NMR relaxometry interpretation of source rock liquid saturation — A holistic approach

Mukhametdinova, Aliya; Habina-Skrzyniarz, Iwona; Kazak, Andrey; Krzyżak, Artur

doi:10.1016/j.marpetgeo.2021.105165

Cited by 34 publications

(10 citation statements)

References 39 publications

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“…After the elimination of all pixels with intensity equal to the background values, the basic images were generated (Figure 4). 3D (for all slices) porosity distribution in the sample was normalized to the value of 13.90 ± 0.01% obtained from 1D‐ T 2 experiment conducted in a low magnetic field (0.05 T; see Section S1, Table S1 and Figure S4 in Supporting Information S1), which is the method of choice for the accurate determination of total porosity (Krzyżak, Habina‐Skrzyniarz et al., 2020; Mukhametdinova et al., 2021). The images in Figure 4 additionally depict slice‐to‐slice porosity distribution, which after normalization to 1D‐ T 2 , can be characterized quantitatively (see Section 4.2) There is only one reservation regarding such normalization, that the contribution to the total porosity of 0.425% is underestimated via DTI due to considerable relaxation mechanisms in proton populations with T 2 ≤ TE .…”

Section: Resultsmentioning

confidence: 99%

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing ¹H in the Pore System of Rock Cores

et al. 2022

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Nuclear magnetic resonance (MR) (NMR) is commonly used for the determination of rock porosity, as well as pore size distribution (PSD) and tortuosity, required to handle hydrocarbon exploitation. Emerging technologies for NMR equipment now enable the visualization of porosity using magnetic resonance imaging (MRI). Currently, single‐point imaging is the most common MRI technique employed, but diffusion‐weighted imaging has been attracting the attention of geologists and geophysicists. In this study, a more advanced technique, diffusion tensor imaging (DTI), is proposed for the characterization of rock core samples. The main purpose of this paper is to demonstrate the usefulness of DTI in the petrophysical characterization of rocks and discuss its strengths. As an example, a carbonate core sample was examined using DTI. The diffusion tensor (DT) was obtained and DT parameters (mean diffusivity, MD; fractional anisotropy, FA; three eigenvalues; DT components; and proton density, PD) were visualized in 2D and 3D. Each parameter was described and its utility in terms of pore space characterization was analyzed. In addition, a new parameter, principal diffusion tracts, was introduced based on the DT tractography performed in the study. The analysis is summarized in a set of DT parameters and a resultant ellipsoid that delivers complementary information about the sample's pore network microgeometry, including referential measurements of anisotropic phantoms. The applications of DTI for the determination of pore size distribution, tortuosity and conductivity are also shown. The study ends with a consideration of the potential prospects and challenges for DTI‐based examination of rock core samples.

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Section: Resultsmentioning

confidence: 99%

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing ¹H in the Pore System of Rock Cores

et al. 2022

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“…Mukhametdinova et al compared several T 1 −T 2 interpretation schemes and proposed a modification in which the existing T 1 − T 2 interpretation scheme is applied to the target area. 24 However, the measurement environment based on an NMR core analyzer in the laboratory is different from that in the NMR logging field, a few cores may not reflect the rock properties of the whole area, and the cost of shale coring is high. During the exploration and development stage, coring in substantial amounts is unlikely and not recommended due to economic factors.…”

Section: Introductionmentioning

confidence: 99%

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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“…T 2 relaxation), additional parameters are employed (i.e. T 1 relaxation or diffusion) to obtain so-called 2D NMR maps 8 – 10 which can theoretically help to separate these overlapping bitumen and water signals. 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.…”

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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NMR relaxometry interpretation of source rock liquid saturation — A holistic approach

Cited by 34 publications

References 39 publications

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing ¹H in the Pore System of Rock Cores

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing ¹H in the Pore System of Rock Cores

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

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

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NMR relaxometry interpretation of source rock liquid saturation — A holistic approach

Cited by 34 publications

References 39 publications

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing 1H in the Pore System of Rock Cores

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing 1H in the Pore System of Rock Cores

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

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

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Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing ¹H in the Pore System of Rock Cores

Prospects and Challenges for the Spatial Quantification of the Diffusion of Fluids Containing ¹H in the Pore System of Rock Cores