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Keywords: machine learning
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Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, June 27–30, 2007
Paper Number: SPE-108710-MS
... of production is called primary recovery. machine learning optimization problem Artificial Intelligence operation cost enhanced recovery quality function deployment evolutionary algorithm Upstream Oil & Gas objective function additional oil recovery system characteristic criteria...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, June 27–30, 2007
Paper Number: SPE-108494-MS
... Intelligence machine learning Upstream Oil & Gas regression fluid modeling PVT measurement variation calculation pressure gradient fluid gradient straight line fluid density pressure data compositional gradient probability equation of state polynomial in-situ fluid measurement standard...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, June 27–30, 2007
Paper Number: SPE-108500-MS
... be used for optimum drilling fluid design during well development in Persian Gulf, Offshore Iran. drilling fluid selection and formulation machine learning drilling fluid chemistry drilling fluid property neural network Artificial Intelligence drilling operation drilling fluids and materials...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, August 31–September 2, 2006
Paper Number: SPE-102228-MS
... reservoir characterization drilling equipment completion installation and operations casing and cementing integrated contract bit selection enhanced recovery well logging project management production monitoring machine learning production control drilling fluid management & disposal...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, August 31–September 2, 2006
Paper Number: SPE-103757-MS
... this objective, the paper will address the following topics: brief introduction of current work practice, processes and workflows descriptions, technology available, real time environment, automated workflow application recommendation and potential benefits. machine learning Artificial Intelligence...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, August 31–September 2, 2006
Paper Number: SPE-103931-MS
..., with the desired precision. Advantages of the EA over the classical Levenberg-Marquardt method for this specific well test analysis problem are also discussed. Artificial Intelligence machine learning optimization problem objective function characteristic drillstem/well testing fractured vuggy...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, August 31–September 2, 2006
Paper Number: SPE-104009-MS
... proposed in this work and to demonstrate that the fractal formulation explains consistently the peculiar behavior observed in some real production decline curves. Artificial Intelligence Upstream Oil & Gas Drillstem Testing machine learning complex reservoir approximation cumulative...
Proceedings Papers

Paper presented at the International Oil Conference and Exhibition in Mexico, August 31–September 2, 2006
Paper Number: SPE-104001-MS
... and in interpretation of test data. The modelling workflow presented in this paper is directly relevant to producing and prospective fractured Middle Cretaceous carbonate reservoirs elsewhere in the Middle East and in Mexico. machine learning Reservoir Characterization flow in porous media Thickness facies...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference in Mexico, November 7–9, 2004
Paper Number: SPE-92039-MS
... that the standard regression techniques are very sensitive to the scatter of the pressure vs. time data. hydraulic fracturing storativity machine learning pressure transient analysis sequential-feed model drillstem testing matrix macrofracture specific storage storativity fraction reservoir...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference in Mexico, November 7–9, 2004
Paper Number: SPE-92240-MS
... computer machine learning type curve society of petroleum engineers type-curve analysis pressure response correlation parameter estimation interference test signal theory spe 92240 procedure drillstem testing spe annual technical conference application reservoir field data displacement...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference in Mexico, November 7–9, 2004
Paper Number: SPE-91920-MS
... mechanistic model mechanism experimental machine learning comparison fluid vaporization Introduction This model has been extensively used for prediction of surface tensions of pure compounds. However, the model gives poor IFT predictions for complex multicomponent hydrocarbon mixtures [ 14...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference in Mexico, November 7–9, 2004
Paper Number: SPE-91755-MS
... described by Voneiff and Cipolla. 8 It is similar in that it consists of a multitude of local analyses, each in an areal window centered around an existing well, shown in Fig. 1 . infill drilling reservoir characterization fast method machine learning upstream oil & gas ozona field infill...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 10–12, 2002
Paper Number: SPE-74387-MS
... artificial intelligence reservoir characterization drillstem/well testing samaniego spej flow rate estimation parameter estimation optimization technique recovery behavior block size distribution spe 74387 probability function upstream oil & gas block size information machine learning...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 10–12, 2002
Paper Number: SPE-74345-MS
... reservoir machine learning upstream oil & gas badarinadh permeability estimation log analysis heterogeneous carbonate spe 74345 activity curve flow in porous media fluid dynamics log-derived permeability porosity artificial intelligence reservoir core porosity core data permeability...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 10–12, 2002
Paper Number: SPE-74338-MS
...). geologic model thickness reservoir characterization gas displacing oil modeling & simulation saturation oil saturation displacing oil interpretation spe 74338 high-resolution geologic model machine learning acquisition information seismic response artificial intelligence gjøystdal...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 10–12, 2002
Paper Number: SPE-74389-MS
... in porous media forecast fluid dynamics permeability reservoir simulation water cut objective function machine learning spe 74389 optimization problem geostatistical modeling aquifer forecast uncertainty realization Introduction A review of reservoir engineering and geology over...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 1–3, 2000
Paper Number: SPE-59012-MS
... drop is present for the case of a non-uniform initial distribution. An expression for this additional pseudo-skin is provided. initial pressure distribution upstream oil & gas drillstem testing pressure profile machine learning drillstem/well testing dimensionless time computation...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 1–3, 2000
Paper Number: SPE-58995-MS
... modeling simulation machine learning reservoir characterisation artificial intelligence reservoir characterization antonio costa silva upstream oil & gas histogram numerical simulation model permeability cggp geological modeling spe 58995 luis guerreiro variogram fracture density...
Proceedings Papers

Paper presented at the SPE International Petroleum Conference and Exhibition in Mexico, February 1–3, 2000
Paper Number: SPE-59030-MS
... transmission network optimization fitness diameter universidad nacional upstream oil & gas mutation probability convergence evolutionary algorithm optimum design genetic algorithm machine learning node operator artificial intelligence algorithm number genetic algorithm applied petroleum...

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