You are here
Home > Others > Shale characteristics impact on Nuclear Magnetic Resonance (NMR) fluid typing methods and correlations

Shale characteristics impact on Nuclear Magnetic Resonance (NMR) fluid typing methods and correlations

by Emanuel Martin - 0
The application of Nuclear Magnetic Resonance techniques in fluid typing and properties estimation is well-developed in conventional reservoirs. However, Shale reservoirs characteristics like pore size, organic matter, clay content, wettability, adsorption, and mineralogy would limit the applicability of the used interpretation methods and correlation.

Shale characteristics impact on Nuclear Magnetic Resonance (NMR) fluid typing methods and correlations

 

Mohamed Mehanaa, Ilham El-monierb

aUniversity of Oklahoma, Suez  University, USA. bUniversity of Oklahoma, USA

 

Abstract

The development of shale reservoirs has brought a paradigm shift in the worldwide energy equation. This  entails developing robust techniques to properly evaluate and unlock the potential of those reservoirs. The  application of  Nuclear Magnetic Resonance techniques in  fluid typing and properties estimation is well-developed in conventional  reservoirs. However, Shale reservoirs characteristics like pore size, organic matter, clay  content, wettability, adsorption, and mineralogy would limit the applicability of  the used interpretation methods and correlation. Some of these limitations include the inapplicability of the controlling equations that were derived assuming fast relaxation regime, the overlap of different fluids peaks and the lack of robust correlation to estimate fluid properties in  shale. This  study presents a  state-of-the-art review of  the main contributions presented on  fluid typing methods and correlations in both experimental and theoretical side. The study involves Dual Tw, Dual Te, and doping agent's  application, T1-T2, D-T2 and  T2sec vs. T1/T2 methods. In addition, fluid properties estimation such as  density, viscosity and the gas-oil ratio is discussed.

This  study investigates the applicability of these methods along with a  study of  the current fluid properties correlations and their limitations. Moreover, it recommends the appropriate method and correlation which are capable of tackling shale heterogeneity.

1. Introduction

Since the introduction of Nuclear Magnetic resonance technology in petroleum industry, its applications answered a lot of questions in  reservoir engineering and provided unambiguous techniques to evaluate rock, rock-fluid  and fluid properties. NMR  applications started with a  tool to calculate the total porosity independently  from rock matrix effects by calculating the  intensity of  hydrogen protons  in  formation. Then,  the differentiation between the bound and free  fraction of  fluid in porous medium became obtainable. Further  improvements  were  performed to extend  its  applicability to determine  capillary pressure [1],  wettability [2],  and relative permeability  [3]   from  NMR   measurements.  Consequently, NMR  became a reliable instrument to diagnose fluid types, properties, and rock  properties as  well for both conventional and unconventional reservoirs [4-8].

Counting  on  NMR methods used in  conventional reservoir to  determine rock  and fluid properties as  a  guide to  analyze shale reservoirs response is  misleading and will  yield unreliable  data [9]. In terms of composition, shale is a heterogeneous rock   with  various clay contents [10]. The clay   distribution affects both NMR response and interpretation [11].  Apart from clay  content, the presence of the organic matter will  add more complexity to the system. Considering the pore size, shale has  a wide spectrum of  pore sizes ranging from nanometer  pores, conventional pores to natural fractures along with the dependence of  the organic pores on  the  maturity of the rock. Moreover,  shale has  different wettabilities and most of  them are  mixed [10].  Due to the complexity of the shale system, an extensive  study  of  rock   and  fluid  characteristics  impact is required to successfully interpret the NMR response.

2.  Theory

NMR measurements procedure starts with exciting the sample by a magnetic field,  which will polarize the hydrogen protons in one direction. Then,   measuring the longitudinal relaxation time (T1) or use  Carr Purcell Meiboom Gill (CPMG) sequence and measure the transverse relaxation time (T2) or use  pulsed-field gradient  sequence  and  measure  the  diffusion coefficient (D) [12].  After that, an  inversion method will  be  adopted to  obtain the decay exponents distribution [13].  This  distribution will  be the base of all succeeding interpretation methods.

The relaxation of fluids in a bulk state is bulk relaxation and tends to be longer as the relaxation will  be due the interactions among fluid protons only. On the other hand, fluids relaxation in porous media will  be  promoted by  the interactions among the fluids and the confining surface protons. Besides, the transverse relaxation time will be more affected by the molecules diffusion. The  diffusion relaxation will  not affect T1 measurement, but it influences T2  where there are  spin dephasing and refocusing [14]. The relaxation  governing equations  in  porous  media  is developed in  fast  diffusion regime as  a  weighted average between bulk and surface relaxation rate. Therefore, they will  be

The relaxation governing equations in porous media is developed in fast diffusion regime as a weighted average between bulk and surface relaxation rate.

The validity of fast  diffusion regime assumption in shale will determine the applicability of those equations. In addition, the impact of the diffusion coupling, homonuclear dipole coupling, heteronuclear dipole coupling, residual dipole coupling and magnetization transfer as relaxation mechanisms should be considered [15].

3.  Shale characteristics impact on the NMR signals

Shale is a clay-rich rock which contains variable content  of clay  minerals and organic matter. This  rock contains different pore sizes which have fractional wettability and host different kinds of fluids. In addition, the Nano-scale pores and adsorption will add more ambiguity to the nature of these rocks. This section will discuss the main characteristics of Shale and their impact on NMR response.

3.1.  Pore  size

The behavior of fluids in confined porous media diverges from their behavior in  bulk state [16].  These deviances will  be  more significant in  Nano-scale pores (Fig. 1). Therefore, this entails a study of these deviances effects on  relaxation mechanisms.

In most conventional pores studies, it is assumed that the main relaxation mechanism is the surface relaxation. Therefore, the position of the fluid peak will be directly proportional to the size of the pore-containing that fluid. With this in mind, many studies managed to estimate pore size distribution from NMR.

Fig. 1. Sizes of molecules and pore throats in siliciclastic rocks on a logarithmic scale. Measurement techniques are shown at the top of the graph [15].

In most conventional pores studies, it is assumed that the main relaxation mechanism is the surface relaxation. Therefore, the position of the fluid peak will  be directly proportional to the size  of the pore-containing that fluid. With this in mind, many studies managed to  estimate pore size  distribution  from NMR. Moving into shale domain, the main relaxation mechanism will be also  the surface relaxation. But  knowing that the relaxation interactions scale  is comparable to the scale  of pores investigated [6], will  render any  study of pore size based on NMR relaxation time useless. Since,  it is evident that Equation (2) is not valid  to calculate the relaxation time of water in Smectite interlayers [17]. Therefore, conventional surface relaxivity concept is not applicable in nano-scale pores anymore.

Furthermore, the heterogeneous distribution of  pore size observed in shale would puzzle the interpretation scheme. Shale contains micro-pores in  the organic matter and nano-pores in the clay minerals and natural fractures [5]. The presence of these different pore size scales in  one rock  complicates the interpretation process. As it  will  be  more challenging to  decide if the peaks  in   relaxation  time  distributions  are resulting  from different pore sizes or from different fluids in these pores.

3.2. Organic matter and clay content

The organic matter and clay content are  considered as matrix constituents and hydrogen protons populations that affect NMR signal [6]. Therefore, the NMR response is not independent of the rock  matrix in  this case [18].  The organic matter affects NMR signals in two ways directly as it is one of the proton population investigated and indirectly by controlling the relaxation time of the fluid contained in  the organic pores where it  acts   as  the relaxation surface. In  both cases, the effect of  organic matter depends on its maturity. Considering the direct effect, the degree of maturation is directly proportional to the mobility of protons [6]. Therefore, the organic matter signature of samples from gas, oil  or  immature windows will  not be  the same. Unfortunately, the organic matter response may be  masked if high hydrogen index fluid is present. On the other hand, the organic matter as a matrix contains organic porosity up  to  20.2% and average pore size approximately 100nm  [19]. Therefore, the surface relaxivity of organic matter and the interactions between the fluid and the surface need further investigation. As, Washburn [18]  suggested that the surface relaxation in organic pores depends on  the homonuclear dipole coupling among hydrogen protons in the fluid and the surface. In contrast to the surface relaxation in conventional  pores  where it is dominated by interactions  among fluid protons and paramagnetic impurities in the surface. Consequently, the  sample  organic content  and  maturation should be  investigated to  properly evaluate their effect. Fig. 2 shows the  morphology of  conventional and  unconventional pores through SEM images along with a schematic representation of shale structure.

Fig. 2 shows the morphology of conventional and unconventional pores through SEM images along with a schematic representation of shale structure.

Fig. 2.  (a)  represents a  comparison between  the  conventional sandstone and the  unconventional organic pores [20].  (b) Represents  schematic illustration of  shale morphology [21].

Clay content  and distribution  add another degree of complexity for fluid identification in these heterogeneous reservoirs.  Similarly, clay minerals are one of the proton population, which have NMR response and influence the responses of the attached fluid in  many different ways. Firstly,  the NMR response of  clay will depend on their distribution  and compaction  in shale [11].  If  the clay particles  present  in structural or laminar distribution, Their NMR response will  be below 0.1  ms  and will  require high resolution equipment to detect their signal [6]. However, if the clay  is dispersed, it will not produce a  separate signal,  but will  affect other fluids responses by providing extra surface area for fluids to relax on.

Secondly, the type of clay minerals present will affect the wettability of the pores. Saada et al. [22] explained that Illite is water-wet while Kaolinite is hydrocarbon-wet. In addition, clay inter-layers fluids will also exhibit restricted relaxation as it will be bonded within the internal structure of the molecule. Interestingly, Zhang et al. [23]'s experimental results suggest that Smectite may absorb hydrocarbons in certain conditions. So, according to clay type, clay will absorb and adsorb different fluids.

3.3. Wettability and adsorption

Wettability determines which fluid will adhere to the surface and subsequently will be affected more by  the surface-fluid interactions. In  contrast to  conventional reservoirs where hydrocarbons accumulations migrate from the source rock to the reservoirs, Shale is considered self-sourced reservoir. Besides, the heterogeneity reported in shale mineralogy. Fig.3 highlights the main constituents of shale matrix and pores. Therefore, Shale pores have different wettabilities and most of them are  mixed. Subsequently, the determination of fluid location within the pore is indefinite and will  depend on  the pore type under investigation [20].

Fig. 3 highlights the main constituents of shale matrix and pores. Therefore, Shale pores have different wettabilities and most of them are mixed. Subsequently, the determination of fluid location within the pore is indefinite and will depend on the pore type under investigation.

Fig. 3. Schematic representations of different shale system constitutions [24].

The electrical charge imbalance on the surfaces of  both organic matter and clay  particles leads to fluid adsorption on these surfaces. There are  two kinds of adsorption experienced in Shale: gas  adsorption in organic pores and water adsorption on clay  particles. Gas  adsorption will be more tangible in high pressures as indicated from Langmuir isotherm. The adsorbed fluid signal will  be  mainly controlled by  the interactions with surface molecules and will exhibit relatively faster relaxation compared to the remaining fluid in the pore. The exchange between molecules between the adsorbed phase and the free phase and its impact on the fluid response needs more investigation.

3.4. Mineralogy

NMR response is assumed to be  independent from the rock matrix, however rock mineralogy will  control the surface chemistry of  the  grains and, in turn, the  surface relaxation mechanism.  Shale is  a  fine-grained  clastic sedimentary rock composed of different portions of clay  minerals, quartz, calcite and organic matter. Interestingly, the resin present in the organic pores also affects the surface chemistry of these pores as it coats the grains. Therefore, the heterogeneity in  the composition reflects on  the surface chemistry of the pores which will  exhibit different relaxitivity characteristics. Therefore, assigning a single value  for   surface relaxivity for the whole rock would be misleading.

Paramagnetic impurities are another aspect that should be considered during shale mineralogy discussions. The most common paramagnetic ions present in shale are iron and manganese. The high paramagnetic content may be misinterpreted to high surface relaxivity. But, in reality, most of the iron content in shale is related to the pyrite and small amount is dispersed as an impurity in pore surfaces and very little in organic matter. Therefore, the paramagnetic ions will not be the reason for faster relaxation [18]. Fig. 4 displays the mineralogy of the main shale gas plays in the United States.

Fig. 4 displays the mineralogy of the main shale gas plays in the United States.

Fig. 4.  Mineralogy of the gas  shale plays in  the United States [25].

4. Fluids NMR responses in shale

The relaxation time of the fluids in bulk volume will  depend mainly on the interactions between the protons within the fluid. But, the relaxation of the fluid in shale pores will  be affected by several competing factors as  indicated in  the previous section. The  resulting signal will  signify massive information about the fluid and the pore size.  This section discusses the NMR response of the fluids in shale. Table  1 shows the qualitative response of different fluid in porous media. Table 2 display the fluids signatures reported in literature.

Table 1 shows the qualitative response of different fluid in porous media.

Table 1 Qualitative T1, T2, and D values for different fluids in porous media [modified after 12].

Table 2 display the fluids signatures reported in literature.

Table 2 Comparison between the response of the brine, oil  and gas in sandstone and shale [6,14,26-29].

4.1. Brine response

The relaxation of the bulk liquids is controlled by dipoleedipole interactions. Consequently, the response is directly proportional to liquid viscosity and inversely to absolute temperature. In addition, the response of brine depends on the type of salts dissolved. As, the presence of small concentrations paramagnetic ions (mn+2) or ferromagnetic ions reduces water response substantially. The combination of the long bulk relaxation time and the tendency of water to fill the small pores are the main reasons for the domination of the surface relaxation mechanism on the brine relaxation in porous media.

4.2. Oil response

Oil is a mixture of different liquid hydrocarbons components and series. Therefore, oil is expected to have board distribution without unique peak characterizing the fluid type as in pure fluids. The more heterogeneous oil composition is  the more broad the oil response would be. The fractions of the light and heavy component in oil determine whether the oil will relax fast due to the heavy components or  relax slowly due to  light components. Moving to the nanoscale pores,  the wettability of the surface determines  which fluid will  be affected by  surface interactions.

However, Minh et al. [5] suggested that the relaxation of heavy oil will be dominated by bulk relaxation mechanism regardless of the wettability of the pore due to the short bulk relaxation of heavy oil. Based on their simulation results, they set 100 cps as the dividing value. Higher than this value, the wettability effect is considered negligible. Based on experimental study results, Tinni et al. [26] agreed that the heavy oil response will not be affected by the surface relaxation. On the other hand, the bulk decay exponent is comparable to the surface decay exponent in light oil. So wettability will determine the essence of the relaxation mechanisms involved.

4.3. Gas response

Dunn et al. [14] showed that the relaxation behavior of gas will not follow the same behavior of liquids. As the bulk relaxation of gas will be dominated by spin-rotation interactions. Therefore, the response is directly proportional to absolute temperature and inversely to fluid viscosity. Moreover, the T1 relaxation dependence on temperature and pressure is opposite to the situation in liquids. T1 is directly proportional to the pressure and inversely to the temperature. Moving to relaxation in shale, the situation is more complicated with the introduction of adsorbed gas to the system. As, gas diffusion coefficient will depend on the diffusion of adsorbed phase, free phase and the exchange between them. Ref. [29] studied the gas dynamics in Shale. Their study proposes a new restricted diffusion model. However the experimental results do not present the diffusion in confined nanopores due to the limited resolution of the used machines. They claims that T1 and T2 are controlled by dipole interactions between gas protons and paramagnetic impurities in the surface. However, Washburn [18] suggested that the relaxation might be due to homonuclear coupling between gas protons and protons on the surface.

5.Interpretation methods

The complex structure and heterogeneous composition of shale require the manipulation of more than one dimension (1-D) distribution to identify the different fluids present in the pores. In addition, the overlap between the fluid relaxation peaks would confound fluid typing methods, for example, the overlap between heavy oil peak and water peak considering 1-D interpretation. Also, Kausik et al. [27] reported an overlap between oil signal (6-20 ms) and methane signal (10-20 ms). However, the implementation of two-dimension (2-D) methods provide better differentiation between the fluid peaks and in some cases may capture clay particles and organic matter response. Unfortunately, there are two expected weaknesses in these methods: I) the wide range of oil viscosities leads to indefinite signature. II) All these methods do not distinguish between free and adsorbed gas fractions. In this section, different interpretation methods will be discussed.

5.1. 1-D method

There are two methods counting on only 1-D measurement. The first method is simply based on the T1 contrast among the fluids (dual TW method).

This method involves polarizing the sample with short and long wait times. Water protons are polarized in the two times, but the hydrocarbons will be polarized only during the long time. So the differential response will define the hydrocarbon response (light oil or gas). The second method depends on the contrast among the diffusion coefficients of fluids (dual TE method). Based on the Equation (2) if the T2 measurement was performed with different TE the responses will be different. The differential response can be used to identify the fluids. This method identifies the gas response and measures its volume accurately. Xie and Xiao [30] proved from numerical simulation that water response in large pores will not be identified by TW method and there will be an overlap between the responses of gas in small pores and irreducible water using TE method. These results suggest the use of 2-D methods for better resolution.

5.2.  2-D methods

2-D methods will overcome most drawbacks encountered in 1-D methods. These methods  include  plotting  diffusion distributions versus T1 or T2, T1 versus T2 and T2SEC versus T1/T2. These methods  yield better  fluid  typing by providing more contrasts between the fluids peaks reveal the overlaps between the fluids signatures.

The T1-T2 method provides better differentiation between the different fluids responses. A number of  experimental studies have been conducted in  this area, including the work of Fleury [6] with low field NMR to quantify fluids through T1-T2 maps. The experiments were performed on both the organic matter (immature, oil  window and gas  window) and shale samples. According to the experimental results, T1-T2  method successfully capture water and methane signature (T1/T2 ~ 2 and 10 respectively) along with a diagnostic map (T1 vs. T2) showing the expected  region of the response from the organic matter and hydroxyls groups in the clays as  indicated in Fig. 5. Washburn [31]  has  studied the T1-T2 response of four  shale samples, three from Piceance basin (outcrop, oil  shale and well cuttings) and outcrop sample from unita basin. This study confirms the ability of the T1-T2 maps to identify different organic maturities present in shale, mainly solid and liquid-like phases. Xie and Xiao  [30] studied the T1-T2   maps with numerical simulation. Their results are consistent with  the previous experimental  work. Therefore T1-T2  method is the applicable tool  for fluid typing in most situations except differentiating between  heavy oil  and bound water response.

According to the experimental results, T1-T2 method successfully capture water and methane signature (T1/T2 ~ 2 and 10 respectively) along with a diagnostic map (T1 vs. T2) showing the expected region of the response from the organic matter and hydroxyls groups in the clays as indicated in Fig. 5.

Fig. 5. T1-T2  diagnostic  map [Modified after 6].

The overlap between the gas  and irreducible water signals resulting from the use of Dual TE method can be resolved by the D-T2 method. Experimental investigation conducted by Zielinski et al. [32] on carbonates introduced the concept of restricted diffusion of fluids in  porous  media, followed by  the  application of  these restricted diffusion maps to interpret the dynamics of restricted gas  in  shale [29], completed by a revised model based on the restricted diffusion to interpret the response from gas and oil shale as shown in Fig. 6.

However, it worth noting that all the diffusion-based methods lack real measurements of restricted diffusion coefficients in shale due to the limited resolution of the machines.

Experimental investigation conducted by Zielinski et al. [32] on carbonates introduced the concept of restricted diffusion of fluids in porous media, followed by the application of these restricted diffusion maps to interpret the dynamics of restricted gas in shale [29], completed by a revised model based on the restricted diffusion to interpret the response from gas and oil shale as shown in Fig. 6.

Fig. 6. D-T2 method in conventional and unconventional reservoirs [5].

Daigle et al. [7]  introduced a method based on  the secular relaxation  (T2sec) to distinguish between  the  fluids through plotting (T2sec vs. T1/T2). Secular relaxation rate is the difference between transverse and longitudinal relaxation rates. Their  experiments were performed in ambient conditions with low  field NMR on  samples from the Bakken, Woodford and shallow Marine mudstone from offshore Japan. This  method succeeded to separate signals of the fluids based on  viscosity and pore size. They provide a diagnostic map to interpret the NMR responses to seven different scenarios as  shown in  Fig. 7.  Followed by  the work of Gips et al. [8] to evaluate the hydrocarbon characteristics based on  the differential response. This  differential response is the absolute difference between the NMR response at  a higher temperature and  at ambient conditions.  This differential response may carry valuable information about the fluid molecular size and viscosity if it is correlated to the correlation time. But, it is worth mentioning that the experimental procedures were performed on a grinded sample. Moreover, Washburn [18] claims that this differential response will be mainly due to  the dependence of the surface relaxivity on temperature.

This method succeeded to separate signals of the fluids based on viscosity and pore size. They provide a diagnostic map to interpret the NMR responses to seven different scenarios as shown in Fig. 7.

Fig. 7. T2sec-T1/T2  method [modified after 7].

5.3. Artificial contrast

Another approach based on introducing an artificial contrast between hydrocarbons and water to separate their signals in the T1-T2 maps which will allow better identification of the fluids.

Mitchell et al. [34] have analyzed the effect of the paramagnetic  doping agent, especially chelated  manganese-EDTA and  unchelated manganese on four   limestone   plugs.  This study recommends the use of  doping agents for  better clear separation among oil  and water signals and the mitigation of unchelated manganese with EDTA whenever clay is present. On the other hand, this study opposes the use  of any  paramagnetic doping agent in  any  system with PH<9,  As, it  will  result in precipitation of  insoluble products which will  affect the flow capacity.  Unfortunately it does not include experimental work in  shale. Using  the same concept, Gannaway [33]  succeeds in providing robust method to  separate water and oil  response. This separation enables to quantify different types of porosities encountered in shale as presented in Fig. 8. However, this study did  not consider the formation damage resulting from the interactions between mn+2  and clays  and PH  effect on  doping effect in  shale.

The application of Nuclear Magnetic Resonance techniques in fluid typing and properties estimation is well-developed in conventional reservoirs. However, Shale reservoirs characteristics like pore size, organic matter, clay content, wettability, adsorption, and mineralogy would limit the applicability of the used interpretation methods and correlation.

Fig.  8.  Gannaway results from using doping agent to quantify the different porosities [modified after 33].

6. Fluid properties estimation from NMR measurement

The  estimation of  the fluid properties from NMR measurements was reported in literature in the experimental work performed by Freedman et al. [35]  in bulk fluid samples, Ref. [36]  in Berea rocks and [37]  in oil sands. Ref. [35]  proposed a mapping function to estimate fluid properties from NMR measurements based on the database that they have established. Ref. [36]  proposed a set  of correlation to  estimate the fluid properties from NMR signal. On the other hand, in one of the earliest studies to calculate the in-situ viscosity of oil  sands based on  NMR measurement, Ref. [37]  assumed that the heavy oil  relaxation will depend only on  internal interactions so they derived a  set  of correlation to estimate in-situ viscosity of oil sands based on T2 and relative hydrogen index.

The  successful estimation  of  fluid properties is  tied to the accuracy of fluid identification by previous  interpretation methods. The fluid properties that could be estimated from NMR include density, Gas-oil ratio (GOR) and viscosity [38-40].

6.1. Density

The  fluid density can be estimated after fluid identification using hydrogen index equation [14]. This equation has twovariables hydrogen index and total porosity. So if the total porosity is known, the fluid density can  be estimated.

This equation has two variables hydrogen index and total porosity.

6.2. Viscosity

Dead oil viscosity of pure alkanes exhibits a linear relationship with both T1, 2 and D [14]. Hirasaki and Zhang [40] proposed a correction for live oil viscosity as a function of Gas Oil Ratio (GOR). However, Chen and Chen [39] recommended a mixing rule to estimate the viscosity of hydrocarbon mixtures. But, this is not the case in the shale reservoirs where the main relaxation mechanism is surface relaxation. Subsequently, the decay exponent is mainly a function of surface-fluid interactions and does not represent the internal reactions within fluid protons.

However, Minh et al. [5]  suggested based on simulation study that the heavy oil (viscosity more than 100cps) relaxation will be controlled by the internal reactions. Therefore, the  heavy oil viscosity could be correlated with their relaxation time following the procedure presented in  Bryan et al.  [37] to estimate the viscosity of oil sands.

Tinni et al. [26]  used the T1/T2 ratio to differentiate between moveable and non-moveable fluids in both conventional and unconventional reservoirs. The experimental results of this study show that the non-moveable hydrocarbons will  not be affected by surface relaxation which was confirmed by  the simulation results of [5]. This study proves that NMR can  estimate the fluid viscosity at least qualitatively.

6.3.  Gas-Oil  Ratio  (GOR)

Winkler et al. [38]  proposed a model to estimate GOR as the ratio between the numbers of the gas  protons to the number of oil protons assuming that oil and gas  molecules maintain their identities microscopically and, in turn, oil and gas molecules will maintain their self-diffusivity. So,  after the differentiation between gas  and oil  molecules using their proposed mixing rule, GOR and viscosity could be  determined. Chen   and Chen   [39] extend this model to  mitigate the invasion of oil base mud and provide a  method to  calibrate NMR  log to estimate GOR and viscosity of live oil signatures. But, this approach requires precise differentiation between gas  and oil.  There is another approach based on the observed deviation from the temperature-viscosity/ relaxation time correlation in the case of live oil. Consequently, this deviation can be correlated empirically to GOR [41]. But, Winkler et al.  [38] disputed the latter approach. As  it  overestimates GOR and they attributed this to the complexity of the competing factors; spin rotation and intramolecular attraction.

The  extension of  the capability of  NMR in  shale to  include fluid properties identification will  make it a stand-alone formation evaluation method. However, the accuracy of the properties derived will  be  doubtful. As the correlations used were derived based on pure alkanes assuming single decay constant. Also, the used correlations will  have limited applicability to  the formations and the fluids used to derive them.

The fast relaxation of protons in shale limits the resolution of low-frequency field measurements. Therefor, the fluid properties will  be  better estimated using high-frequency NMR. However, these high field measurement are  double edged weapons. While it  enables capturing the bulk relaxation response, it magnifies the heterogeneity in  the  system. So capturing this response would be more challenging and inaccurate. Moreover, the small size  of the probe of the common NMR high-frequency machines will not allow sample measurements. Instead, the measurements will  be performed on  grinded samples.

There is another approach based on measuring the response from the sample at  high-temperatures, then subtract the high temperature response from the ambient temperature response. So the differential response could be correlated to the change of fluid viscosity with temperature  assuming that surface relaxation  component  will  remain  constant.  But   this  approach  is disputed by  Washburn [18].  As they proved the dependence of the surface relaxivity of the organic pores on  the temperature.

7. Conclusion

The abundance of different typing methods should boost the identification process, however, unfortunately, shale heterogeneity limit the applicability of most of them. The  T1-T2  method seems to  be  the most appropriate method for  fluid typing in shale. As, the high internal gradients observed in  shale would limit the applicability of all diffusion based methods. Moreover, the accuracy of the fluid properties estimated will depend on the efficiency of extracting the bulk relaxation response. Although most current correlations were derived for pure alkane components, the mixing rules proposed in the literature could extend their applicability to the  multicomponent systems. The  fluid properties estimation from NMR measurements in shale is challenging and  needs  more  experimental  work  to   develop reliable correlations and properly calibrate the devices. The fluid typing in shale is a complicated process, but it would be  better performed by  well-understanding of  the shale system on  one side  and developing robust identification methods on  the other side.

Nomenclature

Shale characteristics impact on Nuclear Magnetic Resonance (NMR) fluid typing methods and correlations

Appendix

Shale characteristics impact on Nuclear Magnetic Resonance (NMR) fluid typing methods and correlations

 

References

 

  1. F.J. Slijkerman, J.P. Hofman, W.J.  Looyestijn, et al.,  A practical approach to obtain primary drainage capillary pressure curves from NMR  core and log data, Petrophysics 42 (04) (2001).
  2. E. Odusina, C.H. Sondergeld, C.S. Rai, An NMR study on shale wettability, in: SPE  147371, Presented at the Canadian Unconventional Resources Conference held in Calgery, Alberta, Canada, 15-17, November, 2011.
  3. T.  AlGhamdi, C.  Arns, Correlations between  NMMR-relaxation response and relative permeability from tomographic reservoir-rock images, SPE Reserv. Eval.  Eng.  16 (04) (2013).
  4. R. Akkurt, H.N. Bachman, C. Cao  Minh, Nuclear Magnetic Resonance comes out of  shell, Oil  Field Rev.  20 (4)  (2008/2009  Winter) 20.
  5. C.C. Minh, S. Crary, L. Zielinski, et al., 2D-NMR applications in unconventional reservoirs, in: SPE 161578, Canadian Unconventional Resources Conference held in Calgary, Alberta, Canada, 30 Octobere1 November, 2012.
  6. M.  Fleury, Characterization  of  shales with low field NMR,  in: The   International Symposium of  Core Analysts, Avignon, France, 8-11 September, SCA2014-014, 2014.
  7. H.  Daigle, A. Johnson, J.P. Gips, Porosity evaluation  of  shales  using NMR secular relaxation, in: Unconventional Resources Technology Conference (URTEC),  Denver, Colorado, 25-27 August, SPE1905272-MS, 2014.
  8. J.P. Gips, H. Dailage, S. Mukul, Characterization of free and bound fluids in hydrocarbon bearing shales using NMR  and  Py-GC-MS, in: SPE-1917689- MS,  Unconventional  Resources Technology Conference (URTEC),  Denver, Colorado, 25-27 August, 2014.
  9. R.F. Sigal, F. Odusina, Laboratory NMR  measurements on methane satu- rated Barnett Shale samples, Petrophysics 52 (1)  (2011) 32-49.
  10. Carl  H.  Sondergeld, Unconventional shale analysis, in: Lecture Notes on Unconventional Reservoirs, The  University of  Oklahoma, Norman, United States of  America, 2014.
  11. V.  Anand, G.J.  Hirasaki, M.  Fleury, NMR   diffusional  coupling:  effects of temperature  and clay distribution, Petrophysics 49 (4)  (2008) 362.
  12. G.R. Coates, L. Xiao,   M.G.  Prammer, NMR  Logging: Principles and Applications, Gulf  Professional Publishing, Houston, 1999 (Reprint).
  13. Boqin Sun, Dunn  Keh-Jim, NMR   inversion methods for   fluid typing, in: SPWLA  44th  Annual Logging Symposium, Galveston, Texas, 22e25  June, SPWLA-2003-GGG, 2003.
  14. K.-J. Dunn, D.J. Bergman, G. Torraca, Nuclear Magnetic Resonance: Petrophysical and Logging Applications, Elsevier, 2002 (Reprint).
  15. H.  Nelson, Pore-throat sizes in sandstones, tight sandstones, and shales, AAPG Bull.  93 (3)  (2008) 329-340.
  16. D. Devegowda, Phase Behavior in Shale. Lecture Notes on Unconventional Reservoirs, The  University of Oklahoma, Norman, United States of America, 2014.
  17. M. Fleury, E. Kohler, F. Norrant, et al., Characterization and quantification of water in smectites with low-field NMR,  J. Phys. Chem. C2013 117 (2013)4551-4560.
  18. E.k Washburn, Relaxation mechanisms and shales, Concept. Magn. Reson. Part A 43 (3)  (2014) 57-89. http://dx.doi.org/.
  19. R. Loucks, R. Reed, S. Ruppel, D.  Jarvie, Morphology, genesis, and distribution of  nanometer scale pores in siliceous mudstones of  the Mississippian Barnett Shale, J. Sediment. Res.  79 (2009) 848-861.
  20. Q.R. Passey, K.M. Bohacs, W.L. Esch, R. Klimentidis, S. Sinha, My source rock is   now  my reservoir e geologic and  petrophysical  characterization  of shale-gas reservoirs, Search Discov. (2012). Article #80231.
  21. F. Civan, D. Devegowda, Rigorous modelling for  data analysis towards accurate  determination  of   shale  gas-permeability  by    multiple-repeated pressure-pluse  transmission tests on crushed samples, in: SPE  170659-PT  Presented at the SPE  Annual Technical Conference and  Exhibition, Amsterdam, the Netherlands, October, 2014, pp. 27-29.
  22. A.  Saada, B.  Siffert, E.  Papirer, Comparison of  the hydrophilicity/hydrophobicity  of   illite  and  kaolinites,  J.  Colloid Interface  Sci.   174  (1995) 185-190.
  23. T. Zhang, G. Ellis,  S. Ruppel, K. Miliken, R. Yang, Effect of  Organic-matter type and thermal maturity on methane adsorption in shale-gas systems, J. Org.  Geochem. 47 (2012) 120-131.
  24. Dyrka, I. Petrophysical Properties of  Shale Rocks. http://infolupki.pgi.gov. pl/en/gas/petrophysical-properties-shale-rocks [accessed 26.12.15].
  25. M.  Schreiber, J. Chermak, Mineralogy and trace element geochemistry of gas shales in the United States: environmental implications, Inter. J. Coal Geol. 126 (2014) (2013) 32-44.
  26. A. Tinni, C. Sundergeld, C. Rai,  NMR  T1-T2 response  of moveable and nonmoveable fluids in conventional and unconventional rocks, in: Presented at the International Symposium of  Core Analysts, Avignon, France,8-11 September,  SCA2014-014, 2014.
  27. R. Kausik, K. Fellah, E. Rylander, P.M.  Singer, R.E. Lewis, S.M. Sinclair, NMR Petrophysics  for  tight  oil  shale  enabled  by  core resaturation, SCA 2014-073 (2014).
  28. A. Tinni, E. Odusina, I. Sulucarian, NMR  response of brine, oil  and methane in organic shales, in: Presented at SPE Unconventional Resources Conference, 1e3 April, Woodlands,  Texas, USA, SPE-168971-ms, 2014.
  29. R. Kausik, C. Cao  Minh, L. Zielinski, et al., Characterization of Gas  dynamics in kerogen nanopores  by   NMR,   in: SPE  147198,  Presented  at  the  SPE Annual  Technical  Conference  and   Exhibition,  Denver,  Colorado,  30 October, 2 November, 2011.
  30. R. Xie, L. Xiao,  Advanced fluid-typing methods for  NMR  logging, Petroleum Sci.  8 (2)  (2011) 163e169. http://dx.doi.org/.
  31. [31]  E.k. Washburn, J.E. Birdwell, A new laboratory approach to shale analysis using NMR  relaxometry,  in: Unconventional Resources Technology Con- ference (URTEC),  Denver, Colorado, 12-14 August, SPE-168798-MS, 2013.
  32. L. Zielinski, R. Ramamoorthy, C. Minh, et al.,  Restricted diffusion effects in saturation  estimates from 2D   diffusion-relaxation NMR   maps,  in:  SPE-134841-MS, Presented at SPE Annual Technical Conference and Exhibition, Florence, Italy, 19-22 September, 2010.
  33. G. Gannaway, NMR  investigation of  pore structure in gas shales, in: SPE 173474 Presented at the SPE Annual Technical Conference and Exhibition, Amsterdam, The  Netherlands, October, 2014, pp. 27-29.
  34. J. Mitchell, J. Staniland, E.J. Fordham, Paramagnetic doping agents in magnetic resonance studies of oil recovery, Petrophysics 54 (4) (2013) 349e367. [35]  R. Freedman, et al., Major advancement in reservoir fluid analysis achieved using a new  high-performance nuclear magnetic resonance  laboratory system, Petrophysics 54 (5)  (2013) 439-456 (GE).
  35. R. Patricia, R. Pedro, Estimation of fluid properties using NMR correlation in Berea rocks, in: SPE 69608, Presented at SPE Latin American and Caribbean Petroleum Engineering Conference, 25e28 March, Buenos Aires, Argentina, 2001.
  36.  J. Bryan, D. Moon, A. Kantzas, In  situ viscosity of  oil  sands using low-field NMR,  J. Can.  Petrol. Technol. 44 (9)  (2003).
  37. M. Winkler, J.J. Freeman, M. Appel, The  limits of fluid property correlations used in NMR  well logging: an experimental study of  reservoir fluids at reservoir conditions, Petrophysics 46 (2)  (2004) 104-112.
  38. J. Chen, S. Chen, A mixing rule for  of  elf  diffusivities in methane hydrocarbon mixtures and determination of  GOR  and oil  viscosities from NMR log  data, Res.  Eval.  Eng.  13 (2)  (2008) 275-282. SPE-115510-PA.
  39. J. Hirasakia, S. Lo, Y. Zhang, NMR  properties of  petroleum reservoir fluids, Magn. Reson. Imagin. 21 (2003) 269-277.
  40. S.-W. Lo, G.J. Hirasaki, W.V.  House, R. Kobayashi, Mixing rules and correlations of NMR  relaxation time with viscosity, diffusivity, and gas/oil ratio of  methane/hydrocarbon  mixtures, in: SPE-77264-PA, Presented  at  SPE Annual  Technical Conference  Proceedings,  1-4   October,  Dallas,  Texas, 2002.

Article history:

  • Received 26 October 2015
  • Received in revised form
  • 25 January 2016
  • Accepted 3 February 2016
  • Corresponding author. (Mohamed Mehana) Tel.: +1 4058873536.
  • E-mail address: M.Mehana@ou.edu (M.  Mehana).
  • Peer review under responsibility of  Southwest Petroleum University.

 

Copyright © 2016, Southwest Petroleum University. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Shale characteristics impact on Nuclear Magnetic Resonance (NMR) fluid typing methods and correlations

The development of shale reservoirs has brought a paradigm shift in the worldwide energy equation. This entails developing robust techniques to properly evaluate and unlock the potential of those reservoirs. The application of Nuclear Magnetic Resonance techniques in fluid typing and properties estimation is well-developed in conventional reservoirs. However, Shale reservoirs characteristics like pore size, organic matter, clay content, wettability, adsorption, and mineralogy would limit the applicability of the used interpretation methods and correlation.
Emanuel Martin
Emanuel Martin is a Petroleum Engineer graduate from the Faculty of Engineering and a musician educate in the Arts Faculty at National University of Cuyo.
http://www.allaboutshale.com

Leave a Reply

five × 5 =

Top