Sedimentology, geochemistry and reservoir potential of the organic-rich Qusaiba Shale, Tabuk Basin, NW Saudi Arabia

Abouelresh, Mohamed O.; Babalola, Lamidi; Bokhari, Abdaseed; Omer, Muhammad; Koithan, Thomas; Boyde, Daniel

doi:10.1016/j.marpetgeo.2019.05.001

Cited by 25 publications

(3 citation statements)

References 73 publications

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“…Moreover, the Mg/Ca, K 2 O + NaO, and G/C 31 H parameters are effective indicators of the change of the paleosalinity. 59,61,62 As shown in Figure 12, the change of the paleosalinity is consistent with the evolution the depositional environment. 4.2.…”

Section: Resultssupporting

confidence: 76%

“…The mudstone, bioclastic limestone, and limestone samples of P 1–2 g–q and C 3 c show high Sr/Ba and log(MgO/Al 2 O 3 ) values, but the Th/U and V/Ni values are relatively low, indicating that the source rocks of P 1–2 g–q and C 3 c were deposited in a marine environment (Figure ). Moreover, the Mg/Ca, K 2 O + NaO, and G/C 31 H parameters are effective indicators of the change of the paleosalinity. ,, As shown in Figure , the change of the paleosalinity is consistent with the evolution the depositional environment.…”

Section: Resultsmentioning

confidence: 77%

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Hydrocarbon Generation Evaluation, Burial History, and Thermal Maturity of the Lower Triassic–Silurian Organic-Rich Sedimentary Rocks in the Central Uplift of the South Yellow Sea Basin, East Asia

et al. 2020

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This study focuses on the organic-rich rocks in the Central Uplift of the South Yellow Sea Basin (SYSB) using the CSDP-2 borehole, which was drilled through the integrated Lower Triassic–Silurian sediments and yielded approximately 2736 m of cores. Geochemical analyses of core samples, including Rock-Eval pyrolysis, elemental analysis, maceral analysis, and gas chromatography–mass spectrometry, revealed multiple sets of effective source rocks developed from the Lower Triassic Qinglong Formation to the Lower Silurian Gaojiabian Formation. The organic matter is dominated by types II1–2–III kerogen, most of which are mature to post-mature. Comprehensive geochemical analysis shows that Permian and Lower Carboniferous mudstones are the most important source rocks and have strong hydrocarbon generation capacity. Most of the source rocks are dominated by planktonic/bacterial sources, deposited mainly in a low-salinity marine environment, and influenced by the input of terrigenous detrital materials. Basin modeling results show that most of the Paleozoic–Mesozoic source rocks in the Central Uplift generally entered the oil-generation window during the Triassic to Late Jurassic. The timing of hydrocarbon generation and expulsion was simultaneous or earlier than the collisional orogenic movement between the Sino–Korean Platform and the Lower Yangtze Craton, which may have some destructive impact on the oil and gas preservation. Therefore, it is preservation condition rather than source rocks that may be one of the most important elements for hydrocarbon accumulation in the Central Uplift of the SYSB.

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Section: Resultssupporting

confidence: 76%

Section: Resultsmentioning

confidence: 77%

Hydrocarbon Generation Evaluation, Burial History, and Thermal Maturity of the Lower Triassic–Silurian Organic-Rich Sedimentary Rocks in the Central Uplift of the South Yellow Sea Basin, East Asia

et al. 2020

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“…It can be produced from both marine and lacustrine shales. Marine shale is generally developed in a stable and gentle tectonic environment, has a few types of lithofacies (Loucks and Ruppel, 2007;Abouelresh et al, 2020), and is widely distributed . In comparison, lacustrine shale formed in a continental rift basin has various types of lithofacies owing to paleogeological conditions such as a relatively small lake area, multiple material sources, and frequent climate changes (Wang et al, 2016;Jia et al, 2018;Li et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Forecast of lacustrine shale lithofacies types in continental rift basins based on machine learning: A case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China

2023

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Lacustrine shale in continental rift basins is complex and features a variety of mineralogical compositions and microstructures. The lithofacies type of shale, mainly determined by mineralogical composition and microstructure, is the most critical factor controlling the quality of shale oil reservoirs. Conventional geophysical methods cannot accurately forecast lacustrine shale lithofacies types, thus restricting the progress of shale oil exploration and development. Considering the lacustrine shale in the upper Es4 member of the Dongying Sag in the Jiyang Depression, Bohai Bay Basin, China, as the research object, the lithofacies type was forecast based on two machine learning methods: support vector machine (SVM) and extreme gradient boosting (XGBoost). To improve the forecast accuracy, we applied the following approaches: first, using core and thin section analyses of consecutively cored wells, the lithofacies were finely reclassified into 22 types according to mineralogical composition and microstructure, and the vertical change of lithofacies types was obtained. Second, in addition to commonly used well logging data, paleoenvironment parameter data (Rb/Sr ratio, paleoclimate parameter; Sr %, paleosalinity parameter; Ti %, paleoprovenance parameter; Fe/Mn ratio, paleo-water depth parameter; P/Ti ratio, paleoproductivity parameter) were applied to the forecast. Third, two sample extraction modes, namely, curve shape-to-points and point-to-point, were used in the machine learning process. Finally, the lithofacies type forecast was carried out under six different conditions. In the condition of selecting the curved shape-to-point sample extraction mode and inputting both well logging and paleoenvironment parameter data, the SVM method achieved the highest average forecast accuracy for all lithofacies types, reaching 68%, as well as the highest average forecast accuracy for favorable lithofacies types at 98%. The forecast accuracy for all lithofacies types improved by 7%–28% by using both well logging and paleoenvironment parameter data rather than using one or the other, and was 7%–8% higher by using the curve shape-to-point sample extraction mode compared to the point-to-point sample extraction mode. In addition, the learning sample quantity and data value overlap of different lithofacies types affected the forecast accuracy. The results of our study confirm that machine learning is an effective solution to forecast lacustrine shale lithofacies. When adopting machine learning methods, increasing the learning sample quantity (>45 groups), selecting the curve shape-to-point sample extraction mode, and using both well logging and paleoenvironment parameter data are effective ways to improve the forecast accuracy of lacustrine shale lithofacies types. The method and results of this study provide guidance to accurately forecast the lacustrine shale lithofacies types in new shale oil wells and will promote the harvest of lacustrine shale oil globally.

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Control of Lithofacies and Geomechanical Characteristics on Natural Fracture Systems in Qusaiba Shale, Rub’ Al-Khali Basin, Saudi Arabia

2021

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Sedimentology, geochemistry and reservoir potential of the organic-rich Qusaiba Shale, Tabuk Basin, NW Saudi Arabia

Cited by 25 publications

References 73 publications

Hydrocarbon Generation Evaluation, Burial History, and Thermal Maturity of the Lower Triassic–Silurian Organic-Rich Sedimentary Rocks in the Central Uplift of the South Yellow Sea Basin, East Asia

Hydrocarbon Generation Evaluation, Burial History, and Thermal Maturity of the Lower Triassic–Silurian Organic-Rich Sedimentary Rocks in the Central Uplift of the South Yellow Sea Basin, East Asia

Forecast of lacustrine shale lithofacies types in continental rift basins based on machine learning: A case study from Dongying Sag, Jiyang Depression, Bohai Bay Basin, China

Control of Lithofacies and Geomechanical Characteristics on Natural Fracture Systems in Qusaiba Shale, Rub’ Al-Khali Basin, Saudi Arabia

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