Acoustic impedance as a lithological and hydrocarbon indicator—a case study from Cauvery Basin

Avadhani, V. L. N.; Anandan, M.; Thattacherry, B. J.; Murthy, K. S. R.; Gariya, B. C.; Dwivedi, Ashish

doi:10.1190/1.2221363

Cited by 8 publications

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“…Acoustic impedance (AI) inversion is described as a key tool for the identification of the reservoir's porosity and lithofacies distribution (Avadhani et al, 2006;Mahgoub et al, 2017;Abdolahi et al, 2022). The post-stack seismic inversion is a cheap, rapid, and effective method for investigating exploration wells that are distant from each other (Abdolahi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

et al. 2022

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Precision porosity and facies determinations are critical in reducing drilling uncertainty and increasing hydrocarbon recoveries from heterogeneous sources. The porosity and facies distribution of the Taiyuan-Shanxi Formations (T9c-T9d), and Shihezi-1 Formation (T9d-T9e) within the Hangjinqi area are uncertain and no studies have covered the spatial distribution on a regional scale. The heterogeneous nature of coal, mudstone, and sandstone makes it challenging to comprehend the distribution of porosity and lithofacies. Also, the seismic resolution is not able to resolve the reservoir heterogeneity. Therefore, we have employed regional 3D seismic and well logs by utilizing the advanced acoustic impedance inversion to accomplish our study. Results of petrophysical analysis conducted on the well J32 showed that Shihezi-1 and Shanxi-1 Formations have potential gas-saturated zones. Crossplot analysis distinguished the lowest impedance coal from the highest impedance tight sandstone facies. The outcomes of the constrained sparse spike inversion (CSSI) reliably distinguished the coal facies from the channel-tight sandstone facies. The tight sandstone facies showed the highest impedance values as compared to coal and mudstone facies on the absolute acoustic impedance section. Impedance and porosity maps of T9d and T9e suggested the presence of a maximum porosity (8%–12% for T9d, and 5%–10% for T9e), and maximum distribution of tight sandstone facies, while T9c shows the lowest porosity (0%–6%) and lowest impedance values due to the presence of coal facies. Thick braided fluvial channels are evident on the T9d impedance and porosity maps, making it the most favorable horizon to produce the maximum gas. Whereas, T9c shows the least distribution of sandstone facies making it the least favorable. We propose that the zones of maximum porosity on the T9c, T9d, and T9e horizons can be exploited for future gas explorations.

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Section: Introductionmentioning

confidence: 99%

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

et al. 2022

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“…It acts as a crucial tool for accurately describing the characteristics of a reservoir (e.g. porosity, saturation and lithofacies) (Abdolahi et al, 2022; Ashraf et al, 2022; Avadhani et al, 2006; Mahgoub et al, 2017; Xu et al, 2009). Hence, usage of this attribute, in particular, becomes more reliable for defining the reservoir property than using a set of multiple attributes such as instantaneous phase, frequency, amplitude, energy, variance and bandwidth.…”

Section: Methodsmentioning

confidence: 99%

Appraisal of reservoir porosity using a machine learning approach: A study from the Eocene‐Miocene interval of Upper Assam Basin, NE, India

Kumar,

Sain

2023

Geological Journal

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Porosity estimation is essential for the exploration of hydrocarbons in sedimentary basins, which are affected by several factors such as the burial depth, lithology changes, sedimentary environment and diagenetic degree. Hence, detailed understanding of reservoir porosity is essential for estimating potential economic reserves and developing an explored hydrocarbon field. The present study uses high‐quality three‐dimensional (3D) seismic reflection data to decipher the porous zones within the Eocene‐Oligocene‐Miocene intervals of the Upper Assam Basin. The coherency seismic attribute has brought out several fault‐bounded structures that control the reservoir geometry. Random forest‐based machine learning technique has been utilized for the porosity estimation within the formations of interest. It is observed that the north‐east (NE) parts of the targeted intervals are porous with varying porosity of 0.28–0.42. The Miocene period witnessed a high amount of sedimentation within the basin. Different structural highs and thicknesses are pronounced in the NE part of the study region. Thus, the Miocene intervals are favourable leads for the exploitation of hydrocarbons.

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“…The TOC of these organic rich formations ranges from 2 to 2.5%. Ro values vary from 1.0 to 1.5% . The samples investigated in the present study were collected from Ramnad Sub basin, lying southwards.…”

Section: Geological Backgroundmentioning

confidence: 99%

A Comparative Study on the Pore Size Distribution of Different Indian Shale Gas Reservoirs for Gas Production and Potential CO₂ Sequestration

2018

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A thorough knowledge on pore size distribution (PSD) is one of the fundamental requirements for characterization of shale gas reservoirs and for accurate estimation of their gas storage potential. However, being an important future source of energy need in India, the pore size distribution of Indian shale gas systems is not scientifically well understood. In the present study, the nanoscale pore size distributions of prospective Indian shale basins, viz., Cambay, Cauvery, Krishna-Godavari (K-G), and Damodar valley (DV), were investigated using mercury injection porosimetry (MIP) and low-pressure gas adsorption (LP-N2 and LP-CO2) techniques. The study focused on identifying the priority basins for shale gas production which can be substituted for sequestration of CO2 based on their PSDs. The samples exhibited higher thermal maturity with increasing organic content. The chemical composition of the shale samples was inferred from XRD data, which depicted higher clay content. The prominent clay minerals identified muscovite, illite, and kaolinite, which are generally flaky in nature. These minerals contributed significantly to the pore size complexity of the studied shale samples. The experimental result suggested that the samples exhibited diversified pore size characteristics. The shales are chiefly bimodal, consisting of mesopores (2–50 nm) and micropores (<2 nm). The micropores were efficiently accessed using CO2, while N2 was effective on characterizing the mesopore region. MIP analysis was used to infer the pore throat area. The average pore diameter of samples ranged from 3.38 nm in Damodar valley to 3.94 nm in thr K-G basin, while Cauvery and Cambay basin samples possessed 3.88 and 3.86 nm, respectively. Both N2 and CO2 adsorption (type II and type I, respectively) suggested the presence of micropore infilling in all of the basin samples. However, Cauvery basin samples were inferred with higher mesopore content as compared to other basins and possessed higher quartz percentage, making it more appropriate for hydraulic fracturing. The Cauvery basin showed enrichment of larger pore sizes. The results signified that the Damodar valley shales have a higher affinity toward CO2 adsorption. This suggested that the basin could be a better host for future carbon storage, compared to other basin pore structures.

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Acoustic impedance as a lithological and hydrocarbon indicator—a case study from Cauvery Basin

Cited by 8 publications

References 0 publications

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Appraisal of reservoir porosity using a machine learning approach: A study from the Eocene‐Miocene interval of Upper Assam Basin, NE, India

A Comparative Study on the Pore Size Distribution of Different Indian Shale Gas Reservoirs for Gas Production and Potential CO₂ Sequestration

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Acoustic impedance as a lithological and hydrocarbon indicator—a case study from Cauvery Basin

Cited by 8 publications

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Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Estimation of porosity and facies distribution through seismic inversion in an unconventional tight sandstone reservoir of Hangjinqi area, Ordos basin

Appraisal of reservoir porosity using a machine learning approach: A study from the Eocene‐Miocene interval of Upper Assam Basin, NE, India

A Comparative Study on the Pore Size Distribution of Different Indian Shale Gas Reservoirs for Gas Production and Potential CO2 Sequestration

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A Comparative Study on the Pore Size Distribution of Different Indian Shale Gas Reservoirs for Gas Production and Potential CO₂ Sequestration