ryptology. This paper presents the beginnings of a distributed omputing system omparable to several others already in existene. Many distributed omputing systems used for sientifi purposes transmit both the ode that proesses the data along with the data or has the proessing ode embedded into the lient software. This paper presents a different system wherein proessing ode is kept separate and remains on the lient omputer in a reserved folder. The lient software aepts tasks in aordane with the proessing

3 ode that is installed on the omputer. This an be referred to as a BoT (Bag of Tasks) approah noted by Costa et. al [1]. MyGrid is a distributed omputing system based on the BoT approah developed and maintained at the Universidade Federal de Campina Grande. The advantage of this sheme is that a wide range of funtionality an be added by installing new proessing ode. 2.3 Multi-Agent Systems Multi-agent systems are based in the field of artifiial intelligene and are developed to solve large problems. These systems are made up of agents, usually software elements, that work together to solve problems. Aording to Gerhard [2], agents have three distint harateristis; autonomy, deentralization and loal views. Autonomy means that the agent ats aording to its own internal variables regardless if the deisions made are detrimental to the overall system. The agents are deentralized meaning there is no agent whih ontrols everything. Lastly the agents have a limited view of the global system meaning that an agent doesn t need to know of the existene of another agent. Many of these systems are based on some sort of bidding for prospetive tasks between the agents. For this reason Multi-agent systems are well suited to model soial reations and make predition. An example of an appliation where a Multi-agent system is Masfit, a system used for fish trading as presented by Cuní et. al [3]. Through the system, traders an partiipate in simultaneous autions remotely through a software agent whih takes bids based on the need of the partiular trader. These virtual trading proxies negotiate for lots of fish. The system presented in this paper uses a multi-agent approah. Parametri studies, whih are olletions of individual simulations, are divided throughout all partiipating omputers. This distribution utilizes a bidding proess where the server sends out a request for bids and the software agents put in bids based on the approximate task ompletion time and other variables. The winning agent will reeive the ase. 3. System Desription 3.1 Client Node Software The lient software was designed with the objetive to optimize the use of omputer resoures. Alloation of the ases by the server is determined in aordane with several variables of the lient omputer. Fators that influene the alloation of ases to a lient omputer inlude speed, storage apaity and urrent omputer usage. Speed is alulated by running a referene ase and reording the time needed to finish the ase. The amount of spae that the lient software an use is defined during the installation of the software. The lient software ontrols any number of proessing threads whih monitor the exeution of the numerial programs. The number of proessing threads is defined during the installation. A lient node (agent) reports diretly to the server or an be onneted to another lient node. The lient node or server is defined as the node s parent. A lient node an have hildren nodes that then report diretly to the node. The objetive of using suh a system is to allow the lient node software to be installed aross various loal area networks. An example of this would be a luster system. A luster an be defined as a olletion of losely related omputer systems, providing a ommon servie or running a ommon parallel appliation by Luke [5]. A luster usually has an internal network and aess to the outside network is usually restrited to one omputer ommonly referred to as the master node whereas the other omputers are reserved solely for proessing and are ommonly referred to as slave nodes. In this ase, the slave nodes have no diret aess to the distributed omputer system s server if it is not installed on the master node. The ability for one lient node to be a parent of others allows that the master node of the luster be the parent of all the hildren nodes thus opening up a mediated line of ommuniation between the slave nodes and the server aross the outside and internal nets. The default arhiteture is a slightly modified server/lient (two-tier) model. Instead of the lients sending requests to the server, the server in the urrent system sends requests in the form of bidding, information and file transfer requests. In this mode the all lients report diretly to the system s server. This model is represented in Figure 2. The other mode is similar to the example given above where lient nodes are organized in a tree struture wherein the server is the entral trunk. Requests are transmitted from the server to its hildren nodes whih are then forward to the hildren of those nodes and so on. Responses are then transmitted from hild to parent until it reahes the server. Eah lient node only has information about its parent and hildren in aordane to the prinipal of loal view of an agent given in hapter 2.3. Figure 3 represents this arhiteture. Figure 4 shows a hybrid model whih an be used to integrate workstations and lusters under the same server using the same lient software. Figure 2. Two-tier model Figure 3. Parent/Child/Server model

4 Figure 4. Hybrid model The hoie between the models is set by three variables. A multi-agent inspired approah is used to define the relationships between the lient nodes and servers. The main variable involved in defining relationships is the lient node s desire to be a parent. This variable is set during the installation and varies between zero to hundred where zero signifies no interest to be a parent and hundred shows a high desire to be a parent. If a lient/server model is desired the variable an be set to zero for all lient nodes. The agents will then request that the server s bid to beome their parent. In the ase of the luster, the master node will have a high desire to be a parent while the slaves will have no interest. The other two variables are the desired number of hildren and the urrent number of hildren. These variables ould be used in the future to optimize the load on any partiular node by limiting the number of hildren a node an have. The relationships are defined by a bidding proess. Eah node will send out a broadast using the User Datagram Protool (UDP) requesting bids on whih node will be its parent. The server will respond and submit a proposal with a value of 70 allowing for the standard server/lient onfiguration if no other lient offers a higher bid. The node will then wait a fixed amount of time allowing all interested nodes to respond. The node will then do an eho test on the highest bidder. This entails sending a simple eho message whih is passed on from hild to parent. If this eho ommand does not return to the node the bid will be aepted and the node of the winning bid beomes the parent. The eho test avoids any ylial relationships between lient nodes. All nodes will periodially hek-up on its parent. If no response is given the relationship is broken and the node searhes for a new parent. In this way there is always a path between the node and server. Messages and files are sent through this path to and from the server. The bid is formulated in a pieewise manner. If the urrent number of hildren (σ ) is less than the desired number of hildren given as σ d, Eq(1) is used, where λ represents the node s desire to be a parent and σ h represents the hypothetial new hildren whih is the number of open bids for hildren. The equation assumes that half of all hildren bids are awarded to the node. If the number of hildren is equal or greater than the desired number of hildren, Eq (2) is used. Equation 2 is a Gaussian funtion used to drastially redue the bid if the number of hildren is greater than desired. if σ σ bid = λ σ 0.5* σ if σ < (1) d 2 ( λ σ 0.5* σ h ) bid = * e 5 (2) σ d ( λ σ 0.5* σ ) If for any ase the server is disonneted or annot be reahed, the lient nodes who were hildren of the server revert to the hybrid model. The nodes will reorganize and the node(s) who annot find parents due to the ylial relationship restrition will temporary manage the hildren lients and manage the urrent simulation ase balane until the server returns. When the server returns the original model onfiguration is resumed. This allows the simulation ases to be proessed without the need for the server. 3.2 Server Software The server is responsible for various tasks suh as aepting ases from users, storing information in the loal database, transferring files to the user, validating user s permissions to aess projets and other related duties. The server reeives requests for information onerning the progress of the projet and sending out information requests to the nodes and then relays the response bak to the user. The server also stores a opy of the input files for all ases and also stores results that were generated from the lient nodes. A unique element of the server software is the use of a plug-in type system that is designed to allow flexible parsing of input and output files of the applied numerial software. Up to ten input variables an be traked for eah numerial program. The user an define these variables. Along with the ten variables, proess time, size of results and error odes are also traked. This is used to analyze the effet of these variables on the proessing time and the size of the results. It is hoped that one an extensive database is reated the server an then use this information to aurately approximate proessing time and size. This data would vastly improve the effiieny of the system by automatially sending more labor intensive simulations to dediated high performane omputers h h

5 while sending less demanding simulations to workstation. This information is also helpful in respet to determine the onvergene spae of the numerial model implemented in the numerial software being run. This information also allows for omparison of performane between versions of the same numerial software or omparisons between different numerial software. Currently the server uses results from limited study of proessing time and results size based solely on the number of elements used in the finite element method analysis. With the estimation of proessing time and results size the server will prepare a bid request for eah inoming simulation ase. The server then sends this bidding request to its hildren nodes and waits a fixed amount of time for the replies. The ase is then awarded to the highest bidder. This proess ontinues until all ases are awarded. If for any reason a lient node is going to be turned off, it sends a message indiating whih proesses it had been awarded and the server thus sends out new bidding requests for these simulations. The server is responsible for heking up on the status of simulations being proessed. This helps inform the user of the status of his or her parametri study but also onfirms that the lient node was not losed before it ould send out a message indiating that it will be turned off. If a lient node does not onfirm the status hek the ase will be put up for bidding one again. The server stores simulation input and results files in ompressed form on a loal FTP (File Transfer Protool) server. The graphi user interfae software an then aess the results from the FTP server if it is authorized to do so by the distributed omputing system server software. 3.3 Parametri Study Generator The parametri study generator ode utilizes a plug-in system desribed in Morooka et. al [6] This plug-in system allows the graphial user interfae to be expanded to enable the modeling of riser systems using various numerial pakages. One of the funtions of this plug-in system is to generalize variables ontained in the numerial software s input and output. Variables are listed and an be aessed and even hanged. This omputational interfae between the input variables and the graphial user interfae is used to reate the ase generator whih is not speifi to any numeri software. Figure 5 shows the parametri study generator onfiguration main window. The user an then hoose two referene ases whih are used to define the limits of the parametri study. The system then analyzes the two ases and finds the variables whih vary between the two ases. The user an then selet up to three variables in that list to vary. The number of divisions for eah variable an then be set. The next step in the generation of the parametri study is the refinement stage shown by Figure 6. The user an then exlude partiular ombinations from the parametri study. The next window is not show but allows the naming rules and desired diretory where the parametri study will be generated if it will be proessed loally or the distributing omputing system an be hosen to be used. Figure 5. Variable and range seletion window Figure 6. Parametri study refinement window 4. Results One of the first uses of this system is to automate disretization tests. These tests involve varying the number of disrete elements used when modeling the riser system. This is often a trade-off between pereived auray and proessing time. Proessing time often inreases exponentially with the number of elements used. The term pereived auray is used to differentiate a onvergene to one result from the absolute auray of the simulation ompared to real time measurement. This test is done before a full parametri study is generated to insure the best ompromise between proess time and stress onvergene. The result of a test disretization study is present in Figure 7. The riser used is a top tensioned riser in one thousand meters of water exposed to a onstant urrent profile of 1 m/s. The lower extremity is used beause the extremities have shown to be the most sensitive to disretization. It an be seen that around 225 elements the von-mises stress begins to level off. There is little advantage of using more than 225 elements in this partiular ase. It an also be seen that if 100 elements were to be used, there would be an error of almost 100%. Alternatively if a point at the middle of the riser (not shown in Figure 7) is taken, the error between 225 and 100 elements is around 2.5%.

6 Figure 7. Results of a Disretization Test Proessed by the Automated System The test is omprised of 27 separate ases that were distributed and proessed on 4 lients. The total run time was 12,742 seonds whih is not as effiient as possible. The most effiient alloation would have yielded 8,554 seonds. This ineffiieny is due to the sparse information onerning the relationship between the proessing time and the number of finite elements used. With further use of the system, this lak of information will be resolved. The system was then applied to generate and proess a parametri study varying wave height and wave period. The wave height was varied from eight meters to twelve meters inrementally with steps of one meter. Wave period was varied from eight to fifteen seonds with steps of one seond. This produed a grid of forty simulations that were exeuted. The total proessing time for the parametri study was forty hours and twenty minutes whih was divided between four test lients. The time to omplete the study with the four lients was 13 hours showing one again the need for further optimization in the alloation of the tasks. The parametri study was applied to a top tensioned riser in five hundred meters of water. The material of the riser was onsidered to be steel with an outside and inside diameter of 0.25 and 0.21 meters respetively. A onstant urrent veloity of 1.0 m/s was assumed and the linear wave theory was utilized. A top tension of 1.5 times the submersed weight was also used. The data was generated from a numerial solver disussed in depth in Morooka et. al [7]. Figures 8 and 9 show the normalized servie life and normalized von-mises stress respetively. The servie life estimate was taken fifty meters beneath the surfae to avoid the effets of the fixed top onnetion on the servie life therefore fousing on the variane of the seleted parameters. Relationships of the dependent variables an learly be seen from Figures 8 and 9. Figure 8. Normalized Servie Life for TTR Figure 9. Normalized von-mises Stress for TTR

7 5. Conlusion The urrent paper presents the beginning steps of a distributed omputing system built with the non-linear riser problem in mind. Due to the highly non-linear nature of riser dynamis, parametri studies are invaluable tools to better understand the interplay between system harateristis. The system an harness remote workstations and luster resoures to quikly proess these parametri studies and separate ases in an optimized manner. Users an submit, view, manage, delete and share parametri studies and isolated ases using a graphial user interfae. Proessed information is ompressed and stored on a entral server for further retrieval by any user who has rights to view the information. A database was implemented to reord up to ten variables for eah numerial program along with reords of all ases, errors and run times. The system redues manual manipulation of input files reduing both man-hours and the possible risk of human error. The system is not fully optimized due partly to a lak of previous ase proessing information but mainly due to the lak of a more omprehensive tehnique to aurately approximate proessing time and results size. This tehnique would allow the server to better desribe the submitted ase to the lient nodes thus resulting in a more optimized alloation. There are many possible tehniques available but more researh must be done to determine the best approah for the partiular appliation. The system urrently laks administration software allowing for easy modifiation and addition of apabilities. Capabilities are added manually for the time being until a better approah an be reated. There also various elements of the system that needs to be better investigated and improved. With the addition of more lient nodes the system will beome even more apable of resolving large sets of simulations. Aknowledgements The authors would like to thank CNPq and FINEP (CTPetro), Petrobras and Capes for supporting the present development. Referenes [1] Costa, L.B., Feitosa L., Araújo, E., Mendes, G, Coelho, R., Cirne, W., Fireman D., MyGrid A omplete solution for running Bag-of-Tasks Appliations. Proeedings of the SBRC Salão de Ferramentas (22nd Brazilian Symposium on Computer Networks - III Speial Tools Session ), 2004, Gramado, Brazil. [2] Coulous G., Dollimore, J., Kindberg, T, Distributed Systems Conepts and Design. 3 rd Edition, Pearson Eduation Ltd, Essex, England, 2001 [3] Cuní, G., Esteva, M., Garia, P., Puertas, E., Sierra, C., Solhaga, T., Masfit: Multi-agent system for fish trading. Proeedings of the 16th European Conferene on Artifiial Intelligene, 2004, Valenia, Spain. [4] Gerhard, W., (editor), Multiagent Systems, A Modern Approah to Distributed Artifiial Intelligene. MIT Press, Cambridge, MA. USA, 1999 [5] Luke, R., Building Clustered Linux Systems, Prentie Hall, NJ [6] Morooka, C.K., Brandt, D.M., Fernandes, W., Jr., Computational Platform Designed to Streamline the Modeling, Proessing and the Analysis of Offshore Riser Systems. Proeedings from the 19th International Conferene of Mehanial Engineering, 2007, Brasília, Brazil. [7] Morooka, C.K., Coelho, F.M., Matt C.G.C., Franiss, R., A Time Domain Simulation Tool for Rigid Riser s Design. Proeedings from the Rio Oil & Gas Expo and Conferene 2006, 2006, Rio de Janeiro, Brazil. [8] Nadiminti, K., Assunção, M.D., Buyya, R., "Distributed Systems and Reent Innovations: Challenges and Benefits". InfoNet Magazine, Volume 16, Issue 3, 2006, Melbourne, Australia. [9] Werthimer, D., Cobb, J., Lebofsky, M., Anderson, D., Kopela, E., SETI@HOME massively distributed omputing for SETI. Computing in Siene and Engineering, IEEE Computer Soiety, 2001, Washington DC. USA, Volume 3, Issue 1, Pages