Waveform classification as a pseudo for reservoir thickness

Lodwick, Bonnie; Grant-Woolley, Lawrence

doi:10.1071/aseg2016ab303

Cited by 3 publications

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“…Paper 2 (Lodwick and Grant-Wooley, 2016) also uses waveform classification, this time to produce a 'probability map', and states 'the map of waveform classification can be used in the surface calculator to generate a probability map of the lower non-reservoir thickness'. This is possibly an example of a Perhaps what the authors meant was the lower non-reservoir has a 50% chance of being at least a certain thickness.…”

Section: Minerals Geophysicsmentioning

confidence: 99%

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Preview Number 184

2016

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Section: Minerals Geophysicsmentioning

confidence: 99%

“…The use of a small window of half a wavelength results in amplitude being the only difference between classes. Lodwick and Grant-Wooley (2016). The main difference between classes is the peak-trough amplitude in the upper part of the analysis window.…”

Section: Minerals Geophysicsmentioning

confidence: 99%

Preview Number 184

2016

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“…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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“…Paper 2 (Lodwick and Grant-Wooley, 2016) also uses waveform classification, this time to produce a 'probability map', and states 'the map of waveform classification can be used in the surface calculator to generate a probability map of the lower non-reservoir thickness'. This is possibly an example of a Nintendo Geo pushing a button but not knowing exactly what happens.…”

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confidence: 99%

Seismic Window

Micenko¹

2016

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Waveform classification as a pseudo for reservoir thickness

Cited by 3 publications

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Preview Number 184

Preview Number 184

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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