A study on assessment of hydrocarbon potential of the lignite deposits of Saurashtra basin, Gujarat (Western India)

Singh, Prakash; Singh, Vikas Kumar; Rajak, Pramod K.; Mathur, Neeraj

doi:10.1007/s40789-017-0186-x

Cited by 23 publications

(6 citation statements)

References 32 publications

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“…The sulphur content in the Cambay lignites is much lower (0.12-0.75%) than the Laisong coals. Similar Eocene lignite deposits of the Khadsaliya Clay Formation in the nearby Saurastra Basin are also characterized by low rank with huminite reflectance ranging from 0.28 to 0.30% (Singh, Singh, Rajak, & Mathur, 2017).…”

Section: Comparisons With Regional Depositsmentioning

confidence: 88%

Organic geochemistry and petrology of the coals in the Laisong Formation, Naga Schuppen Belt, NE India: Inferences on hydrocarbon potential and depositional environment

et al. 2021

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The Oligocene Laisong Formation in the Naga Schuppen Belt is coal bearing along the Dimapur-Kohima Road section, Nagaland. The coals are sulphurous and classified as lignite to sub-bituminous 'C' based on various rank parameters. The coals are dominantly composed of vitrinite followed by inertinite and liptinite in order of abundance. The coals have high-quality Type III kerogens derived mainly from angiosperm plants. The richness and quality of organic matter in the coal is excellent to act as a petroleum source rock. Most of the maturity parameters indicate that maturity ranges from immature to early mature. The biomarker parameters and facies critical maceral associations point to deposition of the coal-bearing strata in non-marine/ terrestrial depositional environment under moist hydrodynamic conditions, possibly in an upper delta plain environment. However, the high sulphur content of the coal points to deposition in a lower delta plain environment. Comparison with other Palaeogene coal deposits of the same sedimentary basin shows that the Laisong coals contain lesser amount of liptinite macerals and much higher amount of sulphur than these deposits. All these deposits are of similar rank, however, the Laisong coals show marginally low thermal maturity from the viewpoint of hydrocarbon generation.

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Section: Comparisons With Regional Depositsmentioning

confidence: 88%

Organic geochemistry and petrology of the coals in the Laisong Formation, Naga Schuppen Belt, NE India: Inferences on hydrocarbon potential and depositional environment

et al. 2021

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“…According to TOC (wt %) values, the studied samples, which have excellent source rock potential, contain type III kerogen. Considering the HI-Tmax diagram , and effective HI values in the HI-Ro diagram, ,, coals of the KP1, KM3, and KM2 seams have gas-prone potential (Table and Figure a,b). Low Production Index (PI) (<0.10), BI (<8), and QI (<144) values and HI-Ro, BI-Ro, BI-Tmax, QI-Ro, and QI-Tmax diagrams support that the three seams are essentially gas-prone (Table and Figure c–f).…”

Section: Discussionmentioning

confidence: 99%

Paleoenvironmental Settings of the Soma Coal Basin (Turkey): Insights from Maceral Data, Biomarker, and Carbon Isotopic Composition

Karadirek

2023

ACS Omega

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Organic geochemical (TOC, pyrolysis, biomarker) and petrographic (maceral analysis) investigations together with organic carbon isotope studies were carried out to characterize in detail the depositional environment, determine the organic matter type, and assess the hydrocarbon production potential of three coal seams (KP1-upper, KM3-middle, and KM2-lower) in the Soma (Manisa, Western Anatolia) coal field in Turkey. The total organic carbon value of the upper coal seam ranged between 11.7 and 55.75%, the middle coal seam between 20.12 and 62.86%, and the lower coal seam between 50.03 and 65.71%. Coals in all three seams are characterized by low hydrogen index (HI) values (<151 mg HC/g TOC), low bitumen index (BI) (<19 g HC/g TOC), and quality index (QI) between 23 and 156 mg HC/g TOC. According to Rock-Eval pyrolysis data, the organic matter type of the coals is type III kerogen. Huminite reflectance, Tmax, and biomarker data (22 S /22S+22R (C 32 ) sterane, ββ/(αα + ββ) (C 29 ) sterane, and MPI-1) indicate that the organic matter is not thermally mature and that the Soma-Manisa coal has reached the sub-bituminous rank. Rock-Eval data shows that coal is gas-prone and has not reached the maturity threshold required for initial gas production. The dominant maceral group is huminite while liptinite and inertinite macerals have been found in minor amounts. Groundwater index (GWI), vegetation index (VI), tissue preservation index (TPI), and gelification index (GI) parameters indicate a transition from limnic-limno-telmatic to limno-telmatic-telmatic environment from the upper seam to the lower seam. N -Alkane distributions show that paleoclimatic conditions have changed from KP1 to KM2. The higher abundance of pristane compared to phytane and low C 35 /C 31 –C 35 homohopane index values demonstrate that the coals were deposited in a suboxic–oxic environment. The predominance of n -alkanes with generally high carbon number, relative variable abundances of C 27 –C 28 –C 29 steranes, δ 13 C values, C/N ratios, and very low gammacerane index indicate a terrestrial ecosystem with nonmarine influence, although algae and microorganisms also contributed to the biomass.

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“…Most of the recent studies are related to the discussion of the hydrocarbon potential and reservoir quality assessment. − A detailed understanding of reservoir lithofacies, rock characteristics, and hydrocarbon potential is critical for successful field development planning. − In carbonates, it is quite challenging to classify lithofacies because of lithological variations and diagenetic processes. − Recently, scientists have used advanced machine-learning techniques to identify lithofacies. − It is quite contemporary to employ robust machine-learning tools for precise prediction in oil and gas fields. − There is a stronger connection between machine learning and unsupervised learning because unsupervised learning can naturally discover patterns that are hidden without human supervision . A self-organizing map (SOM) is an advanced method for lithofacies identification .…”

Section: Introductionmentioning

confidence: 99%

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling

et al. 2022

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The Meyal oil field (MOF) is among the most important contributors to Pakistan's oil and gas industry. Northern Pakistan's Potwar Basin is located in the foreland and thrust bands of the Himalayan mountains. The current research aims to delineate the hydrocarbon potential, reservoir zone evaluation, and lithofacies identification through the utilization of seven conventional well logs (M-01, M-08, M-10, M-12, M-13P, and M-17). We employed the advanced unsupervised machine-learning method of selforganizing maps for lithofacies identification and the novel Quanti Elan model technique for comprehensive multimineral evaluation. The shale volume, porosity, permeability, and water saturation (petrophysical parameters) of six wells were evaluated to identify the reservoir potential and prospective reservoir zones. Well-logging data and self-organizing maps were used in this study to provide a less costly method for the objective and systematic identification of lithofacies. According to the SOM and Pickett plot analyses, the zone of interest is mostly made up of pure limestone oil zone, whereas the sandy and dolomitic behavior with a mixture of shale content shows non-reservoir oil−water and water zones. The reservoir has good porosity values that range from 0 to 18%, but there is a high water saturation of up to 45% in reservoir production zones. The presence of shale in the entire reservoir interval has a negative effect on the permeability value, but the petrophysical properties of the Meyal oil reservoir are good enough to permit hydrocarbon production. According to the petrophysical estimates, the Meyal oil field′s Sakesar and Chorgali Formations are promising reservoirs, and new prospects for drilling wells in the southern and central portions of the eastern portion of the research area are recommended.

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A study on assessment of hydrocarbon potential of the lignite deposits of Saurashtra basin, Gujarat (Western India)

Cited by 23 publications

References 32 publications

Organic geochemistry and petrology of the coals in the Laisong Formation, Naga Schuppen Belt, NE India: Inferences on hydrocarbon potential and depositional environment

Organic geochemistry and petrology of the coals in the Laisong Formation, Naga Schuppen Belt, NE India: Inferences on hydrocarbon potential and depositional environment

Paleoenvironmental Settings of the Soma Coal Basin (Turkey): Insights from Maceral Data, Biomarker, and Carbon Isotopic Composition

Hydrocarbon Potential Assessment of Carbonate-Bearing Sediments in a Meyal Oil Field, Pakistan: Insights from Logging Data Using Machine Learning and Quanti Elan Modeling

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