Use of High Fidelity Models for Real Time Status Detection with Field Examples from Automated MPD Operations in the North Sea

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1 2nd IFAC Wrkshp n Autmatic Cntrl in Offshre Oil and Gas Prductin,, Flrianóplis, Brazil Use f High Fidelity Mdels fr Real Time Status Detectin with Field Examples frm Autmated MPD Operatins in the Nrth Sea Knut Steinar Bjørkevll* *Sintef Petrleum Research, Bergen, Nrway (Tel: ; KnutSteinar.Bjrkevll@sintef.n). Abstract: The paper discusses use f cmprehensive real time mathematical mdels in autmated drilling peratins, with example frm successfully run peratins in the Nrth Sea. Keywrds: Mathematical mdels, rbustness, calibratin, autmatin, decisin supprt systems. 1. INTRODUCTION Cmplex real-time mathematical mdels fr drilling have been used in several Nrth Sea peratins t help autmating parts f peratins where availability f dwnhle measurements in real time is very limited at best. Thus real time calculatins may prvide the infrmatin needed t keep within pressure bundaries in the pen hle, and thereby bth help ptimizing the peratin with respect t time, and help reducing the risk f humans making wrng decisins with regards t keeping pressure at an ptimal level in the pen hle. The backside f this is cst and risk intrduced by the cmplexity f autmatic systems with advanced mathematical mdels within the cntrl lp. Accrdingly wrk is nging t simplify the architecture f autmated drilling systems. The cmplex mdel is mved utside the cntrl lp and replaced by a simplified and very rbust algrithms, which can run the system based n dwnhle real time measurements. This is fully in the line with the authr's view, and will nt t be debated here. The main pint t be made here is that cmplex real-time high-fidelity mdels may add significant value if implemented in a way that handles the varius related challenges adequately. The cncept is then a tw-mdel cncept, where the cmprehensive mdel is replaced by a simplified mdel in the cntrl lp, while the cmprehensive high-fidelity mdel still runs in real time, but is separated frm the cntrl lp. The simplified mdel will handle quick respnses t things like changes in pump rate and string mvements, while the full mdel will run in parallel t prvide infrmatin n lnger term effects. See fr example Gjerstad et al (2012) fr an earlier wrk n develping an accurate simplified mdel. Cntrl lp parameters may be adjusted based n calculatins with the cmprehensive mdel, but nly with a time delay that is sufficient fr a thrugh (and partly r fully autmatic) quality assessment f calculatins with the full mdel. Benefits f the cncept include that the cmplex mdels may Help filling in mre accurately between sensrs in time and space. Prvide synthetic "sensrs" in runs with n dwnhle sensrs, like running casing, liner r cmpletin, and cementing casing r liner. Add redundancy t autmatic systems. Imprve understanding f well status. Realizing these ptential benefits is nly pssible if the implementatin is dne in a very rbust way; the verall bjective being t add benefits frm calculatins with advanced mdels withut reducing stability, rbustness r user peratr-friendliness f the verall system. Rbustness means amng thers understanding and detecting a number f anmalus situatin, and ability t adjust t changing cnditins in the well. A real time high-fidelity mathematical mdel can help btaining this, again under the cnditin that challenges and pitfalls are handled adequately. The cmprehensive type flw mdel has been used in several cntexts in the Nrth Sea, including autmated Managed Pressure Drilling (MPD) by Statil thrugh reservirs where heavily depleted segments were expected, see Syltøy et al (2008) and Bjørkevll et al (2008, 2010), and fr decisin supprt and autmatin n Ekfisk and Statfjrd, see e.g. Rmmetveit et al (2009). The same cmprehensive mdel has been embedded in a high-fidelity training simulatr t btain the mst realistic training fr upcming wells. Requirements n a high-fidelity mdel are in several ways mre challenging in a training simulatr envirnment than in peratins, except that the cnsequences f failure are nrmally much less severe. Thus a training simulatr prvides a useful envirnment fr mdel develpment. Althugh the pints made are relevant fr autmatin f drilling peratins in general, MPD will be used as an example in the discussin belw. A number f well peratins require sme variant f MPD fr imprved Cpyright 2015, IFAC 134

2 dwnhle pressure cntrl, and als fr wells where MPD is nt strictly required it has a large ptential f increasing margins and reducing risk. There are hwever challenges related t additinal cst and cmplexity, and due t the latter sme risk elements may be aggravated. Bth challenges can be addressed by rbust autmatin f parts f the peratin, like fr example autmated chke respnse t changes in pump rate and string mvements. Accrdingly, this paper is written t give input t wrk as utlined by sharing cnsideratins and field experience related t using cmprehensive real time mdels. A nte n terminlgy: We use the term flw mdel r cmprehensive flw mdel t dente a cmprehensive realtime enabled high-fidelity and (partly) dynamic mathematical mdel. Cntrary t this, the term simplified flw mdel dentes a mathematical mdel that is less detailed and ambitius w.r.t. accurate representatin f the physical system, but als less demanding when it cmes t cmputatinal requirement and, due t its simplicity, it is much easier t ensure a high degree f stability and rbustness. 2. PHYSICAL SYSTEM AND COMPLEX MODEL The physical system When trying t predict pressure effects very accurately, either t cntrl pressure at a given measured depth r t detect anmalies early and reliable, many physical effects are significant and need t be cnsidered in the autmated system. Nrmally the tw mst imprtant pressure effects in drilling peratins are related t changes in pump rate and axial mvements f the running string. As a cnvenient generalizatin, the term running string is used t dente bth drill string and cmpletin strings. With sme exceptins nt being addressed here, drilling fluids are much mre viscus than pure water r il, which is necessary t ensure sufficient ability t carry slid particles during pump stps. Due t the high viscsity rapid sund waves are quickly dampened alng lng wells, and the interplay between the cmpressibility and viscsity f the fluid is a much mre predminant effect. When bth cmpressibility and viscsity are large, there will be a large time delay between fr example changing inlet pump rate till stable return at the same level is seen at the utlet secnds is typical, but depends n fluid prperties and the gemetry f the flw trajectry. Meanwhile the change in flw is prpagating thrugh the system, as is the crrespnding change in pressure. Thus, getting the timing f the prcess right is necessary t keep cnstant pressure at a given psitin, in additin t calculating the pressure lss vs. flw rate crrectly at any psitin. Mving pipe up and dwn, nrmally referred t as swab and surge respectively, causes similar flw and pressure effects as pump rate changes. A dwnwards pipe mvement, fr example, will displace fluid belw bit and flw will build up gradually frm bit and upwards at the same time as fluid is being cmpressed due t the pressure increase. The effect f gemetry changes alng the string may mdify the effect f the pipe mvement significantly. Induced flw due t surge mvements may typically be f the same rder as nrmal pump rate. A cmmn cnceptin is that the majr part f the pressure lss is alng the BHA assembly. But nrmally this is nt the case because the drill string is several thusand meters lng, while the part f the BHA with larger diameter is less than 50 m lng. The pressure and temperature dependence f the fluid prperties may be significant. With il base drilling fluids, viscsity may increase by as much as a factr tw r mre when ging frm surface pressure t bttm hle pressure with unchanged temperature. Temperature effect is als large, and will typically cancel sme f the pressure effect. With water base drilling fluids the temperature effect is still large, but pressure effect much smaller. This may cause significant effects, and in additin t the predictin f a cmplex prcess there may be a prprietary issue in getting the mst accurate fluid prperties data. High-fidelity mathematical mdels The effects abve are taken int accunt by state f art dynamic mdels, which will nrmally give a fairly accurate picture prvided relevant input parameters are given with sufficient accuracy. See fr example Bjørkevll et al (2009) and a large number f SPE papers n the subject. The latter is nt always the case, and will be discussed further belw. Other physical effects wrth cnsidering include changes in temperature prfile and cuttings transprt, which bth are imprtant parameters in themselves, and may influence dwnhle pressure significantly. Current mdels include dynamic temperature calculatins, althugh its dependence n a large number f parameters makes accurate predictin under drilling cnditins difficult and uncertain. Accurate cuttings transprt mdelling is still a challenge after decades f wrk in this field. Wrking with mathematical mdels In rder t succeed, taking steps t develp mdels that are bth accurate and at the same time fit fr real time purpses has t be acknwledged as a mst challenging prcess where a number f elements need t be prgressed t a high level f quality t succeed. The sketch in Figure 1 shws hw sme main elements are linked and what they cntain. Cpyright 2015, IFAC 135

3 A. Filling in gaps between sensrs in time and space may be imprtant t get a sufficiently cmplete picture f the situatin in the well. The calibrated mdels may be used t get accurate prfiles between sensrs, taking int accunt discntinuities like changes f gemetry and multiple fluids. This is mst imprtant when sensrs are far frm where pressure margins are narrwest, which fr example may happen in lng pen hle sectins with sensrs lcated in bttm hle assembly (BHA) nly. Fig. 1. Wrking with mathematical mdels t btain safer and faster peratins. In the left hand clumn fcus is kept n mdelling the prcess as accurately as pssible. A cmbinatin f slving basic physical equatins with measurements and data analysis is used, and the level f accuracy btained will depend what is state f art fr mdelling f the prcess being addressed. Here accuracy and predictability is the main fcus. Calculatin speed and stability are f curse f imprtance, but play a mre secndary rle at this stage. Ging frm the left t the middle clumn is abut reducing and pssibly reprgramming the accurate mdels while striving t maintain a sufficient degree f accuracy. At the same time the main gal is t btain a mathematical mdel that is useful fr decisin supprt r fr giving input t autmated lps, where very high requirements t regularity and calculatin speed have t be met. The right clumn puts in the human peratr, wh in sme cases may just mnitr and nly intervene in case f smething ging unexpectedly wrng. In ther cases there will be planned peratr interactin with the system (shwn by dashed arrws in the figure), either regularly r under given cnditins. Level f interactin will typically be large at early stages f new technlgy, with a gal f reducing it as the system becmes mature and sufficient trust has been gained. As time and cst are very high in drilling peratins, aiming fr a system that des nt add time r persnnel t the peratin will be a nrmal gal, and als a strnger gal invlving quicker peratins and fewer persnnel shuld be cnsidered and attempted built int the implementatin plan. A mre detailed descriptin f the rle f human peratrs is given belw. 3. BENEFITS This sectin ges int details n sme main advantages f running real-time mdels during peratins. Quick changes can be handled by a simplified mdel which is very fast and rbust, and may be clsely integrated with the cntrl lp. The first items belw are partly in this categry. Hwever, running withut any sensr fr lnger intervals and under changing cnditins will require a mdel with higher degree f predictability based n first principles. Furthermre, mdels can als be used t imprve results when sensr data are delayed, by calculating effects f peratinal changes befre the respnse shws up n measurements. With cmmnly used mud pulse telemetry, time delay f 30 secnds is quite nrmal, while mathematical mdels can respnd t changes in ne secnd r less. An even larger delay, r rather lack f data, ccurs during pump stps when using mud pulse telemetry, because data transitin stps as sn pump rate gets belw a given threshld. Even with a real time mdel this may be challenging because data fr mdel calibratin is limited r absent. And this als challenges the rbustness f cntrl algrithms, refer fr example Siahaan et al (2014) fr details. B. When a cmprehensive mdel has been used and accurately calibrated in the preceding drilling run, it can be taken further t the fllwing casing, liner r ther cmpletin string run t prvide synthetic "sensrs" in the pen hle. This is valuable because there are nrmally n dwnhle sensrs in such runs, and the peratin is therefre a challenge if narrw pressure cnditins have been cnfirmed when drilling. C. Add redundancy t autmatic systems. If sensrs fall ut, a calibrated real time mdel can take ver and give input crrespnding t the missing sensr data t the cntrl system. D. Imprve understanding f well status, fr example by cmbining mdel calculatins with sensr data and multivariate techniques t btain early and reliable detectin f anmalies like kick, lss, pr hle cleaning, etc. This pint is far frm trivial and still a tpic fr further research, because deviatin between a mdel and measurements may have a number f different causes that can be very hard t separate. Causes include inaccurate mdel input data, inaccurate sensrs and leakages in additin t the varius well anmalies mentined abve. Cpyright 2015, IFAC 136

4 Tuning f the mdel t match dwnhle data, prvided cnsidered trustwrthy. This pint will be elabrated n belw, under data quality and tuning. Mnitr the mdel and transfer t manual cntrl in the case f mdel failing r getting instable. A rbust prcedure must be in place fr this event. Examples seen include: Abrupt peratinal changes causing scillatins in chke system. Fig. 2. Imprtant factrs influencing perfrmance f a mdel based system. CHALLENGES / ROBUST IMPLEMENTATION Realizing the ptential benefits listed abve is nly pssible if the implementatin is dne in a rbust way, such that challenges are handled adequately. Figure 2 shws factrs that are imprtant t btain the required accuracy f pressure cntrl in MPD, and the fllwing discussin ges int details n sme f these. The MPD peratins dne included a number f peratr actins, sme part f regular prcedures and sme included as cntingency measures. Sme tasks require gd training and careful attentin, and shuld therefre be subject fr imprved user interface and rbust autmatin as far as pssible. Manual peratr actins have included: Cllect input data and feed int the mdel, including things like well gemetry, survey data, running string with bttm hle assembly, fluid prperties, frmatin layers, water depth, etc. Testing mdel respnses prir t start f peratins. Update cnfiguratin with new infrmatin, including BHA specificatin between runs. Survey data between runs and a few times during runs, accrding t cnsideratins. Manual update f fluid density. Typically each 15 minutes at best while drilling. Manual update f rhelgy at standard cnditins. Typically up t 4 times per day while drilling. Gd cmmunicatin with mud engineer is imprtant t avid significant time delays. Handle deviatins between mdel and measurements. Tuning f mdels Internal mdel issues. A number f issues have been seen ver the eight wells drilling with the high-fidelity mdel in the lp, and after a number f apprpriate fixes severe issues are nw very rare. But still nt ruled cmpletely ut, and therefre further wrk n rbustness and stability is nging and recmmended. Hardware issues. In ne case instable mdel behaviur was remedied by simply replacing the cmputer. Accurate and rbust mdel tuning is crucial t crrect ffsets between mdel and data that are due t mdel r input data being inaccurate. If there is an ffset due t well anmalies, tuning f mdel may still wrk, but nw the mdel becmes less predictive because it des nt represent imprtant features f the physical system. And als a detectin f the anmaly will be valuable if the system is able t distinguish between different causes f deviatin between data and mdel. A further discussin f this tpic is given in Bjørkevll et al (2015). Data quality issues The data quality issues seen when wrking with real time mdels have turned ut t be a much larger challenge than expected initially, and make bth manual and autmatic interpretatin and autmatin hard. Based n experience this has been addressed tw ways; firstly by imprvements f sensrs and signal transmissin, and secndly by adding data quality checks and crrectins between sensr data and mdels. Even tpside sensrs have turned ut t be less accurate and mre time delayed than desired, and steps have been taken t imprve these things. When it cmes t dwnhle sensrs significant drifting has been seen and must be cnsidered when chsing sensrs and interpreting measurements. The fact that transmissin by mud pulse telemetry stps during cnnectins is t sme extent remedied by sending minimum and maximum pressures during cnnectin after pumps have started again. These measurements are useful fr understanding whether calculated static pressure is accurate. Cpyright 2015, IFAC 137

5 If data is nly available when pumping at high rate it will be hard r impssible t distinguish cntributins frm static pressure and pressure drps due t fluid flw. Hwever, cases have been seen where the static pressure is incnsistent with real time pressure while pumping, as shwn by jumps in Figure 3. The pink dts shw minimum and maximum values transferred after cnnectin, and the full curve including the tw jumps and curve in between were nly available frm the tl memry after the full run. Anther example is shwn by Figure 4. In this case minimum and maximum values were crrect, but an interpretatin based n nly the tw values was very difficult. Seeing the whle curve and cmparing with ther parameters it was pssible t deduce that the largest pressure variatins were related t time delays. Figure 5 illustrates anther challenge. In this case the mdel ran with inaccurate rhelgy input data fr mre than tw days. With gd training and cmmunicatin this can be reduced a lt, but still it shws ptential vulnerability and als ptential benefit f an integrated system with rbust and autmatic update f fluid prperties. Similar challenges have been seen due t inaccurate fluid density values, in particular when displacing t a new fluid. Fig. 5. Measured and calculated dwnhle pressure ver 6 days f drilling, including a shrt trip up t the casing she. A FINAL REMARK The MPD peratins referred t were all cncluded successfully by cmbining all invlved resurces and addressing challenges in the extended drilling team. Issues and anmalies were handled by gd prcedures, cmmunicatin and judgements. Accrdingly, it is apprpriate t include the sequence f washing dwn a few stands shwn by Figures 6-8, where everything wrked well. Further imprvements might be pssible in this case by advanced mdel predictive cntrl, but then again rbustness is imprtant, and things like unexpected lss f pump pwer must be handled accrding t requirements Pump rate and drill string rtatin 200 Fig. 3. Memry recrded dwnhle pressure during cnnectin. Flw rate [l/min] Rtatin rate [RPM] Time [min] Fig. 6. Pump rate and drill string rtatin during a run in hle sequence; cpied frm SPE/IADC Fig. 4. Memry recrded dwnhle pressure during cnnectin. Cpyright 2015, IFAC 138

6 Depth [m MD] Relative bit and hle depth Bit depth Hle depth Time [min] Fig. 7. Bit and hle depth; same sequence as Fig ECD memry Siahaan, H. B., Bjørkevll, K. S., Gravdal, J. E. (2014). Pssibilities f Using Wired Drill Pipe Telemetry During Managed Pressure Drilling in Extended Reach Wells. SPE , SPE Intelligent Energy Cnference and Exhibitin, Nederland, 1-3 April Cnference Prceedings. Sciety f Petrleum Engineers 2014 ISBN s. SINTEF Petrleum and IRIS. Syltøy, S., Eide, S. E., Trvund, S., Berg, P. C., Larsen, T., Fjeldberg, H., Bjørkevll, K. S., McCaskill, J., Prbensen, O. I., Lw, E. (2008). Highly Advanced Multi-Technical MPD Cncept Extends Achievable HPHT Targets in the Nrth Sea. Cnference paper SPE , presented at the 2008 SPE/IADC Managed Pressure Drilling and Underbalanced Operatins Cnference and Exhibitin in Abu Dhabi, UAE, January ECD (s.g.) Time [min] Fig. 8. Measured dwnhle pressure cnverted t equivalent mud weight; same sequence as Fig. 6. REFERENCES Bjørkevll, K. S., Mlde, D. O., Rmmetveit, R., Syltøy, S. (2008). MPD Operatin Slved Drilling Challenges in a Severely Depleted HP/HT Reservir. SPE , SPE/IADC Drilling Cnference, Orland, Flrida, 4-6 March Bjørkevll, K. S. (2009). Design Cnsideratins fr High- Pressure/High-Temperature Well. Bk chapter in Advanced Drilling and Well Technlgy, p , Sciety f Petrleum Engineers. Bjørkevll, K. S., Hvland, S., Aas, I. B., and Vllen, E. (2010). Successful Use f Real Time Dynamic Flw Mdelling t Cntrl a Very Challenging Managed Pressure Drilling Operatin in the Nrth Sea. SPE , presented at the SPE/IADC Managed Pressure Drilling and Underbalanced Operatins Cnference and Exhibitin in Kuala Lumpur, February Bjørkevll, K. S., Daireaux, B., Manum, H. (2015). Pssibilities, Limitatins and Pitfalls in Using Real-Time Well Flw Mdels During Drilling Operatins. T be presented at the SPE Bergen One Day Seminar in Bergen, Nrway, 22 April Gjerstad, K., Time, R. W., Bjørkevll, K. S. (2012). Simplified explicit flw equatins fr Bingham plastics in Cuette Piseuille flw Fr dynamic surge and swab mdelling. Jurnal f Nn-Newtnian Fluid Mechanics (2012) Rmmetveit, R., Ødegård, S. I., Bjørkevll, K. S., Herbert, M. (2009) Testing a new sftware system fr drilling supervisin. Wrld Oil 230 (4), Cpyright 2015, IFAC 139