Asphaltene Deposition Preference and Permeability Reduction

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Asphaltene Deposition Preference and Permeability Reduction Mechanisms in Oil Reservoirs: Evidence from Combining X-ray Micro-Tomography with Fluorescence Microscopy Xiao Feng, Jianhui Zeng, Yong Ma, Kaiyu Jia, Juncheng Qiao, Yongchao Zhang, and Sen Feng Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01389 • Publication Date (Web): 01 Sep 2017 Downloaded from http://pubs.acs.org on September 1, 2017

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Asphaltene Deposition Preference and Permeability Reduction Mechanisms in Oil Reservoirs: Evidence from Combining X-ray Micro-Tomography with Fluorescence Microscopy Xiao Feng1, 2, Jianhui Zeng1*, 2, Yong Ma1, 2, Kaiyu Jia1, 2, Juncheng Qiao1, 2, Yongchao Zhang1, 2 , Sen Feng1, 2 1. The State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China 2. College of Geosciences, China University of Petroleum, Beijing 102249, China

Abstract: Asphaltene deposition in oil reservoirs during acid stimulation, natural depletion and CO2 injection may cause intense formation damage and reduced productivity. Gaining a better understanding of the asphaltene deposition mechanisms and their influence on the reservoir permeability reduction will contribute to the prevention of reservoir damage and the optimization of development schemes. Although numerous models and experiments have been applied to simulate the asphaltene deposition process and evaluate the reservoir permeability loss, few analyses have been performed on natural samples from oil reservoirs undergoing asphaltene deposition. Moreover, permeability reduction simulation due to asphaltene deposition has not yet been performed in 3D micro-scale pore systems. In this work, sandstone samples were collected from natural oil reservoirs with asphaltene deposition and analyzed by both X-ray tomography and fluorescence microscopy to identify the asphaltene. A Navier-Stokes simulator and pore network model are used to study the 3D pore spaces and to calculate the permeabilities and pore radius distributions. Ideal asphaltene deposition models are applied in the 3D pore spaces to simulate the influences of surface adsorption and pore blockage on the permeability reduction. By comparing the calculation results of the ideal models and natural samples, we found that the asphaltene deposition is a coupled effect of the surface adsorption and the pore blockage, which causes a weaker permeability loss than that from the ideal single factor models. Keywords: asphaltene deposition, permeability reduction, pore structure, X-ray tomography

1 Introduction Changes in the pressure, temperature and oil composition during acid stimulation, natural depletion and CO2 injection may lead to asphaltene deposition in oil reservoirs, causing a significant reduction in reservoir permeability and productivity 1-3. Extensive knowledge of asphaltene deposition and its impact on formation permeability is essential for development scheme formulation, reservoir performance evaluation, reservoir damage mitigation and remediation 4, 5. Asphaltene deposition is due to a combined effect of several processes, such as asphaltene association, flocculation, precipitation and adsorption 6, 7. To date, the three primary asphaltene deposition research focuses are the onset condition of asphaltene deposition, the amount of asphaltene deposition and its influence on the reservoir permeability. The first two topics are relatively straightforward and were investigated through asphaltene flocculation and precipitation studies, which are processes normally predicted by a series of asphaltene phase behavior simulations 1, 8-17 and lab measurements 18-27. However, permeability reduction not only depends on the amount of asphaltene precipitation but also relates to the transport properties and adsorption preferences of

*

Correspondence author’s address: China University of Petroleum, Beijing, 18 Fuxue Road, Changping District, Beijing, China.

+86-10-89733325

Email: [email protected]

ACS Paragon Plus Environment

Phone:

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asphaltene precipitates 28. Despite the widely reported effect of asphaltene-induced wettability alteration 29-35, the formation damage by asphaltene deposition is generally considered a consequence of the changes in pore space 36, 37 . Table 1 lists the major existing permeability reduction models due to asphaltene deposition. The earlier correlations suggested between permeability and time, flow rate, flooding agent volume, or asphaltene saturation were based on empirical or ideal models 38-46 and verified primarily by core flooding experiments. Minssieux 47 performed flooding experiments on several types of porous media using crudes with asphaltene contents from 0.1% to 6% and measured the quantities of the asphaltene via RockEval analyses. The three predominant mechanisms were summarized as follows: gradual damage from the homogeneous asphaltene deposition onto accessible pore surfaces, pore blocking or pore throat obstruction by isolated particles, and in situ accumulation or bridging of trapped particles. Ali and Islam 48 proposed a model to couple the asphaltene adsorption with the mechanical plugging caused by the asphaltene deposition, and the model was verified by core flooding experiments. The adsorption was treated with the surface excess theory 49, while the mechanical trapping of asphaltene was treated with a theory developed by Gruesbeck and Collins 40. Leontaritis 37 described the formation damage due to pore-throat blockages by asphaltene particles assuming that particles with diameters 1/3 of the average pore-throat size were retained. Wang and Civan 50, 51 considered both the surface deposition and the entrainment of asphaltene deposits to relate the instantaneous local permeability reduction as a cubic function of the porosity reduction (Kozeny-Carman relationship) 44, 45. Kord, et al. 52, 53 modified the model of Wang and Civan 50, 51 by deriving equations for four different asphaltene mechanisms including the surface deposition, entrainment, pore throat plugging and pore throat opening. Nghiem, et al. 54 coupled the asphaltene phase behavior models with the permeability reduction by an empirical linear correlation between the resistance factor and the adsorbed asphaltene mass fraction. Recent studies 55, 56 integrated the models proposed by Wojtanowicz, Krilov and Langlinais 41 and Minssieux 47 to simultaneously consider three mechanisms: surface deposition, pore bridging, and filtration cake formation. Although these models cover almost all the permeability reduction mechanisms and were effectively applied to some field cases 57-60, they are still unable to sufficiently explain the sequence and contribution of the varied permeability reduction mechanisms 61, 62, the primary control mechanisms in various kinds of porous media 4, 36, 62-67, or the various asphaltene deposition locations or stages within a single reservoir 61, 68 . Disregarding the pore connectivities and morphologies has led to model shortcomings 69. Recently, asphaltene deposition was investigated as a multi-scale phenomenon. Sahimi, et al. 70 adopted a 3D network model 71, which considered most of the micro-scale morphologies and forces, to evaluate asphaltene-induced formation damage. Later, researchers coupled this model with a thermodynamic model to simulate the well behavior under asphaltene deposition 72 and applied scaling laws to reduce the computational efforts 69, 73. Based on the assumption that the smaller pore-throats are plugged due to large asphaltene particles while the larger pore-throat diameters decrease, Nasri and Dabir 74, 75 applied network modeling to predict the asphaltene deposition effects on the petrophysical properties. Mendoza de la Cruz, et al. 76 represented a porous medium as a network of pore bodies (sites) and pore throats (bonds) and described the permeability reduction by two distinct mechanisms: asphaltene adsorption and mechanical trapping. However, neither the single factor models nor the coupled two-factor models could completely fit the previously published data. Boek, et al. 77 investigated asphaltene aggregation and their deposition in capillary flow using multiscale simulation coupled with stochastic rotation dynamics and coarse-grained molecular dynamics. These models, which are regarded as quasi-3D models, made simplifying assumptions about the pore connectivities and morphologies, randomly constructed pore networks with limited pore radius distribution data and used empirical scaling laws to reduce computational efforts. Large amounts of detailed information on the natural pore space, such as pore-throat configuration, pore radius heterogeneity and asphaltene deposition-induced change of pore morphology, is ignored. Techniques such as micro glass models 77-81, X-ray micro tomography (XRT) 82, 83, and scanning electron


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microscopy (SEM) 83, 84 were also applied to observe asphaltene deposition phenomena at the pore scale. However, evidence from these studies were not sufficient to support the pore-scale models due to the lack of quantitative analysis. Table 1 Major Existing Permeability Reduction Models due to Asphaltene Deposition Researchers

Asphaltene Deposition Mechanisms

Permeability Reduction Models

a. Homogeneous Surface Deposition Minssieux

47

b. Pore Blocking by Isolated Particles

Foreign Solids Invasion and Capture Model 41

Verification

Shortage

a. Core Flooding Experiment b. RockEval Analysis

c. Bridging of Trapped Particles Ali and Islam 48 and Kocabas, Islam and Modarress 57

a. Adsorption b. Mechanical Plugging

Leontaritis 37

Particle Retainment

Wang and Civan

a. Surface Deposition

50, 51

a. Sarwar and Islam 49 Model of Surface Excess

a. Core Flooding Experiment

b. Gruesbeck and Collins 40 Model of Mechanical Entrainment

b. Field Cases from Abu Dhabi National Oil

Relationship between Permeability Reduction and Available Area Reduction Kozeny-Carman Relationship between Permeability Reduction and Porosity Reduction 44, 45

b. Entrainment

Previously Published Core Flooding Experiment Data 18, 47 a. Crude Oil PVT Properties

a. Surface Deposition Kord, Miri, Ayatollahi and Escrochi 52, 53

Kozeny-Carman Relationship between Permeability Reduction and Porosity Reduction 44, 45

b. Entrainment c. Pore-Throat Plugging d. Pore-Throat Opening

b. Asphaltene Precipitation Experiment c. Asphaltene Particle Growth Experiment

Simplified Permeability Reduction Model Disregarding Pore Morphologies and Connectivities

d. Core Flooding Experiment

Nghiem, Coombe and Ali 54

Bagherzadeh, Ghazanfari, Kharrat and Rashtchian 55 and Karambeigi, Nikazar and Kharrat 56

Empirical Linear Correlation between the Resistance Factor and the Adsorbed Asphaltene Mass Fraction

a. Adsorption b. Plugging

a. Asphaltene Deposition Envelope Experiment b. Gas Injection Experiment

a. Surface Deposition Model of Foreign Solids Invasion and Capture 41

b. Pore Bridging

Core Flooding Experiment

c. Formation of Filtration Cake

a. Pore Network Modeling 71 Sahimi, Mehrabi, Mirzaee and Rassamdana 70

a. Gradual Surface Precipitation b. Pore Plugging

b. Log-normal Distribution of the Throats’ Sizes c. Monte Carlo simulations

Monteagudo, Rajagopal and Lage 72, 73 and Monteagudo,

a. Adsorption.

a. Pore Network Modeling 71

b. Mechanical Trapping

b. Porosimetric Curve Simulation

c. Thermodynamic model for


Previous Published Oil Characterization and Well Flow Data 37

Quasi-3D models which made simplifying assumptions about the pore connectivities and morphologies,

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Silva and Lage 69

asphaltene deposition

Nasri and Dabir 74,

a. Smaller Throats Plugged Owing to Large Asphaltene particles

75

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c. Scaling Law between Porosity and Permeability Previous Published Core Flooding Experiment Data on Low Permeability Carbonate Cores86

Pore Network Modeling 85

b. Decrement of Larger Throats Diameter

Mendoza de la Cruz, Argüelles-Vivas, Matías-Pérez, Durán-Valencia and López-Ramírez 76

randomly constructed pore networks with limited pore radius distribution data and used empirical scaling laws to reduce computational efforts

a. In-situ Core Flooding Experiment with Reservoir Temperature and Pressure a. Adsorption Pore Network Modeling

b. PVT Apparatus with Solid Detection System to Identify the Onset of Asphaltene Precipitation.

87

b. Entrapment

c. Gas and Liquid Chromatography for SARA fraction Analysis

In this work, genuine 3D modeling is performed in the natural micro pore systems with the verification of quantitative image evidences. XRT analyses are conducted on samples from natural sandstone oil reservoirs that have suffered asphaltene deposition during oil recovery. By comparing the fluorescence microscopy images and XRT images the natural asphaltene deposits are identified at the micro-scale pore space. The permeability losses due to the asphaltene deposition are calculated by locating the asphaltene deposits and conducting a Navier-Stokes flow simulation in the 3D pore spaces. Ideal asphaltene deposition models are designed from the pore spaces to simulate the influences of surface adsorption and pore blockage on the permeability reduction. The asphaltene deposition preference and its influence on the reservoir permeability is discussed through a comparison of these modeling results.

2 Geological Settings The samples were collected from Sanhecun subsag, Zhanhua sag in the southeast Bohai Bay Basin, which is a major Mesozoic-Cenozoic petroliferous basin on the east coast of China. The oil reservoirs are primarily fan delta sandbodies in the Paleogene Dongying and Shahejie Formations and braided channel sandbodies in the Neogene Guantao Formation88. The reservoir rocks are mainly composed of lithic arkose with 41%~70% of quartz, 11%~27% of feldspar and 40%~60% debris. As a result of the various oil sources in the research area, the oil property and composition vary greatly from formation to formation, while the non-hydrocarbon contents are high in all formations (Table 2)89. The Shengli Oilfield Co. Ltd., Sinopec has adopted a CO2-steam huff and puff technique for the development of block Ken119 in the Sanhecun subsag, which has led to large quantities of asphaltene deposition. Table 2 Oil Property and Composition in Sanhecun Subsag Formation

Guantao

Dongying

Shahejie

Density (g· cm )

0.9174~0.9963

0.8847~0.9289

1.0192~1.0746

Viscosity at 50℃ (mPa·s)

51.3~14000

17.6~74.6

9688~22375

-3

Oil Property

Oil Composition

Pour Point (℃)

-15~12

-24~5

19~44

Sulphur Content (%)

1.07~1.74

0.72~2.97

6.51~10.7

Saturated Hydrocarbon (%)

28.45~32.72

29.14~34.22

5.79~12.03

Aromatic Hydrocarbon (%)

26.27~32.26

28.58~39.27

19.52~28.71


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Non-hydrocarbon (%)

26.96~32.50

22.92~29.57

22.45~41.71

Asphaltene (%)

3.62~8.97

3.46~5.89

20.32~41.12

3 Methodology 3.1 Sample Collection and Preparation Three sandstone samples with high, low and no asphaltene contents, qualitatively described by their appearance, were collected from Guantao and Shahejie Formations of a recently drilled development well in block Ken119. Part of each sample was made into thin sections for fluorescence microscopy, and another part of each sample was cored by the TBH Pro II bench drilling machine for XRT imagery. The samples for thin sections were cemented with cyanoacrylate and ground to a thickness of 0.05 mm. The 5 mm-diameter cores were oil-washed with methylbenzene-ethanol solvent, sealed to prevent asphaltene diffusion, and bonded to aluminum tubes with epoxy resin for rotational imaging. 3.2 Image Acquisition The fluorescence microscopy images were acquired by an Eclipse LV100N polarizing microscope at Chinese State Key Laboratory of Petroleum Resources and Prospecting. The thin sections were photographed under the 5X objective and the ultraviolet irradiation of a 100 W mercury lamp. The 3D XRT images were acquired by a Versa XRM-500 X-ray microscope at the same laboratory. The X-ray is induced by an electron beam with an 80 kV voltage and 7 W power and filtered by a fluorite lens 0.213 mm in diameter to reduce the beam-hardening artifact 90. The attenuation projections are magnified by a combined function of cone beam and a 4X objective to achieve a resolution of 2 µm pixel size. The projections are reconstructed and cropped into images of 500×500×500 cubic voxels (1000 µm×1000 µm×1000 µm) (Fig. 1a). In order to preserve the fine details of the raw image to a great extent, no special preprocessing (such as denoising or background homogenization) is performed before image analysis. 3.3 Asphaltene Identification and Segmentation The results of the fluorescence thin sections, as is shown in Table 3, corroborate our initial judgement of the asphaltene content: thin sections of the high asphaltene content sample (S1) have maroon fluorescence under the ultraviolet irradiation; thin sections of the low asphaltene content sample (S2) have yellowish-brown fluorescence; and thin sections of the asphaltene-free sample (S3) have green fluorescence, which is most likely attributed to the residual aromatic hydrocarbon. As for the asphaltene identification in the XRT image, the basic principle is that X-ray will be attenuated when passing through a material, which follows Beer’s law 91 = (1) where and are X-ray intensities before and after X-ray passing through the material, in Sv; is the attenuation coefficient of the material, in m-1; is the distance of X ray passing through the material, in m. The attenuation coefficient chiefly depends on the density and the atomic numbers of the material. In our samples, most of the minerals are composed of silicon dioxide and aluminatesilicate with the highest densities and atomic numbers. The residual pore space is filled with air who has the lowest density and atomic number. The density and atomic number of asphaltene, who is mainly composed of hydrocarbon, lies between the former two materials. The attenuation coefficient differences between the materials are directly reflected by gray level on the XRT image. Comparing the results of the 2D gray scale XRT slices of the three samples (Table 3), it is observed that the material with the highest gray level (white) has nearly the same content and morphology, while the medium-gray level material, which is around the highest gray level material, gradually disappears from sample S1 to S2 and S2 to S3. Thus, the material with the highest gray level is speculated to be the minerals which constitute the rock reservoir skeleton and the material with the medium-gray level is identified as the asphaltene deposited


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during oil recovery. Therefore, the minerals, asphaltene and air can be differentiated according to the intensity range of the XRT images. The air in the XRT images can be segmented directly by setting a gray level threshold for air is homogeneous and there is a vast gray level contrast between air and solid materials. The isolated small particles inside the air are incorporated into the air on the assumption that there is no suspended solid. The mineral segmentation, however, is not that simple since the mineral gray level varies with the mineral’s composition, and there is not a universal gray level criterion for all kinds of minerals in the sample. In this work, a fast watershed algorithm92 is adopted to segment minerals from the whole solid materials. The segmentation starts by picking a set of markers which are visually affirmed to be minerals (usually the points with the regional maximum gray levels). The algorithm will keep immersing the space around the markers by lowering the gray level at a uniform speed until a point of the watershed appears when two distinct fronts join. The procedure is repeated until all watersheds are found and the immersed spaces are regarded as the minerals. The isolated small holes inside the minerals are incorporated into the minerals on the assumption that isolated pores are ineffective for production. Assuming there is no material other than air, minerals and asphaltene in the samples, the asphaltene is segmented by excluding the former two materials from the image. 3.4 XRT Image Analysis The XRT image processing and analysis are carried out via the Avizo 9 software, developed by the VSG of the FEI Company. As an example, we describe the analysis of sample S1. Fig. 1b shows the segmented XRT image with minerals (off-white), asphaltene (red), and air (transparent). The air and asphaltene in the sample is considered the original pore space (Fig. 1c), while the air alone is considered the residual pore space after asphaltene deposition (Fig. 1d). An Navier-Stokes flow simulator93 is applied in these two pore-space systems (Fig. 1e, f) to calculate the instantaneous permeability ( , see appendix Table 1, same as below) and original permeability ( ). A skeleton algorithm 94-97 is adopted to separate the pore spaces into pores (Fig. 1g, h) and extract the pore network models (Fig. 1i, j). Parameters are calculated as follows: A. Original Porosity (∅ ) ∅ =

(2)

where is the voxel quantity of the original pore space and is the voxel quantity of the field of view (500×500×500). B. Residual Porosity (∅ ) ∅ =

(3)

where is the voxel quantity of the residual pore space after the asphaltene volume is removed. C. Porosity of the th Pore (∅ )

∅ =

(4)

where is the voxel quantity of the th pore. D. Porosity of the Pores in the Pore Radii Range of , (∅ ! ) ∅

!

=

∑#$% $&

where ' is the pore radius of the th pore. E. Asphaltene Saturation (( )

(5)

( =

∅ ∅

(6)

∅

G. Asphaltene Content in the Pores in the Pore Radii Range of , () ! ) )

!

∑#$% $& *

=∑

#$% $&

∅

where + is the voxel quantity of the asphaltene in the th pore. The pore radius distribution and asphaltene saturation distribution are obtained using the ∅ statistics in each pore radii.


(7)

!

and (

!

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3.5 Permeability Reduction Modeling In addition to the permeability reduction due to natural asphaltene deposition, theoretical permeability reductions are calculated in the pore spaces based on two single asphaltene deposition models: the gradual surface adsorption model and the pore blockage model 40. The gradual surface adsorption is considered to occur under an ideal condition that the asphaltene particles suspended in the porous medium are fine enough to pass through the extremely narrowed pore-throat and no plugging deposit occurs. The asphaltene can only deposit via the adsorption of the reservoir pore surface. The model is accordingly designed to erode the pore space by adding a 2-µm-thick asphaltene adsorption layer evenly on the pore surface in each iteration, until the conductivity of the pore system is totally lost (Table 4). The pore blockage is considered to occur under another ideal condition that the pore surface no longer adsorbs asphaltene particles. Thereby the asphaltene particles can only deposit by the mechanical retainment of the pore-throats. For a given particle size distribution of the flocculated asphaltene, a pore with a smaller pore-throat radius is more likely to be blocked by the asphaltene particles. The model is therefore designed to block the pore space by filling the pores in order from the smallest to the largest in pore-throat radius (Table 5) assuming asphaltene particles continually grow in diameter with asphaltene flocculation 8. Permeabilities and asphaltene saturation distributions of these two models are determined in the same way as described in section 3.4. The model of Wang and Civan 50, 51 is also introduced for comparison: ,

,

∅

0

= - .∅ /

(8)

where - is a permeability modification coefficient for the correction of the valve (or gate) effect . The valve (or gate) effect means when asphaltene saturation reaches - , the pore system loses all its connectivity. It is obvious that for a given pore system, - varies with the asphaltene deposition preference. In this work, - is chosen to be 1 for a comparison to the other permeability reduction models. 44, 45

Table 3 Sample Thin Section Fluorescence and 2D X-ray Tomography Slices Sample Number Asphaltene Content

S1

S2

S3

High

Low

Free

Thin Sections Fluorescence

2D Slices of The Original Grayscale X-ray Tomography


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2D Slices of Segmented X-ray Tomography with Minerals (white), Asphaltene (red), and Air (blue)

a

b

c

d

e

f

g

h


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i j Figure 1 Analysis of the S1 X-ray CT Image a. Original gray scale image; b. segmented image with minerals (off-white), asphaltene (red), and air (transparent); c. segmented image of the total pore space; d. segmented image of the residual pore space; e. velocity (scalar) field of the N-S simulation in the total pore space; f. velocity field of the N-S simulation in the residual pore space; g. pore separation of the whole pore space; h. pore separation in the residual pore space; i. pore network model of the total pore space; and j. pore network model of the residual pore space after asphaltene is removed. Table 4 2D Slices and 3D Images of the Gradual Surface Adsorption Models Surface 2D Slices with Minerals (white), Segmented Images of the Residual Adsorption Asphaltene (orange), and Air (blue) Pore Space after Surface Adsorption Stages

Total Pore Space Asphaltene Saturation: 0

Surface Adsorption Thickness: 2 µm Asphaltene Saturation: 17.75%



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Table 5 2D Slices and 3D Images of the Pore Blockage Models Pore Blocking Stages

2D Slices with Minerals (white), Asphaltene (green), and Air (blue)

Segmented Images of the Residual Pore Space after Pore Blockage


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Total Pore Space Asphaltene Saturation: 0

Blocked Pore Radii: ≤ 10µm Asphaltene Saturation: 0.49%

Blocked Pore Radii: ≤20 µm Asphaltene Saturation: 10.6%

Blocked Pore Radii: ≤30 µm Asphaltene Saturation: 39.41%



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4 Results The pore volume and the asphaltene content distributions in each pore radii range are presented in Fig. 2. The blue lines represent the sample pore radii distribution; the orange lines represent the asphaltene content distributions at each of the gradual adsorption stage; the green lines represent asphaltene content distributions at all pore blockage stages; and the red lines represent the true asphaltene content distributions. In each of the two samples, when the pore radii are larger than 60 µm, the true asphaltene content has a similar distribution curve to one of the gradual adsorption models: the 4µm thickness gradual adsorption curve for sample S1 (Fig. 2a) and the 3 µm thickness gradual adsorption curve for sample S2. In the pores with radii smaller than 60 µm, the true asphaltene saturations become increasingly higher than those of the previously modeled curves of the gradual adsorption models. The calculation results of the porosity and permeability reduction due to natural asphaltene deposition are shown in Appendix Table 2. Sample S1 and S2 have very similar original porosities and permeabilities, while the initial asphaltene saturation of sample S1 is twice that of S2. As is shown in Table 2, S1 loses almost all its permeability, whereas S2 remains permeable with more than 60% of its initial permeability inact. The resultant permeability reduction based on the gradual adsorption model, the pore blockage model and the Wang and Civan 50, 51 model are listed in Appendix Table 3 to Appendix Table 6. The permeability reduction ( / - asphaltene saturation) curves are plotted in Fig. 3. For the pore space of sample S1, the permeability reduction curves predicted by the Wang and Civan 50, 51 model is closer to the pore blockage model. However, for the pore space of sample S2, the Wang and Civan 50, 51 model has a better fit with the gradual adsorption model. In both cases, the permeabilities simulated in the true residual pore spaces are higher than those from the simple models, although these simplified idealistic models have better fits at higher asphaltene saturations.


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Pore/Asphaltene Content (%)

2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 60 70 80 90 100110 Pore Radius (µm)

0 10 20 30 40 50 60 70 80 90 100110 Pore Radius (µm) a

b

2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0



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0 10 20 30 40 50 60 70 80 90 100110 Pore Radius (µm)

2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 60 70 80 90 100110 Pore Radius (µm)

c d Figure 2 Pore Radii and Asphaltene Content Distribution Diagrams a. Pore radii and asphaltene content distributions of the natural asphaltene and the gradual adsorption model in sample S1; b. pore radii and asphaltene content distributions of the natural asphaltene and the gradual adsorption model in sample S2; c. pore radii and asphaltene content distributions of the natural asphaltene and the pore blockage model in sample S1; and d. pore radii and asphaltene content distributions of the natural asphaltene and the pore blockage model in sample S2.


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1

1

Wang and Civan (2001) Model

0.9

Pore Blockage Model

0.7

Real Sample

Real Sample

0.6 Ki/K0

0.6

Gradual Adsorption Model

0.8

Pore Blockage Model

0.7

Wang and Civan (2001) Model

0.9

Gradual Adsorption Model

0.8

Ki/K0

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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0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0

0 0

10 20 30 40 50 60 70 80 90 100 Asphaltene Saturation (%)

0

10 20 30 40 50 60 70 80 90 100 Asphaltene Saturation (%)

a b Figure 3 Permeability Reduction Curves of the Asphaltene Deposition Models a. Sample S1 pore space modeling results; b. sample S2 pore space modeling results

5 Discussion 5.1 Asphaltene Deposition Preference By comparing the true asphaltene distribution curves with those of the gradual adsorption models (Fig. 2a, b) and the pore blockage models, it is observed that the asphaltene does not evenly adsorb onto pore surface, as depicted by the gradual adsorption model, nor does it completely block the small pores without depositing asphaltene in the larger pores. In all pores, there is consistently a 3- to 4- µm- thick asphaltene layer, whereas in increasingly smaller pores, (especially with pore radii smaller than 60 µm) the asphaltene deposition thickness becomes increasingly thicker. This higher sensitivity of the smaller pores to asphaltene deposition was also observed by Shedid 86, who attributed this phenomenon to the reduction of the surface area open to flow. From the above observations and interpretations, we speculate that the asphaltene deposition is a coupled effect of both the gradual surface adsorption and pore blockage. At the beginning stage of asphaltene deposition, the asphaltene aggregates are transported in the fluid flow without any obstruction (Fig. 4a) and adsorb onto all pore surfaces evenly (Fig. 4b). When the adsorption thickness become approximately 3~4 µm, pores with pore radii smaller than 60µm are narrowed enough to capture large asphaltene particles and form a filter cake (Fig. 4c). Additionally, the formation of the filter cake lowers the concentration of the asphaltene aggregates in the colloidal solution and inhibits further gradual adsorption. Since a pore-throat radius is usually smaller than a pore radius, the asphaltene particle diameters in this study area may be no larger than 120 µm, which the experimental results of Ali 64 also support. 5.2 Permeability Reduction Mechanism Comparing the permeability simulation results (Fig. 3), the permeability reduction curve of pore blockage model matches the one of Wang and Civan 50, 51 model better for sample S1 while the permeability reduction curve of the gradual adsorption model matches the one of Wang and Civan 50, 51 model better for sample S2. This verifies that the permeability reduction mechanism varies with reservoir pore-throat structure even when the asphaltene deposition manner remains the same. However, neither of the two single models provides a permeability close to the permeability simulated in the true pore space. How can we understand that the coupling of the two effects, pore blockage and gradual deposition have a stronger deviation from the true reservoir asphaltene saturation levels compared to a single component predictive model? This is possibly ascribed to the


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Energy & Fuels

pore size heterogeneity in the reservoirs. Although the small pores are easier to be blocked by the narrowing of the pore spaces, their influence on the permeability is limited by the small permeability contribution of small pores. In addition, the blockage of the small pores helps the formation of the filter cake, which retains more asphaltene, lowers the asphaltene contraction in the solution 62 and inhibits the deposition in the larger pores. In the experiments of Takahashi, et al. 98, a larger amount of asphaltene was left behind in the more heterogeneous carbonate core than in the more homogeneous sandstone core although no significant permeability reduction was observed. Rahmani, et al. 99 also found that the asphaltene deposits were confined to sites of occluded pores, which did not result in severe damage to the reservoir. The coupling effect of the gradual adsorption and pore blockage consequently predicts a better reservoir conductivity than single factor models, which, on the other hand, means that the early stage of asphaltene deposition is more inconspicuous than previous predictions have suggested. For this reason, precautions should be taken before clear reservoir damage is detected. 5.3 Future Prospects The combination of micro X-ray tomography with fluorescence microscopy provides an opportunity to carrying out numerical simulations in a 3D pore space and verifying the results with measurements from true samples under asphaltene deposition. In this work, ideal single-factor models are applied to prove that the permeability reduction is a result of multiple factors. We propose three future improvements. The first proposed improvement is to totally remove the deposited asphaltene from samples with a chemical dissolver and rescan the samples to verify the image results. The second proposed improvement is to develop a comprehensive simulator for the 3D pore space which considers both the asphaltene phase behaviors and the asphaltene transport properties. The third proposed improvement is to upscale the micro-scale models to be able to practically apply them to producing fields.

a

b

c

Figure 4 Schematic Diagram of the Asphaltene Deposition Mechanisms


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6 Conclusions Natural asphaltene deposits were detected in the micro-scale pore space of samples through a comprehensive analysis of thin sections fluorescence and 3D X-ray micro-tomography. The true asphaltene distribution and permeability reduction were calculated and compared to the theoretical models. The main conclusions from this work are summarized as follows: (1) Asphaltene deposition occurs through a complex mechanism consisting of both gradual surface adsorption and pore blockage. In this study, the gradual adsorption thickness is 3~4 µm and the blockage occurs in the pores with pore radii smaller than 60 µm. (2) Due to the pore size heterogeneity in natural reservoirs, at equivalent asphaltene saturations, the coupling effect of the gradual surface adsorption and pore blockage will result in a weaker permeability reduction than either factor individually. With lower asphaltene saturations, the prediction of permeability reduction is weaker for this coupled effect than the earlier prediction methods.

Acknowledgments This study is supported by the Chinese National Science and Technology Major Project (No. 2016ZX05006001), the Chinese Key Program of National Natural Science Foundation (No. 41330319) and the 2017 American Association of Petroleum Geologists Foundation Grants-in-Aid Program.

Appendix Term

∅ ∅ ∅ ∅ ! ' ( )! + -

Appendix Table 1 Nomenclature Definition X-ray intensities after X-ray passing through the material X-ray intensities before X-ray passing through the material Attenuation coefficient of the material Distance of X ray passing through the material Instantaneous permeability Original permeability Original porosity Voxel quantity of the original pore space Voxel quantity of the field of view Residual porosity Porosity of the th pore Porosity of the pores in the pore radii range of , Pore radius of the th pore Asphaltene saturation Asphaltene content in the pores in the pore radii range of , Voxel quantity of the asphaltene in the th pore Permeability modification coefficient for the correction of the valve (or gate) effect Appendix Table 2 Porosity and Permeability Calculation Results for Samples S1 and S2 Sample Number S1 S2 Original Porosity

31.91%

35.43%

Residual Porosity

10.09%

22.48%

Asphaltene Saturation

68.37%

36.56%

Original Permeability (D)

3.95

10.01

Instantaneous Permeability (D)

0.23

6.26

Permeability Loss

94.23%

37.46%

Appendix Table 3 Permeability Reduction Results of the Gradual Deposition Modeling for Sample S1 / from the / from the Gradual Adsorption Residual Asphaltene Gradual Wang and Civan Porosity (%) Saturation (%) Thickness (µm) Adsorption Model (2001) Model


Unit Sv Sv m-1 m D D Decimal \ \ Decimal Decimal Decimal µm Decimal Decimal \ \

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Energy & Fuels

0 2 4 6 8 10 12 14

31.91 26.24 20.42 15.13 10.87 7.70 5.46 3.92

0.00 17.75 36.02 52.56 65.92 75.85 82.87 87.71

1.000 0.504 0.217 0.109 0.057 0.036 0.031 0.030

1.000 0.556 0.262 0.107 0.040 0.014 0.005 0.002

Appendix Table 4 Permeability Reduction Results of the Pore Blockage Modeling for Sample S1 / from the / from the Blocked Pore Residual Asphaltene Pore Blockage Wang and Civan Radii (µm) Porosity (%) Saturation (%) Model (2001) Model 0 31.91 0.00 1.000 1.000 ≤10 31.75 0.49 0.983 0.985 ≤20 28.52 10.60 0.714 0.714 ≤30 19.33 39.41 0.211 0.222 ≤40 11.66 63.45 0.053 0.049 ≤50 6.21 80.55 0 0.007 ≤60 3.09 90.33 0 0.001 Appendix Table 5 Permeability Reduction Results of the Gradual Deposition Modeling for Sample S2 / from the / from the Gradual Adsorption Residual Asphaltene Gradual Adsorption Wang and Civan Thickness (µm) Porosity (%) Saturation (%) Model (2001) Model 0 1.000 1.000 35.43 0.00 2 0.529 0.500 28.11 20.65 4 0.237 0.223 21.49 39.36 6 0.122 0.105 16.69 52.89 8 0.063 0.055 13.44 62.06 10 0.038 0.031 11.11 68.65 12 0.028 0.018 9.29 73.77 14 0.024 0.011 7.82 77.94 Appendix Table 6 Permeability Reduction Results of the Pore Blockage Modeling for Sample S2 Blocked Pore Radii (µm)

Residual Porosity (%)

Asphaltene Saturation (%)

/ from the Pore Blockage Model

/ from the Wang and Civan (2001) Model

0 ≤10 ≤20 ≤30 ≤40 ≤50 ≤60

35.43 35.22 29.62 22.65 17.28 13.14 8.73

0.00 0.59 16.39 36.08 51.24 62.92 75.37

1.000 0.975 0.613 0.208 0.027 0.011 0.010

1.000 0.982 0.585 0.261 0.116 0.051 0.015

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Asphaltene Deposition Preference and Permeability Reduction

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