ASPHALTENE DEPOSITION IN PETROLEUM RESERVOIRS: DYNAMIC TEST & CONNECTIONIST MODELING

Transcription

1 CA /7 APHATENE DEPOITION IN PETROEUM REERVOIR: DYNAMIC TET & CONNECTIONIT MODEING. Zendehboud 1*,.A. James 1, A. Bahador 2, and M.A. Ahmad 3 1 Memoral Unversty of Newfoundland, t. John s, N, Canada 2 chool of Envronment, outhern Cross Unversty, smore, NW, Australa 3 Faculty of Petroleum Engneerng, Petroleum Unversty of Technology, Ahwaz, Iran Ths paper was prepared for presentaton at the Internatonal ymposum of the ocety of Core Analysts held n Napa Valley, Calforna,, UA, eptember, 2013 ABTRACT Asphaltene Deposton s a complex process that can lead to a declne n ol producton rates due to permeablty and wettablty alteraton. Asphaltene nstablty occurs due to varatons n thermodynamcs propertes such as pressure, temperature, and mxture composton. In ths study, dynamc experments were conducted usng ol samples to measure mportant phase behavour propertes such as bubble pont pressure (BPP) and the amount deposted as asphaltene. A thermodynamc model was also developed to determne equlbrum composton of the ol samples consderng asphaltenes. We nvestgated the potental applcaton of usng feed-forward Artfcal Neural Network (ANN) optmzed by Imperalst Compettve Algorthm (ICA) and Partcle warm Optmzaton (PO) to estmate the bolng pont pressure and asphaltene deposton. Comparson between the smart technque predctons and the expermental data shows an acceptable match. It s found that pressure drop and temperature are the most mportant factors contrbutng to asphaltene precptaton. Employng laboratory PVT data and connectonst modelng can result n the constructon of an asphaltene phase envelope through an effectve and accurate manner. The outcomes of ths study, n terms of thermodynamc framework and predctve tools, appear to be useful n the desgn stage of more effcent EOR processes. INTRODUCTION Asphaltene precptaton/deposton durng ol producton s consdered unfavourable as t can cause consderable declne n the effectve permeablty, leadng to the reducton n the ol flow rate [1,2]. Beng able to estmate the amount of asphaltene deposton n the reservor n terms of flud and formaton characterstcs would be useful knowledge. Ol recovery methods ncludng natural depleton, gas-lft operatons, acd stmulaton, and mscble gas njecton may cause asphaltene deposton. Asphaltene phase behavour s strongly dependent on the fluds and pressure and temperature. A number of researchers proposed the models to predct the PVT and flud flow behavour of hydrocarbon mxtures durng the onset of asphaltene precptaton [1-6]. A solublty model was ntroduced by Hrschberg et al. (1984) to estmate the heat of

2 CA /7 soluton. The heat was taken nto account as a functon of solublty of asphaltene and the remanng part of the mxture nto the reservor flud. Ths technque s able to capture the nfluence of non-dealty of asphaltene and resn molecules n PVT studes. Predcton of asphaltene precptaton by conventonal tools generally leads to consderable error despte the fact that these technques have been broadly employed. mart technques such as artfcal neural network (ANN) could be an effectve soluton for ths problem because ANN optmsaton s well suted to model ll-defned, ambguous, and non-lnear problems. The authors of ths paper have prevously predcted thermodynamc propertes such as condensate gas rato (CGR) [7]. A seres of dynamc expermental runs were carred out n ths study to nvestgate the effect of temperature, dluton rato, pressure, and fluds composton on asphaltene precptaton. A thermodynamc framework usng a sold model approach was developed to obtan the equlbrum mole fractons of the components nvolved n asphaltene precptaton/deposton. ubsequently, three ANN smart technques; back-propagaton (ANN-BP), partcle swarm optmsaton (ANN-PO), and mperalst compettve algorthm (ANN-ICA) were appled to the expermental data to predct the target functon. tatstcal analyss was used to evaluate the predctve performance of the ANN models. OID MODE Accordng to Pan and Froozabad (2000), the sold fugacty (f ) for a pure component under sothermal condtons s gven as follows [5]: V ( P P ) ln f ln f RT (1) EO b P ln fj ln fj, = 1,,nc ; j = v, l RT (2) where f j and are the fugactes wth and wthout translaton, respectvely; s the EO fj dmensonless volume shft parameter, and b represents bnary nteracton parameter of the EO for the th component. The fugactes of each component are the same n all phases at equlbrum condton as shown by the followng relatonshp: ln f ln f ln fg (3) Based on Nghem and Coombe (1997), the sold phase exsts n the soluton f the followng relatonshp s establshed [8]: nc nc ln f ln f (4) The asphaltene precptaton model should satsfy Equaton (4). The mole fracton of each component s determned usng K-values equatons wrtten below [5,8]: G G x ln f K (5) G G x ln f

3 CA /7 K x x ln ln f f (6) ABORATORY INVETIGATION Asphaltene deposton dynamc tests ncorporate core floodng wth and wthout CO 2. A physcal model (D = 10 cm and = 30 cm) was used n ths part of study. The expermental setup s shown n Fgure 1. The man components of the setup are a core, a core holder, accumulators, nlne analytcal nstruments, flow meters, pressure and temperature gauges, and a pump. Frst, the porous system s saturated wth the ol usng the vacuum method. After that, the CO 2 gas s njected whle the gas flow rate s beng recorded. Durng the floodng process, the pressure dfference between the nput and output s controlled by a backpressure regulator nstalled on the outlet. Hence, the pressure drop along the porous medum length remans constant throughout the njecton. A gradual cylnder s employed to collect the produced flud. After around 5.5 PV gas njected, the core floodng s ended where no ol s produced from the core. Then, the pressure of the physcal model s declned slghtly. When the pressure approaches zero, the core s taken out from the core holder. The core s dred n an oven under vacuum condton whch s dfferent from the process condtons. The magntude of asphaltene deposted durng the producton process was obtaned based on the dfference between mass of the dred porous system before saturaton and after njecton process. It s mportant to note that the process pressure and temperature vares n the ranges of [0, 9000 psa] and [20, 100 C], respectvely. ARTIFICIA NEURA NETWORK (ANN) ANN has been made on the bass of the nformaton processng system of bran. ANN bulds a non-lnear relatonshp between nput and output parameters through ts nherent propertes [9]. The ANN conssts of three dfferent layers, namely; nput, hdden and output layers [9]. The data used are categorzed nto two groups: tranng and testng data seres. The man purpose n the tranng stage s to determne the optmum values of weghts connectng layers to each other. Partcle swarm optmzaton (PO) PO s a stochastc optmzaton technque on the bass of socal behavor of fsh educaton and/or brd collecton [10]. Ths algorthm has capablty to optmze the ANN varables and avod trappng n local optma. Imperalst Compettve Algorthm (ICA) ICA s recognzed as a new algorthm n the evolutonary computaton system based on the human s soco-poltcal evoluton. There are two knds of countres n ICA: colony and mperalst [11]. The ICA n the form of an optmzaton algorthm s joned to the ANN model to construct a sutable structure. The Mean quared Error (ME) s taken

4 CA /7 nto account as a cost functon n the ANN-ICA system. The goal s to mnmze the cost functon throughout the computaton process. REUT & DICUION In ths paper, asphaltene deposton s studed through expermental, modelng and connectonst modelng. Based on the data obtaned from the sold model and the expermental work, the two-phase asphaltene precptaton/deposton envelope s plotted as demonstrated n Fgure 2. When CO 2 njecton s conducted for EOR operaton, the amount of deposted asphaltene wll exhbt a decrease for the ol sample used n our study (lght ol wth API o 33) as the concentraton of CO 2 n the ol sample ncreases (ee Fgure 3a). Ths behavour wll proceed untl bubble pont pressure (BPP) s attaned. However, asphaltene deposton ncreases for the pressures greater than (BPP). The man reason for ths trend s hgh densty of CO 2 compared to the ol sample that may have sgnfcant effect on the solublty of asphaltene partcles n the mxture. Fgure 3b demonstrates the nfluence of pressure drop along the length of porous medum on the amount of asphaltene deposted. As clear ndcated n Fgure 3b, the hgher pressure drop causes more asphaltene deposton on the surface of rocks or partcles n the porous meda. It s also can be concluded the pressure drop exhbts hgher effects n contrast wth the flowng pressure throughout on the asphaltene deposton. Ths study employs varous ANN structures (number of hdden layers and neurons). The optmum parameters for the ANN structure are obtaned based on the R 2 value and ME through tral and error. The algorthm to tran the network s the back propagaton (BP) algorthm. Two evolutonary algorthms ncludng ICA and PO are used to predct AD and BPP. The ANN systems consder temperature, pressure, solvent/ol rato, CO 2 mole fracton, pressure drop, flow rate and molecular weght as nputs. Temperature, molecular weght, and gas/ol rato are the nput parameters for BPP. As Fgure 4 depcts, a comparson s made between the measure amounts of asphaltene deposted and estmated values obtaned from Hu et al. (2000) s model [12] and the testng stage n the BP-ANN, ANN-PO and ANN-ICA systems. It s found that the ANN-PO wth [R 2 = 0.969; ME = ] and ANN-ICA wth [R 2 = 0.976; ME = ] predct asphaltene deposton wth reasonable accuracy, whle the conventonal ANN wth [R 2 = ; ME = ] has lower performance n terms of precson compared to the hybrd ANN systems. However, Hu et al. (2000) s model [12] wth [R 2 = 0.857; ME= ] fals to accurately estmate asphaltene deposton at partcular thermodynamc and process condtons. The same qualtatve concluson s drawn for BPP whle predctng ths parameter usng ANN-BP, ANN-PO and ANN-ICA models. It s mportant to menton here that the ANN-ICA shows greater performance n terms of the number of teratons for convergence and executon tme, compared to the ANN-PO. An ANOVA analyss was performed. The most mportant parameters affectng asphaltene deposton was found to be the pressure drop and temperature. Gas-to-ol rato and temperature were found to be mportant n predctng the bubble pont pressure.

5 CA /7 Better understandng of PVT behavour and precse estmaton of asphaltene precptaton/deposton for hydrocarbon mxtures offers petroleum and chemcal engneers approprate rules of thumb for optmzaton of process condtons n order to mnmze pressure drops n the producton and surface facltes, resultng n separatng the mxture nto the heavy component, ol, and gas. CONCUION Asphaltene deposton and bubble pont pressure were nvestgated through expermental studes, a sold model and predctve connectonst modelng. The followng man conclusons are drawn: 1. The predctve performance of the hybrd smart technque s apprecably superor to the conventonal ANN system and the model developed by Hu et al. (2000). 2. The relatve mportance of the nput varables ndcated that pressure drop and temperature pressure are the most mportant varables contrbutng n the magntude of the AD. It was also concluded that the BPP s mostly affected by the temperature and GOR among the nput parameters. 3. Both ANN-PO and ANN-ICA systems beneft from global and local searchng abltes that help avod optmzaton results gettng stuck n a local mnmum. However, the convergence speeds of the ANN models are n the order of ANN- ICA, ANN-PO and ANN-BP. 4. The optmum structure of the ANN models was manually determned. An alternatve procedure s recommended to be combned wth PO and/or ICA for ths purpose. 5. The effects of pressure and temperature on the amount of asphaltene deposton are strongly dependent on the magntudes of these varables wth respect to the bubble pont pressure of the ol mxture. Fgure 1: Expermental etup Fgure 2: Asphaltene Precptaton Envelope

6 CA /7 Fgure 3: Effect of a) CO 2 Concentraton and b) Pressure Drop on Asphaltene Deposton (a) (b) (c) (d) Fgure 4: Predctve performance of varous models: a) ANN-BP; b)ann-po ; c) ANN- ICA; d) Hu et al. (2000)

7 CA /7 REFERENCE [1] Hrschberg, A.; De Jong,. N. J.; chper, B. A.; Meers, J. G. Influence of Temperature and Pressure on Asphaltene Flocculaton. PE J. 1984, 24, [2] rvastava, R. K.; Huang,..; Dyer,. B.; Mourts, F. M. Quantfcaton of Asphaltene Flocculaton durng Mscble CO2 Floodng n the Weyburn Reservor. J. Can. Pet. Tech. 1995, 34, [3] eontarts, K. J.; Mansoor, G. A. Asphaltene Flocculaton durng Ol Producton and Processng: A Thermodynamc Collodal Model. PE 16258, PE Int. ymp. on Olfeld Chem. an, Antono, Texas, U..A., 4-5 February [4] Mansoor, G. A. Modelng of Asphaltene and Other Heavy Organc Depostons. J. Pet. c. and Eng. 1997, 17, [5] Pan, H.; Froozabad, A. Thermodynamc Mcellzaton Model for Asphaltene Precptaton from Reservor Crudes at Hgh Pressures and Temperatures. PE Prod. and Facl. 2000, 15, [6] peght, J. G.; Plancher, H. Molecular Models for Petroleum Asphaltenes and Implcatons for Asphalt cence and Technology. Proc. Int. ymp. on the Chem. of Btumens, [7] Zendehboud,.; Ahmad, M.A.; James,.; Chatzs, I. Predcton of Condensateto-Gas Rato for Retrograde Gas Condensate Reservors Usng Artfcal Neural Network wth Partcle warm Optmzaton. Energy & Fuels, 2012, 26(6), [8] Nghem,. X.; Coombe D. A. Modelng Asphaltene Precptaton durng Prmary Depleton. PE J. 1997, 2, [9] Hagan, M. T.; Demuth, H. B.; Beal, M. Neural Network Desgn. PW Publshng Company, Boston, [10] Eberhart, R. C.; Kennedy, J. Mcro Machne and Human cence 1995, [11] Garca-Pedrajas, N.; Hervas-Martnez, C.; Munoz-Perez, J. COVNET: A Cooperatve Co Evolutonary Model for Evolvng Artfcal Neural Networks. IEEE Trans. on Neural Net, 2003, 14, [12] Hu, Y. F.; Chen, G. J.; Yang, J. T.; Guo, T. M. A tudy on the Applcaton of calng Equaton for Asphaltene Precptaton. Flud Phase Equl. 2000, 171,