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SPEsoOkItuor?strkEnSPE 18183A Computational Model of Well Cornpletmn Designby S. Dunn-Norman* and J.M. Peden,f Heriot-wati W and D.R. Bedford)Ferranti Computer Systems Md.*SPEMsmbafs

.*WI 1W9, SOClOtyd PotrohumEngirworaThis peperwoopropemdtor proaonklion at thoS3rdAnnualTechnkal Oonkrence and Exhibitionof the Societyof PetrolbumEngineersheld inHouston,lx, CMber 2-5, 19ss.TM peperwasedwtul for&enntatkn by qn SPE ProgramOornmittw foliowlngrwtew ot informMonamtdnad In m abstract$ubmittodbytheauthor(s),OontonmO!Ma pepor,as preoontad,havs notboonr@dewc5W the SooIetyO!Potrohum Enginooraqndqro$ubjwt toowrootkrt bythoauthor(o).Thematuiat, S9praented, don notnemaarlly rottedartyposllknGtthe Soolely at Potrotwm EncIIwor& ItsottkerB,ormern5ere.,Papua H~@~*m~_@MmW *W E_l Wm-dtiqd W*m E_. P~ tooopybrWMctadman aWaotofnot rnomthanwwWda MtratWmmay not boopkd.lhaebmraot clWJ!dcuntainoo@OWu9~otwhWeanllbywhorn the~b~. Wrlta PWoaUam Merug8r,SPE, P.O.,SO!I-, Rkhudmn, TX ~ Telex, 7MSSS SPEOAL.ABSTRACT oxtensi ve array of dealgn $m%metera. In additiodeaignera must optimize designai for ourThe design of well ootttplet ions 1s a Qomplex process requirersenta, proJeoted requiretaenta of fut wewhioh requires the engineer to seleot, rationalize oper,: ions (e.g. stimulation, re-entry), fuand integrate a large num>e~ of design elements. In producing conditions (e.g. produotian souring),the literature, it has been recognized that a potent ial produot ion problems (e.g. sanding, aosyetems epproaoh to well cotnplet ion design must be or oorroaion). ASJwould be expeoted in auoadopted if optimum and coet-effioient Ooatpletioneare to be generated. Historically, diagratmnatioal oomplex prohlam, many resultant completion desare sub-optimal solutions.representations, suoh as logic diagrams, have beenusedto model and implement the systems approach for In an attempt to rationalize the prooess ofwell completion design, completion design, Patton and Abbott proposedsystems approaoh to wel: completion design()This paper discusses, as an alternative, a this approaoh, they suggest an integrated decomputational model of well completion designdeveloped through an prooec!ure in which eaoh oomponent of the designindustry sponsored configured With respect to its attributecollaborative researoh projeot, The modelformalize9 et:vironment, goals, standards, aocess to resourothe relationships and i~teraotion &nd constraints. This approaoh was presentedbetween conventional computations for completiondesign, possible basis for oonstruoting a c!ompletior? desuoh as tubing hydraulic or stress oomputer program.analysis, and design knowledge gained through designand operating experience. The systems approach is significant in that ita framework for describing relationships betThis model provides a comprehensive representatio- varioua wellbore components and operations, tof the systems approaoh to well completion design, effeot on well productivity, and any limitatioand serves as the basis for a computerized imposed on future operations. However, simimplementation. The implementation integrates both identifying these relationships is not sufficieconventional and expert systems programming for oormtruoting a design program. Rather,techniques. Program development and the advantages design knowledge must be expressed in a struotof using a oomputer design program to provide a whiah explicitly details the reasoning and procerational and consistently optimun design capability used to reaoh design oonolusions, Further,are disouesed, struoture must provide a means of constructcompletion design solutions.INTRODUCTION Logio diagrams have been used in an attemptWell completion deeign refers to the selection of struotur,) completion dvsign knowledge(a). Tall downhole equipment, fluids, and procedures diagrammatical teohniquee impart a puneoessary either to establish, or to improve, the procedural, or sequential, view of the compleproduction or injeotion of wellbore fluids. In design prooess, In addition, the diagrammadesigning oomple;ions, engineers must oonsider an approaoh reqiilres that the design knowledgstructured in a binary, query format, that 1Referenoea and illustrations at end of paper question whioh oan only be answered by yes or 27

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.2 A C@lPl?XATI@UiLODl?LOFWE!LLCaMPLS!WCaWmSTCN C!nu.- -.- --. ----- - ----=. -r=Thi8 Btruoture h an awkwardmodel of the completion . The design prooe8s uttl.z~s fotward dridesign prooese. reasoning, and ia non-procedural,Analternative approaoh ror representing the preoess . Design aclutiona muut be Constructed, notof completion design is a computational model which enumerated.provides a dynamic, non-procedural representation.A Oomputatlonal model combinee both empirical design . The design prcblem oan be reduced intoknowledge with nunerioal, engineering computations. sub-problems or eub-oomponenta, such as tubingThe following paper revfevs the procese of well sizing, packer selection, etc.completion deeign, presents a computational model ofthat prooess, and disousses the implementation of , fiesign criteria or requirements are frequenthxs model as a oomputer based design program. updated or deleted ae the design procproceeds.WELLCOMPLETIONESIGN,ROCESS . The teohnical domain of well completion desThe wel,l completion design process refers to the includes sub-tasks such as interpretationfunotions which must be performed in specifying all planning or forecasting.cbwnhole equipment, fluids, and procedures requiredto cmplete a well. The design process, covers a Several of these characteristics are significawide range of produot!on engineering topios. The elements of the computational model, and therefoprincipal techniza; topics are shown in Figure 1. warrant further discussion.It is important tc note that there is actually no First, completion design if a forward,single prooess of designing well completions. The data-driven problem(). This implies that deespecific tasks, or functions performed, .Iepend on solutions muet be constricted ratherthe objective of tfle design. For example, tasks unumor&ted, Enumeration means that it is feasibinvolved in formul+b:.ng a preliminary design for to list all potential eolutlona, and then struotuexpendable exploratory or delineation wells mayvary these in suoh a way that they may be searchedooneiderably from the tasks performed in detailed Find the oorreot answer. It aan be argueddesign of development WellS. Consequently, any enumeration 1s possible if the design problem dcacomputational model of well completion deeign must is sufficiently small(). Clearly, this argumenhave a defii~ed level, or objeotive of the design not valid for well completion design.process to simulate. Well oompletlon design ia also a non-proceduThe computational model presented herein simulates process. It is extremely diffioult to identifythe detailed level of design normally required for ordered set of Stepsw, which defines a geninfield development wells. All funotions, or tasks, method of designing wellbore completions. Furthrequired to select downhole equipment or fluids, are the starting point of the design process is oonmodelled, Examples of these design functions dependent, For example, if the well is subsea,inolude sizing tubing, selecting paakers and engineer may wish to begin with selection ofmovement compensation devices, and specifying a subsea tree, rather than tubing sizidownhole safety valve for the well. Consequently, diagrammatical, or solely proceduapproaches are awkward in representing the enIn addition to ascertaining the level of the design well completion design process. It shouldproc~ss to be modelled, a startir: point of the recognized, however, that thefle are portions ofdesisn process must be defined. Frequently, in the design process whioh are procedural, and thdesign of development well completions, it is necessitate conventional modelling techniques.assumed that the well has already been drilled tototal depth and that production casing is set, It The ability to decompose the problemmay be argued that this assumption constrains the sub-problems, or sub-components providesdesign of the completion string, and that to design manageable approach to constructing solutionsa truly optimum completion requires the assumption the technical domain is extensive. However,that any hole, or easing size, is available, In this approaah, design oonflicts may occur betwsome oases this argument is valid. However, this items which have been configured independently.was deemed to be an unrealistic approaoh for the this case some filtering teohnique must be apppurposes of the computational model, to eliminate conflicting oonfigurations$CHARACTERISTICSFWELLCOMPLETIONESIGN Another important poinb is that completion designa dynamio prooess, As the design develops,In constructing a model or simulation of a problem, design criteria or requirements are frequenit is fundamental to identify the primary task and altered. This is a complicated characteristicits associated oharaoteristios. Accordingly, well model, as it neoessit,ltes an Kindott mechacompletion design was identified as a !Iderign taskil. capable of removing any existing components wIn design tasks, objects are configured under oonfliot with the new design criteriaconstraints in order tq aohieve goals, or satisfy requirements. requirements. Figure 2 depicts this oonce~t. The computational model provides a struoture wThe primary characteristics of well completion embodies these problem oharaateriatics. Howevedesign may be described as follows: attain an exa?t simulation of the completion de

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m%18183 S. DUNN-NORMN~.M. Iprooeaa would require a oomplete understanding ofthe human reasoning procesaea. FOr the Purposes Ofdeveloping the computational model, it has beenassumed that it is sufficient to model the principalproblem ohvacteristi~s.tOMFUTATIONALODEL ~Definitionrhe computational model ie a formal representationDf the well completion degign problem. The model?onsjsts of two principal elements: empirical design?easonlng and engineering computations.rhe empirical design reasoning is simply what 1sknown about designing wellbore completions. Itenocmpasses a description of the objeots andcomponents Whiah comprise the completion aytem,relevant reasoning about selecting thosecomponents, and knowledge of how field operationsmay affeot or aonstrain the d~si gn, Thecomputational model utilizes symbolicrepresentations to describe the components andobjects of the completion system, and the completiondesign reasoning:Engineering amputations refer to 3alculatione suchae tubing hydraulics, stress and expeoted movefaent.These aaloulations are a fundamental element of thewell ,oompletion design proaesa. Calculated valuessuoh se total string expansion, fluid velooity, andthe foruee developed on the packer, are required toobviate impossible design choices.The engineering Computations must be invoked atappropriate pants in the design proaess. Further,the engineering computations and empirical designreasoning must be well co-ordinated, so thatinformation may pass from one element to another atvarious points in the reasoning prooess.Completion ComponentsThe components used in completion design can beola&sed acaording to their type or fUflCtiOfl. Figure3 shows some of the major components, or objeots, ofa wellbore oompletim system.Eaoh oomponent, or object in the completion,possesses attributes. These attributes normallyhave assigned ialues. For example, a paoker has theattribute IInumber of borts!! and this attribute oannormally have a value of 1, 2 or 3. Further, mostobdects possess many attributes, and bhese may beorganized in an objeat-attribute-value struoturefor olarity(s)o In the computational model, mostcompletion components are desoribed using thiseitructuro. Figure 4 shows objeot-attribute-valuestructures for two completion components.The value, or values of an objeot~a attributes maybe determiner, through empirioal design reasoning,direct numerical oomputatlon, or a uomblnation ofthe two methods. For example, optimizing tubingsize means determining a value for the tubingattributed ltODilarid lIDI1, This is aooomplished byfirst computing tubing hydraulics for a range ofpossibilities, and then applying design reasoning to

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EMMD D.R. SSDFCW the Computational results in order to determineoptimuv size. Where it le. not possibleimmediately tietermine the optimum value,alternatives are held for later evaluation?.When an attributes value has been determined,beoomes a constraint on subsequent elements ofdesign problem. As mare components are Speoifieand inc~easing numbers of constraints are addedthe design problem, a non-option design statebe reached. This leaves the designer with a dilemof finding which oonflioting design parameteralter. The designers of 12ECsexpert SYStemWrecognized this phenomena, and simplified thstculation of the design prooess to eliminateprcbiem(c).Mechanical acinpletion components, or completobjects such as wells, reservoirs, anti fluidpossess inherenz rt .atianahips. For example,Figure 3 a subsurface safety valve is a r)mpoofw a completion string, and fluids are .untainin!! a reservoir. These aomponent relationships hbeen formalized in the aomp~tational model,serve se intelligent pointthen < aatians >Figure 5 shaws an example tubing deeign rule wgenerates possible,tubing grades given CO~andpartia~ pressure dkta.Design reasoning is represented symbolically in

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.4 A COMP~ATXOIW,L Q? WSLLCQMS~ON DESIGN SPS

This means that de8igr onoepte, quch se ,,format. used to provide a etruoture o? the completiphysioal oonatraints or oonditionc, may be desoribed prooeas. The atruoture developed may be deaoribedtith a syntax similar to English language. Thus, as having two leve18.the idea that 3 paoker unseats with a 8traight pull, The first, or outw levedefines requirements of the design problem.i.e. no rotation, oan be ooded as: inner level, ie where design reasoning oreates(retrieving-method Paoker etraight-pull) mahtains completion components whioh may satl?raquiremente of the problem, This atruoture

shown in Figure 6,Design rules operate eolely on the ourrent sLate ofdesign knowledge. In order for a rule to exeoute, The design requirements, or I*funotiothe conditions o? the design rule must be validated requiramentalt as they are oalled inby faots. The straight-pull paoker oited above is i?omputational ~del, derine the utilities whioBn example of a faot capable of validating some completion nuat provide. Suoh utilities includedesign rUd. ability t.o produce fluids via tubi!jg, isolate,, annulus, bullhead to kill the well, uirculaDesign rules alter the state of reasoning, either by chemiaal treatments, or provide a meana or liftinadding, deleting or modifying ourrent racta abOUt the well if it dies. Many functional requirementhe completion deeign. Eaah time the state of appear to be related to geographical praoticea,reaa(aning ohanges, new fatts end design knowladgeoauee other design rulee to ooctinue the design Ae functional requirements are asserted inreasoning process. It ehould be ~oted, however, computational model, design reasoning beginsthat rules are not t.esigned to exeoute in a speciry equipment, fluids or ite~s capablepr~-d~!ter-irledrder, aa in procedural Lype satisfying those requirement. Tree-1pr.~grsms. A desi8n faut, which csuses one rula to struoLures are developed which deaoribe the vabe invoked, mayaotua?iy be deleted by the aotiun o! combi~ations or ,attributes for each completenother design rule Def~e the first rule has an component. Theue structures result from the desopportunity to e&cmte. Accordingly, this manner of reaaonlng in the computational model, An exampleooding completion design reasoning h both thie i s shown in Figure 7, whioh depiota alternativenon-prooedur:l and non-determinWio, and obviatea possible attributes. for tubing.the need to explicitly determine the Problemsolution paths. Engineering parameters are an important oriteriaRepreaenttng screening de?)ign alternatives, as the oomponcompletion design knowledge apeciried must meet the physical oonatrainte ofaymbolioally in rule format provides a powerful environment or completion ayatem. Consequentmeohanism for modelling the design reasoning used in engineering, calculations are invoked at appropriaspeeirying wellbore completions. However, the points in the deeign prooeas to return valuee aoomputatlonal model must also have a logioal as fluid velooity, pressure drop, paoker torostructure or rramework for the design rules, so that tubing expanaion or oontraotion, and expeoted wdesign solutions are oonatructed. performance. The numerical values returned are ufor physioal tests, to prevent generationComputational t40del Strgoture impossible doeign choioes. In Figure 7, no tubweights are shown for 3.5 inch) J55 %rade tubiThe struoture of the Computational model rerers to Thie demonstrates that a numerioal test, suohthe manner in whioh the design reasoning has been burst, oollapse or tensile load, has prevenorganized, In order to disousa this struoture, it tubing weights of insuftioient ntrength from beis first necessary to understand the oonoept ofhypothetical worlds. generated for this size and grade of tubing.At some point in the design process, a sufficieHypothetical worlds are states where alternative, number of functional requirements and correspondpossible values or the same parameter are he~d. For completion aomponenta will have been generatedexample, in the initial stages or the design process allow the mtidel to begin constructing deqan engineer may wish to oonsider both permanent and solutions. The valid completion oomponretrievable type paokers, In thie ease twohypothetical wcrlds would be areated. epeoified at this Staga will have been seleoOne world independently, without eignifiaant consideratiowould hold the assumption that the paoker should be of the other equipment to be inoluded in the desiretrievable, while the other world would indioate Thus, some meana of oombining the oomponenta mthat the paoker should be permanent. exist in order to oonstruot final design solutionHypothetical worlds may be oreated, deleted, or :onatruoting Daaign Solutionsmanipulated in a number or ways. Consider thepaoker example cited above. If, at sane point in . At various points in the design prooess, complethe desi8n reasoning, other design parameters are components or items must be oombined t~ veasserted whioh oonrliot with the permanent paoker, oompatibilitiea or perform oaloulations. Inthe world oontalntng the permanent packer ehould be computational model, this ie aooomplished by merdeleted and never recraated. Henoe, hypothetical the hypothetical worlde whioh oontain theee iteworlds provide an /abi ity to explore design Figure 8 depiots merging within the computatiopossibilftiea tentatively ); model, ,Jn t he computational model, hypothetical worlds are Whena merge ooours, the design att:!h:tes, or ?

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. *m 16183 8. DIIMa-UORmaN. J.M. PBDENANDD.R. BBWtMD----- . . .-. --. -.relevant to eaoh independently c!salgned item, are promieing deei~, choices for nerging, the mcwubined in a new state, Thu&, if 3.5 inoh, N80 mskea preliminary determinations concerning deetubing ie combined with an Otis HC paoke~, the optimization.attributes of both items will exiet in the OombinedBtate. If, however., any of the faota to be joined With this approaoh, the computational model proviare incoinpatible, the merge will be prevented from the neoea,aary atruoture for oonstruotlng multiooourring. This is accomplished by explicitly valid design solutions. The optimum solutionidentifying and ooding design contradiotiona or then identified within this set of possiinoompatibflitiea within the model. designs.The points in the design prooesa where mergea should Optimizing Completion Designsooour appear to be either a funotton of the natureof the problem, or a prerequisite for numerioal The optimum completion deeign refers to a dezcomputations. This point is illustrated with two which either maxtmtzea or minimizee oertexamples. parameters, while achieving all specified goalsobjectives of the design. The parameters optimizProfile sizing is an example of the firet reason fop are normally measures suoh as oost, reliability,merging components, It is intuitive that all safety, availability, or ease of workover. Tcomponents whioh have a profile must be {?enaidered measures may be specified by the designer atJointly to ensure that they are.compatibly sized. outset of the design pruoess.Consequently, all profile devices are merged in thecomputational model. There are at least two met~ods of optimizdesigns. The first, and probably most theoreticallThe seoond reason for merging items is to provide aorrect method, is to develop and apply a funotneoeseary inputs for numerioal computations. For to evaluate each measure at every state, or meexample, in order to oalaulate tubing movement and. of the design process. The optimumsolution ie tthe foroee developed on the paoker, . verioue the path whioh maximlzea , or minimizesattribute of the tubing, packer, downhole fluids, measure(s). This forward, aearoh approach wand tubing movement devioee muet be known. If we generate a single deeign solution.assume that many deeign possibilities exist for eachof these items, there will be many oombinationa The problem with devel

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6 A COMPUTATIONALODELOFWELLCWI&TfON DSSI~ SP,The oompu:atlonal model of well ocmpletion design Figure 11.*epreaente a formal atruoture of the eystemsapproaoh to well completion design. Speoifica:ly, The~e are many advantages uhich could be gainedthe computational model provides: implementing the computational model aa acompletion design program. First, a design prc, a formal description and representation of would apply a rational and consistently W#completion components and th~ir attributes. desi~n philosophy. Second, a design program w

rapidly generate the most promit3ing deq a representation for design parameteiw sach aa alternatives, while maintaining all other vconstraints, goals, resources, and conditions. alternatives. In addition, a program aould prothe ?aoility to record design success and f6ilur. a struoture for describing the relationships and and this knowledge could be maintained for tutointeractions between completion components and purposes. Finally, depending on the Oompdesign parameters. environment used, the program could provide portcompletions knowledge.. formalization of the design reasoning andknowledge whioh engineers employ in selecting The advantages of a computerized design programdownhole equipment and fluids. extend and enhance tne manner in Ehioh wellcompletions are currently designed. This. a two level structure of the completion design particularly trUe regdrding the point of generaprocess, .in which design requirements are multiple, promising designidentified and alternatives wthen satisfied with valid maintaining other valid solutions. Enginmechanical designs. currently must limit the number of dealternatives considered at any point in the de. a mechanism for oonstruating multiple, valid prooeas. Consequently, the ability to maintainmechqnieal deeigne. valid alternatives s@nifiaantly enhancesoutrent process of well completion design.. a methodology for optimizing completion designsbased on measures such as oost, equipment Finally, a oomputer design prosrs!n will lik?lyreliability, or safety. more efficient approaoh to completion desPreliminary designo may be generated and verifiedFigure 10 providee a comparison between aspects of a muoh shorter p~riod of time than with cur

the computational model, and the systems appraaah to techniques. In eddttlon, useful Saailities auowell completion design. completions equipmelt database queries, grapfunotions, and completions reccmd keeping wImplementation of the Computational Model as: a alleviate tedious aspects of the design prooese.Computer Design Program CONFUSIONSTesting the validity oF the computational modelrequires an aotual implementatim, or.~imulation, The follOWing Oonolusions are drawn fromwhose results may be oo,apared with solutions to development of a computational model inaotual completion design problemg. Accordingly, the completion designtcomputational model is being implemented as acomputerized design program. 10 The systems approaoh to well completion dprovides a framework for describingThe program acoepts input design data and completion components and their relatiorequirements ,of the probl+m, and incrementally However, this knowledge muet be givedevelops multiple, valid oomplebion designs whichaatlefy those requirements. struoture in order to conetruot final complA provision for designs.ilireutly over-riding the design or speoifioation ufoertain completion components exists in the program. 2, Conventional, procedural techniques providThus, the program functions as a decision suppt.% awkward representation of the completion dsystem oapable of aseisting engineers in formulating prooess,completion designs. 3. It is possible to model the prooessThe implementation of the computational model as a completion design in a non-proceduoomputer program requires both conv~ntional and non-determinisbio manner, This requires thexpert syetems programming techniques. Empirical of both aonventlonal and expert sydesign knowledge is represented symbolically using programming teohniquee.an expert system development environment.Numerioal, engineering programs are aoded in 4, The Computational model of well complconventional languages suoh as Fortran, and are design aan be implemented as a aomputer doo-ordfnated to operate in Conjunction with the program, to enohanoe the way inexpert system environment. completions ard ourrently designed.The implementation is being Oonduoted on a large ACKNONMDOEMENTSworketatior, utillzi.ng bitmap graphios. Thisdevelopment environment allows the program to The work presented herein is the result oproduoe aompletlon design sohematios as shown in industrially eponsored collaborative res

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projeot. The authors would like to expraae~ppreoiatlon to Baker Oil Toola, Ltd., Britoil P1o. ,Wmoo, Chevron Petroleum Ltd., thw UK,Department ofZnergy, Elf Aquitaine, Norak Hydra a.,s., North Seasun Oil Coispany, ,Oooidental Petroleum Ltd., OtisiPreaaure Control, Pall Well Technology, SagaPetroleum as., Total Oil Marine Ltd., and Tristar)ilfield Servioea for their uontributiona to thtsawk and permission to publish the re$mlts.

1. Patton, L.D., and Abbott, A.: Well Completionsand Workovera: Tne Systems Approaohw, EnergyPublications; Dallas, Texas (1985) PP 1-7.2. Peden, J.M., and Leadbett.er, A;: Rationalityin Completion Design and Equipment Seleotion inthe North SC:. paper SPE 15887 presented at the1986 SPE European Petroleum Conference, London,20-22 Ootober.

3; Bromaton; L., Farrell, R., Kant, E.;Martin, N.: PSWPemmingExpert Systems0PS5, Addison-Wesley, Reading (1985) 7.4; Reiohgelt, H;, and Van Harmelin, F.: Critefor Choosing Representation LanguageaControl Regimes for Expert SyatemsW,Knowledge Engineering Review (Deoember

Vl, N4, 10.5. Harmon, P., and King, D.: tArtifioIntelligence in Business: Expert SystemsIt,Riley add Sons, NewYork (1985) 38-39.6. MoDermott, -1.! !~il Rule-based ConfigureComputer Systema*t, Artificial Intelligmcv19, 39-88.7. Hayes-Roth~ F., Waterman, D.A., and Lenat, DlBuilding Expert Systemsw, AddisonWeReading (1983) 85.

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