TECTONOPHYSICS ELSEVIER Tectonophysics 233 (1994) 177-192 The thermal conductivity and heat flow density of the Jeanne d’Arc Basin, offshore eastern Canada Zehui Huang *, Mark A. Williamson zyxwvutsrqponmlkjihgfedcbaZYXW Atlantic Geoscience Centre, Bedford Institute of Oceanography, P.O. Box 1006, Dartmouth, N.S. B2Y 4A2, Canada (Received April 8, 1993; revised version accepted December 13, 1993) zyxwvutsrqponmlkjihgfedcbaZYX Abstract In an effort to understand the present-day thermal structure of the Jeanne d’Arc Basin, we derive matrix thermal conductivity values, using a geometric mean model, from thermal conductivity values of thirteen lithologic end-members (of which seven are measurements on materials from the Jeanne d’Arc Basin) and lithological data. Temperature corrections to thermal conductivity are made for both matrix and pore water. In-situ thermal conductivitics were obtained after correcting for the effect of porosity, which we estimated from both lithological information and digital well logs. Calculated thermal conductivities of the formations range from 1.46 to 2.65 W mP’K-‘. The heterogeneity of formation thermal conductivity is caused by at least two factors. The first is lithological variation due to facies change. The second is due to different degrees of compaction. Under-compacted intervals (overpressure zones) are characteristically of lower thermal conductivity. The surface heat flow density calculated with an inverse method in seventeen wells varies from 37.8 to 60.7 mW/m2. This study indicates that surface heat flow density values estimated without porosity corrections may be 15% higher than those with porosity corrections. We also demonstrate the sensitivity of modelled timing of maturation to thermal conductivity estimations. 1. Intr~uction A sedimentary basin’s thermal structure, which includes thermal conductivity of the rock column and heat flow density, particularly its thermal evolution through time, is important for understanding its petroleum generation, migration and accumulation histories. The Jeanne d’Arc Basin, offshore Newfoundland (Atlantic Canada), is an important hydrocarbon-bearing area and has undergone extensive * This is Geological Survey of Canada Contribution No. 12092. exploration (Bell and Campbeh, 1990). Previous work exploring the basin’s thermal structure can be found in Correia et al. (1990) who reported laboratory measurements on the thermal conductivity of seven major lithologies and thermal gradients from corrected bottom-hole temperatures. Using measured thermal conductivity values as well as data from literature, and corrected thermal gradients, Correia et al. (1990) estimated terrestrial heat flow density at 35 well locations in the basin and proposed a schematic basinal fluid-flow pattern, which describes a downward component in the north and an upward component in the south. Their study, however, did not HO-1951/94/$07.00 0 1994 Etsevier Science B.V. All rights reserved 0040-1951(93)EO278-3 SSDI 178 Z. Huang, MA. Williamson / Tectcmophysics 233 (19941177-192 account for the effect of porosity change with depth upon thermal conductivity, which is an important factor controlling thermal conductivity of sedimental rock. To provide a more detailed examination of the thermal properties of the Jeanne d’Arc Basin, we established in-situ thermal conductivity profiles at seventeen well locations (Fig. 1) using lithological information (percentage of thirteen lithologies from cutting sample examination) and published thermal conductivity values. Porosity corrections were performed using digitized sonic and density logs. On the basis of porosity-corrected thermal conductivity, we used a thermal resistance (Bullard, 1939) and an inversion method (Tarantola and Valette, 1982; Vasseur et al., 1985; Wang, 48* 490 0 I 0 I km I 40 , n t-23 470 /C-92 n l c\ Hebron l-13 46* Fig. 1. The Jeanne d’Arc Basin and well locations (0 = wells used in this study) (modified from Grant et al., 1986). Z. Huang, M.A. Williamson / Tectonophysics 233 (1994) 177-l 92 1992) to calculate the heat flow density at well locations. Derived heat flow densities and thermal conductivity profiles are of use in source rock maturi~ modelling and can be used to test model sensitivity to assumed thermal properties. The in-situ thermal conductivity values were used in a study (Williamson, 1992) that modelled maturity of source rocks within the Jeanne d’Arc Basin. In our study, the stratigraphic framework follows that described in McAlpine (1990). Both lithological data (results of cutting sample examination), logging data and temperature measurements were retrieved from the Atlantic Geoscience Centre’s database. 2. Geological setting The Jeanne d’Arc Basin is about 16,000 km2 in area and is a sub-basin of the East NewfoundIand Basin. It stretches from southwest to northeast and is flanked by the Bonavista Platform to the west and the Outer Ridge Complex to the east (Fig. 1). Formed as a result of the Mesozoic extensional rift tectonics that produced the North Atlantic Ocean (Mc~pine, 1990), the oldest sediments of the basin are Triassic red beds and salt. Jurassic sediments are dominantly shallow-water carbonates, shales and sandstones. The Lower Cretaceous strata record several major progradational deltaic pulses. Upper Cretaceous sediments are mainly calcareous shales deposited during marine transgressions. Tertiary strata are composed of marine shales with minor sandstones. The lithostratigraphy (Fig. 2) (McAlpine, 1990) shows a thick Mesozoic-Cenozoic sequence with major hiatuses at Kimmeridgian, late Barremian, late Aptian and late Albian-Cenomanian times. The Jeanne d’Arc Basin can be divided into three structural-stratigraphic segments by transbasin fauns (Fig. 1). South of the Egret Fault (Enachescu, 1987), uppermost Jurassic and Lower Cretaceous rocks are virtually absent because of erosion and depositional thinning. Between the Egret Fault and the Hibernia-Ben Nevis transbasin fault zone this section thickens dramati- 179 cally, North of the trans-basin fault zone, a thickened Middle Cretaceous sequence has been preserved. The Hibernia oil field is located in the Hibernia-Ben Nevis trans-basin fault zone. The Terra Nova oil field lies south of the trans-basin fault zone. 3. Methods For each well location, the matrix conductivity of an interval of rock (at least 3 m thick) was calculated using the geometric mean model (Eq. (1); Woodside and Messmer, 1961a,b; Brigaud et al., 1990): h, = II@ (1) h, is the matrix conductivity of aggregates of several constituents, Ai is the conductivity of the ith constituent and Pi its fractional volume. The matrix thermal conductivity of thirteen individual lithologies (end-members) are from Correia et al. (1990) and Beach (1985) (Table 1). The values for limestone, dolomite, anhydrite, shale, sandstone, siltstone and claystone (the most commonly encountered lithologies in the study area) were measured on samples from the Jeanne d’Arc Basin and have been corrected for anisotropy (Correia et al., 1990). These values therefore represent thermal conductivity perpendicular to the bedding. The fractional volume of a lithological component is appro~mated by its percentage estimation through cutting sample examination. The thermal gradients (G,) for each well location (Fig. 1) are from Correia et al. (1990) which were calculated from corrected bottom hole temperature (BHT) values (Table 2). These were used to estimate temperature (7’) at specific depths (d) in Eq. (2), for the purpose of correcting temperature effect on the matrix thermal conductivity. The temperature at the sea floor (T,) is taken as 4”C, following Hyndman et al. (1979). The effect of temperature on matrix thermal conductivity has been accounted for following Chapman et al. (1984) in Eq. (3), in which bGiE Ml) I EPOCH AGE FORMAflONS I zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJ IC 2c / SC SANDSTONE 4c SANOUEREAU FORMATION DOMNANTLY SHALE AND MWSTONE t -. -. -_ SILtSTONE SC so 70 so DOLCMKE so 00 SALT 10 VOLCANICS I I zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCB h” ‘AYALON f 20 30 zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA IO IO LiMEStONE BEDS 30 IO lo MNGCOMERATE lo 0 0 0 a 0 _.-.._._.__ -.-._.-.-.-.-.-._ -.-. -. _ “-7__ 9 , E [1 e PRE-MESOZOIC BASEMENT D D c) D.W.0 * s D 0 D Z. Huang, M A. Table 1 Thermal conductivity, sonic travel time and density of thirteen matrix components Limestone Dolomite Anhydrite Shale Sandstone Siltstone Claystone Ma&tone Chert Coal Conglomerate Salt Unclassified 181 W illiamson / Tecronophy sics 233 (1994) 177- 192 Table 2 Corrected thermal gradient (“C/km) in wells analyzed in this study (from Correia et al., 1990) Conductivi~ (W m- “K-‘1 Sonic travel time @,‘ft) Density (g/cm’) Well name Corrected thermal gradient PC/km) 2.41+ 0.96 (1) 3.04k1.22 (1) 5.23*2.09 (1) 2.30 f 0.92 (1) 4.19+ 1.68 (1) 4.24+ 1.70 (1) 3.11+ 1.24 (1) 3.00+ 1.1 (2) 1.40+0.5 (2) 0.20*0.2 (2) 3.28 + 1.8 (2) 5.70+ 1.0 (2) 2.30 i 2.0 (2) 47.5 (3) 43.5 (31 50.0 (3) 70.0 (7) 53.3 (3) 55.0 (5) 70.0 (6) 48.0 (4) 51.0 (8) 120.0 (11) 54.6 (9) 67.0 (3) 50.7 (10) 2.71 (3) 2.87 (3) 2.96 (3) 2.65 (3) 2.65 (3) 2.65 (5) 2.65 (6) 2.70 (4) 2.65 (11) 1.50 (3) 2.69 (9) 2.17 (3) 2.67 (10) Ben Nevis 1-45 Beothuk M-05 Flying Foam I-13 Gambo N-70 Hebron I-13 Hibernia C-96 Hibernia K-14 Hibernia K-18 Hibernia O-35 Mercury K-76 Nautilus C-92 Rankin M-36 South Tempest G-88 Terra Nova K-08 West Flying Foam L-23 Whiterose J-49 Whiterose N-22 25.4 rt 4.8 24.9f 3.7 26.7 + 0.0 25.7 it 2.9 24.Ok3.4 25.1 zt 1.3 22.8t 1.1 26.7rt2.1 23.7 i: 4.7 22.6 k 0.3 28.9 + 3.3 21.3+ 1.8 31.6k6.8 25.7+ 1.2 ’ 29.8k7.9 31.97t 1.s 31.8+2.0 Notes: (1) from Correia et al. (1990); (2) from Beach (1985); (3) from Schlumberger (1989); (4) supposed to be near limestone value; (5) from Hagarty et al. (1988); (6) supposed to be near shale value; (7) from Brigaud et al. (1990); (8) supposed to be near condensed sandstone value; (9) average value of type 1 to type 9 (except type 2); (10) average value of type 1 to type 12 (except type 10); (11) from Doveton (1986). a From a nearby well Terra Nova K-18. lithologic end-members. The lithology weighted Eqs. (5) and (6) (Schlumberger, 1989), respecfor temperature: zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA tively, are used, in which Pi is the fractional T= T,+G,d (2) volume of the ith constituent approximated by its 293 percentage given by cutting sample examination. Am,T = A(3) Matrix sonic travel time (to and matrix density “273+T (pi) of the thirteen lithologies (end-members) are Pore-water thermal conductivity (A,,r), which mainly from Schlumberger (19891, Hagarty et al. also changes with temperature is estimated from (19881, Brigaud et al. (1990) and Doveton (1986) Eqs. (4a) and (4b) (Kappelmeyer and Haenel, (Table 1): 1974): A m,T is the matrix thermal conductivity corrected A w,T= 0.56 + 0.003~“.827 A w,T= 0.442 + 0.0519 In T 0 =GT G 50°C T > 50°C (da) = k AtiPi (5) i=l (4b) An important step in calculating the in-situ thermal conductivity of a rock interval is to account for the effect of porosity. We used sonic and density logs to estimate the porosity-depth profile. The matrix sonic travel time (At,) and matrix density (p,) of an interval of rock are calculated from the lithological data, matrix sonic travel time (ti>, and matrix density (pi) of the Fig. 2. Lithostratigraphic At, Pm= kPipi i=l (6) Porosity values are estimated from averaged readings on the sonic and density logs for each interval, with Eqs. (7) and (8) (Schlumberger, 1989). In this study we did not use the time-average relationship proposed by Wyllie et al. (1956, 1958) to obtain porosity from sonic log, since, in scheme used in this study (from McAIpine, 1990). Z. zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Huang, M A. W illiamson / Tectonophy sics 233 (1994) 177- 192 182 addition to its theoretical oversimplication, in practice it is overly conservative in the 5-25% porosity range and is optimistic for porosities in excess of 30% (Raymer et al., 1980; RaigaClemenceau et al., 1988). Instead, we employed an empirical equation based on the comparison of sonic travel time versus independent porosity measurements (Eq. 7) (Schlumberger, 1989). In Eq. (71, 4, stands for porosity from the sonic log, At,O, represents sonic log response and 0.67 is an empirical constant. Eq. (8) is a simple mass balance relationship in which 4, stands for porosity from the density log, plog for density log response, pr for the density of pore water taken as 1.0245 g/cm3 (Brereton, 1992): +b,, = 0.67(&,, - A&)/&,, 4hp= t&n -Pi&(&n -Pf) (7) (8) Two thermal conductivity vahres (A,,, A,) can be ~al~uIated with the porosity values (&, 4,) using Eqs. (9) and (101, which followed the same geometric mean model (Woodside and Messmer, 1961a,b; Brigaud et al., 1990). The average of these two values is taken as the estimate of the in-situ thermal conductivity (A) (Eq. 11): A = A(; S” W,TSV (9) A = A(~~#~)A~~ (10) w,T A”= (A, + Ap),‘2 (11) where Azj, Ai are the thicknesses and conductivities of lithological layers in a well, respectively. The therma resistance method has been applied to the Jeanne d’Arc Basin by Correia et al+ (1990) due to its simplici~. With this approach, a single thermal gradient obtained from a linear fitting of the subsurface temperature measurements is used. The problem in this approach is that, in reality, due to radiogenic heat production and overpressured intervals, the temperature-depth relationship at a well location is not always linear and generahy, the thermal gradient varies more or less from interval to interval in each well. The inversion method is much more sophisticated than the thermal resistance one. It uses a stochastic inversion algorithm (Tarantola and Valette, 1982; Vasseur et al., 1985; Wang, 1992) based on the thermal resistance method in one dimension. This method starts from the relationship between heat flow density (q), temperature (7’) and thermal conductivity (A): 4 = A(dT/dz) (14) For obtaining the subsurface temperature at depth z CT,), one can integrate Eq. (14) from the sea floor (z = 0) to depth z if the sea floor temperature, surface heat flow density and the thermal conductivities of the sediment layers from the ground to depth z are known: 3.2. Calculation of heat j?ow density T(z) We used two methods to calculate heat flow density at each well. The first one is the reIatively simple thermal resistance method. As proposed by Bullard (1939), where heat production and transient fluid movement are negligible, heat flow density (mW/m2) can be calculated with the following equation: = 7’, +4/e’ dz’/A( z’) (15) The inverse problem occurs when we want to process simultaneousIy all the data, which include m measurements of T,, 12 estimations on the thermal conductivities of the lithological layers in a well, an estimation of sea floor temperature, and an uncertain estimation of surface heat flow 4 = A,,G zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA T density, in order to find the optimal values of (12) these physical quantities which are subject to where G, is the average thermal gradient over errors. Taking all the measurements and estithe driled interval and A, is the average thermates as the a priori information, one can denote mal conductivi~ of the ~rresponding interval, the temperature measurements by the m-dimenwhich can be obtained with Eq. (13): sion vector d (dT = (ti, t,, . . . t,>) (T indicates Aave= ~ AZi/C( AZi/A,) transpose) and the sea floor temperature, ther(13) zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJ i=l mal conductivities and surface heat flow density 2. fiuang, M A. W illiamson / ~ectff~ophy ~ics 233 (1994) 177- 192 (referred to as parameters thereafter) by the (n + 2)-dimension vector p {pT = (to, A,, A,,... A,, 4)). The physical law relating d and p as the forward solution can be written as: (16) d = g(p) where g is a vectorial function defined by Eq. (15). Assuming d and p are random vectors and that the a priori joint probability density function of the two is Gaussian, the joint density function is therefore defined by the mean value (do, po), the covariance matrix of do CC,,) and that of p. (C,,> (for details see Vasseur et al., 1985). The mean value do and p. can be defined with the available a priori information (the temperature measurement set and the parameter set, respectively). The diagonal elements of the covariance matrices C,, and C,, are set equal to the a priori errors which express our confidence in the a priori values. Usually measured values have small variance but subjectively defined (estimated) value(s), which in our case is surface heat flow density, should be assigned with larger variances, The off-diagonal elements of the covariance matrices C,, and C,, express the correlation between the uncertainties in different components of p. or do and are assumed zero. Since Eq. (15) is non-linear, the inverse problem is also non-linear. Tarantola and Valette (1982) present an iteration scheme to search for the most probable estimate of p and d: pk+ 1= PO+ C,,G:(Cdd + G&&~)T x [do - gtp/J + G,(P, - PO>] dk+l = g(pk) + Gk(~k+, - PHI (171 (18) where pk is the estimation of p at the kth iteration and G, is a gradient matrix consisting of the partial derivatives of Eq. (16) with respect to p at point pk. The optimal a posteriori information is yielded when the largest element in the differences between pktl and pk becomes less than a specified small value. In the Jeanne d’Arc Basin, the measured subsurface temperature are given a priori error of f 10°C. Sea floor temperature To is assumed to 183 be 4°C (Evans and Coleman, 1975; Hyndman et al., 1979), with an a priori error of + 2°C. These a priori errors for the temperatures may accommodate the variances related to measurement inaccuracy and seasonal changes. The porosity-corrected thermal conductivities are associated with 40% of their values as the relative a priori error, according to the error estimation by Correia et al. (1990). A priori surface heat flow density is assumed to be 95 mW/m2, with a large a priori error of &90 mW/m2, since this is the least known quantity. 4. Porosity-corrected thermal conductivity In-situ thermal conductivi~ profiles were derived for seventeen well locations (Table 2) where both lithological data and digitized sonic and density logs were available. In the studied wells, each cutting sample represents a 3-20-m interval. The well log readings are at a finer interval (1 m). Therefore, the average of the log responses in the interval represented by the cutting sample is used to estimate the porosity values. A part of the in-situ thermal conductivity profile established for Beothuk M-05 (from the Ben Nevis Formation to Hibernia Formation) is illustrated in Fig. 3. The in-situ thermal conductivity profiles contain errors from various sources. The first is the published thermal conductivity values for the litholo~~ end-members (Beach, 1985; Correia et al., 1990) for calculating matrix thermal conductivities. The second is from the thermal gradients calculated from corrected BHT values (Correia et al., 1990). The third is from our porosity estimates using sonic and density logs. We are unable, however, to estimate the error by comparing systematically the porosity values from well logs to limited porosity measurements. Generally, porosity values calculated from sonic logs and those from density logs differ from each other more or less with different lithologies. Assuming that average porosity value is more representative of the true porosity and the two values from sonic and density logs represent the maximum and minimum porosities, we observed that the differences between the average values and the maximum,Iminimum values are mostly less than 15% This is in agreement with estimated error range (l&20%) of in-situ porosity calculation with logs CDella Yedova and Van Herzen, 1987). In Fig. 3, the range of in-situ thermal conductivity for each interval resulted from the use of two porosity va[ucs for porosity corrections. The extent of the range is rerated to the difference of porosity values from sonic and density togs, which varies from formation to formation (Fig. 3), The difference between the two porosity vah~es is greater in shnly intervals than in other intervals. We observed that shale/claystone intervals have large range of in-situ thermal conductiviw estimates. At Beothuk M-05, the matrix thermal conductivity for Nautilus Shale and Fortune Bay Shale varies from 2.3 to 4.1 W m-‘K-l, and the in-situ crJnductivity purosity WI 20 40 Fig. 3. From feft CO right are shown the Mmlogi4 pmfile, density log, smGc lag, porosity values from kholugical data and logs, and thermal conductivity at Beothuk M-05. The. lithdogical profile shows the percentage of carbonate (A), shaIc,‘clavstone (B), sandstone CC), siltstonc iD1 and coal <El in the Ben Nevis Formatian CO, Avalon I+rmation (21, Eastern Shoal Formation 131, Whiterose Shaie (41, Catalina Formation (5), “B” marker (6) and Hibernia T;ormation (7). The diagram to the far right shows Iroth the matrix thermal conductivity corrected for temperature (triangle) and the in-situ thermal conductivity corrected for temperature and porosi@ (open square), also with the maximum and minimum values being indicated. The depth iu kilometer below rotary table. Z. Huang, MA Wiiiiamson / Tectonophysics 233 (1994) 117-l 92 185 matrix conductivities. The difference between the in-situ thermal conductivity and matrix conductivity in the less compacted sandstone/siltstone/ shale interval of the Catalina Formation is about 1 W m-‘K-l, while that in the more compacted/ cemented limestone interval (lower part of the Catalina Formation and “B” Marker) is less than 0.5 W m-‘K-i. For this reason, although the matrix thermal conductivity of the sandstone/ siltstone/ shale Catalina Formation is higher than that of the carbonate interval, the in-situ thermal conductivities of the sandstone/ siltstone/ shale interval are not much higher than those of the carbonate interval. To apply the inversion method for estimating surface heat flow density, we calculated formation in-situ thermal conductivity at each well. The range and geometric mean of the formation thermal conductivity (A,) from the seventeen wells are presented in Fig. 4. Shale/claystone and zyxwvutsr thermal conductivity from 1.5 to 2.8 W m- ‘K- ‘. We also observed lower in-situ thermal conductivity (1.3 W m-“IX-‘) in other wells (e.g., Hibernia K-18). From Fig. 3, it can be seen that the lithologic variations have a large control on the variations of both matrix conductivi~ and in-situ thermal conductivity. For example, the sandstone/ siltstone/ shale Catalina Formation has higher in-situ thermal conductivity values than the carbonate “B” Marker. The coal-bearing intervals in the Avalon Formation, Whiterose Shale and Hibernia Formation usually show low matrix and in-situ thermal conductivities. In the lower Hibernia Formation, shale/ claystone alternate with sandstone/ siltstone, with corresponding alternation of low and high in-situ thermal conductivities. Porosity also exerts an important control on the variation of in-situ thermal conductivity. Insitu thermal conducti~ties are always lower than Thermal conductivity (W/m/K) 1.0 1.5 2.0 2.5 3.0 l....‘....I....‘....I South Mara Unit - Da~~~FM - EiderFM Nautilus Sh. I BenNevis - Avalon FM FM Eastern Shoals FM Whiiercse SH. (U) _ Catalina FM ‘B’ Marker - Whitercse SH’ (L) Hiimia FM Fortune Bay SH. Jeanne d’Arc FM - RankinFM o # sandstone/siltstine shaleklaystone I sandstone + carbonate 0 sandstone/siltston&hale Fig. 4. Geometric mean and the range of formation thermal conductivity for each formation with the dominant lithology specified, based on the results from seventeen wells. carbonate formations have greater range than other types of formation. The geometric mean of sandstone/ siltstone formations’ therma conductivity progressively increases with depth. However, the geometric mean of the thermal conductivity of shale/claystone formations first increases with depth and then decreases with depth, with the Whiterose Shale (Uf as the turning point. The Fortune Bay Shale has the lowest geometric mean of formation thermal conductivity. The sandstone Hibernia Formation, the Jeanne d’Arc Formation and the Rankin Formation also display a lower geometric mean of formation thermal conductivity than the overlying sandstone Eider Formation and Ben Nevis Formation. A Beothuk Terra Nova K-01 M-05 1 Hebron 1-13 This vertical heterogeneity of formation thermal conductivity may be due to overpressure of formations below the “B” Marker. The top of overpressure is near the top of or in the Fortune Bay Shale (3-4 km below sea level). The fluid retained in these overpressured formations explains their reduction in thermal conductivity. Therefore, the “B” Marker, which has a low geometric mean of formation thermal conductivity, and the overpressured formations can be considered as the barrier for effective thermal transfer in the Jeanne d’Arc Basin. In Fig. 5 the spatial variation of formation thermai conductivity aIong a cross section (the A-B line shown in Fig. 11 is illustrated. Few Hibernia Hibernia Nautilus B Flying Foam zyxwvutsrq Fig. 5. Cross section showing formation thermal conductivity IW m-t Km’) in Beothuk M-OS, Terra Nova K-08, Hebron I-13, Hibernia C-96, Hibernia K-18, Nautilus C-92 and Flying Foam I-13 from south to north along line A-B (see Fig. 1). The depth is kilometer below sea level, and the horizontal space between the wells is in accordance with their distance. Except for FIying Foam f-13, there are oil occurrences along this cross section (see Fig. 1). The formation boundaries are from McAipine (1990). The wavy line between formations stands for unconformity and the straight line for conformable contact. The thick horizontal lines indicate the depth of the top of overpressure zone in Hebron I-13, Hibernia C-96, Hibernia K-18 and Nautilus C-92 Z. Huang, M A. UW iamson / Tectonophy sics 233 (1994) 177- 192 formations exhibit a N-S trend in formation thermal conductivity. The lateral variation can be partly explained by facies changes and unconformaties. Both would change the proportion of lithologies within a formation at different locations. As the shale/claystone ratio increases, the formation thermal conductivity decreases. Another reason is overpressure. In Hebron I-13 and Hibernia K-18, the formations under the top overpressure have rather low thermal conductivities. Although the Fortune Bay Shale and Jeanne d’Arc Fo~ation in Hebron I-13 are buried deeper than their normally compacted counterparts in Terra Nova K-08 and Beothuk M-05, they have lower formation thermal conductivities because of under-compaction. In Nautilus C-92, the top overpressure is much higher (in the Nautilus Shale). The sediments in Nautilus C-92 are deeply buried because of faulting. A comparison of pressure data at the Nautilus C-92, Hibemia K-18 and Hebron I-13 locations (Fig. 6) reveals that Nautilus C-92 has the most overpressured section, i.e., stratigraphically and structurally shallower than the other wells. As the result, most of the overpressured formations in Nautilus C-92, although buried deeper, have lower thermal ~nducti~ties than their counte~arts in Hibemia K-18, Hebron I-13 and other wells which are not overpressured. 5. Swface heat flow density In most of the wells, the uppe~ost intervals (mostly of Tertiary age) which are several hundreds to 2000 m thick do not have both detailed lithological data and logging data. Therefore, there is no in-situ thermal conductivity estimations on these uppermost intervals. To calculate the surface heat flow density with the two methods mentioned, the in-situ thermal conductivities of every lithological interval from the sea floor down to the bottom of the well are needed. To solve this problem, we assume that the thermal conductivity of the uppermost layer is 10% lower than that of the formation immediately below it, because of less compaction (higher porosity). This 187 Pressure zyxwvutsrqponmlkjihgfedcbaZYXWVU (KPa * 1000) 0 .Ir 1000 2000 B n 3 g 3000 4000 5000 Fig. 6. Comparison of subsurface pressure in the overpressurized wells (Nautilus C-92, Hibernia K-18 and Hebron I-13. The short horizontal bar indicates the top of Fortune Bay Shale in Hebron I-13 (a), Hibernia K-18 (b) and Nautilus C-92 (cl. assumption is compatible with our observation made in Fig. 4. The thermal resistance method is quite simple (see Eq. 12). With the inverse method, the assumed surface temperature, subsurface temperature measurements (BHT in our cases), formation thermal conductivity estimation and assumed heat flow density with their assumed standard deviation are taken as the a priori info~ation and the calculation is to find the optimal values (i.e., the a posteriori information, Tables 3 and 4). For example, the inversion reduced the a priori value of surface heat flow density (95.0 rfr90.0 mW/m*) to its optimal value of (50.0 + 7.4 mW/m*) (Table Z. Huang, M .A. W kzamson / Tectonophy sics 233 (19941 177- f 92 188 Table 3 The a priori and a posteriori information of the temperature (T, in “C) at Hebron I-13 Depth (m) a priori value a priori SD. a posteriori value Correction (“0 779.7 2518.7 2569.7 2700.7 2845.7 3359.7 3723.7 3927.7 4052.7 4072.7 4170.7 4195.7 4323.7 4595.7 48.9 57.7 74.4 68.7 79.4 77.7 99.6 103.0 107.8 105.0 106.0 117.0 107.4 125.5 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 50.6 63.4 73.0 69.3 77.2 80.1 97.7 100.1 108.6 104.2 110.3 111.3 114.8 120.7 1.7 5.7 - 1.4 0.6 -2.2 2.4 - 1.9 - 2.9 0.8 -0.8 4.3 5.7 7.4 -4.8 4). Other parameters (for example, fo~at~on thermal conductivity) arc also changed with reduction in standard deviation (Table 4). Our estimates of the surface heat flow density for seventeen wells in the Jeanne d’Arc Basin derived from the two methods are summarized in Table 5. For comparison, the estimations by Correia et al. (19901 are also listed. Our surface heat flow density values from the thermal resistance and inverse methods mentioned are quite close. Usually, the inversion method gives slightly higher value than the thermal resistance method. As the inversion method is more rigorous than the thermal resistance method, we will mainly discuss the results from inversion. We observed that the surface heat flow density calculated with the inverse method varies from 37.8 (Rankin M-36) to 60.7 mW/m2 (two Whiterose wells). The majority of the heat flow density values from this basin is in a much narrower range from 46 to 59 mW/m*. The average of surface heat flow density from the seventeen wells is 51.2 1fl3.1 mW/m2, and that from the fourteen wells (with the maximum/minimum values excluded) is 51.8 f 2.8 mW/m2. Therefore, 51 mW/m2 may be taken as the representative basina1 average for surface heat flow density. In comparison with the results from Correia et al. (19901, our values from either of the methods, with few exceptions (e.g., Gambo N-701, are much lower. According to Allen and Allen (1990), thermally subsiding (post-rift)/ passive margin usually have heat flow ranges from 35 to 65 mW/m2, with an average of 50 mW/m2. Our range (37% 60.7 mW/m2) is more compatible with this typi- Table 4 The a priori and a posteriori information of sea floor temperature (T,, in “C), formation thermal conductivity (A,, in W m-‘K-l) and surface heat flow density (9, in mW/m*) at Hebron I-13 Depth cm) T0 A, uppermost layer A, South Mara Unit Ar Dawson Canyon Fm. A, Nautilus Sh. A, Ben Nevis Fm. A, Avalon Fm. A, Eastern Shoal Fm. A, Whiterose Sh. Kl) A, Catalina Fm. h,“B” Marker Ar Hibemia Fm. AF Fortune Bay Sh. A, Jeanne d’Arc Fm. A, Rankin Fm. 4 o-1221.7 1221.7-1325.7 1325.7-1582.7 1582.7-1737.1 1737.7-1803.1 1803.7-1867.7 1867.7-1969.7 1969.7-2525.7 2.525.7-2707.7 2707.7-2764.7 2764.7-3367.7 3367.7-3647.7 3647.7-4300.7 4300.7-? a priori value a priori SD. a posteriori value a posteriori S.D. 4.0 1.65 1.84 2.10 1.86 2.30 2.12 2.40 2.53 2.65 1.75 2.14 1.62 1.88 2.45 95 2.0 0.66 0.74 0.84 0.74 0.92 0.85 0.96 1.01 1.06 0.70 0.86 0.65 0.72 0.98 95 3.88 1.90 1.91 2.41 1.70 2.32 1.91 2.63 1.99 2.15 1.83 2.12 1.77 1.73 2.34 50 1.99 0.58 0.65 0.68 0.50 0.74 0.57 0.73 0.51 0.64 0.45 0.60 0.50 0.57 0.97 7.4 189 Z. Huang, MA. wiitiamson / Tect~no~~ysic~233 (1994) 177-l 92 cal range than that (48-75 mW/m*) from Correia et al. (1990). If Gambo N-70 is excluded, the difference in surface heat flow density from the inverse method and those estimated by Correia et al. (1990) varies from 2.3 to 20 mW/m2, and that in standard deviation from 0.3 to 16 mW/m*. Our values are 4-29%, with an average of 15%, lower than those estimated by Correia et al. (1990). The differences are due to our porosity corrections. Therefore, on average, surface heat flow densities estimated without correcting for porosity may be 15% higher than porosity-corrected estimates. Another observation is that for the seventeen wells, the difference between the maximum and the minimum surface heat flow density is 33.1 mW/m2 (thermal resistance method) and 23 mW/m2 (inverse method). The difference of the maximum and minimum heat flow values calculated by Correia et al. (1990) is 26 mW/m2. We tend to accept 23 mW/m2 as the representative basinal difference in surface heat flow density and it is quite near that which is based on the estimations by Correia et al. (1990). The cause of this basinal variation is out of the scope of this study, as we do not have enough data to probe Table 5 Surface heat flow density (mW/m’) further. It is generally believed that a basin’s thermal structure is influenced by heat convection and that heat flow density signals vary due to basinal fluid motion. Correia et al. (1990) described an increase in heat flow density with the depth of the deepest BHT in the northern portion of the Jeanne d’Arc basin and a decrease with the depth of the deepest BHT in the southern part. They construed from this that the basin is not in thermal equilibrium and is disturbed by heat transpo~ation through convection, i.e., currently active fluid flow with a downward component in the north and an upward component in the south. Our formation thermal conductivity profiles, corrected for porosity effects, pose a problem with this interpretation. The presence in the basin of a strongly overpressured zone, characterized by low thermal conductivities (Fig. 51, implies by definition separate fluid zones. Furthermore, we noticed that the largest difference in heat flow density is in W-E direction (Rankin M-36 verse two Whiterose wells). Therefore, large-scale N-S basinal fluid movements described by Correia et al. (1990) is unlikely. Alternative reasons for the variation in surface heat flow densities might be the combined effects from two methods used in this study and a comparison with those from Correia et al. (1990) Well name Thermal resistive method Inverse method Correia et al. (1991) Differences a Ben Nevis I-45 Beothuk M-05 Flying Foam I- 13 Gambo N-70 Hebron 1-13 Hibernia C-96 Hibernia K-14 Hibernia K-18 Hibernia O-35 Mercury K-76 Nautilus C-92 Rankin M-36 South Tempest G-88 Terra Nova K-08 W. Flying Foam L-23 Whiterose J-49 Whiterose N-22 48.3 53.1 50.4 47.4 47.0 49.4 41.5 51.3 51.2 43.7 50.9 33.4 51.1 50.7 57.6 66.5 60.7 51.7 + 10.0 56.2 * 10.9 52.6 * 16.7 51.3 f 15.0 50.0 + 7.4 53.5 f 9.8 47.4 * 10.5 52.0 + 8.4 50.4 + 10.0 45.6 i 10.9 50.7 +_ 9.6 37.8 + 8.1 58.9 * 10.5 52.7 rt 9.1 57.9 a 18.5 60.7 * 13.3 60.7 f 11.9 57 * 22 65+ 17 64& 17 48+ ? 58+ 16 64k 17 50 + 16 57 + 20 71 +23 58 + 15 61+ 21 49 f 12 70 f 27 55* ? 70 + 28 75 + 24 72 + 23 5.3 (12) 8.8 (6.1) 11.4 (0.3) - 3.3 (?) 8 (8.6) 10.5 (7.2) 2.6 (5.5) 5 (11.6) 20.6 (13) 12.4 (4.1) 10.3 (11.4) 11.2 (3.9) 11.1 (16.5) 2.3 (7) 12.1 (9.5) 14.3 (10.7) 11.3 (11.1) a Difference between the surface heat flow density values from the inverse method and those estimated by Correia et al. (1990). The first value is difference in surface heat flow density and the second in the brackets is that in S.D. Temperature (degC) zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA 0 20 40 60 80 100 120 140 zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA u IEBRON j r 0.5 i I * I c I / t J 140 120 100 80 161 5 ’ I 60 ’ I 40 ’ I r 20 Time(Ma) Maturity(%Ro) b Fig. 7. (a) Plots of model-predicted maturity/temperature/depth profiles for the Hebron I-13 well using two sets of thermal conductivity assumptions, The crosses are measured values for comparison. (b) ~odei-predicted maturity/ time curves for the Egret source rock using the two conductivity sets. Curve I is based on thermal conductivities from literature. Curve 2 is based on thermal conductivities from this study. of some local factors (e.g., variations in basement heat flow flux, small-scale thermal convection and radiogenic heat production) and errors in data. The effect of each factor may not be very substantial. For example, we have neglected radiogenie heat production in calculation, as there is no published data on heat production in the Jeanne d’Arc Basin. According to Keen and Lewis (1982), the measured heat production in the adjacent continental margin off Nova Scotia ranges from 0.5 to 1.6 pW,/m3. If these values are applicable in the Jeanne d’Arc Basin, we estimated that in a drilled interval of 5000 m, containing 50% shale/dlaystone of high radiogenic heat production value (1.6 pW/m3>, the increase of heat flow density due to heat production in shale/ claystone would be 4 mW/m*. That value alone can not explain why the heat flow at Rankin M-36 is about 23 mW/m* lower than at the Whiterose wells even if we assume that the former had no shale package. 6. Sensitivity of basin modelling to thermal conductivity estimation Part of the impetus behind our examination of this basin’s thermal structure is the need to constrain the thermal assumptions necessary to derive information on the timing of hydrocarbon generation in the region, This involves translating sediment compaction histories into time temperature info~ation by viewing the rock column as a series of stacked beds, each with its own thickness and thermal conductivity (which varies through time with com- ual lithological layers ( 2 3 m) in the Jeanne d’Arc Basin, varies from 1.3 to 2.8 W m- ‘K- ‘. At each well location, in-situ thermal conductivity is controlled by lithological composition. Porosity is also an important control on the in-situ thermal conductivity. In intervals with higher porosity value, in-situ thermal conductivity corrected for porosity can be only about 50% (or lower) of the matrix thermal conductivity. (2) The formation thermal conductivity in the Jeanne d’Arc Basin is heterogeneous. Shale/ claystone formations are more heterogeneous than sandstone and sandstone/ siltstone formations. The range of formation thermal conductivity is 1.46-2.65 W m-‘K-l. From the South Mara Unit down to the Catalina Formation, the formation thermal conductivity generally increases with depth. However, the formations below do not follow this trend. (3) There are at least two reasons for the heterogeneity of the thermal properties in this basin. The first is lateral variation in lithological composition caused by facies change and the second is the development of overpressure zones in some areas of the basin. The heterogeneity in sandstone/ siltstone formation may be mainly explained by the first reason. The heterogeneity of shalc/claystone fo~ation is because of both faties change and overpressure zone and the latter may have played a more important role. 7. zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Summary (4) The surface heat flow density in the studied seventeen wells varies from 37.8 to 60.7 mW/m2, Understanding a basin’s thermal structure, according to the results from the inverse method. particularly the factors that control it, provide The basinal average of the surface heat flow important constraints on models that attempt to density is 51 mW/m”. predict the basin’s thermal evolution through ge(5) This study indicates that on average surface ological times. This in turn influences the quality heat flow density estimated without porosity corand accuracy of a range of models that require rection may be 15% higher than the one with this information as an aid to understanding for porosity correction. example the basin’s rifting history or the genera(6) In basin modelhng, different thermal contion, expulsion and migration of hydrocarbons ductivity estimates are not particularly significant within the basin. Our study of the basin’s thermal for present-day predictions but clearly have substructure is based on porosity-corrected thermal stantial impact on modelled timing of maturation, conductivity and used the inversion method to calculate the surface heat fIow density for seventeen wells. The major results and implications are as the follows: This study was financially supported by a (1) The in-situ thermal conductivity of individCanadian Government Laboratory Visiting Fel- paction). With a constant heat flow through time (see Williamson, 1992), surface temperature (which can vary through time) and assuming steady-state thermal equilibrium, it is possible to calculate temperature-depth profiles for each time increment from the simple formulation expressed earlier. This is then used as a basis for computing levels of organic maturity versus depth at present-day or through time for a specific datum. The level of sensitivity of these maturity predictions to assumed thermal conductivities is demonstrated in Fig. 7. Curve 1 represents maturity predictions based on matrix conductivity values cited in the literature (e.g. Clark, 1966; Gretener, 1981) and based on investigations in other areas. Curve 2 represents maturity predictions using conductivities derived in this study, i.e., correcting for compaction and based on measurements performed on sediments from the Jeanne d’Arc Basin, The differences in these two assumption sets are not particularly significant for present-day predictions but clearly have implications for modelled timing of maturation. The modelling indicates that the source rock reached a certain maturity level about 50 Ma earlier with the thermal conductivities from this study than with the matrix thermal conductivi~ values from literature. 192 Z. zyxwvutsrqponmlkjihgfedcbaZYXWVUTSRQPONMLKJIHGFEDCBA Huang, M A. W illiamson / Tectonophy sics 233 (1994) 177- l 92 lowship to Z, Huang at the Atlantic Geoscience Centre of the Bedford Institute Oceanography, Dartmouth, Nova Scotia. Our study has benefited from fruitful discussion with Serge Levesque. Kelin Wang kindly provided the program for calculating heat flow density with the inversion method. 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