Fusing multiple frequency-decomposed seismic attributes with machine learning for thickness prediction and sedimentary facies interpretation in fluvial reservoirs

Li, Wei; Yue, Dali; Wang, Wenfeng; Wang, Wurong; Wu, Shenghe; Li, Jian; Chen, Depo

doi:10.1016/j.petrol.2019.03.017

Cited by 44 publications

(19 citation statements)

References 49 publications

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“…When the thickness of the sedimentary body is thin, its top and bottom positions are difficult to determine due to the limited seismic resolution. It has been shown that it would be difficult and limited to resolve geological bodies smaller than 1/4 wavelength (1/4λ) based on the conventional idea of improving seismic resolution [33][34][35]. Spectrum analysis found that the dominant frequency of the target layer segment is about 60 Hz.…”

Section: Seismic Lithology Characteristicsmentioning

confidence: 99%

“…It is well known that the amplitude of seismic reflected waves in the layered medium model has a tuning effect, and the amplitude is affected by the thickness of the layered medium [32,36]. It has been reported in the literature that the interpretation of sedimentary facies and the prediction of thin layer thicknesses less than 1/4λ have been achieved by using the tuning amplitude [34,36,37]. Therefore, an attempt was made to use tuning amplitude to predict the thin sand bodies of the SS2 sequence in the study area, and thus to obtain higher resolution sedimentary facies interpretation results.…”

Section: Seismic Lithology Characteristicsmentioning

confidence: 99%

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Seismic-Geological Integrated Study on Sedimentary Evolution and Peat Accumulation Regularity of the Shanxi Formation in Xinjing Mining Area, Qinshui Basin

et al. 2022

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Accurate identification of the lithofacies and sedimentary facies of coal-bearing series is significant in the study of peat accumulation, coal thickness variation and coal-measured unconventional gas. This research integrated core, logging and 3D seismic data to conduct a comprehensive seismic–geological study on the sedimentary evolution characteristics and peat accumulation regularity of the Shanxi Formation in the Xinjing mining area of the Qinshui Basin. Firstly, the high-resolution sequence interface was identified, and the isochronous stratigraphic framework of the coal-bearing series was constructed. Then, the temporal and spatial evolution of sedimentary filling and sedimentary facies was dynamically analyzed using waveform clustering, phase rotation, stratal slice and frequency–division amplitude fusion methods. The results show that the Shanxi Formation in the study area can be divided into one third-order sequence and two fourth-order sequences. It developed a river-dominated deltaic system, mainly with delta plain deposits, and underwent a constructive–abandoned–constructive development stage. The locally distributed No. 6 coal seam was formed in a backswamp environment with distribution constrained by the distributary channels. The delta was abandoned at the later stage of the SS1 sequence, and the peat accumulation rate was balanced with the growth rate of the accommodation, forming a large-area distributed No. 3 thick coal seam. During the formation of the SS2 sequence, the No. 3 coal seam was locally thinned by epigenetic erosion of the river, and the thin coal belt caused by erosion is controlled by the location of the distributary channels and their extension direction. This study can provide a reference for the research on the distribution of thin sand bodies, sedimentary evolution and peat accumulation regularity in the coal-bearing series under the marine–continental transitional environment.

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Section: Seismic Lithology Characteristicsmentioning

confidence: 99%

Section: Seismic Lithology Characteristicsmentioning

confidence: 99%

Seismic-Geological Integrated Study on Sedimentary Evolution and Peat Accumulation Regularity of the Shanxi Formation in Xinjing Mining Area, Qinshui Basin

et al. 2022

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“…The technique of spectral decomposition transforms the seismic signal from the time domain to the frequency domain by mathematical transformation. The reservoir is characterized in the frequency domain to avoid the mutual interference of different frequencies in the time domain (Sinha et al, 2005;Zhang et al, 2017;Li et al, 2019;Wu et al, 2020). Therefore, the seismic response characteristics of the same cavity to seismic waves with different frequencies are also different.…”

Section: Spectral Decompositionmentioning

confidence: 99%

Predicting Granitic Buried-Hill Reservoirs Using Seismic Reflection data—A Case Study From the Bongor Basin, Southwestern Chad

Wang

Zhou

et al. 2022

Front. Earth Sci.

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Predicting the distribution of fractured and dissolved reservoirs in granitic buried hills is a prerequisite for finding high-quality hydrocarbon reservoirs and improving the success rate of exploration activities. Granitic buried-hill reservoirs in the Bongor Basin are characterized by strong heterogeneity and poor lateral continuity. Through the analysis of seismic, core, logging and dynamic production data, this study predicts the distribution of fractured reservoirs and fractured-cave reservoirs in the granitic buried hills in the BC Block of the Bongor Basin. In this study, we predict fractured reservoirs using coherence cubes and linearly enhanced attributes and identify fractured-cave reservoirs with single-frequency attribute bodies. Integrated predictions of fractured reservoirs and fractured-cave reservoirs are conducted using attribute fusion techniques. With these methods, good results have been achieved in reservoir prediction in BC buried hills. Furthermore, this study summarizes a set of granitic buried-hill reservoir prediction techniques for densely inverted rift basins in the Central African Rift System.

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“…Эрозионные палеоврезы бобриковско-радаевского возраста широко распространены на территории Республики Татарстан. С палеоврезовыми отложениями связаны многие месторождения нефти, с чем связан значительный интерес к их изучению [5,7,11].…”

Section: геологическая характеристика объектаunclassified

“…Некоторые авторы [2] применяли мультитрассовые сейсмические атрибуты, такие как доминирующая частота (dominant frequency) и стратиграфические переменные (stratigraphic variables), которые могут быть напрямую связаны с толщиной пласта. Применение спектральной декомпозиции, а также комбинаций различных частотных составляющих сигнала также является возможным решением проблемы [6,11].…”

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Evaluating the thickness of thin-bed seams using seismic data on the example of the Tula-Bobrikovian sediments in the Republic of Tatarstan

Platov

Хайрутдинова

Кадиров

2021

Izvestiya Vysshikh Uchebnykh Zavedeniy. Geologiya i Razvedka

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Background. Determining the productive deposit thickness is of fundamental importance for assessing the reserves of oil and gas fields. 3D seismic data is used to assess the thickness of seams in the interwell space. However, owing to the limited vertical resolution of seismic data, estimating thicknesses of thin deposits (less than 20 m) is challenging.Aim. To evaluate different approaches to calculating the thickness of the productive deposits based on seismic data with the purpose of selecting the most optimal approach.Materials and methods. We compared the results obtained using different approaches to assessing the productive deposit thickness of the Tula-Bobrikovian age in the interwell space, including the convergence method (calculating the thickness for oilwells with no seismic data used), the use of seismic attributes to calculate the “seismic attribute — reservoir thickness” dependency (for attributes, dominant frequency and mono-frequency component at 60 Hz), estimation of the thickness from the seismic signal shape. Cokriging was used to calculate inferred power maps from seismic attribute data and to classify them by waveform. For each of the techniques, the crossvalidation method and calculating the root-mean-square deviation were used as quality criteria.Results. When assessing the accuracy of thickness map development, the root-mean-square deviation was 12.3 m according to convergence method, 10.2 m — to the dominant frequency attribute, 7.2 m — to the attribute of the monofrequency component at 60 Hz and 6.3 m — to the signal shape classification. The latter method yielded the best results, and the developed thickness map allowed paleo-cut to be traced.Conclusions. Applying the thickness estimation method based on the seismic signal shape allows the value of the root-mean-square deviation to be reduced by a factor of 2 compared to that of the widely adopted convergence method. This approach permits productive deposits thickness to be more accurately estimated and hydrocarbon reserves to be determined.

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Fusing multiple frequency-decomposed seismic attributes with machine learning for thickness prediction and sedimentary facies interpretation in fluvial reservoirs

Cited by 44 publications

References 49 publications

Seismic-Geological Integrated Study on Sedimentary Evolution and Peat Accumulation Regularity of the Shanxi Formation in Xinjing Mining Area, Qinshui Basin

Seismic-Geological Integrated Study on Sedimentary Evolution and Peat Accumulation Regularity of the Shanxi Formation in Xinjing Mining Area, Qinshui Basin

Predicting Granitic Buried-Hill Reservoirs Using Seismic Reflection data—A Case Study From the Bongor Basin, Southwestern Chad

Evaluating the thickness of thin-bed seams using seismic data on the example of the Tula-Bobrikovian sediments in the Republic of Tatarstan

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