An Unsupervised Learning Algorithm to Compute Fluid Volumes From NMR T1-T2 Logs in Unconventional Reservoirs

Venkataramanan, Lalitha; Evirgen, Noyan; Allen, David; Mutina, Albina; Cai, Qun; Johnson, Andrew C.; Green, Aaron Y.; Jiang, Tianmin

doi:10.30632/pjv59n5-2018a4

Cited by 9 publications

(5 citation statements)

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“…Anand et al used the BSS method to obtain the T 1 – T 2 features of multiple fluids and combined the volumes of these fluids with spectroscopic and dielectric measurements for shale reservoir evaluation . Venkataramanan et al used a combination of hierarchical clustering and nonnegative matrix factorization (NMF) to quantitatively analyze a shale reservoir and discussed the impact of the dynamic range of fluid saturation on the BSS method . Gu et al divided the T 1 – T 2 spectrum into different regions using NMF and classified the overlapping regions according to the T 1 / T 2 value of the fluid .…”

Section: Introductionmentioning

confidence: 99%

“…19 Venkataramanan et al used a combination of hierarchical clustering and nonnegative matrix factorization (NMF) to quantitatively analyze a shale reservoir and discussed the impact of the dynamic range of fluid saturation on the BSS method. 20 Gu et al divided the T 1 −T 2 spectrum into different regions using NMF and classified the overlapping regions according to the T 1 /T 2 value of the fluid. 21 The weak signal in the T 1 −T 2 spectrum is then transferred to accurately obtain the saturation of fluid.…”

Section: Introductionmentioning

confidence: 99%

“…To date, the T 1 – T 2 interpretation scheme ,− and the blind source separation (BSS) method − have been the two methods most often used for saturation evaluation in shale. Via drying, centrifugation, extraction, and pyrolysis at various temperatures, the T 1 – T 2 spectrum of a few cores at different states is analyzed and the T 1 – T 2 interpretation scheme is drawn to obtain the regions of different fluids.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Fluid Saturation in Shale Using 2D Nuclear Magnetic Resonance

Xie

Guo

et al. 2023

Energy Fuels

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Two-dimensional (2D) nuclear magnetic resonance (NMR) has become an indispensable tool for fluid saturation evaluation in shale reservoir. NMR spectrum derived from echo data with a low signal-to-noise ratio, however, cannot be effectively used for fluid feature extraction and saturation calculation. In this study, we proposed a fluid saturation evaluation method that combines morphology, nonnegative matrix decomposition, and fully constrained least squares. Moreover, an Akaike information criterion (AIC)-based method was proposed to determine the number of fluid types. A shale formation data set with varying fluid saturation was processed to validate the effectiveness of the proposed method. The results show that the proposed method can efficiently and accurately obtain the NMR T 1 −T 2 feature and saturation of different fluid types and has advantages over the comparable method in both computational efficiency and accuracy. Furthermore, the impact of the structure size was investigated, demonstrating that the proposed method is very fault tolerant.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Fluid Saturation in Shale Using 2D Nuclear Magnetic Resonance

Xie

Guo

et al. 2023

Energy Fuels

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“…The low-field nuclear magnetic resonance (LF-NMR) logging tools have proved to be a valuable alternative since the measurements are non-invasive and independent of lithology. In the past 20 years, NMR measurements were used for water–oil emulsions characterization 3 and in recent years for the in-situ fluid saturation determination 4 , 5 . Although the application of NMR measurements have been demonstrated to be successful for emulsion characterization, they are limited to emulsions with high water content (> 10 wt%), and it usually requires reference measurements of oil and water NMR amplitudes necessary for obtaining additional parameters such as relative hydrogen index 3 , 6 .…”

Section: Introductionmentioning

confidence: 99%

“…Application of 2D maps showed considerable success in fluid saturation evaluation, compared to 1D T 2 relaxation distribution analysis, since T 1 relaxation or diffusion of reservoir fluids can be sufficiently different, thus enabling relatively simple separation of their signals. However, 2D NMR is slower and more expensive to run, and there can still be instances where these signals are not distinct, in which case estimation of fluid types and fluid volumes can be challenging and require advanced analysis involving blind-source signal separation (BSS), clustering algorithms, and a certain degree of knowledge in 2D NMR maps interpretation 5 .…”

Section: Introductionmentioning

confidence: 99%

Application of XGBoost model for in-situ water saturation determination in Canadian oil-sands by LF-NMR and density data

Markovic

Bryan²,

Rezaee

et al. 2022

Sci Rep

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Water saturation determination is among the most challenging tasks in petrophysical well-logging, which directly impacts the decision-making process in hydrocarbon exploration and production. Low-field nuclear magnetic resonance (LF-NMR) measurements can provide reliable evaluation. However, quantification of oil and water volumes is problematic when their NMR signals are not distinct. To overcome this, we developed two machine learning frameworks for predicting relative water content in oil-sand samples using LF-NMR spin–spin (T2) relaxation and bulk density data to derive a model based on Extreme Gradient Boosting. The first one facilitates feature engineering based on empirical knowledge from the T2 relaxation distribution analysis domain and mutual information feature extraction technique, while the second model considers whole samples’ NMR T2-relaxation distribution. The NMR T2 distributions were obtained for 82 Canadian oil-sands samples at ambient and reservoir temperatures (164 data points). The true water content was determined by Dean-Stark extraction. The statistical scores confirm the strong generalization ability of the feature engineering LF-NMR model in predicting relative water content by Dean-Stark—root-mean-square error of 0.67% and mean-absolute error of 0.53% (R2 = 0.90). Results indicate that this approach can be extended for the improved in-situ water saturation evaluation by LF-NMR and bulk density measurements.

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