Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

Adebayo, Abdulrauf R.; Babalola, Lamidi; Hussaini, Syed Rizwanullah; Alqubalee, Abdullah; Babu, Rahul S.

doi:10.3390/en12224302

Cited by 17 publications

(6 citation statements)

References 18 publications

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“…In China, unconventional tight gas provides the most contribution of the unconventional resources and accounts for 39.2% reserves and 24.6% annual production of the total natural gas [3]. Therefore, many geoscientists are working on the reservoir quality prediction of sandstone reservoirs [4][5][6][7]. The Ordos basin (OB) is one of the most productive basins with the highest annual gas output in China [8,9].…”

Section: Introductionmentioning

confidence: 99%

Sedimentary Facies Controls for Reservoir Quality Prediction of Lower Shihezi Member-1 of the Hangjinqi Area, Ordos Basin

et al. 2022

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The tight gas reserves in the Hangjinqi area are estimated at 700 × 109 m3. Since the exploration of the Hangjinqi, numerous wells are already drilled. However, the Hangjinqi remains an exploration area and has yet to become a gas field. Identifying a paleo-depositional framework such as braided channels is beneficial for exploration and production companies. Further, braided channels pose drilling risks and must be properly identified prior to drilling. Henceforth, based on the significance of paleochannels, this study is focused on addressing the depositional framework and sedimentary facies of the first member (P2x1) of the lower Shihezi formation (LSF) for reservoir quality prediction. Geological modeling, seismic attributes, and petrophysical modeling using cores, logs, interval velocities, and 3D seismic data are employed. Geological modeling is conducted through structural maps, thickness map, and sand-ratio map, which show that the northeastern region is uplifted compared to northwestern and southern regions. The sand-ratio map showed that sand is accumulated in most of the regions within member-1. Interval velocities are incorporated to calibrate the acoustic impedance differences of mudstone and sandstone lithologies, suggesting that amplitude reflection is reliable and amplitude-dependent seismic attributes can be employed. The Root Mean Square (RMS) attribute confirmed the presence of thick-bedded braided channels. The results of cores and logging also confirmed the presence of braided channels and channel-bars. The test results of wells J34 and J72 shows that the reservoir quality within member-1 of LSF is favorable for gas production within the Hangjinqi area.

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Section: Introductionmentioning

confidence: 99%

Sedimentary Facies Controls for Reservoir Quality Prediction of Lower Shihezi Member-1 of the Hangjinqi Area, Ordos Basin

et al. 2022

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“…10 Tight sandstone gas is an essential unconventional resource, and the exploration of tight sandstone gas is widely distributed and has become a research hotspot. [11][12][13][14] The reservoir pores significantly influence oil and gas production, serving as a storage and leakage space for these fossil fuels of oil and gas. 15,16 The pore content of tight sandstone is comparatively low.…”

Section: Introductionmentioning

confidence: 99%

“…Still, the pore diameter distribution varies widely, spanning from a few nanometers to several hundred micrometers. In contrast, while nanoscale pores account for a relatively high proportion, with strong pore heterogeneity and complex pore structure, [11][12][13][14][17][18][19][20] and it is the apparent feature from traditional reservoirs. 21,22 A comprehensive assessment of pore structure is a key aspect of research.…”

Section: Introductionmentioning

confidence: 99%

Pore structure of tight sandstones with differing permeability: The He 8 Member of the Middle Permian Lower Shihezi Formation, Gaoqiao area, Ordos Basin

Chen,

Zhu,

Wang

et al. 2023

Energy Science & Engineering

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Tight sandstone has strong pore heterogeneity and complex pore structure, and the pore structure of tight sandstone varies with different permeability. To study the differences in the pore structure of tight sandstone with different permeability, this study investigated the tight sandstone of the He 8 Member in the Gaoqiao area of the Ordos Basin. Factors influencing pore formation are analyzed through experiments utilizing methods, such as distinguishing casting thin sections, scanning electron microscopy, high‐pressure mercury intrusion, and low‐temperature nitrogen adsorption. The results indicate that in this area, the pores of the tight sandstone are primarily dissolution and intercrystalline pores, with occasional intergranular pores and microcracks. Type Ⅱ samples (permeability > 0.2 × 10−3 μm2) are primarily composed of dissolution and intercrystalline pores, with a few visible intergranular and microcracks. In contrast, Type Ⅰ samples (permeability < 0.2 × 10−3 μm2) mainly consist of micropores and intercrystalline pores, where microcracks are observed locally. Pore size research demonstrates that Type Ⅰ samples have pore size under 1000 nm, with peaks primarily at 4–5 nm. There are also peaks between 10–1000 nm, but without a consistent pattern. For Type Ⅱ samples with sizes smaller than 1000 nm, pore distribution is evident. Peak values when the pore size exceeds 1000 nm. Type Ⅰ samples have fewer micron‐level pores, while Type Ⅱ samples are primarily dissolution pores. The nanopores of Type Ⅰ samples consist of flat pores with good connectivity, whereas those of Type Ⅱ samples are mainly blind pores with poor connectivity. Type Ⅰ samples have larger pore fractal dimensions, more intricate pore morphology, and rougher and irregular pore throat surfaces compared to Type II samples. Hence, the sedimentary environment, diagenesis, and mineral composition affect the pore distribution in tight sandstone. The research findings highlight the variations in the pore structure of tight sandstone with varying permeability, providing crucial guidance for classifying and assessing pore structure in tight sandstone reservoirs.

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“…The high-quality region in the reservoir is defined as "the zone of maximum commercial productivity under the currently employed technology". This definition, itself, combines the disciplines of geophysics, geology, and petroleum engineering for the optimum production and operative description of the reservoirs [1]. Correct identification of high-quality regions requires a thorough understanding of porosity, permeability, fluid saturation, the volume of shale, areal extent, thickness, and mechanical properties (last but not least) through the integration of seismic data, wireline log, and core data analyses [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

et al. 2020

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This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.

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Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

Cited by 17 publications

References 18 publications

Sedimentary Facies Controls for Reservoir Quality Prediction of Lower Shihezi Member-1 of the Hangjinqi Area, Ordos Basin

Sedimentary Facies Controls for Reservoir Quality Prediction of Lower Shihezi Member-1 of the Hangjinqi Area, Ordos Basin

Pore structure of tight sandstones with differing permeability: The He 8 Member of the Middle Permian Lower Shihezi Formation, Gaoqiao area, Ordos Basin

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

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