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Evaluation of oil sands resources —A case study in the Athabasca Oil Sands, NE Alberta, Canada

Evaluation of oil sands resources —A case study in the Athabasca Oil Sands, NE Alberta, Canada 30 Pet.Sci.(2013)10:30-37 DOI 10.1007/s12182-013-0246-9 Evaluation of oil sands resources NE Alberta, Canada 1 1 2 1 1 Yin Pengfei , Liu Guangdi , Liu Yingqi , Liu Chenglin and Liu Wenping College of Geoscience, China University of Petroleum, Beijing 102249, China Sinocanada Petroleum Corp., Canada JHUOLQ+HLGHOEHUHUODJ%9‹&KLQD8QLYHUVLW\RI3HWUROHXP %HLMLQJ DQG6SULQJHU Abstract: Oil sands are the most important of the oil and gas resources in Canada. So the distribution DQGHYDOXDWLRQRIRLOVDQGVIRUPDFULWLFDOEDVLVIRUULVNLQYH VWPHQWLQ&DQDGD'LVWULEXWLRQRIRLOVDQGV resources is severely controlled by the reservoir heterogeneity. Deterministic modeling is commonly used to solve the heterogeneity problems in the reservoir, but rarely used to evaluate hydrocarbon resources. In this paper, a lithofacies based deterministic method is employed to assess the oil sands resources for a part 7KHWDWLVWLFDODQDO\VV$OEHUWDRIDPLQLQJSURMHFWLQQRUWKHUQ LVRI'HDQWDUN6ZDWHUDQGRLOVDWXUDWLRQGDWD and study of the core description data, regional geology and geophysical logs reveal that the lithofacies LQWKHWXG\VDUHDFDQEHFODVVL¿HGLQWRUHVHUYRLUIDFLHVSRVVL EOHUHVHUYRLUIDFLHVDQGQRQUHVHUYRLUIDFLHV 7KHLQGLFDWRUNULJJLQJPHWKRGLVXVHGWREXLOGD'OLWKRIDFLHVPRGHOEDVHGRQWKHFODVVLILFDWLRQRI VHGLPHQWDU\IDFLHVDQGWKHRUGLQDU\NULJJLQJPHWKRGLVDSSOLHGWRSHWURSK\VLFDOSURSHUW\PRGHOLQJ7KH UHVXOWVVKRZWKDWWKHNULJJLQJHVWLPDWLRQLVRQHRIWKHJRRGFKRLFHVLQRLOVDQGUHVRXUFHVPRGHOLQJLQ YHUWKHJUDGHRQO\EDVHGPRGHOLQJ$OEHUWD/LWKRIDFLHVJUDGHEDVHGPRGHOLQJPD\KDYHDGYDQWDJHVR Key words:WKDEDVFDRLOVDQGVGHWHUPLQLVWLFPHWKRGNULJJLQJPHWKRG'OLWKRIDFLHVPRGHO$ 1 Introduction and 3D oil sands resource modeling can help understand the Oil sands are the most important of the oil and gas distribution (Langenbergetal et al, 2001). Various types of resources in Canada (Carrigy and Kramers, 1973; Flach, 1984; modeling methods have been proposed for bitumen resource +HLQDQG&RWWHULOODE0RVVRS5DQJHU mining assessment. The modeling method we used here is a :9 deterministic interpolation method by integrating lithofacies and bitumen grade (porosity and oil saturation). We chose situ are 1.7 trillion barrels of bitumen and the proven reserves a small area of about 15 sections in the Northern Lights DUHELOOLRQEDUUHOVLQQRUWKHUQ$OEHUWD$ERXWRI 3DUWQHUVKLS3URSHUW\IRUWKLVVWXG\7KLVSDSHUVXPPDUL]HV bitumen resources occur in the surface mineable area . Oil the method and geological analysis of oil sands resources in VDQGVDUHSURGXFHGIURPWKHORZHU&UHWDFHRXV0F0XUUD\ the study area. The hard data available for this study include Formation, in which the depositional environments were FRUHGHVFULSWLRQGDWD'HDQ6WDUNGDWDDQGJHRSK\VLFDOORJ W\SLFDOGHOWDSODLQV )ODFKDQG0RVVRS5DQJHUDQG data . The Northern Lights project area is located about 110 Gingras, 2003). The timing of oil accumulated was earlier than NLORPHWHUVQRUWKHDVWRI)RUW0F0XUUD\LQRZQVKLS7DQG 2002), and biodegradation of pre-existing petroleum created study area is situated in the west lease of the project area (Fig. 1). 6WUDXV] 7KHGLVWULEXWLRQRIWKHRLOVDQGVUHVRXUFHVLV Ɨ http://www.energy.alberta.ca/OilSands/791.asp $OEHUWD(QHUJ\DQG8WLOLWLHV%RDUG3KDVHILQDOSURFHHGLQJ 0 XQGHUELWXPHQFRQVHUYDWLRQUHTXLUHPHQWVLQWKH$WKDEDVFDDELVNDZ: 'HFLVLRQ%  0F0XUUD\ Smith, 1989). Understanding of the spatial distribution of ore 3DXOHQ55LFH5DQG*LQJUDV0*HRORJ\RIWKH)RUW0F0XUUD\ $OEHUWD(GPRQWRQ*HRO6RFLHW\ (GPRQWRQ DUHDQRUWKHDVW KWWSZZZFVSJRUJFRQYHQWLRQVDEVWUDFWV&RUHNLPEDOOBHB *Corresponding author. email: pfyin.sipc@sinopec.com GHVSRVLWLRQDOBHQYLURQPHQWVSGI Received September 29, 2011 (YR\DF*LOOLYUD\DOHW/DQJHQEHUDOHWJ VHYHUHO\FRQWUROOHGE\WKHUHVHUYRLUKHWHURJHQHLW\ %UHNNHDQG 5XELQVWHLQDODQG5LHGLJHU)ODFKHW0RVVRSDQG +HLQDQG/DQJHQEHUJ0RVKLHUDQGDSOHV &UHDQH\UHVRXUFHVDQGV $OODQDQGRLOWKHDO%URRNVHW :05DQJHVWR ZHVW)RXUWKWKHRI0HULGLDQ WKHDQG DOHW%HNHOHDOHW 5LHGLJHUIRUPLQJUHVRXUFHVDQGVRLO $OEHUWD(QHU'HSDUWPHQWJ\LQWKHRLOVDQGVUHVRXUFHVLQ WKH $FFRUGLQJDOWRLJUDVVHWLJKWPDQ DQGZDVWHLVWKHNH\WRWKHPLQLQJHQJLQHHULQJSODQDQGGHVLJQ ņņ$FDVHVWXG\LQWKH$WKDEDVFD2LO6DQGV Pet.Sci.(2013)10:30-37 31 Fort Chipewyan Audet Lake Fort McMurray Edmonton Calgary Mcclelland Lake 10 km 5 mile Fig. 1 Location of the study area 0RGL¿HGIURPKWWSHQYLURQPHQWDOEHUWDFDGRFXPHQWV6\QHQFRB( J\B1RUWKHUQ/LJKWV2LO6DQGV0LQHB3''SGI QHU VHGLPHQWDU\IDFLHVFDQEHFDWHJRUL]HGLQWRGLIIHUHQVWIDFLHV 2 Oil sands geology in Northern Lights Project Leases 1). The reservoir facies group is chiefly composed of sand In the study area, oil sands occur dominantly in the 0LGGOHDQG/RZHU0F0XUUD\)RUPDWLRQVZKLFKZHUH 0F0XUUD\)RUPDWLRQ DQGWKHFRQWLQHQWDOIOXYLDOFKDQQHO deposited in estuarine-tidal-fluvial depositional systems WKH/RZHU0F0XUUD\)RUPDWLRQ 7KHSRVVLEOHUHVHUYRLU $OEHUWD(QHUJ\DQG8WLOLWLHV%RDUG)ODFKDQG+HLQ facies group consists of sand dominated, sand/mud mixed +HLQDQG&RWWHULOODE 7KHHVWXDULQH facies and sand/mud breccia mixed facies deposited in the channel sand, tidal channel sand, tidal flat sand, and fluvial WLGDOÀDWRUWKHFRQWLQHQWDORYHUEDQNKLOHZWKHQRQUHVHUYRLU channel sand serve as the major oil sands reservoirs. Typically facies group includes the mud/coal dominated facies, the sand 0LGGOH0F0XUUD\¶VXSSHUHVWXDULQHVDQGVDUHZHOOVRUWHG IDFLHVGHSRVLWHGLQWKHPDUVKFRDOVZDPSRYHUEDQNDQGWLGDO WKLFNO\EHGGHG¿QHWRPHGLXPJUDLQHGVKHHWVDQGDQGVDQG flats. Fine-medium grained channel sand (Fig. 2) and tidal/ beds are generally on a meter scale. Lower estuarine sands estuarine channel sand (Fig. 3) are the best and typical oil- DUHFRPSRVHGRIZHOOVRUWHGQHPHGLXP¿JUDLQHGDQGVVLWKZ sands reservoirs in Northern Lights Partnership leases. interbedded tidal muds. Tidal flat deposits consist of thin ZHOOVRUWHG¿QHJUDLQHGÀDWVDQGÀDWPXGVDQGPL[WXUHRI 4 Data analysis ¿QHJUDLQHGÀDWVDQGDQGPXGV0XGEUHFFLDZDVGHSRVLWHG LQDVVRFLDWLRQZLWKWKHLGGOH00F0XUUD\HVWXDULQHDQGWLGDO 6WDWLVWLFDODQDO\VLVRI'HDQ6WDUNGDWDLQGLFDWHVWKDWWKH FKDQQHOVDQGV(DUO\XYLDOÀVDQGVDUHFRDUVHVDQGVGHSRVLWHG QG LQDKLJKJ\HQHUHQYLURQPHQW/DWHÀXYLDOGHSRVLWVFRQVLVWRI QRQUHVHUYRLUVLVZHOOUHÀHFWHGE\WKHDEOHELWXPHQFRQWHQW 7 PRGHUDWHO\VRUWHG¿QHPHGLXPJUDLQHGVDQGV0DUVKPXGV 2). The average content of bitumen in reservoirs is generally and the associated coal swamp low energy clays, silts and ZHOODERYHIFXWRIJUDGH  7KDWRIWKHSRVVLEOHUHVHUYRLUV FRDO\GHSRVLWVDUHDOVRGHYHORSHGLQ/RZHUF0XUUD\0 +HLQ is around the cut-off grade and that of the non-reservoir +HLQHWDO DQG'ROE\ is well below the cut-off grade. The bitumen contents are generally high in reservoir facies (Fig. 4). For this modeling, &ODVVL¿FDWLRQRIOLWKRIDFLHV we should determine the distribution of the reservoir facies and the possible reservoir facies, of which the bitumen grade Based on the study of depositional environments, core LVDERYHWKHIFXWRIJUDGH +HLQDQG&RWWHULOODE GDWDVWDWLVWLFDODQDO\VLVRI'HDQ6WDUNGDWDJHRSK\VLFDO +HLQHWDO log data and the facies associations in cross-sections, the OLWKRIDFLHVFODVVL¿FDWLRQRIUHVHUYRLUVSRVVLEOHUHVHUYRLUVD OLWKRIDFLHVGHSRVLWHGLQWKHWLGDOHVWXDULQHFKDQQHO WKH0LGGO JURXSVLQWKH0LGGOHDQG/RZHU0F0XUUD\)RUPDWLRQV 7DEOH 32 Pet.Sci.(2013)10:30-37 &ODVVL¿FDWLRQRIIDFLHVJURXSVLQWKH/RZHUDQG0LGGOH0F0XUUD\)RUPDWLRQV Table 1 Bitumen 0HPEHU Facies group Facies Brief description Depositional environment content Estuarine channel Fine-medium grained, well sorted sand with Estuarine ! sand low-angle cross beddings Reservoir facies 'RPLQDQWO\¿QHJUDLQHGZHOOVRUWHGVDQGZLWK Tidal channel sand Tidal channel ! low-angle cross beddings, and few burrows 6LOWYHU\¿QH¿QHJUDLQHGVDQGZLWKLQWHUEHGGHG 6LOWVDQGÀDW LGDOÀDW7 Possible interlaminated mud, moderately bioturbated reservoir Fine-medium grained sand with greater than facies Channel breccia Tidal/estuarine channel PXGEUHFFLDFKDRWLF 0LGGOH 7KLFNPXGZLWKLQWHUODPLQDWHGYHU\¿QH¿QH 0F0XUUD\ 0XGÀDW LGDOÀDW7 grained sand/silt Formation ,QWHUEHGGHGLQWHUODPLQDWHGYHU\¿QH¿QHJUDLQHGVDQG 0L[HGÀDW and mud, intensively bioturbated Non-reservoir $EDQGRQHGFKDQQHO 7KLFNPXGZLWKLQWHUEHGGHGWKLQ¿QHJUDLQHGVDQG facies mud Fine-medium grained, well sorted sand with Estuarine channel sand Estuarine channel low-angle cross beddings 'RPLQDQWO\¿QHJUDLQHGZHOOVRUWHGVDQGZLWK Tidal channel sand Tidal channel low-angle cross beddings, and few burrows Fluvial channel 0HGLXPYHU\FRDUVHJUDLQHGPRGHUDWHO\VRUWHGVDQG Fluvial channel ! coarse sand with high-angle cross beddings Reservoir facies Fluvial channel Fine-medium-grained, moderately sorted sand with Fluvial channel ! ¿QHVDQG high-angle cross beddings Possible reservoir 2YHUEDQNVDQGVLOW Silty sand with mud layers 2YHUEDQN facies 2YHUEDQNPL[HG ,QWHUEHGGHG¿QHJUDLQHGVDQGDQGPXG Pond mud Thin bedded mud and silt mud Pond Lower 0F0XUUD\ Formation 0DUVKPXG 0XGDQGVLOW\PXGEHGGLQJGLVWXUEHGE\SODQWURRWV 0DU Non-reservoir Coal swamp-coal Coal, may include thin bedded mud Coal swamp facies Coal swamp margin &RDOGDUNGDUNEURZQFDUERQDFHRXVPXG Fine to very coarse grained, moderately sorted sand with Fluvial channel sand high-angle cross beddings Fluvial channel 0L[HG0F0XUUD\VHGLPHQWVIROGHGIDXOWHGE\ Post-depositional slump Post-depositional slump post-depositional slumping VK STUDY STUDY Lower McMurray Middle McMurray Lower McMurray Pet.Sci.(2013)10:30-37 33 Fluvial Marsh channel deposit sand 00QL_results_nlp DEPTH240 PHIT_1 0.6 V/V 0 RHOB_1 RXO_2 mker_paris Formation 1700 K/M3 2700 0.2 OHMM 2000 0 W/W 0.2 Depth NP_1 RT_1 CORE_DATA.PHIT_1 MKER_1 Tops 0.6 V/V 0 0.2 OHMM 2000 50 % 0 0 W/W 0.2 GR_1 PEF_1 RXOZ_1 multimin.phit BULK_MASS_SO_1 0 V/V 0.2 0B/E 5 0 GAPI 200 0.2 OHMM 2000 0.5 V/V 0 Fig. 2 UD\\SLFDOÀXYLDOFKDQQHOVDQGZLWKKLJKUHVLVWLYLW\DQGORZ*DPPD7 3871k DS51 39 00QL_results_nlp 1AA052409807W400 DEPTH240 X PHIT_1 0.6 V/V 0 RXO_1 RHOB_1 mker_paris 0.2 OHMM 2000 1700 K/M3 2700 0 W/W 0.2 Depth NP_1 RT_1 CDRE_DATA.PHIT_1 MKER_1 Formation Tops 0.2 OHMM 2000 0 W/W 0.2 40 0.6 V/V 0 50 % 0 DS53 DS52 GR_1 PEF_1 rxoz multimin.phit BULK_MASS_SO_1 0V/V 0.2 0 GAPI 200 0B/E 5 0.2 OHMM 2000 0.6 V/V 0 DS55 4074 DS54 4171k DS56 42 DS58 DS57 43 DS60 DS59 44 DS62 DS61 Fig. 3 Tidal and estuarine channel sand 34 Pet.Sci.(2013)10:30-37 Table 2 Statistics on bitumen content ,QWKH0LGGOH0F0XUUD\)RUPDWLRQWKHDYHUDJHUHVHUYRLU DFFRXQWVIRUPRUHWKDQRIWKHWRWDOURFNVZKLOHWKH %LWXPHQFRQWHQW reservoirs occur mainly at the top and the bottom in the Facies /RZHU0F0XUUD\)RUPDWLRQ )LJ 0LQ 0D[ YHUDJH$ Estuarine channel 5.03 18.24 12.03 5 Resources modeling method Estuarine channel sand 0.27 4.92 2.74 Deterministic methods and stochastic methods are generally used in oil and gas resources and reservoir modeling Tidal channel sand 5.00 19.59 12.81 depending on the data available. The stochastic methods, VXFKDVVHTXHQWLDO*DXVVLDQVLPXODWLRQDUHHPSOR\HGLQWKH Tidal channel sand 0 4.98 3.09 DUHDVZLWKVSDUVHGDWD+RZHYHUWKHGHWHUPLQLVWLFPHWKRGV Tidal/estuarine channel breccia 0 17.65 6.49 are generally considered in the areas with plenty of data available for modeling. In our study area, the well spacing is $EDQGRQHGWLGDOFKDQQHO 0.01 3.02 0.69 less than 100 meters in some parts and more than 260 holes LGDOÀDWVDQG7 0.24 17.95 7.87 were drilled. In this case, it is believed that the deterministic method is a good choice for resources modeling. For facies LGDOÀDWVDQG7 1.35 2.93 2.25 PRGHOLQJWKHLQGLFDWRUNULJJLQJPHWKRGLVXVHGDQGWKH LGDOÀDWPL[HG7 0 8.98 3.32 RUGLQDU\NULJJLQJHVWLPDWRULVHPSOR\HGWRLQWHUSRODWHWKH bitumen grade on the basis of facies modeling. LGDOÀDWPXG7 0 4.90 1.09 5.1 The method Fluvial channel coarse sand 5.02 17.98 11.07 ,QWKHLQGLFDWRUNULJJLQJLQWHUSRODWLRQRIOLWKRIDFLHVDQG Fluvial channel coarse sand 0.02 12.66 2.86 WKHRUGLQDU\NULJJLQJHVWLPDWLRQRIUHVHUYRLUSHWURSK\VLFDO )OXYLDOFKDQQHO¿QHVDQG 5.00 18.61 11.36 properties, semivariograms, functions indicating the spatial correlation in observations measured at sample locations, )OXYLDOFKDQQHO¿QHVDQG 0 7.26 2.82 should be calculated and the appropriate semivariogram model should be selected for modeling both lithofacies and 2YHUEDQNVDQG 0 17.29 5.75 reservoir petrophysical properties. Flood plain 0 8.89 2.50 5.1.1 Semivariogram 6HPLYDULRJUDPLVGH¿QHGDV &ODUN 0.01 2.60 0.57 1K () Coal swamp 0 4.18 0.94 J ()K=  ( ([ )= ([ )) ¦ LL K 2(1K ) Estuarine channel sand Tidal channel sand 20 30 4-6 6-8 8-10 10-12 12-14 >14 4-6 6-8 8-10 10-12 12-14 >14 Bitumen content, % Bitumen content, % Fluvial channel coarse sand Fluvial channel fine sand 0 0 4-6 6-8 8-10 10-12 12-14 >14 4-6 6-8 8-10 10-12 12-14 >14 Bitumen content, % Bitumen content, % Fig. 4 Bitumen content distribution in reservoir samples Percent of samples, % Percent of samples, % Percent of samples, % Percent of samples, % 0DUVK Pet.Sci.(2013)10:30-37 35 Original facies proportion, % Original facies proportion, % 0 20 40 60 80 100 020 40 60 80 100 10 70 0 20 40 60 80 100 020 40 60 80 100 Reservoir Non-reservoir Possible reservoir Fig. 5HUWLFDOGLVWULEXWLRQRIWKHUHVHUYRLUVLQWKH0LGGOH OHIW D9 QG/RZHU ULJKW 0F0XUUD\)RUPDWLRQ where Ȗ (x , x ) is the semivariogram; N(h) is the number of = () [ LVWKHNULJJLQJHVWLPDWRU Z(x ) (i = 1, 2, ··, n) is i j data pairs separated by distance of h; Z(x ) is the value at grade value at location x . The weight vector (Ȝ) is determined i i the start or “tail” of the pair and Z(x +h) is the variable at the as follows: end or “head” of the pair. The formula above can be used for OJ (,[[ )  J ([[, ) 1 ªº ªº ª º J (,[ [ ) 11 2 1 Q 1 FRQWLQXRXVGDWDDQGGLVFUHWHGDWD+RZHYHULWLVFDOOHGDQ «» «» « » «» « » «» indicator semivariogram, where the indicator is used instead «» «» « » of the property values in the formula above. «» «» « » 5.1.2 Semivariogram models OJ (,[[ )  J[[(, ) 1 J (, [ [ ) «»QQ « 1 Q Q » «» Q There are many semivariogram models in practice. «» «» « » P 11  0 ¬¼ ¬ ¼ ¬¼ +RZHYHUWKHPRVWFRPPRQRQHVDUHVSKHULFDOH[SRQHQWLDO and straight line models. Nugget, range and sill parameters where Ȗ(x , x ) is the semivariogram of the property Z, which i j DUHLPSRUWDQWIRUVHPLYDULRJUDPPRGHOV+RZHYHUWKH is separated by the distance between locations x and x . The i j VWUDLJKWOLQHPRGHOGRHVQRWKDYHVLOODQGUDQJH &ODUN   sum of Ȝ (i=1, 1, 2, ··, n) should be 1. 5.1.3 Indicator semivariogram O 1 ¦ L $QLQGLFDWRULVXVHGLQWKHFDOFXODWLRQRILQGLFDWRU L 1 VHPLYDULRJUDPVDQGLVGH¿QHGDVIROORZV 5.2 Lithofacies modeling 1(=[ )V LN ,[(,V ) N 1,2,. , ,QGLFDWRUNULJJLQJLVXVHGWRLQWHUSRODWHIDFLHV$Q LN 0otherwise indicator semivariogram is calculated for reservoir facies, where x is a vector representing a particular facies location; k i possible reservoir facies and non-reservoir facies in the is the presence of a particular facies. 0LGGOH0F0XUUD\DQG/RZHU0F0XUUD\)RUPDWLRQ7KH If the particular facies is present at location x , its i variograms match the spherical variogram model. For the indicator is assigned to 1; otherwise, it is 0. For the indicator indicator variograms, they match the spherical model very semivariogram, Z(x ) and Z(x +h) are replaced by I(x , s ) and i i i k well (Table 3 and Fig. 6 upper). I(x +h, s ) in the semivariogram formula above. i k The nuggets are generally high for the reservoir facies in 5.1.4 Ordinary krigging method the study area, indicating high variability of reservoir facies in 2UGLQDU\NULJJLQJHVWLPDWLRQLVRQHRIWKHPHWKRGVLQWKH the study area. The ranges in the major and minor directions NULJJLQJHVWLPDWLRQIDPLO\DQGLWXVHVWKHORFDODYHUDJHYDOXH DUHTXLWHIHUHQWGLILQERWKWKH0LGGOHDQG/RZHU0F0XUUD\ 7KHIRUPXODLVGH¿QHGDVWRHVWLPDWHWKHSDUWLFXODUSURSHUW\ )RUPDWLRQVLPSO\LQJWKHÀXYLDOFKDQQHOHVWXDULQHDQGWLGDO FKDQQHOGHSRVLWLRQDOHQYLURQPHQWVRIWKHIWHU$UHVHUYRLUWKH O = () [ ªº ª º 1 L VHPLYDULRJUDPLVFRPSXWHGLQGLFDWRUNULJJLQJLVHPSOR\HG «» « » ˜˜ «» « » to interpolate lithofacies. «» « » =[() ˜ ˜ ˜ «» « » 5.3 Reservoir property modeling ˜˜ «» « » «» « » The semivariogram of bitumen content is calculated O = () [ ¬¼QQ ¬ ¼ Layer Layer 36 Pet.Sci.(2013)10:30-37 &DOFXODWHGYDULRJUDPSDUDPHWHUVIRUWKH0LGGOH0F0XUUD\DQG/R ZHU0F0XUUD\UHVHUYRLUV Table 3 0LGGOH0F0XUUD\ /RZHU0F0XUUD\ %LWXPHQFRQWHQWLQWKH0LGGOH Bitumen content in the Lower Property reservoir facies reservoir facies 0F0XUUD\UHVHUYRLUIDFLHV 0F0XUUD\UHVHUYRLUIDFLHV Variogram model Spherical Spherical Spherical Spherical Search radius, m 2000 2000 2000 2000 Lag distance, m 266.7 266.7 266.7 266.7 Tolerance angle, 15 15 15 15 ROHUDQFHGLVWDQFH7 50 50 50 50 Nugget 0.726 0.468 0.517 0.554 0DMRUUDQJHP 627.5 1096.7 876.2 1029.4 0LQRUUDQJHP 302.3 767.9 793.3 774.2 Vertical range, m 29.2 26 29.9 54.1 1 1 0 0 0 400 800 1200 1600 2000 0 400 800 1200 1600 2000 Distance, m Distance, m 1 1 0 0 0 400 800 1200 1600 2000 0 400 800 1200 1600 2000 Distance, m Distance, m Fig. 6,QGLFDWRUYDULRJUDPRIUHVHUYRLUIDFLHV 8SSHUOHIW0LGGOH0F0XUUD\8SSHUULJKW/RZHU0F0XUUD\ DQGELWXPHQFRQWHQWRIWK H %RWWRPULJKW/RZHU0UHVHUYRLUV %RWWRPOHIW0LGGOH0F0XUUD\ F0XUUD\ for reservoirs, non-reservoirs and possible reservoirs in the petrophysical modeling. 0LGGOHDQG/RZHU0F0XUUD\)RUPDWLRQV7KHVSKHULFDO 6 Quality control of modeling results PRGHOVHHPVWR¿WWKHVHPLYDULRJUDPRIWKHELWXPHQJUDGH Table 3 and Fig. 6 (bottom) show the semivariogram models The facies distribution histogram (Fig. 7) and probability RIELWXPHQJUDGHLQWKHUHVHUYRLUIDFLHVLQWKH0LGGOHDQG curves are calculated before and after modeling and a $JDLQWKHQXJJHWV/RZHU0F0XUUD\)RUPDWLRQVUHVSHFWLYHO\ comparison between them shows a very good match before are high, implying high variability in bitumen grade. The and after modeling. RUGLQDU\NULJJLQJPHWKRGLVXVHGWRLQWHUSRODWHWKHELWXPHQ grade based on the semivariogram model. 7 Discussion 6RPHPRGHOHUVDOVRXVHWKHLQYHUVHGLVWDQFHVTXDUHG method in their resources modeling. They believe that 7KHNULJJLQJHVWLPDWLRQLVRQHRIWKHJRRGFKRLFHVLQRLO NULJJLQJHVWLPDWLRQPD\SURGXFHDPRUHUHDVRQDEOHUHVXOW sand resources modeling if the well space in the modeling LIDORWRIKDUGGDWDDUHDYDLODEOH,QDGGLWLRQWKH0LGGOH DUHDLVVPDOODQGORWRIKDUGGDWD FRUHORJJLQJ'HDQ6WDUN 0F0XUUD\)RUPDWLRQDQGWKH/RZHU0F0XUUD\)RUPDWLRQ and geophysical log data) are available though the inverse LVWUHDWHGDVWZRVHSDUDWH]RQHVLQERWKIDFLHVPRGHOLQJDQG GLVWDQFHVTXDUHGPHWKRGLVH[WHQVLYHO\DFFHSWHGDVRQHRI Semivariance Semivariance Semivariance Semivariance Pet.Sci.(2013)10:30-37 37 80 $OEHUWD*HRO6XUYH\(DUWK6FLHQFHV5HSRUW &' 77.0 70.4 63.6 Q)HL+-&RWWHULOO'.5LFH5HWDO6XEVXUIDFHJHRORJ\RIWKH $WKDEDVFDDELVNDZ0F0XUUD\:VXFFHVVLRQ/HZLV)RUW0F0XUUD\ DUHDQRUWKHDVWHUQ$OEHUWD 176' $OEHUWD(QHUJ\DQG 8WLOLWLHV%RDUG$OEHUWD*HRO6XUYH\(DUWK6FLHQFHV5HSRUW 25.5 Q)+HL-DQG'ROE\*5HJLRQDOOLWKRVWUDWLJUDSK\ELRVWUDWLJUDSK\ 20 17.5 17.2 DQGIDFLHVPRGHOV$WKDEDVFDRLOVDQGVGHSRVLWQRUWKHDVW$OEHUWD 12.3 10.9 10 5RFNWKH)RXQGDWLRQ&RQYHQWLRQ&DQDGLDQ6RFLHW\RI3HWUROHXP 5.6 Geologists (Calgary). 2001. 3: 170-171 Q)-DQG5HSO\J/DQJHQEHUWR&:GLVFXVVLRQRIVHLVPLF+HLPRGHOLQJ Facies_Res Upscaled cells Well logs RIIOXYLDOHVWXDULQHGHSRVLWVLQWKH$WKDEDVFDRLOVDQGVXVLQJ Reservoir Non-reservoir Possible reservoir UD\WUDFHWHFKQLTXHV6WHHSEDQN5LYHUDUHDQRUWKHDVWHUQ$OEHUWD Bulletin of Canadian Petroleum Geology. 2003. 51: 354-366 Fig. 7 Distribution histogram of facies after modeling JHQEHU/DQJ&:+HLQ)-%HUKDQH+HWDO7KUHHGLPHQVLRQDO JHRPHWU\RIÀXYLDOHVWXDULQHRLOVDQGGHSRVLWVRIWKH&ODUNH&U HHN RLOVDQGVUHVRXUFHVPRGHOLQJPHWKRGLQ$OEHUWD/LWKRIDFLHV DUHD 176' QRUWKHDVWHUQ$OEHUWD$OEHUWDJ\(QHUDQG8WLOLW LHV grade based modeling may have advantages over the grade- %RDUG$OEHUWD*HRORJLFDO6XUYH\(DUWK6FLHQFHV5HSRUW only based modeling since the data used for estimation are sourced from the same or the similar sedimentary facies in HVWXDULQHGHSRVLWVLQWKH$WKDEDVFDRLOVDQGVXVLQJUD\WUDFLQJ lithofacies-grade based modeling. WHFKQLTXHV6WHHSEDQN5LYHUDUHDQRUWKHDVWHUQ$OEHUWD%XOOHWL QRI Canadian Petroleum Geology. 2002. 50: 178-204 References *LOOLYUD\0DF -56WUREO56.HLWK'$:HWDO5HVRXUFH HUWD(QHUJ\OE$DQG8WLOLWLHV%RDUG$WKDEDVFDDELVNDZ0F0XUUD: \ FKDUDFWHUL]DWLRQRIWKH0F0XUUD\DELVNDZ: GHSRVLWLQWKH 5HSRUW$ &'520 UHJLRQDOJHRORJLFDOVWXG\ $WKDEDVFD6RXWK5HJLRQRI1RUWKHDVWHUQ$OEHUWD,Q$OEHUWD2LO DQ-O$ODQG&UHDQH\62LOIDPLOLHVRIWKHHVWHUQ: &DQDGD%DVL Q 6DQGVHFKQRORJ\7DQG5HVHDUFK$XWKRULW\HFKQLFDO73XEOLFDWLRQ Bulletin of Canadian Petroleum Geology. 1991. 39: 107-122 Series. 1992. 7: 68 HOH(%%HN3HUVRQ0$5RVWURQ%-HWDO0RGHOLQJVHFRQGDU\ RLO KLHURV062DQGDSOHV: ':4XDQWLWDWLYHHYDOXDWLRQRI/RZHU PLJUDWLRQZLWKFRUHVFDOHGDWDLNLQJ9)RUPDWLRQ$OEHUWD%DVLQ  &UHWDFHRXVDQQYLOOH0*URXSDVVRXUFHURFNVIRU$OEHUWD¶RLOVDQ GV $$3*%XOOHWLQ $$3*%XOOHWLQ NNH+%UHDQG(YR\58VHRIGLSPHWHUGDWDLQWKHGHILQLWLRQRI WKH VRS*0RV'*HRORJ\RIWKH$WKDEDVFDRLOVDQGV6FLHQFH LQWHUQDODUFKLWHFWXUHRISRLQWEDUGHSRVLWVLQWKH$WKDEDVFDRL O 207(4427): 145-152 VDQGV,PSOLFDWLRQVIRUWKH0LGGOH0F0XUUD\)RUPDWLRQLQWKH VRS*'DQG0RV)ODFK3''HHSFKDQQHOVHGLPHQWDWLRQLQWKH/RZHU +DQJLQJVWRQHDUHD$OEHUWD DEV $$3*$QQXDO&RQYHQWLRQ &UHWDFHRXV0F0XUUD\)RUPDWLRQ$WKDEDVFDRLOVDQGV$OEHUWD $$EVWUDFWV Sedimentology. 1983. 30: 493-509 RNV3:)RZOHU%UR0*DQG0DFTXHHQ5:%LRORJLFDOPDUNHUDQG JHU5DQ0-$%DVLQ6WXG\RIWKH6RXWKHUQ$WKDEDVFD2LO6DQGV conventional geochemistry of oil sands/heavy oils, Western Canada $OEHUWD7KHVLV8QLYHUVLW\RI'HSRVLW3K' Basin. Organic Geochemistry. 1988. 12: 519-538 JHU05DQ-DQG*LQJUDV0.'LVFXVVLRQRIVHLVPLFPRGHOLQJRI ULJ\0$DQG&DU.UDPHUV-*XLGH:WRWKH$WKDEDVFDRLOVDQGV DUHD $ ,QIRUPDWLRQ6HULHV95HVHDUFK&RXQFLORI$OEHUWD$$3* WHFKQLTXHV6WHHSEDQN5LYHUDUHDQRUWKHDVWHUQ$OEHUWD%XOOHWL QRI Bulletin. 1973. 69: 213 Canadian Petroleum Geology. 2003. 51: 347-353 UN,3UDFWLFDO*HRVWDWLVWLFV*HRVWRNRV/LPLWHG6FRWODQG&OD  5 GLJHU&LH/1HVV6)RZOHU0HWDOLPLQJ7RIRLOJHQHUDWLRQDQG FK3)OD'2LOVDQGVJHRORJ\²$WKDEDVFDGHSRVLWQRUWK$OEHUWD PLJUDWLRQQRUWKHDVWHUQ%ULWLVK&ROXPELDDQGVRXWKHUQ$OEHUWD Research Council Bulletin. 1984. 46: 31 — Significance for understanding the development of the eastern FK3)OD'DQG+HLQ)-2XWFURSFRUHFRUUHODWLRQRIFKDQQHODQ G $OEHUWDWDUDQGVVGHSRVLWV DEV $$3*$QQXDO&RQYHQWLRQ2I¿FL DO QRQF KDQQHOIDFLHV0F0XUUD\)RUPDWLRQ)RUW0DF.D\$UHD1( $3URJUDP $OEHUWD5RFNWKH)RXQGDWLRQ&RQYHQWLRQ&DQDGLDQ6RFLHW\RI LQVWHLQ,EX5DQG6WUDXV]27KHUPDO3WUHDWPHQWRIWKH$WKDEDVFDRLO Petroleum Geologists (Calgary). 2001. 132-133 sand bitumen and its component parts. Geochimica et Cosmochimica FK3)OD'DQG0RVVRS*''HSRVLWLRQDOHQYLURQPHQWVRIORZHU $FWD &UHWDFHRXV0F0XUUD\)RUPDWLRQ$WKDEDVFDRLOVDQGV$OEHUWD Smi th D G. 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Evaluation of oil sands resources —A case study in the Athabasca Oil Sands, NE Alberta, Canada

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References (33)

Publisher
Springer Journals
Copyright
Copyright © 2013 by China University of Petroleum (Beijing) and Springer-Verlag Berlin Heidelberg
Subject
Earth Sciences; Mineral Resources; Industrial Chemistry/Chemical Engineering; Industrial and Production Engineering; Energy Economics
ISSN
1672-5107
eISSN
1995-8226
DOI
10.1007/s12182-013-0246-9
Publisher site
See Article on Publisher Site

Abstract

30 Pet.Sci.(2013)10:30-37 DOI 10.1007/s12182-013-0246-9 Evaluation of oil sands resources NE Alberta, Canada 1 1 2 1 1 Yin Pengfei , Liu Guangdi , Liu Yingqi , Liu Chenglin and Liu Wenping College of Geoscience, China University of Petroleum, Beijing 102249, China Sinocanada Petroleum Corp., Canada JHUOLQ+HLGHOEHUHUODJ%9‹&KLQD8QLYHUVLW\RI3HWUROHXP %HLMLQJ DQG6SULQJHU Abstract: Oil sands are the most important of the oil and gas resources in Canada. So the distribution DQGHYDOXDWLRQRIRLOVDQGVIRUPDFULWLFDOEDVLVIRUULVNLQYH VWPHQWLQ&DQDGD'LVWULEXWLRQRIRLOVDQGV resources is severely controlled by the reservoir heterogeneity. Deterministic modeling is commonly used to solve the heterogeneity problems in the reservoir, but rarely used to evaluate hydrocarbon resources. In this paper, a lithofacies based deterministic method is employed to assess the oil sands resources for a part 7KHWDWLVWLFDODQDO\VV$OEHUWDRIDPLQLQJSURMHFWLQQRUWKHUQ LVRI'HDQWDUN6ZDWHUDQGRLOVDWXUDWLRQGDWD and study of the core description data, regional geology and geophysical logs reveal that the lithofacies LQWKHWXG\VDUHDFDQEHFODVVL¿HGLQWRUHVHUYRLUIDFLHVSRVVL EOHUHVHUYRLUIDFLHVDQGQRQUHVHUYRLUIDFLHV 7KHLQGLFDWRUNULJJLQJPHWKRGLVXVHGWREXLOGD'OLWKRIDFLHVPRGHOEDVHGRQWKHFODVVLILFDWLRQRI VHGLPHQWDU\IDFLHVDQGWKHRUGLQDU\NULJJLQJPHWKRGLVDSSOLHGWRSHWURSK\VLFDOSURSHUW\PRGHOLQJ7KH UHVXOWVVKRZWKDWWKHNULJJLQJHVWLPDWLRQLVRQHRIWKHJRRGFKRLFHVLQRLOVDQGUHVRXUFHVPRGHOLQJLQ YHUWKHJUDGHRQO\EDVHGPRGHOLQJ$OEHUWD/LWKRIDFLHVJUDGHEDVHGPRGHOLQJPD\KDYHDGYDQWDJHVR Key words:WKDEDVFDRLOVDQGVGHWHUPLQLVWLFPHWKRGNULJJLQJPHWKRG'OLWKRIDFLHVPRGHO$ 1 Introduction and 3D oil sands resource modeling can help understand the Oil sands are the most important of the oil and gas distribution (Langenbergetal et al, 2001). Various types of resources in Canada (Carrigy and Kramers, 1973; Flach, 1984; modeling methods have been proposed for bitumen resource +HLQDQG&RWWHULOODE0RVVRS5DQJHU mining assessment. The modeling method we used here is a :9 deterministic interpolation method by integrating lithofacies and bitumen grade (porosity and oil saturation). We chose situ are 1.7 trillion barrels of bitumen and the proven reserves a small area of about 15 sections in the Northern Lights DUHELOOLRQEDUUHOVLQQRUWKHUQ$OEHUWD$ERXWRI 3DUWQHUVKLS3URSHUW\IRUWKLVVWXG\7KLVSDSHUVXPPDUL]HV bitumen resources occur in the surface mineable area . Oil the method and geological analysis of oil sands resources in VDQGVDUHSURGXFHGIURPWKHORZHU&UHWDFHRXV0F0XUUD\ the study area. The hard data available for this study include Formation, in which the depositional environments were FRUHGHVFULSWLRQGDWD'HDQ6WDUNGDWDDQGJHRSK\VLFDOORJ W\SLFDOGHOWDSODLQV )ODFKDQG0RVVRS5DQJHUDQG data . The Northern Lights project area is located about 110 Gingras, 2003). The timing of oil accumulated was earlier than NLORPHWHUVQRUWKHDVWRI)RUW0F0XUUD\LQRZQVKLS7DQG 2002), and biodegradation of pre-existing petroleum created study area is situated in the west lease of the project area (Fig. 1). 6WUDXV] 7KHGLVWULEXWLRQRIWKHRLOVDQGVUHVRXUFHVLV Ɨ http://www.energy.alberta.ca/OilSands/791.asp $OEHUWD(QHUJ\DQG8WLOLWLHV%RDUG3KDVHILQDOSURFHHGLQJ 0 XQGHUELWXPHQFRQVHUYDWLRQUHTXLUHPHQWVLQWKH$WKDEDVFDDELVNDZ: 'HFLVLRQ%  0F0XUUD\ Smith, 1989). Understanding of the spatial distribution of ore 3DXOHQ55LFH5DQG*LQJUDV0*HRORJ\RIWKH)RUW0F0XUUD\ $OEHUWD(GPRQWRQ*HRO6RFLHW\ (GPRQWRQ DUHDQRUWKHDVW KWWSZZZFVSJRUJFRQYHQWLRQVDEVWUDFWV&RUHNLPEDOOBHB *Corresponding author. email: pfyin.sipc@sinopec.com GHVSRVLWLRQDOBHQYLURQPHQWVSGI Received September 29, 2011 (YR\DF*LOOLYUD\DOHW/DQJHQEHUDOHWJ VHYHUHO\FRQWUROOHGE\WKHUHVHUYRLUKHWHURJHQHLW\ %UHNNHDQG 5XELQVWHLQDODQG5LHGLJHU)ODFKHW0RVVRSDQG +HLQDQG/DQJHQEHUJ0RVKLHUDQGDSOHV &UHDQH\UHVRXUFHVDQGV $OODQDQGRLOWKHDO%URRNVHW :05DQJHVWR ZHVW)RXUWKWKHRI0HULGLDQ WKHDQG DOHW%HNHOHDOHW 5LHGLJHUIRUPLQJUHVRXUFHVDQGVRLO $OEHUWD(QHU'HSDUWPHQWJ\LQWKHRLOVDQGVUHVRXUFHVLQ WKH $FFRUGLQJDOWRLJUDVVHWLJKWPDQ DQGZDVWHLVWKHNH\WRWKHPLQLQJHQJLQHHULQJSODQDQGGHVLJQ ņņ$FDVHVWXG\LQWKH$WKDEDVFD2LO6DQGV Pet.Sci.(2013)10:30-37 31 Fort Chipewyan Audet Lake Fort McMurray Edmonton Calgary Mcclelland Lake 10 km 5 mile Fig. 1 Location of the study area 0RGL¿HGIURPKWWSHQYLURQPHQWDOEHUWDFDGRFXPHQWV6\QHQFRB( J\B1RUWKHUQ/LJKWV2LO6DQGV0LQHB3''SGI QHU VHGLPHQWDU\IDFLHVFDQEHFDWHJRUL]HGLQWRGLIIHUHQVWIDFLHV 2 Oil sands geology in Northern Lights Project Leases 1). The reservoir facies group is chiefly composed of sand In the study area, oil sands occur dominantly in the 0LGGOHDQG/RZHU0F0XUUD\)RUPDWLRQVZKLFKZHUH 0F0XUUD\)RUPDWLRQ DQGWKHFRQWLQHQWDOIOXYLDOFKDQQHO deposited in estuarine-tidal-fluvial depositional systems WKH/RZHU0F0XUUD\)RUPDWLRQ 7KHSRVVLEOHUHVHUYRLU $OEHUWD(QHUJ\DQG8WLOLWLHV%RDUG)ODFKDQG+HLQ facies group consists of sand dominated, sand/mud mixed +HLQDQG&RWWHULOODE 7KHHVWXDULQH facies and sand/mud breccia mixed facies deposited in the channel sand, tidal channel sand, tidal flat sand, and fluvial WLGDOÀDWRUWKHFRQWLQHQWDORYHUEDQNKLOHZWKHQRQUHVHUYRLU channel sand serve as the major oil sands reservoirs. Typically facies group includes the mud/coal dominated facies, the sand 0LGGOH0F0XUUD\¶VXSSHUHVWXDULQHVDQGVDUHZHOOVRUWHG IDFLHVGHSRVLWHGLQWKHPDUVKFRDOVZDPSRYHUEDQNDQGWLGDO WKLFNO\EHGGHG¿QHWRPHGLXPJUDLQHGVKHHWVDQGDQGVDQG flats. Fine-medium grained channel sand (Fig. 2) and tidal/ beds are generally on a meter scale. Lower estuarine sands estuarine channel sand (Fig. 3) are the best and typical oil- DUHFRPSRVHGRIZHOOVRUWHGQHPHGLXP¿JUDLQHGDQGVVLWKZ sands reservoirs in Northern Lights Partnership leases. interbedded tidal muds. Tidal flat deposits consist of thin ZHOOVRUWHG¿QHJUDLQHGÀDWVDQGÀDWPXGVDQGPL[WXUHRI 4 Data analysis ¿QHJUDLQHGÀDWVDQGDQGPXGV0XGEUHFFLDZDVGHSRVLWHG LQDVVRFLDWLRQZLWKWKHLGGOH00F0XUUD\HVWXDULQHDQGWLGDO 6WDWLVWLFDODQDO\VLVRI'HDQ6WDUNGDWDLQGLFDWHVWKDWWKH FKDQQHOVDQGV(DUO\XYLDOÀVDQGVDUHFRDUVHVDQGVGHSRVLWHG QG LQDKLJKJ\HQHUHQYLURQPHQW/DWHÀXYLDOGHSRVLWVFRQVLVWRI QRQUHVHUYRLUVLVZHOOUHÀHFWHGE\WKHDEOHELWXPHQFRQWHQW 7 PRGHUDWHO\VRUWHG¿QHPHGLXPJUDLQHGVDQGV0DUVKPXGV 2). The average content of bitumen in reservoirs is generally and the associated coal swamp low energy clays, silts and ZHOODERYHIFXWRIJUDGH  7KDWRIWKHSRVVLEOHUHVHUYRLUV FRDO\GHSRVLWVDUHDOVRGHYHORSHGLQ/RZHUF0XUUD\0 +HLQ is around the cut-off grade and that of the non-reservoir +HLQHWDO DQG'ROE\ is well below the cut-off grade. The bitumen contents are generally high in reservoir facies (Fig. 4). For this modeling, &ODVVL¿FDWLRQRIOLWKRIDFLHV we should determine the distribution of the reservoir facies and the possible reservoir facies, of which the bitumen grade Based on the study of depositional environments, core LVDERYHWKHIFXWRIJUDGH +HLQDQG&RWWHULOODE GDWDVWDWLVWLFDODQDO\VLVRI'HDQ6WDUNGDWDJHRSK\VLFDO +HLQHWDO log data and the facies associations in cross-sections, the OLWKRIDFLHVFODVVL¿FDWLRQRIUHVHUYRLUVSRVVLEOHUHVHUYRLUVD OLWKRIDFLHVGHSRVLWHGLQWKHWLGDOHVWXDULQHFKDQQHO WKH0LGGO JURXSVLQWKH0LGGOHDQG/RZHU0F0XUUD\)RUPDWLRQV 7DEOH 32 Pet.Sci.(2013)10:30-37 &ODVVL¿FDWLRQRIIDFLHVJURXSVLQWKH/RZHUDQG0LGGOH0F0XUUD\)RUPDWLRQV Table 1 Bitumen 0HPEHU Facies group Facies Brief description Depositional environment content Estuarine channel Fine-medium grained, well sorted sand with Estuarine ! sand low-angle cross beddings Reservoir facies 'RPLQDQWO\¿QHJUDLQHGZHOOVRUWHGVDQGZLWK Tidal channel sand Tidal channel ! low-angle cross beddings, and few burrows 6LOWYHU\¿QH¿QHJUDLQHGVDQGZLWKLQWHUEHGGHG 6LOWVDQGÀDW LGDOÀDW7 Possible interlaminated mud, moderately bioturbated reservoir Fine-medium grained sand with greater than facies Channel breccia Tidal/estuarine channel PXGEUHFFLDFKDRWLF 0LGGOH 7KLFNPXGZLWKLQWHUODPLQDWHGYHU\¿QH¿QH 0F0XUUD\ 0XGÀDW LGDOÀDW7 grained sand/silt Formation ,QWHUEHGGHGLQWHUODPLQDWHGYHU\¿QH¿QHJUDLQHGVDQG 0L[HGÀDW and mud, intensively bioturbated Non-reservoir $EDQGRQHGFKDQQHO 7KLFNPXGZLWKLQWHUEHGGHGWKLQ¿QHJUDLQHGVDQG facies mud Fine-medium grained, well sorted sand with Estuarine channel sand Estuarine channel low-angle cross beddings 'RPLQDQWO\¿QHJUDLQHGZHOOVRUWHGVDQGZLWK Tidal channel sand Tidal channel low-angle cross beddings, and few burrows Fluvial channel 0HGLXPYHU\FRDUVHJUDLQHGPRGHUDWHO\VRUWHGVDQG Fluvial channel ! coarse sand with high-angle cross beddings Reservoir facies Fluvial channel Fine-medium-grained, moderately sorted sand with Fluvial channel ! ¿QHVDQG high-angle cross beddings Possible reservoir 2YHUEDQNVDQGVLOW Silty sand with mud layers 2YHUEDQN facies 2YHUEDQNPL[HG ,QWHUEHGGHG¿QHJUDLQHGVDQGDQGPXG Pond mud Thin bedded mud and silt mud Pond Lower 0F0XUUD\ Formation 0DUVKPXG 0XGDQGVLOW\PXGEHGGLQJGLVWXUEHGE\SODQWURRWV 0DU Non-reservoir Coal swamp-coal Coal, may include thin bedded mud Coal swamp facies Coal swamp margin &RDOGDUNGDUNEURZQFDUERQDFHRXVPXG Fine to very coarse grained, moderately sorted sand with Fluvial channel sand high-angle cross beddings Fluvial channel 0L[HG0F0XUUD\VHGLPHQWVIROGHGIDXOWHGE\ Post-depositional slump Post-depositional slump post-depositional slumping VK STUDY STUDY Lower McMurray Middle McMurray Lower McMurray Pet.Sci.(2013)10:30-37 33 Fluvial Marsh channel deposit sand 00QL_results_nlp DEPTH240 PHIT_1 0.6 V/V 0 RHOB_1 RXO_2 mker_paris Formation 1700 K/M3 2700 0.2 OHMM 2000 0 W/W 0.2 Depth NP_1 RT_1 CORE_DATA.PHIT_1 MKER_1 Tops 0.6 V/V 0 0.2 OHMM 2000 50 % 0 0 W/W 0.2 GR_1 PEF_1 RXOZ_1 multimin.phit BULK_MASS_SO_1 0 V/V 0.2 0B/E 5 0 GAPI 200 0.2 OHMM 2000 0.5 V/V 0 Fig. 2 UD\\SLFDOÀXYLDOFKDQQHOVDQGZLWKKLJKUHVLVWLYLW\DQGORZ*DPPD7 3871k DS51 39 00QL_results_nlp 1AA052409807W400 DEPTH240 X PHIT_1 0.6 V/V 0 RXO_1 RHOB_1 mker_paris 0.2 OHMM 2000 1700 K/M3 2700 0 W/W 0.2 Depth NP_1 RT_1 CDRE_DATA.PHIT_1 MKER_1 Formation Tops 0.2 OHMM 2000 0 W/W 0.2 40 0.6 V/V 0 50 % 0 DS53 DS52 GR_1 PEF_1 rxoz multimin.phit BULK_MASS_SO_1 0V/V 0.2 0 GAPI 200 0B/E 5 0.2 OHMM 2000 0.6 V/V 0 DS55 4074 DS54 4171k DS56 42 DS58 DS57 43 DS60 DS59 44 DS62 DS61 Fig. 3 Tidal and estuarine channel sand 34 Pet.Sci.(2013)10:30-37 Table 2 Statistics on bitumen content ,QWKH0LGGOH0F0XUUD\)RUPDWLRQWKHDYHUDJHUHVHUYRLU DFFRXQWVIRUPRUHWKDQRIWKHWRWDOURFNVZKLOHWKH %LWXPHQFRQWHQW reservoirs occur mainly at the top and the bottom in the Facies /RZHU0F0XUUD\)RUPDWLRQ )LJ 0LQ 0D[ YHUDJH$ Estuarine channel 5.03 18.24 12.03 5 Resources modeling method Estuarine channel sand 0.27 4.92 2.74 Deterministic methods and stochastic methods are generally used in oil and gas resources and reservoir modeling Tidal channel sand 5.00 19.59 12.81 depending on the data available. The stochastic methods, VXFKDVVHTXHQWLDO*DXVVLDQVLPXODWLRQDUHHPSOR\HGLQWKH Tidal channel sand 0 4.98 3.09 DUHDVZLWKVSDUVHGDWD+RZHYHUWKHGHWHUPLQLVWLFPHWKRGV Tidal/estuarine channel breccia 0 17.65 6.49 are generally considered in the areas with plenty of data available for modeling. In our study area, the well spacing is $EDQGRQHGWLGDOFKDQQHO 0.01 3.02 0.69 less than 100 meters in some parts and more than 260 holes LGDOÀDWVDQG7 0.24 17.95 7.87 were drilled. In this case, it is believed that the deterministic method is a good choice for resources modeling. For facies LGDOÀDWVDQG7 1.35 2.93 2.25 PRGHOLQJWKHLQGLFDWRUNULJJLQJPHWKRGLVXVHGDQGWKH LGDOÀDWPL[HG7 0 8.98 3.32 RUGLQDU\NULJJLQJHVWLPDWRULVHPSOR\HGWRLQWHUSRODWHWKH bitumen grade on the basis of facies modeling. LGDOÀDWPXG7 0 4.90 1.09 5.1 The method Fluvial channel coarse sand 5.02 17.98 11.07 ,QWKHLQGLFDWRUNULJJLQJLQWHUSRODWLRQRIOLWKRIDFLHVDQG Fluvial channel coarse sand 0.02 12.66 2.86 WKHRUGLQDU\NULJJLQJHVWLPDWLRQRIUHVHUYRLUSHWURSK\VLFDO )OXYLDOFKDQQHO¿QHVDQG 5.00 18.61 11.36 properties, semivariograms, functions indicating the spatial correlation in observations measured at sample locations, )OXYLDOFKDQQHO¿QHVDQG 0 7.26 2.82 should be calculated and the appropriate semivariogram model should be selected for modeling both lithofacies and 2YHUEDQNVDQG 0 17.29 5.75 reservoir petrophysical properties. Flood plain 0 8.89 2.50 5.1.1 Semivariogram 6HPLYDULRJUDPLVGH¿QHGDV &ODUN 0.01 2.60 0.57 1K () Coal swamp 0 4.18 0.94 J ()K=  ( ([ )= ([ )) ¦ LL K 2(1K ) Estuarine channel sand Tidal channel sand 20 30 4-6 6-8 8-10 10-12 12-14 >14 4-6 6-8 8-10 10-12 12-14 >14 Bitumen content, % Bitumen content, % Fluvial channel coarse sand Fluvial channel fine sand 0 0 4-6 6-8 8-10 10-12 12-14 >14 4-6 6-8 8-10 10-12 12-14 >14 Bitumen content, % Bitumen content, % Fig. 4 Bitumen content distribution in reservoir samples Percent of samples, % Percent of samples, % Percent of samples, % Percent of samples, % 0DUVK Pet.Sci.(2013)10:30-37 35 Original facies proportion, % Original facies proportion, % 0 20 40 60 80 100 020 40 60 80 100 10 70 0 20 40 60 80 100 020 40 60 80 100 Reservoir Non-reservoir Possible reservoir Fig. 5HUWLFDOGLVWULEXWLRQRIWKHUHVHUYRLUVLQWKH0LGGOH OHIW D9 QG/RZHU ULJKW 0F0XUUD\)RUPDWLRQ where Ȗ (x , x ) is the semivariogram; N(h) is the number of = () [ LVWKHNULJJLQJHVWLPDWRU Z(x ) (i = 1, 2, ··, n) is i j data pairs separated by distance of h; Z(x ) is the value at grade value at location x . The weight vector (Ȝ) is determined i i the start or “tail” of the pair and Z(x +h) is the variable at the as follows: end or “head” of the pair. The formula above can be used for OJ (,[[ )  J ([[, ) 1 ªº ªº ª º J (,[ [ ) 11 2 1 Q 1 FRQWLQXRXVGDWDDQGGLVFUHWHGDWD+RZHYHULWLVFDOOHGDQ «» «» « » «» « » «» indicator semivariogram, where the indicator is used instead «» «» « » of the property values in the formula above. «» «» « » 5.1.2 Semivariogram models OJ (,[[ )  J[[(, ) 1 J (, [ [ ) «»QQ « 1 Q Q » «» Q There are many semivariogram models in practice. «» «» « » P 11  0 ¬¼ ¬ ¼ ¬¼ +RZHYHUWKHPRVWFRPPRQRQHVDUHVSKHULFDOH[SRQHQWLDO and straight line models. Nugget, range and sill parameters where Ȗ(x , x ) is the semivariogram of the property Z, which i j DUHLPSRUWDQWIRUVHPLYDULRJUDPPRGHOV+RZHYHUWKH is separated by the distance between locations x and x . The i j VWUDLJKWOLQHPRGHOGRHVQRWKDYHVLOODQGUDQJH &ODUN   sum of Ȝ (i=1, 1, 2, ··, n) should be 1. 5.1.3 Indicator semivariogram O 1 ¦ L $QLQGLFDWRULVXVHGLQWKHFDOFXODWLRQRILQGLFDWRU L 1 VHPLYDULRJUDPVDQGLVGH¿QHGDVIROORZV 5.2 Lithofacies modeling 1(=[ )V LN ,[(,V ) N 1,2,. , ,QGLFDWRUNULJJLQJLVXVHGWRLQWHUSRODWHIDFLHV$Q LN 0otherwise indicator semivariogram is calculated for reservoir facies, where x is a vector representing a particular facies location; k i possible reservoir facies and non-reservoir facies in the is the presence of a particular facies. 0LGGOH0F0XUUD\DQG/RZHU0F0XUUD\)RUPDWLRQ7KH If the particular facies is present at location x , its i variograms match the spherical variogram model. For the indicator is assigned to 1; otherwise, it is 0. For the indicator indicator variograms, they match the spherical model very semivariogram, Z(x ) and Z(x +h) are replaced by I(x , s ) and i i i k well (Table 3 and Fig. 6 upper). I(x +h, s ) in the semivariogram formula above. i k The nuggets are generally high for the reservoir facies in 5.1.4 Ordinary krigging method the study area, indicating high variability of reservoir facies in 2UGLQDU\NULJJLQJHVWLPDWLRQLVRQHRIWKHPHWKRGVLQWKH the study area. The ranges in the major and minor directions NULJJLQJHVWLPDWLRQIDPLO\DQGLWXVHVWKHORFDODYHUDJHYDOXH DUHTXLWHIHUHQWGLILQERWKWKH0LGGOHDQG/RZHU0F0XUUD\ 7KHIRUPXODLVGH¿QHGDVWRHVWLPDWHWKHSDUWLFXODUSURSHUW\ )RUPDWLRQVLPSO\LQJWKHÀXYLDOFKDQQHOHVWXDULQHDQGWLGDO FKDQQHOGHSRVLWLRQDOHQYLURQPHQWVRIWKHIWHU$UHVHUYRLUWKH O = () [ ªº ª º 1 L VHPLYDULRJUDPLVFRPSXWHGLQGLFDWRUNULJJLQJLVHPSOR\HG «» « » ˜˜ «» « » to interpolate lithofacies. «» « » =[() ˜ ˜ ˜ «» « » 5.3 Reservoir property modeling ˜˜ «» « » «» « » The semivariogram of bitumen content is calculated O = () [ ¬¼QQ ¬ ¼ Layer Layer 36 Pet.Sci.(2013)10:30-37 &DOFXODWHGYDULRJUDPSDUDPHWHUVIRUWKH0LGGOH0F0XUUD\DQG/R ZHU0F0XUUD\UHVHUYRLUV Table 3 0LGGOH0F0XUUD\ /RZHU0F0XUUD\ %LWXPHQFRQWHQWLQWKH0LGGOH Bitumen content in the Lower Property reservoir facies reservoir facies 0F0XUUD\UHVHUYRLUIDFLHV 0F0XUUD\UHVHUYRLUIDFLHV Variogram model Spherical Spherical Spherical Spherical Search radius, m 2000 2000 2000 2000 Lag distance, m 266.7 266.7 266.7 266.7 Tolerance angle, 15 15 15 15 ROHUDQFHGLVWDQFH7 50 50 50 50 Nugget 0.726 0.468 0.517 0.554 0DMRUUDQJHP 627.5 1096.7 876.2 1029.4 0LQRUUDQJHP 302.3 767.9 793.3 774.2 Vertical range, m 29.2 26 29.9 54.1 1 1 0 0 0 400 800 1200 1600 2000 0 400 800 1200 1600 2000 Distance, m Distance, m 1 1 0 0 0 400 800 1200 1600 2000 0 400 800 1200 1600 2000 Distance, m Distance, m Fig. 6,QGLFDWRUYDULRJUDPRIUHVHUYRLUIDFLHV 8SSHUOHIW0LGGOH0F0XUUD\8SSHUULJKW/RZHU0F0XUUD\ DQGELWXPHQFRQWHQWRIWK H %RWWRPULJKW/RZHU0UHVHUYRLUV %RWWRPOHIW0LGGOH0F0XUUD\ F0XUUD\ for reservoirs, non-reservoirs and possible reservoirs in the petrophysical modeling. 0LGGOHDQG/RZHU0F0XUUD\)RUPDWLRQV7KHVSKHULFDO 6 Quality control of modeling results PRGHOVHHPVWR¿WWKHVHPLYDULRJUDPRIWKHELWXPHQJUDGH Table 3 and Fig. 6 (bottom) show the semivariogram models The facies distribution histogram (Fig. 7) and probability RIELWXPHQJUDGHLQWKHUHVHUYRLUIDFLHVLQWKH0LGGOHDQG curves are calculated before and after modeling and a $JDLQWKHQXJJHWV/RZHU0F0XUUD\)RUPDWLRQVUHVSHFWLYHO\ comparison between them shows a very good match before are high, implying high variability in bitumen grade. The and after modeling. RUGLQDU\NULJJLQJPHWKRGLVXVHGWRLQWHUSRODWHWKHELWXPHQ grade based on the semivariogram model. 7 Discussion 6RPHPRGHOHUVDOVRXVHWKHLQYHUVHGLVWDQFHVTXDUHG method in their resources modeling. They believe that 7KHNULJJLQJHVWLPDWLRQLVRQHRIWKHJRRGFKRLFHVLQRLO NULJJLQJHVWLPDWLRQPD\SURGXFHDPRUHUHDVRQDEOHUHVXOW sand resources modeling if the well space in the modeling LIDORWRIKDUGGDWDDUHDYDLODEOH,QDGGLWLRQWKH0LGGOH DUHDLVVPDOODQGORWRIKDUGGDWD FRUHORJJLQJ'HDQ6WDUN 0F0XUUD\)RUPDWLRQDQGWKH/RZHU0F0XUUD\)RUPDWLRQ and geophysical log data) are available though the inverse LVWUHDWHGDVWZRVHSDUDWH]RQHVLQERWKIDFLHVPRGHOLQJDQG GLVWDQFHVTXDUHGPHWKRGLVH[WHQVLYHO\DFFHSWHGDVRQHRI Semivariance Semivariance Semivariance Semivariance Pet.Sci.(2013)10:30-37 37 80 $OEHUWD*HRO6XUYH\(DUWK6FLHQFHV5HSRUW &' 77.0 70.4 63.6 Q)HL+-&RWWHULOO'.5LFH5HWDO6XEVXUIDFHJHRORJ\RIWKH $WKDEDVFDDELVNDZ0F0XUUD\:VXFFHVVLRQ/HZLV)RUW0F0XUUD\ DUHDQRUWKHDVWHUQ$OEHUWD 176' $OEHUWD(QHUJ\DQG 8WLOLWLHV%RDUG$OEHUWD*HRO6XUYH\(DUWK6FLHQFHV5HSRUW 25.5 Q)+HL-DQG'ROE\*5HJLRQDOOLWKRVWUDWLJUDSK\ELRVWUDWLJUDSK\ 20 17.5 17.2 DQGIDFLHVPRGHOV$WKDEDVFDRLOVDQGVGHSRVLWQRUWKHDVW$OEHUWD 12.3 10.9 10 5RFNWKH)RXQGDWLRQ&RQYHQWLRQ&DQDGLDQ6RFLHW\RI3HWUROHXP 5.6 Geologists (Calgary). 2001. 3: 170-171 Q)-DQG5HSO\J/DQJHQEHUWR&:GLVFXVVLRQRIVHLVPLF+HLPRGHOLQJ Facies_Res Upscaled cells Well logs RIIOXYLDOHVWXDULQHGHSRVLWVLQWKH$WKDEDVFDRLOVDQGVXVLQJ Reservoir Non-reservoir Possible reservoir UD\WUDFHWHFKQLTXHV6WHHSEDQN5LYHUDUHDQRUWKHDVWHUQ$OEHUWD Bulletin of Canadian Petroleum Geology. 2003. 51: 354-366 Fig. 7 Distribution histogram of facies after modeling JHQEHU/DQJ&:+HLQ)-%HUKDQH+HWDO7KUHHGLPHQVLRQDO JHRPHWU\RIÀXYLDOHVWXDULQHRLOVDQGGHSRVLWVRIWKH&ODUNH&U HHN RLOVDQGVUHVRXUFHVPRGHOLQJPHWKRGLQ$OEHUWD/LWKRIDFLHV DUHD 176' QRUWKHDVWHUQ$OEHUWD$OEHUWDJ\(QHUDQG8WLOLW LHV grade based modeling may have advantages over the grade- %RDUG$OEHUWD*HRORJLFDO6XUYH\(DUWK6FLHQFHV5HSRUW only based modeling since the data used for estimation are sourced from the same or the similar sedimentary facies in HVWXDULQHGHSRVLWVLQWKH$WKDEDVFDRLOVDQGVXVLQJUD\WUDFLQJ lithofacies-grade based modeling. WHFKQLTXHV6WHHSEDQN5LYHUDUHDQRUWKHDVWHUQ$OEHUWD%XOOHWL QRI Canadian Petroleum Geology. 2002. 50: 178-204 References *LOOLYUD\0DF -56WUREO56.HLWK'$:HWDO5HVRXUFH HUWD(QHUJ\OE$DQG8WLOLWLHV%RDUG$WKDEDVFDDELVNDZ0F0XUUD: \ FKDUDFWHUL]DWLRQRIWKH0F0XUUD\DELVNDZ: GHSRVLWLQWKH 5HSRUW$ &'520 UHJLRQDOJHRORJLFDOVWXG\ $WKDEDVFD6RXWK5HJLRQRI1RUWKHDVWHUQ$OEHUWD,Q$OEHUWD2LO DQ-O$ODQG&UHDQH\62LOIDPLOLHVRIWKHHVWHUQ: &DQDGD%DVL Q 6DQGVHFKQRORJ\7DQG5HVHDUFK$XWKRULW\HFKQLFDO73XEOLFDWLRQ Bulletin of Canadian Petroleum Geology. 1991. 39: 107-122 Series. 1992. 7: 68 HOH(%%HN3HUVRQ0$5RVWURQ%-HWDO0RGHOLQJVHFRQGDU\ RLO KLHURV062DQGDSOHV: ':4XDQWLWDWLYHHYDOXDWLRQRI/RZHU PLJUDWLRQZLWKFRUHVFDOHGDWDLNLQJ9)RUPDWLRQ$OEHUWD%DVLQ  &UHWDFHRXVDQQYLOOH0*URXSDVVRXUFHURFNVIRU$OEHUWD¶RLOVDQ GV $$3*%XOOHWLQ $$3*%XOOHWLQ NNH+%UHDQG(YR\58VHRIGLSPHWHUGDWDLQWKHGHILQLWLRQRI WKH VRS*0RV'*HRORJ\RIWKH$WKDEDVFDRLOVDQGV6FLHQFH LQWHUQDODUFKLWHFWXUHRISRLQWEDUGHSRVLWVLQWKH$WKDEDVFDRL O 207(4427): 145-152 VDQGV,PSOLFDWLRQVIRUWKH0LGGOH0F0XUUD\)RUPDWLRQLQWKH VRS*'DQG0RV)ODFK3''HHSFKDQQHOVHGLPHQWDWLRQLQWKH/RZHU +DQJLQJVWRQHDUHD$OEHUWD DEV $$3*$QQXDO&RQYHQWLRQ &UHWDFHRXV0F0XUUD\)RUPDWLRQ$WKDEDVFDRLOVDQGV$OEHUWD $$EVWUDFWV Sedimentology. 1983. 30: 493-509 RNV3:)RZOHU%UR0*DQG0DFTXHHQ5:%LRORJLFDOPDUNHUDQG JHU5DQ0-$%DVLQ6WXG\RIWKH6RXWKHUQ$WKDEDVFD2LO6DQGV conventional geochemistry of oil sands/heavy oils, Western Canada $OEHUWD7KHVLV8QLYHUVLW\RI'HSRVLW3K' Basin. Organic Geochemistry. 1988. 12: 519-538 JHU05DQ-DQG*LQJUDV0.'LVFXVVLRQRIVHLVPLFPRGHOLQJRI ULJ\0$DQG&DU.UDPHUV-*XLGH:WRWKH$WKDEDVFDRLOVDQGV DUHD $ ,QIRUPDWLRQ6HULHV95HVHDUFK&RXQFLORI$OEHUWD$$3* WHFKQLTXHV6WHHSEDQN5LYHUDUHDQRUWKHDVWHUQ$OEHUWD%XOOHWL QRI Bulletin. 1973. 69: 213 Canadian Petroleum Geology. 2003. 51: 347-353 UN,3UDFWLFDO*HRVWDWLVWLFV*HRVWRNRV/LPLWHG6FRWODQG&OD  5 GLJHU&LH/1HVV6)RZOHU0HWDOLPLQJ7RIRLOJHQHUDWLRQDQG FK3)OD'2LOVDQGVJHRORJ\²$WKDEDVFDGHSRVLWQRUWK$OEHUWD PLJUDWLRQQRUWKHDVWHUQ%ULWLVK&ROXPELDDQGVRXWKHUQ$OEHUWD Research Council Bulletin. 1984. 46: 31 — Significance for understanding the development of the eastern FK3)OD'DQG+HLQ)-2XWFURSFRUHFRUUHODWLRQRIFKDQQHODQ G $OEHUWDWDUDQGVVGHSRVLWV DEV $$3*$QQXDO&RQYHQWLRQ2I¿FL DO QRQF KDQQHOIDFLHV0F0XUUD\)RUPDWLRQ)RUW0DF.D\$UHD1( $3URJUDP $OEHUWD5RFNWKH)RXQGDWLRQ&RQYHQWLRQ&DQDGLDQ6RFLHW\RI LQVWHLQ,EX5DQG6WUDXV]27KHUPDO3WUHDWPHQWRIWKH$WKDEDVFDRLO Petroleum Geologists (Calgary). 2001. 132-133 sand bitumen and its component parts. Geochimica et Cosmochimica FK3)OD'DQG0RVVRS*''HSRVLWLRQDOHQYLURQPHQWVRIORZHU $FWD &UHWDFHRXV0F0XUUD\)RUPDWLRQ$WKDEDVFDRLOVDQGV$OEHUWD Smi th D G. Comparative sedimentology of mesotidal (2 to 4 m) $PHULFDQ$VVRFLDWLRQRI3HWUROHXP*HRORJLVWV%XOOHWLQ estuarine channel point-bar deposits from modern examples and 1195-1207 DQFLHQW$WKDEDVFDRLOVDQGV /RZHU&UHWDFHRXV 0F0XUUD\ Q)+HL-DQG&RWWHULOO'.7KH$WKDEDVFDRLOVDQGV²$UHJLRQDO )RUPDWLRQ,Q5HLQVRQ*(HG0RGHUQDQGDQFLHQWH[DPSOHVRI JHRORJLFDOSHUVSHFWLYH)RUW0F0XUUD\$UHD$OEHUWD&DQDGD FRUHDQGSHHOZRUNVKRS&DQDGLDQ6RF$FODVWLFWLGDOGHSRVLWV² LHW\ Natural Resources Research. 2006a. 15: 85-102 of Petroleum Geologists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echnical Publication Series. 1995. 10: 220 $OEHUWD6XUIDFHDQGOEHUWD$J\VXEVXUIDFH(QHUDQG8WLOLWLHV% RDUG (Edited by Sun Yanhua) Frequency, % WUDFLQJ ÀXYLDOHVWXDULQHGHSRVLWVLQWKHWKDEDVFDRLOVDQGVXVLQJUD\ /DQ JHQEHUJ&+HLQ)-/DZWRQ'HWDO6HLVPLFPRGHOLQJRIÀXYLDO &'520 &'520

Journal

Petroleum Science – Springer Journals

Published: Feb 7, 2013

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