Poromechanics of Fractured/Faulted Reservoirs During Fluid Injection Based on Continuum Damage Modeling and Machine Learning
The reactivation of faults is governed by the variation of effective stresses in the fault’s plane. These effective stresses are dependent on the total stresses (solely related to the regional geological stresses and lithology) and pore pressure (strongly affected by rock properties, fluid content,...
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| Veröffentlicht in: | Natural resources research (New York, N.Y.) N.Y.), 2023-02, Vol.32 (1), p.413-430 |
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| description | The reactivation of faults is governed by the variation of effective stresses in the fault’s plane. These effective stresses are dependent on the total stresses (solely related to the regional geological stresses and lithology) and pore pressure (strongly affected by rock properties, fluid content, and saturation conditions). Injecting fluids into a reservoir formation may change the distribution of pore pressures, which influences the effective stresses and may cause the reactivation of existing faults, which has a wide range of consequences. This study investigated the reactivation of preexisting faults due to fluid injection into hydrocarbon reservoirs at different pressures and temperatures. A 3D model containing a continuous normal fault that divides the domain into two compartments was used. A user-defined constitutive model based on continuum damage mechanics implemented as a Fortran subroutine to predict the behavior of fractured and faulted reservoirs was used. A parametric analysis was performed to examine the influence of geometric parameters, such as the fault dip angle, reservoir characteristics, and fluid injection parameters. A machine learning approach based on artificial neural networks (ANNs) is incorporated to predict the enhanced oil recovery using fluid injection. The results predicted by the ANN were further confirmed by numerical modeling. |
| doi_str_mv | 10.1007/s11053-022-10134-8 |
| format | Article |
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A parametric analysis was performed to examine the influence of geometric parameters, such as the fault dip angle, reservoir characteristics, and fluid injection parameters. A machine learning approach based on artificial neural networks (ANNs) is incorporated to predict the enhanced oil recovery using fluid injection. The results predicted by the ANN were further confirmed by numerical modeling.</description><identifier>ISSN: 1520-7439</identifier><identifier>EISSN: 1573-8981</identifier><identifier>DOI: 10.1007/s11053-022-10134-8</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Artificial neural networks ; Chemistry and Earth Sciences ; Computer Science ; Constitutive models ; Continuum damage mechanics ; Damage assessment ; Earth and Environmental Science ; Earth Sciences ; Enhanced oil recovery ; Fault lines ; Faults ; Fluid injection ; Fossil Fuels (incl. Carbon Capture) ; Geography ; Injection ; Learning algorithms ; Lithology ; Machine learning ; Mathematical Modeling and Industrial Mathematics ; Mathematical models ; Mineral Resources ; Neural networks ; Numerical models ; Oil recovery ; Original Paper ; Parameters ; Parametric analysis ; Physics ; Pore pressure ; Pore water pressure ; Reservoirs ; Rock properties ; Statistics for Engineering ; Stresses ; Sustainable Development ; Three dimensional models</subject><ispartof>Natural resources research (New York, N.Y.), 2023-02, Vol.32 (1), p.413-430</ispartof><rights>International Association for Mathematical Geosciences 2022. 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These effective stresses are dependent on the total stresses (solely related to the regional geological stresses and lithology) and pore pressure (strongly affected by rock properties, fluid content, and saturation conditions). Injecting fluids into a reservoir formation may change the distribution of pore pressures, which influences the effective stresses and may cause the reactivation of existing faults, which has a wide range of consequences. This study investigated the reactivation of preexisting faults due to fluid injection into hydrocarbon reservoirs at different pressures and temperatures. A 3D model containing a continuous normal fault that divides the domain into two compartments was used. A user-defined constitutive model based on continuum damage mechanics implemented as a Fortran subroutine to predict the behavior of fractured and faulted reservoirs was used. A parametric analysis was performed to examine the influence of geometric parameters, such as the fault dip angle, reservoir characteristics, and fluid injection parameters. A machine learning approach based on artificial neural networks (ANNs) is incorporated to predict the enhanced oil recovery using fluid injection. The results predicted by the ANN were further confirmed by numerical modeling.</description><subject>Artificial neural networks</subject><subject>Chemistry and Earth Sciences</subject><subject>Computer Science</subject><subject>Constitutive models</subject><subject>Continuum damage mechanics</subject><subject>Damage assessment</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Enhanced oil recovery</subject><subject>Fault lines</subject><subject>Faults</subject><subject>Fluid injection</subject><subject>Fossil Fuels (incl. 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A parametric analysis was performed to examine the influence of geometric parameters, such as the fault dip angle, reservoir characteristics, and fluid injection parameters. A machine learning approach based on artificial neural networks (ANNs) is incorporated to predict the enhanced oil recovery using fluid injection. The results predicted by the ANN were further confirmed by numerical modeling.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11053-022-10134-8</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-7530-2116</orcidid><orcidid>https://orcid.org/0000-0003-3358-5167</orcidid></addata></record> |
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| subjects | Artificial neural networks Chemistry and Earth Sciences Computer Science Constitutive models Continuum damage mechanics Damage assessment Earth and Environmental Science Earth Sciences Enhanced oil recovery Fault lines Faults Fluid injection Fossil Fuels (incl. Carbon Capture) Geography Injection Learning algorithms Lithology Machine learning Mathematical Modeling and Industrial Mathematics Mathematical models Mineral Resources Neural networks Numerical models Oil recovery Original Paper Parameters Parametric analysis Physics Pore pressure Pore water pressure Reservoirs Rock properties Statistics for Engineering Stresses Sustainable Development Three dimensional models |
| title | Poromechanics of Fractured/Faulted Reservoirs During Fluid Injection Based on Continuum Damage Modeling and Machine Learning |
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