Well Inflow Performance Under Fines Migration: Analytical Model, Production Data Treatment
- Grace Ming Yin Loi (Australian School of Petroleum and Energy Resources, University of Adelaide) | Larissa Chequer (Australian School of Petroleum and Energy Resources, University of Adelaide) | Cuong Cao Nguyen (Australian School of Petroleum and Energy Resources, University of Adelaide) | Abbas Zeinijahromi (Australian School of Petroleum and Energy Resources, University of Adelaide) | Pavel Bedrikovetsky (Australian School of Petroleum and Energy Resources, University of Adelaide)
- Document ID
- Society of Petroleum Engineers
- SPE Asia Pacific Oil & Gas Conference and Exhibition, 17-19 November, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 5 Reservoir Desciption & Dynamics, 5.6.8 Well Performance Monitoring, Inflow Performance, 3 Production and Well Operations, 3 Production and Well Operations, 4.1 Processing Systems and Design, 4.1.2 Separation and Treating, 1.8 Formation Damage, 5.6 Formation Evaluation & Management, 5.1.1 Exploration, Development, Structural Geology, 5.1 Reservoir Characterisation, 1.8.3 Fines Migration, 5.5.2 Core Analysis, 4 Facilities Design, Construction and Operation
- Fines Migration, skin factor, Analytical Model, Well Inflow Performance
- 22 in the last 30 days
- 22 since 2007
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Fines migration causes significant formation damage during oil and gas production. The reliable prediction of well performance and extend of the damaged zone allow for planning and design of well stimulation for prevention, mitigation and removal of the damage. We derive a novel analytical model for production well performance during fines migration. The model includes explicit formulae for concentrations of suspended and strained fines, and the expressions for skin factor and well impedance. Ten field cases exhibit close match with the analytical model. Moreover, the tuned parameters belong to common intervals of their variation. The work provides means for reliable well behaviour prediction based on production history and coreflood data.
|File Size||1 MB||Number of Pages||19|
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