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Quantitative prediction of seismic rock physics of hybrid tight oil reservoirs of the Permian Lucaogou Formation, Junggar Basin, Northwest China
Journal of Asian Earth Sciences xxx (xxxx) xxx–xxx
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Quantitative prediction of seismic rock physics of hybrid tight oil reservoirs of the Permian Lucaogou Formation, Junggar Basin, Northwest China ⁎
Minghui Lua, , Hong Caoa, Weitao Sunb, Xinfei Yana, Zhifang Yanga, Youping Xua, Zhenlin Wangc, Min Ouyangc a
Research Institute of Petroleum Exploration and Development (RIPED), PetroChina, Beijing 100083, PR China Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, PR China c Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Karamay 834000, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Tight oil Seismic rock physics Lithology Porosity Total organic carbon Hybrid
Tight oil exploration and development in the Jimusar Sag of southeastern Junggar Basin of Northwest China has led to development of two reservoir segments in the Lucaogou Formation. The upper segment is mostly carbonate rocks, while the lower segment consists mainly of fine-grained clastic rocks. Both segments are mixed with clay, sand, and dolomite, which makes it difficult to identify the lithology and predict the porosity using a traditional seismic rock physics template (RPT). We analyzed the trends of ultrasonic P-wave impedance and Vp/ Vs of laboratory data changing with lithology, porosity, and total organic carbon (TOC) content. Our data provide parametric sensitivity regarding porosity and TOC content by use of a bi-material-matrix–double-inclusion concentration model. We propose a modified RPT of P-wave impedance versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) to predict porosity and lithology of reservoirs. We also present a new RPT of density versus Poisson ratio with the same three end members to predict TOC content and lithology of source rocks. These were validated by comparisons with laboratory measurements and log data. We inverted the porosity, TOC content, and thickness of the upper reservoir segment in the Lucaogou Formation of the J17 work area of Jimusar Sag by using the new RPT quantitative prediction method. The inversion results were identical to oil testing results, supporting the viability and effectiveness of the new RPTs.
1. Introduction
depression that developed on the Pre-Permian fold basement. It is faulted in the western part and overlapped in the eastern part (Fig. 1). The eastern part is bordered by the Santai Fault and Houbaozi Fault to the south, Xidi Fault and Laozhuangwan Fault to the west, Jimusar Fault to the north, and Gucheng uplift to the east (Xi et al., 2015; Cao et al., 2016). Two reservoir segments developed in the Lucaogou Formation. The upper segment is mostly made up of carbonate rocks and the lower segment consists mainly of fine-grained clastic rocks (Kuang et al., 2012; Si et al., 2013; Xi et al., 2015). The high-quality source rocks and the reservoirs of Lucaogou Formation formed in a salty lacustrine environment, and the differences in the lithologies between the upper and lower reservoir segments are mostly controlled by terrestrial inputs. The upper reservoir segment developed in the microfacies of a beach bar of a shallow lake with an inadequate provenance supply. The lower reservoir segment developed in the microfacies of a delta front distal bar and sheet sand with an adequate provenance supply. The reservoirs are fine-grained and subjected to the control of the lithology and pore dissolution. The pore spaces mainly consist of residual
Tight oil is an unconventional oil and gas resource with great potential and significant prospects for future development. The deposition environment of Chinese tight oil reservoirs represents a type of continental lake deposition. This differs from the marine sedimentary deposition types of North America. Tight oil reservoirs have complex lithology, low porosity, low permeability, versatile and minute pore throat structures, and significant lateral heterogeneity. With respect to the lithology and sedimentary environment, tight oil reservoirs can be classified into three categories: tight sandstone, carbonate, and hybrid reservoirs. Each of these reservoir categories has unique physical properties and variable distributions (Jia et al., 2012; Zou et al., 2013; Du et al., 2014). A set of hybrid sedimentations of deep-lake dark mudstone (as major source rocks) and dolomitic rock (as major tight oil reservoirs) has developed in the Permian Lucaogou Formation in the Jimusar Sag of southeastern Junggar basin. The Jimusar Sag is a dustpan-like
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Corresponding author. E-mail address: [email protected] (M. Lu).
https://doi.org/10.1016/j.jseaes.2018.08.014 Received 13 February 2018; Received in revised form 15 August 2018; Accepted 18 August 2018 1367-9120/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Lu, M., Journal of Asian Earth Sciences (2018), https://doi.org/10.1016/j.jseaes.2018.08.014
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Fig. 3. Crossplot of Ip vs Vp/Vs for log data of Lucaogou Formation in Well J174. Color bar represents five different lithologies of reservoir rocks and source rocks. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 1. Tectonic map of Lucaogou Formation of Jimusar Sag. Santai Fault and Houbaozi Fault are in the south, Xidi Fault and Laozhuangwan Fault are in the west, and Jimusar Fault is in the north. Closed box in red is the position of the J17 work area studied in this paper (modified from Xi et al., 2015; Cao et al., 2016). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
lower Vp/Vs and lower AI than dry sand. With increasing porosity, the AI decreases while the Vp/Vs tends to increase. Complicated lithologies and pore structures of hybrid tight oil make it difficult to identify the sweet spots using the traditional RPT of Ip versus Vp/Vs (Fig. 3). The logging P-wave impedance and Vp/Vs of reservoir rocks (green denotes dolarenite, yellow denotes feldspathic litharenite, and orange denotes dolomitic siltstone) and source rocks (cyan denotes carbonaceous mudstone and blue denotes dolomitic mudstone) in Lucaogou Formation in Well J174 are totally mixed together. Therefore, the traditional RPT of Ip versus Vp/Vs cannot be used to identify the lithology of hybrid tight oil reservoirs or to predict their porosity. A common approach for predicting the porosity used by oil field companies is to optimize the most sensitive seismic attributes of porosity, such as impedance by multiple-well correlation, establish the nonlinear relationship between sensitive seismic attributes and porosity through regression analysis, and then apply the relationship to the entire work area to quantitatively predict the porosity (Zhu et al., 2017). The nonlinear relationship between sensitive seismic attributes and porosity will vary with different well quality and reservoir depth, so it may not apply to work areas with great lateral heterogeneity. Although the evaluation of source rocks using logging data is an established technique (Fertl et al., 1988; Passey et al., 1990), it is still difficult to predict the TOC content of source rocks using seismic data. To improve the accuracy of predicting the lithology, porosity, and TOC content of hybrid tight oil reservoirs, we obtained the trends of P-wave impedance and Vp/Vs changing with the lithologies, porosity, and TOC content by analyzing ultrasonic laboratory measurements. We then proposed two new RPTs to predict the porosity, TOC content, and lithology of reservoirs based on analysis of the parametric sensitivity. We applied the new quantitative prediction method to the J17 work area to test its validity.
intergranular pores, solution pores, and fractures with sizes ranging from nanometers to one micron (Si et al., 2013; Huang et al., 2014). The “sweet spots” refer to the tight-oil-rich zones with matured high-quality source rocks, favorable reservoir intervals, and industrial value through artificial stimulation (Zou et al., 2013). Predicting sweet spots from geophysical data is essential for designing and deploying exploration wells, evaluation wells, and development wells. Compared with marine tight oil exploration in North America, the complex lithologies, special petrophysical properties of rocks, and weak hydrocarbon response of continental tight oil reservoirs make it challenging to accurately predict the sweet spots (Du et al., 2016). It is important to predict the lithology, porosity, and total organic carbon (TOC) content for determining the distribution of sweet spots of hybrid tight oil. Rock physics templates (RPTs) are used to establish the relationship between the elastic parameters of rocks and reservoir attributes such as porosity, fluid saturation, and clay content. RPTs are essential tools for quantitative seismic interpretation (Odegaard et al., 2003, 2004). The crossplot of acoustic impedance (AI) versus Vp/Vs (Fig. 2) is the most common RPT used to distinguish shale from sand and to identify gas sand and predict the porosity (Aveseth et al., 2005). Fig. 2 illustrates that shale has a higher Vp/Vs and lower AI than sand. Gas sand has a
2. Rock physics characteristics Seismic rock physics links exist between seismic attributes and reservoir parameters. Laboratory measurements can provide direct observation and understanding of how rock properties (such as lithology, porosity, pore fluids, TOC, and pressure) impact velocity, density, amplitude, attenuation, and anisotropy. We synthetically studied the results of x-ray diffraction, energy dispersive spectrometer, field-emission scanning electronic microscopy (FESEM), and ultrasonic measurements to analyze the relationship between the macro-response (such as velocity) and micro-pores and mineral components. All of the core samples studied were collected from the Lucaogou Formation in Well J174. The depth interval ranged from 3100 to
Fig. 2. RPT presented as a crossplot of Vp/Vs versus AI including porosity trends for different lithologies and increasing gas saturation for sands (Aveseth et al., 2005). 2
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Fig. 4. Three end-member classifications of mineral composition of core samples. Red squares represent samples from reservoirs and gray squares represent source rocks. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3200 m. The core samples were classified into five lithology types: dolarenite, dolomitic siltstone, feldspathic litharenite, carbonaceous mudstone, and dolomitic mudstone. The first three served as the main reservoir rocks and the last two were source rocks. Fig. 4 shows the mineral composition classification of 90 core samples from Lucaogou Formation. The three end members were clay, quartz and feldspar, and dolomite and calcite. The red marks represent the samples from reservoirs and the gray marks represent source rocks. Fig. 4 shows that reservoir rocks and source rocks are mixed with clay, sand, and dolomite. Both the reservoir and source rocks contain a small quantity of clay, which averages less than 20%. However, the difference of mineral composition between them is small and so it is difficult to distinguish the lithologies of hybrid rocks. The distribution histograms of porosity, permeability, and TOC content of upper sweet spots (red color) and lower sweet spots (blue color) are shown in Fig. 5. The porosity ranged from 2% to 20% with a mean of 9%. The reservoir property of upper sweet spots is slightly better than that of lower sweet spots according to the distribution and the peak value of porosity. The air permeability of most samples was 0.01–0.1 mD, and the overburden permeability under reservoir depth was less than 0.01 mD, which means that tight oil production could require fracturing stimulation. TOC content ranged from 0% to 12% and the mean of the upper sweet spots was 5.8%, which was greater than that of the lower sweet spots (4.2%). Using FESEM technology, the microscopic pore characteristics and the microscopic distribution of kerogen reserved in reservoirs and source rocks were observed (Lu et al., 2017). Both reservoir and source rocks have four kinds of pores. These are intragranular pores, intergranular pores, organic pores, and microfractures. The main pore type of reservoir rocks is intergranular pores. The spatial distribution of kerogen appeared as scattered blocks, while the kerogen of source rocks occurred in continuous strip belts. Many microfractures existed at the boundary of organic matter and clay, which may be evidence for expulsion of hydrocarbons from the source rocks. Based on drilling, well logging, core tests, and other data, the sweet spots in the Lucaogou Formation can be classified into three levels of favorable areas considering reservoir porosity, reservoir thickness, TOC content, and maturity of the source rocks. The threshold reservoir porosities of Types I, II, and III are 12%, 8%, and 5%, respectively, and the threshold reservoir thicknesses of Types I, II, and III are 12, 6, and 4 m, respectively. The threshold TOC contents of source rocks of Type I, II, and III are 3.5%, 2%, and 1%, respectively (Bao et al., 2016). To study how the trends of P-wave impedance and Vp/Vs change with lithology, porosity, and TOC content of hybrid tight oil reservoirs, 25 samples were shaped into 2.5-cm-diameter by 4-cm-long cylinders. We operated a set of ultrasonic velocity measurement devices and used
Fig. 5. Distribution of porosity, permeability, and TOC content of upper (red) and lower sweet spots (blue) in order from top to bottom. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the pulse transmission technique to record P- and S-wave travel times of the core samples at a formation pressure of 40 MPa. We then calculated P-wave velocity, S-wave velocity, P-wave impendence, and Vp/Vs using the rock physics formula. Table 1 lists the measured rock properties of three samples, including the density, porosity, TOC content, mineral components, ultrasonic P-wave velocity, and ultrasonic S-wave velocity. Fig. 6 shows the crossplot of P-wave impedance and the Vp/Vs of ultrasonic measurements. The color bar represents TOC content, and the size of the legend circle indicates the porosity. The red circles in Fig. 6 indicate reservoir samples, most of which have TOC contents < 3.5% and porosities > 4%. The other colored circles represent source rock samples. The arrows in Fig. 6 indicate an increase of the variables. If sandstone is chosen as a benchmark, both the P-wave impedance and the Vp/Vs will increase when the composition of dolomite in samples increases. P-wave impedance will decrease and Vp/Vs will increase greatly as the composition of clay in samples increases. In contrast, both P-wave impedance and Vp/Vs will decrease as the TOC content of 3
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Table 1 Tight oil sample rock properties. Samples
Lithology
TOC (%)
Porosity (%)
Density (g/cm3)
Clay (%)
Quartz + feldspar (%)
Dolomite + calcite (%)
Vp (Km/s)
Vs (Km/s)
1 2 3
Dolarenite Feldspathic litharenite Dolomitic mudstone
0.45 1.36 11.9
5.2 9.08 3.7
2.43 2.37 2.09
3.1 3.2 10.4
46.5 64.4 66.4
50.4 32.4 23.2
5.095 4.942 3.488
3.018 2.874 1.614
Fig. 6. Crossplot of Ip vs. Vp/Vs for laboratory data, with colors of marks corresponding to TOC and size of marks corresponding to porosity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 7. Composite cell schematic of BM-DIC model. Blank cells are mineral host matrix and shaded cells are organic patchy matrix (Sun, 2017).
samples increases, while P-wave impedance will decrease and Vp/Vs will increase slightly as the porosity of samples increases. These qualitative trends of elastic properties changing with the lithologies, porosity, and TOC provide the basis for the subsequent rock physics modeling.
where ρi is the density of the ith mineral and vi the volume fraction of the ith mineral. For the organic patch matrix, the kerogen moduli are Kpb = 4 GPa, Gpb = 1 GPa, and the density is ρpb = 1300 kg/m3 (Yan et al., 2013). In the host matrix, the porosity is φ , and the pores are filled with fluid (such as water, oil, or a mixture). The organic patch volume fraction can be calculated from a known TOC weight percentage (Vernik and Nur, 1992). With the host and patchy matrix parameters, we can calculate the elastic bulk modulus K ∗, shear modulus G∗, and density ρ∗of the rock using the BM-DIC model as follows:
3. Rock physics model and template The complicated lithology, high levels of organic matter, compact pore structure, partial microfracture development, and complicated flow distribution make tight oil rock modeling more difficult than in sand and shale reservoirs. The bulk modulus and the density of kerogen are slightly larger than those of water and much less than those of rocks (Yan et al., 2013). Therefore, tight oil rocks are generally composed of a hard mineral matrix and soft embedded kerogen. Eshelby (1957), Hashin (1962), and Kuster-Toksoz (1974) provided guidance on how to calculate the moduli of a medium containing two or more mineral materials. Their models treated background material and the embedded organic material as non-porous media when, in fact, there are pores or cracks both in the background mineral components and in the embedded kerogen. Analysis of a one-material matrix with two different pore inclusion concentrations was made by Ba et al. (2017). In the present study, the elastic parameters of tight oil reservoirs were estimated, based on a bi-material-matrix–double-inclusion concentrations (BM-DIC) model (Sun, 2017). This model assumes that the rock matrix is composed of a mineral host matrix and an organic patchy matrix (Fig. 7). The effective bulk moduli of the overall porous rock were determined by a multi-level effective modulus method. The bulk modulus K¯ s and shear modulus G¯s of host matrix materials containing multiple minerals were calculated using a Voigt-Reuss-Hill average model according to the volume fractions (Mavko et al., 1998). The average density of host matrix material is defined as
K ∗ = K¯ m +
15(1−ν¯m ) G∗
∑ vi ρi , i=1
= G¯m + G¯m 7−5ν¯m +
(
,
(2)
G¯ p −1 G¯ m
)α , ⎤ α ( )⎦
G¯ p G¯ p 2(4−5ν¯m ) ⎡ G¯ − G¯ −1 m m
⎣
ρ∗ = [ρ¯ (1−α ) + ρpb α ](1−φ) + φρw ,
(3) (4)
where K¯ P and G¯ P are the elastic moduli of effective patchy matrix, K¯ m 3K¯ − 2G¯ and G¯m are the elastic moduli of effective host matrix, ν¯m = 2(3Km¯ + G¯ m ) is m m the Poisson’s ratio of effective host matrix, α is the fraction of patchy matrix, and ρw is the density of pore fluid (Sun, 2017). The P-wave velocity, S-wave velocity, and other elastic parameters of rocks (such as Young’s modulus, Poisson's ratio, P-wave impedance, and Vp/Vs) can be calculated in a straightforward manner from the above given set of parameters through rock mechanics relations (Mavko et al., 1998). To determine which elastic parameters are sensitive to porosity and TOC content, we analyzed the relative variations of nine elastic parameters ( ρ , Vp , Vs , Ip , Vp/ Vs , K , μ , E , and σ ) with porosity and TOC content variations of 10%, respectively. As shown in Fig. 8, K , E , and Ip are the top three parameters that are sensitive to porosity. Considering the ease and accuracy of seismic inversion parameters and the need for lithology identification, a crossplot of Ip and Vp/ Vs was drawn to be a
n
ρ¯ =
(K¯ p−K¯ m )(4G¯m + 3K¯ m ) α 4G¯m + 3K¯ p + 3(K¯ m−K¯ p ) α
(1) 4
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Fig. 8. Elastic parameter relative variation with 10% porosity variation (colored by purple) and 10% TOC variation (colored by green). Elastic parameters are density ρ, P-wave velocity Vp, S-wave velocity Vs, P-wave impedance Ip, Vp/Vs, bulk modulus K, shear modulus μ, Young’s modulus E, and Poisson ratio σ. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. Workflow of building RPT and quantitative prediction of reservoir properties.
RPT of the quantitative prediction of porosity; ρ is the most sensitive to TOC content and a crocrossplotssplot of ρ and σ is a RPT of quantitative prediction of TOC content considering the need for lithology identification. Unlike the traditional RPT using a two-elastic-parameter crossplot, different combinations of mineral components must be introduced into building the RPT for hybrid tight oil because of the mixed lithologies of clay, sand, and dolomite. The reservoir lithologies are mainly dolarenite, dolomitic siltstone, and feldspathic litharenite, so there are three combinations of mineral components for RPT building, as follows: (1) a combination of sandstone and dolostone, in which the content of each component varies from 0% to 100%; (2) a combination of sandstone, shale, and dolostone, in which the content of clay has a fixed value of 10% considering the statistics of core measurements (Fig. 4), and the content of sandstone ranges from 30% to 90% with the remainder being dolostone; and (3) a combination of sandstone and shale, in which the content of shale ranges from 10% to 60% and the remainder is sandstone. We selected a wide variation range of shale to obtain the template boundary for data interpretation. Similarly, the lithologies of the source rocks are carbonaceous mudstone and dolomitic mudstone, so the mineral components for RPT building are a combination of sandstone, shale, and dolostone, in which the content of dolostone has a
Fig. 10. RPTs for predicting lithologies and porosity (top), and lithologies and TOC (bottom).
fixed value of 40% considering the statistics of core measurements (Fig. 4), the content of sandstone ranges from 0% to 50%, and the remainder is shale. Fig. 9 shows the workflow of building a RPT and a quantitative prediction of lithology, porosity, and TOC content. The detailed steps are as follows: First, determine the mix ratio of the three skeleton components (sandstone, dolostone, and shale) and the TOC content, porosity, and fluid saturation according to core samples or log data. Second, use the BM-DIC model to calculate the P-wave velocity, S-wave velocity, density, and the required elastic parameters at each point of porosity and lithological association. Third, build a RPT of Ip versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) and another RPT of density versus Poisson ratio with the same three end 5
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Fig. 13. Partial stack profile through Well J174 and Well J31. Highlighted blocks indicate upper and lower sweet spots. Fig. 11. Projection of log interpreted data of upper sweet spots in Lucaogou Formation in Well J174 onto new RPTs of Ip-vs-Vp/Vs crossplot. Colored legends indicate six lithologies and size of legends denotes level of porosity.
and porosity of reservoirs and the lithologies and TOC content of source rocks. For clarity, the porosity variation of the RPT in Fig. 10 ranges from 2% to 16% in increments of 1%. The variation of TOC content ranges from 2% to 12% in increments of 2%. The variation increment of each component in lithological combinations is either 10% or 20%. However, in practice it is usually necessary to have denser data points and also to extend the range of data to improve the interpretation accuracy. Based on core samples and log data, most of the sweet spots consisted of clay at less than 10% and the porosity of reservoirs was generally > 5%. With the addition of Ip > 8 (km/s × g/cm3), Vp/
members. Before outputting the final RPT, compare the template with the corresponding measured data at different frequencies and adjust the input parameters to harmonize the RPT with the distribution of data at the current frequency. Finally, introduce the inverted seismic properties (Ip, Vp/Vs, ρ, and σ) onto the new theoretical RPTs to predict the lithologies, porosity, and TOC content. Fig. 10 shows the new theoretical RPTs for predicting the lithologies
Fig. 12. Logging comprehensive interpretation of upper sweet spots in Well J174. 6
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Fig. 14. Inverted porosity profile through Well J174 and Well J31 by using RPT quantitative prediction method.
Fig. 15. Inverted porosity (left) and thickness (right) of reservoirs of upper sweet spots.
Fig. 16. Inverted TOC content (left) and thickness (right) of source rocks of upper sweet spots.
Vs < 1.85, and 100% sandstone line, all the above five lines in the RPT of Ip versus Vp/Vs comprise the limit lines for predicting high-quality reservoirs. Because the core measurement data were used to calibrate the background parameters of rocks, only the comparison between the template and log data is given in this case. Fig. 11 shows the projection of log data of the upper sweet spots in Well J174 onto the RPT of the Ip versus Vp/Vs crossplot. The colored legends indicate six different lithologies and the size of legends indicates the porosity. Compared with Fig. 3, each data point represented by the lithology combination and porosity has a clear meaning, and the trend of P-wave velocity and Vp/Vs variation with each lithology combination matches that of the laboratory data. The data points tended to be distributed in the upper right template as the dolostone content increased; in contrast, data points tended to be distributed in the upper left template as the mudstone content increased. Because the velocity of tight oil core samples has frequency dispersions, calibration of the broadband measurements was necessary when applying the same RPT to the core laboratory data, log data, and seismic data. Otherwise, the deviation between the
template and the data would be too large to decrease the accuracy of the interpretation results. 4. Case study As a case study, the J17 work area has completed a three-dimensional (3D) seismic acquisition with a bin dimension of 25 m × 50 m, a 48th overall fold, and a total area of 242 km2 in 2004, the scope of which is highlighted in the red box in Fig. 1. Fig. 12 shows logging comprehensive interpretation of upper sweet spots in Well J174. There are three small sections of reservoirs developed in Lucaogou Formation at a depth of 3110–3150 m, with the lithology, from shallow to deep, being dolarenite, feldspathic litharenite, and dolomitic siltstone. The accumulative thickness of reservoirs is only 15 m and the reservoirs in the well logs appear to have slightly higher density, acoustic velocity, and resistivity than mudstone. However, the conventional logging curves cannot identify the lithology of hybrid rocks and are also insensitive to oil-bearing rocks, while nuclear 7
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seismic RPT. We used laboratory measurements and forward modeling of the BM-DIC model and proposed a modified RPT of P-wave impedance versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) to predict the porosity and lithology of reservoirs. A new RPT of density versus Poisson ratio with the same three end members was used to predict the TOC content and lithology of source rocks. This method was verified by applying the new RPT quantitative prediction method to interpret the seismic data of the J17 work area and demonstrating that the inverted reservoir parameters coincided with oil testing results.
magnetic resonance logging technology can better identify lithology, physical properties, and oil saturation. There are two main problems in the 3D seismic inversion of the J17 work area. One is that a single reservoir is very thin and the accumulative thickness of reservoirs is less than the seismic vertical resolution. Seismic inversions can therefore only indicate the general characteristics of sweet spots, but cannot provide a detailed description of a single set of reservoirs. The other problem is that the prediction of seismic reservoirs is difficult because of complicated lithology, large heterogeneity, and unclear physical characteristics of rocks. To address these problems, a series of corresponding measures were adopted. First, we increased the level of research on high-resolution amplitude-preserving processing and improved the precision of thin reservoir description. Second, we strengthened the analysis of the characteristics of rock physics and clarified the influence of lithology, petrophysics, oilgas possibility, and TOC content on the elastic characteristics. Third, we developed the technology associated with pre-stack seismic quantitative prediction to improve the accuracy of tight oil reservoir prediction and reduce the multiple solutions of seismic interpretation. To improve the accuracy of synthetic records of well-seismic calibration, we first analyzed the seismic reflection characteristics of the main target layers of typical wells in the stacked profile. Fig. 13 shows the partial stack profiles through Well J174 and Well J31, and the blocks highlighted in yellow indicate upper and lower sweet spots. The seismic reflection characteristics of Lucaogou Formation are as follows: the seismic reflection events of the top and bottom of the Permian Lucaogou Formation are both wave peaks, which have strong amplitude, good continuity, and can serve as steady marker beds. The seismic reflection event of the top of the upper sweet spots is a wave trough, while the bottom is a wave peak that has moderate amplitude. Conversely, the seismic reflection event at the top of the lower sweet spots is a wave peak, while the bottom is a wave trough that has weak amplitude and poor continuity. After building the new RPT followed by the process shown in Fig. 9, we projected the inverted P-wave impedance and Vp/Vs onto the new template, and then searched the nearest grid node and recorded the corresponding lithology combination and porosity. In the same way, the lithology combination and TOC content of source rocks was inverted by using the new RPT of Poisson ratio and density crossplot. Fig. 14 shows the seismically inverted porosity profile through Well J174 and Well J31 in Lucaogou Formation. The distribution characteristics of upper and lower sweet spots are obvious and the distribution edges are clear. The physical properties of the upper sweet spots are better than those of the lower sweet spots, both of which have a porosity > 5%. Actual drilling information showed that the production of Well J174 was up to 2 T of oil per day, which confirms the validity of the new RPTs. Fig. 15 shows the inverted porosity and thickness of the reservoirs of the upper sweet spots. The thickness of the main reservoirs are approximately 20–30 m. Fig. 16 shows the inverted TOC content and thickness of source rocks of the upper sweet spots. Favorable reservoirs are mainly distributed on the eastern side of the J17 work area, and the reservoir distribution has obvious zonal features. The reservoirs near Well J37 and Well J173 have relatively thicker layers and better physical properties. They are high-yield wells with a production of 5 T of oil per day. In addition, the reservoir types of these two wells are different. The lithology of the main reservoir in Well J173 is dolomite, while the lithology of the main reservoir in Well J37 is dolomitic siltstone.
Acknowledgements We thank Xinjiang Oilfield Company for providing the core samples used in this study. The project was supported by the National Key Basic Research Program Project 973 of China (Grant No. 2014CB239006), the National Science and Technology Major Project of China (Grant No. 2016ZX05046-002), and the China National Petroleum Corporation Science and Technology Project (Grant No. 2016A-3301). References Aveseth, P., Mukerji, T., Mavko, G., 2005. Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge University Press. Ba, J., Xu, W., Fu, L.Y., Carcione, J.M., Zhang, L., 2017. Rock anelasticity due to patchy saturation and fabric heterogeneity: a double double-porosity model of wave propagation. J. Geophys. Res. Solid Earth 122. https://doi.org/10.1002/2016JB013882. Bao, H.J., Liu, X., Zhou, Y.L., et al., 2016. Favorable area and potential analyses of tight oil in Jimsar Sag. Special Oil Gas Reserv. 23 (5), 38–42 (in Chinese with English abstract). Cao, Z., Liu, G., Kong, Y., Wang, C., Niu, Z., Zhang, J., et al., 2016. Lacustrine tight oil accumulation characteristics: permian lucaogou formation in jimusaer sag, junggar basin. Int. J. Coal Geol. 153, 37–51. Du, J.H., He, H.Q., Yang, T., et al., 2014. Progress in China’s tight oil exploration and challenges. China Petrol. Explor. 19 (1), 1–9 (in Chinese with English abstract). Du, J.H., et al., 2016. Continental tight oil in China. Petroleum Industry Press, Beijing, pp. 72–74 (in Chinese). Eshelby, J.D., 1957. The determination of the elastic field of an ellipsoidal inclusion, and related problems. Proc. Roy. Soc., Ser. A 241, 376–396. Fertl, W.H., Chilingar, G.V., 1988. Total organic carbon content determined from well logs. SPE Form. Eval. 3 (2), 407–419. Hashin, Z., 1962. The elastic moduli of heterogeneous materials. J. Appl. Mech. 29E, 143–150. Huang, F.X., Yang, T., Yan, W.P., et al., 2014. Analysis of dominant factors affecting tight oil reservoir properties of China. J. Chengdu Univ. Technol. (Sci. Technol. Ed.) 41 (5), 538–547 (in Chinese with English abstract). Jia, C.Z., Zou, C.N., Li, J.Z., et al., 2012. Assessment criteria, main types, basic features and resource prospects of the tight oil in China. Acta Petrol. Sin. 33 (3), 333–350 (in Chinese with English abstract). Kuang, L., Tang, Y., Lei, D., Chang, Q., Ouyang, M., Hou, L., Liu, D., 2012. Formation conditions and exploration potential of tight oil in the Permian saline lacustrine dolomitic rock, Junggar Basin, NW China. Petrol. Explor. Develop. 39 (6), 700–711. Kuster, G.T., Toksoz, M.N., 1974. Velocity and attenuation of seismic waves in two phase media: part I. Theoretical formulations. Geophysics 39 (5), 587–606. Lu, M H., Cao H., Yan X F., et al., 2017. The Effects of Kerogen on Rock Physics Properties of Tight Oil Reservoirs. EAGE Ann. Mtg Extend Abstract. Mavko, G., Mukerji, T., Dvorkin, J., 1998. The Rock Physics Handbook. Cambridge University Press. Odegaard, E., Aveseth, P., 2003. Interpretation of elastic inversion results using rock physics templates. EAGE Ann. Mtg Extend Abstract. Odegaard, E., Aveseth, P., 2004. Well log and seismic data analysis using rock physics templates. First Break 22, 37–43. Passey, Q.R., Creaney, S., Kulla, J.B., Moretti, F.J., Stroud, J.D., 1990. A practical model for organic richness from porosity and resistivity logs. AAPG Bull. 74 (12), 1777–1794. Si, C., Chen, N., Yu, C., Li, Y., Meng, C., 2013. Sedimentary characteristics of tight oil reservoir in Permian Lucaogou Formation, Jimsar Sag. Petrol. Geol. Experiment 35 (5), 528–533 (in Chinese with English abstract). Sun, W., 2017. Determination of elastic moduli of composite medium containing bimaterial matrix and non-uniform inclusion concentrations. Appl. Math. Mech. 38 (1), 15–28. Vernik, L., Nur, A., 1992. Ultrasonic velocity and anisotropy of hydrocarbon source rocks. Geophysics 57 (5), 727–735. Xi, K.L., Cao, Y.C., Zhu, R.K., et al., 2015. Rock types and charateristics of tight oil reservoir in permian Lucaogou Formation, Jimsar sag. Acta Petrol. Sin. 36 (12), 1495–1507 (in Chinese with English abstract). Yan, F., Han, D., 2013. Measurement of elastic properties of kerogen. SEG 2013 Annual Meeting Houston. Zhu, Y.C., Jiang, Y.Y., Wu, J.J., et al., 2017. Quantitative prediction of tight oil reservoir properties in Jumusar depression. Special Oil Gas Reserv. 24 (4), 42–47 (in Chinese with English abstract). Zou, C.N., Zhang, G.S., Yang, Z., et al., 2013. Geological concepts, characteristics, resource potential and key techniques of unconventional hydrocarbon: on unconventional petroleum geology. Pet. Explor. Dev. 40 (4) 385–399, 454.
5. Conclusions The lithology of tight oil reservoirs in Lucaogou Formation in Jimusar Sag is complex. Dolarenite, dolomitic siltstone, and feldspathic litharenite are the three main lithology types of the upper sweet spots, while dolomitic siltstone is the main lithology type of the lower sweet spots. Most rocks have a mixture of clay, sand, and dolomite, so it is difficult to identify the lithology of sweet spots using the traditional 8
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Full length article
Quantitative prediction of seismic rock physics of hybrid tight oil reservoirs of the Permian Lucaogou Formation, Junggar Basin, Northwest China ⁎
Minghui Lua, , Hong Caoa, Weitao Sunb, Xinfei Yana, Zhifang Yanga, Youping Xua, Zhenlin Wangc, Min Ouyangc a
Research Institute of Petroleum Exploration and Development (RIPED), PetroChina, Beijing 100083, PR China Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, PR China c Research Institute of Exploration and Development, Xinjiang Oilfield Company, PetroChina, Karamay 834000, PR China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Tight oil Seismic rock physics Lithology Porosity Total organic carbon Hybrid
Tight oil exploration and development in the Jimusar Sag of southeastern Junggar Basin of Northwest China has led to development of two reservoir segments in the Lucaogou Formation. The upper segment is mostly carbonate rocks, while the lower segment consists mainly of fine-grained clastic rocks. Both segments are mixed with clay, sand, and dolomite, which makes it difficult to identify the lithology and predict the porosity using a traditional seismic rock physics template (RPT). We analyzed the trends of ultrasonic P-wave impedance and Vp/ Vs of laboratory data changing with lithology, porosity, and total organic carbon (TOC) content. Our data provide parametric sensitivity regarding porosity and TOC content by use of a bi-material-matrix–double-inclusion concentration model. We propose a modified RPT of P-wave impedance versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) to predict porosity and lithology of reservoirs. We also present a new RPT of density versus Poisson ratio with the same three end members to predict TOC content and lithology of source rocks. These were validated by comparisons with laboratory measurements and log data. We inverted the porosity, TOC content, and thickness of the upper reservoir segment in the Lucaogou Formation of the J17 work area of Jimusar Sag by using the new RPT quantitative prediction method. The inversion results were identical to oil testing results, supporting the viability and effectiveness of the new RPTs.
1. Introduction
depression that developed on the Pre-Permian fold basement. It is faulted in the western part and overlapped in the eastern part (Fig. 1). The eastern part is bordered by the Santai Fault and Houbaozi Fault to the south, Xidi Fault and Laozhuangwan Fault to the west, Jimusar Fault to the north, and Gucheng uplift to the east (Xi et al., 2015; Cao et al., 2016). Two reservoir segments developed in the Lucaogou Formation. The upper segment is mostly made up of carbonate rocks and the lower segment consists mainly of fine-grained clastic rocks (Kuang et al., 2012; Si et al., 2013; Xi et al., 2015). The high-quality source rocks and the reservoirs of Lucaogou Formation formed in a salty lacustrine environment, and the differences in the lithologies between the upper and lower reservoir segments are mostly controlled by terrestrial inputs. The upper reservoir segment developed in the microfacies of a beach bar of a shallow lake with an inadequate provenance supply. The lower reservoir segment developed in the microfacies of a delta front distal bar and sheet sand with an adequate provenance supply. The reservoirs are fine-grained and subjected to the control of the lithology and pore dissolution. The pore spaces mainly consist of residual
Tight oil is an unconventional oil and gas resource with great potential and significant prospects for future development. The deposition environment of Chinese tight oil reservoirs represents a type of continental lake deposition. This differs from the marine sedimentary deposition types of North America. Tight oil reservoirs have complex lithology, low porosity, low permeability, versatile and minute pore throat structures, and significant lateral heterogeneity. With respect to the lithology and sedimentary environment, tight oil reservoirs can be classified into three categories: tight sandstone, carbonate, and hybrid reservoirs. Each of these reservoir categories has unique physical properties and variable distributions (Jia et al., 2012; Zou et al., 2013; Du et al., 2014). A set of hybrid sedimentations of deep-lake dark mudstone (as major source rocks) and dolomitic rock (as major tight oil reservoirs) has developed in the Permian Lucaogou Formation in the Jimusar Sag of southeastern Junggar basin. The Jimusar Sag is a dustpan-like
⁎
Corresponding author. E-mail address: [email protected] (M. Lu).
https://doi.org/10.1016/j.jseaes.2018.08.014 Received 13 February 2018; Received in revised form 15 August 2018; Accepted 18 August 2018 1367-9120/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Lu, M., Journal of Asian Earth Sciences (2018), https://doi.org/10.1016/j.jseaes.2018.08.014
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Fig. 3. Crossplot of Ip vs Vp/Vs for log data of Lucaogou Formation in Well J174. Color bar represents five different lithologies of reservoir rocks and source rocks. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 1. Tectonic map of Lucaogou Formation of Jimusar Sag. Santai Fault and Houbaozi Fault are in the south, Xidi Fault and Laozhuangwan Fault are in the west, and Jimusar Fault is in the north. Closed box in red is the position of the J17 work area studied in this paper (modified from Xi et al., 2015; Cao et al., 2016). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
lower Vp/Vs and lower AI than dry sand. With increasing porosity, the AI decreases while the Vp/Vs tends to increase. Complicated lithologies and pore structures of hybrid tight oil make it difficult to identify the sweet spots using the traditional RPT of Ip versus Vp/Vs (Fig. 3). The logging P-wave impedance and Vp/Vs of reservoir rocks (green denotes dolarenite, yellow denotes feldspathic litharenite, and orange denotes dolomitic siltstone) and source rocks (cyan denotes carbonaceous mudstone and blue denotes dolomitic mudstone) in Lucaogou Formation in Well J174 are totally mixed together. Therefore, the traditional RPT of Ip versus Vp/Vs cannot be used to identify the lithology of hybrid tight oil reservoirs or to predict their porosity. A common approach for predicting the porosity used by oil field companies is to optimize the most sensitive seismic attributes of porosity, such as impedance by multiple-well correlation, establish the nonlinear relationship between sensitive seismic attributes and porosity through regression analysis, and then apply the relationship to the entire work area to quantitatively predict the porosity (Zhu et al., 2017). The nonlinear relationship between sensitive seismic attributes and porosity will vary with different well quality and reservoir depth, so it may not apply to work areas with great lateral heterogeneity. Although the evaluation of source rocks using logging data is an established technique (Fertl et al., 1988; Passey et al., 1990), it is still difficult to predict the TOC content of source rocks using seismic data. To improve the accuracy of predicting the lithology, porosity, and TOC content of hybrid tight oil reservoirs, we obtained the trends of P-wave impedance and Vp/Vs changing with the lithologies, porosity, and TOC content by analyzing ultrasonic laboratory measurements. We then proposed two new RPTs to predict the porosity, TOC content, and lithology of reservoirs based on analysis of the parametric sensitivity. We applied the new quantitative prediction method to the J17 work area to test its validity.
intergranular pores, solution pores, and fractures with sizes ranging from nanometers to one micron (Si et al., 2013; Huang et al., 2014). The “sweet spots” refer to the tight-oil-rich zones with matured high-quality source rocks, favorable reservoir intervals, and industrial value through artificial stimulation (Zou et al., 2013). Predicting sweet spots from geophysical data is essential for designing and deploying exploration wells, evaluation wells, and development wells. Compared with marine tight oil exploration in North America, the complex lithologies, special petrophysical properties of rocks, and weak hydrocarbon response of continental tight oil reservoirs make it challenging to accurately predict the sweet spots (Du et al., 2016). It is important to predict the lithology, porosity, and total organic carbon (TOC) content for determining the distribution of sweet spots of hybrid tight oil. Rock physics templates (RPTs) are used to establish the relationship between the elastic parameters of rocks and reservoir attributes such as porosity, fluid saturation, and clay content. RPTs are essential tools for quantitative seismic interpretation (Odegaard et al., 2003, 2004). The crossplot of acoustic impedance (AI) versus Vp/Vs (Fig. 2) is the most common RPT used to distinguish shale from sand and to identify gas sand and predict the porosity (Aveseth et al., 2005). Fig. 2 illustrates that shale has a higher Vp/Vs and lower AI than sand. Gas sand has a
2. Rock physics characteristics Seismic rock physics links exist between seismic attributes and reservoir parameters. Laboratory measurements can provide direct observation and understanding of how rock properties (such as lithology, porosity, pore fluids, TOC, and pressure) impact velocity, density, amplitude, attenuation, and anisotropy. We synthetically studied the results of x-ray diffraction, energy dispersive spectrometer, field-emission scanning electronic microscopy (FESEM), and ultrasonic measurements to analyze the relationship between the macro-response (such as velocity) and micro-pores and mineral components. All of the core samples studied were collected from the Lucaogou Formation in Well J174. The depth interval ranged from 3100 to
Fig. 2. RPT presented as a crossplot of Vp/Vs versus AI including porosity trends for different lithologies and increasing gas saturation for sands (Aveseth et al., 2005). 2
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Fig. 4. Three end-member classifications of mineral composition of core samples. Red squares represent samples from reservoirs and gray squares represent source rocks. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3200 m. The core samples were classified into five lithology types: dolarenite, dolomitic siltstone, feldspathic litharenite, carbonaceous mudstone, and dolomitic mudstone. The first three served as the main reservoir rocks and the last two were source rocks. Fig. 4 shows the mineral composition classification of 90 core samples from Lucaogou Formation. The three end members were clay, quartz and feldspar, and dolomite and calcite. The red marks represent the samples from reservoirs and the gray marks represent source rocks. Fig. 4 shows that reservoir rocks and source rocks are mixed with clay, sand, and dolomite. Both the reservoir and source rocks contain a small quantity of clay, which averages less than 20%. However, the difference of mineral composition between them is small and so it is difficult to distinguish the lithologies of hybrid rocks. The distribution histograms of porosity, permeability, and TOC content of upper sweet spots (red color) and lower sweet spots (blue color) are shown in Fig. 5. The porosity ranged from 2% to 20% with a mean of 9%. The reservoir property of upper sweet spots is slightly better than that of lower sweet spots according to the distribution and the peak value of porosity. The air permeability of most samples was 0.01–0.1 mD, and the overburden permeability under reservoir depth was less than 0.01 mD, which means that tight oil production could require fracturing stimulation. TOC content ranged from 0% to 12% and the mean of the upper sweet spots was 5.8%, which was greater than that of the lower sweet spots (4.2%). Using FESEM technology, the microscopic pore characteristics and the microscopic distribution of kerogen reserved in reservoirs and source rocks were observed (Lu et al., 2017). Both reservoir and source rocks have four kinds of pores. These are intragranular pores, intergranular pores, organic pores, and microfractures. The main pore type of reservoir rocks is intergranular pores. The spatial distribution of kerogen appeared as scattered blocks, while the kerogen of source rocks occurred in continuous strip belts. Many microfractures existed at the boundary of organic matter and clay, which may be evidence for expulsion of hydrocarbons from the source rocks. Based on drilling, well logging, core tests, and other data, the sweet spots in the Lucaogou Formation can be classified into three levels of favorable areas considering reservoir porosity, reservoir thickness, TOC content, and maturity of the source rocks. The threshold reservoir porosities of Types I, II, and III are 12%, 8%, and 5%, respectively, and the threshold reservoir thicknesses of Types I, II, and III are 12, 6, and 4 m, respectively. The threshold TOC contents of source rocks of Type I, II, and III are 3.5%, 2%, and 1%, respectively (Bao et al., 2016). To study how the trends of P-wave impedance and Vp/Vs change with lithology, porosity, and TOC content of hybrid tight oil reservoirs, 25 samples were shaped into 2.5-cm-diameter by 4-cm-long cylinders. We operated a set of ultrasonic velocity measurement devices and used
Fig. 5. Distribution of porosity, permeability, and TOC content of upper (red) and lower sweet spots (blue) in order from top to bottom. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the pulse transmission technique to record P- and S-wave travel times of the core samples at a formation pressure of 40 MPa. We then calculated P-wave velocity, S-wave velocity, P-wave impendence, and Vp/Vs using the rock physics formula. Table 1 lists the measured rock properties of three samples, including the density, porosity, TOC content, mineral components, ultrasonic P-wave velocity, and ultrasonic S-wave velocity. Fig. 6 shows the crossplot of P-wave impedance and the Vp/Vs of ultrasonic measurements. The color bar represents TOC content, and the size of the legend circle indicates the porosity. The red circles in Fig. 6 indicate reservoir samples, most of which have TOC contents < 3.5% and porosities > 4%. The other colored circles represent source rock samples. The arrows in Fig. 6 indicate an increase of the variables. If sandstone is chosen as a benchmark, both the P-wave impedance and the Vp/Vs will increase when the composition of dolomite in samples increases. P-wave impedance will decrease and Vp/Vs will increase greatly as the composition of clay in samples increases. In contrast, both P-wave impedance and Vp/Vs will decrease as the TOC content of 3
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Table 1 Tight oil sample rock properties. Samples
Lithology
TOC (%)
Porosity (%)
Density (g/cm3)
Clay (%)
Quartz + feldspar (%)
Dolomite + calcite (%)
Vp (Km/s)
Vs (Km/s)
1 2 3
Dolarenite Feldspathic litharenite Dolomitic mudstone
0.45 1.36 11.9
5.2 9.08 3.7
2.43 2.37 2.09
3.1 3.2 10.4
46.5 64.4 66.4
50.4 32.4 23.2
5.095 4.942 3.488
3.018 2.874 1.614
Fig. 6. Crossplot of Ip vs. Vp/Vs for laboratory data, with colors of marks corresponding to TOC and size of marks corresponding to porosity. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Fig. 7. Composite cell schematic of BM-DIC model. Blank cells are mineral host matrix and shaded cells are organic patchy matrix (Sun, 2017).
samples increases, while P-wave impedance will decrease and Vp/Vs will increase slightly as the porosity of samples increases. These qualitative trends of elastic properties changing with the lithologies, porosity, and TOC provide the basis for the subsequent rock physics modeling.
where ρi is the density of the ith mineral and vi the volume fraction of the ith mineral. For the organic patch matrix, the kerogen moduli are Kpb = 4 GPa, Gpb = 1 GPa, and the density is ρpb = 1300 kg/m3 (Yan et al., 2013). In the host matrix, the porosity is φ , and the pores are filled with fluid (such as water, oil, or a mixture). The organic patch volume fraction can be calculated from a known TOC weight percentage (Vernik and Nur, 1992). With the host and patchy matrix parameters, we can calculate the elastic bulk modulus K ∗, shear modulus G∗, and density ρ∗of the rock using the BM-DIC model as follows:
3. Rock physics model and template The complicated lithology, high levels of organic matter, compact pore structure, partial microfracture development, and complicated flow distribution make tight oil rock modeling more difficult than in sand and shale reservoirs. The bulk modulus and the density of kerogen are slightly larger than those of water and much less than those of rocks (Yan et al., 2013). Therefore, tight oil rocks are generally composed of a hard mineral matrix and soft embedded kerogen. Eshelby (1957), Hashin (1962), and Kuster-Toksoz (1974) provided guidance on how to calculate the moduli of a medium containing two or more mineral materials. Their models treated background material and the embedded organic material as non-porous media when, in fact, there are pores or cracks both in the background mineral components and in the embedded kerogen. Analysis of a one-material matrix with two different pore inclusion concentrations was made by Ba et al. (2017). In the present study, the elastic parameters of tight oil reservoirs were estimated, based on a bi-material-matrix–double-inclusion concentrations (BM-DIC) model (Sun, 2017). This model assumes that the rock matrix is composed of a mineral host matrix and an organic patchy matrix (Fig. 7). The effective bulk moduli of the overall porous rock were determined by a multi-level effective modulus method. The bulk modulus K¯ s and shear modulus G¯s of host matrix materials containing multiple minerals were calculated using a Voigt-Reuss-Hill average model according to the volume fractions (Mavko et al., 1998). The average density of host matrix material is defined as
K ∗ = K¯ m +
15(1−ν¯m ) G∗
∑ vi ρi , i=1
= G¯m + G¯m 7−5ν¯m +
(
,
(2)
G¯ p −1 G¯ m
)α , ⎤ α ( )⎦
G¯ p G¯ p 2(4−5ν¯m ) ⎡ G¯ − G¯ −1 m m
⎣
ρ∗ = [ρ¯ (1−α ) + ρpb α ](1−φ) + φρw ,
(3) (4)
where K¯ P and G¯ P are the elastic moduli of effective patchy matrix, K¯ m 3K¯ − 2G¯ and G¯m are the elastic moduli of effective host matrix, ν¯m = 2(3Km¯ + G¯ m ) is m m the Poisson’s ratio of effective host matrix, α is the fraction of patchy matrix, and ρw is the density of pore fluid (Sun, 2017). The P-wave velocity, S-wave velocity, and other elastic parameters of rocks (such as Young’s modulus, Poisson's ratio, P-wave impedance, and Vp/Vs) can be calculated in a straightforward manner from the above given set of parameters through rock mechanics relations (Mavko et al., 1998). To determine which elastic parameters are sensitive to porosity and TOC content, we analyzed the relative variations of nine elastic parameters ( ρ , Vp , Vs , Ip , Vp/ Vs , K , μ , E , and σ ) with porosity and TOC content variations of 10%, respectively. As shown in Fig. 8, K , E , and Ip are the top three parameters that are sensitive to porosity. Considering the ease and accuracy of seismic inversion parameters and the need for lithology identification, a crossplot of Ip and Vp/ Vs was drawn to be a
n
ρ¯ =
(K¯ p−K¯ m )(4G¯m + 3K¯ m ) α 4G¯m + 3K¯ p + 3(K¯ m−K¯ p ) α
(1) 4
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Fig. 8. Elastic parameter relative variation with 10% porosity variation (colored by purple) and 10% TOC variation (colored by green). Elastic parameters are density ρ, P-wave velocity Vp, S-wave velocity Vs, P-wave impedance Ip, Vp/Vs, bulk modulus K, shear modulus μ, Young’s modulus E, and Poisson ratio σ. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 9. Workflow of building RPT and quantitative prediction of reservoir properties.
RPT of the quantitative prediction of porosity; ρ is the most sensitive to TOC content and a crocrossplotssplot of ρ and σ is a RPT of quantitative prediction of TOC content considering the need for lithology identification. Unlike the traditional RPT using a two-elastic-parameter crossplot, different combinations of mineral components must be introduced into building the RPT for hybrid tight oil because of the mixed lithologies of clay, sand, and dolomite. The reservoir lithologies are mainly dolarenite, dolomitic siltstone, and feldspathic litharenite, so there are three combinations of mineral components for RPT building, as follows: (1) a combination of sandstone and dolostone, in which the content of each component varies from 0% to 100%; (2) a combination of sandstone, shale, and dolostone, in which the content of clay has a fixed value of 10% considering the statistics of core measurements (Fig. 4), and the content of sandstone ranges from 30% to 90% with the remainder being dolostone; and (3) a combination of sandstone and shale, in which the content of shale ranges from 10% to 60% and the remainder is sandstone. We selected a wide variation range of shale to obtain the template boundary for data interpretation. Similarly, the lithologies of the source rocks are carbonaceous mudstone and dolomitic mudstone, so the mineral components for RPT building are a combination of sandstone, shale, and dolostone, in which the content of dolostone has a
Fig. 10. RPTs for predicting lithologies and porosity (top), and lithologies and TOC (bottom).
fixed value of 40% considering the statistics of core measurements (Fig. 4), the content of sandstone ranges from 0% to 50%, and the remainder is shale. Fig. 9 shows the workflow of building a RPT and a quantitative prediction of lithology, porosity, and TOC content. The detailed steps are as follows: First, determine the mix ratio of the three skeleton components (sandstone, dolostone, and shale) and the TOC content, porosity, and fluid saturation according to core samples or log data. Second, use the BM-DIC model to calculate the P-wave velocity, S-wave velocity, density, and the required elastic parameters at each point of porosity and lithological association. Third, build a RPT of Ip versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) and another RPT of density versus Poisson ratio with the same three end 5
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Fig. 13. Partial stack profile through Well J174 and Well J31. Highlighted blocks indicate upper and lower sweet spots. Fig. 11. Projection of log interpreted data of upper sweet spots in Lucaogou Formation in Well J174 onto new RPTs of Ip-vs-Vp/Vs crossplot. Colored legends indicate six lithologies and size of legends denotes level of porosity.
and porosity of reservoirs and the lithologies and TOC content of source rocks. For clarity, the porosity variation of the RPT in Fig. 10 ranges from 2% to 16% in increments of 1%. The variation of TOC content ranges from 2% to 12% in increments of 2%. The variation increment of each component in lithological combinations is either 10% or 20%. However, in practice it is usually necessary to have denser data points and also to extend the range of data to improve the interpretation accuracy. Based on core samples and log data, most of the sweet spots consisted of clay at less than 10% and the porosity of reservoirs was generally > 5%. With the addition of Ip > 8 (km/s × g/cm3), Vp/
members. Before outputting the final RPT, compare the template with the corresponding measured data at different frequencies and adjust the input parameters to harmonize the RPT with the distribution of data at the current frequency. Finally, introduce the inverted seismic properties (Ip, Vp/Vs, ρ, and σ) onto the new theoretical RPTs to predict the lithologies, porosity, and TOC content. Fig. 10 shows the new theoretical RPTs for predicting the lithologies
Fig. 12. Logging comprehensive interpretation of upper sweet spots in Well J174. 6
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Fig. 14. Inverted porosity profile through Well J174 and Well J31 by using RPT quantitative prediction method.
Fig. 15. Inverted porosity (left) and thickness (right) of reservoirs of upper sweet spots.
Fig. 16. Inverted TOC content (left) and thickness (right) of source rocks of upper sweet spots.
Vs < 1.85, and 100% sandstone line, all the above five lines in the RPT of Ip versus Vp/Vs comprise the limit lines for predicting high-quality reservoirs. Because the core measurement data were used to calibrate the background parameters of rocks, only the comparison between the template and log data is given in this case. Fig. 11 shows the projection of log data of the upper sweet spots in Well J174 onto the RPT of the Ip versus Vp/Vs crossplot. The colored legends indicate six different lithologies and the size of legends indicates the porosity. Compared with Fig. 3, each data point represented by the lithology combination and porosity has a clear meaning, and the trend of P-wave velocity and Vp/Vs variation with each lithology combination matches that of the laboratory data. The data points tended to be distributed in the upper right template as the dolostone content increased; in contrast, data points tended to be distributed in the upper left template as the mudstone content increased. Because the velocity of tight oil core samples has frequency dispersions, calibration of the broadband measurements was necessary when applying the same RPT to the core laboratory data, log data, and seismic data. Otherwise, the deviation between the
template and the data would be too large to decrease the accuracy of the interpretation results. 4. Case study As a case study, the J17 work area has completed a three-dimensional (3D) seismic acquisition with a bin dimension of 25 m × 50 m, a 48th overall fold, and a total area of 242 km2 in 2004, the scope of which is highlighted in the red box in Fig. 1. Fig. 12 shows logging comprehensive interpretation of upper sweet spots in Well J174. There are three small sections of reservoirs developed in Lucaogou Formation at a depth of 3110–3150 m, with the lithology, from shallow to deep, being dolarenite, feldspathic litharenite, and dolomitic siltstone. The accumulative thickness of reservoirs is only 15 m and the reservoirs in the well logs appear to have slightly higher density, acoustic velocity, and resistivity than mudstone. However, the conventional logging curves cannot identify the lithology of hybrid rocks and are also insensitive to oil-bearing rocks, while nuclear 7
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seismic RPT. We used laboratory measurements and forward modeling of the BM-DIC model and proposed a modified RPT of P-wave impedance versus Vp/Vs with three end members (sandstone, dolostone, and mudstone) to predict the porosity and lithology of reservoirs. A new RPT of density versus Poisson ratio with the same three end members was used to predict the TOC content and lithology of source rocks. This method was verified by applying the new RPT quantitative prediction method to interpret the seismic data of the J17 work area and demonstrating that the inverted reservoir parameters coincided with oil testing results.
magnetic resonance logging technology can better identify lithology, physical properties, and oil saturation. There are two main problems in the 3D seismic inversion of the J17 work area. One is that a single reservoir is very thin and the accumulative thickness of reservoirs is less than the seismic vertical resolution. Seismic inversions can therefore only indicate the general characteristics of sweet spots, but cannot provide a detailed description of a single set of reservoirs. The other problem is that the prediction of seismic reservoirs is difficult because of complicated lithology, large heterogeneity, and unclear physical characteristics of rocks. To address these problems, a series of corresponding measures were adopted. First, we increased the level of research on high-resolution amplitude-preserving processing and improved the precision of thin reservoir description. Second, we strengthened the analysis of the characteristics of rock physics and clarified the influence of lithology, petrophysics, oilgas possibility, and TOC content on the elastic characteristics. Third, we developed the technology associated with pre-stack seismic quantitative prediction to improve the accuracy of tight oil reservoir prediction and reduce the multiple solutions of seismic interpretation. To improve the accuracy of synthetic records of well-seismic calibration, we first analyzed the seismic reflection characteristics of the main target layers of typical wells in the stacked profile. Fig. 13 shows the partial stack profiles through Well J174 and Well J31, and the blocks highlighted in yellow indicate upper and lower sweet spots. The seismic reflection characteristics of Lucaogou Formation are as follows: the seismic reflection events of the top and bottom of the Permian Lucaogou Formation are both wave peaks, which have strong amplitude, good continuity, and can serve as steady marker beds. The seismic reflection event of the top of the upper sweet spots is a wave trough, while the bottom is a wave peak that has moderate amplitude. Conversely, the seismic reflection event at the top of the lower sweet spots is a wave peak, while the bottom is a wave trough that has weak amplitude and poor continuity. After building the new RPT followed by the process shown in Fig. 9, we projected the inverted P-wave impedance and Vp/Vs onto the new template, and then searched the nearest grid node and recorded the corresponding lithology combination and porosity. In the same way, the lithology combination and TOC content of source rocks was inverted by using the new RPT of Poisson ratio and density crossplot. Fig. 14 shows the seismically inverted porosity profile through Well J174 and Well J31 in Lucaogou Formation. The distribution characteristics of upper and lower sweet spots are obvious and the distribution edges are clear. The physical properties of the upper sweet spots are better than those of the lower sweet spots, both of which have a porosity > 5%. Actual drilling information showed that the production of Well J174 was up to 2 T of oil per day, which confirms the validity of the new RPTs. Fig. 15 shows the inverted porosity and thickness of the reservoirs of the upper sweet spots. The thickness of the main reservoirs are approximately 20–30 m. Fig. 16 shows the inverted TOC content and thickness of source rocks of the upper sweet spots. Favorable reservoirs are mainly distributed on the eastern side of the J17 work area, and the reservoir distribution has obvious zonal features. The reservoirs near Well J37 and Well J173 have relatively thicker layers and better physical properties. They are high-yield wells with a production of 5 T of oil per day. In addition, the reservoir types of these two wells are different. The lithology of the main reservoir in Well J173 is dolomite, while the lithology of the main reservoir in Well J37 is dolomitic siltstone.
Acknowledgements We thank Xinjiang Oilfield Company for providing the core samples used in this study. The project was supported by the National Key Basic Research Program Project 973 of China (Grant No. 2014CB239006), the National Science and Technology Major Project of China (Grant No. 2016ZX05046-002), and the China National Petroleum Corporation Science and Technology Project (Grant No. 2016A-3301). References Aveseth, P., Mukerji, T., Mavko, G., 2005. Applying Rock Physics Tools to Reduce Interpretation Risk. Cambridge University Press. Ba, J., Xu, W., Fu, L.Y., Carcione, J.M., Zhang, L., 2017. Rock anelasticity due to patchy saturation and fabric heterogeneity: a double double-porosity model of wave propagation. J. Geophys. Res. Solid Earth 122. https://doi.org/10.1002/2016JB013882. Bao, H.J., Liu, X., Zhou, Y.L., et al., 2016. Favorable area and potential analyses of tight oil in Jimsar Sag. Special Oil Gas Reserv. 23 (5), 38–42 (in Chinese with English abstract). Cao, Z., Liu, G., Kong, Y., Wang, C., Niu, Z., Zhang, J., et al., 2016. Lacustrine tight oil accumulation characteristics: permian lucaogou formation in jimusaer sag, junggar basin. Int. J. Coal Geol. 153, 37–51. Du, J.H., He, H.Q., Yang, T., et al., 2014. Progress in China’s tight oil exploration and challenges. China Petrol. Explor. 19 (1), 1–9 (in Chinese with English abstract). Du, J.H., et al., 2016. Continental tight oil in China. Petroleum Industry Press, Beijing, pp. 72–74 (in Chinese). Eshelby, J.D., 1957. The determination of the elastic field of an ellipsoidal inclusion, and related problems. Proc. Roy. Soc., Ser. A 241, 376–396. Fertl, W.H., Chilingar, G.V., 1988. Total organic carbon content determined from well logs. SPE Form. Eval. 3 (2), 407–419. Hashin, Z., 1962. The elastic moduli of heterogeneous materials. J. Appl. Mech. 29E, 143–150. Huang, F.X., Yang, T., Yan, W.P., et al., 2014. Analysis of dominant factors affecting tight oil reservoir properties of China. J. Chengdu Univ. Technol. (Sci. Technol. Ed.) 41 (5), 538–547 (in Chinese with English abstract). Jia, C.Z., Zou, C.N., Li, J.Z., et al., 2012. Assessment criteria, main types, basic features and resource prospects of the tight oil in China. Acta Petrol. Sin. 33 (3), 333–350 (in Chinese with English abstract). Kuang, L., Tang, Y., Lei, D., Chang, Q., Ouyang, M., Hou, L., Liu, D., 2012. Formation conditions and exploration potential of tight oil in the Permian saline lacustrine dolomitic rock, Junggar Basin, NW China. Petrol. Explor. Develop. 39 (6), 700–711. Kuster, G.T., Toksoz, M.N., 1974. Velocity and attenuation of seismic waves in two phase media: part I. Theoretical formulations. Geophysics 39 (5), 587–606. Lu, M H., Cao H., Yan X F., et al., 2017. The Effects of Kerogen on Rock Physics Properties of Tight Oil Reservoirs. EAGE Ann. Mtg Extend Abstract. Mavko, G., Mukerji, T., Dvorkin, J., 1998. The Rock Physics Handbook. Cambridge University Press. Odegaard, E., Aveseth, P., 2003. Interpretation of elastic inversion results using rock physics templates. EAGE Ann. Mtg Extend Abstract. Odegaard, E., Aveseth, P., 2004. Well log and seismic data analysis using rock physics templates. First Break 22, 37–43. Passey, Q.R., Creaney, S., Kulla, J.B., Moretti, F.J., Stroud, J.D., 1990. A practical model for organic richness from porosity and resistivity logs. AAPG Bull. 74 (12), 1777–1794. Si, C., Chen, N., Yu, C., Li, Y., Meng, C., 2013. Sedimentary characteristics of tight oil reservoir in Permian Lucaogou Formation, Jimsar Sag. Petrol. Geol. Experiment 35 (5), 528–533 (in Chinese with English abstract). Sun, W., 2017. Determination of elastic moduli of composite medium containing bimaterial matrix and non-uniform inclusion concentrations. Appl. Math. Mech. 38 (1), 15–28. Vernik, L., Nur, A., 1992. Ultrasonic velocity and anisotropy of hydrocarbon source rocks. Geophysics 57 (5), 727–735. Xi, K.L., Cao, Y.C., Zhu, R.K., et al., 2015. Rock types and charateristics of tight oil reservoir in permian Lucaogou Formation, Jimsar sag. Acta Petrol. Sin. 36 (12), 1495–1507 (in Chinese with English abstract). Yan, F., Han, D., 2013. Measurement of elastic properties of kerogen. SEG 2013 Annual Meeting Houston. Zhu, Y.C., Jiang, Y.Y., Wu, J.J., et al., 2017. Quantitative prediction of tight oil reservoir properties in Jumusar depression. Special Oil Gas Reserv. 24 (4), 42–47 (in Chinese with English abstract). Zou, C.N., Zhang, G.S., Yang, Z., et al., 2013. Geological concepts, characteristics, resource potential and key techniques of unconventional hydrocarbon: on unconventional petroleum geology. Pet. Explor. Dev. 40 (4) 385–399, 454.
5. Conclusions The lithology of tight oil reservoirs in Lucaogou Formation in Jimusar Sag is complex. Dolarenite, dolomitic siltstone, and feldspathic litharenite are the three main lithology types of the upper sweet spots, while dolomitic siltstone is the main lithology type of the lower sweet spots. Most rocks have a mixture of clay, sand, and dolomite, so it is difficult to identify the lithology of sweet spots using the traditional 8