Toward the Scientific Interpretation of Geophysical Well Logs: Typical Misunderstandings and Countermeasures

Lai, Jin; Wang, Guiwen; Fan, Qixuan; Zhao, Fei; Zhao, Xianzheng; Li, Yuhang; Zhao, Yidi; Pang, Xiaojiao

doi:10.1007/s10712-022-09746-9

Cited by 20 publications

(3 citation statements)

References 92 publications

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“…Geophysical logging is a means of various geophysical exploration methods, which is a method to recognize the geological body from one side of different physical properties (Lai et al 2023b).…”

Section: Introductionmentioning

confidence: 99%

Logging curve reconstruction based on multivariate feature fusion of Attribute Co- occurrence Relationships and BiLSTM

Song,

Zhang,

et al. 2024

Preprint

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Logging data play an important role in resource exploration, but in actual production, the logging attributes are often distorted or missing, which affects the geological work based on logging data. In this paper, a multivariate feature fusion model is proposed to address the problem of insufficient extraction of the intrinsic correlation between logging attributes in the traditional logging curve reconstruction method. Analogous to the method of extracting texture features in images, the Gray-Level Co-occurrence Matrices (GLCM) and Gradient Co-occurrence Matrices (GCM)are used to extract some kind of co-occurrence relationship that inherently exists between attribute values and trends, the fused features are fed into the BiLSTM network as a priori features in conjunction with the original attribute features, and the missing data are reconstructed using the trained model. The purpose of using this hybrid architecture is to extract the correlations between logging attributes of human interest in a previous step of network training, and to translate this type of important information into a form that can be learnt by the neural network, helping the network to learn more directly about the effective features in the logging data. In order to verify the effectiveness of the model, this paper uses some common evaluation metrics to assess the feature extraction results and model effectiveness. The experimental results of the test boreholes show that the reconstruction results of this paper's model compared with some common machine learning models (XGBoost, RNN, LSTM, BILSTM) are improved up to 33.18% and 44.12% in accuracy (MAE), and 21.67% and 19.85% in relevance (PCC), which has a practical engineering has some application value.

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Section: Introductionmentioning

confidence: 99%

Logging curve reconstruction based on multivariate feature fusion of Attribute Co- occurrence Relationships and BiLSTM

Song,

Zhang,

et al. 2024

Preprint

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“…FMI is a relatively new logging method with high resolution and intuitiveness, collecting a total of 192 micro-resistivity curves that can be converted into high-resolution (5 mm) images around the wellbore wall (Brekke et al, 2017;Folkestad et al, 2012;Lai et al, 2020;Rajabi et al, 2010;Tao et al, 2018). Therefore, FMI is widely used for structural interpretation and sedimentary characterization, especially in karstology, where it can be used to identify karst elements including fractures, vugs, and dissolution cavities, helping to better understand the formation and evolution of the karst (Folkestad et al, 2012;Huang et al, 2022;Khoshbakht et al, 2012;Lai et al, 2021Lai et al, , 2022Lai et al, , 2023Tao et al, 2018;Xu et al, 2009). However, it appears that the utilization rate of FMI in karst research is relatively low due to the fact that the previous studies have mainly used FMI to identify some typical karst features at certain depths in the well without taking full advantage of the continuous logging data.…”

Section: Introductionmentioning

confidence: 99%

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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“…However, the availability of drilling core data is limited, due to high costs, lack of orientation and low recovery. Petrophysical logs, which comprise a series of conventional well logs, FMI (Fullbore Formation MicroImager) image logs, sonic scanner logs (or array acoustic logging tools) and nuclear magnetic resonance logs (Schlumberger's CMR) play important roles in ‗seven property relationships' (lithology, reservoir property, hydrocarbon bearing property, electronic well log responses, source rock property, brittleness and in situ stress magnitude and direction) evaluation in unconventional hydrocarbon resources (Josh et al, 2012;Iqbal et al, 2018;Shalaby et al, 2019;Lai et al, 2023a). Tight/shale oil and gas reservoirs have complex mineral compositions and heterogeneous pore assemblages, making it difficult to characterize ‗sweet spots' using well logs.…”

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confidence: 99%

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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Toward the Scientific Interpretation of Geophysical Well Logs: Typical Misunderstandings and Countermeasures

Cited by 20 publications

References 92 publications

Logging curve reconstruction based on multivariate feature fusion of Attribute Co- occurrence Relationships and BiLSTM

Logging curve reconstruction based on multivariate feature fusion of Attribute Co- occurrence Relationships and BiLSTM

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

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

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