Integrated Reservoir Characterization in Deepwater Gulf of Mexico Using Nuclear Magnetic Resonance (NMR) Factor Analysis and Fluid Substitution
- Tianmin Jiang (Schlumberger) | Jason Gendur (Schlumberger) | Li Chen (Schlumberger) | Weixin Xu (Schlumberger) | Dan Shan (Schlumberger) | Tom Hall (TALOS Energy) | Tim Wilkinson (TALOS Energy) | Ben Winkelman (TALOS Energy) | Nnadozie Nwosu (Schlumberger) | Jesus Alberta Cañas (Schlumberger) | Ron Hayden (Schlumberger)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 60th Annual Logging Symposium, 15-19 June, The Woodlands, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors
- 6 in the last 30 days
- 158 since 2007
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A novel integrated workflow using Nuclear Magnetic Resonance (NMR) data is developed to evaluate sand reservoirs in deepwater Gulf of Mexico. Accurate characterization of the reservoir properties is the key to predict the formation producibility. Traditional interpretation methods based on Triple-Combo logs (density, neutron, resistivity and gamma ray) have been widely used to characterize clastic formations to provide cost-effective answers of lithology, porosity, saturation and permeability. Nevertheless, zones with fine grained rock texture or clay-rich thin beds represent low resistivity, causing net-to-gross estimation often pessimistic. Grain size variation and clay distribution also affect the vertical permeability and connectivity. Moreover, the traditional methods cannot provide other important quantities of interest such as reservoir properties, sand facies and reservoir quality indicators.
The new approach incorporates modern techniques of NMR factor analysis and fluid substitution to fully characterize the formations by 1) identifying fluid types, evaluating clay distribution, quantifying porosity, saturation and permeability, 2) analyzing fluid facies from NMR factor analysis for rock typing to separate shale, clean sand and laminated sand intervals, 3) computing grain size distribution from a simulated 100% water-filled formation using NMR fluid substitution, 4) evaluating reservoir quality and producibility based on the reservoir properties estimated from 1) to 3).
In this paper, we demonstrate the successful application of the proposed workflow to the wells in deepwater Gulf of Mexico. Interpretation from case studies are presented using wireline NMR data integrated with tri-axial resistivity, borehole image, formation testing and core analysis data. The results provide more accurate reservoir properties for better reservoir quality characterization.
The Gulf of Mexico (GOM) is a major petroleum-producing area in offshore operations of considerable importance. This provided opportunities for oil and gas production, but also brought new challenges for reservoir characterization. Traditional interpretation methods based on Triple-Combo (TCOM) logs including density, neutron, resistivity and gamma ray, have been widely used to characterize clastic formations to provide cost-effective answers of lithology, porosity, saturation and permeability. Deepwater turbidite deposit formed as a result of turbidity current in grain size variations where clay distribution can affect the vertical permeability and connectivity. Zones with fine grained rock texture or thin clay-rich beds suppress resistivity, due to the parallel resistivity effect of conductive shale layers within the resistive hydrocarbon-bearing sand laminations. Net-to-gross estimation in these formations can be pessimistic when using conventional analysis (CA) with standard resistivity measurements from TCOM data. Moreover, traditional methods can be compromised in other important quantities such as clay distribution and reservoir quality.
|File Size||3 MB||Number of Pages||9|