U.S. DEPARTMENT OF THE INTERIOR 

U.S. GEOLOGICAL SURVEY

CALIBRATION OF THE "GAS EFFECT"

USING NEUTRON, DENSITY, AND GAMMA-RAY-INTENSITY

WIRE-LINE LOGS

by 

Timothy C. Hester1

Open-File Report 99-128

This report is preliminary and has not been reviewed for conformity with U.S. Geological 

Survey editorial standards or with the North American Stratigraphic Code. Any use of 

trade, product or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

!U.S. Geological Survey, Box 25046, MS 939, Lakewood, CO 80225

1999
CONTENTS

Page 

Introduction.................................................................................... 3

Gamma ray, neutron, and density tool response.......................................... 4

Effect of clay......................................................................... 5

Effect of gas.......................................................................... 6

Calibration of the gas effect................................................................. 7

Calibration system................................................................... 8

The effect of clay on porosity and gas production....................................... 9

Summary....................................................................................... 11

References cited............................................................................... 13

ILLUSTRATIONS

Figure 1. Crossplot of neutron porosity minus density porosity vs gamma-ray intensity 

showing hypothetical data......................................................... 15

2. Schematic relation of solid and fluid portions of shaly formations

using the dual water model....................................................... 16

3. Crossplot of neutron porosity vs gamma-ray intensity for all gas-producing 

intervals of this report.............................................................. 17

4. Crossplot of neutron porosity minus density porosity vs gamma-ray intensity 

for all gas-producing intervals of this report.................................... 18

5. Crossplot of neutron porosity minus density porosity vs gamma-ray intensity 

showing calibration system........................................................ 19

6. Crossplot of density porosity vs gamma-ray intensity for all gas-producing 

intervals of this report.............................................................. 20

7. Crossplot of gas production in the peak calendar year vs gamma-ray intensity 

for 28 wells in northern Montana................................................ 21

8. Crossplot of gas production in the peak calendar year vs gas-production index 

for 28 wells in northern Montana................................................ 22

9. Summary crossplot showing relative variation in reservoir characteristics with 

plotted position of interval data.................................................. 23
INTRODUCTION

Unconventional (tight), clay-rich reservoirs are not well suited to conventional 

methods of formation evaluation (Kowalchuk et al, 1974; Whitney and Ahlbrandt, 1996). 

Potential pay zones are often poorly defined on geophysical well logs and thus 

overlooked. Both shallow and deep reservoirs from similar depositional environments 

may be equally difficult to evaluate, but with the additional economic constraints 

associated with deep reservoirs, risk is increased. An easy-to-use calibration developed 

here quantifies the "gas effect" (the response of neutron- and density-porosity tools to the 

presence of gas) with respect to actual production and simplifies the identification and 

selection of potential gas-producing intervals at any depth. Geologic factors targeted by 

conventional log analyses, such as effective porosity, clay content, and water saturation, 

although strongly affected by burial, compaction, thermal maturation, and diagenesis, are 

combined and accounted for empirically using a single "gas-production index" (GPI). 

GPI, introduced here for the first time, in effect, links log-derived, in-situ rock properties 

with production potential. The fundamental mechanism for isolating the gas effect for 

calibration is the neutron porosity minus density porosity (N-D) vs gamma-ray intensity 

(GRI) crossplot, which was introduced in the early 1970s with the commercial 

availability of the compensated neutron and density wireline logging tools.

The relation of neutron, density, and gamma-ray tool responses to the presence of 

clay and gas in a formation is expressed graphically (Fig. 1) by plotting the difference 

between neutron and density porosity (N-D, y-axis) against gamma-ray intensity (GRI, xaxis). This method has been used in the past to distinguish gas-producing from waterproducing intervals in very shaly formations where conventional log-analyses are 

ineffective. The inventor of this crossplot and specifically how it came into popular use 

is not in the published literature. Case histories presented in Cutress (1974) document the 

utility of the crossplot as a gas-detection tool in the Viking formation of east-central 

Alberta, Canada, and in Campen (1975) numerous formations in the Cretaceous biogenic 

gas sands of northern Montana. These shallow biogenic sandstones are used as an 

example in this report to test the method, which will be later applied to sandstones in 

general, including deep sandstones of the Anadarko basin.

Effectiveness of the crossplot (Fig. 1) depends on the existence, close proximity, 

and identification of water-saturated zones with geologic and production characteristics 

similar to those of prospective gas-producing intervals. Log measurements from the 

water-saturated zones are plotted to establish a "liquid line" (Cutress, 1974; Campen, 

1975), which then serves to separate gas-producing from water-producing (or nonproducing) intervals. Gas-bearing zones with production potential plot below the liquid 

line. The departure of the gas-bearing zones from the liquid line is the "gas effect", a 

term that refers to the well known opposing response of neutron- and density-porosity 

curves to the presence of gas in the formation. Typically, a liquid line is constructed for 

each formation of interest in a local area. The ideal position of the liquid line is often 

difficult to establish, and may vary depending upon porosity, the degree of water 

saturation, and economic or production criteria specific to the formation or operator. 

Figure 1 shows a schematic N-D vs GRI crossplot with hypothetical liquid line and 

producing-interval data.
In continuous-type (unconventional; Schmoker, 1995) gas accumulations of the 

northern Great Plains, and perhaps in other regions with similar depositional 

environments, 100 percent water-saturated zones are relatively uncommon. In addition, 

existing water sands are not always adjacent to prospective production, similar in clay 

content, or easily recognizable on well logs. Regional studies over broad geographic 

areas or investigations involving multiple formations are additionally complicated by the 

difficulties in constructing and interpreting multiple liquid lines.

A simple, more robust, system of gas detection in shaly sands is developed here. 

The new system uses the same crossplot, but eliminates the need to construct liquid lines 

for each formation, allowing formations with varying geologic characteristics to be 

compared at the same time. In addition, the systematic functioning of the crossplot 

(detailed in a following section) allows the plotted distribution of gas-bearing intervals to 

be explained in terms of relative variation in porosity and water saturation, clay content, 

bound water, and production potential. Thus, the crossplot becomes an interpretive tool 

in support of (or in lieu of) conventional formation evaluation.

This report reviews geophysical tool response to clay and gas. The two 

crossplotted variables N-D and GRI are discussed in terms of porosity using the dual 

water model (Fig. 2) modified from that shown schematically in Schlumberger (1987). A 

series of generalized equations is used to establish a conceptual link among tool response, 

porosity, and the two crossplotted variables. Finally, the calibration of the gas effect is 

explained, and integrated with some geologic interpretations.

An important part of developing this method for a broader application is to 

develop a link between the gas effect and production potential for wells at different 

depths. Cretaceous reservoirs of the northern Great Plains are used here to test the 

method and establish a lower limit for production potential. Deep, high-cost wells are 

economically viable only at higher production rates. Additional data from deep gasproducing wells will be necessary to determine the upper limit of the production range.

This report was funded in part by the Gas Research Institute, Chicago, Illinois 

(through a Cooperative Research and Development Agreement with Advanced Resources 

International, Arlington, Virginia), the U.S. Department of Energy (contract no. DEAT26-98FT40032), Morgantown, West Virginia, and the Energy Resources Program of 

the U.S. Geological Survey, Denver, Colorado.

GAMMA RAY, NEUTRON, AND DENSITY TOOL RESPONSE

The relation of gamma ray, neutron and density tool response is effective in 

accounting for clay, porosity, and water saturation empirically, and for isolating the gas 

effect in a useful and predictable way. Understanding the relation is an important part of 

linking the N-D vs GRI crossplot with geology, calibrating the gas effect, and developing 

the crossplot as an analysis tool for making basic interpretations of the data. To that end, 

tool response to clay and gas, and the crossplotted variables N-D and GRI are explained 

and linked with the dual water model using a series of generalized equations. The term 

"effective" porosity includes free (movable) water and hydrocarbons, "total" porosity 

includes effective porosity and clay-bound (immovable) water (Fig. 2).
Effect of Clay

Heslop (1974) showed that GRI in shaly sandstones increases linearly with clay 

volume. Clay volume is measured independent of "shale" volume using relative gammaray deflections (percent) converted to API units. Clay volume is important because of its 

effect on porosity, permeability, and geophysical tool response. Because his GRI data 

were limited to about 100 API, Heslop was unable to establish an upper limit for his 

linear trend; he therefore suggested that his clay-volume estimates were probably high 

(Heslop, 1974). The data set presented here includes GRI values as high as 175 API for 

intervals in the Cretaceous Bowdoin sandstone, where core and log data indicate clay 

content up to 50 percent (Henry, 1979; Kukal, 1981). Using these new data as an upper 

endpoint, and applying Heslop' s linear trend, a scale for estimated clay volume is shown 

opposite the GRI axis for Figures 3 through 7, and 9. This scale does not account for 

varying proportions of clay minerals with different intrinsic radioactivity levels.

Clay volume estimated in this way is a rough approximation, used here only for 

comparison and to investigate regional trends. As clay volume increases, bound water 

also increases. A linear relation of bound water with clay volume is demonstrated in the 

next section. The following paragraphs outline the response of neutron- and densityporosity tools to the presence of clay and associated bound water.

The neutron tool responds primarily to hydrogen in the formation (Schlumberger, 

1987). Hydrogen is a major component of saturating pore fluids (free water and 

hydrocarbons) and bound water associated with clay, but may also be present in clay 

minerals in the form of hydroxyl ions (-OH) or in non-porous hydrous minerals such as 

gypsum in the form of chemically attached water of crystallization (Schlumberger, 1987). 

Under normal circumstances and in many clean producing formations, saturating pore 

fluids contribute overwhelmingly to the amount of hydrogen per unit volume (hydrogen 

index). Thus, in most conventional liquid-filled reservoirs, the neutron tool measures 

total porosity. The generalized equation,

X'NEUTRON ~~ /^BW ' J^FW ' K'HC ~~ /^TOTAL (, * )

where 0BW is porosity due to bound water, 0FW is porosity due to free water, and J9HC is 

porosity due to hydrocarbons, reflects the response of the neutron tool to liquid-filled 

porosity in terms of the dual water model (Fig. 2).

The density tool responds primarily to electron density, which varies with 

formation bulk density (Schlumberger, 1987). Bulk-density measurements incorporate 

all formation constituents including both solid and fluid components. Porosity is 

determined algebraically by assigning values for matrix and fluid density. In formations 

where lithology and fluid type are well known (and therefore matrix- and fluid-density 

values are correct) the density tool provides a reliable measure of effective porosity. The 

generalized equation,

^DENSITY ~~ J^FW ~*~ T^HC ~ /^EFFECTIVE \4)

reflects the response of the density tool in terms of the dual water model (Fig. 2).
In formations with liquid-filled porosity (under ideal conditions, using correct 

matrix and fluid densities), the crossplotted variable N-D (y-axis, Fig. 1) empirically 

accounts for effective porosity (subtracting equations 1 and 2, and combining terms),

JN-U ^NEUTRON ~ K'DENSITY ~ (J^BW ' J^FW ' V^Hc) ~ (X'FW ' K'HC/

= 0BW (3)

and thus, reflects porosity associated with bound water.

Effect of Gas

The hydrogen index of liquid hydrocarbons is nearly that of water (Schlumberger, 

1987). Therefore, the neutron tool measures total porosity in either oil or water-filled 

formations. Gas, however, contains much less hydrogen than water or liquid 

hydrocarbons and causes the neutron tool to measure porosity too low. In addition, the 

excavation effect (Segesman and Liu, 1971) causes the neutron tool to measure porosity 

even lower than that accounted for by the reduction in hydrogen index. The excavation 

effect is enhanced in combination with water (saturating or bound) and maximized at 

higher porosities, intermediate water saturations, and (or) higher clay volumes. 

Correcting equation 1 for gas using the term -(0g + 0EX), equation 4 becomes,

0NEUTRON = 0BW+ 0FW + 0HC ' (0. + 0Ex) (4)

where, 0g and 0EX are porosity due to gas and the excavation effect, respectively. The 

minus sign reflects a negative contribution to the porosity measurement of the neutron 

tool due to the presence of gas. Thus, neutron porosity in the presence of gas is a 

measure of total porosity minus the effect of gas and excavation.

When neutron porosity is plotted against clay content, the effects of bound water 

become apparent. The regression line of Figure 3, a plot of neutron porosity vs GRI (a 

measure of clay content) for all gas-producing intervals of the data set, shows a 

systematic, linear increase in neutron porosity as clay content (GRI) increases. Neutron 

porosity increases as hydrogen associated with clay-bound water increases. In gascharged formations, such as those represented by the data of Figure 3, bound water is the 

primary contributor to the hydrogen index. Thus, the sample coefficient of determination 

(r2 = 0.66) indicates that about two-thirds of the change in the neutron response is due to 

the linear increase of bound water associated with increasing clay content. Data scatter 

about the regression line reflects variations in effective porosity and gas saturation.
The density of oil and water are similar compared to that of natural gas, which is 

much lower. In the presence of gas, the density tool measures a lower bulk density (and 

calculates a higher porosity), more or less proportional to the percent volume of gas 

present. Adding the term 0G to equation 2, equation 5 becomes,

0DENSITY=0FW +0HC+0G (5)

where 0G is the addition of apparent porosity to the density tool measurement as a result 

of gas in the formation. Without adjustment to the fluid-density value to account for the 

lower density of the gas, calculated effective porosity will be too high, reflecting the 

measurement of effective porosity plus gas porosity.

In formations with gas-charged effective porosity, the variable N-D can be written 

(subtracting equations 4 and 5 and combining terms) as equation 6.

N-D - 0^0* - 0DENs,TY = (0BW + 0FW+ 0HC ' 0g ' 0Ex) ' (0FW+ 0HC + 0G>

= 0BW + 0FW+ 0HC - 0g - 0EX - 0FW 0HC ' 0G

(6)

When the variable N-D is plotted against GRI (Fig. 4), bound water (0BWi equation 6) is 

empirically accounted for as a function of clay content, and vertical variation of the 

plotted data becomes a measure of the cumulative gas effect: - (0g + 0EX + 0G ). The 

negative value reflects displacement below the liquid line (e.g., Fig. 1).

CALIBRATION OF THE GAS EFFECT

The algebraic difference between neutron and density porosity (N-D, y-axis) in 

liquid-filled formations is a measure of bound water (equation 3). In the presence of gas, 

N-D is a measure of bound water minus the gas effect (equation 6). When N-D is plotted 

against clay volume (GRI), the effects of bound water and gas are separated. Figure 4 

shows all gas-producing intervals of this report plotted on the N-D vs GRI crossplot. The 

least-squares regression line shows the dependence of N-D on gamma-ray intensity. 

Clay content (Heslop, 1974) and bound water increase linearly with gamma-ray intensity. 

Thus, the sample coefficient of determination (r2 = 0.58) indicates that more than half of 

the change in N-D is due to variation in clay content and associated bound water. Data 

scatter about the regression line is due to variation in the gas effect.
The term "gas effect" refers to the opposing response of neutron- and densityporosity curves to the presence of gas in the formation. Some scatter, not related to insitu gas volume, is introduced by differing tool responses to porosity and by the irregular 

enhancement of excavation (equation 4) as porosity, water saturation, and clay content 

and mineralogy vary (Segesman and Liu, 1971). Therefore, the magnitude of the gas 

effect (equation 6) is closely, but not precisely, related to the absolute in-situ volume of 

gas measured by the combination of the two porosity tools. This variation is not easily 

quantified and thus imposes a limit on the degree of resolution possible for the gas effect. 

An intermediate index "GPI" (gas-production index), is introduced here to smooth that 

variation and to link the gas effect to production potential. Note the subtle but important 

conceptual difference between the "gas effect" a tool response to a given volume of insitu gas, and "GPI" a measure of the gas effect, with respect to actual production.

The calibration system using GPI consists of two fundamental components: 1) a 

baseline from which the gas effect can be measured (e.g., a liquid line, Fig. 1); and 2) an 

incremental system that maximizes resolution of the gas effect within the limits 

prescribed by the otherwise unaccounted-for variations in tool response and excavation. 

In this system, no water-bearing intervals or liquid lines as such are used. Instead, a 

"zero-production baseline" is empirically determined using 242 producing intervals from 

53 wells of the production data set. This baseline represents the cutoff of commercial gas 

production. In this way, the limits of the data empirically define the ultimate limits of gas 

production for an entire region or several regions. This baseline is the cornerstone of the 

calibration system, which allows intervals or formations within a well, or from different 

wells, to be compared with each other in the context of production potential.

Calibration System

The calibration system (Fig. 5) consists of 2 sets of parallel lines positioned on the 

crossplot to conform to 3 fundamental ideas: 1) the absence of producing-interval data 

represents a theoretical "zero" production potential, therefore, line segments bounding the 

data ultimately define the limits of gas production for the region; 2) the gas effect 

increases vertically downward from these line segments in a systematic and predictable 

way; and 3) there is a definable clay volume, above which, gas-production rapidly 

declines.

Two heavy line segments bound the data of Figure 5 (all gas-producing intervals) 

 a diagonal line and a horizontal line. Both are hand drawn to include the maximum 

number of data points (about 98 percent) and to minimize outliers. Straight-line 

segments are used (both diagonally and horizontally) because they better constrain the 

data throughout the range of GRI than do curved lines. The slopes and point of 

intersection of these line segments (where GRI=100 API) are artifacts of an empirical 

"best fit" to the data. Together, these line segments establish a regional "liquid line" 

(without using water-bearing intervals) for all producing formations. Shallow Cretaceous
reservoirs of northern Montana are used here as a test group to approximate the economic 

lower limit, or "cutoff, of commercial gas production. This economic cutoff is 

represented on the chart by the two heavy line segments, and is referred to as the "zeroproduction baseline". All other lines are parallel to these. Intervals that plot above this 

baseline are unlikely to deliver commercial volumes of gas. Deep gas reservoirs, usually 

hotter and drier than those of the northern Great Plains, may also fit within these criteria.

Below the baseline, gas production without water is not guaranteed, but the 

potential increases with decreasing N-D (Fig. 5). The magnitude of displacement is 

measured using a series of lines parallel to the empirically-defined zero-production 

baseline. These lines (separate and distinct from the baseline) envelope and subdivide the 

data, and scale the gas effect into 12 levels of GPI (Fig. 5). The levels are equally spaced 

in the vertical direction and numbered consecutively, increasing from top to bottom (the 

12th level is open-ended). Thus, GPI increases linearly as a function of the level occupied 

by a data point. This linear index reflects an assumed linear relation of the gas effect to 

in-situ gas volume. This is not precisely the case, however, because the excavation effect 

(equation 4) varies in a non-linear way as geologic conditions change. Thus, the 

cumulative gas effect (equation 6) may vary somewhat with factors other than the volume 

of gas present. The average excavation effect for sandstone with 20 percent porosity (the 

approximate average for production-interval data of this report, Fig. 6) may vary as much 

as 3 percent (Segesman and Liu, 1971). To account for this variation, allow for 

maximum resolution of the gas effect, and preserve the simplicity of the calibration 

system, GPI levels span 3 percent porosity.

The slope break for the parallel lines in Figure 5 (80 API) represents the point at 

which increasing clay volume begins to rapidly degrade reservoir quality. This 

interpretation (discussed in the following section) is determined by the porosity and 

production data of this report, and the porosity and clay content data of Schmoker (1997). 

Using the GPI levels, as opposed to using porosity units of the y-axis (N-D), normalizes 

the gas effect with respect to the zero-production baseline. This approach allows 

intervals or formations to be compared with each other without regard to clay content.

THE EFFECT OF CLAY ON POROSITY AND GAS PRODUCTION

Heslop (1974) shows that GRI in shaly sandstones increases linearly with clay 

volume. Clay volume (as opposed to "shale" volume) is estimated using relative gammaray deflections (percent) and converted to API units. Important in Heslop's correlation is 

a distinction between grain- and matrix-supported lithologies (sand and shale, 

respectively) and the position of the transition between them. In nature, the transition 

between these two endpoints is continuous. Here, the transition is represented by a fixed 

clay volume, marked on the N-D vs GRI crossplot by the intersection of diagonal and 

horizontal lines (Fig. 5; 80 API). The position of the transition on the crossplot separates 

the producing formations of this report by age, depositional environment, and reservoir 

characteristics, and marks the onset of a rapid decline in porosity and gas production. In 

addition, 80 API separates apparent positive and negative influences of clay on reservoir 

character.
Generally, the effects of clay on porosity and gas production are negative, 

reducing overall porosity and permeability by occupying pores, filling pore networks by 

deforming during burial (Schmoker, 1997), and destroying permeability by expanding 

and mobilizing during production. In some cases, however, clay appears to be somewhat 

beneficial to both porosity and production. If permeability is not seriously degraded, 

some clay (or clay types) might actually "bind" water that would otherwise be produced, 

and in moderate amounts can preserve porosity (and perhaps permeability) by retarding 

quartz cementation (Schmoker, 1997).

Schmoker (1997, his Fig. 5) shows that higher porosity (normalized for the level 

of thermal maturity) in the Lower Cretaceous J sandstone (Dakota Group) of the Denver 

basin, Colorado, is associated with intermediate clay content (about 12 percent), and that 

porosity declines as clay content increases or decreases from intermediate values. The 

data of this report indicate a similar porosity trend and show that gas production in the 

northern Great Plains is tightly linked to both porosity and clay content.

Compensated density porosity (Fig. 6) and volume of production in the peak 

calendar year (Fig. 7) are both plotted against clay volume (GRI). Among other trends, 

the two figures show that porosity and gas production co-vary with clay content. Both 

variables increase from lower levels to peak at about 60 API, a clay volume consistent 

with that of the highest porosity values of Schmoker (1997) roughly 12 percent. Above 

about 17 percent clay, the suggested transition point between grain- and matrix-supported 

lithologies (Fig. 5; 80 API), maximum porosity and production decline rapidly. Thus, the 

optimum clay content for higher porosity in the J sandstone, as suggested by Schmoker 

(1997), also appears to be the optimum clay content for maximum porosity and gas 

production in Cretaceous reservoirs of the northern Great Plains, and may range from 

roughly 7 to 17 percent clay (50 to 80 API). Schmoker's data also show that porosity 

may be more rapidly reduced by increasing clay volume than by increasing thermal 

maturation. Therefore, at a given level of thermal maturity, clay content measurements 

may provide an additional level of detail for porosity prediction, above that of using 

thermal maturation alone (Schmoker and Hester, 1990).

Clay-rich, low-permeability sandstones (or siltstones) generally have low 

effective porosity. In contrast, producing intervals tend to be those that retain higherthan-normal porosity as thermal maturity (Hester, 1997) and (or) clay volume increases. 

The data of Figure 6 show that for gas-producing intervals of this report, overall porosity 

decline with increasing clay content is negligible (r2 = 0.03). The lack of porosities below 

8 percent reflects the economic and geologic bias of the producing-interval data, and 

suggests that effective porosities of less than 8 percent are probably inadequate for 

commercial gas production regardless of clay content. As the transition from grainsupported to matrix-supported lithology advances and clay volume exceeds about 35 

percent (130 API), the minimum effective porosity required for gas production increases 

rapidly. As a result, the number of potential producing intervals declines significantly 

(Fig. 6). The data of Figures 6 and 7, in conjunction with those of Schmoker (1997), 

suggest that gas production from intervals where clay content exceeds 50 percent (175 

API) is rare.

10
The potential for gas production can be assessed using the N-D vs GRI crossplot 

and the resulting GPI (Fig. 5). GPI empirically combines porosity, water saturation, and 

clay content into a single parameter that associates the in-situ rock with the potential to 

produce gas. GPI may be favorably compared with direct well testing methods such as 

absolute open flow potential (AOF). AOF was used to evaluate individual well 

productivity for the Eagle Sandstone of the Cedar Creek anticline, southeast Montana 

(Green, 1997). AOF was calculated after all perforated intervals were stimulated and a 

final flow test was conducted. A strong correlation (r2 = 0.88) of AOF with best 12- 

month cumulative gas production (Green, 1997, his Fig. 5) suggests that AOF testing in 

this way can be a good indicator of longer term well performance in the Eagle Sandstone. 

By comparison, using a similar measure of cumulative gas production (Fig. 8, r2 = 0.61), 

GPI also appears to be a good indicator of longer-term well performance.

Figure 8 shows volume of production in the peak calendar year plotted against 

GPI, for each of 28 gas-producing wells of this report. GPI for each well is an average of 

its constituent intervals (Figs. 3-6). The sample coefficient of determination (r2 = 0.61) 

shows a strong dependence of gas production on GPI. Geologic variables not directly 

measured by porosity and gamma-ray logs, such as the presence and distribution of 

natural fractures and clay composition, are accounted for empirically using the zeroproduction baseline (Fig. 5), and are manifested as scatter about the regression line of 

Figure 8. The potential for production is shown by the range (dashed lines) associated 

with a given level of GPI. To the extent that the data are representative of the geology 

and productivity of the northern Great Plains, Figure 8 reflects typical production rates 

reasonably expected for a given quality of reservoir rock or producing interval, as 

measured by GPI. Deep reservoirs are far more expensive to drill and complete than 

shallow reservoirs and thus have rigid economic constraints requiring significantly higher 

production rates. Additional data from deeper wells may show a steepened productionrate curve at the high end of the GPI range.

SUMMARY

The potential exists for large volumes of biogenic methane within only a few 

thousand feet of the surface in the northern Great Plains of the United States. In these 

shallow wells, production costs are generally low. Nevertheless, limited areas have been 

exploited and vast areas remain undeveloped. The primary reasons for the lack of 

development are low production rates (Schmoker, 1995), and the difficulty in recognizing 

and evaluating gas-charged reservoirs (Rice and Spencer, 1995). Unconventional, 

continuous-type, gas-charged lithologies, such as those in the northern Great Plains, are 

often poorly defined on geophysical well logs and not well suited to conventional 

methods of formation evaluation. Potential producing intervals are frequently 

overlooked. Deep targets fit the same geophysical criteria but high drilling costs 

necessitate significantly higher production rates to be economical. The most clay-rich 

reservoirs, often commercially viable in shallow plays, will necessarily be bypassed in 

deep reservoirs.

11
An easy-to-use calibration system developed here (Fig. 5) simplifies 

identification, evaluation, and selection of potential gas-producing intervals in the 

varying geologic settings of the Cretaceous of the northern Great Plains and other 

regions. Geologic parameters targeted by conventional log analyses, such as effective 

porosity, water saturation, and clay content, are accounted for empirically using a single 

gas-production index (GPI), that links the gas effect directly with production. Intervals 

from different formations (or wells) can be compared with each other, on the same plot, 

in the context of production potential and geologic variation. Figure 9 summarizes and 

illustrates the influence of variations in porosity and water saturation, clay volume, bound 

water, and the gas effect, on the relative positions of plotted data.

Intervals that plot below the zero-production baseline (Fig. 5) have a higher 

potential to produce gas than those that do not. The potential for production increases as 

N-D (y-axis, Fig. 5) decreases. Geologic and engineering factors, however, such as 

natural fractures and well-completion techniques, can significantly influence well 

productivity. Data from Campen (1975) indicate that in Cretaceous reservoirs of the 

northern Great Plains some water production can occur at GPI levels as high as 7, and in 

many areas of north-central Montana the success ratio (Schmoker,1995) for gas wells is 

projected to be as low as 50 percent (Rice and Spencer, 1995). In deep gas reservoirs, 

completion techniques and success ratios may vary, and the high end of the GPI scale 

may steepen to reflect higher production rates. The crossplot method described in this 

report is a robust indicator of gas-production potential, and an interpretative tool that 

should be considered in conjunction with (or in lieu of) conventional formation 

evaluation.

12
REFERENCES CITED

Campen, E. B., 1975, Well log analysis in the Cretaceous gas sands of northern Montana, 

in Energy Resources of Montana: Montana Geological Society 22nd Annual Publication, 

p. 15-25.

Cutress, W. G., 1974, An empirical approach to log interpretation in the Viking sand of 

east central Alberta: Society of Professional Well Log Analysts Fifteenth Annual 

Logging Symposium Transactions, p. G1-17.

Green, T., 1997, Shallow gas completions benefit from advanced stimulation technology: 

Gas Research Institute, Gas TIPS, Winter 1996/1997, p.14-19.

Henry, K. C., 1979, Gas detection in the extremely shaly Bowdoin Formation of northern 

Montana: Society of Professional Well Log Analysts Twentieth Annual Logging 

Symposium Transactions, p. Tl-9.

Heslop, A., 1974, Gamma-ray log response of shaly sandstones: Society of Professional 

Well Log Analysts Fifteenth Annual Logging Symposium Transactions, p. Ml-11.

Hester, T.C., 1997, Porosity trends of Pennsylvanian sandstones with respect to thermal 

maturity and thermal regimes in the Anadarko Basin, Oklahoma, in T.S. Dyman, D.D. 

Rice, and W.A. Westcott, eds., Geologic Controls of Natural Gas in Deep Sedimentary 

Basins in the United States: United States Geological Survey Bulletin 2146-1, p. 105-124.

Kowalchuk, H., G. Coates, and L. Wells, 1974, The evaluation of very shaly formations 

in Canada using a systematic approach: Society of Professional Well Log Analysts 

Fifteenth Annual Logging Symposium Transactions, p. HI-21.

Kukal, G.C., 1981, Formation evaluation and gas detection in shallow, low-permeability, 

shaly sands, of the northern Great Plains province: Journal of Petroleum Technology, v. 

33, no. 10, p. 1976-1984.

Rice, D.D., and C.W. Spencer, 1995, Northern Great Plains shallow biogenic gas, in 

Gautier, D.L., Dolton, G.L., Takahashi, K.I., and Varnes, K.L., eds., 1995 National 

Assessment of United States Oil and Gas Resources Results, Methodology, and 

Supporting Data: United States Geological Survey Digital Data Series DDS-30 [CDROM].

Schlumberger, 1987, Log Interpretation Principles/Applications (2d ed.): Houston, TX, 

Schlumberger Educational Services, 198 p.

Schmoker, J.W., 1995, Method for assessing continuous-type (unconventional) 

hydrocarbon accumulations, in D.L. Gautier, G.L. Dolton, K.I. Takahashi, and K.L. 

Varnes, eds., 1995 National Assessment of United States Oil and Gas Resources 

13
Results, Methodology, and Supporting Data: United States Geological Survey Digital 

Data Series DDS-30 [CD-ROM].

Schmoker, J.W., 1997, Porosity predictions in deeply buried sandstones, with examples 

from Cretaceous formations of the Rocky Mountain region, in T.S. Dyman, D.D. Rice, 

and W.A. Westcott, eds., Geologic Controls of Natural Gas in Deep Sedimentary Basins 

in the United States: United States Geological Survey Bulletin 2146-H, p.77-104.

Schmoker, J.W., and T.C. Hester, 1990, Regional trends of sandstone porosity vs vitrinite 

reflectance A preliminary framework, in V.F. Nuccio, and C.E. Barker, eds., 

Applications of thermal maturity studies to energy exploration: Rocky Mountain Section, 

Society of Economic Paleontologists and Mineralogists, p. 53-60.

Segesman, F., and O. Liu, 1971, The excavation effect: Society of Professional Well Log 

Analysts Twelfth Annual Logging Symposium Transactions, p. HI-21.

Whitney, G., and T. S. Ahlbrandt, 1996, Effects of clays on resistivities of clastic rocks 

and reservoir performance: in E.D. Dolly, and J.C. Mullarkey, eds., Hydrocarbon 

production from low contrast, low resistivity reservoirs Rocky Mountain and MidContinent Regions log examples of subtle pays, Rocky Mountain Association of 

Geologists Guidebook, p. 43-52.

14
oc _____ oO

-25

Mostly Water Production 

(or non production)

Mostly Gas Production

40 60 80 100 120

Gamma-Ray Intensity, API

140 160 180

Figure 1. Crossplot of neutron porosity minus density porosity vs gamma-ray intensity 

showing hypothetical liquid line and producing-interval data.

15
Sand

Solids

Silt Clay

ClayBound 

Water

Fluids

Free 

Water

Shale

Grain Matrix

Hydrocarbons

Effective Porosity

Total Porosity

Figure 2. Generalized diagram illustrating schematic relation of solid and 

fluid portions of shaly formations using the dual water model (modified from 

Schlumberger, 1987).

16
60

Estimated Clay Volume, Percent

10 20 30

i____

40

I

50

50

y = 0.23x + 9.75 

r2 = 0.66

20 40 60 80 100

Gamma-Ray Intensity, API

120 160 180

Figure 3. Crossplot of neutron porosity vs gamma-ray intensity for all gas-producing intervals of this report. Linear 

regression line shows average neutron response to bound water associated with clay. Data scatter reflects variations 

in effective porosity and gas saturation. Estimated clay volume is scaled opposite gamma-ray intensity (x-axis). 

Equation and sample coefficient of determination (r2) for regression line are shown in upper left.
Estimated Clay Volume, Percent

50

<D

(A

& 

£

(A

C 

0)

Q

(A

3 

C

0)

s

£

C

o

+rf 

3 

0)

Gas Effect 

Increases

-10

-15

-20

-25

40 60 80 100 120

Gamma-Ray Intensity, API

140 160 180

Figure 4. Crossplot of neutron porosity minus density porosity vs gamma-ray intensity for all gasproducing intervals of this report. Linear regression line shows average response of neutron porosity 

minus density porosity (N-D, y-axis) to bound water associated with clay. Data scatter reflects variations 

in the gas effect. Estimated clay volume is scaled opposite gamma-ray intensity (x-axis). Equation and 

sample coefficient of determination (r2) for regression line are shown in upper left.

18
10

Estimated Clay Volume, Percent

20 30 40 50

-25

80 100 120 140 160 180

c

o

z

x"

<D

_c 

c

U

3

Q_

(/)

(0

O

Gamma-Ray Intensity, API

Figure 5. Crossplot of neutron porosity minus density porosity vs gamma-ray intensity for all gasproducing intervals of this report, combined with a calibration system composed of empirical (hand 

drawn) zero-production baseline (heavy diagonal and horizontal line segments) and 12 indexed levels 

of gas-production potential (thin diagonal and horizontal lines, dashed where inferred). Estimated clay 

volume is scaled opposite gamma-ray intensity (x-axis).

19
Estimated Clay Volume, Percent

= -0.03x + 22.13 

r2 = 0.03

C\J O 

Gamma-Ray Intensity, API

Figure 6. Crossplot of density porosity vs gamma-ray intensity for all gas-producing intervals in this report. Linear 

regression line (heavy solid line) shows average porosity of gas-producing intervals at a given clay volume. Estimated clay 

volume is scaled opposite gamma-ray intensity (x-axis). Equation and sample coefficient of determination (r2) for 

regression line are shown in upper right.
10

Estimated Clay Volume, Percent

20 30

120

140

'CM OLL 

00

TO

Mostly Grain

Supported

Lithology

Mostly Matrix

Supported

Lithology

40

I

50

cvj

20 40 60 80 100 120

Gamma-Ray Intensity, API

140 160

Figure 7. Crossplot of gas production in the peak calendar year vs gamma-ray intensity for 28 wells in northern 

Montana. Approximate transition (80 API) between mostly grain-supported and mostly matrix-supported lithologies is 

dashed. Gamma-ray intensity for each well is averaged from its constituent intervals (Figs. 3-6). Estimated clay volume 

is scaled opposite gamma-ray intensity (x-axis).
140

CVJCVJ 

45678

Gas-Production Index, No Units

Figure 8. Crossplot of gas production in the peak calendar year vs gas-production index (from Fig. 5) for 28 wells in 

northern Montana. Dashed lines indicate approximate range of production associated with a given index level. Gasproduction index for each well is averaged from its constituent intervals (Figs. 3-6). Equation and sample coefficient of 

determination (r2) for regression line are shown in upper left.
Very Low Porosity

or ! 

High Water Saturation

Low Production 

Potential

Clay Replaces

Effective Porosity

Lower Porosity

Clay Replaces 

Higher Water Saturation Grain

Moderate Porosity

and 

High Gas Saturation

Gas Effect 

Increases

-10

-15

-20

-25-

and 

Higher Gas

Mostly Grain 

Supported 

Lithology

High Production 

Potential

Clcay- Volume Iricreases y

r

Mostly Matrix 

Supported 

Lithology

20 40 60 80 100 120 140

Gamma-Ray Intensity, API

160 180

Figure 9. Summary crossplot of neutron porosity minus density porosity vs gamma-ray 

intensity, showing relative variation in porosity, water saturation, clay volume, bound water, 

and production potential, with plotted position of interval data. Heavy solid line is regression 

line of Figure 4. Dashed line is zero-production baseline of Figure 5.

23