reading

Cover
Title page
Copyright page
Dedication
Foreword
Chapter 1: Introduction
1. 1.1 Summary
2. 1.2 Is Sustainable Petroleum Technology Possible?
3. 1.3 Why is it Important to Know the Origin of Petroleum?
4. 1.4 What is the Likelihood of an Organic Source?
5. 1.5 What is the Implication of the Abiogenic Theory of Hydrocarbon?
6. 1.6 How Important are the Fractures for Basement Reservoirs?
7. 1.7 What are we Missing Out?
8. 1.8 Predicting the Future?
9. 1.9 What is the Actual Potential of Basement Hydrocarbons?
Chapter 2: Organic Origin of Basement Hydrocarbons
1. 2.0 Introduction
2. 2.1 Sources of Hydrocarbon
3. 2.2 Non-Conventional Sources of Petroleum Fluids
4. 2.3 What is a Natural Energy Source?
5. 2.4 The Science of Water and Petroleum
6. 2.5 Comparison between Water and Petroleum
7. 2.6 Combustion and Oxidation
Chapter 3: Non-organic Origin of Basement Hydrocarbons
1. 3.0 Introduction
2. 3.1 Theories of Non-organic Origin of Basement Petroleum
3. 3.2 Formation of Magma
4. 3.3 The Composition of Magma
5. 3.4 The Dynamics of Magma
6. 3.5 Water in the Mantle
7. 3.6 The Carbon Cycle and Hydrocarbon
8. 3.7 Role of Magma During the Formation of Hydrocarbon from Organic Sources
9. 3.8 Abiogenic Petroleum Origin Theory
Chapter 4: Characterization of Basement Reservoirs
1. 4.0 Summary
2. 4.1 Introduction
3. 4.2 Natural and Artificial Fractures
4. 4.3 Developing Reservoir Characterization Tools for Basement Reservoirs
5. 4.4 Origin of Fractures
6. 4.5 Seismic Fracture Characterization
7. 4.6 Reservoir Characterization During Drilling
8. 4.7 Reservoir Characterization with Image Log and Core Analysis
9. 4.8 Major Forces of Oil and Gas Reservoirs
10. 4.9 Reservoir Heterogeneity
11. 4.10 Special Considerations for Shale
Chapter 5: Case Studies of Fractured Basement Reservoirs
1. 5.0 Summary
2. 5.1 Introduction
3. 5.2 Geophysical Tools
4. 5.3 Petro-physics in Fracture Modeling, Special Logs and their Importance
5. 5.4 Case Study of Vietnam
6. 5.5 Case Studies from USA
Chapter 6: Scientific Characterization of Basement Reservoirs
1. 6.1 Summary
2. 6.2 Introduction
3. 6.3 Characteristic Time
4. 6.4 Organic and Mechanical Frequencies
5. 6.5 Redefining Force and Energy
6. 6.6 Natural Energy vs. Artificial Energy
7. 6.7 From Natural Energy to Natural Mass
8. 6.8 Organic Origin of Petroleum
9. 6.9 Scientific Ranking of Petroleum
10. 6.10 Placement of Basement Reservoirs in the Energy Picture
Chapter 7: Overview of Reservoir Simulation of Basement Reservoirs
1. 7.1 Summary
2. 7.2 Introduction
3. 7.3 Meaningful Modeling
4. 7.4 Essence of Reservoir Simulation
5. 7.5 Modeling Fractured Networks
6. 7.6 Double Permeability Models
7. 7.7 Reservoir Simulation Data Input
8. 7.8 Geological and Geophysical Modeling
9. 7.9 Reservoir Characterization
10. 7.10 Risk Analysis and Reserve Estimations
11. 7.11 Recent Advances in Reservoir Simulation
12. 7.12 Comprehensive Modeling
13. 7.13 Towards Solving Non-Linear Equations
14. 7.14 Adomian Decomposition of Buckley-Leverett Equation
Chapter 8: Conclusions and Recommendations
1. 8.1 Concluding Remarks
2. 8.2 Answers to the Research Questions
Chapter 9: References and Bibliography
Index
End User License Agreement

List of Illustrations

Chapter 1
1. Figure 1.1 Various steps involved in petroleum technology.
2. Figure 1.2 The distribution of hydrocarbons in and around igneous rocks according to lithology (from Schutter 2003).
3. Figure 1.3 Water plays a more significant role in material production than previously anticipated (from the Guardian, March 12, 2014).
4. Figure 1.4 New science only added more arrogance, blended with ignorance to Dogma science.
Chapter 2
1. Figure 2.1 Basement reservoirs are distributed around the world.
2. Figure 2.2 Schematic of crude oil molecular composition (from USGS website 1).
3. Figure 2.3 General scheme of the evolution of the organic fraction and the hydrocarbon produced (Tissot and Welte, 1984).
4. Figure 2.4 Product distribution with temperature (redrawn from Luo et al., 2014).
5. Figure 2.5 Incondensable gas composition with temperature (Reconstructed from Neven et al., 2011 and Liu et al., 2004).
6. Figure 2.6 Formation of organic-rich sediment layer (from USGS website 1).
7. Figure 2.7 Early burial of sediment layers in basin (from USGS website 1).
8. Figure 2.8 The volume of biomass increases as the size of the living object decreases.
9. Figure 2.9 Continued burial of sediment and rock layers in subsiding basin (from USGS website 1).
10. Figure 2.10 Deeper burial of rock layers in subsiding basin (from USGS website 2).
11. Figure 2.11 Relationship between mobility of particles supported on graphite and their bulk melting temperatures (From Baker, 1982).
12. Figure 2.12 General outline of major sedimentary basins (from USGS website 1).
13. Figure 2.13 Usefulness of cyanobacteria (From Singh et al., 2016).
14. Figure 2.14 Engineered and natural biochemical pathways of cyanobacteria that are employed for production of valuable compounds.
15. Figure 2.15 The use of cyanobacteria to produce cyanodiesel and other valuable products (from Sarsekeyeva et al., 2015).
16. Figure 2.16 The amount of gas available in natural resources increases as one moves from conventional to unconventional resources.
17. Figure 2.18 Scientific characterization is inherently sustainable (From Khan and Islam, 2007).
18. Figure 2.20 Informal classification of organic sediments by their ability to produce Hydrocarbons.
19. Picture 2.1 Gas hydrate burning offers some of the most environment-friendly combustion available to mankind (picture from USGS).
20. Picture 2.2 The difference between charcoal and diamond can be captured in the time function, which is either linearized or altogether eliminated in various economic models that drive modern technology.
21. Picture 2.3 This single-celled green diatom won Rogelio Moreno Gill of Panama fifth place in the BioScapes Imaging Competition. Specimens for this composite image came from a lake (from National Geographic). Picture on the right is a snowflake (from U.S. Dept. of Agriculture).
22. Picture 2.4 Snowflakes are fundamental units of water.
23. Picture 2.5 Pictures of diatoms (picture from Colorado State geological survey, 2013).
24. Picture 2.6 Sun picture taken at 9:19 a.m. EST on Nov. 10, 2004, by the SOHO (Solar and Heliospheric Observatory) spacecraft (NASA/European Space Agency, 2004).
Chapter 3
1. Figure 3.1.A Plinian eruption deposits airfall pumice and ash, blown by winds to north and east (available in USGS website 2).
2. Figure 3.1.B Vent enlarges and eruption column collapses; pyroclastic flows deposit the Wineglass Welded Tuff on north and east flanks (available in USGS website 2).
3. Figure 3.1.C Roof of magma chamber collapses, forming caldera as new vents open above fractures; pyroclastic flows deposit pumice and ash on all flanks of Mount Mazama and in valleys below (available in USGS website 2).
4. Figure 3.1.D Caldera has been partly filled with pumice and ash from the eruption shown in C and with blocks of rock from the caldera walls; weak, dying explosions within the caldera deposit ash on the caldera rim; pyroclastic-flow deposits develop fumaroles and gradually cool (available in USGS website 2).
5. Figure 3.1.E Crater Lake today (available in USGS website 2).
6. Figure 3.2 Composition variation (From Brophy, 2012).
7. Figure 3.3 Time series of Earth’s surface air temperature (black line) and time series corrected for the influence of human activities (red line), Earth’s length of day (green line) and Earth’s core angular momentum (blue line). Image credit: NASA/JPL-Université Paris Diderot - Institut de Physique du Globe de Paris.
8. Figure 3.4 Various layers of the Earth.
9. Figure 3.5 Inner-Core Shear-Wave Anisotropy and Texture from an Observation of PKJKP (from https://www.iris.edu/gallery3/research/example_highlights/globes).
10. Figure 3.6 The sun’s corona, visible during an eclipse (Wikimedia Commons).
11. Figure 3.7 The range of compositions of the Earth crust (modified from Geology.com).
12. Figure 3.8 Triangular diagram of granite rocks (from Geology.com).
13. Figure 3.9 Moon’s inner core (Credit NASA).
14. Figure 3.10 Wave propagations through the Earth (courtesy British Geological Survey).
15. Figure 3.11 Density for various layers of the Earth.
16. Figure 3.12 Mantle convection (courtesy University of Sydney).
17. Figure 3.13 The vapor-saturated solidus and the water-storage capacity of HZ1 lherzolite. (Ref. 2 refers to Green and Fallon, 1998; Ref. 15 refers to Grove et al., 2006, from Green et al., 2010).
18. Figure 3.14 Phase stability fields with different water contents in HZ1 and HZ2 lherzolite compositions at 2.5 GPa.
19. Figure 3.15 Maximum water content (MWC) with three geotherms. (a) Three geotherms (cold, mean, and hot). (b) MWC with cold geotherm. (c) MWC with mean geotherm. (d) MWC with hot geotherm. (From Nakagawa et al., 2015).
20. Figure 3.16 There is a continuous water transfer between the mantle and the surface of the Earth.
21. Figure 3.17 The science of Earth’s core is riddled with many unanswered questions (Figure from Hazen et al., 2012).
22. Figure 3.18 Organic and inorganic origin δ¹³C_CO2 frequency.
23. Figure 3.19 A scheme of genesis of hydrocarbons and petroleum deposits formation (from Kutcherov, 2013).
24. Figure 3.20 Hydrothermal convection from magma (redrawn from Rona, 1988).
25. Figure 3.21 Hydrothermal activities and seafloor spreading.
26. Figure 3.22 The distribution of hydrocarbons in and around igneous rocks according to lithology (from Petford and McCaffery, 2003).
27. Figure 3.23 The Generalized map showing the boundaries of the Pripyat Basin and Dnieper Donets Basin geologic provinces (red lines), centerpoints of oil and gas fields (green and red circles, respectively), and the location of geologic cross section A-A’ shown in Figure 3.24 (green line). Country boundaries are represented by blue lines. (From USGS website 3).
28. Figure 3.24 Geologic cross section for the Dnieper-Donets Basin. See figure 1 for location. Explanation: 1, Upper Devonian; 2, Devonian evaporites; 3, Carboniferous; 4, Permian; 5, Triassic; 6, Jurassic; 7, Cretaceous; 8, Cenozoic; 9, oil accumulation; 10, gas accumulation; 11, top of overpressure; 12, 100° C isotherm; 13, 0.9 percent vitrinite reflectance isochore; 14, stratigraphic boundary.
29. Figure 3.25 Hydrate formation in natural environment (From Islam, 2014).
30. Figure 3.26 Phase diagram of brine and methane (from Kvenvolden, 1993).
31. Figure 3.27 Molecular structure of hydrate.
32. Figure 3.28 Phase diagram of hydrate under arctic and marine environments.
33. Picture 3.1 Crater Lake Caldera: A satellite view of Crater Lake, one of the world’s most famous calderas. Crater Lake formed about 7,700 years ago when a massive volcanic eruption of Mount Mazama emptied a large magma chamber below the mountain. View a Google Map of Crater Lake. (The image above was produced using Landsat GeoCover data from NASA, available in USGS website 2.)
34. Picture 3.2 Satellite image of the Himalayan mountain chain, as imaged by NASA’s Landsat-7 satellite. Credit: NASA.
35. Picture 3.3 Satellite image of the East African Rift, taken on December 18th, 2002. Credit: NASA/GSFC/METI/Japan Space Systems/U.S.-Japan ASTER Science Team.
36. Picture 3.4 Sea floor pillow basalts on the Juan de Fuca Ridge. Credit NOAA.
37. Picture 3.5 Basalt lava flows in Hawaii, credit USGS.
38. Picture 3.6 Columbia River Flood Basalts (Public domain image by Williamborg).
Chapter 4
1. Figure 4.1 The knowledge model: The abstraction process must be bottom up.
2. Figure 4.2 Schematic cross-sections of borehole breakout and drilling-induced fracture (Hillis and Reynolds, 2000).
3. Figure 4.3 Onshore map of distribution of wells logged with borehole imaging data by category across the UK compared with distribution of dual-caliper logs. (from Kingdon et al., 2016)
4. Figure 4.4 Comparison of resistivity images visualising Drilling Induced tensile Fractures (DIFs) from PCM Measures in the Melbourne 1 well, Yorkshire (10 m vertical borehole section). Left-hand panel: conventional logs including perpendicular dual-caliper and gamma-ray log. Right: Unwrapped circumferential resistivity borehole imaging (CMI) (clockwise from north), breakouts highlighted by green boxes, DIFs terminate across coal horizon (lower gamma-ray) at 1352.4 m which shows clear breakout. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Figure from Kingdom et al. (2016).
5. Figure 4.5 Comparison of methods of visualizing a 4 m long borehole breakout from St Bees Shale Formation, from borehole Sellafield 13A in Cumbria (10 m vertical borehole section). Left-hand panel: conventional logs including perpendicular dual-caliper and gamma-ray. Centre panel: Unwrapped circumferential resistivity borehole imaging (FMI) (clockwise from north) with breakout highlighted by the green boxes. Right panel: Unwrapped circumferential acoustic borehole amplitude imaging (UBI) (clockwise from north) with breakout highlighted by green boxes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
6. Figure 4.6 Section of resistivity images visualizing 3 distinct borehole breakouts from PCM from Swinefleet 1, Yorkshire (5 m vertical borehole section). Left-hand panel: conventional logs including perpendicular dual-caliper and gamma-ray log. Right panel: Unwrapped circumferential resistivity borehole imaging (CMI) (clockwise from north) with breakouts highlighted by green boxes. The breakouts on the borehole imaging are clear and distinct but these are not detected by the caliper tools. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
7. Figure 4.7 Total SHmax orientation from borehole breakouts from two different study methods. Left panel: Summary Rose Diagram highlighting SHmax orientations from borehole breakout analysis derived from dual caliper tools only (Evans and Brereton, 1990) mean orientation 149.87° with a circular standard deviation of 66.9°. Right panel: Summary Rose Diagram highlighting SHmax orientations from borehole breakout analysis from this study derived from borehole imaging tools only, mean orientation 150.9° with circular standard deviation of 13.1°.
8. Figure 4.8 Rose diagrams comparing stress field orientations from this study with Evans and Brereton (1990). Grey Rose diagrams show SHmax orientations identified from caliper eccentricity calculated by Evans and Brereton (1990) using dual-caliper eccentricity analysis. Black Rose diagrams showing SHmax orientations from borehole breakouts identified using borehole imaging tools in the Sellafield area of the UK, showing a mean SHmax orientation of 154.5° with a circular standard deviation of 18.5°.
9. Figure 4.9 Map highlighting orientations of SHmax derived from breakouts observed on borehole image logs for Yorkshire, showing a mean SHmax orientation of 147.5° with a circular standard deviation of 7.4°.
10. Figure 4.10 Map highlighting orientations of S_Hmax derived from breakouts observed on borehole image logs for West Staffordshire, showing a mean S_Hmax orientation of 156.7° with a circular standard deviation of 10.7°.
11. Figure 4.11 Map highlighting orientations of S_Hmax derived from breakouts observed on borehole image logs for Lichfield area, showing a mean S_Hmax orientation of 158.8° with a circular standard deviation of 8.4°.
12. Figure 4.12 Diagram of fractures radius and dip angle for the generated subsurface fracture map of the studied reservoir from a typical fractured basement reservoir in southern Vietnam (from Abdelazim and Rahman, 2016).
13. Figure 4.13 Schematic representation of reservoir pressure (Top) after (a) 1 year (left) and (b) 10 years (right) of water injection and fluid velocity (bottom) after (c) 1 year (left) and (d) 10 years (right) of water injection with P_inj=54.9 MPa, Δp=41.14 MPa, and p_i=34.6 MPa. Velocity contour map in (m/s) and pressure contour map in (psi).
14. Figure 4.14 The different steps used in optimizing the subsurface fracture map (from Abdulazim and Rahman, 2016).
15. Figure 4.15 Plot of fracture intensity versus mean square permeability.
16. Figure 4.16 Pressure change and pressure derivatives after inversion at wellbore location using the optimized subsurface fracture map presented in Figure 4.13.
17. Figure 4.17 Reconstructing fracture history.
18. Figure 4.19 The fracture orientations commonly found in the Middle East (Mahmood Akbar et al., 1993).
19. Figure 4.20 Different types of fractures. (a) intercrystal fractures; (b) unfilled tectonic micro-fractures; (c) dissolving fractures; (d) oblique fractures; (e) mesh fractures (from Cui et al., 2013).
20. Figure 4.21 Illustration of the fracture sets in: a folded environment with conceptual plot and stereonets of fractures families (Schmidt lower hemisphere) (From Borghi et al., 2015).
21. Figure 4.22 Illustration of the fracture sets in a reference environment (from Borghi et al., 2015).
22. Figure 4.23 Schematic of the two zones on the Earth’s crustal region.
23. Figure 4.24 Variation in anisotropic parameter as a function of fracture density for gas-saturated sandstone and fracture aspect ratio of 0.01 (redrawn from Shen, 1998).
24. Figure 4.25 Variation in anisotropic parameter as a function of fracture density for gas-saturated sandstone and fracture aspect ratio of 0.05 (redrawn from Shen, 1998).
25. Figure 4.26 Range of variation in anisotropic parameter as a function of fracture density for water-saturated sandstone and fracture aspect ratio of 0.01 (redrawn from Shen, 1998).
26. Figure 4.27 Range of variation in anisotropic parameter as a function of fracture density for water-saturated sandstone and fracture aspect ratio of 0.05 (redrawn from Shen, 1998).
27. Figure 4.28 Variation in anisotropic parameter as a function of fracture density for gas-saturated sandstone and fracture aspect ratio of 0.01 and 0.05 (redrawn from Shen, 1998).
28. Figure 4.29 Range of variation in anisotropic parameter as a function of fracture density for water-saturated sandstone and fracture aspect ratio of 0.01 (redrawn from Shen, 1998).
29. Figure 4.30 Range of variation in anisotropic parameter as a function of fracture density for water-saturated sandstone and fracture aspect ratio of 0.05 (redrawn from Shen, 1998).
30. Figure 4.31 Range of variation in anisotropic parameter as a function of fracture density for gas-saturated sandstone and fracture aspect ratio of 0.01 (redrawn from Shen, 1998).
31. Figure 4.32 Range of variation in anisotropic parameter as a function of fracture density for gas-saturated sandstone and fracture aspect ratio of 0.05 (redrawn from Shen, 1998).
32. Figure 4.33 Range of variation in anisotropic parameter as a function of fracture density for water-saturated sandstone and fracture aspect ratio of 0.01 (redrawn from Shen, 1998).
33. Figure 4.34 Range of variation in anisotropic parameter as a function of fracture density for water-saturated sandstone and fracture aspect ratio of 0.05 (redrawn from Shen, 1998).
34. Figure 4.36 Depiction of Warren and Root model.
35. Figure 4.37 Schematic of mud flow in a tight formation with fractures (after Dyke et al., 1995).
36. Figure 4.38 Data from a well drilled overbalanced until a certain depth and then switching to underbalanced operations. Immediately as UBD begins, the ROP greatly increases. (Redrawn from Woodrow et al., 2008).
37. Figure 4.39 Mud log data from a portion of a well drilled underbalanced in the Piceance Basin. The large gas peaks were attributed to natural fractures that were intersected by the wellbore (redrawn from Myal and Frohne, 1992).
38. Figure 4.40 Schematic of the model used by Norbeck (2012).
39. Figure 4.41 Locations of conductive natural fractures along the lateral of Well A-1 (redrawn from Corbeck, 2010).
40. Figure 4.42 Cross-plot of mud pit volume peak vs. gas peak corresponding to each conductive natural fracture location identified for Well A-1 (redrawn from Corbeck, 2010).
41. Figure 4.43 Locations of conductive natural fractures along the lateral of Well A-2 (redrawn from Corbeck, 2010).
42. Figure 4.44 Cross-plot of mud pit volume peak vs. gas peak corresponding to each conductive natural fracture location identified for Well A-2 (redrawn from Corbeck, 2010).
43. Figure 4.45 Plan view of Field A. Wells A-1 and A-2 are parallel wells drilled in the South – North direction. The lateral spacing between these wells is roughly 800 ft (Redrawn from Corbeck, 2010).
44. Figure 4.46 Natural Fracture System Orientation #1 for Field A. A dominant pattern exists that seems to indicate the presence of a natural fracture system oriented at N65°E.
45. Figure 4.47 REV in fractured reservoirs is greater than core size (redrawn from Islam et al, 2014).
46. Figure 4.48 Comparison of methods of visualizing a 4 m long borehole breakout from St Bees Shale Formation, from borehole Sellafield 13A in Cumbria (10 m vertical borehole section). Left-hand panel: conventional logs including perpendicular dual-caliper and gamma-ray. Centre panel: Unwrapped circumferential resistivity borehole imaging (FMI) (clockwise from north) with breakout highlighted by the green boxes. Right panel: Unwrapped circumferential acoustic borehole amplitude imaging (UBI) (clockwise from north) with breakout highlighted by green boxes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
47. Figure 4.49 Comparison of resistivity images visualizing Drilling Induced tensile Fractures (DIFs) from PCM Measures in the Melbourne 1 well, Yorkshire (10 m vertical borehole section). Left-hand panel: conventional logs including perpendicular dual-caliper and gamma-ray log. Right: Unwrapped circumferential resistivity borehole imaging (CMI). (Figure from Kingdon et al., 2016)
48. Figure 4.50 Example of an AFIT image log. The horizontal axis is azimuth around the wellbore. The sinusoids are interpreted as planar geologic features (from Mclean and McNamara 2011).
49. Figure 4.51 These figures illustrate the concept that critically stressed natural fractures predominantly contribute to fluid flow through reservoirs (redrawn from Barton and Zoback 2002).
50. Figure 4.52 Processing Flow Chart of Density and Acoustic Well logging data (from Batini et al., 2002).
51. Figure 4.53 Processing Flow Chart for Fracture analyses from Well logging.
52. Figure 4.54 Temperature profile shows the existence of a fractured zone (redrawn from Batini et al., 2002).
53. Figure 4.55 Fracture signatures from geophysical logs (from Batini et al., 2002).
54. Figure 4.56 Fracture Analysis from CBIL (from Batini et al., 2002).
55. Figure 4.57 Fracture asset mapped as pole density (from Batini et al., 2002).
56. Figure 4.58 Core permeability vs. core porosity for a heterogeneous formation (from Hamada, 2009).
57. Figure 4.59 Developing filter out of NMR data.
58. Figure 4.60 Filter for Well “A” (from Hamada, 2009).
59. Figure 4.61 Filter for “Well B” (Hamada, 2009).
60. Figure 4.62 Filter for “Well C” (from Hamada, 2009).
61. Figure 4.63 Correlation between core permeability and core porosity (from Hamada, 2009).
62. Figure 4.64 Correlation between pereambility and BG (from Hamada, 2009).
63. Figure 4.65 Correlation of permeability vs. S_gx0 (from Hamada, 2009).
64. Figure 4.66 Permeability distribution (track 6) for Well “A” (from Hamada, 2009).
65. Figure 4.67 Permeability distribution (track 6) for Well “B” (from Hamada, 2009).
66. Figure 4.68 Permeability distribution (track 6) for Well “C” (from Hamada, 2009).
67. Figure 4.69 Correlation between core Pc (blue dots) and NMR Pc (pink line).
68. Figure 4.70 Typical relative permeability and capillary pressure curve for an unconventional gas reservoir (From Islam, 2014).
69. Figure 4.71 Representation of the relationships of the relationships between capillary pressure, and position within a trap of a tight reservoir.
70. Figure 4.72 Porosity is only slightly affected by net stress for carbonate formations (data from Lucia (2007) and Hariri et al. (1995).
71. Figure 4.73 Porosity variation with effective stress (after Okiongbo, 2011).
72. Figure 4.74 Effect of geological age on porosity (from Ehrenberg et al., 2009).
73. Figure 4.75 Porosity variation under net overburden conditions (from Petrowiki.spe.org).
74. Figure 4.76 Effect of overburden stress on matrix and fracture permeability.
75. Figure 4.77 General trend of N_c vs. residual saturation.
76. Figure 4.78 Several correlations between capillary number and residual oil saturation. Breakthrough PV
77. Figure 4.79 General trend of breakthrough recovery and instability number.
78. Figure 4.80 Instability number vs. breakthrough recovery for immiscible gas injection.
79. Figure 4.81 Correlation of mobility ratio with oil recovery for waterflood.
80. Figure 4.82 Correlation between breakthrough recovery and Peters-Flock stability number.
81. Figure 4.83 There is no correlation between capillary number and water breakthrough.
82. Figure 4.84 Typical CO₂ WAG process.
83. Figure 4.85 Breakthrough recovery vs. instability number for miscible flood.
84. Figure 4.86 End-point relative permeability correlates with residual oil saturation.
85. Figure 4.87 Relative permeability curves are altered by lowering of interfacial tension.
86. Figure 4.88 Permeability jail can be removed with thermal or chemical alteration in an unconventional reservoir (from Islam, 2014).
87. Figure 4.89 Critical gas saturation for various permeability values of a gas condensate reservoir.
88. Figure 4.90 Permeability vs. porosity correlation depends largely on the nature of heterogeneity.
89. Figure 4.91 Correlation of porosity vs. permeability for various types of formation.
90. Figure 4.92 Improvement factor due to open fractures.
91. Figure 4.93 The effect of fractures on k_v/k_h.
92. Figure 4.94 Pore size can be affected by fracture distribution and thereby impact residual oil mobilization.
93. Figure 4.95 Rose diagram helps quantify the role of fractures.
94. Figure 4.96 Transiting from macro-pore scale to an initial reservoir model, as experienced in Weyburn project of Canada.
95. Figure 4.97 REV for a reservoir is much larger than the core samples collected.
96. Figure 4.98 Laboratory test results under an overburden pressure of 50 MPa.
97. Figure 4.99 Determination of the nature of fractures from hk data.
98. Picture 4.1 Surface fractures (Akbar et al., 1993).
99. Picture 4.2 (a) Example of borehole breakout taken by a downhole camera. (b) Example of a borehole fracture observed on a downhole camera (Figure 4(b) from Asquith and Krygowski, 2004).
100. Picture 4.3 Photograph of a microresistivity imaging device (left) passing through drillpipe and (right) in the open position (from Kalathingal and Kuchinski 2010).
101. Picture 4.4 Viscous fingering in a miscible displacement process.
102. Picture 4.5 Outcrops often show how fractures must be prevalent in consolidated reservoir formations.
103. Picture 4.6 Thin section photomicrographs of sandstones illustrating A, occurrence and distribution of K-feldspar grains (stained yellow). B, disseminated pyrite (dark grains); C, bitumen (opaque material) filling pores and permeating matrix, and D, secondary intergranular porosity with relict carbonate cement.
104. Picture 4.7 Thin section photomicrographs of sandstones depicting A, open (noncemented), discontinuous fractures parallel to bedding. Such fractures are abundant and form a pervasive network in sandstones adjacent to mature shales, B, secondary porosity associated with horizontal fracture swarms, C, microscopic fractures crosscutting framework quartz grains. Note bitumen filling secondaryintergranular pores, and D, calcite cemented vertical fracture.
105. Picture 4.8 Slabbed sandstone displaying reticulated fracture network on wet surface. Note that the permeable nature and distribution of fractures are not apparent when surface is dry.
106. Picture 4.9 Commercial softwares can help identify fractures in FMS logs.
107. Picture 4.10 The idea is to transit from microscopic to reservoir scale, following the correct scaling laws.
108. Picture 4.11 Different scenarios in fractured shale formation.
Chapter 5
1. Figure 5.1 Schematic representation of shear-wave splitting through an anisotropic material and compression-wave conversion to shear waves from reflection at an isotropic-anisotropic boundary (Bates, 1999).
2. Figure 5.2 Principal stresses and the creation of fractures (Victor Aarre, 2012).
3. Figure 5.3 Folds, faults and fractures along an anticline (Aarre, 2012).
4. Figure 5.4 Computing attributes on a time surface in a 3D seismic volume (Victor Aarre, 2012).
5. Figure 5.5 Synthetic rock samples (top panels) employed to analyze fracture connectivity effects on P wave attenuation. Fractures are denoted by solid “wiggly” lines. Sample a) contains a single sub-horizontal fracture, while samples b) and c) contain two quasi-orthogonal fractures without and with intersection, respectively. The bottom panel shows the corresponding inverse P wave quality factor as a function of frequency for a vertical direction of propagation (from Rubino et al., 2013).
6. Figure 5.6 Real part of the fluid pressure normalized with respect to the amplitude of the stress applied on the top boundaries of the synthetic rock samples, for frequencies of 1.3 kHz (left panels) and 29 kHz (right panels). Top and bottom panels correspond to the rock samples b) and c) in Figure 5.5, respectively.
7. Figure 5.7 Schematic illustration of volume of investigation for typical geophysical measurements in a borehole (Paillet, 1994).
8. Figure 5.8 A, Idealized model of fracture as a planar, uniform-aperture opening between two parallel rock faces; and B, natural fracture model illustrating variable fracture aperture and contacts on asperities (Paillet, 1994).
9. Figure 5.9 Example of conventional geophysical logs for a borehole in foliated granitic schist (Paillet, 1994).
10. Figure 5.10 Examples of fracture imaging using the acoustic televiewer (BHTV): A, Schematic illustration of fracture strike and dip interpretation; B, comparison of core photograph with televiewer log for fractures in granite; and C, comparison of core fracture interpretation with televiewer log for a fracture zone in granite (Paillet, 1994).
11. Figure 5.11 The FMI-HD high-definition formation microimager (SLB, 2015).
12. Figure 5.12 Micro-Conductivity Imager Tool (MCI) (SLB, 2015).
13. Figure 5.13 MCI Log Example (SLB, 2015).
14. Figure 5.14 (a) Fractal dimension contour and fracture orientation diagram calculated by R/S analysis of Y1 Formation in X area; (b) Fractal dimension contour and fracture orientation diagram calculated by R/S analysis of Y2 Formation in X area; (c) Fractal dimension contour and fracture orientation diagram calculated by R/S analysis of Y3 Formation in X area.
15. Figure 5.15 NMR logging-tool response compared to conventional logging tools (Petrowiki).
16. Figure 5.16 Generalized map of the major Cenozoic sedimentary basins and current tectonic elements in the South China (East Vietnam) Sea (from Cuong and Warren, 2009).
17. Figure 5.17 Location of the field within the basin (From Cuong, 2010).
18. Figure 5.18 Seismic profiles across Bach Ho field (From Cuong and Warren, 2009).
19. Figure 5.19 The presence of three zones was clear from the early seismic data.
20. Figure 5.20 Seismic profiles across Bach Ho field.
21. Figure 5.21 Generalized stratigraphic column of Cuu Long basin (From Cuong, 2010).
22. Figure 5.22 Basin Strategraphy (from Cuong and Warren, 2009).
23. Figure 5.23 Classification of Bach Ho basement.
24. Figure 5.24 Interpreted image log.
25. Figure 5.25 Fault and fracture system diagram of Bach Ho field.
26. Figure 5.26 The study area is in the Eagle Ford shale (Jones, 2012).
27. Figure 5.27 Log response in the Eagle Ford (Jones, 2012).
28. Figure 5.28 Seismic response (arrows) of Eagle Ford shale in area of interest (Jones, 2012).
29. Figure 5.29 Time structure map of auto-tracked Eagle Ford event Values range from 2.18 to 2.52 seconds (Jones, 2012).
30. Figure 5.30 Computed Frequency Spectrum from 1.8 to 2.650 seconds (Jones, 2012).
31. Figure 5.31 Wedge Model Tuning Analysis (Jones, 2012).
32. Figure 5.32 Two ways that opening-mode fractures seal (Laubach, 2003).
33. Figure 5.33 Sealed microfractures detected using scanned CL (Laubach, 2003).
34. Figure 5.34 Large and small fractures illustrating emergent threshold (Laubach, 2003).
35. Picture 5.1 Cores show secondary cementing activities (from Cuong and Warren, 2009).
Chapter 6
1. Figure 6.1 Water-fire yin-yang, showing how without one the other is meaningless.
2. Figure 6.2 The sun, Earth, and moon all are moving at a characteristic speed in infinite directions.
3. Figure 6.3 Orbital speed vs size (not to scale) (From Islam, 2014).
4. Figure 6.4 The heartbeat (pictured above) represents the natural frequency of a human, whereas brain waves represent how a human is in harmony with the rest of the universe.
5. Figure 6.5 Maximum and minimum heart rate for different age groups.
6. Figure 6.6 Tangible/intangible duality continues infinitely for maga scale to nano scale, from infinitely large to infinitely small.
7. Figure 6.7 Characteristic speed (or frequency) can act as the unique function that defines the physical state of matter.
8. Figure 6.8 Natural light pathway.
9. Figure 6.9 Wavelength spectrum of sunlight.
10. Figure 6.10 Colors and wave lengths of visible light.
11. Figure 6.11 Artificial and natural lights affect natural material differently.
12. Figure 6.12 Wavelength spectrum of visible part of sunlight.
13. Figure 6.13 Visible natural colors as a function of various wavelengths and intensity of sunlight.
14. Figure 6.14 Wavelength and radiance for forest fire, grass and warm ground (From Li et al., 2005).
15. Figure 6.15 Blue flame radiance for butane.
16. Figure 6.16 Artificial light spectrum.
17. Figure 6.17 Comparison of various artificial light sources with sunlight.
18. Figure 6.18 Comparing within the visible light zone will enable one to rank various artificial light sources.
19. Figure 6.19 Number of particles vs. particle size (not to scale, modified from Khan and Islam, 2012).
20. Figure 6.20 Natural flame colors and temperature.
21. Figure 6.21 Formation of a shield with dark and clear lenses.
22. Figure 6.22 Benefit to environment depends entirely on the organic nature of energy and mass.
23. Figure 6.23 Oxygen cycle in nature involving the Earth.
24. Figure 6.24 Hydrogen cycle in nature involving the Earth.
25. Figure 6.25 Water cycle, involving energy and mass.
26. Figure 6.25 Water cycle, involving energy and mass.
27. Figure 6.26 Even in the short term, the modern age is synonymous with decoupling of economic index from environmental index.
28. Figure 6.27 Crude oil characteristics vary widely, making it difficult to characterize it with a single criterion.
29. Figure 6.28 Throughout history logic and scientific accomplishments have been taken to heights by great savants and prophets (from Islam et al., 2013b).
30. Figure 6.29 Whole rock Rb-Sr isochron diagram, basement samples.
31. Figure 6.30 Natural processing time differs for different types of oils.
32. Figure 6.31 Natural processing enhances intrinsic values of natural products.
33. Figure 6.32 The volume of petroleum resources increases as one moves from conventional to unconventional.
34. Figure 6.33 Cost of production increases as efficiency, environmental benefits and real value of crude oil declines (modified from Islam et al., 2010).
35. Figure 6.34 Overall refining efficiency for various crude oils (modified from Han et al., 2015).
36. Figure 6.35 Crude API gravity and heavy product yield of the studied US and EU.
37. Figure 6.36 Current estimate of conventional and unconventional gas reserve.
38. Figure 6.37 Abundance of natural resources as a function of time.
39. Figure 6.38 As natural processing time increases so does reserve of natural resources (from Chhetri and Islam, 2008).
40. Figure 6.39 ‘Proven’ reserve is miniscule compared to total potential of oil.
41. Figure 6.40 Reserve to production ratio for various regions.
42. Figure 6.41 Proved reserve for various regions.
43. Figure 6.42 Crude oil production continues to rise overall (From EIA, 2017).
44. Figure 6.43 U.S. reserve variation in recent history.
45. Figure 6.44 Technically recoverable oil and gas reserve in the United States.
46. Figure 6.45 Sulfur content of U.S. crude over last few decades.
47. Figure 6.46 Declining API gravity of U.S. crude oil.
48. Figure 6.47 Worldwide crude oil quality.
49. Figure 6.48 Gulf of Mexico Basin region, the petroleum-producing region of the Norphlet and Smackover Formations. Both formations produce in both onshore and offshore locations; the Norphlet produces from Mobile Bay (from USGS, 2008).
50. Figure 6.49 General region from which petroleum is produced from formations discussed in this section, including the Minnelusa (Powder River Basin), Morrow (Anadarko and Denver Basins), Bakken (Williston Basin), and Wasatch (Uinta and Piceance Basins) Formations (From USGS, 2008).
51. Figure 6.50 Area from which petroleum is produced from the Frio Formation, Barnett Shale, Ellenburger Group, and Spraberry Formation. Extent of depositional environments in the Frio (such as the Norias delta complex or the Buna barrier–strandplain) from Galloway et al. (1982). For the Barnett, the locations of the Llano uplift and Ouachita thrust belt mark the southern and eastern limits of the Fort Worth Basin, respectively. Horseshoe Atoll is a Pennsylvanian structure that effectively separates productive rocks of the Spraberry Formation (to the south) from nonproductive rocks (to the north). (From USGS, 2008).
52. Figure 6.51 Three phases of conventional reserve.
53. Figure 6.52 Unconventional reserve growth can be given a boost with scientific characterization.
54. Figure 6.53 Probability distributions for production from wells of an oil or gas field (distributions based on hypothetical data—peak monthly production, peak yearly production, or cumulative production). Each point represents a well, and four fields (VC1–VC4) are depicted. In this type of plot log normal distributions plot as straight lines, and steeper slopes of lines correspond with a greater range of production and thereby greater production variability. The variation coefficient VC = (F5–F95)/F50 provides a dimensionless numerical value for the variability of each data set, and its value increases as slope increases (from USGS, 2008).
55. Figure 6.54 Production data of gas wells in fields in the Ellenburger Group karst and platform categories, Frio Formation fluvial category, Morrow Formation incised-valley category, Minnelusa Formation Minnelusa category, and Wasatch Formation Green River-source category (from USGS, 2008).
56. Picture 6.1 It is reported that two galaxies are on a collision course (Cowan, 2012).
57. Picture 6.2 Wood fire is natural, beneficial, and sustainable.
58. Picture 6.3 Forest fire in Canada is an excellent example of natural flame.
59. Picture 6.4 Burning vehicles are examples of artificial flame.
60. Picture 6.5 Depiction of a flame.
61. Picture 6.6 Fire from wood (top left) is part of the organic cycle whereas smoke from a tungsten bulb (bottom right) is that of the mechanical (hence implosive and non-sustainable) cycle. While these extremes are well known, confusion arises as to how to characterize plastic fire (top right) and smoke from a cigarette (bottom left) that have very similar CO₂ emission as in natural wood burning.
62. Picture 6.7 Images of burning crude oil from shale oil (left) and refined oil (right).
Chapter 7
1. Figure 7.1 Schematic showing the mixing between fracture and matrix flow as a result of convective transport in the matrix.
2. Figure 7.2 Discrete representation in idealized fracture network. Flow and transport is from left to right, with a concentration boundary condition on the left side. The total domain is 100 sectors long.
3. Figure 7.3 The knowledge model.
4. Figure 7.4 The Aphenomenal model (after Islam et al., 2016).
5. Figure 7.5 Sources of errors in modeling petroleum reservoirs.
6. Figure 7.6 Gas content in free and absorbed gas (from Frantz et al., 2005).
7. Figure 7.7 Typical decline curve for a tight gas reservoir (redrawn from Holditch, 2006).
8. Figure 7.8 Comparison of Linear model (homogeneous, zero skin, closed) with Fekete, ECLIPSE and Ozkan Laplace solution (redrawn from Bello, 2009).
9. Figure 7.9 Pressure response in a fractured formation.
10. Figure 7.10 Typical pressure oscillations in slit flow at 150 C (from Delgadillo-Velazquez et al., 2008).
11. Figure 7.11 Number of coin tosses vs. head (+ sign) and tail (sign) in an actual experiment.
12. Figure 7.12 As the number of coin tosses is increased, the head and tail toss ratio converges toward 2 and not 1.
13. Figure 7.13 Number of trials is likely to yield different sets of phenomenal values that apply to different entities.
14. Figure 7.14 Simpson paradox highlights the problem of targeted statistics.
15. Figure 7.15 Using statistical data to develop a theoretical correlation can make an aphenomenal model appealing, depending on which conclusion would appeal to the audience.
16. Figure 7.16 Economic well-being is known to fluctuate with time.
17. Figure 7.16 Major steps used to develop reservoir simulators (redrawn from Abou-Kassem et al., 2006).
18. Figure 7.17 For simulation to be realistic (emulation), one must add new features to the modeling technique.
19. Figure 7.18 Example of double porosity reservoir.
20. Figure 7.19 Radial symmetrical flow through a fracture network similar to a porous media.
21. Figure 7.20 Single fracture simulating a radial symmetrical flow through fracture network (Baker 1955).
22. Figure 7.22 Multi-block system simulating a radial symmetrical flow through fracture network (Warren & Root, 1963).
23. Figure 7.23 Parallel multi-fracture system simulating a radial symmetrical flow through fracture network (Kazemi 1969).
24. Figure 7.24 Multi-sphere blocks having an orthogonal arrangement simulating a radial symmetrical flow through fracture network (De Swaan, 1975).
25. Figure 7.25 Log-log plot showing an example of wells A and B with wellbore storage and skin in a double porosity reservoir, pseudo steady state interporosity flow (Bourdet & Gingartin, 1980).
26. Figure 7.26 Semi-log plot of the wells A and B (Bourdet & Gingartin, 1980).
27. Figure 7.27 Pressure and derivative of examples A and B (Bourdet et al., 1984).
28. Figure 7.28 Influence of ω on pressure derivative log-log curves (Bourdet et al., 1984).
29. Figure 7.29 Influence of ω on semi-log plot for Figure 7.28 (Bourdet and Gingartin, 1980).
30. Figure 7.30 Influence of X on pressure and derivative log-log curves (Bourdet et al., 1984).
31. Figure 7.31 Influence of X on semi-log plot for Figure 7.30 (Bourdet and Gingartin, 1980).
32. Figure 7.32 An example of double permeability model (Bourdet, 2002).
33. Figure 7.33 Pressure derivative response for a well with wellbore storage and skins in double permeability reservoir, the two layers are producing into the well. C_D = 1000, S₁ = S₂ = 0.02, κ = 0.8, λ. = 6 × 10⁻⁸ (Bourdet, 2002).
34. Figure 7.34 Log-log plot of double permeability responses with same values for all the parameters except κ. The two dashed curves correspond to the homogeneous reservoir response (C_D e^2S = 1), and double porosity response (κ = 1) (Bourdet, 2002).
35. Figure 7.35 Semi-log plot for three double permeability examples (Bourdet 2002).
36. Figure 7.36 Log-log plot of double permeability response, the two layers are producing into the well. C_D = 1, S₁ = S₂ = 0, ω = 0.1, λ = 4x10–4, κ = 0.6, 0.9, 0.99, 0.999. The two dashed curves correspond to the homogeneous reservoir response (C_D e^2S = 1), and double porosity response (κ = 1) (Bourdet, 2002).
37. Figure 7.37 Semi-log plot for three double permeability examples of Figure 7.36 (Bourdet, 2002).
38. Figure 7.38 Pressure and derivative response for a well with wellbore storage and skin in double permeability reservoir, only one layer is producing into the well. C_D = 1000, S₁ = 100, S₂ = 0, ω = 0.1, κ = 0.9, λ = 6 x 10⁻⁸ (Bourdet, 2002).
39. Figure 7.39 Log-log plot of double permeability responses, only one layer is producing into the well (Bourdet 2002).
40. Figure 7.40 Semi-log plot of Figure 7.39 double permeability examples (Bourdet 2002).
41. Figure 7.41 A schematic description of history matching and geological activities regarding to a reservoir.
42. Figure 7.42 A detailed description of a reservoir.
43. Figure 7.43 The fluid continuum domain and the variation of fluid density (modified from Bear, 1975).
44. Figure 7.44 Definition of representative elementry volume (REV) and representative elementary property (REP).
45. Figure 7.45 The variation of average porosity as a function one-dimensional proxy, L, of the support (measurement) volume (From lake and Srinivasan, 2004).
46. Figure 7.46 A typical pressure-temperature diagram of crude-oil.
47. Figure 7.47 Schematic of water-oil and gas-oil contacts in a conventional and a fractured reservoir (Luis, 1980).
48. Figure 7.48 Type of displacement from matrix block: a. displacement under capillary forces; b. displacement under capillary and gravity forces (Racht, 1982).
49. Figure 7.49 Illustration of the elements of a fractured reservoir (Luis, 1980).
50. Figure 7.50 Sudation from a matrix element (Luis, 1980).
51. Figure 7.51 Simultaneous recovery mechanism (Luis, 1980).
52. Figure 7.52 Example of production rate versus time for a field (Racht, 1982).
53. Figure 7.53 First stage migration in a fractured reservoir (Racht, 1982).
54. Figure 7.54 Second stage of migration. Equal blocks differently saturated with hydrocarbons as a result of Φ, k, and P_C (Racht, 1982).
55. Figure 7.55 Water drained by migrated oil as a function of block height and capillary pressure curve, in case of uniform matrix but different block heights (Racht, 1982).
56. Figure 7.56 Saturation distribution in a non-fractured reservoir as a function of facies variations (Racht, 1982).
57. Figure 7.57 Water-oil contact in fractures after reservoir fracturing. In zone (1), water displaces oil by imbibition. In zone (2), oil drains water from matrix (Racht, 1982).
58. Figure 7.58 Saturation redistribution after reservoir fracturing (Racht, 1982).
59. Figure 7.59 Data gap in geophysical modeling (after Islam, 2001).
60. Figure 7.60 Identification of the four fractured systems (from Moridis et al., 2010).
61. Figure 7.61 Simplification of fracture geometry.
62. Figure 7.62 Flow profiles for non-planar and non-orthogonal fractures (from Olorode, 2011).
63. Figure 7.63 Conventional simulation technques cannot simulate even fundamental features of various unconventional gas flow.
64. Figure 7.53 Schematic of the porous system considered for derivation of the diffusivity equation.
65. Figure 7.65 Schematic of fluid flow between two parallel plates described by the Navier-Stokes equations.
66. Figure 7.66 Schematic of fluid flow between two parallel plates with pile of grain particles in between causing extra resistance to flow.
67. Figure 7.67 Schematic of fluid flow between two parallel plates filled with grain particles in between representing porous media flow.
68. Figure 7.68 The variation of the water and oil relative permeability, the fractional water flow rate and the differentiation of fractional water flow rate with the water saturation.
69. Figure 7.69 The capillary pressure variation and its first and second derivatives as a function of the water saturation.
70. Figure 7.70 The water saturation distribution with and without the effect of capillary pressure using ADM.