FAST NEUTRON CROSS-SECTION MEASUREMENT PHYSICS AND APPLICATIONS

Transcription

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, ICO 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 7th Annual Logging Symposium held in Reykjavik, Iceland June 2-29, 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 are in the same range, lower for light oil and much lower for hydrocarbon, FNXS can be used for a quantitative saturation measurement. It is particularly useful for differentiating -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 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., 201). It is a measure of the 1

2 formation s ability to interact with fast neutrons. It is sensitive to -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 cm 2. 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 : 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 0.1Me. 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 Me 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 Me 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. Atom Density N a M (1) As an example, Figure 1 shows the three major neutron microscopic cross sections of natural carbon for neutron energies from thermal (0.02e) to 14Me. 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 (~Me) can be neglected. The measurement of the thermal neutron macroscopic capture cross section is traditionally called sigma, which was introduced to the 2 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 Me, hydrogen clearly dominates other elements with a value that is 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 Me, the elastic scattering cross sections of these elements decrease and converge around 14 Me. At 14 Me, 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 Me 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. 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 (~Me) 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 2 Neutron elastic and inelastic microscopic cross section dependency on isotope types. 3 Figure 3 Detection of inelastic gamma rays.

4 To study the dependency to the three formation properties, we used MCNP (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 MCNP 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, 2, 34, 4, 6 Calcite 4, 10, 17, 2, 34, 4, 6 4, 10, 17, 2, 34, 4, 6 Almandine 0 Anhydrite 0 Ankerite 0 Kyanite 0 Shale (0% Quartz+0% Clinochlore) 10, 17 Shale (0% Quartz+0% Illite) 10, 17 Shale (0% Quartz+0% Kaolinite) 10, 17 Shale (0% Quartz+0% 10, 17 Montmorillonite) 100-p.u. Water p.u. Diesel 100 Table 2 Formation fluids in the modeling database and the legend used in the following figures. log(normalized Pure Inelastic Count Rate) Quartz Calcite Almandine Anhydrite -0.1 Ankerite Kyanite -0.2 Shale -0.3 Water Diesel Elastic Scattering Macroscopic Cross Section (1/m) Figure 4 The crossplot of the modeled pure inelastic count rate and elastic macroscopic cross section at 14 Me. 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 14Me 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-Me 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. response is off the trend of sandstone and limestone. Yet, overall, though, the 14-Me elastic cross section is the dominant term describing the inelastic gamma ray response. Figure shows the log of the inelastic gamma ray count rate as a function of the 14-Me 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. 4 Further study shows that the 14-Me 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-Me inelastic scattering cross section in all formation conditions.

5 log(normalized Pure Inelastic Count Rate) Figure Crossplot of the modeled pure inelastic count rate and inelastic macroscopic cross section at 14 Me in the modeling database. The legend is the same as in Figure 4. Density (g/cc) Me Inelastic Scattering Macroscopic Cross Section (1/m) Quartz Calcite Almandine Anhydrite Ankerite Kyanite Shale Water Diesel Quartz Calcite Almandine Anhydrite Ankerite Kyanite Shale Water Diesel Me Inelastic Scattering Macroscopic Cross Section (1/m) Figure 6 Crossplot of the bulk density and inelastic macroscopic cross section at 14 Me 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-Me 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-Me 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-Me 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 -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. FNXS (1/m) Sandstone Limestone 100pu HI 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 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

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 6 considerations, was recently introduced. It outputs a 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., 201). 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,.-in. casing size, 1. lbm/ft casing weight, class H cement, and the borehole filled with fresh. 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.-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 in the borehole and 20-ppk in the borehole, so it has very little borehole salinity sensitivity. It reads much higher for 0.3 g/cm 3 methane in the borehole because it is very sensitive to, in both the borehole and the formation. The -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 1 shows the standoff effect, which mainly requires a gain correction. As shown in Figure 8 to Figure 1, 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/ 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 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. log(grat) in other borehole conditions Sandstone 4.4 Limestone pu log(grat) in the standard borehole condition Figure 8 Log of GRAT at the borehole condition with casing size (CSIZ) of 4. in., casing thickness (CSTH) of 0.2 in., and BS of 6 in. versus its values for the same formation condition in the standard borehole condition. log(grat) in other borehole conditions Sandstone 4.4 Limestone pu log(grat) in the standard borehole condition Figure 9 Log of GRAT at the borehole condition with CSIZ=9.62 in., CSTH=0.4 in., and BS=12 in. versus its values for the same formation condition in the standard borehole condition. 7

8 log(grat) in other borehole conditions Sandstone 4.4 Limestone pu log(grat) in the standard borehole condition log(grat) in other borehole conditions Sandstone Limestone pu log(grat) in the standard borehole condition 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. log(grat) in other borehole conditions Sandstone 4.4 Limestone pu log(grat) 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. in. versus its values for the same formation condition in the standard borehole condition. A solid red line (4 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 4 line. log(grat) in other borehole conditions Sandstone Limestone pu log(grat) in the standard borehole condition Figure 13 Log of GRAT at the borehole condition with 260-ppk brine in the borehole (on the black 4 line) and 0.3g/cm 3 methane in the borehole (above the line) versus its values for the same formation condition in the standard borehole condition. 8

9 log(grat) in other borehole conditions Sandstone 4.4 Limestone pu log(grat) in the standard borehole condition 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 -filled formations and -filled boreholes. One difference between modeled and measured results is the 100-p.u. point. The laboratory measurement shows the 100-p.u. 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. 1 FNXS a3 a1 log GRAT a (2) 2 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 4 line) or towards the formation..6 log(grat) in other borehole conditions Sandstone Limestone pu log(grat) in the standard borehole condition Figure 1 Log of GRAT for a 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. A solid red line (4 with an offset) fits those data points very well. Log of GRAT for the borehole condition with the same casing and additional 1.-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 9 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.

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). ery 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 -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 / 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. 1 FNXS a3 a1 gain log GRAT offset0 offset1 a2 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 is the volume fraction of each component. Formations typically contain rock matrix and pore space, which can be filled with various fluids (oil,, or ). 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. FNXS FNXS FNXS formation formation formation fluid oil fluid oil fluid matrix oil matrix fluid... matrix Equation 4 shows the interpretation based on formation sigma, thermal neutron porosity (TPHI), and FNXS measurements to solve volumes of,,, and clay. The log analyst will provide the theoretical values of,,, 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 shows another interpretation example by adding elemental dry weights (DW) from the spectroscopy measurements. In this case, the four unknowns are volumes of, calcite,, and. 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. SIGMA TPHI FNXS 1 TPHI SIGMA SIGMA TPHI clay clay clay SIGMA TPHI clay clay SIGMA clay clay TPHI (3)... (4) 10

11 SIGMA TPHI FNXS DWCA DWSI 1 SIGMA TPHI calcite SIGMA TPHI DWSI DWCA calcite calcite calcite calcite TPHI SIGMA TPHI calcite calcite SIGMA calcite calcite () Table 3 Theoretical values of sigma, TPHI, and FNXS for typical formation components. Material Sigma (c.u.) TPHI FNXS (1/m) Quartz Calcite Orthoclase Albite Anhydrite Pyrite Bituminous Coal Dry Illite a Wet Illite a Dry Smectite a Wet Smectite a Water Kerogen (CH 1.3g/cm 3 ) CH 4 (0.0 g/cm 3 ) CH 4 (0.1 g/cm 3 ) CH 4 (0.2 g/cm 3 ) Oil (C 3 H 8 0.g/cm 3 ) Oil (C 3 H 8 0.6g/cm 3 ) Diesel (CH g/cm 3 ) CO 2 (0.6 g/cm 3 ) 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 11 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 and are injected in the reservoir in cycles of alternated with (WAG). The well was completed as a horizontal 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.-in. hole. The logging objective in this case study was to monitor the and saturation across the uppermost reservoirs, behind the 7-in. casing and 3.-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,, and 6 show the basic PNL tool ratios between the nearfar (NF) and near-deep (ND) detectors: thermal ratios (TRAT_NF, TRAT_ND), burst ratios (BRAT_NF, BRAT_ND) and the inelastic 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 -filled porous intervals. Hence, to use TRATs or BRATs to detect, 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 -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 - or oil-filled limestone with various porosity (x000 to x140 ft and x400 to x80 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. 1/m) and are lower in the -filled zones. Note that TPHI, TRAT, BRAT, sigma, or gamma ray cannot differentiate between 0- p.u. limestone (X160 to X220 ft) and -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 in the X220 to X400 ft interval. The 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 trapped by an invisible permeability barrier (perhaps a stylolite) at the bottom of the layer. Notice that CH TPHI has a stronger 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- split from the OH neutrondensity 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 12 the volume. Track 11 corresponds to the CH fluids analysis integrating OH porosity and mineral volumes with sigma, TPHI and FNXS. Sigma drives mostly the volume, and TPHI and FNXS drive the oil/ 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 1, where no OH log information is used in the interpretation. Example 2. Example 2 is from a producing oil and field in the USA. The interval shown in Figure A-2 was drilled with an 8.7-in. bit size and completed with 4. 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 -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 8.7-in. bit size, 4.-in. casing size, and 11.6 lbm/ft casing weight with 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 / 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 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 (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. in. bit size) was drilled in Pennsylvania, USA, and the logging objective was to evaluate the complex shale formation including mineralogy, kerogen volume, and 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 volume and total porosity because they have contrasting responses to kerogen and. Note the transition from low porosity, low kerogen, and low volume above ~x0 ft to higher porosity, higher kerogen volume, and higher 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 formation in CH, a log was run after the well was cased (. in., 23 lbm/ft casing) but before completion. The borehole was filled with fresh. 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 largerdiameter-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 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 transition at ~x0 ft. The transition is not obvious with the sigma and TPHI measurements. 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 ( Example 4 Example 4 is from a producing oil and field in Southeast Asia. The objective of the casedhole 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.- in. bit size completed with two strings of 3.-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

14 SPWLA 7 th Annual Logging Symposium, June 2-29, 2016 lithology is complex with interbeddedd sands, shales, limestones and coals. Open-hole logs were available. Figure 20 shows the TPHI versus FNXS crossplot where the FNXS was computed with the default gain and offset based on 8.-in. bit size, 3.-in. tubing size and 9.3 lbm/ft tubing weight with in the bore ehole. The actual conditions are significantly different with the dual 3.-in. tubing cemented in the 8.-in. borehole and with light hydrocarbons in the boreholee rather than. The FNXS with the default correction is clearly too low in this case and requires an additional offset correction (offset1 in Figure 17). Figure 21 shows a crossplot of TPHI versus FNXS where FNX XS has been computed from log(g GRAT) with an appropriate additional offset to account for the light hydrocarbon in the borehole and the dual tubing configuration. This additional offset is determined to make the corrected FNXS value close to the theoretical value in the shale zone es. Compared to Figure 20, FNXS values in Figure 21 are much more consistent with the sandstone ne and limestone / envelope. The interval covered in Figure A-4 shows varying lithologies including a low porosity limestone with shale, thin sands and coal. The GRAT and FNXS show the thin sand zones at x ft and x80 may have some producible while the limestone has very low porosity. osity A standalone ne PNL volumetric interpretation ion is shown in the far right track using SIGM, FNXS, TPHI and the CH PNL spectroscopy copy data and it can be contrasted to the open n-ho hole logs, which were not used in the interpretation etation to show what is possible with standalone PNL logs and FNXS. Note that the BRAT, SIGM and TPHI logs respond very similarly between the -filleporosity limestone zone. porosity in the thin sands zones and the low SUMMARY Figure 20 Example 4, crossplot of TPHI versus FNXS where FNXS has the default GRAT offset correction. The FNXS is clearly reading too low, whic ch is consistent with to light hydrocarbon in the borehole. We introduced a new formation property, FNXS and its measurement, to the logging industry. FNXS is independent of the existing formation nuclear properties HI, sigma, or density and brings additional information ion for formation evaluation applications. A slim PNL device was optimized for this measurement. We studied d the FNXS response in a wid de range of downhole ole environmental conditions ons by both simulations and experiments and provided d the characterization ion and borehole correction algorithms. Four log examples demonstrate te the FNXS measurement men performance in different environments and its added value in challengingg CH conditions. Standalone CH formation evaluation is enabled by the addition of this new measurement ement when is present. ACKNOWLEDGMENTS Figure 21 Example 4, crossplot of TPHI versus FNXS where FNXS has been comp puted from GRAT and an appropriate GRAT offset to account for the complex boreholee condition. The authors wish to thank the managements of Schlumberger, ADCO, ICO Indonesia and the other operating comp panies involved for their support in this endeavor, and their permission to publish thesee findings. 14

15 REFERENCES Los Alamos National Laboratory, 2003, MCNP a general Monte Carlo N-particle transport code, ersion, report LA-UR McKeon, D. C., and Scott, H. D., 1989, SNUPAR A nuclear parameter code for nuclear geophysics applications, IEEE Transactions on Nuclear Science, 36(1), 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 3rd Annual Logging Symposium, Cartagena, Colombia, 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., 201, An innovative slim pulsed neutron logging tool, Paper XXXX, Transactions, SPWLA 6th Annual Logging Symposium, Long Beach, California, USA, 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). 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 (199), 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 He worked with Schlumberger as a General Wireline field Engineer between 2000 and 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 He started his career back in 200 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 Ade Lukman is currently working as Petroleum Engineering Team Leader for ICO, Indonesia. He started his career back in 2002 as a Petroleum Engineer at ICO 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 James Thornton worked as a Physics Advisor at Schlumberger s Princeton Technology Center (retired). He holds a PhD in physics from Stanford University (USA). 1

16 SPWLA 7th Annual Logging Symposium, June , 29, 2016 APPENDIX A. INTEGRATED INTERPRET INTERPRETATION ATION EXAMPLES Figure A A-1, 1, Example 11, a log example from Abu Dhabi. 16

17 Figure A-2, Example 2, a log example from USA. 17

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 volume. The FNXS is a critical input in the pulsed neutron interpretation because it enables the computation of an accurate volume. 18

19 Figure A-4, Example 4, a log example from Southeast Asia from a complex completion. 19