Geophysical Well-Log Evaluation in the Era of Unconventional Hydrocarbon Resources: A Review on Current Status and Prospects

Lai, Jin; Wang, Guiwen; Fan, Qixuan; Pang, Xiaojiao; Li, Hongbin; Zhao, Fei; Li, Yuhang; Zhao, Xianzheng; Zhao, Yidi; Huang, Yuyue; Bao, Meng; Qin, Z.; Wang, Qiqi

doi:10.1007/s10712-022-09705-4

Cited by 53 publications

(15 citation statements)

References 146 publications

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“…The elastic parameters (Young's modulus, Poisson's ratio) calculated from well logs are dynamic, while the elastic parameters obtained from laboratory tests are static elastic parameters. The well log used to calculate Young's modulus and Poisson's ratio should be calibrated with static elastic parameters to improve accuracy (Iqbal et al, 2018;Guo et al, 2019;Ma et al, 2022;Lai et al, 2022b).…”

Section: Brittleness Indexmentioning

confidence: 99%

“…where α is the effective stress coefficient, ε is the strain factor (Zoback et al, 2003;Iqbal et al, 2018;Lai et al, 2022b). The E and v should be static elastic parameters in Eqs.…”

Section: In Situ Stress Status and Directionmentioning

confidence: 99%

“…13). Furthermore, the key well can be further used for well log evaluation of newly-drilled wells (Lai et al, 2022b).…”

Section: Establishment Of a Key Wellmentioning

confidence: 99%

“…Geological ‗sweet spots' should comprehensively evaluate the lithology, reservoir (fracture) properties and oil-bearing properties of unconventional hydrocarbon resources (Zhao et al, 2019). In addition, optimization of engineering ‗sweet spots' should evaluate brittleness, frackability and in situ stress anisotropy (Rybacki et al, 2016;Iqbal et al, 2018;Zhao et al, 2019;Lai et al, 2022b). Table 1 lists the parameters used in evaluating geological and engineering ‗sweet spots' of the Lucaogou Formation (Table 1).…”

Section: Prediction Of Geological and Engineering ‗Sweet Spots'mentioning

confidence: 99%

“…Oil saturation should also be high and preferrably higher than 50%. Engineering ‗sweet spots' should be characterized by a high brittleness index (>50%), while differences between SH max and Sh min should be low (Guo et al, 2019;Lai et al, 2022b).…”

Section: Prediction Of Geological and Engineering ‗Sweet Spots'mentioning

confidence: 99%

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Geological and Engineering ‘Sweet Spots’ in the Permian Lucaogou Formation, Jimusar Sag, Junggar Basin

Lai

Bai

et al. 2023

Acta Geologica Sinica (Eng)

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Unconventional oil and gas resources require petrophysical logs to answer the question “How to optimize the geological and engineering sweet spots?”. Therefore the establishment of key well with comprehensive descriptions of lithology, reservoir property, hydrocarbon bearing property, electronic well log responses, source rock property, brittleness, and in situ stress magnitude and direction is important for exploration and production of unconventional hydrocarbon resources. Cores, thin section, scanning electron microscopy (SEM) and comprehensive well log suits are used to build a key well of the Permian Lucaogou Formation, Jimusar Sag of Junggar Basin. The results show that there are mainly three types of lithologies including siltstone, mudstone, and dolostone. Lithologies can be predicted using the combination of conventional well and image logs. The pore spaces consist of interparticle pores, intragranular dissolution pores, and micropores. Nuclear Magnetic Resonance (NMR) T2 components longer than 1.7 ms are superposed as effective porosity. Permeability is calculated using the Coates model from NMR T2 spectra. The ratio of T2 components >7.0 ms to the T2 components >0.3 ms is used to calculate oil saturation. TOC is calculated using ΔlogR method. Brittleness index is calculated using Poisson‐ Young's method, and brittleness index ranges from 13.42%–70.53%. In situ stress direction is determined, and in situ stress magnitudes (Maximum horizontal stress SHmax, Minimum horizontal stress Shmin, Vertical stress Sv) are calculated using density and sonic logs. The strike‐slip stress type (SHmax>Sv>Shmin) is encountered. The key well which comprehensively includes the above seven properties is established. Then geological and engineering (geomechanical) sweet spots are optimized from the key well by fully analyzing lithology, reservoir property, oil bearing potential, in situ stress magnitude and brittleness. The results are hoped to support engineers' and geologists' decisions for exploitation of unconventional hydrocarbon resources.

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Section: Brittleness Indexmentioning

confidence: 99%

“…where α is the effective stress coefficient, ε is the strain factor (Zoback et al, 2003;Iqbal et al, 2018;Lai et al, 2022b). The E and v should be static elastic parameters in Eqs.…”

Section: In Situ Stress Status and Directionmentioning

confidence: 99%

“…13). Furthermore, the key well can be further used for well log evaluation of newly-drilled wells (Lai et al, 2022b).…”

Section: Establishment Of a Key Wellmentioning

confidence: 99%

Section: Prediction Of Geological and Engineering ‗Sweet Spots'mentioning

confidence: 99%

Section: Prediction Of Geological and Engineering ‗Sweet Spots'mentioning

confidence: 99%

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Geological and Engineering ‘Sweet Spots’ in the Permian Lucaogou Formation, Jimusar Sag, Junggar Basin

Lai

Bai

et al. 2023

Acta Geologica Sinica (Eng)

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Prediction of lithology in lacustrine carbonates using well logs: The Cretaceous Barra Velha Formation in Santos Basin, offshore Brazil

Wang¹,

Zhang

Fan³

et al. 2023

Geological Journal

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Abundant hydrocarbons have been produced from the pre‐salt lacustrine carbonates of the Cretaceous Barra Velha Formation in the Santos Basin of offshore Brazil. The highly heterogeneous lithology and pore systems make it difficult to predict the reservoir quality via petrophysical well logs. Core description, computed tomography (CT) scanning, thin sections and wireline well logs are utilized to characterize the lithology, pore spaces are reservoir quality of the Cretaceous Barra Velha Formation pre‐salt lacustrine carbonates in the Santos Basin. The results show that the lithology mainly includes shrubstone, grainstone, laminated micrite and massive micrite. The pore systems are dominated by secondary pores, such as intergranular pores, dissolution pores and intercrystalline pores as well as modified primary pores. Geological well logs (conventional well logs, electrical and sonic image logs and nuclear magnetic resonance [NMR] logs) are integrated to build the predictable models of the carbonate reservoirs. The shrubstone, which is dominated by intergranular pores, has the highest reservoir quality, which can be derived from NMR logs. Grainstone contains both primary intergranular pores and abundant secondary dissolution pores. The laminated and massive micrite contain only micropores and therefore have the lowest reservoir quality. Lastly, conventional well logs integrated with electrical and sonic image logs are utilized to predict the distribution of lithology and reservoir quality in single wells, and the results are testified by the NMR log data. The shrubstone and grainstone are future exploration targets of the Cretaceous Barra Velha Formation in the Santos Basin. The results are hoped to give insights into the petrophysical behaviours of various lithologies, and provide theoretical and technical guidance for reservoir evaluation and prediction.

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Karst architecture characterization of deep carbonate reservoir using image logs in Tarim Basin, West China

Liu

Zhang

et al. 2023

Geological Journal

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This paper has conducted a high‐resolution characterization of a deeply buried carbonate karst in the Tarim Basin of China by utilizing the FMI (Full‐bore Formation Micro Image) method integrated with seismic data, drilling cores and thin sections. It was found that the type and scale of wellbore karst architecture are mainly controlled by karst paleogeomorphology and vertical faults. The karst highlands and slopes, respectively, develop vadose karst and phreatic karst, with different compositions of karst elements. In terms of the karst thickness in the wellbore, the vertical fault development zone is almost twice that of the non‐development zone, both for the vadose karst and phreatic karst. Furthermore, the vertical fault is also able to promote the development of mixing karst. The mixing zone is located 55 m below the water table, with a strata thickness of approximately 25 m, a net reservoir thickness of 5–15 m, and an average porosity of 4%–8%, which serves as an effective hydrocarbon reservoir in the Yingshan Formation. The results will provide insights into the vertical division of karst reservoir and help predict reservoirs using well logs.

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Geophysical Well-Log Evaluation in the Era of Unconventional Hydrocarbon Resources: A Review on Current Status and Prospects

Cited by 53 publications

References 146 publications

Geological and Engineering ‘Sweet Spots’ in the Permian Lucaogou Formation, Jimusar Sag, Junggar Basin

Geological and Engineering ‘Sweet Spots’ in the Permian Lucaogou Formation, Jimusar Sag, Junggar Basin

Prediction of lithology in lacustrine carbonates using well logs: The Cretaceous Barra Velha Formation in Santos Basin, offshore Brazil

Karst architecture characterization of deep carbonate reservoir using image logs in Tarim Basin, West China

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