A new method for predicting sweet spots of shale oil using conventional well logs

Journal Pre-proof A new method for predicting sweet spots of shale oil using conventional well logs Jinbu Li, Min Wang, Shuangfang Lu, Guohui Chen, Weichao Tian, Chunqing Jiang, Zheng Li PII:

S0264-8172(19)30533-1

DOI:

https://doi.org/10.1016/j.marpetgeo.2019.104097

Reference:

JMPG 104097

To appear in:

Marine and Petroleum Geology

Received Date: 30 March 2019 Revised Date:

14 October 2019

Accepted Date: 16 October 2019

Please cite this article as: Li, J., Wang, M., Lu, S., Chen, G., Tian, W., Jiang, C., Li, Z., A new method for predicting sweet spots of shale oil using conventional well logs, Marine and Petroleum Geology (2019), doi: https://doi.org/10.1016/j.marpetgeo.2019.104097. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

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A new method for predicting sweet spots of shale oil using

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conventional well logs

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Jinbu Li1,2, Min Wang*1, Shuangfang Lu**1, Guohui Chen3, Weichao Tian1,

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Chunqing Jiang2, Zheng Li4

5

1

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Qingdao 266580, Shandong, PR China

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2

Geological Survey of Canada, Alberta T2L 2A7, Canada

8

3

Key laboratory of tectonics and petroleum resources, Ministry of Education, China

9

University of Geosciences, Wuhan 430074, PR China

Key laboratory of deep oil and gas, China University of Petroleum (East China),

10

4

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Dongying 257015, Shandong, PR China

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*Corresponding author:

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Prof. Min Wang

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School of Geosciences, China University of Petroleum (East China), Qingdao 266580,

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Shandong, China

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Phone (or Mobile) No.: +86-18661856595

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Email: [email protected]

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Prof. Shuangfang Lu

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School of Geosciences, China University of Petroleum (East China), Qingdao 266580,

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Shandong, China

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Phone (or Mobile) No.: +86-18661856596

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Email: [email protected]

Geology Scientific Research Institute of Shengli Oilfield Company, Sinopec,

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Abstract

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In this paper, a new method called sweet spot index (SSI) is proposed by the combination of shale

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oil mobility and shale reservoir fracability to predict the vertical distribution of shale oil sweet

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spots using conventional logs. In the SSI method, logging evaluation models for TOC, S1 (volatile

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petroleum), and mineral content are initially established. Mobile oil content is obtained by

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subtracting the amount of adsorbed oil from the total oil content, which is the S1 value after the

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recovery of light and heavy hydrocarbons. The adsorbed oil content is calculated based on the oil

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adsorption model established by a stepwise pyrolysis experiment. The formation fracability is

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estimated by a combination of estimates of brittle mineral content and Young’s modulus.

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Formations with higher brittleness and lower Young’s modulus are considered better simulation

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candidates. The SSI value is the product of the normalized mobility and the fracability index,

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which minimizes the section of just an organic matter sweet spot (i.e., high oil content) or an

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inorganic sweet spot (i.e., easily fractured) and has the advantage of accurately predicting its

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vertical distribution. In the case study, the new method is successfully implemented to predict

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sweet spots of the Es3L (lower sub member of the third member of the Eocene Shahejie

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Formation) in the Bonan Sag, Bohai Bay Basin, China. The lower limit value of SSI is set to 0.1

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based on its relationship with shale oil production. The effectiveness, reliability and adaptability of

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the SSI method have been verified by three wells in the Bonan Sag.

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Keywords: Shale oil; mobility; sweet spots; fracability; brittleness; adsorbed oil

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1. Introduction

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As a self-contained source-reservoir system, shale oil has attracted great attention from many

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geologists because of its huge resource potential (Clarkson et al., 2013; Jarvie, 2012; Jia et al.,

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2013; Kirschbaum and Mercier, 2013; Lu et al., 2012; Zou et al., 2013; Zou et al., 2012). The

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United States Energy Information Administration (EIA) (2013) estimated that technologically

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feasible recovery of shale oil in 41 countries stands at 345 billion barrels, of which China ranks

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third with 32 billion barrels (Wang et al., 2019). At present, China has completed geological

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evaluation and resource estimation in a few oilfields, and the results show that the potential shale

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reservoirs studied have the characteristics of high organic matter abundance, moderate vitrinite

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reflectance, low porosity and permeability, wide geographic distribution, etc. (Chen et al., 2017a;

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Hua et al., 2016; Jiang et al., 2014; Li et al., 2014; Pan et al., 2017; Song et al., 2013; Wang et al.,

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2015). Although many shale oil wells have been drilled in Bohai Bay, Songliao, Jianghan and

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other basins, shale oil production is still low, such as Boye-HF 1 well in the Dongying Sag, which

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produced 8.22 m3 of oil per day initially, but decreased to 1.6 m3 per day over the course of

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several months with total production of only 100 m3 (Lu et al., 2016). The Biye-HF1 well in the

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Biyang Sag had an initial production of 23.6 m3 of oil per day, but the productivity also decreased

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sharply, and now it has been shut-in (Zhang et al., 2012a). At present, the results of exploration

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and development of shale oil resources in China are less than anticipated. One reason is that the

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intervals with shale oil sweet spots are not clear (Lu et al., 2016; Zhang et al., 2014).

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Several methods, including multi-parameter plane folding (Heege et al., 2015; Yang et al.,

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2015), fuzzy optimization (Liu and Wang, 2016), and factor analysis (Chen et al., 2016; Chen et

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al., 2017b; Glaser et al., 2013; Hakami et al., 2016; Jarvie, 2012; Liang et al., 2016) have been

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used for predicting shale oil sweet spots in previous studies. These methods are generally

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performed by analyzing the geological settings, shale composition and shale properties. The

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indicators selected are various, including vitrinite reflectance (%Ro), total organic carbon (TOC),

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porosity (φ), oil saturation (So), volatile hydrocarbon content (S1), thickness (H), oil saturation

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index (OSI = S1/TOC × 100, mg-HC per g-TOC), permeability (K), brittleness, mechanical

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parameters, etc. There are several issues with utilization of the above data in the previous studies.

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First, many of these parameters are related to each other, such as oil content, oil saturation, and

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porosity, such that the controlling factors of shale oil sweet spots are not clear. In previous studies

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on shale oil sweet spots prediction, many researchers paid more attention to the total oil content

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evaluation. Yang et al., (2015) suggests that shale oil sweet spots are areas of high oil content and

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high fracability. However, the areas with high shale oil content may not be favorable zones, e.g.,

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the facies of massive mudstone with high oil adsorption capacity and low proportion of free oil

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(Yong et al., 2016). Shale oil mobility should be considered in the prediction of shale oil sweet

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spots (Lu et al., 2016; Zhang et al., 2014). Second, with the large thickness and significant

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longitudinal heterogeneity of shale reservoirs, limited core sample data is not sufficient to interpret

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the detailed information of the formation. Therefore, predicting sweet spots’ vertical distribution

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using well log interpretation calibrated with core analyses is needed (Chen et al., 2016). Third,

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when the sum of the normalized parameters characterizing oiliness and fracability is used as the

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evaluation standard for sweet spots detection, the prediction result is likely to be only an organic

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matter sweet spot (a zone with high oil content) or an inorganic sweet spot (a zone that is easily

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fractured). However, the product of these normalized parameters could clarify this situation.

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Fourth, the lower limit value of the parameters used for identifying sweet spots is from field

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experience in other basins, such as S1×100/TOC =100 mg/g proposed by Jarvie et al. (2012),

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which may not be suitable for the current research area. Standards should be established based on

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measured data or shale oil production volume in the study area.

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A shale oil sweet spot should have the presence of high volumes of highly mobile oil and

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susceptibility to hydraulic fracturing (Lu et al., 2016). Therefore, a new method called the sweet

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spot index (SSI) is proposed based on the shale oil mobility and ability to create a fracture

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network by stimulation, and is applied to prediction of sweet spots in the Es3L (lower sub member

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of the third member of the Eocene Shahejie Formation) lacustrine shale in the Bonan Sag, Bohai

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Bay Basin, China. The innovative aspects of the SSI method include multiple aspects. First, it

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accounts for the two most important indicators (shale oil mobility and shale reservoir fracability)

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(Lu et al., 2016) and can determine the sweet spot’s variations in the vertical distribution of shale

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reservoirs with significant heterogeneity. Second, the SSI value is the product of the normalized

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mobility and the fracability index, which avoids the situation where just an organic matter sweet

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spot or an inorganic sweet spot is identified. Third, the lower limit value of the SSI is determined

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based on their relationship with shale oil production of the current research area. The effectiveness,

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reliability and adaptability of the SSI method have been tested on the three wells of the Bonan

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Sag.

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2. SSI method

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The distribution of shale oil sweet spots is varied with the large thickness and significant

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longitudinal heterogeneity of the lacustrine shale reservoirs in China. It has been observed that the

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higher mobile oil content and reservoir fracability, the higher shale oil production (Wang et al.,

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2019). Based on the above reasoning, the concept of the SSI method has been proposed as

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follows:

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SSI = M n × Fn

(1)

where SSI is the sweet spot index, Mn and Fn are the normalized mobility index and the fracability

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index, and are defined below:

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M

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m in

)

(2)

Fn = ( FI − FI m in ) / ( F I m ax − FI m in

)

(3)

n

= (M − M

m in

) / ( M m ax

−M

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where Mmin and Mmax are the minimum and maximum mobile oil content for the investigated

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formation. FImin and FImax are the minimum and maximum fracability index for the investigated

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formation. M and FI are the mobile oil content and fracability index through depth. Mmin, Mmax,

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FImin, and FImax are constants and M and FI are variables.

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For the preliminary predictions of the SSI method, it is necessary to determine the shale oil

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mobility and shale reservoir fracability. Therefore, in this paper, organic and inorganic

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heterogeneity evaluation methods have been established using a combination of measured core

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data and conventional logging curves. The shale organic matter heterogeneity is evaluated by the

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vertical variation of TOC and S1. The total oil content can be obtained after the light and heavy

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hydrocarbon measurement of S1, which subtracts the amount of adsorbed oil to get the maximum

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amount of mobile oil. The shale inorganic heterogeneity is evaluated by the brittleness and rock

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mechanics parameters to estimate the shale reservoir fracability. The lower limit value of the SSI

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is determined by the shale oil production of several wells. SSI is applied to other wells to clarify

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the distribution of shale oil sweet spots in the Bonan Sag. The workflow is shown in Figure 1.

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3. Case study

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3.1 Geological settings

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The Bonan Sag is a secondary structural unit in the central-southern of Zhanhua Sag in

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Jiyang Depression of the Bohai Bay Basin, China (Shi et al., 2005). To the south, it is connected to

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the Chenjiazhuang uplift, and to the east, it is connected with the Gubei Sag and the Gudao buried

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mountain structural belt. The western part of the Bonan Sag is bounded by the Yihezhuang uplift,

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whereas the northern part is adjacent to the Chengdong uplift. The exploration area of the Bonan

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Sag is 600 km2 (Figure 2).

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During the deposition period of the Es3L, a lake basin enlarged with a warm and humid

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climate and formed a series of rocks with different lithologies, such as dark gray mudstone, dark

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gray lime mudstone, gray argillaceous dolomite, gray sandy mudstone, and dolomitic siltstone (Jiu

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et al., 2013; Shi et al., 2005; Wang et al., 2015). The Es3L is the main source rock formation in the

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Bonan Sag with thickness from 100 to 600 m (328 to 1968 ft). In our previous study, geochemical

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analysis of the Es3L shale indicated the presence of highly organic-rich, early-mature, Types I and

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II1 lacustrine oil prone kerogens, and high-density oil. In particular, TOC values range from 0.71

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to 9.32 wt%, with an average of 3.1 wt%; S1 ranges from 0.03 to 13.12 mg HC/g rock, with an

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average of 2.14 mg HC/g rock. Hydrogen index (HI) values range from 160.58 to 1041.82 mg/g

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TOC, and with an average of 495.87 mg/g TOC. %Ro ranges from 0.55 to 0.9 % and pyrolysis

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peak temperature (Tmax) ranges from 424 to 447 °C, with an average of 440 °C (Wang et al.,

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2016; Wang et al., 2015). Commercial oil flow has been obtained from the Es3L for many wells,

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such as well Luo 42, well Luo 19 and well Xinyishen 9 (Table 1). The proved cumulative oil in

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place is 1.84 × 108 tons (1.38 billion barrels) and the predicted oil in place 1.68 × 108 tons (1.23

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billion barrels) (Wang et al., 2015), demonstrating the great production potential of the study area.

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(Note: the detailed explanation of the proved cumulative oil and the predicted oil can be seen in

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GB/T 19492-2004)

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In general, the Luo 42 and Xinyishen 9 wells with good oil production rates, are more

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suitable to establish the SSI method. However, the limited coring data of these two wells makes it

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difficult to establish effective logging evaluation models. Well Luo 69, which was cored

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systematically from 2911.00 to 3140.75 m (9550 to 10304 ft) in 2010, provided details and

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characteristics of the shale oil reservoirs of Es3L and provided good basic data for the exploration

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and development of shale oil in the Bonan Sag. In this study, organic and inorganic logging

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evaluation models were established based on core experiments and conventional logging data of

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the Luo 69 well, and validated with data from other wells (e.g. Luo 67 and Xinyishen 9 wells).

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3.2 Samples and experiments

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From top to bottom of Es3L of the Luo 69 well, 180 lacustrine shale samples were selected at

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1 m intervals, except for some depth intervals without core data, to carry out the routine pyrolysis

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experiments. After surface cleaning and powdering to 100 mesh, the shale samples were placed

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into a Rock-Eval-VI instrument. The samples were then heated to 300°C for 3 minute followed by

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heating to 650°C at a heating rate of 50°C per minute. Organic geochemistry parameters, such as

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TOC, S1, cracking hydrocarbons (S2), and Tmax were obtained (Espitalié et al., 1984; Espitalie et

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al., 1977).

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Forty-five lacustrine shale samples of Es3L in the Bonan Sag were selected to determine the

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bitumen content using chloroform extraction. The whole-rock samples were powdered to 100

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mesh after surface cleaning, and subsequently extracted with chloroform for 8 h.

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Forty lacustrine shale samples of Es3L in the Bonan Sag (two samples from well Luo 67,

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three samples from well Xinyishen 9, and thirty-five samples from well Luo 69) were selected to

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carry out the stepwise pyrolysis experiment. The schematic diagram of the stepwise heating

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pyrolysis is shown in Figure 3. The process of pyrolysis and the products of each stage in this

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study were as follows: powdered samples (100 mesh) were placed into the Rock-Eval 6 instrument,

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and heated at a rate of 25°C per minute from 90°C to the first target temperature of 200°C and

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held for 3 minute to get the S1-1, then heated at a rate of 25°C per minute from 200°C to the

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second target temperature of 300°C and held for 3 minute to get the S1-2a, and then heated at a rate

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of 25°C per minute from 300°C to the third target temperature of 350°C and held for 3 minute to

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get the S1-2b. After the third target temperature, the samples continued to be heated at a rate of

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25°C per minute from 350°C to the third target temperature of 450°C and held for 3 minute to get

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the S2-1, and then heated at a rate of 25°C per minute from 450°C to the fifth target temperature of

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650°C to get the S2-2. The physical meaning of the S1-1, S1-2a, S1-2b, S2-1, and S2-2 had been

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interpreted by (Jiang et al., 2016b): the sum of S1-1 and S1-2 is the free hydrocarbons which

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represent the maximum movable hydrocarbon content; S2-1 is the adsorbed hydrocarbons, and S2-2

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is the cracking hydrocarbons of kerogen. Although some less stable kerogen components have

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been cracked at 450°C and lower, there are some heavy components in shale oil that would be

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cracked or evaporated at the temperature above 450°C as well. And the total oil released before

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450°C is equal to the extracts obtained from the solvent extraction. Thus the stepwise pyrolysis

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method used in this study is considered reliable for the characterization of adsorbed oil (S2-1) from

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the perspective of oil content (Jiang et al., 2016b; Li et al., 2018).

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Three hundred and fifty lacustrine shale samples of Es3L were selected from well Luo 69 and

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analyzed for mineral composition. Each sample was crushed and mixed with ethanol, ground in a

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mortar and pestle, and smeared on a glass slide. The X-ray diffraction experiments were

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performed using a Panalytical X'Pert PRO Diffractometer (Petroleum Exploration and Production

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Research Institute, Sinopec) with Cu Kα radiation (40 kV, 30 mA) and a scanning speed of 2° 2θ

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per minute (θ) to analyze all the minerals. The experiment was carried out at room temperature

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(25°C).

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3.3 Organic heterogeneity logging evaluation

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3.3.1 TOC and S1 logging evaluation

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The ∆logR method is widely used for the TOC prediction of source rock because of its

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convenience and versatility (Modica and Lapierre, 2012; Passey et al., 1990; Sharma and Chopra,

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2016). However, the accuracy of the traditional ∆logR method is often influenced by artificial

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factors, such as the baseline selection, the fixed congruence coefficient, the TOC background

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value, and sample maturity, all of which may cause difficulty in achieving the desired goals (Liu et

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al., 2014). In this study, an improved ∆logR method was used to evaluate TOC and S1. This

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method automatically selected the baseline and optimized the proportionality coefficient D based

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on the minimum error between the log prediction value and the measured value from the core

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analysis. The calculation process of the improved ∆logR method was completed by computer, and

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the model was (Liu et al., 2014):

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∆ log R = D × log

R + (1 − D ) × ( ∆t − ∆tbaseline ) Rbaseline

TOC = A × ∆ log R + B

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(4) (5)

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where Rbaseline and ∆tbaseline are the baseline values of the resistivity log and the acoustic log, in Ωm

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and µs/ft, respectively. R and ∆t are the values of the resistivity log and the acoustic log along the

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depth, in Ωm and µs/ft, respectively. D is the proportionality coefficient, which represents the

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relative proportion of the resistivity curve in ∆logR, ranging from 0 to 1. A and B are model

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coefficients, both greater than zero.

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Based on the minimum error between the predicted TOC and the measured TOC, the value of

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the proportionality coefficient D of the improved ∆logR method for TOC prediction is optimized

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as 0.1. The calculation, A and B in equation 5, were obtained from the relationship between the

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TOC from the core analysis and the log data (Figure 4(a)). The correlation coefficient (R2) is

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approximately as high as 0.882, which indicates that the improved ∆logR method is suitable for

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predicting the TOC. The formula to predict the TOC can be expressed as:

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∆ log RTOC = 0.1 × log

R + 0.9 × ( ∆ t − ∆ tbaseline ) Rbaseline

TOC = 1.202 × ∆ log RTOC + 0.561

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(6) (7)

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Similarly, the improved ∆logR method was also applied to the prediction of S1, as shown in

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Figure 4(b). The value of the proportional coefficient D for S1 prediction is 0.35. For the improved

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∆logR method, the proportional coefficient D plays a role in identifying the contribution of

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kerogen and hydrocarbon fluid in organic matter. It is easy to understand that the proportional

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coefficient D for S1 calculation is greater than that of the TOC calculation because the resistivity

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logs are more sensitive to the hydrocarbon fluid, while the acoustic logs are sensitive to the

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kerogen (Liu et al., 2014). The formula to predict the S1 can be expressed as:

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∆ log RS1 = 0.35 × log

S1 = 0.973 × ∆ log RS1 + 0.264

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R + 0.65 × ( ∆ t − ∆ t baseline ) Rbaseline

(8) (9)

3.3.2 Oil content evaluation

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The oil content, characterized directly by the pyrolysis parameter S1 is not equal to that of the

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background because of the loss of light hydrocarbons and heavy hydrocarbons during the process

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of the core storage and experimental analysis (Jarvie, 2012). Generally, the loss of light

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hydrocarbons typically the C15- fractions, while the heavy hydrocarbons appear within S2 peak

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because of the “carry-over” effect and confinement effect (Delvaux et al., 1990). At present, there

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are several methods for the recovery of S1, such as the chromatographic method, combined

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extraction with pyrolysis, chemical kinetic theory, and pressure coring (Jiang et al., 2016a; Wang

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et al., 2014; Zhu et al., 2015).

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In this study, for the recovery of heavy hydrocarbons, we directly corrected S1 according to

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the relationship between S1 and bitumen “A”. Figure 5 shows that the correction coefficient for

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heavy hydrocarbons (KH) is 3.358 based on the relationship between the overall hydrocarbon

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content and S1, which is close to the value of 3.20 found by Wang et al. (2014) for the Chinese

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eastern basins. For the recovery of light hydrocarbons, we use the relationship between the light

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hydrocarbon recovery coefficient and the maturity/depth of the Jiyang Depression built by Zhu et

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al. (2015). As shown in Figure 6, the light hydrocarbon recovery coefficient (KL) becomes larger

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as the depth increases. The reason for this can be interpreted that the deeper source rock produces

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lighter hydrocarbon components, which are easily lost during the processes of core storage and

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experimental analysis.

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After recovery of S1, the oil content (Ot) can be estimated by equation 10: Ot = S1 × K H × K L

256

(10)

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where S1 is the volatile hydrocarbon content in mg/g, which can be obtained from pyrolysis or the

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logging evaluation method. KH is the correction coefficient for heavy hydrocarbons, the value is

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3.358 in the Bonan Sag (Figure 5). KL is the correction coefficient for light hydrocarbons, which is

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related to the maturity.

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3.3.3 Mobile oil content evaluation

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There are a few reports available on the evaluation of the mobility of shale oil, and the

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evaluation methods are: OSI > 100 mg/g, the value of OSI at the hydrocarbon expulsion threshold,

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the elastic/dissolved gas driving model method, and the hydrocarbon adsorption and vapor

265

adsorption method (Jarvie, 2012; Li et al., 2017; Li et al., 2016; Zhang et al., 2014). The OSI >

266

100 mg/g indicator (Jarvie, 2012) is based on the statistics of marine shale oil production capacity,

267

which may not be suitable for the current research area since the adsorption capacity of kerogen is

268

affected by the kerogen type, maturity (Zhang et al., 2012b), and oil properties (Wei et al., 2012),

269

etc. The elastic/dissolved gas driving model is important for the prediction of mobile oil, but it is

270

difficult to obtain all the geological parameters required for this method (Zhang et al., 2014).

271

In this study, the adsorbed oil content of ten samples was tested by a stepwise heating

272

pyrolysis experiment performed on shales and routine Rock-Eval performed on shales and solvent

273

extracted shales, respectively. The relationship between the heavy oil content (S2-S2’) of the

274

routine Rock-Eval and adsorbed oil content (S2-1) of the stepwise Rock-Eval at the temperature

275

interval ranging from 350°C to 450°C was shown in the Figure 7a. The coefficient between these

276

two parameters is 0.9068, indicating that heavy oil content characterized by the extraction method

277

is slightly higher than the adsorbed oil content obtained from the stepwise experiment. The reason

278

can be interpreted by the factor that the temperature interval of the routine pyrolysis used for the

279

extraction method is relatively wide. Also, the solvent-based extraction method extracts both free

280

oil and adsorbed oil (Sonnenfeld and Canter, 2016). The adsorbed oil content (Oa) is controlled by

281

TOC values (Figure 7b), which can be calculated as shown below:

282

Oa = 2.007 × TOC

(11)

283

The physical meaning of equation 11 is that the coefficient of adsorbed oil content is

284

approximately 2.007, which is greater than the coefficient values of heavy hydrocarbons (S2-S2’)

285

and TOC given in the previous reports, such as the average value of 1.01 reported by Han (Han et

286

al., 2015)) and 0.82 by Li (Li et al., 2018)). Jiang et al. (2016b) show that the kerogen of well Luo

287

69 is rich in sulfur with low hydrocarbon-generating activation energy and it can generate heavy

288

oil in the early stage (low thermal maturities). The shale oil produced from the Es3L of the three

289

wells in the Bonan Sag have high densities (ranging from 0.87 to 0.91 g/cm3), which are greater

290

than that produced in Barnett Shale (Table 2). Therefore, the coefficient of adsorbed oil content in

291

the Bonan Sag is relatively high.

292 293

In this study, the mobile oil content (M) can be obtained from the combination of total oil content (Ot) and the adsorbed oil content (Oa):

M = Ot − Oa

294

= S1 × K H × K L − TOC × 2.007

295

3.4 Inorganic heterogeneity logging evaluation

296

3.4.1 Mineral content evaluation

(12)

297

Since special logging technologies like element-capture spectroscopy logging (ECS) and

298

natural gamma spectrum (NGS) have not been widely used in China, it is necessary to predict the

299

mineral composition from conventional logs. Multivariate fitting and full-volume linear models

300

are two methods widely used to evaluate inorganic minerals (Adiguna, 2012; Mahmood et al.,

301

2018; Zhong et al., 2006; Zorski et al., 2011). However, the volume summary of each component

302

evaluated by the multivariate fitting method is not equal to 100%, which has caused the

303

multivariate fitting method to be questioned. Moreover, previous studies have claimed that not all

304

values obtained from well logging instruments are a simple linear superposition of log response

305

values for each component of the reservoir (Adiguna, 2012; Lai et al., 2015; Xiao et al., 2016). It

306

is therefore difficult to describe the relationship between the content of each mineral and well

307

logs values with a simple linear formula. Fortunately, due to the advantages of an

308

unstructured calculation, the neural network technique has been successfully applied to

309

reservoir parameter predictions (Jamshidian et al., 2015; Shi et al., 2016). We have introduced

310

the principles and workflows of back propagation (BP) network in detail in our previous

311

study (Li et al., 2019), and will give a brief introduction here.

312

BP neural network is a multi-layer feed forward network trained according to an error

313

feedback propagation algorithm. Its structure has three layers: the input layer, the hidden layer,

314

and the output layer. Three layers are connected by weights (Wij and Wjk) and threshold values (aj

315

and bk). Through repeated calibration using a large number of measured samples, the weights,

316

threshold values, and the number of hidden layer nodes, can be optimized (Hecht-Nielsen, 1989).

317

The parameters of the input layer are well logs, while the parameters of the output layer are

318

mineral composition. The shale mineralogy was divided into three groups since it is difficult to

319

evaluate all the mineral contents using the limited conventional logs. These groups consist of clay

320

minerals (illite, chlorite, kaolinite and mixed layer), siliceous minerals (quartz and feldspar-based),

321

and calcareous minerals (calcite, dolomite and siderite). The relationships between the each

322

mineral group and conventional logs were analyzed using equation 13 (Table 3). The logs with a

323

significant correlation coefficient at the 0.01 level were selected as the input parameters of the BP

324

neural network, which were acoustic (AC), density (DEN), neutron (CNL), gamma ray (GR), and

325

resistivity (RT).
=

326

 ∑   ∑  ∑   ∑  ∑   ∑  ∑ 

(13)

327

where r is the correlation coefficient, xi and yi are variables, and N is the number involved in the

328

calculation.

329

Before the BP network optimization, all input parameters were normalized from -1 to 1 to

330

eliminate the effects of the different dimensions of the input log values and to accelerate the rate

331

of convergence (Lai et al., 2015; Li et al., 2019; Tan et al., 2013). In addition, to promote the

332

representativeness and applicability of the BP network, 200 samples were randomly selected as

333

training samples, 80 samples were selected as validating samples and 70 samples were selected as

334

testing samples. The BP neural network is optimized by using a cross-validation method for

335

training samples and validating samples, and the optimal network is tested by the testing samples.

336

As shown in Figure 8, the predicted values of the training and validating samples of the BP neural

337

network and the measured values of each mineral group show a good linear relationship, the linear

338

coefficients are approximately equal to 1, and the correlation coefficients (R) are more than 0.7. In

339

addition, the distribution of the testing sample points on both sides of the diagonal guarantees the

340

predictive performance of the model, indicating that the BP neural network can be applied to the

341

other wells.

342

3.4.2 Fracability evaluation

343

The low-permeability characteristics of shale reservoirs determine that they have to be

344

fractured to achieve oil production. The formation of large-scale fracture networks should have a

345

high brittleness index and low fracture toughness to generate more fracture/flow channels at a

346

given energy (Jin et al., 2014, 2015; Sun et al., 2015). There was a positive relationship between

347

the fracture toughness and the Young's modulus (Jin et al., 2014, 2015). Therefore, Young's

348

modulus was used to define the fracability index in this study. An ideal fracturing zone should

349

have a high brittleness index and a low Young's modulus. The mathematical model of the

350

fracability index (FI) can be expressed as:

351 352

FI =

1 ( Bn + En ) 2

(14)

where Bn and En are the normalized brittleness and the normalized Young's modulus, and are

353

defined as:

354

B n = ( B − B m in ) / ( B m a x − B m in

)

(15)

355

E n = ( E m a x − E ) / ( E m a x − E m in

)

(16)

356

where Bmin and Bmax are the minimum and maximum brittleness indexes for the investigated

357

formation, Emin and Emax are the minimum and maximum Young's modulus for the investigated

358

formation. Bmin, Bmax, Emin, Emax are constants. B and E are the brittleness index and the Young's

359

modulus through depth, which are defined as (Jin et al., 2014, 2015):

360

361

B =

E=

V sil + V cal V total

ρ ×( 3×∆ts 2 − 4 ×∆t p2 ) ∆ts 2 ×( ∆ts 2 −∆t p2 )

(17)

(18)

362

where Vsil and Vcal are the siliceous minerals content and the calcareous minerals content. Vtotal is

363

the total mineral content. ρ is the density log, g/cm3. ∆ts is the transverse wave logging data, µs/ft.

364

∆tp is the longitudinal wave logging data, µs/ft.

365

Since transverse wave logging data is limited, 15 samples were selected from Es3L of well

366

Luo 69 to test the transverse wave logging and longitudinal wave logging in the laboratory. The

367

lithology of these samples contains dark shale, gray mudstone, and argillaceous dolomite, which

368

cover the main lithology types of the Es3L in the Bonan Sag. The depth of these samples varies

369

from 2947 m to 3123 m, within the Es3L of well Luo 69. The relationship between the transverse

370

wave logging and longitudinal wave logging are shown in Figure 9. Therefore, the transverse

371

wave logging data can be obtained from the following relationship:

372

∆ t s = 2 .4 9 6 × ∆ t p - 5 1 .1 8 5

(19)

373

The brittleness index of the Es3L of well Luo 69 was evaluated using equation 17 based on

374

the content of calcareous and siliceous minerals. The fracability index (FI) was calculated by the

375

combination of the brittleness index and Young's modulus using equation 14, as shown in Figure

376

10. The color indicates the value of the fracability index. As the color changes from deep blue to

377

red, the shale changes to a more readily fractured rock matrix. High energy may be required to

378

generate cracks in the formations with a high brittleness index and a large Young's modulus, and

379

so these areas should not be used as development targets at the early stage of shale oil exploration.

380

3.5 Lower limit value of the SSI

381

The statistical data of the shale oil productivity (daily output per meter, t/d ▪m-1) and the SSI

382

of the production intervals of the seven wells (see locations in Figure 2) of Es3L in the Bonan Sag

383

have been analyzed, as shown Figure 11. The shale oil productivity increases with the increase of

384

SSI, and there exists an inflection point. When the SSI is less than 0.1, the shale oil productivity is

385

low, and the trend is flat. When the SSI is greater than 0.1, the shale oil productivity increases

386

rapidly. Therefore, we take 0.1 as the lower limit value of the SSI for the favorable shale oil

387

section of the Bonan Sag, and the area where the SSI is greater than 0.1 is the sweet spot of the

388

shale oil.

389

In addition, the relationships between the mobile oil content, the fracability and the shale oil

390

productivity are shown in Figure 12. The shale oil productivity increases with the increase of the

391

mobile oil content and the fracability. The trend of increasing shale oil production with mobile oil

392

content similar to that of the SSI, not the fracability, indicates that the SSI is mainly controlled by

393

the mobile oil content in this area. There also exists an inflection point in the trend of increasing

394

shale oil production with mobile oil content (Figure 12 (a)), and the lower limit value of the

395

mobile oil content can be set as 5 mg/g. The scatter plot between the shale oil productivity and the

396

reservoir fracability is mainly divided into two areas, as shown in Figure 12(b). The high oil

397

production area and the low oil production area can be separated by a fracability of approximately

398

0.7, which is equal to the value of used by Jin et al. (2015) for the Barnett Shale.

399

The statistical data of the shale oil wells show that the mobile oil content of the Es3L in the

400

Bonan Sag ranges from 0 to 30 mg/g, and the fracability ranges from 0.4 to 0.9, i.e, Mmin and Mmax

401

in the Equation (2) are 0 and 30, Fmin and Fmax in the Equation (3) are 0.4 and 0.9, respectively. As

402

shown in Figure 12, when the mobile oil content reaches 5 mg/g and the reservoir fracability

403

reaches 0.7, the shale oil productivity increases and the SSI value calculated by Equation (1) is 0.1,

404

which equal to the lower limit value of the SSI for the favorable shale oil section (Figure 11).

405

Therefore, the geological meaning of the lower limit value of the SSI of 0.1 in the Bonan Sag can

406

be interpreted that a minimum of 5 mg/g mobile oil and a fracability index greater than 0.7 are

407

required to qualify as a sweet spot for a shale oil reservoir.

408

4. Examples and discussion

409

The following section uses the experimental core data and log data from the various shale oil

410

wells of the Bonan Sag to select shale oil sweet spots using the SSI method. Many of the examples

411

in this study are combined with actual oil production and compared with the method proposed by

412

Yang et al. (2015) using the overlapping parameters, thereby demonstrating the effectiveness of

413

the SSI method in shale oil sweet spots selection.

414

4.1 Well Luo 69

415

The first example is well Luo 69, which was systematically cored from 2911.00 to 3140.75 m

416

(9550 to 10,304 ft) in the Es3L and provided good basic data for the exploration and development

417

of shale oil in the Bonan Sag. However, the production testing on the interval from 3040 to 3066

418

m (9974 to 10,059 ft) yielded only 0.85 tons (6.2 bbl) of oil per day with a density of 0.89 g/cm3

419

(Tables 1 and 2).

420

The vertical distribution of the oiliness, fracability, SSI, etc. of Es3L of well Luo 69 have

421

been predicted using conventional logs, as shown in Figure 13. Tracks 1 and 2 are the main well

422

logging curves used for the organic and inorganic evaluation. Track 3 is the depth and track 4 is

423

the lithology. Tracks 5 and 6 are the logging evaluation results of the TOC and S1, which are

424

consistent well with the measured values (black dots). Tracks 7 and 8 are the porosity and oil

425

saturation from the experiments. Track 9 contains total oil and mobile oil. The total oil content is

426

the S1 value after the recovery of light and heavy hydrocarbons, the mobile oil content is obtained

427

by subtracting the amount of adsorbed oil from the total oil content, and the intervals with the

428

mobile oil content greater than 5 mg/g are filled with a yellow color. Tracks 10-12 are the logging

429

evaluation results of the minerals, such as clay minerals, siliceous minerals and calcareous

430

minerals, which are very consistent with the measured values (black dots). Track 13 is the Young’s

431

modulus and track 14 is the brittleness index calculated from the brittleness minerals content.

432

Track 15 is the fracability estimated using the combination of the brittleness index and the

433

Young’s modulus, and the intervals with the fracability greater than 0.7 are filled with a yellow

434

color. Track 16 is the vertical distribution of the SSI based on the mobile oil content and the

435

fracability of the shale reservoirs, and the intervals with the SSI greater than 0.1 are filled with a

436

yellow color in this track. Track 17 shows the sweet spots selected by the previous method

437

proposed by Yang et al. (2015) using the superposition of TOC, S1, porosity, oil saturation and

438

brittleness index. Track 18 shows the sweet spots selected by the SSI method proposed in this

439

study.

440

There are almost no depth intervals with the SSI greater than 0.1 in the Es3L of well Luo 69

441

(the thickness of the intervals at 3043 m (9983 ft) and 3059 m (10,036 ft) with the SSI greater than

442

0.1 are too thin to be considered in this study), therefore, nothing was shown in the track 18 of

443

Figure 13. The area enclosed by the red box from 3040 to 3066 m (9974 to 10,059 ft) is the oil

444

testing interval, although this interval has the characteristics of moderate maturity (Ro range from

445

0.7% to 0.93% in Es3L, with an average value of 0.8% (Lu et al., 2017; Wang et al., 2013)), high

446

organic matter, porosity, oil saturation, and brittleness (Table 4). The average values of the mobile

447

oil content and reservoir fracability in this interval are only 3.94 mg/g and 0.68, respectively. The

448

values of the SSI in this oil production test interval are generally less than 0.1, indicating that this

449

interval is not an appropriate shale oil sweet spot of well Luo 69, as shown by the oil production

450

testing results with low oil production of 0.85 tons per day.

451

Using the previous methods based on the combination of TOC, S1, porosity, oil saturation,

452

and brittleness index (Yang et al., 2015; Zou et al., 2013) suggests that interval from 2990 to 3066

453

m (9809 to 10,059 ft) as a sweet spot. The standard parameters used in previous methods are

454

shown in Table 5. The intervals of each parameter used by the previous method beyond the

455

standards are filled with peach color, as shown in Figure 13. It is evident that the sweet spots

456

selected by the previous methods in Es3L of Luo 69 are not consistent with the actual oil testing

457

results.

458

4.2 Well Luo 67

459

The second example is well Luo 67. Its location is shown in Figure 2. As a low oil production

460

well in the Bonan Sag, well Luo 67 yielded 2.1 tons (15.4 bbl) of oil per day with a density of 0.91

461

g/cm3 in the interval from 3287 to 3310 m (10,784 to 10,859 ft) (Table 1). Unlike well Luo 69, as

462

the coring was limited, therefore, the analysis for TOC, pyrolysis, porosity, oil saturation and

463

mineral compositions of this well were only carried on several samples in the Es3L. For this

464

reason, we used the organic and inorganic logging evaluation models established by well Luo 69

465

to calculate the TOC, S1 and mineral contents, and validated the results with measured values from

466

core data of well Luo 67. The vertical distribution of the oiliness, fracability, and SSI in Es3L of

467

well Luo 67 have been predicted using the conventional logs, as shown in Figure 14. The curves

468

of tracks 5 and 6 are the logging evaluation results of the TOC and S1 based on the organic

469

logging evaluation models established by the data of well Luo 69, and the curves of tracks 8-10

470

are the logging evaluation results of each mineral group based on the inorganic logging evaluation

471

models. The vertical distribution of the predicted values match well with the measured values,

472

indicating that the organic and inorganic logging evaluation models established by well Luo 69 are

473

reliable and can be applied to the other wells in the study area.

474

The abundance of organic matter (oiliness) and reservoir fracability calculated from

475

conventional logs shows a decrease with the depth increase in Es3L of well Luo 67. There exists

476

an oiliness peak at the interval from 3300 to 3310 m (10,826 to 10,859 ft) from the measured

477

values of cuttings, and this peak is located in the range of the oil testing interval from 3287 to

478

3310 m (10,784 to 10,859 ft) (the area enclosed by the red box in Figure 14). Compared with the

479

oil testing interval of well Luo 69, although well Luo 67 has higher mobile oil content (with an

480

average value of 6.2 mg/g), the oil production of well Luo 67 is also low after fracturing. The

481

reason for this may be interpreted from the fracability calculated from the conventional logs,

482

which ranges from 0.53 to 0.77, with an average value of 0.63, which is smaller than the lower

483

limit value of 0.7 and indicates poor fracability.

484

The depth intervals with the SSI greater than 0.1 in the Es3L of well Luo 67 range from 3178

485

to 3230 m (10,426 to 10,597 ft) and from 3302 to 3306 m (10,833 to 10,846 ft). Since the

486

thickness of the interval from 3302 to 3306 m is too thin to economically produce oil, we only

487

take the interval from 3178 to 3230 m as the sweet spot in Es3L of well Luo 67 (Track 16 of

488

Figure 14).

489

Track 15 in Figure 14 shows the three sweet spots of well Luo 67 selected by the method

490

proposed by Yang et al. (2015) using the superposition of the TOC, S1 and brittleness index. The

491

first sweet spot is located from 3170 to 3230 m (10,400 to 10,597 ft), which is roughly consistent

492

with the results of the SSI method proposed in this paper. The second and third sections are

493

located in the intervals from 3264 to 3277 m (10,708 to 10,751 ft) and the intervals from 3299 to

494

3312 m (10,823 to 10,866 ft), respectively, which are not selected by the SSI method. Although

495

the oiliness (TOC and S1) and brittleness index in the second and the third sections are greater

496

than the values of Yang’s method (Yang et al., 2015), the low shale oil mobility characters of the

497

second section (mobile oil content less than 5 mg/g) and the poor fracability characters of the third

498

section (average value of fracability is 0.69) suggests that these two intervals are not suitable

499

sweet spots of well Luo 67. The oil productivity of the third section (Table 1) in well Luo 67 also

500

supports this view.

501

4.3 Well Xinyishen 9

502

The third example is applied on well Xinyishen 9. Unlike wells Luo 69 and Luo 67, well

503

Xinyishen 9 is a commercial oil well with high oil production of 38.5 tons (283 bbl) per day in

504

Es3L, and the accumulated oil production has reached 13,164 tons (96,755 bbl) (Wang et al.,

505

2013). The location of well Xinyishen 9 is shown in Figure 2.

506

The organic and inorganic logging evaluation models of well Luo 69 were used to understand

507

the oiliness and reservoir fracability, and to predict the vertical distribution of the SSI of well

508

Xinyishen 9, as shown in Figure 15. The interval from 3388 to 3405 m (11,115 to 11,171 ft) is

509

perforated for oil production (the area of red box circled in Figure 15), although this interval does

510

not have accompanying core analysis. The logging evaluation results indicate that this interval is

511

characterized by high oil abundance of organic matter, high mobility, and can easily be fractured

512

(Table 4). The SSI ranges from 0.08 to 0.31 with an average value of 0.17, suggesting that this

513

interval is a true sweet spot in Es3L for well Xinyishen 9. The result of the oil testing production

514

in this interval is 38 tons per day, which provides sufficient evidence to test the SSI method for

515

shale oil sweet spots prediction. In addition, the values of the mobile oil content, fracability, SSI,

516

and the thickness of the oil tested intervals are much larger than those of well Luo 67 (Figure 14),

517

resulting in a comparatively higher productivity for well Xinyishen 9. The other interval with SSI

518

greater than 0.1 in Es3L for well Xinyishen 9 ranges from 3361 to 3384 m (11,027 to 11,102 ft),

519

which could also be considered as a shale oil sweet spot for well Xinyishen 9, as shown in track

520

16 of Figure 15.

521

The interval from 3361 to 3424 m (11,027 to 11,233 ft) can be selected as the shale oil sweet

522

spot of well Xinyishen 9 based on the Yang’s method (Yang et al., 2015) (track 15 of Figure 15).

523

Although this zones is characterized by high oil production, two sections, such as the interval from

524

3384 to 3388 m (11,102 to 11,115 ft) and the interval from 3407 to 3424 m (11,178 to 11,233 ft)

525

(the areas enclosed by the blue boxes in Figure 15), indicate low shale oil mobility (mobile oil

526

content is only 4.1 mg/g on average) and relative low shale fracability, suggesting that these zones

527

are not good quality sweet spots.

528

Based on the above analysis, the shale oil mobility and the shale reservoir fracability are two

529

critical factors that must be taken into account for the prediction of shale oil sweet spots. As a

530

novel approach proposed in this study, the SSI method is suggested to be feasible for the

531

identification of shale oil sweet spots, and has the following advantages compared with previous

532

methods: (1) It combines the properties of shale oil mobility and shale reservoir fracability and

533

eliminates the instances of only organic matter sweet spots or inorganic sweet spots. (2) It has

534

better consistency with the actual output of shale oil. (3) With the advantage of high-resolution

535

logging (i.e., FMI provides data at much finer scales, such as 0.0254m per sample), the SSI

536

method can accurately evaluate the vertical distribution of shale oil sweet spots even if there is no

537

core analysis data available.

538

It should be noted that the method for selecting sweet spots proposed in this study is from the

539

perspective of mobile oil content and reservoir fracability. However, as lacustrine shales often

540

generate polar-rich petroleum with high molecular weight waxes, oil flow can be hindered even in

541

highly stimulated wells. Other factors, such as oil quality, reservoir pressure, gas-to-oil ratio (GOR)

542

etc. are also important for the shale oil production. Therefore, during the entire process of risking

543

targets, various geological characteristics or parameters should be considered comprehensively. In

544

addition, gas injection, in situ formation heating, microwave, and other technologies could be

545

adopted to highly help move the petroleum out of the source rock after reservoir stimulation.

546

5. Conclusion

547

This paper proposed a new method (SSI) that uses a combination of shale oil mobility and shale

548

reservoir fracability to predict the sweet spots of shale oil using conventional logs.

549

The SSI is obtained by the product of the normalized mobility and the fracability index. The

550

higher the mobile oil content and fracability, the higher SSI value will be, and the better the

551

reservoir properties will be. The new method avoids the situation where just an organic matter

552

sweet spot or an inorganic sweet spot is defined. Together, with the advantage of a conventional

553

logging suite, this method can predict the vertical distribution of sweet spots even if there is no

554

core data or special logging data.

555

The SSI method was successfully applied to the sweet spots prediction of the Es3L lacustrine

556

shale in the Bonan Sag, Bohai Bay Basin, China. The organic and inorganic logging evaluation

557

models have been established by the improved ∆logR method and the BP neural network based on

558

the use of the core measurements and conventional logs of well Luo 69 to predict the oiliness and

559

fracability, and the method were verified by well Luo 67 and well Xinyishen 9. The lower limit

560

value of the SSI used to define the sweet spots was set to 0.1 through its correlation with the shale

561

oil production. Three wells of the Bonan Sag have verified the effectiveness, reliability and

562

adaptability of the SSI method in the prediction of sweet spots using conventional logs.

563

Acknowledgements

564

This study was funded by the National Natural Science Foundation of China (no. 41922015,

565

41672116),

566

2017ZX05049-004-003), and the Fundamental Research Funds for the Central Universities (no.

567

17CX02057 and no. 18CX06031A). The authors also thank Dan Jarvie for his valuable advice and

568

comments that improved this paper.

569

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Figure captions

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Figure 1. Workflow of shale oil sweet spots evaluations. TOC = total organic carbon; S1 = volatile hydrocarbon content; SSI = sweet spot index Figure 2. (A) Location of the Zhanhua Sag in China. (B) Location of the Bonan Sag and wells distribution (modified after Wang et al., 2015). Figure 3. Schematic diagram of the stepwise heating pyrolysis procedure. The red line represents the temperature; the blue line represents the signal intensity. Figure 4. (A) Crossplot of total organic carbon (TOC) from core analysis vs. ∆logRTOC. (B) Crossplot of volatile hydrocarbon content (S1) from core analysis vs. ∆logRS1. R2 = correlation coefficient; y= TOC or S1; x = ∆logRTOC or ∆logRS1. Figure 5. Crossplot of bitumen “A” vs. S1. S1 = volatile hydrocarbon content; R2 = correlation coefficient; x = S1; y = chloroform asphalt “A”. Figure 6. Relationships between light hydrocarbon recovery coefficient (KL) and (A) maturity (Ro) and (B) depth (modified after Zhu et al., 2015). Figure 7. (A) Crossplot of adsorbed oil content vs. heavy oil content (S2-S2’). (B) Crossplot of adsorbed oil content vs. total organic carbon (TOC). R2 = correlation coefficient; x = TOC or heavy oil; y = adsorbed oil content. Figure 8. Crossplot of measured values vs. predicted values. (A) Training and validating samples of clay minerals. (B) Testing samples of clay minerals. (C) Training and validating samples of siliceous minerals. (D) Testing samples of siliceous minerals. (E) Training and validating samples of calcareous minerals. (F) Testing samples of calcareous minerals. x = predicted values; y = measured values. Figure 9. Relationship between the transverse wave logging and longitudinal wave logging. x = longitudinal wave; y = transverse wave. Figure 10. Evaluation model for shale reservoir fracability. The colors represent the value of the fracability index. Figure 11. Crossplot of shale oil productivity vs. the SSI in the Bonan Sag. Shale oil productivity presents the daily output per meter of the testing interval; the pink dotted line represents the trend. Figure 12. (A) Crossplot of shale oil productivity vs. mobile oil content in the Bonan Sag. (B) Crossplot of shale oil productivity vs. fracability index in the Bonan Sag. Figure 13. Prediction of the vertical distribution of shale oil sweet spots in Es3L of well Luo 69 in the Bonan Sag. RT = resistivity log; DEN = density log; AC = acoustic log; GR = gamma ray log; TOC = total organic carbon; S1 = volatile hydrocarbon content; BI = brittleness index; FI = fracability index; SSI = sweet spot index; SS1 = the sweet spots selected by the previous method proposed by Yang et al. (2015); SS2 = the sweet spots selected by the SSI method; the area enclosed by the red box is the oil testing interval. Figure 14. Prediction of the vertical distribution of shale oil sweet spots in Es3L of well Luo 67, in the Bonan Sag. RT = resistivity log; DEN = density log; AC = acoustic log; GR = gamma ray log; TOC = total organic carbon; S1 = volatile hydrocarbon content; BI = brittleness index; FI = fracability index; SSI = sweet spot index; SS2 = the sweet spots selected by the SSI method; the area enclosed by the red box is the oil testing interval. Figure 15. Prediction of the vertical distribution of shale oil sweet spots in Es3L of well Xinyishen 9, Bonan Sag. RT = resistivity log; DEN = density log; AC = acoustic log; GR = gamma ray log; TOC = total organic carbon; S1 = volatile hydrocarbon content; BI = brittleness index; FI = fracability index; SSI = sweet spot index; SS2 = the sweet spots selected by the SSI method. The area enclosed by the red box is the oil testing interval. Table 1. Well Oil Production of Es3L in Bonan Sag Table 2. Comparison of Shale Oil Densities from Different Locations Table 3. Correlations between Mineral Groups and Log Data Table 4. Characteristics of the Testing Oil Intervals of Well Luo 69, Well Luo 67 and Well Xinyishen 9 in the Bonan Sag Table 5. Lower Limit Values for the Shale Oil Sweet Spots Selection of the Previous Methods

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Figure 1. Workflow of shale oil sweet spots evaluations. TOC = total organic carbon; S1 = volatile hydrocarbon content; SSI = sweet spot index.

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Figure 2. (A) Location of the Zhanhua Sag in China. (B) Location of the Bonan Sag and wells distribution (modified after Wang et al., 2015).

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Figure 3. Schematic diagram of the stepwise heating pyrolysis procedure. The red line represents the temperature; the blue line represents the signal intensity. FID = flame ionization detector.

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Figure 4. (A) Crossplot of total organic carbon (TOC) from core analysis vs. ∆logRTOC. (B) Crossplot of volatile hydrocarbon content (S1) from core analysis vs. ∆logRS1. R = correlation coefficient; y= TOC or S1; x = ∆logRTOC or ∆logRS1.

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Figure 5. Crossplot of bitumen “A” vs. S1. S1 = volatile hydrocarbon content; R = correlation coefficient; x = S1; y = bitumen “A”.

820 821 822

Figure 6. Relationships between light hydrocarbon recovery coefficient (KL) and (A) maturity (Ro) and (B) depth (modified after Zhu et al., 2015).

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Figure 7. (A) Crossplot of adsorbed oil content vs. heavy oil content (S2-S2’). (B) Crossplot of adsorbed oil content vs. total organic carbon (TOC). R = correlation coefficient; x = TOC or heavy oil; y = adsorbed oil content.

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Figure 8. Crossplot of measured values vs. predicted values. (A) Training and validating samples of clay minerals. (B) Testing samples of clay minerals. (C) Training and validating samples of siliceous minerals. (D) Testing samples of siliceous minerals. (E) Training and validating samples of calcareous minerals. (F) Testing samples of calcareous minerals. x = predicted values; y = measured values.

833 834 835

Figure 9. Relationship between the transverse wave logging and longitudinal wave logging. x = longitudinal wave; y = transverse wave.

836 837 838

Figure 10. Evaluation model for shale reservoir fracability. The colors represent the value of the fracability index.

839 840 841

Figure 11. Crossplot of shale oil productivity vs. the SSI in the Bonan Sag. Shale oil productivity presents the daily output per meter of the testing interval; the pink dotted line represents the trend.

842 843 844

Figure 12. (A) Crossplot of shale oil productivity vs. mobile oil content in the Bonan Sag. (B) Crossplot of shale oil productivity vs. fracability index in the Bonan Sag.

845 846 847 848 849 850 851

Figure 13. Prediction of the vertical distribution of shale oil sweet spots in Es3L of well Luo 69 in the Bonan Sag. RT = resistivity log; DEN = density log; AC = acoustic log; GR = gamma ray log; TOC = total organic carbon; S1 = volatile hydrocarbon content; BI = brittleness index; FI = fracability index; SSI = sweet spot index; SS1 = the sweet spots selected by the previous method proposed by Yang et al. (2015); SS2 = the sweet spots selected by the SSI method; the area enclosed by the red box is the oil testing interval.

852 853 854 855 856 857

Figure 14. Prediction of the vertical distribution of shale oil sweet spots in Es3L of well Luo 67, in the Bonan Sag. RT = resistivity log; DEN = density log; AC = acoustic log; GR = gamma ray log; TOC = total organic carbon; S1 = volatile hydrocarbon content; BI = brittleness index; FI = fracability index; SSI = sweet spot index; SS2 = the sweet spots selected by the SSI method; the area enclosed by the red box is the oil testing interval.

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Figure 15. Prediction of the vertical distribution of shale oil sweet spots in Es3L of well Xinyishen 9, Bonan Sag. RT = resistivity log; DEN = density log; AC = acoustic log; GR = gamma ray log; TOC = total organic carbon; S1 = volatile hydrocarbon content; BI = brittleness index; FI = fracability index; SSI = sweet spot index; SS2 = the sweet spots selected by the SSI method. The area enclosed by the red box is the oil testing interval.

864

865 866

Table 1. Well Oil Production of Es3L in Bonan Sag Well name

Interval

Top depth (m [ft])

Bottom depth(m [ft])

Oil Rate(t/day [b/day])

Gas Rate(t/day [b/day])

Method

Thickness (m [ft])

Luo 69 Yi 142 Yi 283 Xinyishen 9 Yi 172 Bogu 4 Luo 67 Yi 97 Luo 19 Luo 42

Es3L Es3L Es3L Es3L Es3L Es3L Es3L Es3L Es3L Es3L

3040 (9974) 2901.5 (9519) 3646.15 (11962) 3355.11 (11007) 3230.4 (10598) 3913.2 (12838) 3287 (10784) 2977 (9767) 2936 (9632) 2828.13 (9279)

3066 (10059) 2908.3 (9542) 3700 (12139) 3435.29 (11270) 3252 (10669) 3926.3 (12881) 3310 (10859) 3016 (9895) 2962 (9718) 2861 (9386)

0.85 (6.2) 7 (51.5) 10.2 (75) 38.5 (283) 0.2 (1.2) 1.6 (11.4) 2.1 (15.4) 23.1 (169.8) 43.5 (319.7) 79.9 (587.3)

nd nd nd 870 (30720) nd nd nd nd nd 7750 (273653)

Testing Testing Fracturing Flowing Testing Testing Acidification Swabbing Acidification Flowing

26 (85.3) 5.7 (18.7) 53.85 (176.7) 80 (262.5) 11.5 (37.7) 6.2 (20.3) 17 (55.8) 18 (59.1) 26 (85.3) 32.9 (107.8)

Each well location is described in Figure 2. Abbreviations: Es3L = lower sub member of the third member of the Eocene Shahejie Formation; nd = no data available.

867

868 869

Table 2. Comparison of Shale Oil Densities from Different Locations Type

Density (g/cm3)

Gravity (API)

Location

Shale oil Shale oil Shale oil Shale oil Shale oil

0.89 0.87 0.91 0.84 0.78-0.88

27 31 24 36 30-50

Well Luo 69, Es3L shale of Jiyang Depression, China Well Xinyishen 9, Es3L shale of Jiyang Depression, China Well Luo 67, Es3L shale of Jiyang Depression, China Well 3-Mitcham, Barnett Shale of Fort Worth Basin, USA Eagle Ford Shale

References: (Jarvie, 2012)and (Zanganeh et al., 2015). Abbreviation: Es3L = lower sub member of the third member of the Eocene Shahejie Formation.

870

Table 3. Correlations between Mineral Groups and Log Data AC (us/ft)

DEN (g/cm3)

Clay minerals

0.53

-0.55

Siliceous minerals

0.44

Calcareous minerals

-0.53

Groups

871 872

CNL (%)

GR (API)

RT (Ω·m)

CAL (IN)

PE (b/e)

Sample numbers

0.71

0.65

-0.46

0.12

-0.13

353

-0.46

0.56

0.53

0.11

-0.04

353

0.55

-0.69

-0.64

-0.15

0.12

353

-0.15 0.23

The band marked with is significantly correlated at the 0.01 level Abbreviations: AC = acoustic log; DEN = density log; CNL = neutron log; GR = gamma ray log; RT = resistivity log; CAL = caliper log; PE = photo electricity.

873

Table 4. Characteristics of the Testing Oil Intervals of Well Luo 69, Well Luo 67 and Well Xinyishen 9 in the Bonan Sag Well name

Luo 69 Luo 67 Xinyishen 9 874 875

Testing oil Interval (m [ft])

TOC (%)

S1 (mg/g)

Porosity (%)

OS (%)

BI

3040-3046 (9974-10059) 3287-3310 (10784-10859) 3388-3405 (11115-11171)

1.48-7.52 (3.83) 1.14-4.14 (2.37) 1.50-5.21 (3.39)

0.40-6.18 (2.61) 1.01-4.03 (2.29) 2.70-7.40 (5.00)

3.50-15.30 (7.10)

65.90-96.40 (87.80)

-

-

-

-

0.64-0.94 (0.81) 0.72-0.92 (0.82) 0.72-0.92 (0.83)

MC

(mg/g)

0.03-23.16 (3.94) 2.5-10.8 (6.15) 4.6-12.32 (6.52)

FI

SSI

0.56-0.93 (0.68) 0.53-0.78 (0.67) 0.49-0.91 (0.72)

0-0.59 (0.07 0.05-0.2 (0.09 0.08-0.31 (0.13

Abbreviations: OS = oil saturation; BI = brittleness index; MC = mobile oil content; FI = fracability index; SSI = sweet spot index; A-B/C = A represents the minimum value, B represents the maximum value, C represents the average value.

876

877 878

Table 5. Lower Limit Values for the Shale Oil Sweet Spots Selection of the Previous Methods Parameters

Ro %

TOC %

S1 mg/g

Porosity %

OS %

BI

standards

>0.8

>2

>2

>3

>60

>0.7

References: Yang et al. (2015), and Zou et al. (2013). Abbreviations: Ro = Vitrinite reflectance; TOC = total organic carbon; S1 = volatile hydrocarbon content; OS = oil saturation; BI = brittleness index.

1.

The organic and inorganic heterogeneous evaluation methods were established by conventional logs and validated by measured core data.

2.

An adsorbed oil model was established using the stepwise heating pyrolysis experiment.

3.

A new method called sweet spot index (SSI) model was developed by the combination of shale oil mobility and shale reservoir fracability.

4.

The lower limit value of the SSI was set to 0.1 based on the shale oil production data in Bonan Sag.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: