Development of a Network and Gas Lift Allocation Model for Production Optimization in the Ras Budran Field

8/7/2019 Development of a Network and Gas Lift Allocation Model for Production Optimization in the Ras Budran Field

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m lSPE 29782Developmentof a Networkand Gas LiftAllocationModel forProductionOptimizationinthe Ras BudranFieldY.A.-W.E1-Massryand A.D. Price, Suez 011 Co.-...,-.. -.arc MelnData~ 1W5.OcktYdP.bobum EwIIN61s, klhbpwwwm ~torpmMMiIM@HwsPE Mklflb@a OUShwhdd in Sduah, tl-14W 1S65mbmwmnla2i9dti PmnmmimbymPEPmgmmcamllMu-~~wnhhd blmSlmImct4wMtodtyttwauthq).cllnl,md*~,-~. titim~wti md~wwmtimw m~hbtims). m~, u~, ad~m~POdt&mdt&O&lhcccdObvlwm_, b_, w~. b~a=Qmameh h@~*mhdti~malwultoan~l)tnalnmwmlme.01 whom md by whom =kma. WIS, lJbIWUI, SPE, P.O. Son SSSSSS, shb8tbNnnynmb0w. nN~tiammin~~~, TX 7S0GSWS, U.SA., Tolmt, 1SS24S SPEUT.ABSTRACT

Z& Ros Budran Field lies cm the eazt side ofthe GLlf of Suez 5 KM offshore. l%e field umwnen-ced pdUCt&Wl in Jan 1983 and gas lift was imple-mented in 1985. The pmdudion System ~sts of 3well-head platforma delivering prodwtia to a centralprocess piatfonn via 12 pipelines. The produced

rwons to the first atage gas la-l separation at theprocess platform, taking W accamt the interac-tim of the other wells in predicting pressure andffow rate responses. The gaz liftaihcation modelhas been used forrcutine produdon optimizationand ahcatia of lift gas in a mwfti well netwo-rked model, as well as for predictkm of fwture sys-tem r~rements and identificatitm of deMtle-


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EL-Maaary,Y.and Priec, A. 3The network model is constructed through

accurately modeling the following:1) Reservoir fluid properties.2) Well performance.3) Pipeline performance.RESERVOIR FLUID PROPERTIES

Reservoir Fluid properties play an importantrole in calculating pressure drop in wellbores, flowl -ines and transmitting pipelines in single and mu-ltiphase flow. It is necessary to obtain numericalvalues for fluid properties such as oil formation VOIume factor J30,gas solubilit y Ra, Z-factor, as well asPOand Pg, oil and gas viscosities. These parametersmay be obtained from laboratory PVT analysis, butoften it is necessary to estimate them, especially attemperatures different from reservoir temperature.During the model setup, the PVT properties werecalculated based on a previous simulation study ofthe Ras Budran field carried out in August 19873.The different PVT properties at reservoir tempera-ture were compared with the PVT data calculatedfrom four different PVT correlations namely:Standing,4 Lasater,5 Glaso,6 and Vaaquez-Beggs7correlations. Laaaters correlation was found to be

A calibration factor (~) was selected foreach PVT property to reduce the difference be-tween calculated and measured PVT propertiesto a minimum value.

Measured PropertyvaKc= ............ (4)Calculated PropertywaA calibrated Lasaters correlation was

then used to compute PVT properties at anypressure and temperature in the current study.Table (1) shows the final calculated PVT prop-erties using Lasaters correlation.WELL PERFORMANCE AND HISTORYMATCHING

The well profile of each of the 17 wellswas divided into a number of nodes. Each noderepresents a change in tubing inner diameter(I.D.), deviation angle, the presence of gas liftmandrels or a change in flowing temperature.The total depth for all wells was taken as themiddle of the perforations in each well, whichwas assumed to represent the well flow entrypoint. The reference point for all depth anddistance calculations was the mean sea level.


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4performancethe bottomstraight line

Dcvdopment of a Network qnd (h LttAlfocation Modelfor Production Optimizationmodel was the reservoir pressure andhole pressure was calculated using aproductivity index (P.I.)8.

The multiphase flow simulator used con -tained a number of different multiphese flowcorrelations for calculating pressu~~ ~osses, holdupand flow- regimes in verticai fiow--. These correc-tions are summarized in Table (2). Only 7 correla-tions were used for the performance history match-ing part of the study.

Having specified the correlations to be usedin the well performance history matching, thepressure losses from the mid point of perforationup to the well-head were calculated. Variouscorrelations were evaluated by applying two errormeasures, namely :

{D -* V \t ~ Xlcto-loo~Emr% = ;i;-Pw-) .......(AP--AP~)De&aReas.ihw% (SaxionEmu%)= p+

where:P~ = bottom hole flowing pressure.

(s)

(6)

The failure of correlation(s) in predictingpressure losses at any station could be attribut-ed not only to the correlation performance butalso to error in measured pressures and/ormissing parameters in describing well perfor-mance. Thus, evaluating each station separatelywill eliminate the station calculated error de-pendance on each other and wiii highfight theshortcomings in measured pressures and wellperformance data.

Another type of error measurementmethod will enhance evaluating pressure losscalculation for each station separately and willincrease the confidence in the selected correla-tion. This error is assigned delta pressurepercentage error or station error 90.

In addition to the above, two error mea-sures for evaluating the best correlation forsimulating well performance, the final governingselection parameter is that the closer the givencorrelation is in predicting the well-head pres-sure the more preference is given to that corre -lation whether it is the best or the 2nd bestcorrelation, provided that the cumulative erroris always kept less than 5 %. The accurate


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13L-Ma8cry,Y.and Prica,A. 5lated pressures. The approach used in the modelwas to eliminate these changes and let the changein the correlation be the modifying parameter asthis was models the well performance better in thepredictive mode.PIPELINE PERFORMANCE AND HISTORYMATCHING

All pipeline profiles are divided into anumber of nodes in a similar way to that used forthe Ras Budran wells. The reference point for allelevation and distance calculations was selected tobe the mean sea level point at the productionmanifold. The pressure data used in history match-ing was taken from field measurements at specificpipel~ne !Q~a$iQ~~ c=rrie~ ~1~$ ~uri=g the pericd91911993 to 15/12/1993.

The multiphase flow simulator provided anumber of correlations for calculating pressure loss,imki-up and flow regimes in horizontal pipeiinesa~.These correlations are summarixcd in Table (3).The various correlations were evaluated by calcu-lating the percentage error defined by the followingequation :

(Pi. -P*) ~~(to-100 ........0..... (7)

tween Band bPP-B is taking place within theB platform itself.

This may suggest a deposition (scale,aapha!tene;etci,.,) Q~ u ~rga Qf ~~~~r~$~~~g [@~-t ia l closure of a control and/or isrdation valve).This represents a production system bottleneck(location of excessive pressure losses) affectingother producers connected to the system.NETWORK MODEL CONSTRUCTION ANDVERIFICATION

A good description of the individualcomponents of the Ras Budran productionsystem was achieved through the performanceL:=*---- --t~:-- A-. --AL-AU-busy -a buAu&steps UGDGKIUCU ~~ove for ~~ewells and pipelines.

The approach of an integrated networksystem analysis extends the NODAL ANALYS -lS1Omethod, for calculating the overail systempressure losses taking into consideration theconnection of all wells to the correspondingmanifolds and pipelines through to the separa-tor. The overalk solution for pressure losses wasperformed by iterating for pressures and flowrates at all nodes having only two known values,


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6 Development of a Network and Gas LtitAffocationModel for Production OptimizationNetwork Model Verification

In order to verify the network model, all thewell input data files, including the well profile,reservoir data and gas lift quantities, have beenupdated along with weii test information to repre -sent actual field data during the verification period.

The absence of bottom hole pressure surveydata (after the date of the pressure survey used inthe history matching part of the study) for all thewelis in addition to the noticeable decline in theproduction performance of some of the weiis ( e.g.increase in gas lift rate, increase in water cut 70,loss in P. I. due to scale deposition) necessitatedthe introduction of a number of assumptions to thewell input data files to reflect the observed changestaking piace . These assumptions were limited tochanges of the gas lift entry point and the produc-tivi~~ hidex (P.L) ef scme ive;h. These parameterswere assumed as different values until the measu -red and calculated parameters were matched. Theassumed matched values were then kept unchangedduring the verification and prediction modes.

The static reservoir pressure for each welland the (lst stage separation) production separator

(3) For this period of interest we have surfacenetwork measured pressures from 09/09/93 to15/12/93. Minor changes in flowing parameterson piatform and/or a well basis have occurred,(+/- 1,500 STBLPD variance in the total fieldproduction). Therefore, matchtng the networkperformance at different dates will not be ofmuch use.Discussion

The comparison between the networkmodel performance and the measured liquidrates, on a weil- by- well basis, showed that themodel performance matched the actual condi -tions with the exception of 3 welis (B5, B8 andC4) which are producing with Klgh water cuts .The impact of these differences on net oilproduction was evaluated.

A quantitative comparison between thenetwork model and the actual system perfor-mance is presented in Table (4). In this Table,two different error measures were used forevaluating rate differences between the network

11, 12,13model and actual net oil production rates .These are defined ax


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EL-Massry,Y. and Price,A. 7the overall model performance.

The comparison between the calculated andthe measured oil production rates (shown in Table4) indicates that the network model is over-predict-ing meaimred net oil production by 1980 BOPD(cumulative error % = 4.4) during the validationperiod (i.e. Dec. 1993). The major gross rate dif -ference between actual and calculated flow rates inthe system come from wells B5, B8 and C4. Howev-er, injecting the gas shallower in the last two wellsutilizing gas lift calculations improves the situation,suggesting that scale deposition had been takenplace resulting in a loss in the well P.I.

All the previously identified changes werebased upon field observations and gas lift perfor-mance calculations and the results of the networkmodel verification run are shown in Table (4). The% error (variance between calculated and testseparator measured gross liquid and oil rates) are490 and 4.790 respectively.CONSTRUCTING THE NETWORK AND GASLIFT ALLOCATION MODEL AND MODELVERIFICATIONHistorical gas lift optimization in Ras Budran

artificial lift quhntity versus liquid flow rate, foreach well using the pre -defined multiphasevertical flow, correlation. An example is plottedin Figure (5). Examination of system perfor-mance curve data points showed that althoughthe gross liquid is a function of many variables,the underlying trend is dependant upon the totalquantity of gas injected. The gas lift allocationmodel utilizes this set of system performancecurves as its data base.

From this data base the performancecurve for a well, j, is selected and a mathe-matical function of the form16;

~(x) = $ b+,ix ....................... (11)where x is the gas injection rate and the coef fi -cients are determined by least squares. A func-tion of the above form is provided for everywell in the system.

The next stage of optimization is to findthe unconstrained optimum assuming an ynlim -ited supply of lift gas. The optimization can beperformed on either the gross or on stock-tankbarrels of oil. This is defined as the sum of the


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8 Development of a Network and Gas LtitAlfocationModel for Production Optimization

dl(~l) 4(%) df (X=) . . ........=~ = G ................. (13)d% dx dxwhere G (bbl/d per mmscf injected) is the gradientof all wells (m) in the system. This optimum ean beobtained for all wells providing that each well, j;1) Can flow at the specified gas lift value, xj

and2) Is not bound by any other overriding con-

strains.There are many numerical techniques

available for solving linear system of the form;Ma: fl(xl) +f~(q +......+f(x)

% * % *+% s ~ .. :=:,=,, ,:::.::: (14).,%, 2 0

these range from sophisticated optimization meth-ods to simple iteration procedures. However, theintroduction of more complicated non-linear con-straints eg. maximum flow through specific pipe-iines, etc, makes the setting up of more constraints

Thus an iterative tech-

curves. The solution node for the network wasset at the process platform B. (i.e. at the inletof the off-shore production separator) with aspecific sink (separator) pressure.

Fixing the amount of lift gas to each wellas defined from field measurements, the actualand model field production are matched with aminimum %error between 1.170 and 1.5% (i.e.485 and 674 BOPD respectively) under differentwater cut and gas lift system volumes changes.

For the optimization mode ( i:e. byremoving the fried input gas quantity constraint)using the pre-described iterative technique threeruns were performed to re-allocate the gas liftdistribution for the wells under various gas liftcompression capacities, they are :(a) Optimizing the case during the second haifof Dec. 1993 , using 36.16 MMSCFID, (4 com -pressers) resulted in a 771 STBOPD gain com-pared to the actual gas lift distribution.

(b) Optimizing the case dated 24101194, using36 MMSCF/D, (4 compressors) resulted in a538 STBOPD gain compared to the actwd gas


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EL-Mas s ry ,Y. and Price, A. 9has the capability to calculate what-if scenarios byintroducing new and/or closing- in low priority wellsand also predicting future lift gas demand.

Having identified a production systembottleneck taking place through the pipeline fromB platform to the process platform B, a decisionwas taken to twin this short pipeline (120 feet) byutilizing the existing, unused, water- injection linewith appropriate pipe- work looping. The estimatedincremental production due to this modificationusing the model was 800 STBOPD. An estimatedincrease in production of 600 STBOPD was real-ized after completing this modification.

The introduction of a newly proposed wellto the production system was investigated using themodel. The results indicate that the well wouldproduce more than 4000 STBOPD with a minimumimpact on the production rate of other producerseither in the amount of gas required to lift thatwell or through induced back pressure effects onother producers within the system.

Furthermore, to calculate the field produc-tion performance under additional lift gas volumesanother two identical compressors to the existingones were added to the system (each with 9 MMsc -

3-

4-

5-

850 STBOPD (1.3% and 1.5 Yo increase)for three and four compressor availability.Choosing one multiphase vertical flowcorrelation to describe all Ras Budran wellswas not found possible based upon datafrom bottom hole pressures surveys foreach well. It was concluded that 11 wellsare best modelled using Orldsaewsklscorrelation, 4 wells using Hagedorn andBrowns correlation, and only one welI usingMukhjee and Br!!ls Ccme!aticm.The pipeline performance connecting Bwand C platforms to the process platformRB-PP is best described using the Muk-herjee and Brills correlation, whereas thepipeline from Aplatform to PP-B is bestdescribed using Oliemans correlation.The removal of excessive pressure lossestaking place through the very short pipelinefrom B to PP-B platforms (as indicatedby a g!~ba! ~der estirnatica Cf ~Z~SSiii$2lo sses by all correlations), resulted in anaddition of 600 STBOPD to the field pro-duction.


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10 Dcvetopment of a Network and Gas LiftAffocationModel for Production Optimization10- Further sensitivity runs were performed to

refllze the effect of introducing a new well(RB-CIA) on field performance in parallelwith lower P.I. wells performance and in rela-tion to changes in gas lift allocation betweenthem.

~~. ~~~u~~ gas lift volumes for the field undervarious operating scenarios with time can nowbe easily assessed.

ACKNOWLEDGEMENTThe authors would !ike to thank Suco Manage-

ment for their approval to publish this paper.

REFERENCES1. Bristow, P. G.and Thambynayagam, R. K. M,.-Design Calculation for Three-Phase Flow Behav-ior in Wells and Flowlines for Naturally and Arti-ficially Lifted Wells, presented at the 6th EGPCSeminar, Cairo,Egypt,24 November,1982 .2. El-Maaary, Y. :Construction of a Network Modelfor an Integrated Production System and Appli-cation to the Ziet Bay Field MS thesis, CairoUniversity, Giza, (1994).3.Ras Budran Reservoir Engineering Study,

)p. 1751-1763.11. Mandhane, J. M., Gregory, G. A. md Aziz,K.:Critical Evaluation of Friction Pressure-Drop Prediction Methods For Gas- Liquid Flowin Horizontal Pipes, JPT (Oct. 1977) .12. Mnndhame, J, M., Gregory, G. A. and Aziz,K.:Critical Evaluation of Holdup PredictionMethods For Gas-Liquid Flow in HorizontalPipes, JPT (Aug. 1975) .13. Mandhane, J. M., Gregory, G. A. and Aziz,. a.. T : ..:.4 Rlmw ~~K .:-A Fiow ~att~fii Map f~i G-a- =.q~. . ...Horizontal Pipes, Int. J. of Multiphase Flow(1974),537.14. E1-Massry, Y. and Abo-El-Yazid, M.:Op-timizing Continuous Flow Gas- Lift Wells ForRas Budran Field, paper presented for EGPC,ninth production and exploration conference,Cairo, 22-24 November 1988.15. E1-Massry, Y,, Housny A., and Abo-El-Yazid, M.:Application and Optimization ofContinuous- Flow Gas-Lift Wells for Zlet Bayand Ras Budran Wells, SPE 25624, presentedat Sl?E 8 ~ Mitidie EM Oil ~h.~iv ~iid ~ofif~i =ence, Bahrain, 3-6 April 1993.16. Marshall, D.L., Edwards, R.J.E., Wade, K.C.:*A Gas Lift Optimization and Allocation ModelFor Manifolded Subsea wells SPE 20979 pre-sented at the European Petroleum Conference,


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k !MTable 1 Measured Versus Calculated PVT for Ras Budran FieldlFo r B#nd GORPr22mur9 Bo Ro9B/STB G~. KF12TB I Uo, Cp(Iow.r Uni t )p-h Mou. t ic . .ss.1 Cak . M*SSs 6s9 4 1S7 + $67 s12 I S* 2 I 9 .20 3.20-k 1l=H--

.000 l.let l.tee S*2 I 392 I 2 .94 I 3.5*4.000 t .leo 1.%79 9* 2 3?2 2.se I 2 .mee ,000 * .* 79 1.*75 312 I -* - I 9 .* nl c .sn 2,000 * l ee l.*es *4 2 1Lt oa l.l oa 1.203

, --- , ---- ------- 1 342 I Z.*O I 2.*S3 I 3* 2 3* 2 1 .2s 3.22 2H=]

verage Tot a l Absol lu t o Error% (TAE%)

So TAE % = 0. 6 s55!

QOR TAE %- SO

Uo TAE % - 2.87

hble4ResultsfNetworkodelVerificationDuringTheSecondHalfof Dec.1993

l T d i e 2 Su n n wy d Va l i i Mi c a l f h n v Cmk i i n Cu n b k t i o f jT wq o I WMMMt i - s jPressure Loss I HOLDUP I Flow Me@ ill Pmswe Lose I NOLDLIP I F&M-OR OR DWTD OR OR DR/TO 1.-

Beo 1 s s 0 I BB/TD lmGGICKAGAITDlI BBR I BBR I BB/TD II ORK I ORK I ORK II HB I HE I BB /DR/BJA I

W*N --vy- --,:. --%%- - = - :, %ou - -RR-M 4ss9 MS.7J M S* 1M Mu I.U..w 4,10 4J8S .2UA6 3.076 139.7s LSO1 2.73S 66A1 1.910 1Ss.7s um S .l U .142A9 1.U9 Mam SAW S.W .249Bl 2.720 1s9.42 1.* 2.119 294n 2,s1 1S9.U 4.2S7 4AM .247m 2.Ss6 M.* 4.4Ss 4.890 . l=St 1.W4 LW.42 MM 2>74 -2.S8m 1.%0 L..* X4% I 3.9U .437M SW lm.u S,44J km ! .1.6et Lc n M9.u ZSa 1s19 NJw Mm I.w.u lam 1.617 4S0es 4.s20 1s.42 m 890 4C4 s.m9 141-W 4.1s 4.6 t9 .S.4

TOTAL -- IU.1J I 4s,0n 41>m -1.-

Total Field Network Model Verification :Cumulative Error % (CE%)= 4.4Absolute!Error % (AE%) = 7.7

HBO HBO OBIQRIBJABJA BJA1/BJA2 TDMB MB MBAny BRIMIN1 AnyAny BRIMIN2 Any

NOSLIP NOSLIP NOBLIP 1hciex o f A bb re vl at l on s. .

C k D . a s k Ro a HBO.Ha@un &Mom L%@M= h J2dr@! AS& TO= l a i dWMl = ~ ni MA I i d & px f a i a i i m ~ K. w-MM= M& d MA H d @ k w da i m GA = GM, M& k q a sBWN 1 02 =M 6 h n i H4 JpCc Wh W = M&@ 4 Mlm= B 4$ qb M r x g md m= k g gs6 M Ra w

I SJA1 i WA1/SJA2/EATDN I TD Jrml SRIMIN1 I Atw I

Any I SRIMIN2 I AcqNOSUP NOSUP NOSUP {Index Of Abbreviations

Wi=ra.nlsih! MO :H a g s ! mh ( k @B m= B q g a ! we i i d W. Ha@amk Bmnha dB B C =@q a 6 M Oi j d w : Ma a ! mC al d i k lf U AI Z h i j r i dMH ~ km kM I o = l w o l w~ 1 ~ :~ ~ Hd @WM w: Ma @ 6 hi l.. OK m Ow ( Aq w= ( k i t@WN M B I I6 h a mHd @k u a Mi W No$ Aaaurt@

WELLHEADPLATFORMRB-B kFw r 4,,-PAELLIIEAD w PPLATFORM TRR-C wLEGEND:P= 1 Z Pr c.d uc l io n L in e PROCESSPLATFORMT =S- T est L lnc RB. PP-BI14C=S# la h P rr ss . Llt t GasLP =20 Low Press.Pr od . Ga s WELLREAI)PLATFORMW= 12 Wa ler I nJ cc llon y$wSIIORE P RODUCT ION FACI LI Ti E !S

FIG.(1) lUS BLJ DRAN P RODUCTION ROUTING


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W[ l L t wo HI S I ORVMA I CH , ? RwJ n [ h . IJIPIIL(SUMV on 28/1}/1!!3)

fMtASURIO PRCSSURt OURISIGSURV[Y W 28/11/1993.

-4000=g -5600-s -s000-=:= -lobo -

-Boon--9000-- I6boo--11000 -

100 400 600 ma 1000 1?00 1400 1600 1800 2000 2100P, ,, ,, , * ( PSI A)r,, (2-0) [.~le ot V* I1 Pe,l.,mm te I l i slw , mat ch

12 PIP ELIN[ f R OI A A 1 0 PRODUCT I O N MA l b 31 3 : * j ; i ,Pw to l ,,1> 940s111ss1 II *1 1s 11, S U

I M

W[LL RU-96 rii SiOi!Y iAAitfi , ~fit~~ti?i Y:. ~~~!!!

tIIKJOOROIJ& BRWII RIVISIO CORR[ l A IIM?MIWJRIO PR[SSURC OURIHGSURVCV ON 20/6/9J.

=g -s000s.- -6060-~- -70@o-=

-M60 --s000 --Imoo -- I IQOO-

500 1000 1500 Xmo MooP,e,s,Ir (PSIA)f,q (1-b)etmole . 1 .rll Pttlwmo.ce Ili.lety Melcb

1 2 PI PEL INE fROU O 10 PP-B PLATfORU.NM 4.11/9/ wfi111M6 11/61/s$IO:66:DA Pwcslum,,ti 84411q


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-,.

+CALCULATE FLOW RATES ININPUT SRANCHES (WELLS)

+MIX FUJ I09 PROM WRLLS TO

ODTAIN COMPOOITt ON IN PIPELINR6 I+ITERATE FOR PRESSURECALCULATION AT THE SEPARATOR,

+COM PA RE PRESSU RE CA LCUL AT IONWITI+ THE ONE SPECIFIED AT THECJ AND IF IT IS WITHIN TOLERANCE

[, 1 II ~Fig. 4 NETWORK iViODEL CALCUWI iwN A~~~~i~i+ivil. .-- . . .l


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Documents

Development of a Network and Gas Lift Allocation Model for Production Optimization in the Ras Budran Field