Where:
Gcad air-dry gas content, scf/ton
Gco organic fraction gas content, scf/ton β slope, scf/ton
wa ash content, weight fraction ww moisture content, weight fraction
The vertical axis in Figure 4-1 is the air-dry gas content estimate obtained from each sample. The hori-zontal axis is the inorganic content (ash + moisture content) of each sample determined by proximate analy-sis. The intercept at an ash plus moisture content of zero is equal to the organic fraction gas content estimate for
Figure 4-1.
GRI #2 Air-Dry Gas Content vs. Inorganic Content
(
a w)
co
cad G w w
G = b+ +
the reservoir(s) of interest. We will use this value to compute the in-situ gas content once we determine the average in-situ inorganic content.
The relationship for the seven samples from GRI Observation Well
#2 resulted in an intercept of 908.8 ± 199.4 scf/ton. We estimated the range in the intercept from the confidence intervals. The 95% statistical confi-dence interval estimates (represented by the dashed lines in Figure 4-1) indicates the fit accuracy. The dis-tance between the two 95% confi-dence interval is less when the varia-tion in the sample inorganic content is greater and more samples are avail-able. Had there been more samples with greater inorganic content
varia-tion from this well, the variavaria-tion in the intercept would have been less.
In all GRI western basin research efforts, the cor-relation of total gas content to the inorganic content has resulted in an estimate of zero gas content at an ash plus moisture content less than one. Generally, the zero gas content point was within statistical significance levels of one. For example, the slope of the line illustrated in Figure 4-1 is equal to -1,037.5 ± 379.2 scf/ton and intersects the zero gas content abscissa at 0.876. The range between the intersection of the confidence inter-vals with the horizontal axis includes one.
The extrapolation to a organic fraction gas content may not be statistically valid in the event that few data points are available or if the samples have a narrow range of ash and moisture contents. Such a situation is illustrated by Figure 4-2 for data collected from GRI Well #1 at the COAL Site. The intercept and slope for these six points was 910.7 ± 902.0 scf/ton and -1,264.9
± 3,644.6 scf/ton, respectively. Surprisingly, the inter-cept was very close to the result obtained from the GRI
#2 samples but the confidence in the intercept and slope was much lower.
Averaging the individual sample data does not result in an accurate estimate of the average gas content
of the reservoir as shown by Example 4-1. The problem is that the average of a few samples is not an accurate predictor of the average reservoir condition. The Ex-ample 4-1 estimate of the gas-in-place volume would have been low by 28.7% if based upon the average of the samples. Based upon Example 2-1, the correct gas-in-place volume was 60.28 Bscf per square mile. The error is equivalent to 17.3 Bscf per square mile.
A later section of this chapter will show you how to estimate the number of samples required to obtain statistically significant results. One important point to remember is that the range of the inorganic content of the samples must exceed the standard deviation of the inorganic content of the reservoir for accurate esti-mates.
Composition and Density Relationship
The composition of coal gas reservoirs can be estimated from open-hole density log data and core data. We must relate the inorganic content to density to use the relationship between gas content and inorganic content.
The sample or reservoir density is related to the density of the ash, moisture, and organic fractions by Equation 4-2.
GRI #1 Air-Dry Gas Content vs. Inorganic Content
Where:
ρ density, g/cm3 ρa ash density, g/cm3
ρo organic fraction density, g/cm3 ρw sorbed water density, g/cm3
If we know the sample ash and equilibrium mois-ture contents, we can estimate the density. Therefore, we can convert the horizontal axis of Figure 1 or 4-2 to density. Example 4-4-2 shows you how to apply Equation 4-2.
The density of the ash and organic portions of reservoirs can differ. Sample density can be measured and related to the ash, organic, and moisture contents.
We found that accurate density measurements require helium pycnometry. A helium pycnometer measures
the volume of the sample by helium expansion. Helium is used since it enters coal micropores without adsorp-tion and does not add moisture to the sample. The test is not destructive and accurate results are obtained on samples as small as three grams. Sample density is determined in this manner during sorption isotherm measurements. Instruct the laboratory staff to report the sample density when measuring storage capacity data.
An interpretation method to determine ash and organic fraction density is based upon a rearranged version of Equation 4-2 listed in Equation 4-3.
(4-3)
The relationship illustrated in Figure 4-2 between air-dry gas content and inorganic content is statistically insignificant. Insignificance can be caused by two few points, a narrow variation in the sample inorganic content, or by improper sample split selection for inorganic content measurements. One approach to esti-mating the organic fraction gas content is to average the dry, ash-free gas content estimates from each sample. These are summarized below for the six samples in Figure 4-2.
Sample Ash Moisture Air-Dry Dry, Ash-Free
ID No. Content Content Gas Content Gas Content fraction fraction scf/ton scf/ton
35-1 0.418 0.026 386.2 694.1
35-3 0.349 0.045 450.0 741.7
35-4 0.404 0.044 308.8 559.2
35-6 0.426 0.033 346.8 641.2
35-7 0.350 0.049 372.2 620.0
35-8 0.388 0.041 372.0 650.7
Average 0.389 0.040 372.6 651.2
The average dry, ash-free gas content estimate severely underestimates the organic fraction gas content estimate. The benchmark for these data is 913 scf/ton. Why is the average dry, ash-free gas content so much lower? There are two reasons. The first is that there are insufficient samples for errors to be normally distributed and cancel opposite errors. Secondly, there are insufficient samples to obtain a statistically significant estimate of the average dry, ash-free gas content of the reservoir. If you are reduced to analyzing gas content data in this manner, keep in mind that your estimates of the gas-in-place volume may be quite low. In this case, the gas-in-place estimate would have been low by 28.7%.
Example 4-1. Average Dry, Ash-Free Gas Content Estimates for Low Confidence Data Sets
Prepare a graph, such as in Figure 4-3, of the left-hand side of Equation 4-3, [the reciprocal of the dry den-sity], versus wa/(1-ww) [the dry ash content] to inter-pret density – proximate data. We assume that the sorbed water density is one g/cm3. The graph results in a vertical axis intercept at zero equal to the reciprocal of the organic fraction density. The vertical axis inter-cept at one is equal to the reciprocal of the ash density.
The analysis results in estimates of the coal density equal to 1.276 ± 0.023 g/cm3 and the ash density equal to 2.497 ± 0.310 g/cm3. The densities of the organic and ash fractions are functions of the compositions of each.
The organic composition is quantified by the maceral composition.1 ,2 The three pri-mary maceral groups are vitrinite, exinite (liptinite) and inertinite. Each of these groups includes sub-groups that have similar proper-ties. Vitrinite is the most common maceral group in Upper Cretaceous Western Interior bituminous humic coal seams of the U.S.
With maceral density data, you can com-pute the expected organic fraction density based upon the volumetric average density of each of three components; vitrinite, inertinite, and exinite. Equation 4-4 lists the relation-ship. The maceral density data is rarely mea-sured. However, published data are available.
Use estimates of the ash density (2.497 g/cm3) and the organic density (1.295 g/cm3) to estimate the density at an ash content of 0.30 and 0.55. Use an equilibrium moisture content of 0.0089 to approximate the in-situ moisture content. Use Equation 4-2 for the calculation.
wa = 0.30;
Example 4-2. Relationship between Density & Inorganic Content
Figure 4-3.
Relationship between Sample Density and Ash Content Where:
ρo organic fraction density, g/cm3 ρv vitrinite maceral density, g/cm3
ρi inertinite maceral density, g/cm3 ρe exinite maceral density, g/cm3
Vv vitrinite content, volume fraction Vi inertinite content, volume fraction
Ve exinite content, volume fraction
e
Primary Study Minimum Mode Maximum
Maceral Group Density Density Density
g/cm3 g/cm3 g/cm3
Vitrinite 1.21 1.28 1.36
Inertinite Dyrkacz & Horwitz1 1.25 1.35 1.46
Exinite 1.08 1.20 1.25
Vitrinite 1.22 1.30
Inertinite Crelling2
Exinite 1.06 1.22
Vitrinite 1.27 1.29 1.30
Inertinite University of Utah3 1.39
Exinite 1.14 1.18 1.21
Vitrinite 1.28
Inertinite Australian4 1.34
Exinite 1.12
Vitrinite 1.29
Inertinite Average 1.35
Exinite 1.18
Well Sample Vitrinite Inertinite Exinite Organic
Content Content Content Fraction Density Volume Volume Volume g/cm3 Fraction Fraction Fraction
Valencia Canyon 32-1 Intermediate Fruitland Test 1 0.830 0.135 0.035 1.294 Valencia Canyon 32-1 Intermediate Fruitland Composite 0.905 0.063 0.032 1.290 Valencia Canyon 32-1 Basal Fruitland Test 2 0.892 0.095 0.013 1.294 Valencia Canyon 32-1 Basal Fruitland Composite 0.758 0.198 0.044 1.297
Valencia Canyon 32-1 Average 0.846 0.123 0.031 1.294
Southern Ute 5-7 Intermediate Fruitland Composite 0.852 0.125 0.023 1.295 Southern Ute 5-7 Intermediate Fruitland Test 1 0.821 0.155 0.024 1.297
Southern Ute 5-7 Basal Fruitland Composite 0.798 0.159 0.043 1.295
Southern Ute 5-7 Basal Fruitland Test 3 0.830 0.140 0.030 1.295
Southern Ute 5-7 Average 0.825 0.145 0.030 1.295
COAL Site GRI #1 0.916 0.083 0.001 1.295
Overall Average 0.845 0.128 0.027 1.295
Table 4-1
Summary of Published Maceral Density Ranges
Table 4-2
Fruitland Formation Organic Fraction Density Estimates
Table 4-1 summarizes published density data for the three coal maceral types. Based upon Table 4-1, the average density values for vitrinite, inertinite, and exinite are 1.29, 1.35, and 1.18 g/cm3. These values were used to compute the possible range in Fruitland Formation coal density for nine samples obtained from the Valencia Canyon 32-1, Southern Ute 5-7, and COAL Site locations.1 The composition and density estimates for the samples are summarized in Table 4-2.
Table 4-2 shows that the average value of 1.295 g/
cm3 for the organic fraction density was remarkably similar to the value for all of the samples.
There is an alternative method of estimating or-ganic density from ultimate analysis data based upon a correlation. Ultimate analyses were discussed in Chap-ter 3. Coal composition data from a set of 66 coal samples ranging in rank from lignite to low-volatile bituminous were correlated as a function of ultimate analysis data.1
A correlation for the organic fraction density based upon an ultimate analysis is listed in Equation 4-5.
where:
estimated organic fraction density, g/cm3 wH hydrogen content, weight fraction wO oxygen content, weight fraction wN nitrogen content, weight fraction wS sulfur content, weight fraction
Equation 4-5 results in estimates of the organic fraction density similar to those derived from labora-tory measurements as summarized in Table 4-3. The average of 1.296 g/cm3 is very similar to the estimate obtained by density measurements. We recommend that you perform ultimate analyses and use Equation 4-5 when density data are unavailable.
Fruitland coal was deposited in an environment that included a variety of rock types including sand-stone, shale, and volcanic ash deposits. The ash con-tained within the coal contains a high proportion of kaolinite (density 2.42 g/cm3) with quartz (density:
ˆo
Sample Hydrogen Carbon Nitrogen Sulfur Oxygen Equation Content, Content, Content, Content, Content, 4-5 Dry Ash- Dry Ash- Dry Ash- Dry Ash- Dry Ash- Organic Free Free Free Free Free Density weight weight weight weight weight g/cm3 fraction fraction fraction fraction fraction
GRI #1 Test 1 0.0470 0.8330 0.0143 0.0174 0.0883 1.326 GRI #2 0.0492 0.8014 0.0296 0.0134 0.1064 1.289 S. Ute Tribal I PLA 9 #2 0.0519 0.8453 0.0140 0.0086 0.0802 1.292 SSR #11-15 Test 2 0.0549 0.8545 0.0127 0.0104 0.0675 1.271 SU 5-7 Intermediate Composite 0.0583 0.7946 0.0007 0.0095 0.1369 1.334 SU 5-7 Intermediate Tests 1 2 3 4 5 0.0546 0.8287 0.0006 0.0080 0.1081 1.332 SU 5-7 Basal Composite 0.0571 0.8134 0.0006 0.0183 0.1106 1.324 SU 5-7 Basal Test 3 0.0538 0.8289 0.0006 0.0094 0.1073 1.336 VC 32-1 Intermediate Test 1 0.0557 0.8285 0.0107 0.0063 0.0988 1.293 VC 32-1 Intermediate Composite 0.0597 0.8326 0.0156 0.0076 0.0845 1.250 VC 32-1 Basal Test 2 0.0593 0.8427 0.0144 0.0078 0.0758 1.249 VC 32-1 Basal Composite 0.0600 0.8338 0.0153 0.0073 0.0836 1.249 Average 0.0551 0.8281 0.0108 0.0103 0.0957 1.296
Table 4-3
Summary of Equation 4-5 Organic Fraction Density Estimates
2.65 g/cm3) and feldspar (density range:
2.55 to 2.76 g/cm3) components in lesser amounts.1 This composition suggests that the density of kaolinite would be a minimum estimate of the density of the ash. The estimated ash density of 2.497 g/cm3 shown in Figure 4-3 is in line with the kaolinite density. Example 4-2 shows you how to use these values to compute the relationship between den-sity and inorganic composition.
With estimates of the density of the ash and organic fractions, it is possible to compute the ash content from the density. We will do this in Chapter 5 to evaluate open-hole density log data for estimates of the in-situ ash content.
Equation 4-6 lists the relationship for ash content from density data.
(4-6)
Where:
wwe equilibrium moisture content, weight fraction The moisture content is determined independently by equilibrium moisture content measurements on coal samples.
Equilibrium Moisture Content
You should base estimates of the in-situ gas con-tent and density upon the in-situ moisture concon-tent. The moisture content obtained from the proximate analyses performed on air-dried samples is not equal to the in-situ moisture content. The sample moisture content can be greater or less than the in-situ moisture content depending upon the sample type, coal rank, handling, and drying procedures. Presently, we recommend that
a
ASTM equilibrium moisture content data (measured with ASTM D14121) be used to approximate the in-situ moisture content.
The equilibrium moisture content measurement involves placing a weighed, pre-wetted coal sample contained in an uncovered bottle into a vacuum desic-cator. The desiccator includes a dish containing a saturated solution of K2SO4 that maintains the relative humidity at 96% to 97%. The desiccator is evacuated to a pressure of 30-mm Hg and placed in an 86 oF convec-tion oven. The sample remains in the oven for 48 hours or until a constant weight is achieved. The sample is removed and reweighed. The sample is then dried for 1.5 hours at 105 oC and the final dry weight measured.
equilibrated coal weight.
One of the limitations of this method is that the equilibrium moisture content is not measured at reser-voir temperature. The results vary with temperature.
Figure 4-4 illustrates the moisture dependence upon temperature for two Valencia Canyon 32-1 reservoirs.
The reservoir temperature at this location is 100 oF Increasing the temperature from 86 to 100 oF decreases the equilibrium moisture content by 10% to 12% of the 86 oF value. This change is insignificant for
gas-in-Figure 4-4
Valencia Canyon 32-1 Equilibrium Moisture Content vs. Temperature
place estimates but can affect sorption isotherm measurements.1
We recommend that you select at least three samples per reservoir for the mois-ture measurements to determine consis-tency. The equilibrium moisture content is often available from samples selected for sorption isotherm measurements.
Number of Samples Required One of the questions that arises con-cerns the number of samples that are re-quired to accurately determine the gas con-tent. You can answer this question by sta-tistical analysis based upon operating char-acteristic curves such as illustrated in Fig-ure 4-5.12
The statistical problem involves selecting a subset of the reservoir such that the subset has the same average properties as the reservoir. You must specify two significance levels. The first level is the probability that the analysis concludes that the average property of the sample set is equal to the average property of the reservoir when they are equal. We chose 95% for this level. The second level is the risk that the analysis concludes that the average property of the sample set is equal to the average property of the reservoir when the sample set average is different by a significant amount.
We use a risk of 10% to 20% for the second level.
In Figure 4-5, the horizontal axis value is the difference between the sample set average property, µ, and the reservoir average property, µ0, divided by the standard deviation, σ, of the reservoir property. The horizontal axis value is zero when the sample set average and reservoir property average are the same.
For a given error, the value of the horizontal axis increases as the standard deviation of the reservoir property decreases. The vertical axis is value of the first or second significance level and is a function of the number of samples, n. For the first level, the vertical axis is the value of the probability that the sample set property average is equal to the reservoir property average. Since we chose a level of 95%, the intercept at
zero (when the averages are equal) is 0.95.
The standard deviation must be estimated to determine the number of samples required, n. We are most inter-ested in the density distribution in the samples and in the reservoir when we adjust open-hole log analyses to agree with core analyses. Therefore, we will use open-hole density logs in the statistical analysis. Be-fore drilling, you can use open-hole density data from offset or analogous wells to determine the number of samples required. After logging, you should verify the statistical analysis with logs from the cored well.
How do we use Figure 4-5? Suppose that the value of the horizontal axis is 2 as would be the case for a reservoir with a narrow density standard deviation. If only two samples are available, there is a 20% chance that the sample set average differs more than 10% from the in-situ average. Consider the case when the stan-dard deviation of the in-situ density is twice as wide resulting in a horizontal axis value of one. Now eight samples are required to have no more than a 20%
chance of the sample set average density differing from the in-situ average density by more than 10%. If only two samples are available for the wider standard devia-tion, the risk that the average is incorrect increases to over 0.7.
Figure 4-5
Operating Charateristic Curves
In practical terms, if the in-situ density does not vary much, the standard deviation is small and we do not need many samples to determine the average in-situ density. As the in-situ density variation increases, more samples are required to accurately determine the aver-age.
Example 4-3 shows you how to apply the statistical analysis to determine the number of samples required for GRI Well #1 at the COAL Site. This example shows that at least 21 samples are required due to the wide variation in in-situ density in the San Juan Basin Fruitland Formation.
We cut core samples into one-foot lengths for the desorption measurements. For 95% statistical certainty, 21 feet of the 90 feet (or 23%) of the reservoir must be sampled to obtain a sample average density that is within 10% of the average reservoir density. In addi-tion, the standard deviation of the inorganic fraction of the samples must be equal to or greater than the stan-dard deviation of the reservoir density values. For this reason, a wide range in the sample inorganic fractions must be selected for desorption experiments As an example of a properly sized sample set, Figure 4-6
illustrates the gas vs. inorganic content correlation for the Southern Ute 5-7 well. We desorbed 26 samples with a range in inorganic content from 0.185 to 0.749.
There was a much “tighter” correlation (i.e., the differ-ence between the 95% confiddiffer-ence intervals was re-duced) when the data encompassed the entire density range. The intercept of the zero air-dry gas content point was also much closer to one. For these data, the intercept was at 0.961.
To improve accuracy, do not attempt to high-grade samples by selecting only the lowest density samples for desorption. If you do, you will have difficulties correlating gas content to density. This was the case for the six GRI Observation Well #1 samples (see Figure 4-2) that had a narrow range in inorganic content (0.394 to 0.459) when the range should have including inor-ganic contents from 0.12 to 0.70. GRI Observation Well #2 (see Figure 4-1) had a much greater range of
To improve accuracy, do not attempt to high-grade samples by selecting only the lowest density samples for desorption. If you do, you will have difficulties correlating gas content to density. This was the case for the six GRI Observation Well #1 samples (see Figure 4-2) that had a narrow range in inorganic content (0.394 to 0.459) when the range should have including inor-ganic contents from 0.12 to 0.70. GRI Observation Well #2 (see Figure 4-1) had a much greater range of