Constraining basin thermal history and ... - Wiley Online Library

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Basin Research (2016) 1–12, doi: 10.1111/bre.12213

Constraining basin thermal history and petroleum generation using palaeoclimate data in the Piceance Basin, Colorado Yao Tong,* Daniel E. Ibarra,† Jeremy K. Caves,† Tapan Mukerji* and Stephan A. Graham‡ *Department of Energy Resources Engineering, Stanford University, Stanford, CA, USA †Department of Earth System Science, Stanford University, Stanford, CA, USA ‡Department of Geological Sciences, Stanford University, Stanford, CA, USA

ABSTRACT Careful assessment of basin thermal history is critical to modelling petroleum generation in sedimentary basins. In this paper, we propose a novel approach to constraining basin thermal history using palaeoclimate temperature reconstructions and study its impact on estimating source rock maturation and hydrocarbon generation in a terrestrial sedimentary basin. We compile mean annual temperature (MAT) estimates from macroflora assemblage data to capture past surface temperature variation for the Piceance Basin, a high-elevation, intermontane, sedimentary basin in Colorado, USA. We use macroflora assemblage data to constrain the temporal evolution of the upper thermal boundary condition and to capture the temperature change with basin uplift. We compare these results with the case where the upper thermal boundary condition is based solely upon a simplified latitudinal temperature estimate with no elevation effect. For illustrative purposes, 2 one-dimensional (1-D) basin models are constructed using these two different upper thermal boundary condition scenarios and additional geological and geochemical input data in order to investigate the impact of the upper thermal boundary condition on petroleum source rock maturation and kerogen transformation processes. The basin model predictions indicate that the source rock maturation is very sensitive to the upper thermal boundary condition for terrestrial basins with variable elevation histories. The models show substantial differences in source rock maturation histories and kerogen transformation ratio over geologic time. Vitrinite reflectance decreases by 0.21%Ro, source rock transformation ratio decreases 10.5% and hydrocarbon mass generation decreases by 16% using the macroflora assemblage data. In addition, we find that by using the macroflora assemblage data, the modelled depth profiles of vitrinite reflectance better matches present-day measurements. These differences demonstrate the importance of constraining thermal boundary conditions, which can be addressed by palaeotemperature reconstructions from palaeoclimate and palaeo-elevation data for many terrestrial basins. Although the palaeotemperature reconstruction compiled for this study is region specific, the approach presented here is generally applicable for other terrestrial basin settings, particularly basins which have undergone substantial subaerial elevation change over time.

INTRODUCTION Reconstructing a basin’s thermal history is critical in basin and petroleum system modelling. This is especially true for the study of petroleum source rock maturation and hydrocarbon generation modelling, as the thermal history acts as a boundary condition when solving Arrhenius-type reaction rates and conversion equations (Hantschel & Kauerauf, 2009). In most cases, the basin thermal history can be quantitatively modelled given constraints on the upper and lower thermal boundary conditions (Al-Hajeri Correspondence: Yao Tong, Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305-2220, USA. E-mail: [email protected]

et al., 2009; Hantschel & Kauerauf, 2009). The lower boundary condition is determined by the basal heat flow, which is the product of lithology-dependent thermal conductivities and the regional geothermal gradient, i.e. Fourier’s law (e.g. Hantschel & Kauerauf, 2009). The upper boundary condition is the sediment–water interface temperature (TSWI) in a marine basin, defined as the temperature at the boundary between the top sediment layer and the overlying water column. The TSWI provides a basis for estimating marine basin upper thermal boundary temperatures (Senglaub et al., 2006; Paton et al., 2007; Baur et al., 2010; Kuhlmann et al., 2011), and is integrated into basin modelling program (e.g. PetroModâ). If, however, the basin is subaerial, the actual upper boundary temperature is the sediment–air surface

© 2016 The Authors Basin Research © 2016 John Wiley & Sons Ltd, European Association of Geoscientists & Engineers and International Association of Sedimentologists This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Y. Tong et al. temperature. The TSWI are no longer robust characterizations of the surface temperatures in terrestrial basins, which are likely experienced more complex temperature variations relative to the stable depositional environment of many marine basins. The TSWI estimation tool included in basin modelling program (e.g. PetroModâ) is not able to capture local surface temperature variations during basin evolution. For general notation purposes, we will use the term, the sediment-surface-temperature (SST), to present basin upper thermal boundary condition, with no specific implication regarding the environment of the basin sediment (e.g. marine, lacustrine or terrestrial). For subaerial systems, SST may be impacted by many factors, such as local geological, topographic and climate settings, and varies greatly in magnitude on shorter timescales compared to the lower boundary condition. Both boundary conditions are essential elements in constraining basin thermal history. Our main focus for this paper was to constrain the upper thermal boundary conditions for the Piceance Basin, with a complex deposition history involving a marine to terrestrial transition and subsequent orogenic uplift. As a consequence of this geologic history, optimal source rock maturity and hydrocarbon generation estimates for the Piceance Basin require accurate surface temperature reconstructions. Numerous geochemical- and floral-based temperature reconstruction have been used to estimate Cenozoic surface air temperatures in western North America (Mix et al., 2011). Palaeobotanical reconstructions using macrofloral assemblages provide the most robust surface temperature reconstructions and are widely used for testing climate models and reconstructing past elevations (e.g. Huber & Caballero, 2011; Goldner et al., 2012, 2014). Other geochemical methods that measure surface temperature include paired dD–d18O measurements of clay minerals, typically kaolinite or smectite (Sjostrom et al., 2006; Mix & Chamberlain, 2014), and clumped isotope measurements of palaeosol and lacustrine carbonate (Huntington et al., 2010; Fan et al., 2014). However, potential seasonal biases in mineral formation suggest that these geochemical methods may only record summertime average temperatures (Breecker et al., 2009; Peters et al., 2013; Hough et al., 2014; Mix & Chamberlain, 2014). Using robust palaeobotanical reconstructions and the presence of suitable data sets near the Piceance Basin, we compiled a regional palaeotemperature time-series for the Piceance Basin using palaeobotanical methods. We use these temperature estimates as the upper thermal boundary condition for the basin model. In the process, we demonstrate the sensitivity of SST in terrestrial basins for petroleum source rock maturation. Finally, we show how changing the upper thermal boundary conditions significantly impacts source rock thermal maturation and hydrocarbon generation.

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GEOLOGIC SETTING The Piceance Basin is an intermontane basin located in northwest Colorado. During the Cretaceous, the region was part of the expansive epicontinental Western Interior Seaway (Fig. 2), which subsequently was uplifted and partitioned as a discrete sedimentary basin during the Late Cretaceous through Eocene Laramide Orogeny (Fig. 2; Chase et al., 1998; McQuarrie & Chase, 2000; Davis et al., 2009; Smith et al., 2014; DeCelles & Graham, 2015), and by the late Eocene, was uplifted several kilometres (Mix et al., 2011; Chamberlain et al., 2012). As such, annually averaged terrestrial surface temperatures in the uplifted Piceance Basin would have been colder than the annually averaged temperature at sea level due to lapse rate effects (Forest, 1995). As a consequence, Piceance Basin surface air temperatures likely diverged from the globally averaged sea-surface temperature during the Cenozoic, which is characterized by a period of long-term cooling, from peak warmth in the early Eocene to the icehouse conditions of the late Neogene (Zachos et al., 2001). Today, the basin is bounded by several Laramide uplifts and structural arches: White River uplift to the east, Douglas Creek arch to the west, Uncompahgre uplift to the southwest, Axial Basin anticline to the northeast, Gunnison Uplift to the southeast and Uinta Mountains to the northwest (Fig. 1). The basin is asymmetric with steeply dipping strata on the eastern flank and gently dipping strata on the western and southwestern part of the basin (Fig. 1). The steeply dipping eastern flank, the Grand Hogback, reflects a deeper west-vergent reverse or thrust fault (Johnson & Nuccio, 1986; Johnson, 1989; Johnson & Roberts, 2003). To the west, the Douglas Creek Arch separates the Piceance Basin from the Uinta Basin. The stratigraphic sequence includes a major transition from shallow marine, coastal plain sediments deposited in during the Late Cretaceous to terrestrial sediments (highly uplifted and eroded) deposited during Tertiary (Figs 2 and 3). The lowermost Upper Cretaceous strata contains the Mancos Group, a thick (>1500 m) marine shale (Johnson & Flores, 2003). The Mancos Group is in turn overlain by the Mesaverde Group, which is the primary reservoir of the basin’s tight-gas resources (Cole & Cumella, 2003; Hood & Yurewicz, 2008; Yurewicz et al., 2008) and is composed, in ascending order, of the Castlegate, Sega, Iles, and Williams Fork Formations (Johnson, 1989). The wide-spread Cameo coal, deposited during the Late Cretaceous, occurs at the top of the Iles formation, and is thought to be the major source rock (Johnson & Rice, 1990; Yurewicz et al., 2003) for the extensive gas resources within the overlying Williams Fork Formation tight sandstone (Soeder et al., 1987; Fall et al., 2012, 2014). Overlying Tertiary strata include the Wasatch (Palaeocene–Eocene), Green River (lower Eocene) and Uinta (upper Eocene) formations (Fig. 3).

© 2016 The Authors Basin Research © 2016 John Wiley & Sons Ltd, European Association of Geoscientists & Engineers and International Association of Sedimentologists

Basin thermal history paleoclimate data (a) Axial Basin

Uinta Mountains

White River Dome

Danforth Hills

Powell Park Rangley Meeker (1,951 m)

Well Location

Douglas Creek Arch

ack gb Ho e nd clin Gra no Mo xis lA

a clin Syn

Piceance Creek Anticline

White River Uplift

Roan Plateau

Mesagar

New Castle

Glenwood Springs (1,768 m )

Cross Section Divide Creek Anticline

Colorado River

Bo ok

Battlement Mesa (3,300 m)

Cl s iff Grand Junction (1,417 m)

re hg pa com plift U

Un

Colorado Denver

Gunnison Uplift

N 0

10 20 Kilometers

30

(b) Orchard Grand Parachute Rulison Valley

Mount Garfield

Mamm Creek

Grand Hogback

E

W

Depth Subsea (m)

2164

0 Wasatch –1067

Ohio Creek Conglomerate Williams Fork Formation Mancos Shale Formation

METHODS Sediment–water interface temperature estimation Current basin modelling practices require an estimate of the SST for the upper thermal boundary condition (Hantschel & Kauerauf, 2009). For estimates of past SST variations, an average palaeo-air-surface temperature (TS)

1200 m

Fig. 1. (a) Map of the Piceance Basin with major uplift arch structures, green polygons indicate the Mesaverde outcrop, and red well symbol indicates the Exxon Love Ranch #1 well analysed in this study (modified from Leibovitz, 2010). Inset map shows the state of Colorado, USA. (b) Piceance Basin structural cross-section showing asymmetry of the basin and strata from Upper Cretaceous through lower Tertiary strata based on well logs. Upper Tertiary strata (Green River and Uinta Fm.) are missing due to erosion. Colour-filled gamma ray (GR) and resistivity (ILD) are shown. Key landmarks and gas fields are labelled. (modified from Rogers, 2012).

Piceance Basin

16 Km Vertical Exaggeration = 10x

is first estimated from palaeo-latitude (Wygrala, 1989), which requires knowledge of latitudinal changes of the study basin. Then, TS is corrected to the actual sediment–water interface temperature (TSWI) accounting for the palaeo-water depth. TSWI is therefore based on both the palaeo-water depth model and global temperature variations, assuming marine temperatures, interpolated to the Wygrala (1989) latitude-dependent seawater


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Y. Tong et al.

Fig. 2. Palaeogeographic maps of North America from Late Cretaceous to early Miocene. In each map, red outline defines the state of Colorado and the red polygon indicates the location of the Piceance Basin. (modified from Blakey, 2014).

magnitude of this temperature decrease would be reasonable for marine basin temperatures derived from the d18O of benthic foraminifera (Zachos et al., 2001). This TSWI is then used as the upper thermal boundary condition (SST). For the modern sediment–water interface temperature, the annual mean ground surface temperature is often obtained from mean air temperature (www.worldclimate.com), using the present-day latitude of the study area, and water depth is accounted for following the equations from Beardsmore & Cull (2001) and Hantschel & Kauerauf (2009).

Terrestrial surface temperature reconstruction

Fig. 3. Piceance Basin stratigraphic column (modified from Yurewicz et al., 2003).

temperature time-series. For the Piceance Basin, this method interpolates mean annual temperature to be between approximately 21–24 °C from the latest Cretaceous through the Early Eocene Climatic Optimum, followed by a decrease through the Cenozoic to ~17 °C. The

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We propose a new method to estimate the upper thermal boundary conditions using palaeoclimate data. We use palaeobotanical data to quantify changes in the regional Mean Annual Surface Temperature (Table 1). Two primary approaches have been used to estimate palaeotemperatures using macroflora: Leaf Margin Analysis (LMA) (Wilf, 1997) and the Climate-Leaf Analysis Multivariate Program (CLAMP) (Wolfe, 1995). These two methods rely on modern calibration data sets (primarily calibrated in the western United States) and have been applied to macroflora localities throughout western North America. Both methods assume that the morphological characteristics of the plant assemblage can be used as a proxy for past climate. LMA relies on the percentage of fossil leaves with toothed vs. untoothed (smoothed) leaves, which has been shown to correlate with temperature assuming no significant precipitation limitations (Wolfe, 1979; Wilf, 1997). CLAMP is similar but involves canonical correspondence analysis using a multivariate approach to distinguish other climatic effects on the assemblage morphology; this allows for simultaneous estimates of temperature and precipitation (Wolfe, 1995). In this compilation, we assume that both methods give valid temperature reconstructions, and where possible use LMA and


Basin thermal history paleoclimate data Table 1. Compilation of mean annual temperature (MAT) estimates form macroflora assemblage studies. Methods: CLAMP – Climate-Leaf Analysis Multivariate Program; LMA – Leaf Margin Analysis; MR – Multiple Regression Analysis; P – Physiognomy (see text for details). For temperature estimates without a reported error (16 of the 33 data points), we assign a typical error of 2.5 °C (1r) Age (Ma)

Latitude (°N)

Longitude (°W)

MAT (°C)

12.5 12.5 15 16 27.2 27.2 27.2 27.2 29.75 30.95 30.95 33.6 34.05 34.05 34.05 35 35 35.5 45 45 46.5 46.5 46.5 46.5 50.5 50.5 50.5 50.5 51 51 51 51 51

39.40 38.50 38.60 38.60 37.80 37.80 37.85 37.85 37.30 38.40 38.40 39.10 38.50 38.50 38.50 38.95 38.95 39.30 41.00 41.00 41.00 39.80 39.80 39.80 44.72 44.72 41.69 44.72 42.00 41.40 42.00 41.40 41.69

119.60 116.20 119.00 119.00 107.00 107.00 106.93 106.93 106.50 106.30 106.30 105.60 105.20 105.20 105.20 105.29 105.29 113.30 109.00 109.00 109.00 108.50 108.50 108.50 107.29 107.29 107.83 107.29 108.00 109.30 108.00 109.30 107.83

11.9 13.4 10.6 9 4.2 6.25 10.8 4.2 6.25 12.7 7.5 4.5 12.8 10.8 14 11.8 10.8 13.2 16.4 16.7 16.7 14.3 15.2 14.3 17.2 19.6 16.4 20.4 21.3 23 26.1 24.1 20

Error (1r)

1.25 1.3 1.3 1.25 2.5

1.3 2.9 1.3

1.3 2.1 4.4 3.6 2.3 3.7 3.6 3.6 3.6

CLAMP values from the same localities (e.g. data from Florissant, Colorado compiled by Chase et al., 1998). Additional older palaeotemperature estimation methods, such as qualitative physiognomy (e.g. Wolfe, 1979), and multiple regression analysis (Wing & Greenwood, 1994) are included if no LMA or CLAMP temperature estimates are available. We compiled all available temperature estimates from Colorado, Utah, Nevada and Wyoming (Fig. 4). The inclusion of estimates from Nevada is necessary to provide mean palaeotemperature estimates during the Miocene, in particular during the mid-Miocene, a period of known global warmth relative to the Oligocene and late Neogene (Zachos et al., 2001; You et al., 2009; Jagniecki & Lowenstein, 2015). The resulting temperature compilation suggests temperatures of 17–25 °C during the Eocene, decreasing through the Cenozoic to modern temperatures of 5–12 °C. This trend corresponds well to both timing of uplift of the Piceance Basin (Mix et al., 2011;

Method

Location name

References

CLAMP CLAMP CLAMP CLAMP CLAMP P CLAMP CLAMP P MR P P MR CLAMP P CLAMP CLAMP MR CLAMP CLAMP CLAMP MR LMA MR CLAMP LMA LMA LMA LMA LMA LMA LMA LMA

Chalk Hills, NV Aldrich Station, NV Stewart Valley, NV Fingerrock, NV Creede, CO Creede, CO Creede, CO Creede, CO Platora, CO Pitch-Pinnacle, CO Pitch-Pinnacle, CO Antero, CO Florissant, CO Florissant, CO Florissant, CO Florissant, CO Florissant, CO House Range, UT Green River, WY Green River, WY Green River, WY Green River, WY Green River, WY Green River, WY Little Mountain, WY Green River (Little Mtn.) Green River (Latham) Green River (Little Mtn.) Green River (Sourdough) Green River (Niland) Green River (Sourdough) Green River (Niland) Green River (Latham)

Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Wolfe et al. (1998) Wolfe et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Wolfe et al. (1998) Wolfe (1994) Chase et al. (1998) Wolfe et al. (1998) Chase et al. (1998) Chase et al. (1998) Chase et al. (1998) Wing & Greenwood (1994) Wing & Greenwood (1994) Wolfe et al. (1998) Fricke and Wing (2004) Fricke and Wing (2004) Fricke and Wing (2004) Fricke and Wing (2004) Fricke and Wing (2004) Fricke and Wing (2004) Fricke and Wing (2004) Fricke and Wing (2004)

Smith et al., 2014), and of global cooling and long-term pCO2 decrease from the early Eocene to the Neogene (Zachos et al., 2001; Beerling & Royer, 2011). The compilation of surface temperature data spanning 51 Ma to present is interpolated to an evenly spaced time series (0.5 Myr) using an error-weighted kernel smoothing routine (Hayfield & Racine, 2008; Gao et al., 2015) accounting for the reported error in the temperature estimates (Fig. 4a, Table 1). For temperature estimates without a reported error (n = 16 of 33, i.e. in the compilation of Chase et al., 1998), we assign a typical error of 2.5 °C (1r). To estimate the post-mid-Miocene temperature evolution of the Piceance Basin and encapsulate possible uncertainties in this evolution, we also utilize all historical weather station temperature data located in or near the Piceance Basin (long-term mean and error; Fig. 4a, b, Table 2). The mean and 1r of the modern temperature data are used to fix the temperature at 0 Ma. They were combined with the palaeotemperature data to


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Y. Tong et al. 30

(a)

Mean Annual Temperature (°C , MAT)

Mean Annual Temperature (°C , MAT)

30 25 20 15 10

SST Scenario 1 SST Scenario 2

5 0

Floral Modern Stage 1

80

Stage 2

60

40

25

20

15

10

5

0

Stage 3 Stage 4

20

(b)

0

1000

Age (Ma)

2000

3000

Modern elevation (m)

(c)

45 °N

CO

NV

40 °N

Elevation (m)

WY

UT

35 °N

120 °W

110 °W

100 °W

Fig. 4. (a) The compiled Mean Annual Surface Temperature (MAT) for Piceance Basin using palaeobotanical data. Black are macroflora data from previously published studies; red dots are MAT measurements from present-day weather stations; all error bars are 1r; black line is the mean, kernel-smoothed MAT; and the dashed lines are 1r on the kernel-smoothed MAT. (b) Modern MAT temperature plotted against modern-day elevation (m) on x-axis. (c) Locations of the palaeobotanical data (black dots) and the modern weather station locations (red dots).

interpolate the temperature time-series to the present day. The modern elevation–temperature lapse rate in the Piceance Basin shown in Fig. 4b was not used to correct or estimate any of our compiled palaeotemperature estimates; nonetheless, these data illustrate how changes in the basin elevation impact the surface temperature.

BASIN MODEL We construct a basin model for the Exxon Love Ranch #1 well using a one-dimensional (1-D) basin

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modelling program (PetroMod 1Dâ). We select this well because (1) it is located in the north-central part of the basin where the source rock burial history is well known, and (2) abundant geologic and geochemical data are available from previous studies (Yurewicz et al., 2003, 2008). In order to evaluate different thermal boundary conditions and isolate their impact, we construct two model scenarios using two different upper thermal boundary condition inputs, whereas all other model input is unchanged. Detailed model assumptions and input data are summarized in the following sections.


Basin thermal history paleoclimate data Table 2. Compilation of mean annual temperature (MAT) from weather stations Name of location

Latitude (°N) Longitude (°W) MAT (°C) Error (1r) Elevation (m) Duration

Dinosaur National Monument Rangely 1E Massadona 3 E Little Hills Altenbern Grand Valley Rifle Collbran Colorado National Monument Grand Junction 6 ESE Meeker 2 Craig 4 SW Palisade Grand Junction Walker Fruita Bonham Reservoir Paonia 1 SW Glenwood Springs 2 Marvine Marvine Ranch

40.23 40.08 40.28 40.00 39.05 39.45 39.53 39.25 39.10 39.05 40.03 40.45 39.12 39.10 39.02 39.10 38.85 39.57 40.03 40.03

108.97 108.60 108.60 108.20 108.38 108.05 107.80 107.97 108.73 108.45 107.92 107.60 108.53 108.55 108.73 107.90 107.62 107.32 107.52 107.47

Thermal history of the basin model The thermal evolution of the source rock is modelled by solving the 1-D heat flow equation with given thermal conductivity based on formation lithology, and assumed upper and lower thermal boundary conditions (Hantschel & Kauerauf, 2009). We construct two surface temperature histories and use these as the upper thermal boundary condition for the two model scenarios. Figure 4a shows the comparisons between two different upper boundary temperature profiles. The grey line shows the first SST scenario, which we estimate using the globally averaged, latitudinally dependent temperature reconstructions from Wygrala (1989). The black lines show the second SST scenario, which we reconstruct using palaeobotanical temperature data, as described above. Both scenarios show decreasing SST from the Cretaceous to the present, which is consistent with global cooling and long-term decreases in pCO2 (Zachos et al., 2001; Beerling & Royer, 2011) from the Late Cretaceous to the Neogene. However, the temperature history in the second scenario displays a substantial decrease in the surface temperature changes due to uplift of the Piceance Basin in the early Cenozoic (Mix et al., 2011; Smith et al., 2014), which is not captured in the first scenario. For a detailed comparison between these two SST scenarios, we divide both temperature profiles into four stages: (1) From Late Cretaceous to middle Palaeocene, we assume the same SST for both scenarios and estimate the palaeotemperatures from the corresponding palaeo-water depth. This is a regional assumption considering that the depositional environment of the

8.5 8.3 8.3 6.0 2.7 9.7 8.6 8.0 11.3 11.9 6.9 6.0 12.2 11.5 10.4 0.1 9.9 8.7 4.7 2.6

0.9 1.1 1.1 0.7 0.7 0.7 0.7 1.0 0.8 0.8 0.7 0.9 0.9 0.8 0.9 1.4 1.0 0.7 0.7 0.5

1804 1612 1884 1871 1734 1551 1622 1823 1765 1451 1938 1963 1463 1475 1366 2996 1701 1753 2240 2377

1963–2015 1894–2015 1986–2009 1946–1991 1958–2015 1904–1914 & 1965–1981 1910–2009 1900–1999 1940–2015 1962–2015 1970–1992 1977–2015 1911–2015 1900–2015 1893–2012 2003–2015 1905–2015 1988–2015 1959–1971 1972–1998

Piceance Basin region was primarily shallow marine to warm coastal plain settings (Johnson & Nuccio, 1986; Johnson, 1989). Variations in SST during this period are small, except for some minor fluctuations due to the palaeo-water depth changes. For instance, the palaeotemperature slightly decreases from 112 Ma to approximately 80 Ma, and after 80 Ma, it gradually increases again. These fluctuations correspond well with the palaeo-water depth cycles due to the retreat of the Western Interior Seaway in the study area, from shallower marine to deeper marine then returning to a shallow coastal plain environment (Johnson & Nuccio, 1986; Johnson & Flores, 2003; Patterson et al., 2003). (2) From the middle Palaeocene to middle Oligocene, both scenarios show decreasing temperatures as the depositional environment changes from shallow marine/coastal plain to a cooler terrestrial setting, though the temperature estimates from palaeobotanical data (scenario 2) display a greater decrease (Fig. 4a). This is a result of the regional uplift of the Piceance Basin (Fig. 2), as the uplifted basin experiences colder annually averaged surface temperatures than the annually averaged temperature at sea level due to the effect of decreasing temperature with increasing elevation (Forest, 1995). We note that neither scenario captures the prolonged warmth of the Early Eocene Climatic Optimum (EECO) (Zachos et al., 2001); however, because divergence of the two scenarios begins just at the end of the EECO, the impact of EECO on our results should be minimal. (3) From the late Oligocene to mid-Miocene, the two scenarios increasingly diverge: the palaeobotanical data indicate a temperature increase into the mid-


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Y. Tong et al. Miocene, whereas the other scenario continues a slight decrease in temperature. We believe that the second scenario is more realistic considering the abundant evidence regarding globally warmer conditions in the mid-Miocene relative to the Oligocene and late Miocene (Zachos et al., 2001; You et al., 2009; Goldner et al., 2014). (4) From the mid-Miocene to the present—both scenarios indicate a decrease in temperature. This is consistent both with known regional uplift (Johnson & Nuccio, 1986; Johnson, 1989) and consequent decreases in surface temperature as elevation increased and with increasingly cooler conditions into the Quaternary, particularly in the Northern Hemisphere (Lisiecki & Raymo, 2005; Pagani et al., 2010). The second scenario suggests a colder temperature history than scenario one based on both the palaeobotanical data and the modern-day weather station MAT data. To construct the lower thermal boundary condition, we assume a constant heat flow value of 65 mW m 2. This value is based on information from previous studies conducted in the Piceance Basin (Johnson & Nuccio, 1986; Yurewicz et al., 2003; Zhang et al., 2008). Based on these studies, heat flow values show a relatively invariant trend, except for the possible impact of magmatic activity in the southern part of the basin from the Oligocene through the Miocene (Yurewicz et al., 2003). However, as our 1-D model is located in the northern part of the basin, we assume no significant thermal impact from this igneous activity.

system elements were not developed until the Late Cretaceous, we only include strata from the Upper Cretaceous to Tertiary formations in our analysis. Pre-Upper Cretaceous strata present in the Piceance Basin are therefore not considered in this basin modelling. Table 3 summarizes the burial history and major events based on previous studies (Johnson & Flores, 2003; Johnson & Roberts, 2003; Patterson et al., 2003; Yurewicz et al., 2003, 2008; Zhang et al., 2008; Dickinson et al., 2012).

Source rock properties Previous studies of the Piceance Basin (Johnson & Roberts, 2003; Yurewicz et al., 2003; Zhang et al., 2008) provide abundant source rock information. The name “Cameo” is used to represent the Cretaceous coastal plain coal zone (Yurewicz et al., 2003). This Cameo coal zone is the source interval modelled in our study. The Hydrogen index (HI), total organic carbon (TOC) and kinetic model used in the basin model are summarized in Table 4. The source rock HI and TOC varies across the basin, but at the well location, we assume constant HI and TOC values based on the average coal quality of the northern part of the Piceance Basin (Yurewicz et al., 2003). The Pepper & Corvi (1995) type III-IV(F) kinetic model was assumed in the basin model, as a pyrolysis study of the source rock samples suggests that the Cameo coal was deposited in coastal plain environment and follows an evolution pathway typical for type III kerogen (Zhang et al., 2008).

RESULTS AND DISCUSSION Stratigraphic and burial history Deposition occurred in the Piceance basin region as early as the Palaeozoic; however, as the essential petroleum

Two basin model scenarios were constructed following the data discussed in the previous sections. The only difference between the two models is the upper thermal

Table 3. Stratigraphic and lithology data used to construct burial history for the Exxon Love Ranch 1 well. Thermal conductivity in each layer is based on formation lithology

Formation, unit or event

Age range (Ma)

Quaternary Uplift and erosion Hiatus Uinta Fm. Green River Fm. Wasatch Fm. Hiatus KTunconformity Ohio Creek Fm. Uplift and erosion Williams Fork Fm. Cameo Rollins Mbr. Corzette/Corcoran Mbr. Sego-Castlegate Fm. Mancos Fm.

0–0.1 0.1–10 25–10 40–45 45–50 50–58 58–59 59–61 61–66.5 66.5–70 73–73.5 73.5–74.5 74.5–75 75–78.5 78.5–90

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Thickness (metre) 9 1000 0 232 418 1364 0 88 265 1076 75 58 253 396 1105

Thermal conductivity (W m Major lithology

at 20 °C

at 100 °C

Sandstone NA NA Shale, siltstone and sandstone Shale, kerogen, carbonate Shale, sandstone NA Conglomerate NA Sandstone, Shale Coal Sandstone, Shale Sandstone, Shale Sandstone, Shale Shale

3.95

3.88

2.04 2.38 2.04

1.98 2.24 1.99

2.3

2.18

2.33 0.3 3.09 2.27 2.15 1.67

2.2 0.71 2.71 2.14 2.06 1.71

1

K 1)


Hydrogen index (mgHC/gTOC)

TOC (wt %)

Kinetic model

150

60

Pepper & Corvi (1995)_TIII-IV(F)

2.0

(a)

Scenario 1 Scenario 2 (± 1 st. dev.)

1.5 1.0 0.5 0.0

100 80

(b)

60 40 20

8 Mass (106 Tonnes)

constraint, which permits us to isolate the impact of this factor on the model predictions of source rock maturity, transformation ratio and hydrocarbon generation (Fig. 5). Vitrinite reflectance at the source rock layer is calculated following the method given by Sweeney & Burnham (1990) and is a widely used indicator to present source rock maturation. Both models show gradually increasing maturity resulting from continuously depositing overburden sediment until the latest Cenozoic. However, the maturation rates over time show different behaviour. From the Late Cretaceous until the early-mid Eocene, both models show similar maturation patterns; however, from that point on, the maturation prediction from scenario two diverges from scenario one and remains nearly parallel but cooler. This observation closely corresponds to the steep temperature decrease in scenario two, as the palaeobotanical data indicate a colder surface temperature resulting from uplift after the early-mid Eocene. For the maturation prediction from scenario two, there is a pronounced slope change from mid-Oligocene to mid-Miocene. The increasing slope of the maturation prediction indicates faster source rock maturation rate, which is correlated with SST increases during this interval. Overall, we observe that the source rock maturity predictions are sensitive to the choice of SST history. The local temperature variations captured by palaeobotanical data provide extra information to constrain basin thermal history. The source rock transformation ratio and hydrocarbon generation predictions show a similar evolution over time. The source rock transformation ratio is the fraction of source rock that has been thermally matured and generated hydrocarbons relative to total source rock; when this fraction of matured source rock increases, we expect greater generation of hydrocarbons from the source rock. Comparing the model scenarios, the slope changes given by the second model scenario are noteworthy. For instance, model scenario two (red curve) shows a slope change in source rock transformation ratio prediction around 25 Ma, indicating that the source rock transformation ratio increased in the Oligocene. This corresponds well with the upper thermal boundary condition change in SST scenario two – the increase of SST during stage 3 (Fig. 4a). The overall fraction of matured source rock and hydrocarbon generation increases cumulatively in both scenarios; however, the evolution through time differs, indicating that the source rock “cooking process” is significantly different. In the late Oligocene, scenario one predicts around 60% transformation ratio as compared to only 42 5% as predicted by scenario two. This difference in transformation ratio indicates large differences in

(c)

Fraction (%)

Table 4. Cameo coal source rock properties input data

Vitrinite Reflectance (%Ro)

Basin thermal history paleoclimate data

0

6 4 2 0 Q Po

0

Miocene

15

Oligocene

30

Eocene

45

Paleocene

Cretaceous

60

75

Age (Ma)

Fig. 5. Model comparisons of source rock maturation, transformation ratio and hydrocarbon generation mass, black curves are predictions from model scenario one, red curves are predictions from model scenario two, red dashed lines present the uncertainty range based on 1r of model scenario two (shown in Fig. 4).

petroleum generation at given times and has major implications for the timing of hydrocarbon trapping and accumulation. As timing is crucial to determine whether the hydrocarbon generation is able to be trapped and favourable for petroleum accumulation or not, constraining the uncertainties in the thermal conditions will ultimately reduce petroleum exploration risk. In addition to model predictions of source rock maturation history and hydrocarbon generation process over geological time, we also compare source rock maturation

Fig. 6. Model predictions compared with vitrinite reflectance data, green squares are vitrinite measurements with error bar at different depth, black curve shows prediction from model scenario one, red curve shows prediction from model scenario two, dashed red lines are uncertainty range for model scenario two.


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Y. Tong et al. (vitrinite reflectance) predictions at present day with depth profiles of measured vitrinite reflectance data (Yurewicz et al., 2003) (Fig. 6). Model scenario two shows a better match to the vitrinite reflectance data at depths with measured samples, whereas model scenario one tends to overestimate the present-day maturation level. The mean residual between model scenario two and the data is 0.10%Ro compared to 0.17%Ro for model scenario one. The vitrinite reflectance depth profile further demonstrates the utility of using palaeobotanical data to constrain the upper thermal boundary condition to predict the evolution of petroleum generation and presentday maturation levels of the source rock. Importantly and in spite of the relatively large uncertainty range in the temperature reconstructions based upon palaeobotanical data, the predictions from scenario one still do not overlap with the one sigma error from scenario two. This result verifies that using temperature estimates from scenario one as the upper thermal boundary condition alone could significantly overestimate source rock maturation levels. However, for marine basins or other basins without complicated uplift histories and also sufficient palaeo-bathymetric controls (Baur et al., 2010; Kuhlmann et al., 2011), the method used in scenario one remains a robust method to estimate upper thermal boundary conditions.

the Tibetan Plateau, where elevation changed from sea level to up to 5–6 km from the Cretaceous to the Cenozoic (Ritts et al., 2008; Wang et al., 2008, 2014).

ACKNOWLEDGEMENTS The authors thank the thoughtful review from Dr. Alan Carroll and Dr. Cynthia Ebinger. We thank the following individuals and groups for their contributions to this study: Dr. Paul Weimer and his research group at University of Colorado at Boulder; Dr. Kenneth Peters, Dr. Allegra Hosford Scheirer, Leslie Magoon, Dr. Carolyn Lampe. Funding for this research was provided by the affiliates of the Stanford Basin and Petroleum System Modeling Research Group, with current members including California Resources Corporation, Chevron, ConocoPhillips, Ecopetrol, Hess, Murphy, Nexen, Pemex, Petrobras and Saudi Aramco. With special thanks to Schlumberger who provided academic licenses and support for PetroModâ. Daniel E. Ibarra is supported by a Stanford University EDGE-STEM Fellowship. Jeremy K. Caves is supported by a U.S. NSF Graduate Research Fellowship (grant DGE-1147470) and a Stanford Graduate Fellowship.

CONFLICT OF INTEREST SUMMARY AND CONCLUSIONS Results from two basin model scenarios for the Piceance Basin show that the simulated source rock maturation, transformation ratio and hydrocarbon generation varies significantly, given different SST conditions. This in and of itself is not surprising, but our results underscore the importance of correctly capturing surface thermal history in terrestrial basins where elevation changes occur over time. The differences shown in the model prediction represent the uncertainties given by different upper thermal boundary conditions. These uncertainties could be reduced using our proposed approach, where terrestrial palaeoclimate data are utilized to assist in adding basin thermal history constraints. Importantly, the two scenarios show significant differences despite the large uncertainty in the palaeobotanical temperature reconstructions. The methodology is generally applicable for any terrestrial basin with reliable palaeoclimate data. Indeed, the importance of accurately constraining the upper thermal boundary for understanding basin evolution should spur efforts at improving palaeoclimatic reconstructions, particularly for areas where Meso-Cenozoic uplift is known to have occurred, but temperature reconstructions are sparse, such as basins on the Mongolia Plateau and elsewhere in interior Asia (Hendrix et al., 1992; Caves et al., 2014). Presumably, the effects demonstrated here would be even more pronounced in basins where surface elevation changes are even more substantial than the North American Cordillera, such as similar aged basins found on

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No conflict of interest declared.

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Manuscript received 2 December 2015; In revised form 22 July 2016; Manuscript accepted 30 July 2016.
