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A new method of source and reservoir rock pyrolysis to determine the boiling point distribution of petroleum in rock samples
Journal of Petroleum Science and Engineering 170 (2018) 71–80
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A new method of source and reservoir rock pyrolysis to determine the boiling point distribution of petroleum in rock samples
T
Dhrupad R. Betia,b,∗, David J. Thula, Terry A. Ringb, John D. McLennana,b, Raymond Leveya a b
Energy & Geoscience Institute, The University of Utah, UT, 84108, USA Department of Chemical Engineering, The University of Utah, UT, 84112, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Pyrolysis S1 Incremental S1 Free petroleum Refractive index HAWK™
This paper presents a new method of source and reservoir rock pyrolysis used to understand the boiling point distribution of petroleum in rock samples. Experiments and simulations of the new incremental S1 method were performed on a range of rock types and API gravities (Black oil < 40, Volatile oil 40–45, Gas condensate ≥ 50 and Wet gas > 50). Comparison of the results from experiments and simulations are presented. The boiling point distribution results from the new incremental method can be used to infer the carbon number distribution of oil present in rock samples. However, the boiling point distribution data from the incremental S1 method is not adequate to determine the API Gravity of oil present in rock samples. Refractive index (RI) and density of oil have a straight line correlation. The boiling point distribution data from the Incremental S1 can be used in conjunction with the refractive index data to predict the composition of oil present in rock samples.
1. Introduction The objective of this study is to experimentally determine the boiling point distribution of crude oil present in a rock sample. A rapid and economical method for characterizing the boiling point distribution of oil present in the pores of the rock - in the absence of a liquid sample - is highly valuable. The boiling point distribution can indicate the carbon number associated with hydrocarbon molecules (Riazi, 2005). This information is useful in a wide range of disciplines in the oil and gas industry including; exploration, drilling, well completions, production engineering, reservoir engineering and distillation. For instance, a geologist can use this technique in the laboratory and at the well site to understand boiling point distribution and further understand the carbon numbers of oil in rocks, to identify a reservoir and further locate a target zone. A drilling or completions engineer can use the same information to identify loading zones for a horizontal well and where to perforate. A production and reservoir engineer can use this information to identify producible zones in the reservoir. A chemical engineer in a refinery can understand the distillate fractions even before the product comes from a new discovery. Speight (2006) describes a distillation procedure to determine the boiling point distribution of a crude oil. Following Speight's procedures for determining the boiling point distribution of a crude oil sample is relatively straightforward. For example, an oil sample might be acquired from an active seep or an oil producing well by surface sampling.
∗
When a liquid sample is not available from an exploration program or before a well has been completed and brought on to production, the ability to understand a boiling point distribution of the crude oil present in situ is limited. However, in the presence of a source or a reservoir rock sample, source rock pyrolysis has historically been used to determine the quantity of free petroleum in such a rock sample (Espitalie et al., 1977, 1985a, 1985b, 1986). The analysis of rock samples, with conventional source rock pyrolysis methods, does not provide boiling point distributions or API gravity. In order to attain this more specific information, standard source rock pyrolysis methods have to be altered. Very few techniques enable classification of crude from rock sample pyrolysis. The HAWK-PAM™ - Petroleum Assessment Method, (Maende, n. d.) is one such pyrolysis method. This method is an alteration of conventional source rock pyrolysis and allows prediction of API gravity by analysis of a rock sample itself. This study presents one such method, another alteration of the conventional source rock pyrolysis method, to attain the boiling point distribution of oil in rock samples. The incremental S1 method was first introduced in a previous work (Beti, 2016). Yiadom (2017) adapted Incremental S1 method to understand the quality of petroleum in the Utica play, USA. Abrams et al. (2017) used a similar method to propose an extraction protocol to evaluate oil in place in unconventional plays. The incremental S1 method presented in this study provides new insights that allow for carbon number classification of oil-containing rock samples, through boiling point distribution. Unlike other distillation-related experimental techniques
Corresponding author. Energy & Geoscience Institute, The University of Utah, UT, 84108, USA. E-mail address: [email protected] (D.R. Beti).
https://doi.org/10.1016/j.petrol.2018.06.036 Received 26 March 2018; Received in revised form 3 June 2018; Accepted 16 June 2018 Available online 19 June 2018 0920-4105/ Published by Elsevier B.V.
Journal of Petroleum Science and Engineering 170 (2018) 71–80
D.R. Beti et al.
Fig. 1. FID and IR signals of a sample in the pyrolysis with TOC method, showing the assigned S1, S2, S3, S4, and S5 parameters as a function of temperature modified from (Mccarthy et al. n. d.).
increases the fidelity of the S1 peak. In the conventional pyrolysis method, S1 measures the quantity of free petroleum. This peak is measured at a temperature range of 100 °C to 300 °C. In the new Incremental S1 method, multiple temperature steps (isotherms) are created with a rapid temperature increase between each of the isotherms. The isotherms in this method are in the range of 50 °C–300 °C. Based on the boiling points of the hydrocarbon molecules, these multiple FID peaks measured at each temperature step, achieve fractionation of petroleum present in the sample. Consequently, the quantity of free petroleum measured in the conventional S1 peak is split into multiple peaks in the new incremental S1 method providing a quantitative measurement of free petroleum with higher fidelity (boiling point distribution). To exclude the possibility of fractionating bitumen or kerogen in the rock sample, the maximum temperature of the incremental S1 method is set to 300 °C.
presented by Speight (2006), this method only requires a rock sample for analysis. The sample can be a small subset of a core or collected drill-cuttings. The new method presented in this paper is based on source rock pyrolysis that was first introduced by Espitalié and his colleges in the 70s and 80s (Espitalié et al., 1977, 1985a, 1985b, 1986). These pyrolysis experimental methods are widely accepted and applied to understand petroleum systems (Bordenave, 1993; Huc, 2013). One of the most important experimental methods is “pyrolysis with total organic carbon (TOC)” method informally known as ”PyroS3650_TOC750 or classic pyrolysis”. A general schematic of this experimental method is shown in Fig. 1. In this conventional pyrolysis experimental method, the raw signals from three detectors (FID: Flame Ionization Detector, IR CO: Infrared CO cell, and IR CO2: Infrared CO2 cell) are recorded as a function of time, temperature, and experimental environment. From these data, a set of parameters; S1, S2, S3, S4, S5, and Tmax are experimentally determined (Espitalié et al., 1977, 1985a; 1985b). Both, Bordenave (1993) and Huc (2013) have presented the procedure to calculate these parameters, a brief summary is presented in the following paragraphs. The FID signal is studied in two sections, S1 and S2. The area under the FID signal, produced by the initial heat up from 100 °C to 300 °C followed by a 3 min isotherm, corresponds to the S1. S1 is an indication of the quantity of free petroleum present in a rock sample at the time of analysis. The area under the FID signal, produced by increasing the temperature from 300 °C to 650 °C, at a heating rate of 25 °C/min corresponds to S2. S2 is an indication of the kerogen content present in a rock sample. The temperature recorded for the maximum of S2 peak varying as a function of thermal maturity is Tmax, it is an indication of the maturity of kerogen present in the sample. Both S1 and S2 have units of milligrams of hydrocarbons per gram of rock (mg of HC/g of rock). These units are applied from a calibration. While the FID signal is the focus of this paper, a brief description of the other parameters follows. As shown in Fig. 1, the IR signals from the carbon monoxide and carbon dioxide detectors are segregated into the S3, S4, and S5 parameters. S3 which includes S3 CO and S3 CO2 is a measure of the amount of CO and CO2 released as a result of decomposition of organic matter in an inert, He atmosphere. S4 which includes S4 CO and S4 CO2 is a measure of the amount of CO and CO2 released as a result of decomposition of inorganic matter in the presence of an air atmosphere. S5 is the measures of the amount of CO2 released as a result of decomposition of inorganic matter at higher temperatures in presence of an air atmosphere. A new “incremental S1 method” is discussed in this paper. This method is a result of a series of trial and error experiments presented in a previous study (Beti, 2016). The new Incremental S1 method
2. Material and methods In order to understand the results from the multiple peaks of this incremental S1 method, knowing the composition and/or API gravity of oil present in the rock sample is essential. API gravity is used universally to classify oils. Comparisons of the experimentally determined boiling point distribution and simulated boiling point distribution results corresponding to API gravity have been made in this study. 2.1. Samples First, a set of artificial samples were created. Eleven oil samples with an API gravity ranging from 18° to 53.5° (refer to Table 1) were introduced into 1 × 0.5 × 0.5 inch3 sandstone samples (mean porosity 14%, and mean pearmibility 463 millidarcy - Uygur and Picard, 1980), containing no prior petroleum with the help of a custom built, vacuum setup. The sandstone cubes were placed on the vacuum setup which was connected to a suction pump creating a suction pressure of −15 mm Hg. Approximately 2–5 ml of oil were then back-flooded into each sample, due to the pressure difference applied to the rock, the oil percolated into the rock sample. Depending on the oil's density and viscosity, the oil entered the pores of the rock within 5–60 min. Multi-stage two phase flash separation simulations of temperature isotherms of the incremental S1 method were also performed, in order to verify and compare the results of the artificial samples measured using the incremental S1 method. One hundred ninety two (192) oil samples were used for the simulations. These oils were sourced from the oil library of ProMax Software; with known API gravities ranging from 18 to 72. This oil library contains boiling point data for these 192 oils which have been - curated from all over the world. 72
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Table 1 The summary of the results from artificial sample experiments performed with the incremental S1 method. Sample names
S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug.
2016.00065 2016.00068 2016.00084 2016.00085 2016.00093 2016.00098 2016.00100 2016.00101 2016.00104 2016.00122 2016.00125
API gravity
33.7 27.0 32.7 33.1 29.3 18.0 33.7 53.5 32.5 28.3 48.1
Formation
NA NA NA NA NA NA NA NA NA NA NA
Sample type
Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered
Normalized Data (after curve correction) - Boiling Point Distribution
S1 (mg of HC/g of rock)
S1_1
S1_2
S1_3
S1_4
S1_5
S1_6
0% 0% 0% 0% 1% 0% 0% 0% 0% 1% 1%
12% 15% 17% 16% 13% 17% 23% 11% 25% 23% 35%
22% 25% 26% 25% 23% 26% 28% 16% 30% 29% 34%
29% 27% 28% 27% 28% 28% 24% 32% 24% 25% 16%
21% 19% 16% 17% 19% 16% 16% 22% 12% 13% 8%
16% 15% 13% 15% 16% 13% 9% 19% 9% 8% 7%
In order to compare the results from the artificial sample – incremental S1 experiments and the comparable numerical simulations, a set of real (real-world) samples were also subjected to the incremental S1 method. These real-world samples, chosen for confirmation experiments, are representative of three major reservoir rock types (shale, carbonate, and sandstone). The accurate API gravity of oil present in these “real-world” samples is difficult to estimate. The currently available method to estimate the API gravity entails analyzing the oil produced from these rock formations recovered at the surface or occasionally sampled downhole. It is important to note that such produced oil could be sourced from one or many perforate zones, and likely a part oil in the rock is immobile. Furthermore, there is a possibility of crude oil homogenization in the wellbore. However, the following is the sample information and some studies that state the API gravity of the oil produced from these formations.
1.44 3.98 4.22 3.97 4.53 4.22 4.00 1.98 7.26 8.75 2.40
the supporting document. All these samples were pulverized as for the sample specification of HAWK™ Instrument and approximately 75 mg of each sample was used for the pyrolysis experiments. 2.2. Incremental S1 method
# of Well samples API#
Basin
Sample Type
Geologic Unit
Reference Oil API gravity
4
43 0195 0019
Paradox
Core
(Clem and Brown, 1984)
38.4°
4
43 0195 0019
Paradox
Core
(Clem and Brown, 1984)
38.4°
6
43 037 30165
Paradox
Core
43 037 30165
Paradox
Core
(Heath et al., 2017) (Lauth, 1978)
41
5
6
43 041 30036
Utah Core Hingeline
Cane Creek Shale Unit A (fine grained silty dolomite/ shale) Cane Creek Shale Unit B (fine grained silty dolomite/ shale) Gothic Shale (shale) Desert Creek fm. (limestone and dolomite) Upper Navajo SS. (sand stone)
(Chidsey et al., 2011)
48°
40°
As indicated in the introduction, the new incremental S1 method focuses on improving the fidelity of the S1 peak. The following paragraph describes the method. It is programmable on anhydrous pyrolysis instruments. Like the conventional pyrolysis method, the experiments with the incremental S1 method are performed in an inert, He atmosphere at 0.8 bar (atmospheric pressure). Fig. 2 shows the schematic of the incremental S1 method, illustrated with an example. This method is designed such that the sample undergoes multiple temperature steps. Fig. 2 shows the FID signal as a function of step changes in temperature. The temperature profile, indicated in red, is comprised of 6 isotherms at < 50 °C, 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C. Each temperature increment is imposed with a ramp rate of 200 °C/min at every 50° interval (from 100 °C to 150 °C, 150 °C–200 °C, and so on until 300 °C). The isotherms at < 50 °C, 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C, are referred to as Incremental S1 temperatures from here on. The FID signal peaks, indicated in black in Fig. 2, shows 6 peaks corresponding to the isotherms (< 50 °C, 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C) these peaks are named S1_1, S1_2, S1_3, S1_4, S1_5, and S1_6 respectively. All the 6 peaks use a calibration to give the same units as S1 (mg of HC/g of rock). In the incremental S1 method, the initial temperature increase from ∼50 °C to 100 °C is due to the introduction of the sample into the oven. The sample is not in the oven for the first 5 min of the method, making it difficult to directly control the sample temperature. Hence, the first isotherm is not exactly at 50 °C. In the interest of a minimum, effective, runtime, the isotherm durations are set at 5 min, except for the last isotherm at 300 °C for 10 min. If the FID signal does not return to baseline at the end of each isotherm, the peak decline data is used to numerically extrapolate the exponential decay curve. The area under this extrapolated exponential curve (tail) is then subtracted from the respective subsequent curves and added back to the original curve. This extrapolation process can be performed on one or several peaks. For example, the extrapolation process was applied for the peaks S1_2, S1_3, S1_4 and S1_5 in Fig. 2. The last isotherm at 300 °C is set at 10 min, assuming the FID signal reaches baseline in 10 min. 2.3. Data processing The extrapolation of the FID signal curves cannot be performed using the pyrolysis instrument's software. Therefore, all the data processing in this study is performed using MatLab™. The processing of the experimental data is divided into 3 steps. The
All of the sample information gathered for this study is provided in 73
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D.R. Beti et al.
Fig. 2. An example pyrogram illustrating the schematic of the incremental S1 method.
Fig. 3. Comparison of un-extrapolated and extrapolated data of a sample processed in MatLab software.
first step includes calculating the pyrolysis coefficient using a standard. Then, the pyrolysis coefficient is used to calibrate the data, and further calculate the mg of HC/g of rock for each incremental S1 peak. The last step includes curve correction by extrapolation of the FID signal curve and assigning a corrected value to S1_1, S1_2, and so on through S1_6 values respectively. These steps are explained in the following paragraphs. In the first step, the uncalibrated raw experimental data of all the samples (including standard samples data) is used as an input to a Matlab routine. First, the area under the FID signal curve is calculated for all the samples (including the calibration standard). Then only the raw experimental data of a standard sample with known mg of HC/g of rock is used to calculate the pyrolysis coefficient in a Matlab code; pyrolysis coefficient is a conversion factor used for calibration purpose. In the second step, the calibration process involves the conversion of the raw FID signal measured in millivolts to mg of HC/gm of rock, for each unit area under the FID signal curve. A validation of this calibration process was performed. Around 50 samples that have been tested with the conventional pyrolysis method (PyroS3650_TOC750) were used for validation. For these 50 samples, raw signal data were collected and calibrated using both the HAWK instrument software and a Matlab code. The results calibrated by the pyrolysis instrument were compared with the results calibrated using the pyrolysis coefficient generated using Matlab. The comparison of the two datasets resulted in a root mean square value of > 0.98. This indicates that there is little or no difference in the results using the two approaches. After the
validation, the pyrolysis coefficient was used to calculate the results (HC content corresponding to each peak) from the experiments performed using the incremental S1 method. In the last step, the FID signal data is extrapolated for the incremental S1 peaks which do not reach the FID baseline and further subtraction of area from the subsequent peaks was performed. The R squared value for the exponential decay curve fit with FID signal decline for each peak was ≥0.98. It is important to ensure that, though the incremental S1 values after the extrapolation vary from the initial incremental S1 values the overall free petroleum content remains the same. Hence, a validation of all the overall free petroleum content results (S1 or sum of S1_1, S1_2, S1_3, S1_4, S1_5, and S1_6) calculated with and without the extrapolation was also performed. The comparison of the two datasets resulted in a root mean square value of > 0.99. This indicates that the process of extrapolation of the curves followed by subtraction of the tail area from the subsequent curves was performed accurately, and there was no over-estimation or under-estimation of the total area (free petroleum content) as a result of extrapolation method. Fig. 3 shows a comparison between un-extrapolated and extrapolated data for the same sample. It is important to note that the incremental S1 values in Fig. 3 are different, but the overall free petroleum content (S1) is the same.
3. Experiments, simulations and initial observations Table 1 is a summary of the predicted boiling point distributions for 74
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Table 2 The summary of the results from real (real-world) sample experiments performed with the incremental S1 method. Sample names
API gravity
S.Sep. 2015.00033
38
S.Sep. 2015.00034
38
S.Sep. 2015.00035
38
S.Sep. 2015.00036
38
S.Sep. 2015.00037
38
S.Sep. 2015.00038
38
S.Sep. 2015.00039
38
S.Sep. 2015.00041
38
S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep.
41 41 41 41 41 41 40 40 40 40 48 48 48 48 48 48
2015.00042 2015.00043 2015.00044 2015.00045 2015.00046 2015.00047 2015.00052 2015.00053 2015.00054 2015.00055 2015.00057 2015.00058 2015.00059 2015.00060 2015.00061 2015.00062
Formation
Cane Creek Shale A Cane Creek Shale A Cane Creek Shale A Cane Creek Shale A Cane Creek Shale B Cane Creek Shale B Cane Creek Shale B Cane Creek Shale B Gothic Shale Gothic Shale Gothic Shale Gothic Shale Gothic Shale Gothic Shale Desert Creek fm. Desert Creek fm. Desert Creek fm. Desert Creek fm. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS.
Sample type
Normalized Data (after curve correction) - Boiling Point Distribution S1_1
S1_2
S1_3
S1_4
S1_5
S1_6
S1 (mg of HC/gm of rock)
Unit
Real
0%
15%
33%
24%
15%
12%
1.96
Unit
Real
0%
13%
28%
24%
19%
16%
8.29
Unit
Real
2%
22%
28%
24%
16%
9%
0.97
Unit
Real
0%
27%
32%
20%
12%
8%
4.77
Unit
Real
1%
20%
35%
22%
12%
11%
0.48
Unit
Real
0%
9%
31%
30%
19%
12%
3.58
Unit
Real
0%
17%
35%
26%
13%
8%
1.79
Unit
Real
0%
24%
37%
20%
10%
8%
2.32
Real Real Real Real Real Real Real Real Real Real Real Real Real Real Real Real
0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
10% 11% 11% 10% 11% 9% 20% 19% 16% 18% 19% 20% 19% 18% 15% 20%
20% 20% 21% 19% 20% 19% 31% 32% 31% 28% 29% 29% 30% 29% 29% 29%
25% 24% 25% 25% 25% 25% 27% 27% 27% 28% 28% 27% 27% 28% 30% 26%
24% 23% 23% 24% 23% 24% 15% 15% 17% 18% 16% 16% 16% 17% 17% 15%
22% 22% 21% 22% 21% 22% 6% 7% 8% 8% 8% 8% 8% 8% 9% 10%
3.43 3.45 3.03 2.84 2.91 3.18 2.70 3.09 5.73 5.81 4.35 5.08 8.51 6.01 6.46 5.34
between the experiments and the simulations, the feed of the first flash separator was set to be mass flow (100 kg/s). Table 3 is a summary of results from the 192 simulations of the incremental S1 method. In this table, the results from all the 192 oil simulations are grouped according to the API gravity. All of the individual simulation results are provided in the supporting documents. The results are segregated into 10 API groups, with each group having a 5° API gravity range, starting from 18° going to 72°. The normalized (for 6 parameters – S1_1, S1_2, S1_3, S1_4, S1_5 and, S1_6) vapor fractions of each API gravity group at the incremental S1 temperatures is presented in this table (Table 3). The non-normalized vapor fractions of each API gravity group at the incremental S1 temperatures is presented in the supporting documents. The minimum, average and maximum percentages of each API gravity group, recorded at all the incremental S1 temperature are provided in these tables. The data presented in Table 3 shows no clear trend between the API gravity group and boiling point distribution. Moreover, depending on the density of oil, a specific amount oil vaporizes at temperatures above 300 °C; see Fig. 5. For instance, in a crude oil with an API gravity < 20° the average fraction of petroleum vaporizing at temperatures above 300 °C is 87%. Not surprisingly, in a crude oil with an API gravity > 60°, the average fraction of petroleum vaporizing at temperatures above 300 °C is 2%. This indicates that the quantity of free petroleum is likely under-estimated regardless of the pyrolysis method. Furthermore, it is not possible to estimate the overall fraction of free petroleum available to be detected by S1 or Incremental S1 (at temperatures higher than 300 °C), without prior knowledge of the API gravity.
the artificial sample experiments, performed with the incremental S1 method. Table 2 is a summary of the predicted boiling point distributions for the real-world sample experiments, performed with the incremental S1 method. Both of these tables show the overall quantity of free petroleum denoted by S1 and the boiling point distribution (in percentage) of hydrocarbon groups present in the sample. The Incremental S1 values presented in these tables show the extrapolated and normalized data from the 6 peaks. Non-normalized, pre - and post extrapolation results for both sets of experiments are provided in the supporting documents. From the results presented in Tables 1 and 2, it can be observed that there is no clear trend in the boiling point distribution denoted by the incremental S1 percentages. For most of the samples, irrespective of the API gravity, S1_3, and S1_4 values are greater than the rest of the incremental S1 peak values. This indicates that, for these samples, the majority of the hydrocarbon molecules, have the boiling point in the range of 100 °C–200 °C. From the data in Table 2, it can be observed that for Cane Creek Shale Unit A samples, the overall free petroleum content (S1) and the boiling point distribution vary within the same formation. This indicates heterogeneity of oil in the Cane Creek Shale Unit A samples. 192 simulations of the incremental S1 method were also performed to compare simulations results with the Incremental S1 experimental results. A 2 phase, 7 stage flash vaporization was performed in ProMax software, using the Peng-Robinson equation of state. The first stage of flash vaporization is at room temperature and atmospheric pressure. This mimics the same initial conditions that are applied in the actual incremental S1 experiments. The subsequent, six consecutive stages in the simulation are designed to replicate the incremental S1 experimental conditions. Fig. 4 is a schematic of the ProMax two-phase flash separation at incremental S1 temperatures. The pyrolysis experiment results have mass units. In order to have consistent units for comparison
4. Results The boiling point distribution data from the simulations are presented as vapor fractions (vapor – mass fractions) in Table 3. This data 75
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Fig. 4. Schematic of ProMax two phase separation at IS1 temperatures and atmospheric pressure.
heterogeneous boiling point distribution of Cane Creek shale unit A samples, the incremental S1 values from the experiments and simulations are consistent, except for one sample (with 53° API gravity). The experiments are consistent with the simulations. If a relationship between API gravity and boiling point distribution is established, it could be used for interpretation of the incremental S1 data. The observations made from the simulation results presented in Table 3 are as follows. The overall quantity of hydrocarbon molecules vaporizing at > 300 °C, increases with an increase in the density of the oil. The boiling point distribution data do not correspond to a unique API gravity group. For example, the boiling point range of 31°–35 °API gravity at 100 °C is 0%–7%. Alternatively, the boiling point range of a 35°- 40 °API gravity oil at 100 °C is 0%–10%. In other words, each API gravity group does not correspond to a unique boiling point distribution. Consequently, the boiling point distribution data alone cannot be used to classify the oil present in the rocks.
is comparable to the experimentally determined boiling point distribution data (vapor – mass fractions) of the artificial and real samples presented in Tables 1 and 2 respectively. The following are the comparison made. The boiling point distribution data of one artificial sample containing 53.5° API gravity oil presented in Table 1, does not fall in the boiling point distribution range (minimum-maximum) of API gravity ranging from 51° to 55° presented in Table 3. The experimental data for this sample in Table 1 indicates the presence of hydrocarbon molecules with higher boiling points than are predicted by the simulations. One explanation for this could be that the oil might have lost its lighter hydrocarbon molecules before the actual experiment. Excluding this one sample with 53.5°API, the boiling point distribution of all of the experimental data presented in Tables 1 and 2 falls within the respective boiling point distribution range of the simulation data presented in Table 3, with a few outliers. For example, Fig. 6 shows the boiling point distribution results from the incremental S1 experiments with artificial and real samples, overlaying the simulation results from the API gravity group (46°–50°). In this Figure, the y-axis represents the boiling point distribution (in mass fractions), and the x-axis represents the boiling point temperature (in °C). This figure show results from both the simulations and experiments of the all the samples corresponding to API gravity ranging from 46° to 50°. The orange data points indicate the results from the simulations, the blue, and red data points indicate the results from experiments with real and artificial samples, respectively. This figure indicates that, the experimentally determined boiling point fractions at higher temperatures (250 °C and 300 °C) fall in the lower portion of the simulations data, whereas the same experimentally determined boiling point fractions at lower temperatures (100 °C and 150 °C) fall in the upper end of the simulation data. This trait was observed in all the simulation and experiment comparisons when plots similar to Fig. 6 are used (presented in supporting documents). From the comparisons stated above, it can be stated that the majority of the boiling point distribution data generated from the experiments correspond to the respective boiling point distribution ranges generated from the simulations. This indicates, in spite of, the absence of a clear API - boiling point correlation in the experimental data,
5. Discussion In order to further understand the nonunique API gravity - boiling point distribution relationship, Fig. 7 compares of the incremental S1 peaks with the hydrocarbon boiling point data. The lower half of the figure shows the boiling point of some of the hydrocarbon molecules commonly found in crude oil samples (Haynes, 2014). The color of the data points indicates the hydrocarbon structure. The upper half of the figure is an incremental S1 pyrogram, showing the FID signal as a function of temperature. The arrows corresponding to temperature indicate the hydrocarbon molecules vaporizing at the respective incremental S1 temperatures. This figure indicates that each incremental S1 peak may contain different amounts and types of hydrocarbon structures like paraffins, naphthenes, and aromatics etc, which has a significant effect on the density of oil (Tissot and Welte, 1978). Fig. 7, does not include the boiling point data of complex molecules, such as olefins, or NSO (nitrogen, sulfur, and/or oxygen) compounds. Yet, it can be observed that it is highly likely to have an eclectic mix of hydrocarbon molecule structures that contribute to each of the incremental S1 peaks. The density of these different structures contributes to the overall 76
Journal of Petroleum Science and Engineering 170 (2018) 71–80
100% 100% 60% 63% 79% 46% 26% 16% 9% 10% 47% 50% 33% 30% 28% 23% 20% 9% 6% 3% 32% 48% 40% 37% 32% 51% 27% 17% 19% 14% 20% 28% 26% 25% 23% 24% 21% 11% 14% 6%
0% 22% 23% 22% 19% 14% 15% 0% 1% 0%
Max. Ave. Max. Ave.
Min.
density of the crude (API gravity). Consequently, it is not possible to predict the API gravity of oil present in rocks from the incremental S1 data (boiling point distribution) alone. In the book, The Chemistry and Technology of Petroleum, fourth. Ed (Speight, 2006), the author states that “A method for the classification of crude oils can only be efficient, first, if it indicates the distribution of components according to volatility, and second, if it indicates the characteristic properties of the various distillate fractions”. Consequently, in order to identify the oil present in a rock sample using the incremental S1 method, along with understanding the boiling point distribution, knowing the properties of the distillate fractions is important. Speight (2006) presents two formula to classify petroleum. The first being “Universal Oil Product (UOP) Characterization Factor”, and second being “Correlation Index”. The UOP characterization factor is defined as: 1
0% 0% 21% 21% 17% 15% 15% 2% 8% 0%
K= Min.
Vapor fraction at 250 °C
Vapor fraction at 300 °C
D.R. Beti et al.
(TB ) 3 . d
(5.1)
22% 44% 27% 48% 39% 39% 35% 24% 22% 14%
CI = 473.7d − 458.6 +
48,640 . (TB)
(5.2)
0% 0% 0% 1% 1% 2% 0% 0% 0% 2%
0% 1% 0% 3% 2% 3% 0% 0% 0% 4%
0% 0% 0% 0% 0% 0% 0% 0% 18% 0%
8% 5% 5% 7% 7% 9% 13% 22% 36% 52%
21% 10% 11% 15% 20% 22% 25% 42% 50% 96%
0% 0% 0% 0% 0% 0% 18% 21% 21% 0%
15% 13% 20% 18% 23% 26% 24% 40% 27% 31%
27% 28% 34% 33% 41% 47% 37% 74% 35% 99%
0% 0% 0% 19% 0% 5% 19% 13% 10% 0%
14% 19% 20% 24% 23% 22% 23% 18% 18% 7%
where “TB” is the average boiling point in degrees Rankine and “d” is the specific gravity 60°/60 °F. The characterization factor “K” and the correlation index “CI” imply the presence of paraffin or naphthene or aromatic concentration in the crude oil sample. Both of these formulas used for oil classification are a function of boiling point and density. Fig. 8 shows the density of some of the most commonly found petroleum molecules plotted against their boiling points (Haynes, 2014) and refractive index. The size of the data points correspond to carbon number and the color of the data points indicates the structure the hydrocarbon molecule (paraffinic, naphthenic, and aromatic). It is clear from the plot in Fig. 8 that the boiling point is a non-linear function of density and the refractive index is a linear function of density. For instance, paraffinic C6 H14, naphthenic C6 H12, and aromatic C6 H6 all have boiling points in the range of 50 °C–100 °C (represented as S1_2 in the incremental S1 method). However, the density of these molecules that correspond to API gravity are different. Therefore, it is impossible to establish a correlation between API gravity and boiling point distribution from the results presented above. However, Table 4 presents the carbon numbers associated with each of the incremental S1 parameters on the basis of boiling points information sourced from Riazi (2005). For example, a sample with S1_5 measured as 30% implies, that the mass percent of C12eC13 for paraffin and naphthenes, (C11eC13 for aromatics) normalized to C5-C16 is 30%. Hence, the carbon number distribution of a rock sample can be inferred by using the new Incremental S1 method. Fig. 8 also shows that density plotted against the refractive index has a straight line correlation. In the book, Characterization, and Properties of Petroleum Fractions, by Riazi (2005) he argues for using the refractive index as a factor for characterizing crude oils. George (2015) presented a correlation of refractive index and density, another study mentioning the importance of refractive index in determining the density of crude oil. Hence, we strongly argue that, the refractive index of oil present in a rock sample can be attained using FTIR (Fourier transform infrared) instruments (Kronig and Kramers, 1927) with rock background subtraction. Then refractive index data determined from FTIR and the boiling point data can be used in equations (5.1) and (5.2) to solve for “K” and “CI”. After solving for the oil classification parameter “K” and “CI”, these parameters can be used in conjunction with the boiling point data, to predict the approximate composition of oil present in a candidate rock sample. In summary, although the information from the new incremental S1 method allows for identifying the boiling point fractions corresponding to a carbon number of hydrocarbon molecules, it is not complete to
API API API API API API API API API API
Gravity Gravity Gravity Gravity Gravity Gravity Gravity Gravity Gravity Gravity
(< 20) (20–25) (26–30) (31–35) (36–40) (41–45) (46–50) (51–55) (56–60) (> 60)
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Ave. Min. Ave. Min. Ave.
Max.
Ave. Min. Min.
Max.
Vapor fraction at 150 °C Vapor fraction at 100 °C Vapor fraction at 50 °C
Normalized to six Peaks API group
Table 3 The summary of the results from 192 simulations of the incremental S1 method (Normalized).
Max.
Vapor fraction at 200 °C
Max.
The formula for the Correlation Index is the following:
77
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Fig. 5. Fraction of petroleum vaporizing at temperatures above 300 °C (at atmospheric pressure) verses API gravity.
Fig. 6. Boiling point distribution results from incremental S1 experiments with artificial and real samples overlaid on simulation results, for API gravity range of 46°–50°.
artificial and real samples, providing a new possibility for determining the boiling point distribution of oil-saturated (or partially saturated) rock samples. 2: A new data processing schema which includes extrapolation of source rock pyrolysis results is presented. 3: The incremental S1 experiments are validated by an extensive set of numerical simulations based on oils from around the world. 4: A new insight on the effect of API gravity on petroleum fractions vaporizing at temperatures > 300 °C in rock samples is presented. From the results of the simulations, it is clear that with decreasing API gravity of oil the fraction of petroleum vaporizing above 300 °C increases. 5: A comprehensive set of boiling point results to infer the distribution of carbon number using the novel incremental S1 method is demonstrated. Finally, the value of using the Refractive Index in crude oil
predict the density (API gravity) or composition of crude oil in rock samples. However, when this boiling point information is used in conjunction with refractive index data, an approximate composition of oil present in rock can be predicted, not to mention the density based (API gravity) classification of oil present in the rock samples. Therefore, this new incremental S1 method is a necessary first step towards attaining a complete composition assay from rock sample in the absence of liquid samples.
6. Conclusion The summary is as follows; 1: Experiments were undertaken with the new incremental S1 method using a wide API gravity range of 78
Journal of Petroleum Science and Engineering 170 (2018) 71–80
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Fig. 7. A comparison of the pyrolysis of incremental S1 peaks and hydrocarbons boiling point data.
Fig. 8. Density versus refractive index and boiling point of hydrocarbon molecules. Sourced from (Haynes, 2014). Table 4 Carbon numbers of hydrocarbon molecules corresponding to boiling points and incremental S1 parameters; data from (Riazi, 2005). Incremental S1 parameters (mg or HC/g of rock)
S1_1
S1_2
S1_3
S1_4
S1_5
S1_6
Boiling points (°C @ 1 atm)
< 50
50–100
100–150
150–200
200–250
250–300
> 300
5 5 5
6–7 6–7 6–7 6
8–9 8–9 8 7–8
10–11 10 9–11 9–10
12–13
14–17
17 +
12–13 11–13
14–16 14–16
17 + 17 +
Carbon Number
Paraffins Olefins Naphthenes Aromatics
References
classification for density (API gravity) is demonstrated.
Abrams, M.A., Gong, C., Garnier, C., Sephton, M.A., 2017. A new thermal extraction protocol to evaluate liquid rich unconventional oil in place and in-situ fluid chemistry. Mar. Petrol. Geol. 88, 659–675. Beti, D.R., 2016. Determination of American Petroleum Institute Gravity of Petroleum in the Rock Using Pyrolysis. The University of Utah, Salt Lake City. Bordenave, M.L., 1993. Applied Petroleum Geochemistry. Editions Technip, Paris. Chidsey, T.C., Hartwick, E.E., Johnson, K.R., Schelling, D.D., 2011. Petroleum geology of providence oil field, Central Utah Thrust Belt. UGA Publ. 40, 213–231. Clem, Keith M., Brown, K.W., 1984. Petroleum Resources of the Paradox Basin - Keith M. Clem, Karl W. Brown - Google Books. [WWW Document]. Pet. Resour. Parad. Basin. https://books.google.com/books?id=x0VrRXzp_nIC&pg=PA42&lpg=PA42&dq= Cane+Creek+shale+formation+api+gravity&source=bl&ots=pKtzjANqKY& sig=4ZOkfZQh53qOt9DmuXnesoZkFSc&hl=en&sa=X&ved= 0ahUKEwjK7trZkbTVAhUqqVQKHSLkAg0Q6AEITDAG#v=onepage&q&f=false (accessed 8.5.17).
Acknowledgment We acknowledge the support of the Department of Chemical Engineering and the Energy & Geoscience Institute at the University of Utah.
Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.bbrc.2017.04.153 79
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Kronig, R.L., Kramers, H.A., 1927. Atti Congr. Intern. Fisici. Como 2, 545. Lauth, R.E., 1978. Oil and Gas Fields of the Four Corners Area. Four Corners Geological Society. Maende, A., n.d. Wildcat compositional analysis for conventional and unconventional reservoir assessments HAWK petroleum assessment method (H-PAM)™. Application Note (052016–1). Riazi, M.R., 2005. Characterization and Properties of Petroleum Fractions, first ed. ASTM, Philadelphia. Speight, J., 2006. The Chemistry and Technology of Petroleum, fourth. ed. CRC Press. Tissot, B.P., Welte, D.H., 1978. Petroleum Formation and Occurrence. Springer Berlin Heidelberg, Berlin, Heidelberg. Uygur, K., Picard, M.D., 1980. Reservoir characteristics of jurassic Navajo sandstone, southern Utah. In: Henry Mountain Symposium, vol. 8. Utah Geological Association Publication, pp. 277–286. Yiadom, R.B., 2017. Petroleum Quality Analysis within the Utica-Point Pleasant Play of Ohio, United States. The University of Utah, Salt Lake City.
Espitalié, J., Laporte, J.L., Madec, M., Marquis, F., Leplat, P., Paulet, J., Boutefeu, A., 1977. Méthode rapide de caractérisation des roches mètres, de leur potentiel pétrolier et de leur degré d’évolution. Rev. l'Institut Français du Pétrole 32, 23–42. Espitalie, J., Deroo, G., Marquis, F., 1985a. La pyrolyse Rock-Eval et ses applications. Première partie. Rev. l'Institut Français du Pétrole 40, 563–579. Espitalie, J., Deroo, G., Marquis, F., 1985b. La pyrolyse Rock-Eval et ses applications. Deuxième partie. Rev. l'Institut Français du Pétrole 40, 755–784. Espitalie, J., Deroo, G., Marquis, F., 1986. La pyrolyse Rock-Eval et ses applications. Troisième partie. Rev. l'Institut Français du Pétrole 41, 73–89. George, A.K.,S.R.N., 2015. Correlation of refractive index and density of crude oil and liquid hydrocarbon. Int. J. Chem. Environ. Biol. Sci. 3. Haynes, W.M., 2014. CRC Handbook of Chemistry and Physics, 95th Edition. CRC Press. Heath, J.E., Dewers, T.A., Chidsey, T.C., Carney, S.M., Bereskin, S.R., 2017. The Gothic Shale of the Pennsylvanian Paradox Formation, Greater Aneth Field (aneth unit), Southeastern Utah: Seal for Hydrocarbons and Carbon Dioxide. Huc, A.-Y., 2013. Geochemistry of Fossil Fuels from Conventional to Unconventional. Editions Technip, Paris.
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Journal of Petroleum Science and Engineering journal homepage: www.elsevier.com/locate/petrol
A new method of source and reservoir rock pyrolysis to determine the boiling point distribution of petroleum in rock samples
T
Dhrupad R. Betia,b,∗, David J. Thula, Terry A. Ringb, John D. McLennana,b, Raymond Leveya a b
Energy & Geoscience Institute, The University of Utah, UT, 84108, USA Department of Chemical Engineering, The University of Utah, UT, 84112, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Pyrolysis S1 Incremental S1 Free petroleum Refractive index HAWK™
This paper presents a new method of source and reservoir rock pyrolysis used to understand the boiling point distribution of petroleum in rock samples. Experiments and simulations of the new incremental S1 method were performed on a range of rock types and API gravities (Black oil < 40, Volatile oil 40–45, Gas condensate ≥ 50 and Wet gas > 50). Comparison of the results from experiments and simulations are presented. The boiling point distribution results from the new incremental method can be used to infer the carbon number distribution of oil present in rock samples. However, the boiling point distribution data from the incremental S1 method is not adequate to determine the API Gravity of oil present in rock samples. Refractive index (RI) and density of oil have a straight line correlation. The boiling point distribution data from the Incremental S1 can be used in conjunction with the refractive index data to predict the composition of oil present in rock samples.
1. Introduction The objective of this study is to experimentally determine the boiling point distribution of crude oil present in a rock sample. A rapid and economical method for characterizing the boiling point distribution of oil present in the pores of the rock - in the absence of a liquid sample - is highly valuable. The boiling point distribution can indicate the carbon number associated with hydrocarbon molecules (Riazi, 2005). This information is useful in a wide range of disciplines in the oil and gas industry including; exploration, drilling, well completions, production engineering, reservoir engineering and distillation. For instance, a geologist can use this technique in the laboratory and at the well site to understand boiling point distribution and further understand the carbon numbers of oil in rocks, to identify a reservoir and further locate a target zone. A drilling or completions engineer can use the same information to identify loading zones for a horizontal well and where to perforate. A production and reservoir engineer can use this information to identify producible zones in the reservoir. A chemical engineer in a refinery can understand the distillate fractions even before the product comes from a new discovery. Speight (2006) describes a distillation procedure to determine the boiling point distribution of a crude oil. Following Speight's procedures for determining the boiling point distribution of a crude oil sample is relatively straightforward. For example, an oil sample might be acquired from an active seep or an oil producing well by surface sampling.
∗
When a liquid sample is not available from an exploration program or before a well has been completed and brought on to production, the ability to understand a boiling point distribution of the crude oil present in situ is limited. However, in the presence of a source or a reservoir rock sample, source rock pyrolysis has historically been used to determine the quantity of free petroleum in such a rock sample (Espitalie et al., 1977, 1985a, 1985b, 1986). The analysis of rock samples, with conventional source rock pyrolysis methods, does not provide boiling point distributions or API gravity. In order to attain this more specific information, standard source rock pyrolysis methods have to be altered. Very few techniques enable classification of crude from rock sample pyrolysis. The HAWK-PAM™ - Petroleum Assessment Method, (Maende, n. d.) is one such pyrolysis method. This method is an alteration of conventional source rock pyrolysis and allows prediction of API gravity by analysis of a rock sample itself. This study presents one such method, another alteration of the conventional source rock pyrolysis method, to attain the boiling point distribution of oil in rock samples. The incremental S1 method was first introduced in a previous work (Beti, 2016). Yiadom (2017) adapted Incremental S1 method to understand the quality of petroleum in the Utica play, USA. Abrams et al. (2017) used a similar method to propose an extraction protocol to evaluate oil in place in unconventional plays. The incremental S1 method presented in this study provides new insights that allow for carbon number classification of oil-containing rock samples, through boiling point distribution. Unlike other distillation-related experimental techniques
Corresponding author. Energy & Geoscience Institute, The University of Utah, UT, 84108, USA. E-mail address: [email protected] (D.R. Beti).
https://doi.org/10.1016/j.petrol.2018.06.036 Received 26 March 2018; Received in revised form 3 June 2018; Accepted 16 June 2018 Available online 19 June 2018 0920-4105/ Published by Elsevier B.V.
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Fig. 1. FID and IR signals of a sample in the pyrolysis with TOC method, showing the assigned S1, S2, S3, S4, and S5 parameters as a function of temperature modified from (Mccarthy et al. n. d.).
increases the fidelity of the S1 peak. In the conventional pyrolysis method, S1 measures the quantity of free petroleum. This peak is measured at a temperature range of 100 °C to 300 °C. In the new Incremental S1 method, multiple temperature steps (isotherms) are created with a rapid temperature increase between each of the isotherms. The isotherms in this method are in the range of 50 °C–300 °C. Based on the boiling points of the hydrocarbon molecules, these multiple FID peaks measured at each temperature step, achieve fractionation of petroleum present in the sample. Consequently, the quantity of free petroleum measured in the conventional S1 peak is split into multiple peaks in the new incremental S1 method providing a quantitative measurement of free petroleum with higher fidelity (boiling point distribution). To exclude the possibility of fractionating bitumen or kerogen in the rock sample, the maximum temperature of the incremental S1 method is set to 300 °C.
presented by Speight (2006), this method only requires a rock sample for analysis. The sample can be a small subset of a core or collected drill-cuttings. The new method presented in this paper is based on source rock pyrolysis that was first introduced by Espitalié and his colleges in the 70s and 80s (Espitalié et al., 1977, 1985a, 1985b, 1986). These pyrolysis experimental methods are widely accepted and applied to understand petroleum systems (Bordenave, 1993; Huc, 2013). One of the most important experimental methods is “pyrolysis with total organic carbon (TOC)” method informally known as ”PyroS3650_TOC750 or classic pyrolysis”. A general schematic of this experimental method is shown in Fig. 1. In this conventional pyrolysis experimental method, the raw signals from three detectors (FID: Flame Ionization Detector, IR CO: Infrared CO cell, and IR CO2: Infrared CO2 cell) are recorded as a function of time, temperature, and experimental environment. From these data, a set of parameters; S1, S2, S3, S4, S5, and Tmax are experimentally determined (Espitalié et al., 1977, 1985a; 1985b). Both, Bordenave (1993) and Huc (2013) have presented the procedure to calculate these parameters, a brief summary is presented in the following paragraphs. The FID signal is studied in two sections, S1 and S2. The area under the FID signal, produced by the initial heat up from 100 °C to 300 °C followed by a 3 min isotherm, corresponds to the S1. S1 is an indication of the quantity of free petroleum present in a rock sample at the time of analysis. The area under the FID signal, produced by increasing the temperature from 300 °C to 650 °C, at a heating rate of 25 °C/min corresponds to S2. S2 is an indication of the kerogen content present in a rock sample. The temperature recorded for the maximum of S2 peak varying as a function of thermal maturity is Tmax, it is an indication of the maturity of kerogen present in the sample. Both S1 and S2 have units of milligrams of hydrocarbons per gram of rock (mg of HC/g of rock). These units are applied from a calibration. While the FID signal is the focus of this paper, a brief description of the other parameters follows. As shown in Fig. 1, the IR signals from the carbon monoxide and carbon dioxide detectors are segregated into the S3, S4, and S5 parameters. S3 which includes S3 CO and S3 CO2 is a measure of the amount of CO and CO2 released as a result of decomposition of organic matter in an inert, He atmosphere. S4 which includes S4 CO and S4 CO2 is a measure of the amount of CO and CO2 released as a result of decomposition of inorganic matter in the presence of an air atmosphere. S5 is the measures of the amount of CO2 released as a result of decomposition of inorganic matter at higher temperatures in presence of an air atmosphere. A new “incremental S1 method” is discussed in this paper. This method is a result of a series of trial and error experiments presented in a previous study (Beti, 2016). The new Incremental S1 method
2. Material and methods In order to understand the results from the multiple peaks of this incremental S1 method, knowing the composition and/or API gravity of oil present in the rock sample is essential. API gravity is used universally to classify oils. Comparisons of the experimentally determined boiling point distribution and simulated boiling point distribution results corresponding to API gravity have been made in this study. 2.1. Samples First, a set of artificial samples were created. Eleven oil samples with an API gravity ranging from 18° to 53.5° (refer to Table 1) were introduced into 1 × 0.5 × 0.5 inch3 sandstone samples (mean porosity 14%, and mean pearmibility 463 millidarcy - Uygur and Picard, 1980), containing no prior petroleum with the help of a custom built, vacuum setup. The sandstone cubes were placed on the vacuum setup which was connected to a suction pump creating a suction pressure of −15 mm Hg. Approximately 2–5 ml of oil were then back-flooded into each sample, due to the pressure difference applied to the rock, the oil percolated into the rock sample. Depending on the oil's density and viscosity, the oil entered the pores of the rock within 5–60 min. Multi-stage two phase flash separation simulations of temperature isotherms of the incremental S1 method were also performed, in order to verify and compare the results of the artificial samples measured using the incremental S1 method. One hundred ninety two (192) oil samples were used for the simulations. These oils were sourced from the oil library of ProMax Software; with known API gravities ranging from 18 to 72. This oil library contains boiling point data for these 192 oils which have been - curated from all over the world. 72
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Table 1 The summary of the results from artificial sample experiments performed with the incremental S1 method. Sample names
S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug. S.Aug.
2016.00065 2016.00068 2016.00084 2016.00085 2016.00093 2016.00098 2016.00100 2016.00101 2016.00104 2016.00122 2016.00125
API gravity
33.7 27.0 32.7 33.1 29.3 18.0 33.7 53.5 32.5 28.3 48.1
Formation
NA NA NA NA NA NA NA NA NA NA NA
Sample type
Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered Engineered
Normalized Data (after curve correction) - Boiling Point Distribution
S1 (mg of HC/g of rock)
S1_1
S1_2
S1_3
S1_4
S1_5
S1_6
0% 0% 0% 0% 1% 0% 0% 0% 0% 1% 1%
12% 15% 17% 16% 13% 17% 23% 11% 25% 23% 35%
22% 25% 26% 25% 23% 26% 28% 16% 30% 29% 34%
29% 27% 28% 27% 28% 28% 24% 32% 24% 25% 16%
21% 19% 16% 17% 19% 16% 16% 22% 12% 13% 8%
16% 15% 13% 15% 16% 13% 9% 19% 9% 8% 7%
In order to compare the results from the artificial sample – incremental S1 experiments and the comparable numerical simulations, a set of real (real-world) samples were also subjected to the incremental S1 method. These real-world samples, chosen for confirmation experiments, are representative of three major reservoir rock types (shale, carbonate, and sandstone). The accurate API gravity of oil present in these “real-world” samples is difficult to estimate. The currently available method to estimate the API gravity entails analyzing the oil produced from these rock formations recovered at the surface or occasionally sampled downhole. It is important to note that such produced oil could be sourced from one or many perforate zones, and likely a part oil in the rock is immobile. Furthermore, there is a possibility of crude oil homogenization in the wellbore. However, the following is the sample information and some studies that state the API gravity of the oil produced from these formations.
1.44 3.98 4.22 3.97 4.53 4.22 4.00 1.98 7.26 8.75 2.40
the supporting document. All these samples were pulverized as for the sample specification of HAWK™ Instrument and approximately 75 mg of each sample was used for the pyrolysis experiments. 2.2. Incremental S1 method
# of Well samples API#
Basin
Sample Type
Geologic Unit
Reference Oil API gravity
4
43 0195 0019
Paradox
Core
(Clem and Brown, 1984)
38.4°
4
43 0195 0019
Paradox
Core
(Clem and Brown, 1984)
38.4°
6
43 037 30165
Paradox
Core
43 037 30165
Paradox
Core
(Heath et al., 2017) (Lauth, 1978)
41
5
6
43 041 30036
Utah Core Hingeline
Cane Creek Shale Unit A (fine grained silty dolomite/ shale) Cane Creek Shale Unit B (fine grained silty dolomite/ shale) Gothic Shale (shale) Desert Creek fm. (limestone and dolomite) Upper Navajo SS. (sand stone)
(Chidsey et al., 2011)
48°
40°
As indicated in the introduction, the new incremental S1 method focuses on improving the fidelity of the S1 peak. The following paragraph describes the method. It is programmable on anhydrous pyrolysis instruments. Like the conventional pyrolysis method, the experiments with the incremental S1 method are performed in an inert, He atmosphere at 0.8 bar (atmospheric pressure). Fig. 2 shows the schematic of the incremental S1 method, illustrated with an example. This method is designed such that the sample undergoes multiple temperature steps. Fig. 2 shows the FID signal as a function of step changes in temperature. The temperature profile, indicated in red, is comprised of 6 isotherms at < 50 °C, 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C. Each temperature increment is imposed with a ramp rate of 200 °C/min at every 50° interval (from 100 °C to 150 °C, 150 °C–200 °C, and so on until 300 °C). The isotherms at < 50 °C, 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C, are referred to as Incremental S1 temperatures from here on. The FID signal peaks, indicated in black in Fig. 2, shows 6 peaks corresponding to the isotherms (< 50 °C, 100 °C, 150 °C, 200 °C, 250 °C, and 300 °C) these peaks are named S1_1, S1_2, S1_3, S1_4, S1_5, and S1_6 respectively. All the 6 peaks use a calibration to give the same units as S1 (mg of HC/g of rock). In the incremental S1 method, the initial temperature increase from ∼50 °C to 100 °C is due to the introduction of the sample into the oven. The sample is not in the oven for the first 5 min of the method, making it difficult to directly control the sample temperature. Hence, the first isotherm is not exactly at 50 °C. In the interest of a minimum, effective, runtime, the isotherm durations are set at 5 min, except for the last isotherm at 300 °C for 10 min. If the FID signal does not return to baseline at the end of each isotherm, the peak decline data is used to numerically extrapolate the exponential decay curve. The area under this extrapolated exponential curve (tail) is then subtracted from the respective subsequent curves and added back to the original curve. This extrapolation process can be performed on one or several peaks. For example, the extrapolation process was applied for the peaks S1_2, S1_3, S1_4 and S1_5 in Fig. 2. The last isotherm at 300 °C is set at 10 min, assuming the FID signal reaches baseline in 10 min. 2.3. Data processing The extrapolation of the FID signal curves cannot be performed using the pyrolysis instrument's software. Therefore, all the data processing in this study is performed using MatLab™. The processing of the experimental data is divided into 3 steps. The
All of the sample information gathered for this study is provided in 73
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Fig. 2. An example pyrogram illustrating the schematic of the incremental S1 method.
Fig. 3. Comparison of un-extrapolated and extrapolated data of a sample processed in MatLab software.
first step includes calculating the pyrolysis coefficient using a standard. Then, the pyrolysis coefficient is used to calibrate the data, and further calculate the mg of HC/g of rock for each incremental S1 peak. The last step includes curve correction by extrapolation of the FID signal curve and assigning a corrected value to S1_1, S1_2, and so on through S1_6 values respectively. These steps are explained in the following paragraphs. In the first step, the uncalibrated raw experimental data of all the samples (including standard samples data) is used as an input to a Matlab routine. First, the area under the FID signal curve is calculated for all the samples (including the calibration standard). Then only the raw experimental data of a standard sample with known mg of HC/g of rock is used to calculate the pyrolysis coefficient in a Matlab code; pyrolysis coefficient is a conversion factor used for calibration purpose. In the second step, the calibration process involves the conversion of the raw FID signal measured in millivolts to mg of HC/gm of rock, for each unit area under the FID signal curve. A validation of this calibration process was performed. Around 50 samples that have been tested with the conventional pyrolysis method (PyroS3650_TOC750) were used for validation. For these 50 samples, raw signal data were collected and calibrated using both the HAWK instrument software and a Matlab code. The results calibrated by the pyrolysis instrument were compared with the results calibrated using the pyrolysis coefficient generated using Matlab. The comparison of the two datasets resulted in a root mean square value of > 0.98. This indicates that there is little or no difference in the results using the two approaches. After the
validation, the pyrolysis coefficient was used to calculate the results (HC content corresponding to each peak) from the experiments performed using the incremental S1 method. In the last step, the FID signal data is extrapolated for the incremental S1 peaks which do not reach the FID baseline and further subtraction of area from the subsequent peaks was performed. The R squared value for the exponential decay curve fit with FID signal decline for each peak was ≥0.98. It is important to ensure that, though the incremental S1 values after the extrapolation vary from the initial incremental S1 values the overall free petroleum content remains the same. Hence, a validation of all the overall free petroleum content results (S1 or sum of S1_1, S1_2, S1_3, S1_4, S1_5, and S1_6) calculated with and without the extrapolation was also performed. The comparison of the two datasets resulted in a root mean square value of > 0.99. This indicates that the process of extrapolation of the curves followed by subtraction of the tail area from the subsequent curves was performed accurately, and there was no over-estimation or under-estimation of the total area (free petroleum content) as a result of extrapolation method. Fig. 3 shows a comparison between un-extrapolated and extrapolated data for the same sample. It is important to note that the incremental S1 values in Fig. 3 are different, but the overall free petroleum content (S1) is the same.
3. Experiments, simulations and initial observations Table 1 is a summary of the predicted boiling point distributions for 74
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Table 2 The summary of the results from real (real-world) sample experiments performed with the incremental S1 method. Sample names
API gravity
S.Sep. 2015.00033
38
S.Sep. 2015.00034
38
S.Sep. 2015.00035
38
S.Sep. 2015.00036
38
S.Sep. 2015.00037
38
S.Sep. 2015.00038
38
S.Sep. 2015.00039
38
S.Sep. 2015.00041
38
S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep. S.Sep.
41 41 41 41 41 41 40 40 40 40 48 48 48 48 48 48
2015.00042 2015.00043 2015.00044 2015.00045 2015.00046 2015.00047 2015.00052 2015.00053 2015.00054 2015.00055 2015.00057 2015.00058 2015.00059 2015.00060 2015.00061 2015.00062
Formation
Cane Creek Shale A Cane Creek Shale A Cane Creek Shale A Cane Creek Shale A Cane Creek Shale B Cane Creek Shale B Cane Creek Shale B Cane Creek Shale B Gothic Shale Gothic Shale Gothic Shale Gothic Shale Gothic Shale Gothic Shale Desert Creek fm. Desert Creek fm. Desert Creek fm. Desert Creek fm. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS. Upper Navajo SS.
Sample type
Normalized Data (after curve correction) - Boiling Point Distribution S1_1
S1_2
S1_3
S1_4
S1_5
S1_6
S1 (mg of HC/gm of rock)
Unit
Real
0%
15%
33%
24%
15%
12%
1.96
Unit
Real
0%
13%
28%
24%
19%
16%
8.29
Unit
Real
2%
22%
28%
24%
16%
9%
0.97
Unit
Real
0%
27%
32%
20%
12%
8%
4.77
Unit
Real
1%
20%
35%
22%
12%
11%
0.48
Unit
Real
0%
9%
31%
30%
19%
12%
3.58
Unit
Real
0%
17%
35%
26%
13%
8%
1.79
Unit
Real
0%
24%
37%
20%
10%
8%
2.32
Real Real Real Real Real Real Real Real Real Real Real Real Real Real Real Real
0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
10% 11% 11% 10% 11% 9% 20% 19% 16% 18% 19% 20% 19% 18% 15% 20%
20% 20% 21% 19% 20% 19% 31% 32% 31% 28% 29% 29% 30% 29% 29% 29%
25% 24% 25% 25% 25% 25% 27% 27% 27% 28% 28% 27% 27% 28% 30% 26%
24% 23% 23% 24% 23% 24% 15% 15% 17% 18% 16% 16% 16% 17% 17% 15%
22% 22% 21% 22% 21% 22% 6% 7% 8% 8% 8% 8% 8% 8% 9% 10%
3.43 3.45 3.03 2.84 2.91 3.18 2.70 3.09 5.73 5.81 4.35 5.08 8.51 6.01 6.46 5.34
between the experiments and the simulations, the feed of the first flash separator was set to be mass flow (100 kg/s). Table 3 is a summary of results from the 192 simulations of the incremental S1 method. In this table, the results from all the 192 oil simulations are grouped according to the API gravity. All of the individual simulation results are provided in the supporting documents. The results are segregated into 10 API groups, with each group having a 5° API gravity range, starting from 18° going to 72°. The normalized (for 6 parameters – S1_1, S1_2, S1_3, S1_4, S1_5 and, S1_6) vapor fractions of each API gravity group at the incremental S1 temperatures is presented in this table (Table 3). The non-normalized vapor fractions of each API gravity group at the incremental S1 temperatures is presented in the supporting documents. The minimum, average and maximum percentages of each API gravity group, recorded at all the incremental S1 temperature are provided in these tables. The data presented in Table 3 shows no clear trend between the API gravity group and boiling point distribution. Moreover, depending on the density of oil, a specific amount oil vaporizes at temperatures above 300 °C; see Fig. 5. For instance, in a crude oil with an API gravity < 20° the average fraction of petroleum vaporizing at temperatures above 300 °C is 87%. Not surprisingly, in a crude oil with an API gravity > 60°, the average fraction of petroleum vaporizing at temperatures above 300 °C is 2%. This indicates that the quantity of free petroleum is likely under-estimated regardless of the pyrolysis method. Furthermore, it is not possible to estimate the overall fraction of free petroleum available to be detected by S1 or Incremental S1 (at temperatures higher than 300 °C), without prior knowledge of the API gravity.
the artificial sample experiments, performed with the incremental S1 method. Table 2 is a summary of the predicted boiling point distributions for the real-world sample experiments, performed with the incremental S1 method. Both of these tables show the overall quantity of free petroleum denoted by S1 and the boiling point distribution (in percentage) of hydrocarbon groups present in the sample. The Incremental S1 values presented in these tables show the extrapolated and normalized data from the 6 peaks. Non-normalized, pre - and post extrapolation results for both sets of experiments are provided in the supporting documents. From the results presented in Tables 1 and 2, it can be observed that there is no clear trend in the boiling point distribution denoted by the incremental S1 percentages. For most of the samples, irrespective of the API gravity, S1_3, and S1_4 values are greater than the rest of the incremental S1 peak values. This indicates that, for these samples, the majority of the hydrocarbon molecules, have the boiling point in the range of 100 °C–200 °C. From the data in Table 2, it can be observed that for Cane Creek Shale Unit A samples, the overall free petroleum content (S1) and the boiling point distribution vary within the same formation. This indicates heterogeneity of oil in the Cane Creek Shale Unit A samples. 192 simulations of the incremental S1 method were also performed to compare simulations results with the Incremental S1 experimental results. A 2 phase, 7 stage flash vaporization was performed in ProMax software, using the Peng-Robinson equation of state. The first stage of flash vaporization is at room temperature and atmospheric pressure. This mimics the same initial conditions that are applied in the actual incremental S1 experiments. The subsequent, six consecutive stages in the simulation are designed to replicate the incremental S1 experimental conditions. Fig. 4 is a schematic of the ProMax two-phase flash separation at incremental S1 temperatures. The pyrolysis experiment results have mass units. In order to have consistent units for comparison
4. Results The boiling point distribution data from the simulations are presented as vapor fractions (vapor – mass fractions) in Table 3. This data 75
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Fig. 4. Schematic of ProMax two phase separation at IS1 temperatures and atmospheric pressure.
heterogeneous boiling point distribution of Cane Creek shale unit A samples, the incremental S1 values from the experiments and simulations are consistent, except for one sample (with 53° API gravity). The experiments are consistent with the simulations. If a relationship between API gravity and boiling point distribution is established, it could be used for interpretation of the incremental S1 data. The observations made from the simulation results presented in Table 3 are as follows. The overall quantity of hydrocarbon molecules vaporizing at > 300 °C, increases with an increase in the density of the oil. The boiling point distribution data do not correspond to a unique API gravity group. For example, the boiling point range of 31°–35 °API gravity at 100 °C is 0%–7%. Alternatively, the boiling point range of a 35°- 40 °API gravity oil at 100 °C is 0%–10%. In other words, each API gravity group does not correspond to a unique boiling point distribution. Consequently, the boiling point distribution data alone cannot be used to classify the oil present in the rocks.
is comparable to the experimentally determined boiling point distribution data (vapor – mass fractions) of the artificial and real samples presented in Tables 1 and 2 respectively. The following are the comparison made. The boiling point distribution data of one artificial sample containing 53.5° API gravity oil presented in Table 1, does not fall in the boiling point distribution range (minimum-maximum) of API gravity ranging from 51° to 55° presented in Table 3. The experimental data for this sample in Table 1 indicates the presence of hydrocarbon molecules with higher boiling points than are predicted by the simulations. One explanation for this could be that the oil might have lost its lighter hydrocarbon molecules before the actual experiment. Excluding this one sample with 53.5°API, the boiling point distribution of all of the experimental data presented in Tables 1 and 2 falls within the respective boiling point distribution range of the simulation data presented in Table 3, with a few outliers. For example, Fig. 6 shows the boiling point distribution results from the incremental S1 experiments with artificial and real samples, overlaying the simulation results from the API gravity group (46°–50°). In this Figure, the y-axis represents the boiling point distribution (in mass fractions), and the x-axis represents the boiling point temperature (in °C). This figure show results from both the simulations and experiments of the all the samples corresponding to API gravity ranging from 46° to 50°. The orange data points indicate the results from the simulations, the blue, and red data points indicate the results from experiments with real and artificial samples, respectively. This figure indicates that, the experimentally determined boiling point fractions at higher temperatures (250 °C and 300 °C) fall in the lower portion of the simulations data, whereas the same experimentally determined boiling point fractions at lower temperatures (100 °C and 150 °C) fall in the upper end of the simulation data. This trait was observed in all the simulation and experiment comparisons when plots similar to Fig. 6 are used (presented in supporting documents). From the comparisons stated above, it can be stated that the majority of the boiling point distribution data generated from the experiments correspond to the respective boiling point distribution ranges generated from the simulations. This indicates, in spite of, the absence of a clear API - boiling point correlation in the experimental data,
5. Discussion In order to further understand the nonunique API gravity - boiling point distribution relationship, Fig. 7 compares of the incremental S1 peaks with the hydrocarbon boiling point data. The lower half of the figure shows the boiling point of some of the hydrocarbon molecules commonly found in crude oil samples (Haynes, 2014). The color of the data points indicates the hydrocarbon structure. The upper half of the figure is an incremental S1 pyrogram, showing the FID signal as a function of temperature. The arrows corresponding to temperature indicate the hydrocarbon molecules vaporizing at the respective incremental S1 temperatures. This figure indicates that each incremental S1 peak may contain different amounts and types of hydrocarbon structures like paraffins, naphthenes, and aromatics etc, which has a significant effect on the density of oil (Tissot and Welte, 1978). Fig. 7, does not include the boiling point data of complex molecules, such as olefins, or NSO (nitrogen, sulfur, and/or oxygen) compounds. Yet, it can be observed that it is highly likely to have an eclectic mix of hydrocarbon molecule structures that contribute to each of the incremental S1 peaks. The density of these different structures contributes to the overall 76
Journal of Petroleum Science and Engineering 170 (2018) 71–80
100% 100% 60% 63% 79% 46% 26% 16% 9% 10% 47% 50% 33% 30% 28% 23% 20% 9% 6% 3% 32% 48% 40% 37% 32% 51% 27% 17% 19% 14% 20% 28% 26% 25% 23% 24% 21% 11% 14% 6%
0% 22% 23% 22% 19% 14% 15% 0% 1% 0%
Max. Ave. Max. Ave.
Min.
density of the crude (API gravity). Consequently, it is not possible to predict the API gravity of oil present in rocks from the incremental S1 data (boiling point distribution) alone. In the book, The Chemistry and Technology of Petroleum, fourth. Ed (Speight, 2006), the author states that “A method for the classification of crude oils can only be efficient, first, if it indicates the distribution of components according to volatility, and second, if it indicates the characteristic properties of the various distillate fractions”. Consequently, in order to identify the oil present in a rock sample using the incremental S1 method, along with understanding the boiling point distribution, knowing the properties of the distillate fractions is important. Speight (2006) presents two formula to classify petroleum. The first being “Universal Oil Product (UOP) Characterization Factor”, and second being “Correlation Index”. The UOP characterization factor is defined as: 1
0% 0% 21% 21% 17% 15% 15% 2% 8% 0%
K= Min.
Vapor fraction at 250 °C
Vapor fraction at 300 °C
D.R. Beti et al.
(TB ) 3 . d
(5.1)
22% 44% 27% 48% 39% 39% 35% 24% 22% 14%
CI = 473.7d − 458.6 +
48,640 . (TB)
(5.2)
0% 0% 0% 1% 1% 2% 0% 0% 0% 2%
0% 1% 0% 3% 2% 3% 0% 0% 0% 4%
0% 0% 0% 0% 0% 0% 0% 0% 18% 0%
8% 5% 5% 7% 7% 9% 13% 22% 36% 52%
21% 10% 11% 15% 20% 22% 25% 42% 50% 96%
0% 0% 0% 0% 0% 0% 18% 21% 21% 0%
15% 13% 20% 18% 23% 26% 24% 40% 27% 31%
27% 28% 34% 33% 41% 47% 37% 74% 35% 99%
0% 0% 0% 19% 0% 5% 19% 13% 10% 0%
14% 19% 20% 24% 23% 22% 23% 18% 18% 7%
where “TB” is the average boiling point in degrees Rankine and “d” is the specific gravity 60°/60 °F. The characterization factor “K” and the correlation index “CI” imply the presence of paraffin or naphthene or aromatic concentration in the crude oil sample. Both of these formulas used for oil classification are a function of boiling point and density. Fig. 8 shows the density of some of the most commonly found petroleum molecules plotted against their boiling points (Haynes, 2014) and refractive index. The size of the data points correspond to carbon number and the color of the data points indicates the structure the hydrocarbon molecule (paraffinic, naphthenic, and aromatic). It is clear from the plot in Fig. 8 that the boiling point is a non-linear function of density and the refractive index is a linear function of density. For instance, paraffinic C6 H14, naphthenic C6 H12, and aromatic C6 H6 all have boiling points in the range of 50 °C–100 °C (represented as S1_2 in the incremental S1 method). However, the density of these molecules that correspond to API gravity are different. Therefore, it is impossible to establish a correlation between API gravity and boiling point distribution from the results presented above. However, Table 4 presents the carbon numbers associated with each of the incremental S1 parameters on the basis of boiling points information sourced from Riazi (2005). For example, a sample with S1_5 measured as 30% implies, that the mass percent of C12eC13 for paraffin and naphthenes, (C11eC13 for aromatics) normalized to C5-C16 is 30%. Hence, the carbon number distribution of a rock sample can be inferred by using the new Incremental S1 method. Fig. 8 also shows that density plotted against the refractive index has a straight line correlation. In the book, Characterization, and Properties of Petroleum Fractions, by Riazi (2005) he argues for using the refractive index as a factor for characterizing crude oils. George (2015) presented a correlation of refractive index and density, another study mentioning the importance of refractive index in determining the density of crude oil. Hence, we strongly argue that, the refractive index of oil present in a rock sample can be attained using FTIR (Fourier transform infrared) instruments (Kronig and Kramers, 1927) with rock background subtraction. Then refractive index data determined from FTIR and the boiling point data can be used in equations (5.1) and (5.2) to solve for “K” and “CI”. After solving for the oil classification parameter “K” and “CI”, these parameters can be used in conjunction with the boiling point data, to predict the approximate composition of oil present in a candidate rock sample. In summary, although the information from the new incremental S1 method allows for identifying the boiling point fractions corresponding to a carbon number of hydrocarbon molecules, it is not complete to
API API API API API API API API API API
Gravity Gravity Gravity Gravity Gravity Gravity Gravity Gravity Gravity Gravity
(< 20) (20–25) (26–30) (31–35) (36–40) (41–45) (46–50) (51–55) (56–60) (> 60)
0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Ave. Min. Ave. Min. Ave.
Max.
Ave. Min. Min.
Max.
Vapor fraction at 150 °C Vapor fraction at 100 °C Vapor fraction at 50 °C
Normalized to six Peaks API group
Table 3 The summary of the results from 192 simulations of the incremental S1 method (Normalized).
Max.
Vapor fraction at 200 °C
Max.
The formula for the Correlation Index is the following:
77
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Fig. 5. Fraction of petroleum vaporizing at temperatures above 300 °C (at atmospheric pressure) verses API gravity.
Fig. 6. Boiling point distribution results from incremental S1 experiments with artificial and real samples overlaid on simulation results, for API gravity range of 46°–50°.
artificial and real samples, providing a new possibility for determining the boiling point distribution of oil-saturated (or partially saturated) rock samples. 2: A new data processing schema which includes extrapolation of source rock pyrolysis results is presented. 3: The incremental S1 experiments are validated by an extensive set of numerical simulations based on oils from around the world. 4: A new insight on the effect of API gravity on petroleum fractions vaporizing at temperatures > 300 °C in rock samples is presented. From the results of the simulations, it is clear that with decreasing API gravity of oil the fraction of petroleum vaporizing above 300 °C increases. 5: A comprehensive set of boiling point results to infer the distribution of carbon number using the novel incremental S1 method is demonstrated. Finally, the value of using the Refractive Index in crude oil
predict the density (API gravity) or composition of crude oil in rock samples. However, when this boiling point information is used in conjunction with refractive index data, an approximate composition of oil present in rock can be predicted, not to mention the density based (API gravity) classification of oil present in the rock samples. Therefore, this new incremental S1 method is a necessary first step towards attaining a complete composition assay from rock sample in the absence of liquid samples.
6. Conclusion The summary is as follows; 1: Experiments were undertaken with the new incremental S1 method using a wide API gravity range of 78
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Fig. 7. A comparison of the pyrolysis of incremental S1 peaks and hydrocarbons boiling point data.
Fig. 8. Density versus refractive index and boiling point of hydrocarbon molecules. Sourced from (Haynes, 2014). Table 4 Carbon numbers of hydrocarbon molecules corresponding to boiling points and incremental S1 parameters; data from (Riazi, 2005). Incremental S1 parameters (mg or HC/g of rock)
S1_1
S1_2
S1_3
S1_4
S1_5
S1_6
Boiling points (°C @ 1 atm)
< 50
50–100
100–150
150–200
200–250
250–300
> 300
5 5 5
6–7 6–7 6–7 6
8–9 8–9 8 7–8
10–11 10 9–11 9–10
12–13
14–17
17 +
12–13 11–13
14–16 14–16
17 + 17 +
Carbon Number
Paraffins Olefins Naphthenes Aromatics
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Acknowledgment We acknowledge the support of the Department of Chemical Engineering and the Energy & Geoscience Institute at the University of Utah.
Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx. doi.org/10.1016/j.bbrc.2017.04.153 79
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