Electromagnetic technology for prospecting unconventional


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Yan L., 2022,Unconventional Oil and Gas exploration with EM methods

Electromagnetic technology for prospecting unconventional hydrocarbon resources
Liangjun Yan1,2,3 1Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of
Education, China, 430100. [email protected] 2Cooperative innovation center of Unconventional oil and gas, Hubei, China
3Key Laboratory of Geophysical prospecting, CNPC

SUMMARY
Unconventional oil and gas (normally tight gas and oil, shale gas and oil, coal seam gas, natural gas hydrate, etc.), which generally have the characteristics of source-reservoir symbiosis, are widely distributed. Still, their high-quality reservoirs have large burial depths, small targets, complex electrical properties, and considerable inhomogeneity, making their a great challenge for electromagnetic (EM) exploration to detect them. In recent decade, A series of achievements have been made in the field of EM exploration of unconventional oil and gas worldwide, including the EM response mechanism of unconventional reservoir rocks, new methods and technologies in controlled-source EM exploration on land, identification and evaluation methods for oil and gas using EM parameters. These technologies have been successfully applied in unconventional oil and gas exploration and development with good effect and have been recognized by petroleum geology and development circles. Firstly, this paper introduced the complex resistivity characteristics of organic-rich shale, tight sandstone, and dolomite in southern China occurring in low porosity and permeability reservoirs, and discovered that organic-rich shale has the characteristics of low resistivity and high polarization. At next, an IP model for the shale reservoirs is established. Based on the mechanism of source-reservoir symbiosis in shale reservoirs, the identification mode for the sweet spot is proposed. It is then proved this paper that there exist a good petrophysics foundation for the EM exploration in the field of shale gas exploration and development. Secondly, there has been a research focus on how to use the multiple IP parameters, such as resistivity and polarizability, to estimate the characteristic parameters of the sweet spot of reservoir. The prediction method based on IP model for the parameters of reservoir is introduced. Thirdly, there is an outspring of the controlledsource EM exploration methods for unconventional oil and gas exploration, such as Wide Field Electromagnetic Method (WFEM), Wire-less Electromagnetic Method (WEM), Time-Frequency Electromagnetic Method (TFEM), Long Offset and Window Transient Electromagnetic Method (LowTEM) and Focused Source Electromagnetic Method (FSEM). These methods and technologies share a common feature of using a long wire source, with high-power, large current multi-waveform transmission, multi-component array acquisition and hybrid processing and inversion. Therefore, not only the signal-to-noise ratio, exploration depth, resolution and reliability, but also the efficiency, resolution, cost and adaptability have been significantly improved, making these methods able to deal with the geology problems in the exploration and development of unconventional oil and gas under complex conditions. Finally, several cases are given to indicate the apparent application effects of the new methods and technologies of controlled-source EM method in unconventional oil and gas exploration, sweet spot detection, fluid identification, fracturing monitoring, and at the same time, look into the broad application prospect of EM methods in the exploration and development of unconventional oil and gas. However, there are still plenty of unsuccessful cases. It is a long way to go in the effective application of EM methods in unconventional oil and gas exploration and development. Therefore, more achievements are expected to be made, especially on the EM response mechanism of unconventional oil and gas reservoirs, 3D high spatial and temporal density data collection technology under complex geological and topographical conditions, fast, stable and reliable high-precision inversion and imaging methods with constraints based on prior logging and seismic data, reservoir parameters prediction method based on refined IP model. Due to the limitation of the author's ability and the paper’s length, the new methods, technologies, and application cases referred to in this paper are mainly from China.
Keywords: Unconventional oil and gas, Resistivity, Chargeability, Sweet spot testing, Controlled source EM

INTRODUCTION Due to the greenhouse effect and environmental

concerns, human’s yearn for clean energy has been inspired. However, the conventional oil and gas resources shortage has been a global headache in

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the Post-Petroleum Era, therefore, the exploitation and development of unconventional oil and gas reserves are gaining more and more attentions worldwide among governments and energy companies. Unconventional oil and gas resources including tight gas and oil, shale gas and oil, heavy oil, coal seam gas, and natural gas hydrate, are significant parts of the global energy structure, which occupy about 80% of the total oil and gas reserves on the earth. The successful development of unconventional oil and gas resources, especially shale gas and tight sandstone gas in the United States (US) has enabled the country to become self-sufficient in natural gas for the first time in the last over 40 years. In 2009, the output of unconventional natural gas in the US first exceeded that of conventional natural gas. Since then, the US turned into an important shale gas exporter and the supply pattern of the global natural gas market has changed. China is the world's second-largest energy consumer, and its unconventional oil and gas reserves is equivalent to the US. To meet the social needs and to reduce the energy dependency, China has accelerated the research on unconventional oil and gas related geological theories, exploration technologies, development methods and technical equipment. So far, many advances have been made and huge achievements have been achieved. With the continuous rising of unconventional oil and gas output, the national energy structure is considerably improved.
1.1 Geological and geophysical characteristics of unconventional oil and gas reservoirs
● Geological characteristics
Unconventional oil and gas resources are different kinds of oil/gas reserves that cannot be explored and developed simply with regular technologies and methods. Generally, the reservoir forming conditions of unconventional oil and gas resources are less demanding than those of conventional oil and gas resources, thus, unconventional reservoirs are more common and their occurrence modes are more diverse, Unconventional reservoirs possess the following geological characteristics (Zou, 2015): (a) source-reservoir symbiosis; (b) large distribution area, deep burial depth, and blurred boundaries; (c) tight, poor physical properties, low porosity, low permeability, extensive nanoscale cracks, and strong heterogeneity; (d) lithological diversity (sandstone, limestone, shale, coal and migmatite, etc.), small effective reservoir; (e) rich in organic matter, high maturity, poor phase segregation, no unified boundaries of oil, gas, and water. ● Geophysical characteristics
Reservoir rocks containing oil and gas usually

possess the characteristics of high resistivity and high polarization (Zonge,1975; Lima and Sharma, 1992; Sigel et al., 2007; Davydycheva et al., 2006; Burtman, 2015; Hu 2022). He and Wang (2007) proposed the ‘Ring three storey’ model for oil and gas identification based on EM and IP parameters. They confirmed that oil-gas reservoirs were of high resistivity and high polarization, while the waterbearing reservoirs were generally of low resistivity and high polarization. Based on the spatial resistivity and polarization differences, oil, gas and water could be easily and effectively identified. With this model, numerous favorable geological results have been obtained by applying the EM method in oil and gas exploration (He, 2005; Davydycheva, 2006; He, 2010). However, the differences in geological characteristics make the electrical features of unconventional reservoirs noticeably different. Unconventional reservoirs are mainly shale, mud shale, tight sandstone, and mudstone, which present the features of low resistance, low permeability and low polarization. A great quantity of petrophysical experiments indicated that shale gas reservoirs in southern China generally demonstrated the characteristics of low density, low speed, high resistance, and low magnetism (Wang, 2015). By conducting substantive complex resistivity measurements, Yan et al. (2014) and Xiang et al. (2014) found that richorganic shale in southern China had the characteristics of low resistivity and high polarization. Similar results were obtained by Burtman (2014) using TerraTek shale rock samples. Similarly, changes in electrical characteristics are also noticeable during thermal flooding and hydraulic fracturing process. For example, when SAGD technique is applied, steam chambers and possible steam channeling-paths may form, and phase-transformation zones would form surround the steam-injection wells. The resistivity variations within different phase-transformation zones can provide physical-property foundation to identify the steam-flooding front. (Yang et al. 2005; Hu, 2022). As for hydraulic fracturing in shale gas reservoir, low resistance and high polarization anomalous body would form when thousands of tons of fracturing fluid are injected into the reservoir (Chen, 2000). Therefore, EM method can be used to monitor the spatial distribution of fracturing fluid and shed light on fracturing optimization.
1.2 Exploration features of unconventional oil and gas
Unconventional reservoirs demonstrate different source, lithology and physical properties, as well as hydrocarbon conditions from conventional reservoirs, thus, the exploration methods, development and evaluation techniques can be totally different. To explore the unconventional oil

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and gas reservoirs, there are three stages: firstly, screen out the core area based on the high-quality reservoir evaluation criteria; secondly, identify the ‘Sweet Spots Area (Segment)’ based on the reservoir integral/local tectonic characteristics, and fault and microfracture features; finally, drilling, production and ‘Sweet Spots Area (Segment)’ evaluation. Compared with conventional oil and gas, there are three changes in unconventional reservoirs exploration (Zou, 2015), one is from ‘finding oil in trap’ to ‘finding oil in the strata’, second is from ‘oil/gas reservoir’ to ‘oil/gas layer’, and third is from ‘Sweet Spot’ to ‘Sweet Spot Area (Segment)’. The ‘Sweet Spot Area (Segment)’ of unconventional reservoirs refers to as the source-reservoir symbiosis development area, which is an unconventional hydrocarbon enrichment area with large distribution range, specific thickness, highquality source rock, good physical properties, high oil/gas saturation, high formation energy (i.e., high gas-oil ratio, high formation pressure), high brittleness index, rich fracture and local structure developed. Evaluation and optimization of “Sweet Spots Area (Segment)” is the key of unconventional oil and gas exploration and development, which runs through the whole exploration and development process. The evaluation parameters slightly vary with different unconventional reservoirs. The main evaluation parameters include TOC, porosity, the development degree of microfractures, brittleness, range (burial depth and distribution area), etc. (Zou et al., 2015; Yang, 2019). To improve the economic recovery efficiency of unconventional reservoir, it is necessary to improve reservoir permeability or fluid viscosity by horizontal drilling, fracturing, and other techniques to guarantee the continuous or quasi continuous accumulation of oil/gas resources. According to the specific geological problems and electrical characteristics in the three stages of unconventional oil and gas exploration, EM exploration technology can give full play to the advantages to achieve the exploration, detection and evaluation of sweet spots.
1.3 Opportunities and challenges of EM
exploration technologies
Due to the limitation of inherent method and theory, EM exploration is far less important than seismic exploration in oil and gas exploration, and only applied in the early stage of oil and gas exploration in basin survey. In unconventional reservoirs, the difference of seismic wave impedance between oil, gas and water is small, which makes seismic exploration difficult (Yuan, 2013). However, the difference of resistivity and dielectric constant between oil, gas and water is very large, and electromagnetic method has superior conditions for fluid identification and sweet spot detection (Yan,

2014). The high-power EM method can capture the high-resolution anomaly field distribution caused by resistivity and polarizability differences. When combined with seismic exploration methods, the distribution of ‘Sweet Spots Area (Segment)’, residual oil distribution, spatial distribution of steam or water injected can be elaborately described, and the reservoir permeability and oil/gas saturation can be accurately predicted. With the development of unconventional and ultra-deep oil and gas exploration, the cost of seismic exploration is increasing and the exploration is becoming more and more difficult. Electromagnetic exploration methods are facing great opportunities as well as severe challenges. ⚫ Inherent limitations of EM methods
The essential difference between EM and seismic methods lies indifferent field properties. Seismic wave field belongs to the wave field, while electromagnetic field belongs to the diffusion field. Low-frequency EM methods are generally used for oil and gas exploration, which can be easily affected by volume effect, static offset, field source effect, overprint effect, shadow effect, etc. Fortunately, the development of high-precision 3D EM modeling technique has provided an effective tool to mitigate these adverse effects. For instance, the joint inversion of multiple field sources can effectively suppress the field source effect (He, 2019). Fictitious wave transformation for transient electromagnetic field improves the interpretation reliability and resolution (Chen, 1999; Li, 2005; Mittet, 2015, 2018; Stoffa et al. 2018). Full domain, full coverage, full waveform field, and uniform illumination three-dimensional EM data acquisition and processing, special wave field transformation and inversion imaging techniques are the keys to overcome the limitations of the EM exploration methods. ● EM response mechanism of reservoir rocks
EM response mechanism is closely related to the reservoir physical properties. The simplified resistivity model based EM methods is now facing great challenges to solve complicate geological problems, and the induced polarization (IP) mechanism of reservoir rocks should be valued ( Kavian, 2012; Fiandaca, 2012). The time and frequency dispersion characteristics caused by the fluids and minerals in reservoir should be comprehensively considered and studied (Burtman, 2014, 2015). Meanwhile, the physical and structural characteristics of the conventional and unconventional oil and gas reservoirs should be distinguished, and an equivalent complex resistivity model comprehensively considering EM induction, IP and other effects should be established (Zhdanov, 2008). Moreover, the foundation of controllable source electromagnetic (CSEM) technology should be established based on the IP mechanism of reservoir rocks. Only by fully utilizing the sensitivity

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of the IP parameters in complex resistivity model can the purpose of precise multi-parameter joint detection of oil and gas be realized. Therefore, it is essential to study the IP mechanism and anomaly extraction method of unconventional oil and gas reservoirs to improve the detection accuracy and the interpretation reliability. It is an inevitable trend in the development of EM exploration to seek IP multi-parameters based CSEM method. ● EM data acquisition
The effectiveness of exploration highly relies on the space-time density and the quality of EM data, and data acquisition technique is the prerequisite and the guarantee for the development of EM exploration methods. Acquisition technology requires a deep combination of methods, instruments, acquisition software, field geology and terrain conditions, geological targets and other factors, through feasibility analysis and simulation test, to develop an effective acquisition scheme. Firstly, In the acquisition method, it is necessary to achieve a substantial breakthrough in 3D acquisition, realize multi-field source coverage and uniform illumination, and synchronize acquisition with hundreds of thousands of instruments to record massive electromagnetic multi-component time series data, so as to meet the requirements of high spatial and temporal density data for high precision inversion. Secondly, high-quality acquisition software capable to conduct noise level analysis, feasibility analysis and data quality evaluation should be developed to improve the exploration efficiency. Finally, portable, intelligent, low-cost, lowpower and low-noise EM multichannel receivers should also be manufactured to simplify the acquisition process. ● EM data processing
Data processing is significant part of the successful application of EM methods. Without a proper data processing technique, no matter how sensitive the instrument, how advanced the data collection techniques, and how high the quality of data, the results obtained will be worthless. Data processing mainly include data preprocessing, attribute parameter definition and extraction. Denoising and correcting is the first step in the data processing. Due to the cultural noise and the signal distortion caused by the method or the geological and topographical factors, targeted de-noising and correction methods are required. Many modern signal processing methods have been adapted to data de-noising, such as the robust estimation, median filtering, wavelet transform, recursive flood wave, and coherent analysis methods (Kass & Li 2011; Streich, 2011; Ji et al. 2016; Rasmussen et al. 2017; Wu, 2021). In contrast, the correction methods, such as the field source correction and static shift correction, are developed based on the characteristics of the methods. Because of the complexity of EM methods and noise, there is no

universal correction and denoising techniques. Attribute parameter extraction is guaranteed to improve the reliability and effect of the inversion and interpretation. Conventional method is to define the apparent resistivity parameter. However, the complicated multidimensional source creates great difficulties in defining the apparent resistivity. Thus, using different field components or combinations of methods to define the attribute parameters by numerical calculations, such as the all-time apparent resistivity and all-time vertical conductivity, oil-water identification factor, differentially normalized quantities, has attracted research interest, and several good application results have been achieved (Yan, 1999; Davydycheva et al., 2006; He, 2015; Xue, 2020; He, 2021). ● EM inversion and interpretation methods
Theoretically, all geophysical inversions are underdetermined problems. It is difficult to infer the information about the abnormal underground targets accurately from a single type of geophysical data. Comprehensive inversion and interpretation by multiple geophysical datasets are becoming a significant research trend. Joint inversion using as many types of prior information as possible is an effective means of reducing the multiplicity of inversion, improving the data utilization, and modifying the inconsistency of the inversion model. Seismic information is relatively abundant in oil and gas exploration, and the method of joint inversion of EM and seismic data has attracted research attention and achieved better specific results (Peng and Liu, 2020, He, 2020). In unconventional oil and gas exploration, EM signals are sensitive to the sweet spot, and the electrical properties are consistent with the characteristic parameters, such as the porosity, brittleness, permeability, and TOC. With the constraints provided by the reservoir information obtained from the seismic data, the inversion resolution and interpretation reliability can be improved using EM data to predict the reservoir parameters. Furthermore, this method can reduce the risk of exploration and development. There are two types of joint inversion: one type is based on petrophysical relationships and the other is based on structural similarity relationships (Peng and Liu, 2020). In oil and gas exploration and development, petrophysical and geological information can be acquired as the prior information. Integrating the prior information, such as petrophysical and geological data, into the inversion can reduce the non-uniqueness of the optimization problems and help obtain proper inversion results (Giraud et al., 2017; Astic and Oldenburg, 2019). Joint inversion based on the empirical relationships among the various geophysical parameters has undergone vigorous development (Jegen et al., 2009; Heincke et al., 2010; Lelievre et al., 2012), and the petrophysical empirical or statistical formula can be applied in industry. Via the intersection

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analysis of the P-wave velocity and resistivity, Jegen et al. (2009) obtained the sectional empirical relationship between the P-wave velocity and resistivity. They confirmed that the empirical relationship can be locally effective under a specific geological background. Xu et al. (2016) and Peng et al. (2018) created marine EM and seismic data joint inversion frameworks based on the relationship between the reservoir and petrophysics and using the Archie formula and Gassmann formula, respectively. They both achieved global optimization one-dimensional joint inversion and extracted the reservoir porosity and saturation information. Hu et al. (2020) utilized the prior information obtained from the logging and seismic profile to perform constraint modeling, and they realized the artificial fish swarm joint inversion using the magnetotelluric (MT) and gravity data. Yang et al. (2021) proposed the petrophysical relationship between the velocity and resistivity based on the cross-variation function, and then, they adopted the guided fuzzy c-means clustering algorithm to carry out the multi-constraint inversion and completed the two-dimensional MT and seismic data joint inversion. However, there is still no standard petrophysical model that can link all of the physical parameters. Therefore, it is difficult to the directly integrate the petrophysical or other geological features into a standard geophysical inversion (Astic and Oldenburg, 2019). Compared with the constrained inversion based on the empirical petrophysical relationship, the joint inversion built via structural coupling requires less prior information. Haber and Oldenburg (1997) first proposed the concept of structural coupling constraint inversion, and Gallando and Meju (2003 and 2004) subsequently proposed the crossgradient inversion method. Multiple parameters joint inversion based on spatial structure similarity has gradually become the mainstream algorithm. Hu et al. (2009) applied the cross-gradient algorithm technique to joint inversion of two-dimensional seismic and EM data with multiplicative regularization and showed that the reservoir evaluation based on the joint inversion was better than that obtained through imaging interpretation of the individual seismic data. Zhdanov (2012) proposed a universal spatial structure constraint algorithm named Gramian spatial constraint method. It has many choices of operators for Gramian space; and when the operator is transformed into a gradient type, the cross-gradient method is obtained. Lelievre and Farquharson (2013) obtained a linear variation expression of the local model parameters and a solution for the change rate in all directions using the construction method, constructed Gramian spatial constraint operator using the obtained gradient of the model parameters, and finally achieved joint inversion of multiple datasets. Gao et al. (2017) proposed a cross-gradient joint

inversion strategy using alternately updating model parameters, which reduced the complexity of algorithm. Yan et al. (2020) derived the threedimensional (3D) discrete form of the cross-gradient operator and realized 3D joint inversion of gravity, magnetic, and MT data. Currently, the joint inversion of seismic and EM data is based on the petrophysical information constraints. Research on the structural coupling algorithm has primarily focused on joint inversion of gravity, magnetic, and EM data, and structural coupling joint inversion using seismic and EM data is the future goal in unconventional oil and gas EM exploration.
1.4 Level and ability of the EM exploration method
The EM method is an effective means of resource and energy exploration. The physical property foundation is the differences of the resistivity, dielectric constant, and polarization in rocks. The detection of underground geological targets can be realized by observing the spatial-temporal distribution of the EM fields. Although EM method has a good electrical basis in unconventional oil and gas exploration, the sweet spots of unconventional oil and gas reservoirs feature large burial depth, small target and strong heterogeneity, which require higher resolution, exploration depth and level of data processing and interpretation of electromagnetic exploration method. In the last 20 years of development, the methods of petroleum EM exploration have been comprehensively improved. In terms of the instruments, geophysical instrument companies such as Phoenix, Zong, KMS, Metronix, and Geonics have developed various types of high-power and strong current transmitting systems and multi-functional networked portable receivers. The wide frequency electromagnetic instrument developed by China's Jishan High-tech company, the high-power time-frequency electromagnetic system developed by BGP, CNPC, and the ultra-high power and ultra-low frequency transmitting and receiver developed by the Institute of Geophysics, Chinese Academy of Sciences, have also been put into oil and gas exploration. The Mag-power transmitter through a few kilometers to dozens of kilometers long ground wire to feed hundreds of amperes of current underground, can be convenient to emit a variety of waveform, for the time domain and frequency domain electromagnetic method to provide a powerful source of electromagnetic field. Low power consumption, broadband, low noise level, large dynamic range, portable, light weight, multi-function, distributed receivers can realize the data acquisition to thousands of channels array, which provided a hardware guarantee for 3D EM data acquisition and hydraulic fracturing monitoring under complex geology and terrain conditions. Lots of new control

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source EM methods were emerging to improve the resolution, detection depth, and interpretation reliability, such as the Wide Frequency EM method (WFEM), Wire-less EM method (WEM), Timefrequency EM method (TFEM), Long offset, long window Transient EM method (LowTEM), and Focused Source EM method (FSEM). These methods have greatly improved the anti-noise ability, detection depth, resolution, reliability, work efficiency, cost, and adaptability. The use of new data processing, 3D inversion and interpretation techniques had significantly enhanced the ability of electromagnetic methods to serve unconventional oil and gas exploration. At the same time, the study of unconventional reservoir rocks physics of has been deepening, and the induced polarization mechanism of unconventional reservoirs has made new progress. The model that accurately describes the IP mechanism of unconventional reservoirs has been preliminarily established, and the parameter prediction method of unconventional reservoirs based on this model has also been basically formed (Tong, 2020; Burtman, 2015). Lots of reviewers have summarized the EM methods and technologies in oil and gas exploration and development (He, 2010,2019; Strack, 2013,2014;Tietze 2014; Streich 2015; Di 2019, 2020; Xue 2016, 2020; Constable, 2010; Liu 2021). This paper focuses on the study of electrical characteristics of unconventional reservoir, the sweet spot detection and its characteristic parameters prediction methods based on the IP model, as well as the research progresses in regarding the new methods and technologies in EM exploration. In addition, several cases studies of shale gas the sweet spot detection and hydraulic fracturing monitoring with LowTEM were presented. The summary of the cases studies will benefit the wide application of new EM methods and technologies in unconventional oil and gas exploration and development.
2. Sweet spot detection and its character parameters prediction
The detection and evaluation technology of shale gas sweet spot is the key to shale gas exploration and development. Seismic exploration method undoubtedly plays an important role in sweet spot detection and development because of their high resolution and attribute identification capability. However, most of the shale gas exploration areas in southern China are rugged areas, and covered by limestone, seismic exploration is much more expensive and difficult to obtain high-quality data. In addition, the sweet spots are distributed in argillaceous source rocks with deep burial depth, high degree of evolution and rich organic matter. The seismic impedance characteristics are not obvious and there are no clear trap characteristics.

The shale reservoir itself is a source rock with extremely low porosity and permeability, and strong heterogeneity. As a result, the seismic characteristics of the sweet spot are not obvious, which makes prediction difficult. Therefore, the development of high-precision seismic prediction methods for shale gas reservoirs faces great challenges, and the development of cost-effective non-seismic methods is an effective way to detect sweet spot in unconventional reservoirs. As the most effective supplementary means of seismic exploration, the electromagnetic (EM) exploration method has the advantages of large exploration depth, high work efficiency, low cost and strong adaptability, and has been playing an important role in the early stage of basin oil and gas exploration. The EM exploration of oil and gas based on EM diffusion can only be effective when the resistivity of the oil and gas reservoir is high enough compared with the surrounding rocks. The importance of IP effect in rocks has been realized, and two IP parameters (resistivity and chargeability) have been tried to jointly detect oil and gas reservoirs (Davydycheva, 2006; He, 2010; Commer, 2011). Complex resistivity (CR) method, which is based on the IP theory, has been tried to identify conventional oil and gas in China for more than 30 years (Wu, 1996; Xu,2004; Su et al., 2005), and two kinds of IP anomaly models were put forward to interpret the CR date. One considered that the reservoir itself was a dynamic multiphase balance system, in which the double electric layer formed from bi-phase medium, and was the source in which the IP field came from (He, 2007). Another was called ‘the micro-seepage model’ which considered IP effect happened above the reservoir because the hydrocarbons migrated to the upper reducing zone, and resulted in the formation of the rich pyrite halo which was the source of strong IP effect (Veeken, 2009). Shale gas is stored in the reservoir in free or adsorbed state, and has the typical characteristics of self-generation and self-storage and in-situ accumulation, which inevitably determines the difference in the IP anomaly model of data interpretation in shale gas exploration. Are the above two models suitable for shale gas exploration? What are the geoelectric characteristics of organicrich shale? Does the shale gas reservoir have a strong IP effect? Can the resistivity and chargeability be integrated to identify the sweet spot? What is the sweet spot detecting pattern for EM exploration? Yan et al., (2014), Xiang (2014) collected hundreds of shale samples in southern China, performed compositional analysis, complex resistivity and Total Organic Carbon (TOC) measurements, and found a qualitative relationship between pyrite content and TOC. On this basis, combined with the IP theory study, the methods to predict the sweet spot’s characteristic parameters

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and the detection pattern suitable for EM method were proposed.
2.1 Electrical characteristics of shale in southern China
Xiang et al. (2014) collected 243 geological outcrop samples in Yibin, Sichuan and Zunyi, Guizhou in southern China. The lithologies are mainly organicrich shale, sandstone, dolomite and basalt (surrounding rock formations). Through the complex resistivity measurement and analysis, the IP parameters of the samples were obtained, and the physical properties of each formation were shown in Figure 1 and Table 1. Compared with the surrounding sandstone, dolomite and basalt, the average resistivity of the organic-rich shale is about 120 Ω·m, and the average polarizability is about 20% in Wufeng Group-Longmaxi Group; the average resistivity of the organic-rich shale is about 147 Ω·m, and the average polarizability is about 42% in the Qiongzhusi Group. These indicated that the organic-rich shale exhibited the characteristics of low resistivity and high chargeability, which was inconsistent with the low chargeability characteristics of conventional shale reservoirs.

Organic-rich shale is abundant in Zhaotong, Yunnan, with shallow burial depth and large thickness. China National Petroleum Corporation (CNPC) has made breakthroughs in shale gas exploration in this area. The Well Z104 has successfully produced industrial gas, with a daily gas production of 20,000 cubic meters. The logging curves of Well Z104 (Figure 2) showed that the shale gas reservoir had obvious characteristics of low resistivity, low velocity and high gamma value. The mineral composition analysis of 34 cores from the groups of S1l-O3w and Є1n in Well Z104 showed that the average content of quartz and feldspar was 30% to 60%, the average content of carbonate minerals is generally less than 30% (except mud shale), the average content of clay minerals is 10% to 35%, the content of pyrite is generally high, with a maximum value of 20% and an average content of 5% (Figure 3).

Polarizability(%)

Figure 2. Z104 Geology Histogram and Logging Curves

Resistivity/Ωm

Figure 1. Polarizability and resistivity results of CR measurement for geological outcrop rocks at different groups (top: chargeability; bottom: resistivity)
2.2 Prediction of the sweet spot characteristic parameters
● TOC predicting

Figure 3. The mineral composition in the shales from S1 l-O3w and Є1n (red: pyrite)
The complex resistivity measurement and TOC analysis were performed on 11 cores from Well Z104. Figure 4 indicated that the chargeability was

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consistent with the variation of pyrite content, with a high positive correlation. Figure 5 presented a plot of TOC and pyrite content versus chargeability. The results showed that there was also a good correspondence between pyrite content and TOC. Apparently, pyrite is an indicator mineral for IP, and is it also an indicator mineral for TOC? If the relationship between TOC and chargeability could be established, it will have great significance for EM exploration. Why is pyrite abundant in organic-rich shales? Is it necessarily related to TOC? A significant phenomenon is that pyrite is not only a characteristic mineral formed in organic-rich deposits, but also a marker of sedimentary environment and a characteristic mineral of strong IP effect. Veeken (2009) explained its formation process and controlling factors. The depositional environment of shale was a deep-water anoxic environment (reduction reaction environment), coupled with its self-generation and self-storage reservoir characteristics, the increase in TOC acted as a catalyst, providing a chemical reaction environment for the formation of secondary pyrite in the shale clay, the chemical process was as follows CH4 + Ca2+ + SO42− → CaCO3 + H2S  +H2O H2S + Fe2+ + 2OH − → FeS2 + H2O
Figure 4. Relationship between pyrite and polarizability
Figure 5. Relationship between TOC and pyrite
In addition, biodegradation and bacterial activity might give rise to organic origin of pyrite in the sedimentary pile. It was proved empirically that significant enrichment in pyrite was often related to hydrocarbon occurrences at deeper levels. Based on the resistivity, polarizability and TOC of

organic-rich shale, Xiang (2016) and Xu (2020) gave an empirical relationship model between polarizability and TOC, as shown in Figure 6.

10

8

TOC(%)

6

4
y = -13.048x2 + 21.038x - 1.2677
R²= 0.888
2

0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Chargeability
Figure 6. The Cross-plot of TOC and Polarizability

● Prediction of brittleness index
The mineral composition of shale reflected the depositional environment and depositional conditions of shale, and also determines the brittleness index of shale, which affects the fracturing and development of shale gas. It is of great significance to study the relationship between brittleness index and electrical parameters from the perspective of shale mineral composition. Quartz content is the main reason for the shale’s brittleness, but the roles of other important brittle minerals cannot be ignored. There were many definitions of brittleness index. Xu et al. (2020) defined the brittleness index B as follows:
B = quartz + feldspar+Pyrite (1) quartz + feldspar + Pyrite+carbonate + clay
The above formula highlighted the relationship between pyrite content, brittleness index and electrical parameters (resistivity and polarizability). The brittleness of Well Z104 shale was statistically analyzed by formula (1), and the relationship between the brittleness index and the polarizability was established, as shown in Figure 7. It can be seen that the quadratic nonlinear relationship was obvious
100 B = 18.772ln(m) + 81.373
80 R²= 0.9229

Brittleness Index(%)

60

40

20

0

0.0

0.2

0.4

0.6

0.8

1.0

Chargeability

Figure 7. The cross-plot of brittleness index and

polarizability

● Prediction of the permeability Unconventional reservoirs usually have low porosity

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and low permeability characteristics (porosity less than 10%, pore throat diameter less than 1 μm or permeability less than 1 mD). In order to achieve sustainable and effective oil and gas production, it is necessary to combine horizontal well drilling, formation fracturing and other technologies to increase reservoir permeability or reduce fluid viscosity (Zou et al., 2013).

Figure 8. The schematic diagram of the effective-

medium theory. A heterogeneous media contains

many different types of perturbed media, each of

which contains resistivity l , effective radius rl , and

surface-polarizability

coefficient



l s

.

The

resistivity

of heterogeneous medium is equivalent to the CR of

an effective medium through the effective-medium

approach. (Tong et al., 2020)

Combining theoretical and experimental analysis of the electromagnetic characteristics of the ‘sweet spot’ region, establishing the theoretical relationship model between electromagnetic response and reservoir physical properties is the key to realize the EM detection and quantitative evaluation of sweet spot. Reservoir physical properties such as porosity, saturation, and permeability are sensitive parameters for reservoir IP characteristics (Slater, 2007). Archie (1942) gave the empirical relationship between pure sandstone conductivity and formation water conductivity, connected porosity and formation water saturation. However, the rock mineral composition and pore structure of unconventional reservoirs with low porosity and low permeability was complex, and the Archie’s relationship was no longer applicable. The polarization effects (electric double layer polarization, Maxwell-Wagner dielectric polarization, etc.) existing at the interface of different minerals and pore fluids will cause the IP phenomenon of reservoir in the electromagnetic exploration frequency range. Pelton et al., (1978) first used the Cole-Cole model to characterize rock IP phenomena, and it was widely used in EM exploration. Zhdanov (2008) proposed the generalized effective-medium theory of induced polarization (GEMTIP) model based on strict mathematical and physical equations. GEMTIP is suitable for IP problems in anisotropic and multiphase media, and established a quantitative relationship between model parameters and rock physical properties, which is more conducive to

reservoir prediction. Tong et al., (2020) extended the theoretical relationship between macroscopic IP parameters and microstructure in the GEMTIP, and established MGEMTIP model under the assumption of isotropic spherical perturbation (Figure 8). MGEMTIP:

 -1



=

0

 1+

N



 1 −

1

  

(2)

e

M 0 

l
l =1

il +1

N
 where M0 = 1- 3 fl 2 l =1

l = 90 fl M0(4l +20 )

l = rl 2sl (2l + 0 ) . 0 is the DC resistivity of the

background medium

f

l

,



l

,

rl

,



l s

reseectively

correseond to the volume comeonent of the l tyee
eerturbed medium DC resistivity equivalent seherical radius and surface eolarization
earameters N is the total amount of eerturbed
medium the equivalent medium eolarizability is
N
t = l . hhe relationshie between t and the
l =1
eolarizability m of the Cole-Cole model is

m = t (1+t ) . GEMGhP is consistent with the

eolarization characteristics of discrete metallic minerals (Wong 1979) and also exelains the high eolarization ehenomenon in which the GawellWagner eolarization shifts to low frequencies under high oil saturation condition (Burtman and Zhdanov 2015). hhe electrical Kozeny-Carman (K-C) equation characterized the rock eorosity and tortuosity

r through the formation factor F and characterized
the caeillary radius through the seecific surface

area S por . hhe eermeability relationshie can be

exeressed as

Q

k= b c

(3)

F S por

Where Q,b, c are the fitting earameters (Slater

2007).Since the seeeage in unconventional reservoirs was dominated by the diffusion of nonDarcy flow the validity of the electrical K-C equation was greatly reduced in low eermeability rocks. Gany studies eroeosed to use comelex resistivity to estimate thereby imeroving the erediction accuracy of eermeability (Börner et al. 1996; de Lima and Niwas 2000; Slater and Lesmes 2002). Figure 9 showed the eermeability erediction relationshie based on normalized eolarizability (Weller et al. 2015) but there was a large error in the erediction results of low eermeability rocks. Figure 10 showed

that Spor correlated with the imaginary conductivity

  (which mainly controls the eolarizability strength) but the quantitative relationshie needed to be further determined in combination with clay minerals (Revil et al. 2013). Conductive minerals

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

9 / 29

Yan L., 2022,Unconventional Oil and Gas exploration with EM methods
such as clay and metals in unconventional reservoirs usually do not earticieate in the seeeage erocess but directly affect the rock P ehenomenon.

Figure 11. Relationship between relative polarizability difference and permeability of tight rock

Figure 9. Permeability relationship between experimental measurement and electrical K-C model estimation based on normalized polarizability
mn ( mn = m0 )(Weller et al., 2015)

Figure 12. Permeability relationship between experimental measurement and MGEMTIP estimation based on polarizability difference

Figure 10. Experimental relationship between the imaginary conductivity and specific surface area between sandstone and clayey sandstone (Revil et al., 2013)
Whether conductive minerals actually eroduce conductance or eolarization can be effectively analyzed by combining the mineral size connectivity and frequency band (Vinegar and Waxman 1984; Xiang et al. 2014; Revil et al. 2015). Numerical simulations showed that there was a nonlinear effect between the eermeability of

fractured tight reservoirs (limestone) and the

conductivity of formation water (Kirkby et al. 2016).

hhe electrical characteristics of these

unconventional reservoirs seriously affect the

eermeability erediction based on the electrical K-C

model. hhe existence of clay and minerals in

unconventional reservoirs makes it difficult to

distinguish the P mechanism of unconventional

reservoirs. Removing or sueeressing eolarization

indeeendent of eore fluid can effectively imerove the

erediction accuracy of eermeability. hhere is a

difference between the theoretical eolarizability of

the GEMGhP (no seeeage) and the measured

eolarizability of tight rocks (weak seeeage). hhis

difference has a good correlation with the rock

eermeability (Figure 11). hhe eermeability

erediction formula based on GEMGhP was

eroeosed

k = Q(t -e )b = Q b

(4)

ec

ec

Where t and e correseond to the theoretical

eolarizability and measured eolarizability of the rock

and Q,b, c are the rock characteristic indices. With

Abstract, 25th EM Induction Workshop, Çeşme, Turkey, September 11-17, 2022

10 / 29

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Electromagnetic technology for prospecting unconventional