Reservoir Characterization

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].

1 Chapter 3 Table 3.1 Mean values of false and true discovery rates for three classifers in ... Table 3.2 Mean and three quantiles of distribution of AUC values. Table 3.3 Percent of values of anomalyIndicator exceeding significance cutoff α ...

2 Chapter 4 Table 4.1 Results of TOC and Rock-Evaol Pyrolysis for the rock samples analyzed. Table 4.2 Typical biomarker characteristics of shale and carbonate-derived hydro...

3 Chapter 6Table 6.1 Prior rates of false discovery of anomalous clusters, and cluster sets...Table 6.2 Parameters of clusters, labeled as anomalous with individual cluster a...Table 6.3 Thresholds, true, and false discovery rates of anomalous records using...

4 Chapter 7Table 7.1 Dissimilarities among values of 14 parameters in 4 combinations of gas...Table 7.2 AUC values for four classification methods and 14 predictor parameters...

5 Chapter 8Table 8.1 Input parameters for Example 1.Table 8.2 Comparison of analyses for Example 1.Table 8.3 Input parameters for Example 2.Table 8.4 Comparison of analyses for Example 2.Table 8.5 Input parameters for Example 3.Table 8.6 Simulation input vs. analysis results for Example 3.Table 8.7 Input parameters for Example 4.Table 8.8 Simulation input vs. analysis results for Example 4.

6 Chapter 9Table 9.1 Percent of identified outliers among individual forecasts for five val...Table 9.2 Number of outliers at the output of the first level committee machine....Table 9.3 Bias of the forecast with four predictors for four permeability models...Table 9.4 Absolute bias of the output of first level committee machine produced ...Table 9.5 Root Mean Squared error (mD) for individual forecasts with hybrid mode...Table 9.6 Mean instability of individual forecasts with hybrid model for the rec...Table 9.7 Outliers of permeability forecasts with multiplicative model and their...

7 Chapter 11Table 11.1 Parameters of distribution of a number of individual forecasts for in...Table 11.2 Percent of identified outliers among individual forecasts for five va...Table 11.3 Mean absolute bias of the forecasts by Monte Carlo committee machines...Table 11.4 Instability index of individual forecasts with six machine learning m...Table 11.5 Correlation coefficients between outputs of Monte Carlo committee mac...Table 11.6 Instability of individual forecasts for eight permeability records.

8 Chapter 13Table 13.1 Example of the estimated parameters of the SEPD-type decline curve.Table 13.2 Example of estimated covariance matrix for parameters of SEPD decline...Table 13.3 Monte Carlo simulated coefficients of the SEPD model. Mean 10,000: me...Table 13.4 Forecasted mean and median production values derived from Monte Carlo...

9 Chapter 14Table 14.1 Model variables and definitions.Table 14.2 Integrated oil company reserves and financial data (2010).Table 14.3 Statistical summary of the independent oil and gas companies sampled ...Table 14.4 Regression results of integrated oil companies (2010).Table 14.5 Regression results of independent oil companies (2010). CAP.Table 14.6 Regression results of independent oil companies – three independent v...Table 14.7 Regression results of independent oil companies – four independent va...Table 14.8 Estimated reserves and assets of private companies (2010).Table 14.9 Effective market capitalization of National Oil Companies in OPEC (20...Table 14.10 Actual and computed market capitalization of companies outside North...Table A.1 Sample of independent oil producers 3(2010).Table A.2 Sample of independent gas producers a(2010).

10 Chapter 15Table 15.1 Data used in comparison of analytical solutions.

11 Chapter 16Table 16.1 Well and operation parameter values in drilling well NGHP-01-17A.Table 16.2 Estimated material properties in drilling well NGHP-01-17A.