Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

Zhang, Fan; Zhang, Chi

doi:10.46690/ager.2021.01.09

Cited by 15 publications

(3 citation statements)

References 48 publications

(67 reference statements)

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“…NMR technology can reflect the occurrence of fluid in pore throats of different sizes. − Therefore, we used NMR technology to quantitatively evaluate the sand production characteristics and microscopic mechanisms of unconsolidated sandstone reservoirs during water flooding. As shown in Figure , the initial saturated crude oil in the pore throat with a radius of 10 1 –10 2 ms is represented by ( S i ), the resaturated oil in this area after water flooding is represented by S o , and the damage degree of sand production in unconsolidated sandstone can be calculated as follows b = prefix− S i − S normalo S i × 100 % where b is the damage degree of sand production (%), S i is the T 2 spectrum frequency area of the secondary saturated oil, and S o is the T 2 spectrum frequency area of the initial saturated oil.…”

Section: Methodsmentioning

confidence: 99%

Influence of Sand Production Damage in Unconsolidated Sandstone Reservoirs on Pore Structure Characteristics and Oil Recovery at the Microscopic Scale

Luo²,

Yu³

et al. 2022

ACS Omega

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Unconsolidated sandstone reservoirs have low rock strength and are easily damaged by sand production. To evaluate the effect of the microscopic pore system on the degree of recovery and sand production damage, five typical unconsolidated sandstone core samples were selected. The nuclear magnetic resonance T 2 spectrum of the water-flooding experiments was used to analyze the oil displacement mechanism and sand production damage of the pore throat structure, and the control mechanism of the injection parameters was used to evaluate the recovery and sand production damage degree. The results showed that the recovery of the unconsolidated sandstone core samples was the highest when the injection volume was 6 PV, and the overall pore throat recovery increased from 14.37 to 48.72%. The recovery and sand production damage increased with increasing injection pressure. The overall pore throat recovery was the highest when the pressure was 20 MPa (48.42%), and the sand production damage index was the maximum when the pressure was 25 MPa (19.98%). Under a lower injection pressure, sand production damage mainly originates from loose sand. The main target of loose sand production damage is a smaller pore throat, and the sand production index increases slowly. The recovery increased with the pressure and increased relatively quickly. Under a higher injection pressure, sand production damage mainly comes from loose sand and skeleton sand, causing damage to both smaller and larger pore throats. The sand production damage is positively correlated with injection pressure. The recovery slows down with an increase in pressure or even decreases.

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Section: Methodsmentioning

confidence: 99%

Influence of Sand Production Damage in Unconsolidated Sandstone Reservoirs on Pore Structure Characteristics and Oil Recovery at the Microscopic Scale

Luo²,

Yu³

et al. 2022

ACS Omega

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“…There is no doubt that permeability is one of the most indispensable parameters that directly impacts oil production. The heterogeneity and anisotropy of permeability are also related to oil recovery efficiency [1][2][3][4][5]. In order to evaluate the physical properties of rocks more efficiently, scholars have decided to define relevant parameters in two-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Microscopic Anisotropic Permeabilities of Tight Oil Reservoirs Impacted by Heterogeneous Minerals

Wang

Wei

et al. 2022

Energies

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This study aimed to reveal the anisotropic permeabilities of tight oil reservoirs impacted by heterogeneous minerals. SEM imaging, image processing, fractal calculation, microscopic reservoir modeling, and visual flow simulation were carried out to investigate the above problems. Results show that the variation coefficient of two-dimensional permeability for the studied tight reservoir samples ranges from 0.09 to 0.95, with an average value of 0.68. The penetration coefficient ranges from 1.16 to 2.64, with an average value of 2.13. The ratio of maximum to minimum permeability is between 1.25 and 7.67, with an average value of 5.62. The fluid flow in tight reservoirs has significant anisotropy comprising dominant flow through conductive channels. Flow in tight oil reservoirs tends to involve minor hydraulic fracturing with no proppant.

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“…Traditional loggingbased lithology identification methods, such as mathematical modeling [5], cross plot [6] and statistical analysis [7], establish the theoretical relationship between logging response and lithology. Recently, Zhang et al deciphered nuclear magnetic resonance logging data in carbonate reservoirs and used the ratio between longitudinal and transverse relaxation time as a potential lithology indicator to determine carbonate sub-facies [8]. Nevertheless, the corresponding lithology interpretation depends on expert experience and is time-consuming [9].…”

Section: Introductionmentioning

confidence: 99%

Sequential data-driven cross-domain lithology identification under logging data distribution discrepancy

Liu

Zhou

et al. 2021

Meas. Sci. Technol.

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Since traditional data-driven methods are not effective in cross-domain lithology prediction, researches recently focus on improving the model through learning the feature distribution across the datasets of different wells. However, the sequential relation in logging curves has not been fully extracted in logging data processing, and the distribution discrepancy of lithology class proportion is neglected by the classification model. In this paper, a sequential logging data-driven cross-domain lithology identification model is proposed, which mines spatial neighborhood information while learning data distribution discrepancy of both features and classes. To extract variation trend with depth of logging curves, a temporal convolutional network is designed for lithology prediction. An improved class-balanced self-training algorithm is developed based on semi-supervised learning, where the data distribution in target well is shared with source well. Besides, a model ensemble method is adopted in lithology identification by conducting results averaging to promote stability. Verification experiments on logging datasets of a shale oil field show that the proposed method can learn the sequential characteristics as well as data distribution and realize accurate cross-domain lithology identification.

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Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

Cited by 15 publications

References 48 publications

Influence of Sand Production Damage in Unconsolidated Sandstone Reservoirs on Pore Structure Characteristics and Oil Recovery at the Microscopic Scale

Influence of Sand Production Damage in Unconsolidated Sandstone Reservoirs on Pore Structure Characteristics and Oil Recovery at the Microscopic Scale

Characterizing the Microscopic Anisotropic Permeabilities of Tight Oil Reservoirs Impacted by Heterogeneous Minerals

Sequential data-driven cross-domain lithology identification under logging data distribution discrepancy

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