Investigation of Organic Related Pores in Unconventional Reservoir

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Investigation of organic related pores in unconventional reservoir and its quantitative evaluation Xinmin GE, Yiren Fan, Yingchang Cao, Jiangtao Li, Jianchao Cai, Jianyu Liu, and Sudong Wei Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b00590 • Publication Date (Web): 25 May 2016 Downloaded from http://pubs.acs.org on May 31, 2016

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Investigation of organic related pores in unconventional reservoir and its quantitative evaluation Xinmin Ge*,†,‡ , Yiren Fan†,‡, Yingchang Cao†,‡, Jiangtao Li§, Jianchao Cai||, Jianyu Liu†,‡, and Sudong Wei⊥ †

School of Geosciences, China University of Petroleum, Qingdao 266580, China

‡

Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine

Science and Technology, Qingdao 266071, China §

Research Institute of Exploration and Development, Qinghai Oilfield, CNPC,

Dunhuang 736202, China ||

Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and

Geomatics, China University of Geosciences, Wuhan 430074, China ⊥

China Oilfield Services Limited, CNOOC, Tang’gu 300450, China.

ABSTRACT: Pores in organic matters are important for unconventional reservoirs since a large amount of absorbed hydrocarbons are resided in these spaces. An integrated method to quantify organic pores using the low field nuclear magnetic resonance (NMR) is introduced in this paper. Relationships between the organic related porosity and geochemical parameters are also discussed. Resistivity, velocity, density, and natural gamma ray spectrums are measured simultaneously to investigate 1

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petrophysical responses of organic pores, aiming to predict the organic related porosity using conventional petrophysical data. Results show that the NMR signal of samples under the dry state is a good indicator of organic pores, and can be calibrated to the organic related porosity. The organic related porosity is positively correlated with total organic carbon contents (TOC), absorbed free gaseous hydrocarbons (S0), absorbed free liquid hydrocarbons (S0), and residual petroleum potential (S0), but negatively correlated with residual carbons (RC). The organic related porosity is positively correlated with thorium content (Th), natural gamma intensity (GR) and resistivity (R), whereas negatively correlated with density (DEN), compression wave velocity (Vp) and shear wave velocity (Vs). The model achieves favorable result, which can be generalized to predict the in-situ organic related porosity.

1. INTRODUCTION With the rapid demand of fossil energies, unconventional resources such as tight oil and shale gas are gaining more and more attention in recent decades. These reservoirs are featured by near source accumulation, or self-generating and self-preserving. The organic matter plays an important role in these reservoir since the type and content of organic carbon controls the reservoir quality and hydrocarbon potential.1-4 There are growing advancements to estimate the total organic carbon (TOC) using petrophysical and well logging data.5-11 However, few reports are published on the characterization and quantitative evaluation of organic related pores using petrophysical methods.

Generally, the organic related pores are resultant decompositions of organic 2


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material during the hydrocarbon generation process. The diameter of organic related pore ranges from several to hundred nanometers, and can be filled by hydrocarbons.9, 12-13

The organic porosity, its structure and connectivity, can influence the storage

capacity and mobility significantly. Hence, a comprehensive investigation and evaluation for the organic porosity is vital in the formation evaluation.

We put forward an integrated method to quantify the organic pores combing petrophysical, geochemical, and petrographic experiments. We characterize and quantify organic pores by the low field nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive X-ray spectrometer (EDS). We investigate relationships between the organic related porosity with geochemical parameters including TOC, absorbed free gaseous hydrocarbons (S0), absorbed free liquid hydrocarbons (S1), residual petroleum potential (S2), residual carbon (RC), and vitrinite reflectance (Ro) to investigate influential factors of the organic related porosity. Relationships between the organic related porosity and petrophysical responses are studied to establish a prediction model for the organic related porosity.

2. MATERIALS AND METHODS 2.1. Geological settings. We utilize 26 samples drilled from Luchaogou Formation of Malang Sag in Santanghu Basin, a typical self-generating and self-preserving shale reservoir. The Santanghu Basin is located in northeastern Xinjiang, China. It is a small intermountain basin sandwiched between 3


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Dahafutike-Suhaitu and Moqin Ural mountains and covers a total area of 2.3×104 km2. Its first-order structural units can be divided into northern uplift zone, central depression zone and southern thrust belt. Malang Sag is the main area for oil accumulation of the central depression zone. The sedimentary strata of this sag include Carboniferous, Permian, Triassic, Jurassic, Cretaceous, Paleogene, Neogene and Quaternary. Lucaogou Formation of the Middle Permian is the major oil bed in this region, with complex lithology and mineral distribution. Carbonate, clastic and sedimentary

tuffs

are

unevenly

distributed

due

to

unstable

sedimentary

environments.14 The location and its tectonic units are shown in Figure 1.

All samples are analyzed for natural gamma ray and gammy ray spectrums firstly, and divided into three groups, the first group for conventional petrophysical measurements, the second group for organic geochemical test, and the third group for SEM, EDS, and XRD analysis.

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Niu quan hu tectonic zone

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Jurassic

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Hei dun tectonic zone Ma zhong tectonic zone

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5

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0

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Mesozoic Erathem

Strata Thick- Depth Lithology Era. Sys. Formation ness/m /m

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Oil area

Secondary fracture P2l denudation line

P2h

600 ~ 1200

3000

Figure 1. Geological settings of Santanghu Basin and Malang Sag; (a) Location of Santanghu Basin and Malang Sag; (b) Distribution of tectonic units and the section of Malang Sag.

2.2. Low field NMR test. Low field NMR is a sophisticated technique in geophysical prospecting. It can be classified into two categories, one dimensional NMR such as longitudinal relaxation (T1) and transversal relaxation (T2) spectrum, two dimensional NMR including transversal-diffusion spectrum (T2-D) and longitudinal-transversal spectrum (T1-T2). Although great achievements published to characterize the tight oil and shale gas using two dimensional NMR spectrums,15-19 T2 spectrum is still the most popular technique among all of them and serves as a significant role in the reservoir exploration.20-22 Thus, it is essential to extract the valuable information in T2 spectrum, to characterize the unconventional reservoir.

We conduct NMR experiments using the MARAN-II ultra-rock spectrometer. We use the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence to excite the signal and inverse them to T2 spectrums by the Butler–Reeds–Dawson (BRD) algorithm.23

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Samples are polished to cylinder shapes with diameter of 2.54cm and length from 3 to 4cm, and then heated under an ambient temperature of 100℃, to remove drilling muds and most of the free water, capillary bound water, and light hydrocarbon resided in large pores. Signal at this state is measured, representing responses of clay bound water, monolayer water on mineral surfaces, bitumen and other high viscous hydrocarbons. Next, we put these samples into an auto-saturator container for 72 hours, under confining pressure of 60MPa to ensure most pores are fully saturated with water, and then measured the signal. At last, we use a high velocity centrifugal machine to displace the movable water from pores, and measure the signal. To sum up, each sample is measured at three saturation states: (1) dry state (2) full water saturation state (3) irreducible water saturation state.

Compared with those of conventional reservoir, NMR signals of unconventional reservoir are weaker and more difficult to detect. Acquisition parameters should be designed properly to ensure the quality. After many trials, the waiting time (TW), the echo time (T E), and the number of echoes (NECH) are set as 6000 milliseconds (ms), 0.1 milliseconds (ms) and 4096, respectively. What’s more , the number of scans (NS) is also important since it determines the signal to noise ratio (SNR). Five samples under different states are selected randomly to investigate the relationship between NS and SNR. As is shown in Figure 2(a) to Figure 2(c), SNR is nonlinearly positive correlated with NS. Samples with 100% water saturation reach the largest SNR under a certain NS. Each sample attains SNR higher than 30 when NS is larger than 512, showing favorable quality of the data. Figure 2(d) shows inversion results of a 6


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representative sample in different NS. It is observed that there are no obvious variations when NS is higher than 512. Therefore, NS is chosen as 512 in subsequent experiments.

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Figure 2. Relationship between SNR and NS for five random selected samples at (a) dry state; (b) fully water saturated state; (c) irreducible water saturation state; (d) T2 spectrums of a representative sample in different NS.

2.3. Petrophysical measurements. We used the AP-608 automated Permeameter-Porosimeter to obtain Helium porosity and permeability for samples of the first group.24 Density (DEN), resistivity (R), compression velocity (Vp) and shear velocity (Vs) are recorded simultaneously. In addition, natural gammy intensity and 7


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the nature gammy ray spectrums are measured to obtain the total natural gamma intensity (GR) and contents of potassium (K), thorium (Th) and uranium (U). These experiments are performed before classification of these samples.

2.4. Geochemical experiments. Samples of the second group are used to measure both TOC and pyrolysis parameters such as S0, S1, S2, RC, and Ro. TOC and Ro are measured by LECO CS-200 Carbon-sulfur analyzer and TIDAS MSP 200 Microscope Photometer. Rock-Eval pyrolysis measurements are conducted by DELSI INC Rock-Eval.25

2.5. Petrographic and mineralogy analysis. Compared to macroscopic averaging methods such as NMR and mercury injection capillary pressure (MICP), SEM can characterize the microstructure with pore size in a few to tens of nanometers, and can image the individual pore directly.26-27 We applied the high resolution backscatter electron (BSE) model of FEI Quanta 250 FEG to collect images, with the superiority that BSE detector allows for better contrast due to high-energy electron being back-scattered by elastic scattering interactions with specimen atoms. All samples are viewed on surfaces oriented perpendicular to bedding. The lowest and the highest intensity value are scaled black and white in images, whereas the intermediate intensity are represented by various shades of gray. Therefore, components with different atom numbers can be identified from the image. Pores developed in organic matters with the lowest atomic number are located in the darkest area of the image. Meanwhile, EDS involving the generation of an X-ray spectrum from the entire scan

8


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area of the SEM is capture and analyzed quantitatively to provide specific data on elemental composition.28-29 We use XRD (Rigaku D/max-2600/pc) to quantify the mineral composition and their contents of the third group samples, after SEM and EDS analysis. Table 1 and Figure 3 list weight percentages of minerals of the given samples, and their mean values. The mineralogy of samples can vary considerable, but the main minerals are quartz, dolomite, and plagioclase, clay content only accounts for small portion of these samples, with an average value of 0.82%.

Table 1. Mineral constitution of the measured samples by XRD Sample No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Clay 0.90 0.85 0.87 0.70 1.20 0.90 1.30 0.88 0.90 0.60 0.70 0.50 0.60 0.65 0.80 0.90 0.70 0.80 0.70 0.80 0.60 0.90 0.50 1.20 0.60 1.20

Anhydrite 1.00 0.00 0.00 1.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.30 0.00 0.00 0.00 0.00 0.00 0.00

Mineralogical content (weight %) Quartz K-feldspar Plagioclase 16.30 0.00 30.10 10.25 0.00 8.10 8.33 2.40 11.00 24.50 0.00 24.10 19.70 0.00 14.00 26.20 0.00 17.00 41.80 0.00 9.10 16.62 0.00 17.40 25.90 5.30 5.80 25.30 4.10 0.00 23.90 7.10 0.00 54.50 3.50 5.40 15.20 0.90 2.00 23.05 2.60 7.00 65.30 3.20 6.30 45.00 3.80 22.20 23.50 0.00 15.50 38.00 6.00 13.80 30.90 8.10 24.10 24.90 0.00 31.10 32.50 0.00 22.20 21.20 0.00 26.00 45.20 7.70 8.00 56.20 0.00 12.80 16.60 2.30 7.30 53.30 4.70 18.90

9


Calcite 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.70 0.00 0.00 4.10 8.70 0.00 4.60 13.90 13.50 23.10 16.80 17.70 11.50 2.10 8.20 22.90 12.80 5.50 17.30

Dolomite 51.70 80.80 77.40 49.60 65.10 55.90 47.80 63.40 62.10 70.00 64.20 27.40 81.30 62.10 10.50 14.60 37.20 24.60 18.50 30.40 42.60 43.70 15.70 17.00 67.70 4.60

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Clay Anhydrite Quartz K-feldspar Plagioclase Calcite Dolomite

Figure 3. Pie chart of mineral constitution for given samples

3. RESULTS AND DISCUSSIONS 3.1. Quantification of the organic related pores. Figure 4 shows BSE images of a representative sample with different magnifications. We observed dissolution pores, micro pores and organic pores from the image with a magnification of 4000, and organic micro pores from the image with a magnification of 6000. Proportions of elements picked by EDS are shown in Table 2. These organic pores and organic micro pores are confirmed by the EDS analysis since most signals are contributed by the carbon (C). With the help of EDS and digital analysis of BSE images, the organic porosity for each sample can be quantified conveniently. Table 2. Relative proportions of elements quantified by EDS Number 1 2

Test result Mass fraction (%) Atom fraction (%) Mass fraction (%) Atom fraction (%)

Carbon 91.84 96.04 92.84 99.1

Elements Oxygen Silicon 4.78 0 3.75 0 0 1.12 0 0.51

Aurum 3.39 0.22 6.04 0.39

Figure 5(a) shows NMR decay curve of the same sample in different states. It is observed that the decaying rate is slower and the signal quality is higher when the 10


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sample is fully saturated with water. However, the decaying rate becomes faster when the moveable water is displaced. Oscillations in decay curves are observed for the sample on dry and irreducible state. Figure 5(b) depicts T2 spectrums under different saturation states. Spectrum of the dry state is irregular bimodal-peak distributed with T2 ranges from 0.1ms to 30ms, which is similar to results published elsewhere.30-31 (a)

(b)

Figure 4. BSE images of a representative sample with different magnifications: (a) 4000 magnification; (b) 6000 magnification. Table 2. Relative proportions of elements quantified by EDS

Number

1

2

Test result

Elements Carbon

Oxygen

Silicon

Aurum

Mass fraction (%)

91.84

4.78

0

3.39

Atom fraction (%)

96.04

3.75

0

0.22

Mass fraction (%)

92.84

0

1.12

6.04

Atom fraction (%)

99.1

0

0.51

0.39

The amplitude and range of T2 is expanded with the injection of water. The main peak moves right to about 10ms, revealing intergranular pores of larger size are

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occupied with moveable water. Undergoing the centrifugal process, most of the movable water is driven out, leading to decrease of long relaxation signals. Meanwhile, the main peak moves left to fast relaxation range (about 3ms). It is observed the left part of T2 spectrum remains unaltered during the centrifugal process, revealing signals of irreducible water. In addition, the maximal and minimal value of T2 spectrum keeps invariant between the dry state and the irreducible state.

As mentioned above, most fluids including free water, capillary bound water, drilling mud, and light hydrocarbons are evaporated during the heating process. Thus, remaining components containing hydrogen nucleus are mainly composed by bitumen, monolayer water on mineral surfaces, and clay bound water. Conventionally, monolayer water on mineral surfaces relaxes too fast to be measured, and signals can be attributed to the organic related substances such as bitumen, and clay bound water. As is seen from Table 2 and Figure 3, all of these samples are clean and have relative low clay count (less than 1%), NMR signals of clay bound water are weak and can be neglected. With this assumption, we can deduce that NMR signals of samples in dry state are contributed by organic related substances.

From the comparison of T2 spectrums between dry and irreducible water saturation state, it is known that NMR signals of organic related pores are overlapped with the capillary bound water. Signals with T2 smaller than 0.3ms are almost organic related, since no obvious variations are observed in three different states. Nevertheless, signals with T2 located between 0.3 ms and 30 ms are joint contributions of organic

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related substances and capillary bound water, and cannot be easily separated. It tells that conventional cutoff method used to discriminate the movable and irreducible components from T2 spectrum may be invalid when facing the unconventional reservoir.32

(a)

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Dry state Fully saturated state Irredicuble state

800000

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Figure 5. Measured decay curves and T2 spectrums of a dolomitic shale under different states: (a) decay curves; (b) T2 spectrums. Using the relationship between amplitude and porosity of standard samples,33 we can obtain the apparent porosity of three different states, which is 1.02%, 2.48% and 4.55%, respectively. The porosity difference between fully water saturated state and dry state (3.53%) is equivalent to the measured Helium porosity (3.6%). Similar results are attained for remaining samples, as shown in Figure 6(a). These results further explain that NMR signals in dry state are almost organic related. Considering influences of organic related substances, the NMR based irreducible water saturation for unconventional resources is modified as,

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SwiNMR =

φNMR −irreducible − φNMR −organic ×100% φNMR − saturated − φNMR −organic

(1)

where φNMR − saturated is the calibrated porosity measured of sample in fully water saturated state, φNMR−irreducible is the calibrated porosity of sample in irreducible state, and φNMR −organic is the calibrated porosity of sample in dry state.

Figure 6(b) shows the relationship between the irreducible water saturation obtained by Equation 1 and that of centrifugal experiments. It is seen that the modified irreducible water saturation from NMR data bears good correlation with the conventional centrifugal irreducible water saturation. The comparison supports the ideal that NMR signal of the dry state is almost contributed by the organic components, not the clay bound water. Therefore, the calibrated NMR porosity of samples measured at the dry state is defined as the organic related porosity. Figure 6(c) compares the organic porosity of given samples obtained by NMR and SEM-EDS analysis, respectively. It is noticeable that the organic related porosity obtained by low field NMR presents a good fit with that extracted by SEM-EDS analysis.

(a)

(b) 20

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1.5

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Φorganic-NMR/ %

Figure 6. Comparisons of total porosity, irreducible water saturation, and organic related porosity by different methods: (a) Helium and NMR porosity; (b) irreducible water saturation of different methods; (c) NMR and SEM organic porosity.

3.2. Relationships with geochemical parameters. Relationships between the organic related porosity and geochemical parameters are shown in Figure 7. Clearly, the organic related porosity is positively correlated with TOC, S0, S1, S2, S1+S2, and S0+S1+S2. It is easy to interpret since NMR signals measured are contributed high viscous hydrocarbon such as bitumen and kerogen, and the measured S0, S1, and S2 are also positively correlated with the amount of hydrocarbons. TOC describes the quantity of organic carbon including both kerogen and bitumen, and bear good linear relationship with the organic related porosity. Ro reflects the thermal maturity of sedimentary rocks may influence kerogen types and oil-generating windows, but it cannot be used to characterize the hydrocarbon potential. Hence, it is reasonable that the organic related porosity shows no dependence upon Ro. Figure 7(h) depicts the relationship between the organic related porosity and RC. It is observed that the organic related porosity is inversely proportional to RC. RC indicates 15


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remaining organic matters after pyrolysis, encountered as background in sedimentary rocks. Generally, it is an unfavorable factor to the development of organic pores, and is negatively correlated with the organic related porosity.

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(g)

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Figure 7. Relationship between the organic porosity and geochemical parameters: (a) TOC; (b) Ro; (c) S0; (d) S1; (e) S2; (f) S1+S2; (g) S0+S1+S2; (h) RC.

Table 3 lists statistical results between the organic related porosity and these geochemical parameters, including residual sum of squares (RSS) and adjusted R squares ( Adj. R2). The organic related porosity bears the best linear relationship with TOC, then S1+S2, and S0+S1+S2. Therefore, we can conclude that the organic related pores may have some similar properties as the organic matters. Table 3. Statistical results of geochemical parameters Parameter

TOC

S0

S1

S2

S1+S2

S0+S1+S2

RC

RSS

0.289

1.616

1.298

0.797

0.764

0.762

1.282

Adj.R2

0.845

0.134

0.304

0.573

0.591

0.592

0.314

3.3. Prediction using petrophysical data. It is vital to investigate petrophysical responses of organic related pores since these parameters can be measured directly by wireline logs. It will be convenient to predict the in-situ organic related porosity if the relationship between the organic related porosity and conventional well logging or petrophysical data is established by core calibration. 17


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Figure 8(a) to Figure 8(d) show the relationship between the organic related porosity and radioactive parameters. It is seen that the organic porosity has a good correlation with Thorium (Th) content and Total Gamma ray intensity (GR), but a weak correlation with Potassium (K) content and Uranium (U) content. High K content may be caused by the presence of potassium feldspars or micas, and U is not only resided in organic matter, but also affected by the depositional environment. Inversely, Th content is simply correlated with organic related porosity for samples with low clay volumes. Hence, the coefficient between the organic related porosity and GR is lower than that of Th. The result shows that U and K are unsuitable to indicate the volume of organic related pores in this region.

The values of DEN, R, Vp and Vs are plotted against the organic related porosity, as shown in Figure 8(e) to Figure 8(h). Generally, petrophysical responses of the organic related pores are approximately the same as the organic carbon.4,11,34-35 Substances resided in organic related pores like the bitumen, kerogen are non-conductive and low-weighted, yielding high resistivity and low density in petrophysical experiments. Organic related pores are composited by soft matters showing the characteristics of high acoustic transit time. As expected, increase in organic related pores generally produces decrease in sonic velocity and density, but cause increases in resistivity. Furthermore, it is found that for resistivity and organic related porosity, the fitting result of exponential function is better than that of the linear function. Though the intrinsic mechanism is not clear, we guess the phenomenon is similar to the relationship between water saturation and resistivity.36-37 18


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(b)

2.5

2.5

2.0

2.0

Φ organic / %

Φorganic /%

(a)

1.5

1.0

1.0

0.0

0.0 0

2

4

6

K/%

0

8

(c)

2

4

6

U/ µg/g

8

(d) 2.5

2.5

2.0

2.0

Φorganic /%

Φorganic /%

1.5

0.5

0.5

1.5

1.0

0.5

1.5

1.0

0.5

0.0 0

2

4

6

Th/ µg/g

0.0

8

0

40

60

80

100

120

(f)

2.5

2.5

2.0

2.0

Φ organic /%

1.5

1.0

0.5

0.0 1.8

20

GR/API

(e)

Φorganic /%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.5

1.0

0.5

2.0

2.2

2.4 3

2.6

0.0

2.8

0

DEN/ g/cm

500

1000

R/ Ω.m

(g)

(h)

19


1500

2000

2.5

2.5

2.0

2.0

Φ organic /%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Φ organic /%

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1.5

1.0

0.5

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1.5

1.0

0.5

0.0 3000

4000

Vp/ m/s

5000

0.0 1000

6000

2000

Vs/ m/s

3000

4000

Figure 8. Relationships between the organic porosity and petrophysical parameters: (a) K; (b) U; (c) Th; (d) GR; (e) DEN; (f) R; (g) Vp; (h) Vs. Table 4. Fitting parameters of petrophysical responses Parameter

K

U

Th

GR

DEN

R

Vp

Vs

RSS

1.938

1.929

0.633

1.438

0.732

0.029

0.496

0.737

Adj.R2

0.038

0.003

0.661

0.230

0.608

0.629

0.734

0.605

Table 4 summarizes the Fitting parameters between the organic related porosity 2

and these petrophysical responses. The Adj.R of Vp is the highest, and then followed by Th. According to these univariate analyses, the following formula is obtained by,

φorganic = −0.0002Vp +0.0001Vs − 0.3921DEN + 0.04Th + 2.1044 R0.0396

(2)

where φorganic is the organic porosity.

It is noted that a positive coefficient of Vs occurred in Equation 2, which is inconsistent with the univariate analysis. To satisfy the physical meaning, a new regression model which excluded the Vs data is present as,

φorganic = −0.00014Vp − 0.33495DEN + 0.04431Th + 2.0261R0.0374 .

20


(3)

Page 21 of 30

Both models achieve favorable performances, with correlation coefficient of 0.91 and 0.89, and average relative errors of 6.2% and 6.1%, respectively. However, Equation 3 is superior since it requires less input information, and all fitting parameters are physical feasible.

(a)

(b) 2.5

4

Equation 2 Equation 3

2.0

Φ organic-predicted / %

3

Φ organic-predicted / %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.5

1.0

2

1

0.5

0.0 0.0

0.5

1.0

1.5

2.0

0

2.5

0

1

2

3

4

Φ organic-experiment/ %

Φ organic-experiment/ %

Figure 9. Model establishment and validations: (a) Relationships between predicted and measured organic related porosity; (b) model validation. To validate the effectiveness of the empirical formula (Equation 3), we test the complementary 14 samples which are not used for the forward model. As shown in Figure 9(b), the predicted organic related porosity is highly coherent with the experimental data, with an average relative error of 10.88%. The fitting result shows the good performance of our model.

We can adjust the model to more general case with the help of core calibration,

which is expressed as,

φorganic = a ×Vp +b × DEN + c × Th + d × Re

(4)

where a , b , c , d and e are empirical fitting parameters, which may vary from 21


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Page 22 of 30

region to region, or in lithologies within a given formation.

We can also rewrite the model as the form of an unspecified function,

φorganic = f (V p , DEN , Th, R)

(5)

where f (⋅) denotes the unspecified function, which can be obtained by experiments.

One of the most important implications for above analysis is that quantitative information of organic related pores determined by low field NMR experiments is in accordance with the value measured by image analysis of SEM and EDS, and can be predicted through petrophysical responses by core calibration.

4. CONCLUSION The paper presents a comprehensive approach to characterize organic related pores of tight samples. The organic related porosity quantified by low field NMR experiment is confirmed by other measurements such as Helium porosity, BSE and EDS. Geochemical and petrophysical responses of organic related pores are investigated to know intrinsic properties of substances located in these pores. A comprehensive empirical model is established to facilitate the predication of organic related porosity via petrophysical data. The following conclusions are summarized as:

(1) During the heating pretreatment under temperature of 100℃, most of free and capillary bound water, light hydrocarbon, and drilling muds are evaporated, whereas high viscous hydrocarbons such as bitumen and kerogen are remaining

22


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invariant. Monolayer water on mineral surfaces cannot be moved during the heating process, but did not contribute relaxation signals in low field NMR experiments. Clay bound water can be neglected for clean samples with clay content less than 1%. NMR signals for samples under the dry state are mainly contributed by these high viscous hydrocarbons, and can be calibrated to the organic related porosity.

(2) The abundance of pores in organic matter is related to geochemical properties. Generally, the organic related porosity is positively correlated with TOC, S0, S1 and S2, whereas negatively correlated with RC.

(3) Petrophysical responses of the organic related pores are similar to the organic carbon. The organic related porosity increases with the increase of Th, GR and R, but decreases with the increase of Vp, Vs and DEN. Traces of these petrophysical responses can be adopted to predict the in-situ organic related porosity via core calibration.

The integrated experiments and analysis provides an innovative way to quantify organic related pores of unconventional samples with low clay content, which can be served as a useful tool for geologist and geophysicist. In addition, petrophysicist should be aware of the intrinsic meaning of porosity measured by different methods. Generally, the porosity from Helium analysis stands for total inorganic pores, and the porosity estimated by conventional well log is contributed by inorganic and organic pores. Inorganic and organic pores can be discriminated by low field NMR analysis.

AUTHOR INFORMATION 23


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Corresponding Author

*E-mail: [email protected]; Tel 8613646428362.

Notes

The authors declare no competing financial interest.

ACKNOWLEDGEMENTS This work was supported by the Fundamental Research Funds for the Central Universities (16CX05004A), Natural Science Foundation of Shandong Province, China (ZR2014DQ007), National Natural Science Foundation of China (41404086), China Postdoctoral Science Foundation (2015T80759), and National Key Foundation for Exploring Scientific Instrument of China (2013YQ170463).

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Investigation of Organic Related Pores in Unconventional Reservoir

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