Two effective methods for calculating water saturations in shale-gas reservoirs

Xu, Jingling; Xu, Lei; Qin, Yuxing

doi:10.1190/geo2016-0462.1

Cited by 23 publications

(11 citation statements)

References 21 publications

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“…In addition to methane, also liquid water (connate water and injected water/retained fracturing fluid) is present in shale nanopores (Li et al 2018;Xu et al 2017). When it comes to multi-phase transport in porous media, wettability-usually characterized by contact angle and surface tension-is a significant factor controlling fluid distribution and fluid flow (Zhou et al 2016).…”

Section: Introductionmentioning

confidence: 99%

A Molecular Dynamics Investigation on Methane Flow and Water Droplets Sliding in Organic Shale Pores with Nano-structured Roughness

Wei

Zhou

2021

Transp Porous Med

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Roughness of surfaces significantly influences how methane and water flow in shale nanopores. We perform molecular dynamics simulations to investigate the influence of surface roughness on pore-scale transport of pure methane as well as of two-phase methane–water systems with the water sliding as droplets over the pore surface. For single-phase methane flow, surface roughness shows a limited influence on bulk methane density, while it significantly reduces the methane flow capacity. In methane–water systems, the mobility of water is a strong function of surface roughness including a clear transition between immobile and mobile water droplets. For cases with mobile water, droplet sliding speeds were correlated with pressure gradient and surface roughness. Sliding water droplets hardly deform, i.e., there is little difference between their advancing and receding contact angle with structured roughness.

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Section: Introductionmentioning

confidence: 99%

A Molecular Dynamics Investigation on Methane Flow and Water Droplets Sliding in Organic Shale Pores with Nano-structured Roughness

Wei

Zhou

2021

Transp Porous Med

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“…Refer to the previous publications by Xu et al, 2017 andZhang et al, 2016, their developed method of the revised water-saturation-TOC presented a very good result on water saturation prediction in the Longmaxi-Wufeng shale, south eastern Sichuan Basin, China. The applied equation of the revised watersaturation-TOC is formulated as follows:…”

Section: Water Saturationmentioning

confidence: 79%

K-Mean Cluster Analysis for Better Determining the Sweet Spot Intervals of the Unconventional Organic-Rich Shale: A Case Study

Akbar¹,

Nugraha²

2018

Contemporary Trends in Geoscience

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The petrophysical analysis is the crucial task for evaluating the quality of unconventional organic-rich shale and tight gas reservoirs. The presence of organic matter and the ultra-tight with over complex pore system have remained a lack of understanding of how to evaluate the extensive parameters of porosity considering organic content, gas saturation, organic richness, brittleness index, and sweet spot interval by only using conventional log. Therefore, this study offers effectively applied techniques and better analysis for interpreting these parameters by maximizing and integrating geological, geochemical, rock mechanical and engineering data. In general, the field data used in this study are from the first dedicated well for source rock exploration in the North Sumatra Basin, Indonesia. The developed method was derived by using conventional log. All interpretation results were validated by laboratory data measurements of routine and special core analysis, petrography, total organic carbon (TOC) and organic maturation, and brittleness index (BI) calculation. Moreover, the high quality of NMR log data was used as well to ensure our developed techniques present good estimations. Briefly about the methods, we started to determine the total and effective porosity based on the density log by including the presence of organic matter and multi-mineral analysis in these estimations. Then, we used the revised water saturation-TOC of water saturation while the TOC was predicted in advance by averaging three results from the correlation of TOC-Density, modified CARBOLOG and Passey’s ΔlogR methods. Equally important, in order to obtain the reliable gas saturation prediction, we used saturation exponent (n), cementation factor (m), and the tortuosity factor (a) parameters which obtained from laboratory measurement of formation resistivity factor and resistivity index (FFRI). In addition, the brittleness index was predicted based on sonic log data. Finally, all parameters needed for determining gas shale sweet spot have been made. Then, we developed a way to evaluate the sweet spot interval by using K-mean clustering. In conclusion, this clustering result properly follows the shale quality index parameters which consist of organic richness and maturation, brittleness index, the storage capacity of porosity and gas saturation. This study shows that these petrophysical applied techniques leads us to interpret the best position of shale interval to be developed with a simple, fast, and accurate prediction way. Furthermore, as a novelty, this method can be used as rock typing method and obviously can reduce uncertainty and risks in organic-rich shale exploration.

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“…In this study, the Simandoux (1963) equation is used where a is the tortuosity factor, m is the cementation exponent, Φ is the neutron-porosity, R w is the formation water resistivity, R t is the true resistivity, R sh is the shale resistivity. In organic-rich shale reservoirs, the total organic matter content (TOC) (and conductive minerals if present) is advised to be integrated into the calculations to give a more accurate estimate of water/gas saturation (Xu et al 2017). The TOC [%] can be related to bulk density where A and B are gas and formation specific constants (Schmoker and Hester, 1983).…”

Section: Deterministic Interpretation Of Real Well Logging Datamentioning

confidence: 99%

“…Since the unconventional reservoirs are usually complex multi-mineral formations, the conventional well-log-analysis methods are rarely applicable, because several petrophysical properties as unknowns may influence the measurements. Empirical estimation methods of water saturation (Archie 1942) and total organic content (Passey et al 1990) should be revised in the given exploration area (Bibor and Szabó 2016;Xu et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Improved well logs clustering algorithm for shale gas identification and formation evaluation

et al. 2021

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The identification of lithology, fluid types, and total organic carbon content are of great priority in the exploration of unconventional hydrocarbons. As a new alternative, a further developed K-means type clustering method is suggested for the evaluation of shale gas formations. The traditional approach of cluster analysis is mainly based on the use of the Euclidean distance for grouping the objects of multivariate observations into different clusters. The high sensitivity of the L2 norm applied to non-Gaussian distributed measurement noises is well-known, which can be reduced by selecting a more suitable norm as distance metrics. To suppress the harmful effect of non-systematic errors and outlying data, the Most Frequent Value method as a robust statistical estimator is combined with the K-means clustering algorithm. The Cauchy-Steiner weights calculated by the Most Frequent Value procedure is applied to measure the weighted distance between the objects, which improves the performance of cluster analysis compared to the Euclidean norm. At the same time, the centroids are also calculated as a weighted average (using the Most Frequent Value method), instead of applying arithmetic mean. The suggested statistical method is tested using synthetic datasets as well as observed wireline logs, mud-logging data and core samples collected from the Barnett Shale Formation, USA. The synthetic experiment using extremely noisy well logs demonstrates that the newly developed robust clustering procedure is able to separate the geological-lithological units in hydrocarbon formations and provide additional information to standard well log analysis. It is also shown that the Cauchy-Steiner weighted cluster analysis is affected less by outliers, which allows a more efficient processing of poor-quality wireline logs and an improved evaluation of shale gas reservoirs.

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Two effective methods for calculating water saturations in shale-gas reservoirs

Cited by 23 publications

References 21 publications

A Molecular Dynamics Investigation on Methane Flow and Water Droplets Sliding in Organic Shale Pores with Nano-structured Roughness

A Molecular Dynamics Investigation on Methane Flow and Water Droplets Sliding in Organic Shale Pores with Nano-structured Roughness

K-Mean Cluster Analysis for Better Determining the Sweet Spot Intervals of the Unconventional Organic-Rich Shale: A Case Study

Improved well logs clustering algorithm for shale gas identification and formation evaluation

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