A Comprehensive Workflow Using Nuclear Magnetic Resonance (NMR) Data To Evaluate and Characterize Low Resistivity Low Contrast Reservoirs
- Tianmin Jiang (Schlumberger) | Michiko Hamada (Schlumberger) | Yuki Maehara (Schlumberger) | Samira Ahmad (Schlumberger) | Aldrick Garcia Mayans (Schlumberger) | Niranjan Aryal (Schlumberger) | Hanatu Kadir (Schlumberger)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 24th Formation Evaluation Symposium of Japan, 11-12 October, Chiba, Japan
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petrophysicists and Well Log Analysts
- 2 in the last 30 days
- 116 since 2007
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A novel integrated workflow using Nuclear Magnetic Resonance (NMR) data is developed and is presented in this paper to evaluate laminated low resistivity low contrast (LRLC) clastic formations in offshore deepwater wells. Traditional interpretation methods on thin laminations such as laminated sand analysis (LSA) requires a high resistivity contrast between sand and shale laminations to be able to estimate resistivity and volume fraction corresponding to sand which are then used to compute total hydrocarbon in place. In LRLC environment, the method fails because of insufficient resistivity contrast. Moreover, the traditional methods lack the ability to output other important quantities of interest such as sand facies, hydrocarbon properties, and reservoir quality indicators.
The proposed workflow uses NMR data and incorporates modern techniques of factor analysis and fluid substitution to fully evaluate and characterize LRLC formations by 1) quantifying accurate sand fraction, porosity, permeability and water saturation using a modified laminated sand analysis with NMR factor analysis, 2) analyzing fluid facies from NMR factor analysis to separate shale, block pay sand, laminated pay sand and wet sand intervals, 3) estimating hydrocarbon properties, grain size distribution and corresponding reservoir quality using fluid substitution which removes hydrocarbon contamination from NMR signal to simulate a 100% water filled formation.
|File Size||1 MB||Number of Pages||7|