fast neutron cross-section measurement physics and...

SPWLA 57th Annual Logging Symposium, June 25-29, 2016

1

FAST NEUTRON CROSS-SECTION MEASUREMENT PHYSICS AND APPLICATIONS

Tong Zhou, David Rose, Tim Quinlan, James Thornton, Pablo Saldungaray, Schlumberger; Nader Gerges, Firdaus Bin Mohamed Noordin, Abu Dhabi Company for Onshore Petroleum Operations Ltd; Ade Lukman, VICO Indonesia.

Copyright 2016, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors. This paper was prepared for presentation at the SPWLA 57th Annual Logging Symposium held in Reykjavik, Iceland June 25-29, 2016.

ABSTRACT

A new formation nuclear property, the fast neutron cross section (FNXS), is introduced to the well logging industry. It is a measure of the formation’s ability to interact with fast neutrons. For sigma and porosity, the other two commonly used neutron measurements, certain elements tend to dominate, such as B, Cl and Gd for the sigma measurement and H for the porosity measurement. However, for the FNXS measurement, there is no single element dominating the response. This is explained by the complex dependence of neutron interactions on energy and elemental composition. Therefore, FNXS can provide information independent of the other neutron measurements for formation evaluation applications.

FNXS can be measured by a pulsed neutron logging tool that has been designed for that purpose. The corresponding raw measurements are the detected gamma rays that are induced by fast neutron inelastic scattering. However, the purely inelastic gamma ray events cannot be measured directly and are always mixed with the gamma ray events induced by thermal or epithermal neutron capture. It is difficult to consistently separate inelastic and capture gamma ray events in a wide range of downhole conditions. Several critical innovative tool design features are required to overcome this challenge. The detailed physical processes leading to the detected inelastic gamma rays, which involve both neutron and gamma ray transport, were modeled explicitly using Monte Carlo techniques in a wide range of formation and borehole conditions. It was found that the inelastic gamma ray response is dominated by FNXS and thus can be described approximately by FNXS. This approximation can be improved by introducing additional formation properties such as bulk density and atomic density. The tool measurement is characterized based on laboratory

data to provide formation FNXS values, with corrections to account for the hole size and casing impact. The impact of other typically unknown borehole conditions, such as cement variation, standoff, and eccentered casing, is assessed using modeling.

Because FNXS values of the rock matrix and water are in the same range, lower for light oil and much lower for hydrocarbon gas, FNXS can be used for a quantitative gas saturation measurement. It is particularly useful for differentiating gas-filled porosity from very low porosity in cased-hole formation evaluation if openhole density is not available. Log examples are provided to illustrate the FNXS measurement applications and performance.

INTRODUCTION

Formation evaluation based on cased-hole (CH) logging can provide valuable information. Compared to openhole (OH) logging, CH logging is operationally very flexible and has much lower risk and cost. On the other hand, CH logging has many more interpretation challenges. An accurate formation measurement requires accounting for the often-complex completion (casing, tubing, etc.), cement, and borehole fluid conditions. Pulsed neutron logging tools have long been popular for CH logging, providing measurements that are sensitive to formation hydrogen index (HI) and sigma. However, one of the most accurate nuclear measurements in open hole, the gamma-gamma density, is often missing in CH logging. When the CH density is available, the casing-cement corrections can be very challenging, leading to degraded accuracy compared to OH application and questionable reliability. Without a robust density measurement (either from OH or CH), it is mathematically underdetermined to solve gas saturation based on HI and sigma measurements.

In this paper, we expand on a new formation nuclear property, fast neutron cross section (FNXS), introduced recently (Rose et al., 2015). It is a measure of the


2

formation’s ability to interact with fast neutrons. It is sensitive to gas-filled porosity variation, but insensitive to liquid-filled porosity variation. It can be measured by a specifically designed pulsed neutron logging tool, by detecting the gamma rays induced from fast neutron inelastic scattering. It can provide interpretation functionality similar to that of density logging, but with a different response. A standalone CH formation evaluation is possible based on FNXS, HI, and sigma measurements, which can all come from a pulsed neutron logging tool. The FNXS measurement can also be used in OH applications, when gamma-gamma density measurement is not available, such as situations where radioisotope sources are prohibited.

BASIC PRINCIPLE

When a neutron has a collision with a nucleus, a variety of nuclear reactions will happen depending on the neutron energy and the type of the nucleus. The likelihood of an interaction between an incident neutron and a target nucleus is expressed by the concept of neutron cross section. The cross section per atom is referred to as microscopic cross section, with the standard unit of “barn”, which is equal to 10-24 cm2. It can be visualized by an area of the target nucleus: if an incident neutron strikes the target nucleus within that area, an interaction will happen; otherwise, the neutron will just pass through. Thus, a large cross section means a high likelihood a neutron will react with a nucleus. The bulk property of a material that relates to the likelihood of an interaction is referred to as the macroscopic cross section, defined as the product of atom density and microscopic cross section as shown in Equation 1, with the standard unit of cm-1:

aNM

DensityAtom (1)

As an example, Figure 1 shows the three major neutron microscopic cross sections of natural carbon for neutron energies from thermal (0.025eV) to 14MeV. They are elastic scattering (red), inelastic scattering (blue), and capture (magenta) cross sections. Starting from thermal energy, one can observe that the capture cross section decreases dramatically as neutron energy increases (described as E-1/2). For neutron die-away measurement, neutron capture at thermal energy dominates the response and neutron capture at high energy (~MeV) can be neglected. The measurement of the thermal neutron macroscopic capture cross section is traditionally called sigma, which was introduced to the

well-logging industry several decades ago. The default unit of sigma is the capture unit (c.u.), which is equal to 1000 cm-1. In contrast to the capture cross section, the elastic cross section has almost no dependency on neutron energy from thermal energy to 0.1MeV. In this energy region, the incident neutrons are simply scattered elastically by nuclei just like billiard balls. Elastic scattering is the dominant mechanism for the neutron slowing down process. The well-logging measurement of the ability of a formation to slow down neutrons from source energy to epithermal or thermal energy is generally called the neutron porosity measurement, which was invented several decades ago as well. The spiky structure in the elastic scattering cross section above 1 MeV is associated with resonance reaction mechanisms. The peaks correspond to the nuclear energy levels in the carbon-13 compound nucleus. Such resonance structure will appear at a lower energy for a heavier nucleus since the nuclear energy levels in heavy nuclei are lower than those in light nuclei. Inelastic scattering is not possible for neutrons with energies below a given threshold (typically in the MeV range). A heavier nucleus typically has a lower energy threshold for inelastic scattering than a light nucleus. Under inelastic scattering, some of the kinetic energy of the incident neutron will be transferred to an excited state of the nucleus. A gamma ray will be released almost instantly when the excited nucleus decays back to the ground state.

Figure 1 Neutron microscopic cross sections for natural carbon.

The neutron cross section values also depend on the isotope types. Figure 2 shows the elastic (top panel) and inelastic (bottom panel) scattering cross sections for five common earth elements, H, C, O, Si, and Ca. For


3

elastic scattering cross sections below 0.01 MeV, hydrogen clearly dominates other elements with a value that is 5 to 10 times higher. This phenomenon, and the fact that hydrogen has the highest average neutron energy loss per collision, makes the HI dominate the neutron porosity measurement. However, above 0.01 MeV, the elastic scattering cross sections of these elements decrease and converge around 14 MeV. At 14 MeV, the elastic scattering cross sections of many different elements have a very similar value and no isotope is dominant. Also at this energy level, the inelastic scattering cross section is about half of the elastic scattering cross section for multiple elements, with the exception of hydrogen, for which inelastic scattering is forbidden completely. This indicates that the formation property governing fast neutron interactions with energy in the MeV range is different from and independent of the other two neutron properties, neutron thermalization (HI) and neutron capture (sigma). A measurement based on fast neutrons can bring valuable information, especially for CH formation evaluation, where density information is often lacking.

Figure 2 Neutron elastic and inelastic microscopic cross section dependency on isotope types.

In a logging tool, direct fast neutron detection is not very practical due to the extremely low efficiency of fast neutron detectors. The detection of induced inelastic gamma rays is a feasible indirect alternative because only neutrons with energy above the threshold (~MeV) can trigger inelastic scattering, as the bottom panel of Figure 2 shows. The inelastic gamma ray measurement involves complex physics. Any detected inelastic gamma ray goes through three steps (Figure 3): 1) fast neutron transport from the source neutron to the location where the inelastic scattering happens; 2) generation of the inelastic gamma ray; and 3) gamma ray transport from where it is generated to the detector. The first step can be explained by a simple attenuation model: the higher the formation fast neutron cross section, the more neutrons get attenuated, and fewer neutrons will penetrate deep into the formation. Both elastic and inelastic scattering can attenuate neutrons in the first step. In the second step, gamma rays are generated by neutron inelastic scattering. Therefore, the higher the inelastic cross section, the more inelastic gamma rays will be generated. The last step of gamma ray transport can be explained by the gamma ray attenuation model. A gamma ray can be attenuated by the formation in three ways: pair production, Compton scattering, and photoelectric absorption. All three types of gamma ray attenuation are dominated by formation bulk density, with some dependency on atomic number (Z). The probability of inelastic gamma ray detection per source neutron can be computed as the total probability integrated over all possible paths. Thus, one would expect that the inelastic gamma ray count rate should depend strongly on the fast neutron elastic scattering cross section, the fast neutron inelastic scattering cross section, and the bulk density.

Figure 3 Detection of inelastic gamma rays.


4

To study the dependency to the three formation properties, we used MCNP5 (Los Alamos National Laboratory, 2003) to model the response of the inelastic gamma ray count rate in a wide range of CH environments. A special MCNP5 patch was used to flag the detected gamma rays in the detector by the type of originating reaction, i.e. capture or inelastic scattering. The formation conditions are listed in Table 1 and Table 2.

Table 1 Lithology and porosity conditions in the modeling database.

Lithology Porosity Quartz 4, 10, 17,

25, 34, 45, 65

Calcite 4, 10, 17, 25, 34, 45, 65

Dolomite 4, 10, 17, 25, 34, 45, 65

Almandine 0 Anhydrite 0 Ankerite 0 Kyanite 0 Shale (50% Quartz+50% Clinochlore) 10, 17 Shale (50% Quartz+50% Illite) 10, 17 Shale (50% Quartz+50% Kaolinite) 10, 17 Shale (50% Quartz+50% Montmorillonite)

10, 17

100-p.u. Water 100 100-p.u. Diesel 100

Table 2 Formation fluids in the modeling database and the legend used in the following figures.

Figure 4 The crossplot of the modeled pure inelastic count rate and elastic macroscopic cross section at 14 MeV. The colors represent the lithologies and fluids listed in Table 1. The symbols represent various formation fluids as explained in Table 2.

Figure 4 shows the log of the inelastic gamma ray count rate of a gamma ray detector in a logging tool as a function of the 14MeV elastic scattering cross section for numerous formation conditions. Compared to Figure 21 in the reference paper (Rose et al., 2013), we include many additional lithologies. There is a clear and strong correlation between the inelastic gamma ray count rate and the 14-MeV elastic cross section. In particular, the results for the various different formation fluids are almost on the same curve. There are still some lithology dependencies as a second-order term, e.g. the 0-p.u. anhydrite is an outlier from the main trend, the dolomite response is slightly different from that of sandstone and limestone, and the 100-p.u. water response is off the trend of sandstone and limestone. Yet, overall, though, the 14-MeV elastic cross section is the dominant term describing the inelastic gamma ray response.

Figure 5 shows the log of the inelastic gamma ray count rate as a function of the 14-MeV inelastic scattering cross section for numerous formation conditions. We did not observe a clear correlation in this figure. In particular, the various different formation fluids are all separated.

Further study shows that the 14-MeV inelastic scattering cross section is highly correlated with, and almost proportional to, the bulk density in the entire modeling database. Figure 6 shows the formation bulk density as a function of the 14-MeV inelastic scattering cross section in all formation conditions.

4 5 6 7 8 9 10-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

14 Elastic Scattering Macroscopic Cross Section (1/m)

log(N

orm

aliz

ed

Pu

re Ine

lastic

Co

un

t Ra

te)

Quartz

Calcite

Dolomite

Almandine

Anhydrite

Ankerite

Kyanite

Shale

Water

Diesel


5

Figure 5 Crossplot of the modeled pure inelastic count rate and inelastic macroscopic cross section at 14 MeV in the modeling database. The legend is the same as in Figure 4.

Figure 6 Crossplot of the bulk density and inelastic macroscopic cross section at 14 MeV in the modeling database. The legend is the same as in Figure 4.

As discussed earlier, the inelastic gamma ray generation is directly proportional to the inelastic scattering cross section, and the generated inelastic gamma rays will be attenuated before being detected. The attenuation is dominated by the bulk density, and the probability of detecting a generated inelastic gamma ray decreases as the bulk density increases. Since the 14-MeV inelastic scattering cross section is almost directly proportional to the bulk density as shown in Figure 6, the inelastic gamma ray generation and the gamma ray attenuation are competing against each other. As a result, the final detected inelastic gamma ray count rate is not correlated with either bulk density or the inelastic scattering cross section, but is highly correlated with

the third formation property, the 14-MeV elastic scattering cross section. The inelastic scattering and bulk density attenuation will not be canceled out perfectly, and there are many other second-order effects. Due to the complexity of the physics, there is no analytical solution for the inelastic gamma ray response as a function of all related formation properties. On top of that, the impact of many borehole conditions on the measured inelastic gamma rays can be much larger than those second-order effects. Finally, we decided to define the FNXS as the 14-MeV elastic scattering macroscopic cross section of the formation, neglecting the formation inelastic scattering cross section and bulk density in a first order approximation, and use FNXS alone to describe the tool response to the detected inelastic gamma ray count rate.

As shown in Figure 7, which shows FNXS as a function of formation HI, FNXS is not sensitive to liquid-filled porosity variations (from 0 p.u. to 100 p.u.) or formation salinity variations (from 0 ppk to 260 ppk), but it is very sensitive to gas-filled porosity variations (shown from 0 p.u. to 34 p.u.). This shows that FNXS is clearly an independent formation property unrelated to HI and sigma. Therefore, it can provide additional information for formation evaluation applications.

Figure 7 Crossplot of FNXS and HI for the selected formation conditions demonstrates the independency of FNXS to HI or sigma and its unique property of gas sensitivity.

CHARACTERIZATION BASED ON MEASURED AND MODELED DATA

The inelastic gamma ray count rate is an indirect measurement of fast neutrons. In reality, we cannot directly measure the inelastic gamma ray counts; we

1 2 3 4 5 6 7-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

14MeV Inelastic Scattering Macroscopic Cross Section (1/m)

log(N

orm

aliz

ed

Pu

re Ine

lastic

Co

un

t Ra

te)

Quartz

Calcite

Dolomite

Almandine

Anhydrite

Ankerite

Kyanite

Shale

Water

Diesel

0 1 2 3 4 5 6 70

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

14MeV Inelastic Scattering Macroscopic Cross Section (1/m)

Den

sity

(g

/cc)

Quartz

Calcite

Dolomite

Almandine

Anhydrite

Ankerite

Kyanite

Shale

Water

Diesel

0 0.2 0.4 0.6 0.8 14.5

5

5.5

6

6.5

7

7.5

8

8.5

HI

FN

XS

(1

/m)

Sandstone

Limestone

Dolomite

100pu water


6

can only measure the total gamma ray counts during the neutron burst. The burst count rate contains gamma rays that are generated by both capture and inelastic scattering. The capture gamma rays are induced by thermal neutrons or epithermal neutrons, which are dominated by formation HI and sigma. To extract the inelastic gamma ray count rate from the burst count rate, one needs to consistently predict and subtract the capture gamma ray background during the burst using other available measurements (such as a capture count rate after the burst). This is very important and necessary, because the sensitivity of capture gamma rays to formation HI or sigma can be an order of magnitude higher than the one of inelastic gamma rays to formation FNXS. Any residual capture gamma rays will mask the FNXS response of the inelastic gamma ray measurement. In addition, the borehole environmental effects on capture gamma rays add complexity.

It is very challenging to have a consistent and robust capture subtraction in all downhole environments, and this requires special tool design considerations. A gamma ray scintillation crystal with very little, or no, sensitivity to thermal and epithermal neutron capture is preferred. This helps to minimize the contribution of capture gamma rays during source neutron bursts, and makes the capture subtraction easier. A good example of such a crystal is YAP (yttrium aluminum perovskite). With the major elements of yttrium, aluminum, and oxygen, its thermal and epithermal neutron capture cross section is extremely low.

Another design choice is the source neutron pulsing scheme. A short neutron burst (about 20 µs) is preferred

because the thermal and epi-thermal neutron population does not fully build up. The capture gamma rays immediately after the burst can be used to predict the capture background during the burst. The structure of a short burst-on time plus a short burst-off time can be repeated many times to ensure injecting enough neutrons into the formation for other measurement such as HI, sigma, or spectroscopy. This kind of pulsing scheme requires a pulsed neutron generator (PNG) that can be turned on or off rapidly (with a rise and fall time of a few hundred nanoseconds).

Last, but not least, a neutron monitor is advantageous to normalize-out any PNG neutron output variation for the inelastic gamma ray measurements.

A pulsed neutron logging tool optimized for the FNXS measurement, with all the discussed design

considerations, was recently introduced. It outputs a gas ratio (GRAT) channel, which highly correlates to the net inelastic gamma ray count rate measured in the farthest-spaced detector. It is corrected for capture background and normalized by a neutron monitor. The robustness of the capture background subtraction for the inelastic gamma ray measurement is discussed in the previous paper for a wide range of downhole conditions (Rose et al., 2015).

The capture background subtraction is only the first step towards an accurate FNXS measurement. There can be many different types of borehole effects on an inelastic gamma ray count rate, even with perfect capture background subtraction. Using modeling techniques, we simulate the GRAT response of the newly designed pulsed neutron tool in a variety of borehole conditions.

In the modeling database, we define a standard borehole condition as 8-in. hole size, 5.5-in. casing size, 15.5 lbm/ft casing weight, class H cement, and the borehole filled with fresh water. We studied the GRAT measurement in other borehole conditions as a function of the GRAT measurement in this standard condition. We found the logarithm of GRAT is better behaved than GRAT itself, and all borehole effects can be corrected by applying a gain and an offset to the logarithm of GRAT. The following figures show the details.

Figure 8 and Figure 9 show the impact of the hole size and casing on the modeled log(GRAT) values. The diagonal line indicates the log(GRAT) that is the same for a given environment as the one in the standard borehole condition. The hole size effect on log(GRAT) is best corrected back to the standard environment with a gain and offset. This can be explained by the concept of the sensitive volume, which is a cylindrical volume with the tool in the center. In a small borehole, the ratio of the formation component to the borehole component in the sensitive volume is larger than in a large borehole. This indicates a higher sensitivity of the measurement to the formation for a small borehole size, and reduced formation sensitivity for larger boreholes. Therefore, there will be a gain correction for the log(GRAT) measurement depending on the hole size.

Figure 10 shows the impact of casing on the modeled log(GRAT). The GRAT measurement reads higher in 7-in. casing than in the standard 5.5-in. casing and requires an offset correction to the log(GRAT). Figure 11 shows the cement impact on the measurement. The


7

lighter cement will cause a high log(GRAT) reading and requires an offset correction to log(GRAT). Figure 12 shows the tubing effect. Tubing will introduce more metal in the borehole and cause lower reading of log(GRAT). An offset correction is required to correct it to the standard conditions. Figure 13 shows the borehole fluid impact. GRAT reads almost the same for fresh water in the borehole and 250-ppk water in the borehole, so it has very little borehole salinity sensitivity. It reads much higher for 0.3 g/cm3 methane gas in the borehole because it is very sensitive to gas, in both the borehole and the formation. The gas-filled borehole requires largely an offset correction for log(GRAT). Figure 14 shows the eccentric casing effect. Depending on whether the tool is closer to the formation or farther from the formation, log(GRAT) will need a small gain correction or no correction. Figure 15 shows the standoff effect, which mainly requires a gain correction.

As shown in Figure 8 to Figure 15, one will need a gain and an offset to correct for all different types of borehole effects on log(GRAT) to match its values in the standard borehole condition. The gain can be determined from the borehole size. The offset depends on casing size, casing weight, borehole fluid, standoff, cement, and so on. Not all those conditions are well known. Based on the laboratory measurements, we can characterize the offset correction based on the casing size and weight as a default offset correction. In reality, one often will need to apply an additional offset correction for other unknown conditions, such as cement density, borehole fluid density, standoff, tubing, and so on. In some cases, the offset needs to be zoned due to borehole condition changes as a function of depth, such as hole size change, casing change, tubing end, oil/water contact in borehole, and so on. In one of the most difficult cases, if the borehole condition varies continuously, such as in a flowing well with varying oil or gas holdup, the offset will not be a constant and offset determination will be problematic. Conditions such as this represent the limitation of the FNXS measurement for quantitative use.

Figure 8 Log of GRAT at the borehole condition with casing size (CSIZ) of 4.5 in., casing thickness (CSTH) of 0.25 in., and BS of 6 in. versus its values for the same formation condition in the standard borehole condition.

Figure 9 Log of GRAT at the borehole condition with CSIZ=9.625 in., CSTH=0.545 in., and BS=12 in. versus its values for the same formation condition in the standard borehole condition.

4.2 4.4 4.6 4.8 5 5.24.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2

log(GRAT) in the standard borehole condition

log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water

4.2 4.4 4.6 4.8 5 5.24.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water


8

Figure 10 Log of GRAT at the borehole condition with CSIZ=7 in., CSTH=0.32 in., and BS=8 in. versus its values for the same formation condition in the standard borehole condition.

Figure 11 Log of GRAT at the borehole condition with light cement versus its values for the same formation condition in the standard borehole condition.

Figure 12 Log of GRAT for the borehole condition with CSIZ=7 in., CSTH=0.32 in., and BS=8.5 in. versus its values for the same formation condition in the standard borehole condition. A solid red line (45 with an offset) fits those data points very well. Log of GRAT for a borehole condition with the same casing and additional tubing with the 2.88-in. outside diameter and 0.22-in. thickness are also plotted, and are below the black 45 line.

Figure 13 Log of GRAT at the borehole condition with 260-ppk brine in the borehole (on the black 45 line) and 0.3g/cm3 methane in the borehole (above the line) versus its values for the same formation condition in the standard borehole condition.

4.2 4.4 4.6 4.8 5 5.24.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water

4.2 4.4 4.6 4.8 5 5.24.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water

4.2 4.4 4.6 4.8 5 5.24.2

4.4

4.6

4.8

5

5.2

5.4

5.6


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water

4.2 4.4 4.6 4.8 5 5.24.2

4.4

4.6

4.8

5

5.2

5.4

5.6

5.8


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water


9

Figure 14 Log of GRAT for a borehole condition with the casing 1 in. off from the center versus its values for the same formation condition in the standard borehole condition. The tool is oriented either towards the borehole (data points are on the 45 line) or towards the formation.

Figure 15 Log of GRAT for a borehole condition with CSIZ=7 in., CSTH=0.32 in., and BS=8.5 in. versus its values for the same formation condition in the standard borehole condition. A solid red line (45 with an offset) fits those data points very well. Log of GRAT for the borehole condition with the same casing and additional 1.5-in. standoff between the tool and casing are also plotted, and are above the red line.

After the gain and offset corrections are done for all the borehole effects, the log(GRAT) can be converted to FNXS. Figure 16 shows the modeled (top panel) and measured (bottom panel) log(GRAT) as a function of FNXS in the standard borehole condition for similar formation conditions. The similarity of the two figures illustrates the consistency between the modeled and

measured results. It also validates the study based on the modeling results for the conditions that are hard to reproduce in the laboratory, such as gas-filled formations and gas-filled boreholes. One difference between modeled and measured results is the 100-p.u. water point. The laboratory measurement shows the 100-p.u. water point closer to the sandstone, limestone, and dolomite trend than what modeling predicts. The conversion of log(GRAT) to FNXS is the solid line in Figure 16, defined by Equation 2.

3

21 GRATlog

1FNXS a

aa

(2)

Figure 16 Log of GRAT versus FNXS for both the modeled (top panel) and the measured (bottom panel) databases. Note the similarity between modeling and measurement. The conversion of GRAT to FNXS is based on the solid line.

4.2 4.4 4.6 4.8 5 5.24.2

4.3

4.4

4.5

4.6

4.7

4.8

4.9

5

5.1

5.2


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water

4.2 4.4 4.6 4.8 5 5.24.2

4.4

4.6

4.8

5

5.2

5.4

5.6


log(G

RA

T)

in o

the

r b

ore

hole

co

nditi

on

s

Sandstone

Limestone

Dolomite

100pu water


10

WORKFLOW

Figure 17 illustrates the workflow to compute FNXS from the raw measurement. The raw channel provided by the tool is GRAT. The proper borehole correction for log(GRAT) includes gain and offset corrections, which need inputs from users. The gain is computed based on the borehole size (BS). A default offset (offset0) can be computed based on the borehole size, casing size (CSIZ) and casing weight (CWEI). Very often, in reality, an additional offset is needed to get an accurate FNXS measurement because the actual logged condition does not match the condition in the laboratory where the default offset is characterized. The log analyst will interactively adjust the additional offset “offset1” until the computed FNXS is reasonable in a zone that has no gas-filled porosity. In practice, the offset could be determined in a liquid-filled clean limestone in carbonates or a shale zone in clastics, and these are commonly present. If there is a change in the borehole condition, such as hole size, casing, tubing, or borehole fluid change, like a gas/water contact, the log analyst will need to provide an appropriate offset for each zone. At the last step, an FNXS value can be computed from the borehole-corrected GRAT using Equation 2.

Because of the additional offset provided by the log analyst, the absolute accuracy of the default offset “offset0” is not very critical. As long as the sum of the two offsets is appropriate, the final output of FNXS will be accurate. Therefore, the user inputs of casing size and casing weight are not critical.

3

21 log

1a

aoffset1offset0GRATgainaFNXS

Figure 17 – Workflow for GRAT environmental corrections and FNXS computation.

INTERPRETATION

FNXS is a bulk formation property that follows a linear volumetric mixing law, as shown in Equation 3, where FNXS with different subscripts represent values for different components, Φ is porosity, and V is the

volume fraction of each component. Formations typically contain rock matrix and pore space, which can be filled with various fluids (oil, water, or gas). The formation FNXS is the volume weighted sum of the FNXS of all these components. This is very similar to the formation bulk density mixing law. The interpretation based on FNXS and other available bulk measurements is to solve a series of linear equations, as shown in the following examples.

oiloil

gasgaswaterwater

matrixoilgaswaterformation

gasgasfluidfluid

matrixgasfluidformation

fluidmatrixformation

FNXSV

FNXSVFNXSV

FNXSVVVFNXS

FNXSVFNXSV

FNXSVVFNXS

FNXSFNXSFNXS

...

...1

...1

1

(3) Equation 4 shows the interpretation based on formation sigma, thermal neutron porosity (TPHI), and FNXS measurements to solve volumes of quartz, water, gas, and clay. The log analyst will provide the theoretical values of quartz, water, gas, and clay for sigma, TPHI and FNXS. The interpretation method is to solve for the four unknowns based on four linear equations, which have a unique solution. Equation 5 shows another interpretation example by adding elemental dry weights (DW) from the spectroscopy measurements. In this case, the four unknowns are volumes of quartz, calcite, water, and gas. There are six linear equations to determine four unknowns, so it is an overdetermined problem with a unique solution. Table 3 lists theoretical values for typical formation components. A modified SNUPAR (McKeon and Scott, 1989) code has been developed to compute those values for any given chemical formula.

claygaswaterquartz

clayclaygasgas

waterwaterquartzquartz

clayclaygasgas


clayclaygasgas


VVVV

FNXSVFNXSV

FNXSVFNXSVFNXS

TPHIVTPHIV

TPHIVTPHIVTPHI

SIGMAVSIGMAV

SIGMAVSIGMAVSIGMA

1

...

...

...

(4)


11

calcitegaswaterquartz

quartzquartz

calcitecalcite

calcitecalcitegasgas






VVVV

DWSIVDWSI

DWCAVDWCA

FNXSVFNXSV

FNXSVFNXSVFNXS

TPHIVTPHIV

TPHIVTPHIVTPHI

SIGMAVSIGMAV

SIGMAVSIGMAVSIGMA

1

...

...

...

(5)

Table 3 Theoretical values of sigma, TPHI, and FNXS for typical formation components.

Material Sigma (c.u.)

TPHI FNXS (1/m)

Quartz 4.55 –0.03 6.84

Calcite 7.08 0.00 7.51

Dolomite 4.70 0.03 8.51 Orthoclase 15.82 –0.05 6.33

Albite 7.65 –0.04 6.69

Anhydrite 12.45 –0.03 7.14

Pyrite 90.53 0.01 6.60

Bituminous Coal 15.79 0.68 7.72

Dry Illite 20.79a 0.22 8.06 Wet Illite 21.00 a 0.34 8.02

Dry Smectite 14.36 a 0.29 8.36

Wet Smectite 19.23 a 0.68 8.60 Water 22.20 1.00 7.80

Kerogen (CH 1.3g/cm3) 20.18 0.98 9.07

CH4 (0.05 g/cm3) 2.50 –0.05 0.67 CH4 (0.15 g/cm3) 7.50 0.21 2.01

CH4 (0.25 g/cm3) 12.50 0.47 3.36

Oil (C3H8 0.5g/cm3) 18.21 0.78 5.44

Oil (C3H8 0.6g/cm3) 21.85 0.97 6.53

Diesel (CH1.8 0.89 g/cm3) 23.30 1.08 7.98

CO2 (0.6 g/cm3) 0.03 –0.12 2.24 a Field observations typically higher due to variable boron content

An interesting property of FNXS compared to sigma and TPHI is that the clay (shale) effect is small when computing saturations. Note in Table 3 that FNXS values of illite and smectite are similar to those of the other typical minerals, whereas the sigma and TPHI

values are drastically different. In practice, clay (shale) values used in the interpretation may need to be adjusted because clay properties can vary, and the use of the TPHI versus FNXS crossplot can help with this determination.

LOG EXAMPLES

Example 1. The well in this example was drilled in a giant onshore oil field located in Abu Dhabi. The target reservoirs are thick, porous Cretaceous shelf carbonates bounded by a shaly formation at the top, which acts as a regional seal for the underlying carbonates. To enhance the oil recovery, both water and gas are injected in the reservoir in cycles of water alternated with gas (WAG). The well was completed as a horizontal water injector, but the zone of interest for the logging operation is located in a slanted section (60 average deviation) building angle for the lateral, completed with 7-in. casing in 8.5-in. hole. The logging objective in this case study was to monitor the water and gas saturation across the uppermost reservoirs, behind the 7-in. casing and 3.5-in. tubing.

Figure A-1 shows a composite display with the pulsed neutron log (PNL) data, OH logs and interpretations. In track 1, the fluid density and borehole sigma (SIBH) from the PNL indicate that the tubing is filled with brine. Presumably, this is also the case for the annular space between the tubing and the 7-in. casing. There are no open perforations in the logged interval. Tracks 4, 5, and 6 show the basic PNL tool ratios between the near-far (NF) and near-deep (ND) detectors: thermal ratios (TRAT_NF, TRAT_ND), burst ratios (BRAT_NF, BRAT_ND) and the inelastic gas ratio measurement (GRAT).

The TRATs are computed from the capture counts after the PNG burst. The ratios are used for the thermal capture neutron porosity (TPHI) computation. In this example, using the appropriate scales, both ratios overlay; therefore, increasing the spacing does not necessarily bring additional information, although a larger formation volume is probed. The BRATs, also referred to as the “inelastic ratios” in some industry literature, are the “burst ratios”, and computed from counts detected during the neutron burst after subtracting activation background counts. Notice that again the BRATs overlay when using the appropriate scale, so that although increasing the spacing augments the dynamic range, it does not necessarily bring additional information. When evaluating the benefit of increasing the detector spacing, not only should the


12

increase in dynamic range be considered, but also the degradation in the statistical uncertainty. A proportion of the detected gamma rays in the BRAT window are inelastic, produced by high energy or “fast” neutrons interacting with matter, but there is a significant fraction of capture gamma rays as well. One interesting point to notice in this case is that the TRATs and the BRATs have a similar profile and, in fact, they correlate with TPHI. This is because all these measurements are strongly influenced by the HI of the formation and they carry similar information. This means that they will behave similarly in very low porosity or gas-filled porous intervals. Hence, to use TRATs or BRATs to detect gas, an external true porosity measurement is required.

Track 6 shows the raw channel GRAT with a distinctly different response compared to TPHI, TRAT, or BRAT. It reads high in gas-filled porosity zones, which are flagged in the X220 to X400 ft and X140 to X160 ft intervals. It stays almost the same in other zones, including the 0-p.u. limestone zone (x160 to x220 ft) and the water- or oil-filled limestone with various porosity (x000 to x140 ft and x400 to x580 ft). The characterized formation property, FNXS, is shown in track 7; FNXS is derived from GRAT with the proper borehole corrections applied. The FNXS values in liquid-filled limestone zones are very close to the theoretical value for limestone (7.5 1/m) and are lower in the gas-filled zones. Note that TPHI, TRAT, BRAT, sigma, or gamma ray cannot differentiate between 0-p.u. limestone (X160 to X220 ft) and gas-filled limestone (X220 to X400 ft).

Tracks 8 and 9 show the OH logging-while-drilling (LWD) data, acquired 3 years earlier than this interpretation. The OH neutron-density crossover confirms the gas in the X220 to X400 ft interval. The gas flagged by GRAT and FNXS in the X140 to X160 ft zone does not show in the OH logs, but it is evident from the OH NPHI versus CH TPHI deficit shown in track 3. This could be some gas trapped by an invisible permeability barrier (perhaps a stylolite) at the bottom of the layer. Notice that CH TPHI has a stronger gas effect than OH NPHI because the latter has some drilling mud invasion effect.

Tracks 12 and 13 display the interpretation of the OH logs. The hydrocarbon volume is computed from resistivity and the oil-gas split from the OH neutron-density separation. FNXS has well-defined endpoints for fluids and minerals and follows a linear mixing law; therefore, it is easy to use in a solver routine to quantify

the gas volume. Track 11 corresponds to the CH fluids analysis integrating OH porosity and mineral volumes with sigma, TPHI and FNXS. Sigma drives mostly the water volume, and TPHI and FNXS drive the oil/gas split. Alternatively, by combining sigma, TPHI, and FNXS with the elemental dry weights from the new PNL tool spectroscopy, it is also possible to make a standalone PNL evaluation, as shown in tracks 14 and 15, where no OH log information is used in the interpretation.

Example 2. Example 2 is from a producing oil and gas field in the USA. The interval shown in Figure A-2 was drilled with an 8.75-in. bit size and completed with 4.5 in. casing with 11.6 lbm/ft casing weight. This completion configuration has a relatively large cement volume, with a cement thickness more than 2 in. The formation lithology is shaly sand, with low-porosity gas-filled and very low porosity zones alternating. OH logs were available.

Figure 18 shows the TPHI versus FNXS crossplot where the FNXS was computed with the default gain and offset based on 8.75-in. bit size, 4.5-in. casing size, and 11.6 lbm/ft casing weight with water in the borehole (i.e., only gain and offset0 used as per Figure 17). The cement used in this well was lighter than the cement used in the laboratory conditions in which the characterization database was measured. The light cement will cause GRAT to read higher than the default, as shown in Figure 11. Thus, FNXS with the default correction is clearly too low in this case and requires an additional offset correction (offset1 in Figure 17). Figure 19 shows a crossplot of TPHI versus FNXS where FNXS has been computed from log(GRAT) with an appropriate additional offset to account for the light cement effect. This additional offset is determined to make the corrected FNXS value close to the theoretical value in the shale zones. Compared to Figure 18, FNXS values in Figure 19 are much more consistent with the sandstone and limestone gas/water envelope and would be appropriate to use in a quantitative interpretation using theoretical values.

The majority of the interval covered in Figure A-2 is shaly sand. There are two interesting zones. One is at the top section from x160 to x180 ft, and the other is at the bottom section from x270 to x330 ft. GRAT and FNXS show the bottom section to have some gas while the top section has very low porosity. A standalone PNL volumetric interpretation can be performed using Equation 4 based on the sigma, FNXS, and TPHI measurements and is shown in tracks 10 and 11. The


13

standalone PNL interpretation was validated by the OH logs shown in tracks 8 and 9. Note that the BRAT, sigma, TPHI, and gamma ray respond very similarly between the top (very low porosity) and bottom (gas-filled porosity) zones.

Figure 18 Example 2, crossplot of TPHI versus FNXS where FNXS has the default GRAT offset correction. FNXS is clearly reading too low due to the light cement.

Figure 19 Example 2, crossplot of TPHI versus FNXS where FNXS has been computed from GRAT and an appropriate GRAT offset to account for the light cement.

Example 3. Figure A-3 shows an example of a CH pulsed neutron log in a well that also had a full suite of OH logs, including triple-combo, nuclear magnetic resonance (NMR), and a larger-diameter OH spectroscopy tool (Radtke et al., 2012). The well (8.75-

in. bit size) was drilled in Pennsylvania, USA, and the logging objective was to evaluate the complex shale gas formation including mineralogy, kerogen volume, and gas volume quantification. An OH interpretation was made using the extensive suite of logs. The spectroscopy data were used to compute the complex mineralogy. The spectroscopy dry weight total organic carbon (DWTOC) measurement is a key input for computing the kerogen volume. Density and NMR are used for computing gas volume and total porosity because they have contrasting responses to kerogen and gas. Note the transition from low porosity, low kerogen, and low gas volume above ~x550 ft to higher porosity, higher kerogen volume, and higher gas volume below. The key OH logs responding to this transition are the density and DWTOC logs.

To test the ability of this pulsed neutron tool to evaluate a complex shale gas formation in CH, a log was run after the well was cased (5.5 in., 23 lbm/ft casing) but before completion. The borehole was filled with fresh water. Three separate passes were made at 300 ft/hr each in GSH-LTH (Gas Sigma HI Lithology) mode, in which the time domain and energy spectroscopy data are simultaneously acquired. The data were stacked, and a standalone interpretation was made using the sigma, TPHI, FNXS, and spectroscopy data, all from the same PNL tool. At this slow logging speed, the spectroscopy data had very good precision, including the DWTOC, and compare favorably to the larger-diameter-tool spectroscopy data acquired in OH. A standalone CH interpretation was performed using all the data in a weighted linear solver with standard endpoint values (Table 3). Note in Figure A-3 the favorable comparison of the interpreted volumes, including the gas volumes and total porosity, even though no OH logs were used in the CH interpretation. The FNXS and DWTOC appear to be responding to the low porosity–gas transition at ~x550 ft. The transition is not obvious with the sigma and TPHI measurements.

Example 4 – Example 4 is from a producing oil and gas field in Southeast Asia. The objective of the cased-hole log was to identify possible productive zones to target for recompletion. The logging conditions for the cased-hole PNL log were very challenging for the borehole corrections as it is a non-standard completion. The interval shown in Figure A-4 was drilled with 8.5-in. bit size completed with two strings of 3.5-in. tubing. Each tubing was independently perforated at different intervals and the well is producing through both tubing strings. The PNL was logged in the long string with light hydrocarbon in the borehole. The formation

SPWLA 57

lithology is complex with interbedded sands, shales, limestones and coals. Open

Figure where the FNXS was computed with the default gain and offset based on 8.and 9.3 lbThe actual conditions are significanthe dual 3.5and with light hydrocarbons in the borehole rather than water. The FNXS with too low in this case and requires correction (offset1 in

Figure where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

Figure where FNXS has been computed from GRAT and an appropriate GRAT offset to account for borehole

SPWLA 57



Figure where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

Figure where FNXS has been computed from GRAT and an appropriate GRAT offset to account for borehole

SPWLA 57



Figure 20where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

Figure 21where FNXS has been computed from GRAT and an appropriate GRAT offset to account for borehole

SPWLA 57


Figure 20 where the FNXS was computed with the default gain and offset based on 8.and 9.3 lbmThe actual conditions are significanthe dual 3.5and with light hydrocarbons in the borehole rather than water. The FNXS with too low in this case and requires correction (offset1 in

20 Example where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

21 Example where FNXS has been computed from GRAT and an appropriate GRAT offset to account for borehole condition.

SPWLA 57th Annual Logging Symposium,


shows where the FNXS was computed with the default gain and offset based on 8.

m/ft tubing weight with water in the borehole. The actual conditions are significanthe dual 3.5-in. and with light hydrocarbons in the borehole rather than water. The FNXS with too low in this case and requires correction (offset1 in

Example where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

Example where FNXS has been computed from GRAT and an appropriate GRAT offset to account for

condition.

Annual Logging Symposium,


shows where the FNXS was computed with the default gain and offset based on 8.

/ft tubing weight with water in the borehole. The actual conditions are significan

in. tubing cementedand with light hydrocarbons in the borehole rather than water. The FNXS with too low in this case and requires correction (offset1 in

Example where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

Example where FNXS has been computed from GRAT and an appropriate GRAT offset to account for

condition.



shows the where the FNXS was computed with the default gain and offset based on 8.


tubing cementedand with light hydrocarbons in the borehole rather than water. The FNXS with too low in this case and requires correction (offset1 in Figure

Example 4,where FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

Example 4,where FNXS has been computed from GRAT and an appropriate GRAT offset to account for

condition.



the TPHI where the FNXS was computed with the default gain and offset based on 8.5-in.


tubing cementedand with light hydrocarbons in the borehole rather than water. The FNXS with the too low in this case and requires

Figure

4, crossplot of TPHI vwhere FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

4, crossplot of TPHI where FNXS has been computed from GRAT and an appropriate GRAT offset to account for


lithology is complex with interbedded sands, shales, limestones and coals. Open-hole logs were available.

TPHI where the FNXS was computed with the default gain

in. bit size, 3.5/ft tubing weight with water in the borehole.

The actual conditions are significantubing cemented

and with light hydrocarbons in the borehole rather than the default correction is clearly

too low in this case and requires Figure 17

rossplot of TPHI vwhere FNXS has the default The FNXS is clearly reading too low, which is consistent with to light hydrocarbo

rossplot of TPHI where FNXS has been computed from GRAT and an appropriate GRAT offset to account for


lithology is complex with interbedded sands, shales, hole logs were available.

TPHI versuswhere the FNXS was computed with the default gain

bit size, 3.5/ft tubing weight with water in the borehole.

The actual conditions are significantubing cemented

and with light hydrocarbons in the borehole rather than default correction is clearly

too low in this case and requires 17).

rossplot of TPHI vwhere FNXS has the default GRAT offset correction. The FNXS is clearly reading too low, which is consistent with to light hydrocarbo




versuswhere the FNXS was computed with the default gain


The actual conditions are significantubing cemented in the 8.5


too low in this case and requires an

rossplot of TPHI vGRAT offset correction.

The FNXS is clearly reading too low, which is consistent with to light hydrocarbon in the borehole.




versus FNXS crossplot where the FNXS was computed with the default gain


The actual conditions are significantly different with in the 8.5


an additional offset


The FNXS is clearly reading too low, which is n in the borehole.




FNXS crossplot where the FNXS was computed with the default gain

bit size, 3.5-in./ft tubing weight with water in the borehole.

tly different with in the 8.5-


additional offset



rossplot of TPHI versus where FNXS has been computed from GRAT and an appropriate GRAT offset to account for the complex




. tubing size /ft tubing weight with water in the borehole.

tly different with -in.


additional offset

rossplot of TPHI versuGRAT offset correction.


versus where FNXS has been computed from GRAT and an

the complex

Annual Logging Symposium, June 25



tubing size /ft tubing weight with water in the borehole.

tly different with borehole


additional offset

ersus FNXS GRAT offset correction.


versus FNXS where FNXS has been computed from GRAT and an

the complex

June 25






additional offset

s FNXS GRAT offset correction.


FNXS where FNXS has been computed from GRAT and an

the complex

June 25-29, 2016

14

lithology is complex with interbedded sands, shales,





additional offset

s FNXS GRAT offset correction.

The FNXS is clearly reading too low, which is

FNXS where FNXS has been computed from GRAT and an

the complex

29, 2016

14

Figure where FNXS has been computed from anhydrocarbon in the boreholeconfigurationmake the corrected FNXS value close to the theoretical value in the shale zonesvalues in sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between the low porosity

SUMMARY

We introduced a new formation property, FNXS and its FNXSnuclearadditional information for formation evaluation applications. this measurement. in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environmentschformation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS

The authors wish to thank the managements of Schlumberger, ADCO, VICO Indonesia and the other operating comin this endeavor, and their permission to publish these findings.

29, 2016

Figure where FNXS has been computed from an appropriate hydrocarbon in the boreholeconfigurationmake the corrected FNXS value close to the theoretical value in the shale zonesvalues in sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between the gaslow porosity

SUMMARY

We introduced a new formation property, FNXS and its FNXSnuclearadditional information for formation evaluation applications. this measurement. in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environmentschallengingformation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS


29, 2016

Figure 21where FNXS has been computed from

appropriate hydrocarbon in the boreholeconfigurationmake the corrected FNXS value close to the theoretical value in the shale zonesvalues in sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

gas-low porosity

SUMMARY

We introduced a new formation property, FNXS and its FNXS nuclearadditional information for formation evaluation applications. this measurement. in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

allengingformation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS


21 where FNXS has been computed from

appropriate hydrocarbon in the boreholeconfigurationmake the corrected FNXS value close to the theoretical value in the shale zonesvalues in Figure sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

-filled porosity in the thin low porosity

SUMMARY

We introduced a new formation property, FNXS and its measurement

is independent nuclear properties HIadditional information for formation evaluation applications. this measurement. in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

allengingformation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS


shows a cwhere FNXS has been computed from

appropriate hydrocarbon in the boreholeconfiguration. make the corrected FNXS value close to the theoretical value in the shale zones

Figure sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

filled porosity in the thin low porosity limestone

SUMMARY

We introduced a new formation property, FNXS measurement

is independent properties HI

additional information for formation evaluation applications. A slim this measurement. in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

allenging formation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS



appropriate additionalhydrocarbon in the borehole

This additional offset is determined to make the corrected FNXS value close to the theoretical value in the shale zones

Figure 21sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

filled porosity in the thin limestone



additional information for formation evaluation A slim

this measurement. in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

CHformation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS



additionalhydrocarbon in the borehole

This additional offset is determined to make the corrected FNXS value close to the theoretical value in the shale zones

21 are much more consistent with the sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the openin the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

filled porosity in the thin limestone



additional information for formation evaluation A slim

this measurement. We studied the FNXS response in a wide range oconditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

CH conditions.formation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS

The authors wish to thank the managements of Schlumberger, ADCO, VICO Indonesia and the other operating companies involved for their support in this endeavor, and their permission to publish

shows a crossplot of TPHI where FNXS has been computed from

additional hydrocarbon in the borehole

This additional offset is determined to make the corrected FNXS value close to the theoretical value in the shale zones. Compared to

are much more consistent with the sandstone and limestone gas/water envelope. The interval covered in Figure Alithologies including a low porosity limestone with shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be contrasted to the open-hole logs, which were not used in the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

filled porosity in the thin limestone zone

We introduced a new formation property, FNXS measurement,

is independent properties HI, sigma,

additional information for formation evaluation A slim PNL

We studied the FNXS response in a wide range of downhole environmental conditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

conditions.formation evaluation is this new measurement when gas is present.

ACKNOWLEDGMENTS

The authors wish to thank the managements of Schlumberger, ADCO, VICO Indonesia and the

panies involved for their support in this endeavor, and their permission to publish

rossplot of TPHI where FNXS has been computed from

offset to account for the light hydrocarbon in the borehole

This additional offset is determined to make the corrected FNXS value close to the theoretical

Compared to are much more consistent with the

sandstone and limestone gas/water envelope. The interval covered in Figure A

a low porosity limestone with shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretatis shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be

hole logs, which were not used in the interpretation to show what is possible with standalone PNL logs and FNXS. Note SIGM and TPHI logs respond very similarly between

filled porosity in the thin zone.

We introduced a new formation property, FNXS to the

is independent of sigma,

additional information for formation evaluation PNL device was optimized for

We studied the FNXS response f downhole environmental

conditions by both simulations and experiments and provided the characterization and borehole correction algorithms. demonstrate the FNXS measurement pin different environments

conditions.formation evaluation is enabled bythis new measurement when gas is present.

ACKNOWLEDGMENTS




offset to account for the light hydrocarbon in the borehole



sandstone and limestone gas/water envelope. The interval covered in Figure A



filled porosity in the thin

We introduced a new formation property, FNXS to theof the existing formation

sigma, additional information for formation evaluation

device was optimized for We studied the FNXS response

f downhole environmental conditions by both simulations and experiments and provided the characterization and borehole correction algorithms. Fourdemonstrate the FNXS measurement p

and its added value in conditions.

enabled bythis new measurement when gas is present.




offset to account for the light and the dual tubing



sandstone and limestone gas/water envelope. The interval covered in Figure A-4 shows



filled porosity in the thin sands

We introduced a new formation property, FNXS to the

the existing formation sigma, or density

additional information for formation evaluation device was optimized for


conditions by both simulations and experiments and provided the characterization and borehole

Four demonstrate the FNXS measurement p

and its added value in conditions.

enabled bythis new measurement when gas is present.



rossplot of TPHI where FNXS has been computed from log(



Compared to Figure are much more consistent with the

sandstone and limestone gas/water envelope. The 4 shows



sands

We introduced a new formation property, FNXS logging industry

the existing formation or density




log examples demonstrate the FNXS measurement p

and its added value in Standalone

enabled by the addition of this new measurement when gas is present.



rossplot of TPHI versus log(GRAT



Figure are much more consistent with the



hole logs, which were not used in the interpretation to show what is possible with standalone PNL logs and FNXS. Note that SIGM and TPHI logs respond very similarly between

sands zones


the existing formation or density




log examples demonstrate the FNXS measurement p


the addition of this new measurement when gas is present.



versus GRAT



Figure 20are much more consistent with the



hole logs, which were not used in the interpretation to show what is possible with

that theSIGM and TPHI logs respond very similarly between

zones


the existing formation or density and brings




log examples demonstrate the FNXS measurement performance


the addition of this new measurement when gas is present.



versus FNXS GRAT)



20, FNXS are much more consistent with the

sandstone and limestone gas/water envelope. The 4 shows varying



the BRAT, SIGM and TPHI logs respond very similarly between

zones and the


the existing formation and brings




log examples erformance


the addition of



FNXS ) with



, FNXS are much more consistent with the

sandstone and limestone gas/water envelope. The varying

a low porosity limestone with shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low porosity. A standalone PNL volumetric interpretation is shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be


BRAT, SIGM and TPHI logs respond very similarly between

and the







and its added value in Standalone CH

the addition of



FNXS with



, FNXS are much more consistent with the

sandstone and limestone gas/water envelope. The varying

a low porosity limestone with shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x778 ft and x850 may have some producible gas while the limestone has very low

ion is shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy data and it can be


BRAT, SIGM and TPHI logs respond very similarly between

and the

We introduced a new formation property, FNXS logging industry.






and its added value in CH

the addition of




15

REFERENCES

Los Alamos National Laboratory, 2003, MCNP a general Monte Carlo N-particle transport code, Version 5, report LA-UR-03-1987.

McKeon, D. C., and Scott, H. D., 1989, SNUPAR—A nuclear parameter code for nuclear geophysics applications, IEEE Transactions on Nuclear Science, 36(1), 1215-1219.

Radtke, R.J., Lorente, M., Adolph, R., Berheide, M., Fricke S., Grau, J., Herron, S., Horkowitz, J., Jorion, B., Madio, D., May, D., Miles, J., Philip, O., Roscoe, B., Rose, D., and Stoller, C., 2012, A new capture and inelastic spectroscopy tool takes geochemical logging to the next level, Paper 103, Transactions, SPWLA 53rd Annual Logging Symposium, Cartagena, Colombia, 16–20 June.

Rose, D., Zhou, T., Beekman, S., Quinlan, T., Delgadillo, M., Gonzalez, G., Fricke, S., Thornton, J., Clinton, D., Gicquel, F., Shestakova, I., Stephenson, K., Stoller, C., Philip, O., La Rotta Marin, J., Mainier, S., Perchonok, B., and Bailly, J.-P., 2015, An innovative slim pulsed neutron logging tool, Paper XXXX, Transactions, SPWLA 56th Annual Logging Symposium, Long Beach, California, USA, 18–22 July.

ABOUT THE AUTHORS

Tong Zhou is a Senior Tool Physicist at Schlumberger’s Houston Formation Evaluation Center. He holds a PhD in nuclear engineering from North Carolina State University (USA).

David Rose is a Principal Petrophysicist and Manager of Interpretation Engineering for Nuclear Answer Products at Schlumberger’s Houston Formation Evaluation Center. He holds a BS degree in geophysical engineering from Colorado School of Mines (USA).

Tim Quinlan is a Senior Petrophysicist at Schlumberger’s Houston Formation Evaluation Center. He holds a BS in hydrogeology from University of Arizona Tucson (USA).

James Thornton worked as a Physics Advisor at Schlumberger’s Princeton Technology Center (retired). He holds a PhD in physics from Stanford University (USA).

Pablo Saldungaray is a Principal Petrophysicist working in customer support and interpretation development. Since he joined Schlumberger in 1989, he has held several positions in the field and data processing centers in Africa, Europe, Latin America and the Middle East. Pablo has a BS degree in Electrical Engineering from the National South University (1987) and an MBA from the Austral University (1995), Argentina. Pablo is an active member of the SPWLA, SPE and EAGE.

Nader Gerges is a Senior Petrophysicist working with Abu Dhabi Company for Onshore Petroleum Operations (ADCO) and supporting the Bu Hasa asset team with the surveillance and studies petrophysical activities. Nader graduated with a BS in Electrical Engineering from Cairo University in 2000. He worked with Schlumberger as a General Wireline field Engineer between 2000 and 2004. He joined Nexen Petroleum and Statoil Canada as a Senior Petrophysicist. Nader is an active member of the SPWLA, SPE and EAGE and participated as an author and co-author on several industry related publications.

Firdaus Bin Mohamed Noordin is current working as surveillance petrophysicist for ADCO, U.A.E since 2012. He started his career back in 2005 for PETRONAS in various projects from exploration to development and finally in resource assessment team supporting business development unit. His formation evaluation experience covers fields in Southeast & Central Asia, Africa & Middle East. He obtained his BS in Applied Physics from University of Science Malaysia (USM) in 2004 & MSc in Petroleum Engineering from University of Technology PETRONAS in 2011.

Ade Lukman is currently working as Petroleum Engineering Team Leader for VICO, Indonesia. He started his career back in 2002 as a Petroleum Engineer at VICO and was involved in various projects including current the project where he is responsible for development of Badak and Semberah field. He obtained his Bachelor’s degree from Institut Teknologi Bandung in 2002.

SPWLA 57

APPENDIX A.

Figure

SPWLA 57

APPENDIX A.

Figure

SPWLA 57

APPENDIX A.

Figure A

SPWLA 57

APPENDIX A.

A-1,

SPWLA 57th Annual Logging Symposium,

APPENDIX A.

1, Example 1


APPENDIX A. INTEGRATED INTERPRET

Example 1


INTEGRATED INTERPRET

Example 1



Example 1, a



log example from Abu Dhabi.
















Annual Logging Symposium, June 25



June 25

INTEGRATED INTERPRETATION EXAMPLES


June 25-29, 2016

16

ATION EXAMPLES


29, 2016

16

ATION EXAMPLES

29, 2016

ATION EXAMPLES

29, 2016

ATION EXAMPLESATION EXAMPLESATION EXAMPLES


17

Figure A-2, Example 2, a log example from USA.


18

Figure A-3, Example 3, an example from Pennsylvania, USA, comparing OH logs, CH PNL, and two separate volumetric interpretations using just the OH data and just the CH pulsed neutron data. The independently computed volumetric interpretations are favorable, including the total porosity and gas volume. The FNXS is a critical input in the pulsed neutron interpretation because it enables the computation of an accurate gas volume.


19

Figure A-4, Example 4, a log example from Southeast Asia from a complex completion.

fast neutron cross-section measurement physics and...

Documents