Note: Descriptions are shown in the official language in which they were submitted.
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METHOD FOR EVALUATION OF HYDROCARBON CONTENT OF SHALE
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This
application is a non-provisional application which claims the benefit of
and priority to US provisional application serial number 61/495186 dated June
9, 2011,
entitled "Method for evaluation of hydrocarbon content of shale," which is
hereby
incorporated by reference in its entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0002] None.
FIELD OF THE INVENTION
[0003] This
invention relates to the evaluation of the hydrocarbon content, for
example the hydrocarbon gas and/or liquid content, of a subterranean shale
deposit.
BACKGROUND OF THE INVENTION
[0004] Shale
is an increasingly important source of hydrocarbon resources. Such
unconventional resources, however, present challenges not only in their
extraction but
also in the analysis of a deposit to determine its hydrocarbon content.
Clearly, analysis of
the potential of a shale deposit prior to committing to the substantial cost
of extracting the
hydrocarbon is essential. Analysis is also essential to inform and guide the
location,
development and completion of wells in the deposit.
[0005] The
evaluation of shale resources, especially shale gas resources, is
challenging because of a variety of factors including low values of porosity
and
permeability, and complicated and variable mineralogy.
[0006] Shale
gas formations can also contain oil and valuable condensate deposits.
The formations are generally characterized by low to moderate clay volumes and
low to
high quartz or calcite content, where decreasing quartz volume is generally
offset with
increasing calcite volume. These formations also often contain small but
significant
amounts of heavy minerals, usually dominated by pyrite (and marcasite). The
formations
may contain up to 12 to 15% by volume of kerogen (organic matter, transformed
by heat
and pressure) which is a source of methane gas and liquid hydrocarbons present
in pores
in the rock. There is normally also adsorbed gas present in association with
the kerogen;
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the kerogen itself is porous and can contain gas. Finally, the formations
normally contain
trace amounts of uranium and other radioactive elements which can render the
Gamma-
Ray log essentially useless for quantitative interpretation of clay content.
[0007] A great deal
of effort has been expended in attempting to develop methods for
evaluation of shale gas founations. Much of this effort has involved efforts
to use
specialized logging measurements such as spectral elemental analysis to solve
for all of
the significant elements present in the formation. However, the volume of
kerogen, which
is one of the most important parameters, cannot be determined with the
spectral tools due
to the presence of carbon in various minerals as well as in the kerogen.
Independent
methods are normally used to determine the volume of organic material.
[0008] Kerogen from
logs is typically determined using the method outlined in the
paper: Passey, Q., et al., A practical model for organic richness from
porosity and
resistivity logs, AAPG Bulletin, 74, No. 12, p. 1777 ¨ 1794. The Passey method
yields
kerogen content, but nothing more. Other formation properties such as porosity
and
water saturation must be determined independently. The Passey method was
originally
derived for use in evaluation of the total organic content of hydrocarbon
source rocks.
The method requires knowledge of the maturity of the organic material, and it
is less
accurate for sediments that are over-mature, such as shale gas formations. See
also:
Schmoker, James W. and Hester, Timothy C., 1983, Organic carbon in Bakken
Foimation, United States portion of Williston Basin: AAPG Bulletin, v. 67, no.
12, p.
2165 - 2174.
[0009] This sort of
approach involves use of geochemical or spectral logs (which
measure elemental composition of the formation), which are then combined with
conventional logs, such as sonic slowness (DT), gamma ray (GR), bulk density
(RHOB),
and resistivity (Re) to determine the mineralogical composition of the
formation along
with porosity and water saturation. These methods rely on empirical
correlations
between kerogen volume and formation bulk density (or other logs) to compute
kerogen
volume. This is then used as an input curve in the multi-mineral solution that
also yields
porosity. These methods require multiple input curves that increase the cost
of data
acquisition and complexity of data analysis.
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[0010] Still
other methods utilize apparent matrix methods, where the mineralogically
complex shale gas formations are represented by apparent values of matrix
response for
each of the input logs. The interpretation depends strongly on the judgment of
the
interpreter in selecting the values of the apparent matrix property for each
log.
[0011]
Generally, in these prior methods, the volume of kerogen is first derived
using
any one of a variety of techniques. The value is then used together with log
data in
conventional methodologies to derive a value for the total porosity of the
formation.
These methods do not distinguish between porosity of the mineral matrix and
porosity
contained within the kerogen. This is shown diagrammatically in Figure 2a.
[0012] The
overall porosity (1)t (sometimes referred to as "Phit") gives a good
indication of hydrocarbon content, particularly if combined with a figure for
water
saturation (that is to say, how much of the total porosity of the shale, which
would
otherwise be occupied by hydrocarbon, is in fact occupied by water).
[0013] Other
approaches use brute-force empirical methods to calibrate models
directly from core data. These methods require a full suite of conventional
logs and
abundant core data to use in establishing the correlations between logs and
core data.
These methods will work only if there is no change in the log to core
correlation that
might be present due to changes in geology.
[0014] For
example, where there is plentiful core data, at least in a small number of
key wells, it's possible to use neural networks or the so-called clustering
technique. Well
log and core measurements acquired in a handful of wells are used to derive an
empirical
correlation between the desired and measurable parameters. Measurable
parameters may
include e.g. gamma ray, bulk density, neutron porosity, photoelectric factor
and deep
sensing conductivity (M). Desired parameters to be predicted may include
volume of
kerogen, total porosity, grain density, total water saturation and gas-filled
porosity. The
principal disadvantage of this technique is the reliance on abundant core data
in a given
formation.
[0015] To
summarize, the prior models for evaluating shale gas do not intrinsically
include kerogen. Instead, kerogen volume is predicted as described above, e.g.
using
empirical relationships between core kerogen content and other logs (e.g.
gamma ray or
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bulk density). Once kerogen volume is estimated, then overall porosity and
water content
are calculated using established techniques.
[0016] There
is a need for a method of evaluating hydrocarbon content, especially
hydrocarbon gas content, in a shale deposit, which is simple, fast and
relatively accurate
and which takes into account kerogen.
BRIEF SUMMARY OF THE DISCLOSURE
[0017] The
inventors have realized that kerogen volume, together with total porosity,
in a shale deposit may in fact be estimated with a reasonable degree of
accuracy from a
small number of logs without resorting to expensive, complicated and
potentially
unreliable elemental analysis, and which does not rely on core data, but may
be verified
by subsequent core sampling if necessary.
[0018] In the
new technique, kerogen with associated porosity may be an intrinsic
part of the model - see Figure 3. Either or both of the density of solid
kerogen and the
porosity of kerogen may be known with reasonable accuracy and can be used as
inputs.
The measured bulk density (RHOB log), together with one or more measurements
indicative of porosity and water content (e.g. DT log, resistivity log) may
then be
mathematically combined with the kerogen density and/or kerogen porosity to
give an
estimate for solid kerogen volume and total porosity and water content. See
Figure 2b.
[0019] In one
embodiment, a method for evaluating the volume of hydrocarbon gas
or liquid in a shale deposit comprises combining known kerogen density and/or
kerogen
porosity values with log data in a mathematical analysis to derive directly
values for
kerogen volume, total porosity and water content. Kerogen volume can be either
solid
kerogen volume or porous kerogen volume; since the kerogen porosity is known,
either
value can easily be derived from the other.
[0020] In this
method, the mathematical analysis may comprise the solution of
simultaneous equations incorporating said known kerogen density and/or kerogen
porosity values and log data. The log data may include: (i) log measurements
of bulk
density (RHOB) and (ii) log measurements indicative of porosity. The log
measurements
indicative of porosity may comprise either or both of neutron log measurements
and
slowness measurements, and may also include resistivity, which may provide an
indication of water saturation.
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[0021] The
mathematical analysis may incorporate at least one known, assumed or
estimated parameter from the group comprising: solid matrix grain density,
matrix
slowness and slowness of solid kerogen, or it may incorporate all of these.
[0022]
Another option is to adjust the derived value for kerogen volume to be
consistent with a value for pyrite and/or marcasite volume (which may be
obtained using
X ray diffraction) based on an empirically derived relationship between
kerogen volume
and pyrite and/or marcasite volume; this can be done using an iterative
process.
[0023] If the
derived value for kerogen volume is below a threshold value (for porous
kerogen volume, between 0.01 and 0.05, e.g. about 0.03), an input value for
matrix
slowness may be increased iteratively until the kerogen volume is above the
threshold.
[0024] If the
derived value for the matrix porosity is below a threshold value (0.03),
then an input value for matrix slowness may be decreased iteratively until the
matrix
porosity is above the threshold.
[0025] The
log data may include neutron log measurements. In this case, if the value
for kerogen volume is below a threshold value (for porous kerogen volume,
between 0.01
and 0.05, preferably about 0.03), an input value for solid matrix grain
density is
iteratively increased until the value for porous kerogen volume is above the
threshold.
[0026]
Alternatively, if the log data includes neutron log measurements and a value
for porosity of mineral matrix is derived from said mathematical analysis,
then, if the
porosity of mineral matrix is below the same threshold value, the solid matrix
neutron
response may be increased iteratively until porosity of the mineral matrix is
above the
threshold. The above adjustments are summarized in Table 1 below
[0027] Table 1
RHOB ¨ DT model
Threshold parameter: Response:
Vpk < 0.03 Increase DTsm (sonic slowness)
Phipm < 0.03 Decrease DT. (sonic slowness)
RHOB - NPHI model
Threshold parameter: Response:
Vpk < 0.03
Increase RHO. (solid matrix grain
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density) up to the limit of 2.75
Phipm < 0.03
Increase NPsm (solid matrix neutron
response) up to the limit of 0.20
[0028] In another optional, but very useful step, values derived from the
mathematical analysis may be adjusted so that they are consistent with one or
more log
data inputs selected from bulk density, sonic slowness and neutron data.
[0029] In another embodiment, a method for evaluating the volume of
hydrocarbon
gas in a shale deposit comprises the steps of:
(i) Taking a log measurement of bulk density (RHOB);
(ii) Taking log measurements indicative of porosity and water content (e.g.
DT log, neutron log, resistivity log);
(iii) Mathematically combining an estimated value for density or porosity
of kerogen with the log measurements of steps (i) and (ii) to derive directly
an
estimate for porous kerogen volume and total porosity and water content.
[0030] The optional features explained above all apply to this embodiment.
[0031] In a further embodiment, a method for evaluating the volume of
hydrocarbon
gas in a shale deposit comprises the steps of:
drilling a well into the shale deposit and measuring log data using a logging
tool,
said log data comprising at least:
(i) bulk density data; and
(ii) either (a) neutron response data or (b) bulk slowness data together with
either resistivity data or neutron response data;
from said log data, together with at least one known, estimated or assumed
parameter, directly computing an estimate of total porosity, and porous
kerogen volume, for the deposit;
wherein, said at least one known, estimated or assumed parameter is selected
from
the group comprising solid mineral grain density, slowness of the solid
matrix, porosity of kerogen, density of solid kerogen and slowness of solid
kerogen.
[0032] The optional features explained above all apply to this further
embodiment.
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[0033] In a further embodiment, a difference value Delphi is derived,
essentially
representing the difference between the neutron log (or slowness log) and
porosity
derived from the RHOB log. This value is found to be related to the clay
volume
VclayND, which in turn can be used to compute the solid matrix neutron
response NPsm
(or slowness matrix response DT,m). This NPsin or DTsm value is then used as
an input
parameter in the methods described above.
[0034] This further embodiment has the advantage that it eliminates one of
the
feedback loops which might otherwise used to put bounds around the results.
This is
explained in more detail later.
[0035] Definitions
[0036] Porosity is (0 ¨ sometimes referred to as "Phit") is the volume
fraction of
pores in a matrix, either of mineral (Opp,) or of kerogen (I)pk).
[0037] Sonic slowness (DT) is a measure of the amount of time it takes a
sound wave
to travel a certain distance, the inverse of velocity. It is usually reported
in micro-
seconds/foot and symbolized as DT.
BRIEF DESCRIPTION OF THE DRAWINGS
[0038] A more complete understanding of the present invention and benefits
thereof
may be acquired by referring to the following description taken in conjunction
with the
accompanying drawings in which:
[0039] Figure 1 is a schematic illustration of the structure of a region of
shale, with
porosity;
[0040] Figure 2a and 2b are diagrams illustrating a difference in approach
between
prior art methods for evaluating shale (Figure 2a) and that of the invention
(Figure 2b);
[0041] Figure 3 is a diagram summarizing a model for the composition of a
shale
deposit; and
[0042] Figure 4 is a plot of various log measurements and outputs from
Example 1;
[0043] Figure 5 is a plot of various log measurements and outputs from
Example 2;
[0044] Figure 6 is a plot of various log measurements and outputs from
Example 3;
[0045] Figures 7a and 7b are plots of volume of pyrite vs. volume of
kerogen for
Examples 1 and 2 respectively;
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[0046] Figure
8 is a flow diagram showing the iterative solutions of the third and
fourth embodiments, used to include effects of pyrite, water saturation, and
changes in
matrix properties;
[0047] Figure
9 is a plot of Vpk vs. RHOB showing maximum and minimum matrix
porosity bounds used in the methodology of the fifth embodiment; and
[0048] Figure 10 is a plot of various log measurements and outputs from
Example 4.
DETAILED DESCRIPTION
[0049] Turning
now to the detailed description of the preferred arrangement or
arrangements of the present invention, it should be understood that the
inventive features
and concepts may be manifested in other arrangements and that the scope of the
invention
is not limited to the embodiments described or illustrated. The scope of the
invention is
intended only to be limited by the scope of the claims that follow.
[0050] In a
first embodiment of the invention, an evaluation method is based on bulk
density and compressional sonic logs and, optionally, a resistivity log. These
porosity
logs were chosen since they each have a robust response to organic material.
The
advantage of this approach is the simplicity and minimal number of input logs
that
minimize data acquisition costs. Of course, the smaller number of inputs may
require
greater reliance on the use of assumptions.
[0051] This
approach yields total porosity, volume of kerogen, volume of pyrite,
whole rock grain density and water content, all five of which can be directly
compared to
core data if necessary to verify the results. Model input parameters (i.e.
estimated
parameters or parameters which are based on information from elsewhere)
include log
response properties of dry kerogen, kerogen porosity, properties of the
saturating fluids,
and properties of the non-kerogen mineral matrix. The model parameters are
mostly
assumed to be constant with depth, although they could be varied by
petrofacies or zone.
[0052] The
method is based on a new petrophysical model, shown in Figure 3. This
model explicitly includes solid kerogen and kerogen porosity in addition to
other
components normally observed in petrophysical models. The total porosity is
the sum of
the volumes of adsorbed gas, free gas (and liquid, if any) in kerogen pores,
free gas and
water (and liquid hydrocarbon, if any) in mineral matrix pores, and bound
water
associated with clay particles.
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[0053] Water
saturation for this model is computed as the sum of irreducible water
plus free water divided by the total porosity, and is typically computed using
Archie's
Equation, though there are other saturation equations which could be used such
as
Simandoux, Dual Water or Waxman-Smits, all of which are well known to those
skilled
in this art. Archie's equation is given below:
[ aRw ¨1/n
Sw =
Phir Rt _
Sw = water saturation, volume fraction of the pore space that is occupied by
water
a = constant, usually a = 1
Rw = resistivity of water contained in the pore space, ohm-m
Phit = porosity (volume fraction pore space)
m = cementation exponent, often m = 2.0
Rt = formation resistivity, ohm-m
n = saturation exponent, often n = 2.
[0054] In
addition to using Archie's equation, which requires a resistivity log, it is
also possible to compute water saturation using core data, for example core
porosity and
core bulk volume gas which often display a strong correlation. Intervals with
low
porosity are more likely to contain relatively smaller amounts of gas, and
thus have
higher values of water saturation.
(Phit ¨ BVG)
Sw =
Phit
[0055]
Referring to Figure 1, in this embodiment, the formation is assumed to be
composed of two components: porous mineral matrix and porous kerogen 1. The
porous
mineral matrix is composed of mineral grains 2 plus porosity 3, which can be
gas-filled to
its irreducible state, as presented in the model just described. The kerogen
also contains
porosity 4, which we assume to be hydrocarbon gas or liquid-filled based on
the
assumption that this material is oil wet; see Wang, F.P, and Reed, R. M.,
2009, Pore
Networks and Fluid Flow in Gas Shales: SPE 124253, presented at the 2009 SPE
Annual
conference, New Orleans, USA, 4 -7 October, 2009.
[0056] Clay
volume can be determined using an average of the two estimators based
on resistivity and the neutron porosity, or using other methods. Although not
specifically
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defined here as part of the petrophysical model, clay effects on the input
logs can be
accounted for in either of two ways. The first is to simply apply a correction
to the input
logs that is proportional to the clay volume. The second is to correct the
matrix properties
(DT., RHO., or NP,m) by an amount proportional to the clay volume. The results
in the
Examples below were obtained using an adjustment to input parameter DTsm
related to
the clay volume for the RHOB ¨ DT model, and by subtracting an amount related
to the
clay volume from the input log NPHI for the RHOB ¨ NPHI model.
[0057] Nomenclature
sm ¨ solid matrix (dry, crystalline, includes clay)
pm ¨ porous matrix
pmfl ¨ fluid contained within porous matrix
sk ¨ solid kerogen (dry)
pk ¨ porous kerogen
pkfl ¨ fluid contained within porous kerogen
DT ¨ component slowness
RHO ¨ component density
NP ¨ component thermal neutron response
Input logs
RHOB
Resistivity
DT or NPHI
Input parameters
RHOsn, = solid mineral matrix grain density
RHOpmfl = fluid density in solid matrix
RHOsk = density of solid kerogen (nominally 1.3 g/c3)
RHOpkfl = density of fluid contained in kerogen
RHOpk = density of porous kerogen, including fluids OR Opk kerogen porosity
DTsm = slowness of the solid matrix
DTpinfl = slowness of the fluid in porous matrix
DTsk = slowness of solid kerogen
DTpkfl = slowness of the fluid in porous kerogen
Or
NPsin = neutron response of the solid matrix
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NPpinfl = neutron response of the fluid in solid matrix
NPsk = neutron response of solid kerogen
NPpkfl = neutron response of the fluid in solid matrix
Basic Equations
RHOB = RHOpmVpm + RHOpkVpk
DT = DTpmVpm + DTpkVpk
1 = Vpm + Vpk
RHOpm RH0sm (14pm) + RHOpmflOpm
Or
RHOB = RHOpmVpm + RHOpkVpk
Nphi = NPpmVpm + NPpkVpk
1 = Vpm + Vpk
RHOpm = RHO., (1-Opm) + RHOpmflOpm
Solution for the model based on RHOB and DT:
Volume of solid kerogen:
Vk = Vpk(1 ¨ 0pk)
Where clDipk is the kerogen porosity, either given by (in the case in which
RHOpk is an
input parameter)
(rhosk ¨ rhopk)
Opk =
(rhosk ¨ rho )
Plifir
or is an input parameter, in which case RHOpk is given by:
rhopk = rho sk(1. ¨ 0pk + rhoplif10 pk
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The density of solid kerogen RHOsk (nominally 1.3 g/c3), together with either
the
porosity of the kerogen Opk, or the density of porous kerogen RHOpk, are input
parameters.
The volume of porous kerogen Vpk is given by
1
DT ¨ DTsm ¨ ¨ vho ¨ rhob)
C b sm
V pk = _________________________________________________
c t
DTpk ¨ DTsm ¨ ¨V.hosm ¨ rhopk)
b
where b = (RHO. - RHOpmfl ), and
C =(DTpmfl - DTsm)
and DTpk = DTA (1 - cOpk) DTpkflil) pk
The porosity of the mineral matrix is
(rhob ¨ rho pkVpk)
rho sm
(1 ¨ V pk)
¨ b
Total porosity is the Phit = VpmcD pm + Vpicl) pk
Finally, the grain density of the whole rock, including the kerogen is
rho sm(1 ¨ V Phit) + rho skV sk
rhog = sk ¨
(1 ¨ Phit)
[0058] The
high degree of correlation between different input parameters, the
uncertainty in their value, and the iterative solution make it possible and
desirable to have
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a means of checking that the final output results are consistent with the
inputs logs. The
approach is to compute the formation bulk density using the computed results:
RHOB =VkRhosk +VpkOpkRhopv +(Vsm + V py Vciay)Rho,n (V sin + V py Vaay)0 pmRhO
pno
A similar calculation is carried out for either DT or NPHI logs. If the
computed RHOB,
DT, or NPHI agrees with the input log, then the results are internally
consistent. If there
are differences between the computed and measured logs, then the various input
parameters may be systematically adjusted until the logs agree, or the
differences are
minimized.
[0059] In a
second embodiment of the invention, neutron logs are used for the
porosity logs when the sonic log is not available. Neutron response is a
technique for
measuring porosity which is well known per se in this field. The second
embodiment is
otherwise identical to the first apart from substituting equivalent values for
NP (e.g.
NPs,,,, NPpmfl, etc.) in place of the values for DT. In the remaining
examples, when DT /
slowness values or parameters are discussed, it should be taken that these are
interchangeable with NP values or parameters.
[0060] X-ray
diffraction (XRD) data for all shale gas formations included in the
examples below show measurable amounts of pyrite (and its dimorph marcasite,
present
in smaller amounts). Both minerals are composed of FeS2, and have values of
grain
density of 5.02 and 4.88 g/cc. These values are significantly greater than the
density
values of the dominant host mineral matrix of quartz or calcite of 2.65 or
2.71 g/cc.
When pyrite is present in small quantities, as in the case of shale gas
formations, it can
measurably increase the grain density of the matrix
[0061]
Examination of core XRD data for a number of wells suggests that the volume
of pyrite present is often correlated with the kerogen content. Figure 7 shows
data from
two wells that establish this relationship.
[0062] In a
third embodiment of the invention, the correlation of the pyrite volume
with the kerogen volume is taken into account in the petrophysical model in
addition to
the procedure of the first or second embodiments. A direct linear relation of
the volume
of pyrite with the volume of kerogen is assumed; Figures 8a and 8b show this
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relationship based on data from Examples 1 and 2 below, respectively. The
solid mineral
matrix grain density in the model is re-computed to account for the pyrite
content, as part
of an interative solution used to correct for the effects of fluid saturation
changes. A
modification of the third embodiment might be to include the effects of pyrite
on the
matrix sonic slowness or the solid matrix neutron response. However, these
adjustments
would be small relative to other sources of variation in this parameter, and
also do not
have core data for verification.
[0063] The
adjustment to correlate pyrite volume with kerogen volume is performed
iteratively, as set out in the flow diagram of Figure 8.
[0064] An
additional complication may require further refinement of the solution. In
some circumstances the computed volume of porous kerogen (Vpk) is a non-
physical
negative value, or near zero positive value. This is likely due to violated
assumptions. A
fourth embodiment of the invention involves adding a further procedure to the
first,
second or third embodiments in order to address this problem. The approach is
iteratively to decrease the values of the matrix sonic slowness until Vpk is
greater than an
input threshold value (nominally a volume ratio of 0.03). Similarly, at times
the
computed matrix porosity has a negative value or near zero positive value.
This can be
handled by iteratively increasing the matrix slowness until the computed
matrix porosity
exceeds an input threshold value (nominally 0.03). Adjusting the slowness of
the solid
mineral matrix thus has the effect of maintaining lower bounds on both the
volume of
porous matrix and the mineral matrix porosity.
[0065] In a
modification of the fourth embodiment, for the case where NPHI is used
in place of DT, different adjustments are required to maintain lower bounds on
the
volume of porous kerogen and the porosity of the mineral matrix. If the volume
of porous
kerogen is less than some input threshold value (nominally 0.03), then the
grain density
of the solid matrix is systematically increased up to a limiting value
(nominally 2.75
g/cc). If, on the other hand, the porosity of the mineral matrix is less than
some threshold
value (nominally 0.03), then the NPHI response parameter for the mineral
matrix (NP,m)
is systematically increased up to a limiting value (nominally 0.20).
[0066] The
results of these adjustments are generally consistent with core data, total
porosity values that are rarely less than 0.03, and porous kerogen values that
are rarely
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less than 0.03. These two iterative processes are shown together with the
pyrite iteration
of the third embodiment in Figure 8.
[0067] A fifth embodiment makes use of the difference value Delphi, as
discussed
above. Delphi essentially represents the difference between the neutron log
(or slowness
log) and porosity derived from the RHOB log.
[0068] We start with the basic, well known equation for RHOB:
RHOB = RHO.V. + RHOfi Phit,
where
RHO.= density of the rock matrix;
RHOfi = density of the fluid in the pore space;
Vs. = 1 ¨ Phit;
Phit = volume of the total pore space.
The equation for RHOB can be solved for Phit:
Phit = (RHO. - RHOB)/( RHO. - RHOfi).
[0069] By taking a nominal assumed value for matrix density RHO. (in the
equations
below it has been taken as 2.71 g/cm3 which is appropriate for limestone, but
it could be a
different value such as 2.65 g/cm3 which would be appropriate for quartz) and
a nominal
value for water density RHOfi (in the equations below it is 1.04 g/cm3 ¨ this
will vary
with assumed level of salinity), then the following equation can be derived:
Delphi = Nphi ¨(2.71 ¨RHOB)/(2.71 ¨1.04)
[0070] This equation, and the remainder of the discussion below, assumes
that the
neutron log is being used, but the slowness log may be substituted and the
analysis
remains equally valid provided the slowness log is converted to a sonic
porosity using the
equation:
PhiDT = (DTrna - DT)/(DTma - DTfl)
[0071] Delphi essentially represents the separation between the two logs
when they are
plotted in the same track in a log plot. The total porosity values as measured
by each tool
cancel or offset each other in Delphi. The values of apparent porosity due to
solid kerogen
also tend to cancel out as well. Thus Delphi primarily reflects the influence
of various
minerals on the two logs. The difference has a slight influence from the
dominant matrix
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mineral (often quartz or calcite) and a stronger influence from the clay
content.
Therefore the difference term can be used to estimate the volume of clay
VclayND,
VclayND = X*( Delphi + Y), where X and Y are scale and offset parameters.
[0072] Next, VclayND is used to compute the neutron response (NPshf) and
grain
density (RHO,,h) of the solid mineral matrix using values of scale and offset
parameters
that lead to a match between the computed results and the measured logs.
[0073] The values of these two quantities are used in the general solution
given by
the equations in section [0055] above, along with the iterative solution used
to obtain
values for the pyrite content (dependent on kerogen content), and to account
for the
effects of changes in fluid saturation. This embodiment thus employs the outer
iterative
loop shown in Figure 9, but replaces the inner iterative loops that produce
change in the
matrix properties.
[0074] The solution as just described sometimes results in kerogen content
that
exceeds physical bounds, determined by considering the existence of maximum
and
minimum values of the matrix porosity. Consider a cross plot of volume of
porous
kerogen (Vpk) on the vertical axis, and the formation bulk density (RHOB) on
the
horizontal axis as shown in Figure 8. An upper bound on Vpk is formed by the
straight
line that connects the two points A and B as shown on Figure 9.
Point A: Porous kerogen endpoint:
Vpk = 1.0, RHOB = rho5k(1 ¨Opk) + rhofi (131pk
Point B: Porous matrix endpoint (minimum porosity case):
Vpk = 0, RHOB = rhopip(1 ¨ (1Dpm,Min) rhofi Opm,Min
[0075] Similarly, the lower bound on Vpk is formed by the straight line
that connects
the two points A and C, with point C given as follows.
Point C: Porous matrix endpoint (maximum porosity case):
Vpk = 0, RHOB = rhoph,(1 ¨ Ophf,max) + rhofi Opm,Max
[0076] The Vpk is also constrained to be non-negative.
[0077] These constraints are shown in Figure 9 which is a plot of Vpk vs.
RHOB.
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[0078] In the
foregoing embodiments, various model input parameters are used which
are known or estimated from previous experiences with shale deposits. Table 2
shows
some values for these parameters.
Table 2
Log Model Component Properties
Component Density DT NPHI* Reference
(g/cc) lsec/ft VN
Quartz 2.64 56.0 -0.02 Schlumberger **
Calcite 2.71 49.0 0.0 Schlumberger **
Dolomite 2.85 44.0 0.01 Schlumberger **
Pyrite 4.99 39.2 -0.03 Schlumberger **
Lignite 1.19 160 0.52
Bitumin 1.24 120 0.60+
Kerogen 1.0 ¨ 1.1 160 50 - 65 2
H.I.
Water at 200 F
0.98 208 1.0
Water + 10%
NaC1 at 200 F 1.05 192.3 0.96
Water + 20%
NaC1 at 200 F 1.125 181.8 0.92
Kerosene 60 F
0.82 230 1.04
Methane 7,000 psi, 200 F**
0.24 0.48
Air at 3,000 psi,
212 F 780
1 density of water at 200 F and 7,000 psi
* Limestone units
** Schlumberger Log Interpretation Charts 2005 Edition. Published by
Schlumberger Marketing
Communications
2 New Evaluation Techniques for Gas Shale Reservoirs, Lewis, R., et al,
Schlumberger Reservoir
Symposium 2004
Example 1
[0079] Figure
4 shows a plot for a ConocoPhillips field location which will be
referred to as field location A. This plot shows measured and computed logs
which are
detailed further in Table 2 below. The solid computed lines were obtained
using a
RHOB-DT model according to the first, third and fourth embodiments; the dashed
lines
17
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were obtained using a RHOB-NPHI model according to the second, third and
fourth
embodiments. Where there is more than one label, the upper label for each
track is the
name of data from core samples, which are shown as open circles on the
respective track.
The middle and lower labels represent data from the RHOB-DT and RHOB-NPHI
models, respectively. For each track the scale is indicated at the top; this
scale is
reproduced in Table 3 since it can be hard to read in the Figure.
[0080] Figure
4 shows the excellent correlation between the derived results with core
data, shown as open circles on the plot. Figures 5 and 6, relating to Examples
2 and 3
below, also show good correlation with core data.
[0081] Table 3
Track labels Parameter (unit) Data range
GR Gamma (GAPI) 0 to 200
RD Deep resistivity 0.2 to 2000
(Ohm-meters)
DTC Compressional slowness 140 to 40
(microseconds/ft)
RHOB Bulk density (g/cc) 1.95 to 2.95
VCLAY Clay volume (volume ratio) 0 to 1
PYR1TENPY (volume ratio) 0 to 0.1
KEROGENNSK (volume ratio) 0 to 0.2
PHI/PHIT (volume ratio) 0 to 0.2
RHOMA/RHOG (volume ratio) 2.6 to 2.8
SW/SWT (volume ratio) 0 to 1
BVH/BVG (volume ratio) 0 to 0.1
Example2
[0082] Figure
5 shows a plot for a ConocoPhillips field location which will be
referred to as field location B. This plot shows measured and computed logs
whose
details are given in Table 3 above The solid computed lines were obtained
using a
RHOB-DT model according to the first, third and fourth embodiments; the dashed
lines
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were obtained using a RHOB-NPHI model according to the second, third and
fourth
embodiments.
Example 3
[0083] Figure 5
shows a plot for a ConocoPhillips field location which will be
referred to as field location C. This plot shows measured and computed logs
whose
details are given in Table 3 above. The solid computed lines were obtained
using a
RHOB-DT model according to the first, third and fourth embodiments; the dashed
lines
were obtained using a RHOB-NPHI model according to the second, third and
fourth
embodiments.
Example 4
[0084] Figure 10
shows a plot for a ConocoPhillips field location which will be
referred to as field location D. This plot shows measured and computed logs
similar to
those of the previous examples. The computed lines were obtained using a
methodology
according to the fifth embodiment.
[0085] In closing,
it should be noted that the discussion of any reference is not an
admission that it is prior art to the present invention, especially any
reference that may
have a publication date after the priority date of this application. At the
same time, each
and every claim below is hereby incorporated into this detailed description or
specification as a additional embodiments of the present invention.
[0086] Although the
systems and processes described herein have been described in
detail, it should be understood that various changes, substitutions, and
alterations can be
made without departing from the spirit and scope of the invention as defined
by the
following claims. Those skilled in the art may be able to study the preferred
embodiments and identify other ways to practice the invention that are not
exactly as
described herein. It is the intent of the inventors that variations and
equivalents of the
invention are within the scope of the claims while the description, abstract
and drawings
are not to be used to limit the scope of the invention. The invention is
specifically
intended to be as broad as the claims below and their equivalents.
REFERENCES
[0087] All of the
references cited herein are expressly incorporated by reference. The
discussion of any reference is not an admission that it is prior art to the
present invention,
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especially any reference that may have a publication data after the priority
date of this
application. Incorporated references are listed again here for convenience:
1. Passey, Q., et al., A practical model for organic richness from porosity
and resistivity
logs, AAPG Bulletin, 74, No. 12, p. 1777¨ 1794.
2. Lewis, R., et al, New Evaluation Techniques for Gas Shale Reservoirs,
Schlumberger
Reservoir Symposium 2004
3. Wang, F.P, and Reed, R. M., 2009, Pore Networks and Fluid Flow in Gas
Shales:
SPE 124253, presented at the 2009 SPE Annual conference, New Orleans, USA, 4 -
7
October, 2009.
4. Schmoker, James W. and Hester, Timothy C., 1983, Organic carbon in Bakken
Foitnation, United States portion of Williston Basin: AAPG Bulletin, v. 67,
no. 12, p.
2165 - 2174
