Reservoir Characterization

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This second volume in the series, “Sustainable Energy Engineering,” covers reservoir characterization, a huge part of the production process and crucial to the power generation supply chain. Long though of as not being “sustainable,” newly discovered sources of petroleum and newly developed methods for petroleum extraction have made it clear that not only can the petroleum industry march toward sustainability, but it can be made “greener” and more environmentally friendly. Sustainable energy engineering is where the technical, economic, and environmental aspects of energy production intersect and affect each other.
This collection of papers covers the strategic and economic implications of methods used to characterize petroleum reservoirs. Born out of the journal by the same name, formerly published by Scrivener Publishing, most of the articles in this volume have been updated, and there are some new additions, as well, to keep the engineer abreast of any updates and new methods in the industry.
Truly a snapshot of the state-of-the-art, this groundbreaking volume is a must-have for any petroleum engineer working in the field, environmental engineers, petroleum engineering students, and any other engineer or scientist working with reservoirs.

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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 m 3/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/V s 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 pversus 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.

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