| 000 | 02392 a2200289 4500 | ||
|---|---|---|---|
| 003 | OSt | ||
| 005 | 20231122121701.0 | ||
| 008 | 220201b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9781119434290 | ||
| 040 | _cIIT Kanpur | ||
| 041 | _aeng | ||
| 082 |
_a333.79 _bP965m |
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| 100 | _aPukite, Paul | ||
| 245 |
_aMathematical geoenergy _bdiscovery, depletion, and renewal _cPaul Pukite, Dennis Coyne and Daniel Challou |
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| 260 |
_bWiley _aNew Jersey _c2019 |
||
| 260 |
_bAmerican Geophysical Union _c2019 _aWashington |
||
| 300 | _aviii, 365p | ||
| 490 | _aGeophysical monograph series; no. 241 | ||
| 500 | _aAGU 100 Advancing earth and space science | ||
| 505 | _a | ||
| 520 | _aGeoEnergy encompasses the range of energy technologies and sources that interact with the geological subsurface. Fossil fuel availability studies have historically lacked concise modeling, tending instead toward heuristics and overly-complex processes. Mathematical GeoEnergy: Oil Discovery, Depletion and Renewal details leading-edge research based on a mathematically-oriented approach to geoenergy analysis. Volume highlights include: Applies a formal mathematical framework to oil discovery, depletion, and analysis Employs first-order applied physics modeling, decreasing computational resource requirements Illustrates model interpolation and extrapolation to fill out missing or indeterminate data Covers both stochastic and deterministic mathematical processes for historical analysis and prediction Emphasizes the importance of up-to-date data, accessed through the companion website Demonstrates the advantages of mathematical modeling over conventional heuristic and empirical approaches Accurately analyzes the past and predicts the future of geoenergy depletion and renewal using models derived from observed production data Intuitive mathematical models and readily available algorithms make Mathematical GeoEnergy: Oil Discovery, Depletion, and Renewal an insightful and invaluable resource for scientists and engineers using robust statistical and analytical tools applicable to oil discovery, reservoir sizing, dispersion, production models, reserve growth, and more. | ||
| 650 | _aPower resources -- Mathematical models | ||
| 650 | _a Ressources énergétiques--Modèles mathématiques. | ||
| 700 | _aCoyne, Dennis | ||
| 700 | _aChallou, Daniel | ||
| 942 | _cBK | ||
| 999 |
_c565334 _d565334 |
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