Petrophysical Characterization and Fluids Transport in Unconventional Reservoirs

Petrophysical Characterization and Fluids Transport in Unconventional Reservoirs presents a comprehensive look at these new methods and technologies for the petrophysical characterization of unconventional reservoirs, including recent theoretical advances and modeling on fluids transport in unconventional reservoirs. The book is a valuable tool for geoscientists and engineers working in academia and industry. Many novel technologies and approaches, including petrophysics, multi-scale modelling, rock reconstruction and upscaling approaches are discussed, along with the challenge of the development of unconventional reservoirs and the mechanism of multi-phase/multi-scale flow and transport in these structures.

• Includes both practical and theoretical research for the characterization of unconventional reservoirs
• Covers the basic approaches and mechanisms for enhanced recovery techniques in unconventional reservoirs
• Presents the latest research in the fluid transport processes in unconventional reservoirs

• Language: English
• Publisher: Elsevier Science
• Release date: Jan 24, 2019
• ISBN: 9780128172896

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China

Preface

Jianchao Cai; Xiangyun Hu, Wuhan, China

Unconventional reservoirs, which primarily include shale, coal, and tight sandstone reservoirs, have recently found an important role in the production of oil and gas as other reservoirs are either depleted or too expensive to produce during times of low oil prices. Industry’s lack of experience with these reservoirs, relative to conventional resources, requires attention from the perspective of technology and theoretical understanding to fill the gap between classical theory of fluid flow in porous media and the unconventional nature of these reservoir rocks. One of the major issues with unconventional reservoirs is their low recovery factor; therefore, enhancing oil and gas recovery from these reservoirs is crucial for their economic development. Some of the important challenges in enhancing oil and gas recovery from unconventional reservoirs are accurate characterization of the microstructure and the ability of fluid transport, especially in understanding which transport mechanisms play important roles in the flow of oil and gas at different scales and rock types.

This book presents a comprehensive overview of new methods and technologies for petrophysical characterization of unconventional reservoirs, recent theoretical advances and modeling of fluid flow and transport, multiscale modeling, rock reconstruction, and upscaling approaches in unconventional reservoirs.

The book is intended for graduate students, geoscientists, and engineers engaging in research and development of unconventional oil and gas reservoirs in academia and industry. This book is divided into two parts: Part 1 (Petrophysical Characterization) and Part 2 (Porous Flow Dynamics). In Part I (Petrophysical Characterization), Chapter 1 focuses on characterizing pore size distributions of shale, in which three current widely used techniques and methods, i.e. field emission scanning electron microscope, nitrogen adsorption and mercury intrusion capillary pressure, are introduced and summarized to analyze pore microstructure properties of shale rocks. Chapter 2 presents petrophysical characterization of the pore structure of coal by means of low-field nuclear magnetic resonance technology. In Chapter 3, the characteristics of tight sandstone petrophysical properties are reviewed and pore structure characterization techniques as well as different methods of calculating fractal dimensions are introduced and discussed. In addition, mathematical models for predicting permeability of tight sandstones are also briefly introduced. Chapter 4 further studies the pore structure and heterogeneity of unconventional tight oil reservoirs based on multifractal analysis and low-field nuclear magnetic resonance technology. In Chapter 5, an integrated method to quantify organic pores using low-field nuclear magnetic resonance is introduced, combined with backscattered electron images and energy dispersive X-ray spectrometer. Relationships between the organic-related porosity and geochemical parameters are analyzed using petrophysical data. Chapter 6 presents a critical overview of methods available to predict shale permeability from experimental and modeling perspectives at both laboratory and field-scales, and also presents a nonempirical model for two-phase relative permeability in shale fractures. Chapter 7 introduces the characteristics of pore structure, connectivity, and wettability of several typical core samples from organic-rich shale formation as well as their coupled effects on fluid migration. Chapter 8 aims at understanding the wettability behavior of tight rocks by performing spontaneous imbibition experiments and contact angle measurements.

In Part 2 (Porous Flow Dynamics), Chapter 9 presents a general fracture wing model to analyze the transient pressure response of complex fracture systems in low permeability reservoirs. Chapter 10 explores the impact of fluid circulation rates on the heterogeneity of thermal drawdown and its potential impact on the timing and magnitude of induced seismicity, and develops a dimensionless semianalytical model to determine thresholds for the evolution of uniform or shock-front distributions of thermal drawdown within rock comprising the reservoirs. Chapter 11 establishes a basic overview of the applicability of some commonly used pore-scale modeling methods in shale formations and discusses the hydrocarbon transport and storage mechanisms in shale nanopores. Chapter 12 presents high-pressure methane adsorption experiments on shale samples and proposes a novel adsorption model by combining the micropore filling and monolayer coverage theories to describe methane adsorption process in shale. Chapter 13 proposes a coal permeability model based on the nonconstant vertical stress condition by considering roof deformation acting to hold part of the overburden load. Chapter 14 develops a model to predict adsorbing-gas (e.g., CO2) permeability change by coupling pore size evolution with permeability change during gas pressure depletion, and also proposes two parameters (change ratio of permeability and total loss of flow rate) to quantitatively evaluate the dynamic process of CO2 flow in different coal ranks. Chapter 15 introduces a workflow to integrate all the physics affecting the dynamic permeability evolution. The complex relationships between apparent porosity, adsorption, and apparent permeability based on the pulse-decay experiment are also described.

We would like to thank the individual chapter authors of the book for their inspiring contributions and diligent work. We also specially acknowledge the Elsevier press for providing this opportunity to bring this book to readers. Special thanks is also reserved for Amy M. Shapiro, Ashwathi P. Aravindakshan, Ali Afzal-Khan, and Nilesh K. Shah of Elsevier’s book production group for editing this book. We also acknowledge the National Natural Science Foundation of China (41722403, 41630317, 41572116), and the Hubei Provincial Natural Science Foundation of China (2018CFA051) for supporting the series of studies on petrophysical characterization and flow and transport in porous media.

Part 1

Petrophysical Characterization

Chapter 1

Characterizing Pore Size Distributions of Shale

Kouqi Liu⁎; Mehdi Ostadhassan⁎; Jianchao Cai†    * Department of Petroleum Engineering, University of North Dakota, Grand Forks, ND, United States

† Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, P.R. China

Abstract

Research on unconventional shale reservoirs has increased dramatically due to the decline of shale production from conventional reserves. Pore structure analysis can assist in accurately understanding the storage and migration properties of the gas and oil that are very critical for the numerical simulation and overall production estimation. In this chapter, three methods (field emission scanning electron microscope (FESEM), nitrogen adsorption, and mercury intrusion capillary pressure (MICP)) are introduced and applied to analyze the microstructures of shale rocks. Pore information is derived and analyzed using the three methods. Limitations and strengths of the three methods are also described in this chapter. The results showed that nanopores were widely distributed in the shale samples. FESEM is a straightforward way to view the pores but is limited in characterizing the pores in two dimensions. Nitrogen adsorption can quantify the pores that are < 200 nm in size. MICP can detect pores with a broad size range from a few nanometers to few hundred micrometers. The pore information from the MICP method reflects the pore throat characteristics.

Keywords

Shale; Pore microstructures; Different characterization methods; Limitations and strengths; Field emission scanning electron microscope (FESEM); Nitrogen adsorption; Mercury intrusion capillary pressure (MICP)

Chapter Outline

1Introduction

2Scanning Electron Microscopy

2.1Sample Preparation

2.2Image Analysis

2.3Bakken Example Analysis

3Gas Adsorption

3.1Sample Preparation

3.2Longmaxi Sample Analysis

3.3Model Choice

4Mercury Intrusion Capillary Pressure Test

4.1Case Study

4.2Comparison of the PSD from MICP and Gas Adsorption

5Conclusions

Acknowledgments

References

Acknowledgments

The authors would like to thank Ms. Hallie Fowler, Core Laboratories (Houston) for performing the milling process and for taking the FESEM photographs, Dr. Wang Liang from Southwest Petroleum University for sharing the gas adsorption data from Longmaxi and Dr. Zoya Heidari from University of Texas at Austin for running the MICP tests. Support from the China Scholarship Council (CSC) and the National Natural Science Foundation of China (41722403; 41572116) are also gratefully acknowledged.

1 Introduction

Since commercial production from shale-gas reservoirs began, research on shale has increased significantly. Understanding the pore structure of unconventional reservoirs, such as shale, can assist in estimating their elastic transport and storage properties, thus enhancing the hydrocarbon recovery from such massive resources.

Porosity is defined as the pore fraction. The pores can either be closed, isolated or open and connected. The matrix-related pores (with a size range from a nanometer to micrometer) are commonly found in shale rocks. Based on the pore classification suggested by Loucks et al. [1], pores in shale can be separated into three main pore types: interparticle pores, intraparticle pores, and organic pores. According to the International Union of Pure and Applied Chemistry (IUPAC) [2], pores can be divided into three categories: micropores (< 2 nm), mesopores (2–50 nm), and macropores (> 50 nm). In the past decades, many shale-gas reservoirs in the world have been studied extensively by researchers, such as the Barnett shale (USA) [1, 3], Marcellus shale (USA) [4, 5], Longxi shale (China) [6, 7], Toarcian shale (Germany) [8], Boom Clay (Belgium) [9], Posidonia shale (Germany) [10], Longmaxi shale [11], and Silurian shale [12]. Multiple methods have been employed to study the pore structure of these reservoirs, including mercury intrusion porosimetry (MIP) [13], nuclear magnetic resonance [14], gas adsorption [6], small angle neutron scattering [15], and scanning electron microscopy (SEM) image analysis [1, 10, 16, 17].

In this chapter, we summarize the current widely used techniques that can assist in future experiment research work on the pore structures of shale formations. Three commonly used methods will be explained in this chapter and some real sample analysis will also be introduced. It should be kept in mind that these three methods are based on different working mechanisms and each method has its own strengths and limitations. Different methods can yield different pore structure analysis results and the combination of these methods may elucidate different features of the pore structure and provide more information about the pores.

2 Scanning Electron Microscopy

SEM is one of the most popular imaging techniques. SEM, unlike conventional light microscopy, produces images by recording various signals resulting from interactions of an electron beam with the sample as it is scanned in a raster pattern across the sample surface [18]. Field emission SEM (FESEM), which has a much higher resolution than conventional SEM, has a greater ability in characterizing the nanopores in shale formations.

2.1 Sample Preparation

Mechanical polishing is widely applied by researchers for sample preparation. Usually, the shale samples are resin coated. When the resin is solid, sand paper (with different sizes of grit from 600 to 1200) is used to polish the sample surface. Finally, we used diamond polishers, with various grain sizes (5, 3, and 0.5 μm), to polish the sample [19]. This procedure can produce surface topographic irregularities because of the differential hardness of the components (Fig. 1A). These irregularities sometimes greatly exceed the size of the nanopores in the shale and therefore, this method of sample preparation is inadequate for pore quantification using the SEM technique. To eliminate the limitations of mechanical polishing, argon ion milling was used to produce a much flatter surface (Fig. 1B). Samples, which were taken parallel to the bedding, were trimmed down to a 0.5 cm square cube. Then all faces of the cube were smoothed out by hand with a Buehler polishing wheel using 600-grit silicon carbide grinding paper. The samples were mounted to the ion mill sample holder and placed in the Leica EM TIC 3 × argon ion mill. Thereafter, all samples were milled at an accelerating voltage of 8 kV, with a gun current at 3 mA for 8 h. Samples were then removed from the ion mill's sample holder and mounted on a clean SEM stub using carbon paint [20].

Fig. 1 SEM images of the topography surfaces of samples by (A) mechanical polishing and (B) argon ion milling.

2.2 Image Analysis

In order to investigate the pore structure from the grayscale SEM images, ImageJ, a powerful commercial computer program that is widely used in image analysis in other fields, was used [21, 22]. For the backscattering electron images or the secondary images of shale, the pores have low grayscale values while the solid components have a high grayscale value. After selecting an appropriate threshold value, the images were segmented into a binary image where the black pixels represent the pores and the white pixels show the solid matrix. Fig. 2 shows the imaging process for one shale sample. Following this, porosity and pore size were quantified.

Fig. 2 Image process for one shale sample.

2.3 Bakken Example Analysis

Samples from the Bakken Formation were collected for the real case study. To quantitatively analyze the pore structure of the samples, the representative elementary area (REA) method was used. The REA for a porous medium has a minimum averaging area that will yield a value representative of the whole. The porosity indicator was used to determine the REA by analyzing the influence of the magnification on porosity [23]. For the samples studied, it was found that as the magnification was reduced (scan area increased), the porosity value oscillated until it remained steady under a critical magnification. The scan area under this critical magnification point was used as the REA. Fig. 3 shows the process of determining the REA of one Bakken sample.

Fig. 3 Determination of the REA of Bakken sample (#9) using a porosity indicator.

Based on the REA, the surface porosity of the sample was calculated. Table 1 shows that the samples had abundant pore counts and most of the samples had very low porosity values (< 10%). The data in Table 1 show that samples with high pore counts do not necessarily have high porosity values. This is because pore counts refer to the number of pores in the sample while porosity refers to the ratio of the pore volume to the total sample volume. The study by Saraji and Piri [23] shows that samples with large pore size and small pore counts can still have large porosity values. Thus, porosity should be determined by the combination of pore size and pore counts, not by pore counts alone.

Table 1

The correlations between mineralogical composition and TOC (total organic carbon) content on one hand and the pore counts and porosity on the other were also investigated. The results are shown in Table 2. The data illustrate that the samples used in this study contain the same minerals but in different weight ratios. In order to not eliminate the influence of one parameter on the other based on the bivariate plots, when in fact the parameter may exert a significant influence when it is joined by another independent parameter, partial least squares (PLS) regression was applied. The composition of the samples (quartz, pyrite, clay, feldspar, and TOC) was treated as the independent parameter, and porosity and pore counts were used as the dependent variables. The fit parameters of the PLS model are shown in Table 3. The results show that for the porosity/pore counts, clays and feldspar have a positive influence on the porosity/pore counts while quartz and pyrite have negative effects on the porosity/pore counts. The increase of TOC can decrease the porosity/pore counts due to the extensive occurrence of nonporous organic matter. Of all of the minerals, TOC affected the porosity and pore count the most [20].

Table 2

Table 3

We used the SEM quantitative analysis method to determine the pore parameters that could cause uncertainty due to resolution limitation. The minimum pore size that we could analyze was 10 nm, which means that pores smaller than 10 nm may exist in the shale samples but cannot be detected by the SEM.

3 Gas Adsorption

Gas adsorption is of major importance in measuring the pore structure over a wide range of porous materials. Since Dewar [24] reported the adsorption of nitrogen and other gases at liquid air temperature when studying the composition of atmospheric gases, nitrogen has become a potentially available adsorption material. The monumental work on the monolayer adsorption by Langmuir [25] attracted great interest from researchers for the interpretation of adsorption data. In the 1930s, Benton and White [26] published on the existence of the multilayer adsorption of nitrogen at the temperature of 77 K. Brunauer and Emmett applied gas adsorption to analyze the surface area of samples [27]. In 1938, the publication of the Brunauer-Emmett-Teller (BET) theory [28], which is the extension of the Langmuir monolayer adsorption model to a multilayer adsorption model, provided researchers the theoretical method to determine the surface area of porous media. In the late 1940s, by using the Kelvin equation, Barrett, Joyner, and Halenda (BJH) proposed a method [29] to derive the pore size distributions (PSDs) from an appropriate nitrogen isotherm. The BJH method is still one of the most popular methods used to date. In the early 2000s, based on the established principles of statistical mechanics and assuming a model solid structure and pore topology, the density functional theory (DFT) method was proposed and has been an important tool in characterizing the PSD of porous samples [30].

3.1 Sample Preparation

The samples were crushed to < 0.2 mm and then out-gassed at 423 K (301.73°F) for 12 h to remove existing bound water that is absorbed by clay minerals. Then, nitrogen adsorption isotherms were obtained for relative pressure (P/Po) (P and P0 are the equilibrium and the saturation pressure of adsorbates at the temperature of adsorption), varying from 0.01 to 0.995 at 77 K (− 321 °F) [31].

3.2 Longmaxi Sample Analysis

Fig. 4 depicts nitrogen adsorption isotherms in four samples from the Longmaxi Formation [32]. All nitrogen adsorption isotherms exhibited the same characteristics. For the adsorption branch, as the relative pressure increased from 0, the adsorption quantity increased rapidly and then followed a slow increase. The deflection point was used to separate the monolayer and multilayer adsorption phase [31]. When the relative pressure was larger than 0.8, the rate of the gas adsorption increased until the relative pressure approached 1. Hysteresis was observed for the samples, indicating the existence of mesopores in the Longmaxi samples. Based on IUPAC [2], the adsorption isotherms that were detected in the samples would belong to type IV and the shape of the pores in the Longmaxi Formation are mainly ink-bottle.

Fig. 4 Examples of nitrogen adsorption isotherms in Longmaxi shale samples.

The BJH model (BJHAD (Barrett, Joyner, and Halenda adsorption branch) and BJHDE (Barrett, Joyner, and Halenda desorption branch)) and DFT model were applied to the data to analyze pore structure from the isotherms. The results can be found in Table 4, which show different outcomes from each method for surface area and pore volume calculation. Surface area of the samples that was calculated from the BJHAD model varied from 1.817 to 5.051 m²/g, which is smaller than the surface area calculated from the BJHDE and DFT models, which varied from 3.004 to 13.120 m²/g and 6.204 to 16.441 m²/g, respectively. Considering Sample #17 for example, the surface area calculated using the DFT method was around five times of the surface area calculated from the BJHAD method, while the surface area calculated from BJHDE model was around four times the surface area calculated from the BJHAD model. Regarding pore volume, the DFT model yielded, on average, a larger pore volume than both BJHAD and BJHDE models. The pore volume estimated for Sample #17 is 1 × 10− 2 cc/g while the pore volume from the same sample calculated from the BJHAD and BJHDE models was 0.6 × 10− 2 and 0.9 × 10− 2 cc/g, respectively. There was no clear correlation between pore volume derived from the BJHAD and BJHDE methods. This discrepancy in the results will be explained later in the