| 000 | 09578cam a2200817 i 4500 | ||
|---|---|---|---|
| 001 | ocn880425786 | ||
| 003 | OCoLC | ||
| 005 | 20171030101116.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 140411s2014 nju obs 001 0 eng | ||
| 010 | _a 2014014610 | ||
| 020 |
_a9781118910955 _q(electronic bk.) |
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| 020 |
_a1118910958 _q(electronic bk.) |
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| 020 |
_a9781118910894 _q(electronic bk.) |
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| 020 |
_z9781118779316 _q(hardback) |
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| 020 | _z1306802490 | ||
| 020 | _z9781306802499 | ||
| 020 | _z9781118910948 | ||
| 020 | _z111891094X | ||
| 020 | _z1118779312 | ||
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_a(OCoLC)880425786 _z(OCoLC)880409134 _z(OCoLC)907477607 _z(OCoLC)915731983 _z(OCoLC)961579406 _z(OCoLC)962612373 _z(OCoLC)966385626 |
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_a611500 _bMIL |
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| 037 |
_a8D478EE7-BBB3-40D4-AAC1-315364DB3007 _bOverDrive, Inc. _nhttp://www.overdrive.com |
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| 040 |
_aDLC _beng _erda _epn _cDLC _dYDX _dN$T _dEBLCP _dIDEBK _dYDXCP _dDG1 _dE7B _dRECBK _dCUI _dCDX _dOCLCF _dB24X7 _dCOO _dTEFOD _dUMI _dDEBSZ _dOCLCO _dOCLCQ _dOCLCO _dDEBBG _dTEFOD _dVT2 |
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| 100 | 1 | _aHoldaway, Keith R. | |
| 245 | 1 | 0 |
_aHarness oil and gas big data with analytics : optimize exploration and production with data driven models / _cKeith R. Holdaway. _h[electronic resource] |
| 264 | 1 |
_aHoboken, New Jersey : _bJohn Wiley & Sons, Inc., _c2014. |
|
| 300 | _a1 online resource. | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aWiley & SAS business series | |
| 500 | _aIncludes index. | ||
| 500 | _aMachine generated contents note: Preface Chapter 01: Fundamentals of Soft Computing Current Landscape in Upstream Data Analysis Evolution from Plato to Aristotle Descriptive and Predictive Models The SEMMA Process High Performance Analytics Three Tenets of Upstream Data Exploration and Production Value Propositions Oilfield Analytics I am a ... Notes Chapter 02: Data Management Exploration and Production Value Proposition Data Management Platform Array of Data Repositories Structured Data and Unstructured Data Extraction, Transformation, and Loading Processes Big Data Big Analytics Standard Data Sources Case Study: Production Data Quality Control Framework Best Practices Notes Chapter 03: Seismic Attribute Analysis Exploration and Production Value Propositions Time-Lapse Seismic Exploration Seismic Attributes Reservoir Characterization Reservoir Management Seismic Trace Analysis Case Studies Reservoir Properties defined by Seismic Attributes Notes Chapter 04 Reservoir Characterization and Simulation Exploration and Production Value Propositions Exploratory Data Analysis Reservoir Characterization Cycle Traditional Data Analysis Reservoir Simulation Models Case Studies Notes Chapter 05: Drilling and Completion Optimization Exploration and Production Value Propositions Workflow One: Mitigation of Non-Productive Time Workflow Two: Drilling Parameter Optimization 5.5 Case Studies: Steam Assisted Gravity Drainage Completion Notes Chapter 06: Reservoir Management Exploration and Production Value Propositions Digital Oilfield of the Future Analytical Centers of Excellence Analytical Workflows: Best Practices Case Studies Notes Chapter 07: Production Forecasting Exploration and Production Value Propositions Web-Based Decline Curve Analysis Solution Unconventional Reserves Estimation Case Study: Oil Production Prediction for Infill Well Notes Chapter 08: Production Optimization Exploration and Production Value Propositions Case Studies Notes Chapter 09: Exploratory and Predictive Data Analysis Exploration and Production Value Propositions EDA Components EDA Statistical Graphs and Plots Ensemble Segmentations Data Visualization Case Studies Notes Chapter 10: Big Data: Structured and Unstructured Exploration and Production Value Propositions Hybrid Expert and Data Driven System Case Studies Multivariate Geostatistics Big Data Workflows Integration of Soft Computing Techniques Notes Glossary About the Author Index. | ||
| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aPreface -- Chapter 01: Fundamentals of Soft Computing Current Landscape in Upstream Data Analysis Evolution from Plato to Aristotle Descriptive and Predictive Models The SEMMA Process High Performance Analytics Three Tenets of Upstream Data Exploration and Production Value Propositions Oilfield Analytics I am a ... Notes -- Chapter 02: Data Management Exploration and Production Value Proposition Data Management Platform Array of Data Repositories Structured Data and Unstructured Data Extraction, Transformation, and Loading Processes Big Data Big Analytics Standard Data Sources Case Study: Production Data Quality Control Framework Best Practices Notes -- Chapter 03: Seismic Attribute Analysis Exploration and Production Value Propositions Time-Lapse Seismic Exploration Seismic Attributes Reservoir Characterization Reservoir Management Seismic Trace Analysis Case Studies Reservoir Properties defined by Seismic Attributes Notes -- Chapter 04 Reservoir Characterization and Simulation Exploration and Production Value Propositions Exploratory Data Analysis Reservoir Characterization Cycle Traditional Data Analysis Reservoir Simulation Models Case Studies Notes -- Chapter 05: Drilling and Completion Optimization Exploration and Production Value Propositions Workflow One: Mitigation of Non-Productive Time Workflow Two: Drilling Parameter Optimization 5.5 Case Studies: Steam Assisted Gravity Drainage Completion Notes -- Chapter 06: Reservoir Management Exploration and Production Value Propositions Digital Oilfield of the Future Analytical Centers of Excellence Analytical Workflows: Best Practices Case Studies Notes -- Chapter 07: Production Forecasting Exploration and Production Value Propositions Web-Based Decline Curve Analysis Solution Unconventional Reserves Estimation Case Study: Oil Production Prediction for Infill Well Notes -- Chapter 08: Production Optimization Exploration and Production Value Propositions Case Studies Notes -- Chapter 09: Exploratory and Predictive Data Analysis Exploration and Production Value Propositions EDA Components EDA Statistical Graphs and Plots Ensemble Segmentations Data Visualization Case Studies Notes -- Chapter 10: Big Data: Structured and Unstructured Exploration and Production Value Propositions Hybrid Expert and Data Driven System Case Studies Multivariate Geostatistics Big Data Workflows Integration of Soft Computing Techniques Notes Glossary About the Author Index. | |
| 520 |
_a"Use big data analytics to efficiently drive oil and gas exploration and productionHarness Oil and Gas Big Data with Analytics provides a complete view of big data and analytics techniques as they are applied to the oil and gas industry. Including a compendium of specific case studies, the book underscores the acute need for optimization in the oil and gas exploration and production stages and shows how data analytics can provide such optimization. This spans exploration, development, production and rejuvenation of oil and gas assets. The book serves as a guide for fully leveraging data, statistical, and quantitative analysis, exploratory and predictive modeling, and fact-based management to drive decision making in oil and gas operations. This comprehensive resource delves into the three major issues that face the oil and gas industry during the exploration and production stages: Data management, including storing massive quantities of data in a manner conducive to analysis and effectively retrieving, backing up, and purging data Quantification of uncertainty, including a look at the statistical and data analytics methods for making predictions and determining the certainty of those predictions Risk assessment, including predictive analysis of the likelihood that known risks are realized and how to properly deal with unknown risks Covering the major issues facing the oil and gas industry in the exploration and production stages, Harness Big Data with Analytics reveals how to model big data to realize efficiencies and business benefits"-- _cProvided by publisher. |
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| 588 | 0 | _aPrint version record and CIP data provided by publisher. | |
| 650 | 0 |
_aPetroleum industry and trade _vStatistics. |
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| 650 | 0 |
_aGas industry _vStatistics. |
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| 650 | 0 | _aBig data. | |
| 650 | 7 |
_aBUSINESS & ECONOMICS _xIndustries _xEnergy Industries. _2bisacsh |
|
| 650 | 7 |
_aBig data. _2fast _0(OCoLC)fst01892965 |
|
| 650 | 7 |
_aGas industry. _2fast _0(OCoLC)fst00938285 |
|
| 650 | 7 |
_aPetroleum industry and trade. _2fast _0(OCoLC)fst01059546 |
|
| 655 | 4 | _aElectronic books. | |
| 655 | 7 |
_aStatistics. _2fast _0(OCoLC)fst01423727 |
|
| 655 | 0 | _aElectronic books. | |
| 776 | 0 | 8 |
_iPrint version: _aHoldaway, Keith R. _tHarness oil and gas big data with analytics. _dHoboken : Wiley, 2014 _z9781118779316 _w(DLC) 2014005234 |
| 830 | 0 | _aWiley and SAS business series. | |
| 856 | 4 | 0 |
_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118910948 _zWiley Online Library |
| 942 |
_2ddc _cBK |
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| 999 |
_c207456 _d207456 |
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