Integrated Reservoir Characterization Using Unsupervised Learning on Nuclear Magnetic Resonance (NMR)
T1– T2 Logs
- Tianmin Jiang (ConocoPhillips) | Ron J. M. Bonnie (ConocoPhillips) | Thiago Simoes Correa (ConocoPhillips) | Martin C. Krueger (ConocoPhillips) | Shaina A. Kelly (ConocoPhillips) | Matt S. Wasson (ConocoPhillips)
- Document ID
- Society of Petrophysicists and Well-Log Analysts
- SPWLA 61st Annual Logging Symposium - Online, 24 June - 29 July, Virtual Online Webinar
- Publication Date
- Document Type
- Conference Paper
- 2020. held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors
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A novel interpretation workflow was developed using an automated unsupervised learning algorithm on Nuclear Magnetic Resonance (NMR) T1–T2 log data to quantify fluid filled porosity and saturation, producible oil volumes, and to characterize matrix pore sizes and formation wettability. Core porosity and saturation measurements, scanning electron microscope images (SEM), rock-eval pyrolysis, wettability measurements and mercury injection capillary pressure (MICP) tests are compared with the NMR interpretation for calibration and validation.
Understanding in-situ fluid types and volumetrics is key for reservoir characterization. The traditional static formation evaluation model based on triple-combo logs (density, neutron, resistivity and gamma ray) has been widely used to characterize formations to provide costeffective answers of lithology, total porosity and water saturation. Nevertheless, the dynamic result from production often shows quite different water cut than total water saturation, because the static model cannot distinguish immobile hydrocarbons from producible oil. NMR T1–T2 log data show unique signatures of formation fluids, such as gas, immobile hydrocarbon, producible oil, immobile and free water. The NMR data also provide a method to interpret fluid and matrix properties, including fluid viscosity, pore geometry and fluid-pore interaction. However, due to the downhole environment and the resolution limitation of the logging tool, the signatures of the fluids are not always well-separated. It is challenging to visually separate the signal contributions of different formation fluids on T1–T2 maps.
An automated unsupervised learning algorithm is implemented in the new workflow to separate different T1–T2 signatures of pore fluids for fluid typing and provides fluid porosities and saturations. T1–T2 signatures of separated fluids are used to characterize fluid mobility, pore sizes and formation wettability.
The new approach is successfully applied to multiple wells for a field case study to characterize the saturation and producibility of hydrocarbon and water, which routine petrophysical models are unable to distinguish. Results are corroborated with dynamic production data showing high free water and high residual oil. This is also validated by routine and special core analyses. Integration of NMR, MICP and SEM gives pore body and pore throat size distributions with body to throat ratio (BTR), increasing the precision of estimated formation permeability. High T1/T2 ratio of the oil suggests that the formation is partially oil-wet. The wettability results from NMR are consistent with the core wettability test and production results. Understanding which portion of a reservoir contains mobile fluids impacts target zone selection and reserves estimation.
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