Geocellular Modeling of the Cambrian to Eocene Multi-Reservoirs, Upper Indus Basin, Pakistan

Ehsan, Muhsan; Latif, Muhammad Ali Umair; Ali, Abid; Radwan, Ahmed E.; Amer, Muhammad Attique; Abdelrahman, Kamal

doi:10.1007/s11053-023-10256-7

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Potwar sub-basin is a part of the Upper Indus Basin (UIB) in which the Precambrian Indian Shield (basal crystalline rocks) is covered by a substantial sedimentary succession of Phanerozoic to recent rocks. ,, The UIB is further subdivided into Potwar (East) and Kohat (West) sub-basins, which the Indus River separates. Both sub-basins are tectonically deformed and represent facies variations (Figure ).…”

Section: Geological Settings and Stratigraphymentioning

confidence: 99%

Unconventional Reservoir Characterization of Patala Formation, Upper Indus Basin, Pakistan

Ehsan,

Chen,

Latif

et al. 2024

ACS Omega

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Unconventional hydrocarbon exploration is needed in the current oil and gas crisis scenario. Therefore, the development of conditions for unconventional hydrocarbon exploration is needed. In the Upper Indus Basin (UIB), Pakistan, the Patala Formation is one of the potential candidates for this unconventional exploration. It is a proven source rock at the regional level in the Kohat-Potwar sub-basin of UIB. This study aims to evaluate the shale gas potential of the rock in the Minwal-Joyamair area of the sub-basin. Developing a shale rock physics model is important for exploring and developing shale reservoirs due to the difference between unconventional shale and conventional sand reservoirs. These differences include mineral types, mineral characteristics, matrix pores, and fluid properties. To achieve the study's objectives, an integrated strategy provides for evaluating rock physics parameters, petrophysics, and geochemical analyses. This integrated approach indicates that the Patala Formation is a good potential reservoir for shale gas exploration. The Formation has a significant thickness (around 40−50 m), higher total organic carbon content (02−10%), higher brittleness index (0.44−0.56), and relatively shallow depth (2136−3223 m). These research findings suggested that the presence of organic and quartz-rich lithofacies can be considered as highly favorable "sweet spots" for shale-gas exploration in the UIB, Pakistan. Through proper understanding of the spatial and temporal distribution of these "sweet spots", shale-gas exploration can be developed as an effective strategy to exploit shale gas.

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Section: Geological Settings and Stratigraphymentioning

confidence: 99%

Unconventional Reservoir Characterization of Patala Formation, Upper Indus Basin, Pakistan

Ehsan,

Chen,

Latif

et al. 2024

ACS Omega

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“…Paleocene-aged rocks are considered to be source rocks that have the potential for generation in the Kohat Sub-Basin and Potwar Sub-Basin . Previous research in the Kohat-Potwar Basin in North Pakistan has identified numerous source rocks that have the potential for hydrocarbons, including the Salt Range formation (Precambrian), the Dandot, Sardhai, and Chidru formations (Permian), and the Lockhart Limestone and Patala formations (Paleocene). − The total organic content (TOC) ranges from 0.5 to less than 3.5%. Hydrocarbons are considered to be generated from the source rocks with Types II and III kerogen .…”

Section: Introductionmentioning

confidence: 99%

“…Extensive research has been carried out on different aspects, such as surface geology, biostratigraphy, sedimentology, hydrocarbon prospect interpretation via petrophysical analysis, seismic studies, and reservoir evaluation of Paleocene-aged formations in the Kohat sub-basin. 19 However, comparatively limited research has been done on the source rock evaluation of Paleocene-aged formations in the Kohat sub-basin. This research focuses on determining the geochemical evaluation of source rocks of Paleocene age in the Tolanj-01 well, Kushal Garh Block, and Kohat sub-basin.…”

Section: Introductionmentioning

confidence: 99%

Geochemical Evaluation of Paleocene Source Rocks in the Kohat Sub-Basin, Pakistan

Sohail,

Mehmood,

Jahandad

et al. 2024

ACS Omega

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The Kohat sub-basin is one of the main hydrocarbon-producing sedimentary basins located in the northwest extension of the Indus Basin in Pakistan. It contains numerous proven and potential petroleum from the Cambrian to the Miocene. Conventional petroleum resources have been depleting rapidly over the last couple of years. Therefore, unconventional resources should be explored using a variety of geochemical and geophysical techniques to address the energy demands. Geochemical techniques, including total organic carbon (TOC) assessment, Rock-Eval pyrolysis, organic petrography, and biomarker studies, are essential for evaluating the potential of shale gas reservoirs to delineate future prospects in a basin. The source rock potential of the Paleocene rocks, including the Patala, Lockhart, and Hangu formations of the sub-basin, is evaluated using geochemical analyses on well cuttings from the Tolanj-01 well. The analyses include estimation of total organic carbon (TOC), Rock-Eval pyrolysis, and organic petrography (vitrinite reflectance) to evaluate the organic richness, thermal maturity, kerogen type, hydrocarbon type, and environment of deposition. Other techniques for extractable organic matter (EOM) include solid−liquid chromatographic separation of fractions, gas chromatography (GC-FID)/whole oil chromatography, and gas chromatography−mass spectrometry (GC-MS). The organic matter (TOC, wt %) analysis reveals that 18 (18) samples of the Hangu formation (0.08−1.8 wt %) show poor values, 12 (12) samples of the Lockhart formation (0.05−0.5 wt %) have poor to fair content, and 26 (26) samples of the Patala formation have poor to fair (0.08−0.19 wt %) TOC content. Rock-Eval pyrolysis studies including hydrogen index, oxygen index, T max , quantities of free hydrocarbons (S 1 , mg/ g), and hydrocarbons produced from pyrolysis (S 2 , mg/g) are determined for the well-cut samples (56) of the Paleocene rocks. The hydrogen index values for the Hangu formation are lower than 200, and those for the Lockhart and Patala formations range between 100 and 250. A maceral analysis is also conducted on these samples, which reveal that the majority of the samples of the Paleocene units present in the basin belong to kerogen types II/III. The thermal maturity of the Hangu and Lockhart formations falls in the late-stage oil window, while that of the Patala formation falls in the peak to late oil window. The genetic potential (GP) for these rock units is also determined based on S 1 and S 2 values, which reveals that it is generally poor except for a few samples of the Hangu and Lockhart formations, which show fair GP values. For the organic petrography (vitrinite reflectance, R 0 ), one sample from each unit is selected, which shows that the Hangu, Lockhart, and Patala formations fall in the category of the mature oil window with their R 0 (%) values being 0.95, 0.89, and 0.82, respectively. The extracts (EOM) from these rock units are also analyzed to assess the depositional settings, biological source input, biodegradation, thermal ...

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“…Well-logging data can offer an effective tool to obtain extensive details on subsurface formation and rock properties. Well logs provide such information as lithology information, formation resistivity, clay content, porosity, rock density, water saturation, rock physics, and flow capacity, which are very important for reservoir characterization [8][9][10][11]. Estimating P p from borehole data has been accomplished through established theoretic techniques, which originated from the Terzaghi and Biot effective stress law [12][13][14][15], to accurately estimate P p in formations, ensuring safe drilling operations and reducing the environmental effect.…”

Section: Introductionmentioning

confidence: 99%

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

Feng,

Wang,

et al. 2024

JMSE

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A growing number of large data sets have created challenges for the oil and gas industry in predicting reservoir parameters and assessing well productivity through efficient and cost-effective techniques. The design of drilling plans for a high-pressure tight-sand reservoir requires accurate estimations of pore pressure (Pp) and reservoir parameters. The objective of this study is to predict and compare the Pp of Huizhou Sag, Pearl River Mouth Basin, China, using conventional techniques and machine learning (ML) algorithms. We investigated the characteristics of low-permeability reservoirs by observing well-logging data sets and cores and examining thin sections under a microscope. In the reservoir zone, the average hydrocarbon saturation is 55%, and the average effective porosity is 11%. The tight sandstone reservoirs consist of fine- to extremely fine-grained argillaceous feldspathic sandstone. The mean absolute error for reservoir property prediction is 1.3%, 2.2%, and 4.8%, respectively, for effective porosity, shale volume, and water saturation. Moreover, the ML algorithm was employed to cross-check the validity of the prediction of Pp. Combining conventional and ML techniques with the core data demonstrates a correlation coefficient (R2) of 0.9587, indicating that ML techniques are the most effective in testing well data. This study shows that ML can effectively predict Pp at subsequent depths in adjacent geologically similar locations. Compared to conventional methods, a substantial data set and ML algorithms improve the precision of Pp predictions.

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Geocellular Modeling of the Cambrian to Eocene Multi-Reservoirs, Upper Indus Basin, Pakistan

Cited by 8 publications

References 29 publications

Unconventional Reservoir Characterization of Patala Formation, Upper Indus Basin, Pakistan

Unconventional Reservoir Characterization of Patala Formation, Upper Indus Basin, Pakistan

Geochemical Evaluation of Paleocene Source Rocks in the Kohat Sub-Basin, Pakistan

Pore Pressure Prediction for High-Pressure Tight Sandstone in the Huizhou Sag, Pearl River Mouth Basin, China: A Machine Learning-Based Approach

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