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1 Chapter 1Figure 1.1 Different components of reservoir characterization, from Fornel and E...Figure 1.2 Wide range of physical scale for different data types associated with...Figure 1.3 SURE Challenge: Having to deal with the wide ranges of Scale, Uncerta...Figure 1.4 Areal coverage of well data is complemented by the larger areal sampl...Figure 1.5 Vertical and spatial resolution of various geophysical, well logs and...Figure 1.6 Time-lapse seismic response changes caused by different positions of ...Figure 1.7 Use of conventional seismic, well log data and MEQ data to create a 3...Figure 1.8 The entire process of reservoir model updating through 4D seismic mod...Figure 1.9 Reservoir modeling process workflow. The process takes control of the...Figure 1.10 Integrated reservoir modeling, fluid simulation update and reiterati...

2 Chapter 2Figure 2.1 Common methods for estimating the shear wave velocity.Figure 2.2 The placement of the test device is shown in schemati.Figure 2.3 Schematic, placement of sample with transducer and the top cap.Figure 2.4 (a) The core flooding system, (b) Image of the holder connected to th...Figure 2.5 Compressional and shear wave velocity vs different effective pressure...Figure 2.6 P-wave velocity (experimental and estimated) at different effective p...Figure 2.7 S-wave velocity (experimental and estimated) at different effective p...Figure 2.8 Cross plot of estimated P-wave velocities vs. laboratory measurements...Figure 2.9 Cross plot of the estimated S-wave velocities vs. laboratory measurem...Figure 2.10 Plot of experimental shear wave velocity against compressional wave ...Figure 2.11 Plot of estimated shear wave velocity against compressional wave vel...Figure 2.12 Rate of variability of experimental/estimated velocities with increa...Figure 2.13 Plot of Laboratory vs. estimated Bulk modulus (K) of rock sample.Figure 2.14 Plot of laboratory vs. estimated shear modulus.Figure 2.15 Plot of laboratory vs. estimated Young’s modulus.

3 Chapter 3Figure 3.1 Divergence values for records in training and test set. The horizonta...Figure 3.2 Expected versus posterior false discovery rate for two sizes of the t...Figure 3.3 Histograms of area under posterior ROC Curve (AUC) for three anomaly ...Figure 3.4 AUC histograms and quantile regions calculated from 1000 pairs of tra...Figure 3.5 Median of posterior AUC for three AD classifiers as a function of the...Figure 3.6 Quantile width of AUC distribution calculated on anomaly detection re...Figure 3.7 Sparsity values on the records of the training and test sets. Horizon...Figure 3.8 Anomaly detection. Histograms of posterior true discovery rate (TDR) ...Figure 3.9 Histograms of anomalyIndicator for three types of classifiers. (a)-ad...

4 Chapter 4Figure 4.1 Type of Kerogen present in source rocks.Figure 4.2 It shows kerogen conversion and maturity (Tmax).Figure 4.3 Bulk compositions of oils and bitumen studied.Figure 4.4 GC-Fingerprint of a carbonate-derived oil.Figure 4.5 GC-Fingerprint of a shale facies derived oil.Figure 4.6 GC- Fingerprint of Tertiary Oil No. 3 which is severely biodegraded.Figure 4.7 GC- Fingerprint of Cretaceous Oil No. 3 which is not biodegraded.Figure 4.8 Triterpane fingerprints of carbonate-derived oil.Figure 4.9 Triterpane fingerprint of shale-derived oil.Figure 4.10 Cross plot of dibenzothiophene/phenanthrene versus pristane/phaytane...

5 Chapter 5Figure 5.1a Prototype single-siren tool (assembled).Figure 5.1b Prototype single-siren tool (disassembled).Figure 5.2a Example MWD collar used for siren frequency and source placement opt...Figure 5.2b Drillpipe p/Dp to 12 Hz.Figure 5.2c Drillpipe p/Dp to 50 Hz.Figure 5.2d Drillpipe p/Dp to 100 Hz.Figure 5.3a Three step pulse recovery in noisy environment (pressure, vertical; ...Figure 5.3b Three step pulse recovery (very noisy environment).Figure 5.4a Early 1980s “stable closed’ siren (left) and improved 1990s “stable-...Figure 5.4b Streamline traces for erosion analysis.Figure 5.4c Velocities for erosion and pressure analysis.Figure 5.5a Short “hydraulic” wind tunnel system.Figure 5.5b Very long “acoustic” wind tunnel.Figure 5.5c A pair of ganged or tandem mud sirens.Figure 5.5d Some sirens tested in wind tunnel.Figure 5.5e Evaluation of hub convergence effects on signal strength and torque.Figure 5.5f Flow straighteners (PVC tubing) for upstream and downstream use.Figure 5.5g Flow meter.Figure 5.5h Siren test section with differential transducers.Figure 5.5i Real-time data acquisition and control system.Figure 5.5j Torque, position and rpm counter.Figure 5.5k Short wind tunnel, “bird’s eye” view.Figure 5.5l Test shed window overlooking long wind tunnel.Figure 5.5m Piezoelectric transducer closest to siren for constructive interfere...Figure 5.5n Distant multiple transducer array setup.Figure 5.5o Fireworks for low frequency noise generation, when all else is unava...Figure 5.6a Siren Dp vs ω at with flow rate fixed.Figure 5.6b Low-frequency (10 Hz) long wind tunnel data.Figure 5.6c Low-frequency (10 Hz) signal recovery.Figure 5.6d High-frequency (45 Hz) long wind tunnel data.Figure 5.6e High-frequency (45 Hz) signal recovery.Figure 5.7a Method 4-3, A-002 (8 feet).Figure 5.7b Method 4-3, A-003 (12 feet).Figure 5.7c Method 4-3, A-004 (16 feet).Figure 5.7d Method 4-3, A-005 (20 feet).Figure 5.7e Method 4-3, B-002 (8 feet).Figure 5.7f Method 4-3, B-004 (16 feet).Figure 5.7g Method 4-3, A-002 (8 feet).Figure 5.7h Method 4-3, A-003 (12 feet).Figure 5.7i Method 4-3, A-004 (16 feet).Figure 5.7j Method 4-3, A-005 (20 feet).Figure 5.7k Method 4-3, B-003 (12 feet).Figure 5.7l Method 4-3, B-006 (24 feet).Figure 5.7m Method 4-3, A-002 (8 feet).Figure 5.7n Method 4-3, A-004 (16 feet).Figure 5.7o Method 4-3, A-006 (24 feet).Figure 5.7p Method 4-3, B-006 (24 feet).Figure 5.7q Method 4-3, B-008 (32 feet).Figure 5.8a Method 4-4, Run C-1.Figure 5.8b Method 4-4, Run C-2.Figure 5.8c Method 4-4, Run C-3.Figure 5.8d Method 4-4, Run C-4.Figure 5.8e Method 4-4, Run C-5.

6 Chapter 6Figure 6.1 Histograms of the number of clusters and the number of records in ind...Figure 6.2 Clustering of a randomized test set that includes regular and anomalo...Figure 6.3 Number of appearances of individual records in different clusters of ...Figure 6.4 Histograms of three prior anomaly parameters.Figure 6.5 Histograms of three posterior parameters characterizing presence or a...Figure 6.6 Anomaly indexes of individual records. High permeability anomaly.Figure 6.7 Histograms of three prior anomaly indexes.Figure 6.8 Histograms of three posterior anomaly indexes.Figure 6.9 Values of anomaly index of individual records in gas-sand and brine-s...

7 Chapter 7Figure 7.1 Dissimilarities for 14 parameters in four combinations of conditions....Figure 7.2 Histograms of the values of the Group_A parameter calculated for gas-...Figure 7.3 Histograms of the Group_A parameter calculated for brine-sand and sha...Figure 7.4 ROC curves for four high performing parameters. Method: LDA.Figure 7.5 Mean area under ROC curve for 14 parameters. Area under Rock Curve av...Figure 7.6 Group_A vs. Poisson’s ratio cross section of gas sand vs. brine sand ...Figure 7.7 Vp/Vs versus λ cross section of gas-sand vs. brine-sand or shale.Figure 7.8 ROC Curves for KNN and LDA classification techniques for 2D predictor...

8 Chapter 8Figure 8.1 Comparison of solutions for n = 0.3.Figure 8.2 Comparison of solutions for n = 0.8.Figure 8.3 Determining n for Example 4.Figure 8.4 Matching cart for Example 1.Figure 8.5 Matching chart for Example 2.Figure 8.6 Matching chart for Example 3.Figure 8.7 Matching chart for Example 4.Figure B1 Pressure and pressure derivative type c.Figure B2 Pressure and pressure derivative type curve for n=0.4.Figure B3 Pressure and pressure derivative type curve for n=0.5.Figure B4 Pressure and pressure derivative type curve for n=0.6.Figure B5 Pressure and pressure derivative type curve for n=0.7.Figure B6 Pressure and pressure derivative type curve for n=0.8.Figure B7 Pressure and pressure derivative type curve for n=0.9.Figure B8 Pressure and pressure derivative type curve for n=1.0.

9 Chapter 9Figure 9.1 Plot of sorted values of individual permeability forecasts generated ...Figure 9.2 Diagram of the structure of the second level committee machine. Weigh...Figure 9.3 Mean absolute bias of permeability forecast with additive multiplicat...Figure 9.4 Individual forecasts, output of the first level committee machine, an...Figure 9.5 Bias of permeability forecast by the first level committee machine. O...Figure 9.6 Bias of permeability forecast by the first level committee machine. O...Figure 9.7 Permeability forecast with multiplicative, exponential, and additive ...Figure 9.8 Permeability forecast with second level committee machine.Figure 9.9 Workflow for permeability prediction.

10 Chapter 10Figure 10.1 Distribution of elastic deformation modules of coalbeds: (a) distrib...Figure 10.2 Geostatic pressure in wells.Figure 10.3 Distribution of the lateral thrust of coalbeds.Figure 10.4 Gas flow rates of Taldinskaya area wells.Figure 10.5 Results of experimental studies using the multiwave VSP technique: (...

11 Chapter 11Figure 11.1 Histogram of a number of individual forecasts in Monte Carlo cycle o...Figure 11.2 Bias of the forecast with permeability Models 1 and 3. Bias is calcu...Figure 11.3 Ordered values of permeability and their forecasts with permeability...Figure 11.4 Forecast bias as a function of permeability values for two machine l...Figure 11.5 Individual and committee machine permeability forecasts in compariso...Figure 11.6 Instability indexes of individual permeability forecasts by support ...Figure A2.1 Configuration of a Committee Machine.Figure A2.2 Multilayer Perceptron (a) without and (b) with short cut connections...

12 Chapter 12Figure 12.1 Map of regional fault zones, folds, and salt occurrences in the nort...Figure 12.2 The general location of identified deepwater hydrocarbon trends in t...

13 Chapter 13Figure 13.1 SEPD decline curve and autocorrelation of its random component.Figure 13.2 Approximation of the SEPD curve via multidimensional grid search. Fo...Figure 13.3 Approximated decline curve and four starting decline curves with par...Figure 13.4 Approximating curves produced by Levenberg-Marquardt minimization of...Figure 13.5 Approximation errors for grid search followed by iterative minimizat...Figure 13.6 Example of histogram of production values generated by Monte Carlo s...Figure 13.7 Example of fifty Monte Carlo generated SEPD curves.Figure 13.8 Two quantile ranges for Monte Carlo generated production curves. App...Figure 13.9 Bootstrap quantile uncertainty regions for SEPD approximating curve....Figure 13.10 Upper and lower quantiles for Monte Carlo and bootstrap generated d...

14 Chapter 14Figure 14.1 Independents sampled from the 2010 OGJ150 are shown as dark lines.Figure 14.2 Market capitalization and proved reserves – integrated oil companies...Figure 14.3 Market capitalization and production – integrated oil companies (201...Figure 14.4 Market capitalization and total assets – integrated oil companies (2...Figure 14.5 Market capitalization and proved reserves — large-cap independents (...Figure 14.6 Market capitalization and proved reserves — small-cap independents (...Figure 14.7 Market capitalization and total assets – independents (2010).Figure 14.8 Market capitalization and proved reserves – independents (2010).Figure 14.9 Multinational vs. domestic independents market capitalization (2010)...Figure 14.10 Conventional vs. unconventional independents market capitalization ...Figure 14.11 Proved reserves and annual production – independents (2010).Figure 14.12 Market capitalization and proved reserves – state-owned majors and ...

15 Chapter 15Figure 15.1 A comparison of temperature profiles given by different analytical m...Figure 15.2 Effect of formation fluid influx on the temperature profiles.Figure 15.3 Effect of Joule-Thomson cooling on the temperature profiles.Figure 15.4 Effect of entrained drill cuttings on the temperature profiles.Figure 15.5 Sketch illustrating heat transfer in a borehole section.

16 Chapter 16Figure 16.1 Sketch illustrating heat transfer in a borehole section.Figure 16.2 Model-predicted temperature profiles in drilling well NGHP-01-17A.Figure 16.3 Model-predicted temperature profiles with injection mud temperature ...Figure 16.4 Model-predicted temperature profiles with mud flow rate 0.05 m³/s.Figure 16.5 Model-predicted temperature profiles with 3 °C-temperature drop at t...

17 Chapter 17Figure 17.1 Gas-filled formations. The standard deviations of calculated Vp, Vs ...Figure 17.2 Standard deviations of the Vp/Vs ratio for gas and brine saturated f...Figure 17.3 Clusters of (Vp,Vs) velocities for gas-filled and brine-filled forma...Figure 17.4 K-nearest neighbor and the probability of true discovery.Figure 17.5 K-nearest neighbor and the probability of false discovery.Figure 17.6 Recursive partitioning and the probability of true discovery.Figure 17.7 Recursive partition.Figure 17.8 Linear discriminant analysis and the probability of true discovery.Figure 17.9 Linear discriminant analysis and the probability of false discovery.Figure 17.10 The probability of true discovery and the three classification tech...Figure 17.11 The probability of false discovery.

18 Chapter 18Figure 18.1 V_p versus effective pressure for sandstone samples [9] (Zhang and Be...Figure 18.2 Typical Mohr rupture diagram for concrete [13] (Wuerker, 1959).Figure 18.3 Poisson’s ratio versus total porosity [19] (Spikes and Dvorkin, 2004...Figure 18.4 The relationship of Young’s modulus and porosity [20] (Kumar et al ....Figure 18.5 (a) The variation of Young’s modulus with RH [29] (Pham et al ., 200...Figure 18.6 The relationship of Vp-fast & Vs-fast and Young’s modulus with TOC [...Figure 18.7 The relationship of Young’s modulus and TOC [21] (Kumar et al. , 201...Figure 18.8 The relationship of Young’s modulus and clay content [21] (Kumar et ...Figure 18.9 The influence of clay content on UCS [41] (Sone and Zoback, 2011).Figure 18.10 Experimental data showing strength anisotropy in shales [42] (Wills...Figure 18.11 Strength of horizontally and vertically cored samples from Upper an...Figure 18.12 The strength of shale core in different direction [45] (Li et al .,...Figure 18.13 Effect of the bedding angle on compressive failure strength [46] (A...Figure 18.14 The bulk and shear moduli with different mineral concentrations of ...Figure 18.15 The dependence of Young’s modulus on quartz and carbonate [20] (Kum...Figure 18.16 The relationship between Sw and Young’s modulus.Figure 18.17 The relationship between Sw and Poisson’s ratio.Figure 18.18 The relationship between Sw and UCS.Figure 18.19 (a) and (b) The fitting line and the “best” line for E and UCS.Figure 18.20 (a) and (b) The data range analysis for E.Figure 18.21 (a) and (b) The data range analysis for UCS.

19 Chapter 19Figure 19.1 Flowchart of solution for mathematical models for one time step.Figure 19.2 Simulation results under two different time steps.Figure 19.3 Oil and water relative permeability curves (repotted from Parvazdava...Figure 19.4 Comparison of oil recovery change with time after water flooding or ...Figure 19.5 Compared results of normalized effluent concentrations of nanopartic...Figure 19.6 Compared results of normalized effluent concentrations of nanopartic...Figure 19.7 Influence of nanofuids injection time on oil recovery.Figure 19.8 Amount of nanoparticles trapped under different injection time.Figure 19.9 Water saturation distribution at 0.5 PV, 1.5 PV, 2.25 PV and 3 PV re...Figure 19.10 Nanoparticle distribution in the reservoir at the end of the nanofu...Figure 19.11 Oil recovery under different nanofuids injection rates.Figure 19.12 The amounts of nanoparticles trapped in the reservoir at the end of...Figure 19.13 Oil recovery with slug injection of nanofuids under different injec...Figure 19.14 Oil recovery and Water cut against injection time (nanofuids inject...Figure 19.15 Oil recovery with slug injection of nanofuids at different slug siz...Figure 19.16 Oil recovery curves under different length of nanofuids injection t...Figure 19.17 Oil recovery under different flow rate ratio between water flooding...Figure 19.18 Dimensionless nanoparticle effluent concentration at production wel...Figure 19.19 Porosity of the SPE 10B model.Figure 19.20 Comparison results of (a) water saturation under water flooding, (b...Figure 19.21 The distribution of Nanoparticles concentration in water phase afte...Figure 19.22 Nanofuids concentration distribution in the reservoir at different ...Figure 19.23 Nanoparticles concentration distribution on the rock surface at dif...

20 Chapter 20Figure 20.1 (a) The Tensleep horizon interpreted from 3D seismic data shown in s...Figure 20.2 (a) West–east seismic section across the Teapot Dome. The reverse fa...Figure 20.3 Apertures are log-normally distributed in the wireline image logs, C...Figure 20.4 Distributions of the fracture intensity attribute for each zone in t...Figure 20.5 (a) Possible deformation bands are shown by red arrows, note that th...Figure 20.6 The permeability barriers are roughly parallel the S1 fault. The gri...Figure 20.7 Locations of 8 Wells with core data used in Chiaramonte (2009) reser...Figure 20.8 Saturation pressure and swelling factor calculated in the swelling e...Figure 20.9 Fluid viscosity is plotted vs. bubble point pressure at each swellin...Figure 20.10 Streamline analysis on 03-28-2004 for water saturation around produ...Figure 20.11 (a) Model 1 has injectors (I4, I5, and I6) parallel to the main fra...Figure 20.12 A high mole fraction of CO2 is observed at the producers beginning ...Figure 20.13 The oil production for Models 1 and 2 are compared to an oil produc...Figure 20.14 Increasing fault multiplier results in higher oil production for bo...

21 Chapter 21Figure 21.1 Different components of Reservoir Characterization [32].Figure 21.2 Different types of clusters [52].Figure 21.3 Fuzzy clustering to monitor fluid movement in a geothermal reservoir...Figure 21.4 Hierarchical clustering with different proximity functions [42].Figure 21.5 Overview of the ensemble learning method [52].Figure 21.6 (a) Perceptron learning process [11]. (b) Multi layer perceptron arc...Figure 21.7 An illustrative example of CNN architecture [11].Figure 21.8 Illustration of the RNN architecture [49].Figure 21.9 Illustration of the Auto encoder architecture [59].Figure 21.10 Illustration of GANs architecture [41].Figure 21.11 Elements of a 3D structural model [43].Figure 21.12 Proxy dataset maps aligned to data types (right) and concept layers...Figure 21.13 Illustrative workflow from Justman et al. [27].Figure 21.14 Example of domain boundary revision and iteration using an example ...Figure 21.15 Subsurface Trend Analysis (STA) results. Data-based prediction vs. ...Figure 21.16 ANN-based workflow from Thanh et al. [60].Figure 21.17 EOR screening process workflow [35].