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Pore pressure modeling and in situ stress determination in Raniganj basin, India
Abstract Pore pressure and stress analysis are vital to avoid well complications while drilling. A methodology was used to map pore pressure in the Raniganj basin located in India using sonic log and seismic data. A model-based seismic inversion technique was used to characterize the reservoir. Moreover, seismic attributes generated from inversion were integrated with estimated pore pressure from Bower’s and Eaton’s method using sonic logs to map sub-surface pore pressure. The probabilistic neural network architecture was used as an integration algorithm for mapping the pore pressure. Besides, the magnitude of overburden (Sv), maximum (SH), and minimum (Sh) horizontal stress were estimated, and the present-day SH direction was determined from drilling-induced fractures in the resistivity image log. Pore pressure equals hydrostatic pressure at shallower depth while marginal overpressure was observed at deeper depth as the estimated pore pressure deviates from the normal compaction trend line. Bower’s method slightly overestimates the pore pressure at deeper depth compared to Eaton’s, though the minimum difference between estimated and measured pore pressure supports the result. Stress analysis identifies a normal fault stress regime (Sv > SH > Sh), in which existing normal faults were stable based on frictional faulting theory. The SH is oriented along NE-SW direction and the estimated Sh magnitude corroborates with direct field measurements. The result from this study helps to understand pore pressure distribution and stress profiles, mitigates associated drilling risk, predicts the pressure limit for the reactivation of existing faults during fluid injection in hydraulic fracturing treatments, and also provides vital inputs for future geomechanical studies.
Pore pressure modeling and in situ stress determination in Raniganj basin, India
Abstract Pore pressure and stress analysis are vital to avoid well complications while drilling. A methodology was used to map pore pressure in the Raniganj basin located in India using sonic log and seismic data. A model-based seismic inversion technique was used to characterize the reservoir. Moreover, seismic attributes generated from inversion were integrated with estimated pore pressure from Bower’s and Eaton’s method using sonic logs to map sub-surface pore pressure. The probabilistic neural network architecture was used as an integration algorithm for mapping the pore pressure. Besides, the magnitude of overburden (Sv), maximum (SH), and minimum (Sh) horizontal stress were estimated, and the present-day SH direction was determined from drilling-induced fractures in the resistivity image log. Pore pressure equals hydrostatic pressure at shallower depth while marginal overpressure was observed at deeper depth as the estimated pore pressure deviates from the normal compaction trend line. Bower’s method slightly overestimates the pore pressure at deeper depth compared to Eaton’s, though the minimum difference between estimated and measured pore pressure supports the result. Stress analysis identifies a normal fault stress regime (Sv > SH > Sh), in which existing normal faults were stable based on frictional faulting theory. The SH is oriented along NE-SW direction and the estimated Sh magnitude corroborates with direct field measurements. The result from this study helps to understand pore pressure distribution and stress profiles, mitigates associated drilling risk, predicts the pressure limit for the reactivation of existing faults during fluid injection in hydraulic fracturing treatments, and also provides vital inputs for future geomechanical studies.
Pore pressure modeling and in situ stress determination in Raniganj basin, India
Banerjee, Abir (author) / Chatterjee, Rima (author)
2021
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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