Time‐lapse impedance inversion of post‐stack seismic data

Gluck, S.; Deschizeaux, B.; Mignot, A.; Pinson, C.; Huguet, F.

doi:10.1190/1.1815693

Cited by 12 publications

(7 citation statements)

References 2 publications

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“…It is feasible to monitor the reservoir dynamic variations on seismic difference when the time-lapse seismic difference is measurable, which has been proved by theory and real application. But time-lapse seismic difference may be caused by other reservoir physical parameters [4][5][6] , rather than oil saturation, since time-lapse seismic responses are impacted by coupled effect of effective pressure, oil saturation and temperature variations, especially during the production of water-driven reservoir, in which increasing pressure,…”

Section: Study On Characterizing Reservoir Parameters Dynamic Variatimentioning

confidence: 99%

Study on attribute characterization for reservoir dynamic monitoring by seismic

Chen

Zhao

2008

Sci. China Ser. D-Earth Sci.

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Study on characterizing reservoir parameters dynamic variations by time-lapse seismic attributes is the theoretical basis for effectively distinguishing reservoir parameters variations and conductingtime-lapse seismic interpretation, and it is also a key step for time-lapse seismic application in real oil fields. Based on the rock physical model of unconsolidated sandstone, the different effects of oil saturation and effective pressure variations on seismic P-wave and S-wave velocities are calculated and analyzed. Using numerical simulation on decoupled wave equations, the responses of seismic amplitude with different offsets to reservoir oil saturation variations are analyzed, pre-stack time-lapse seismic attributes differences for oil saturation and effective pressure variations of P-P wave and P-S converted wave are calculated, and time-lapse seismic AVO (Amplitude Versus Offset) response rules of P-P wave and P-S converted wave to effective pressure and oil saturation variations are compared. The theoretical modeling study shows that it is feasible to distinguish different reservoir parameters dynamic variations by pre-stack time-lapse seismic information, including pre-stack time-lapse seismic attributes and AVO information, which has great potential in improving time-lapse seismic interpretation precision. It also shows that the time-lapse seismic response mechanism study on objective oil fields is especially important in establishing effective time-lapse seismic data process and interpretation scheme.time-lapse seismic, dynamic monitoring, rock physical model, pre-stack attribute, Amplitude Versus Offset (AVO) After the special time-lapse seismic data processing, difference analysis and computer visualization of seismic data measured at different periods in the same oil and gas field, the physical properties variations in the reservoir, such as porosity, saturation, pressure and temperature, can be described, and fluid front can be traced by the seismic responses variations with time. The reason is that the changes of reservoir fluids and physical properties during the production can cause the changes of seismic responses.Rock physics measurement in lab shows that reservoir production can cause the velocity change of the seismic wave [1,2] , which is the petrophysical basis [3] for time-lapse seismic study. It is feasible to monitor the reservoir dynamic variations on seismic difference when the time-lapse seismic difference is measurable, which has been proved by theory and real application. But time-lapse seismic difference may be caused by other reservoir physical parameters [4][5][6] , rather than oil saturation, since time-lapse seismic responses are impacted by coupled effect of effective pressure, oil saturation and temperature variations, especially during the production of water-driven reservoir, in which increasing pressure,

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Section: Study On Characterizing Reservoir Parameters Dynamic Variatimentioning

confidence: 99%

Study on attribute characterization for reservoir dynamic monitoring by seismic

Chen

Zhao

2008

Sci. China Ser. D-Earth Sci.

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“…Time lapse inversion of 4D seismics and reservoir behaviour monitoring facilitate the extraction of saturation and pressure effects induced by hydrocarbon production (e.g. Gluck 2000, Oldenziel 2003.…”

Section: Technical Articlementioning

confidence: 99%

“…Seismic inversion is gradually becoming a routine processing step in field development studies as well as for exploration purposes. Even time lapse inversions are now conducted (Gluck et al 2000, Oldenziel 2003. The AI attribute is gradually replacing the normal amplitude seismic representation as inversion is becoming an integral part of the reservoir characterisation workflow (cf Latimer et al 2000, Van Riel 2000.…”

Section: A Word Of Cautionmentioning

confidence: 99%

Seismic Inversion Methods and some of their constraints

Veeken¹,

Silva²

2004

First Break

145

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“…As we input data for structural imaging and property inversion, the presence of noise will directly affect the accuracy and resolu- * Email: feng_zhenjie2016@126.com tion of the results. In addition, high-fidelity signal-and-noise separation is the basis for microseismic or time-lapse seismic monitoring (Gluck et al, 2000;Maharramov et al, 2016;Qu and Verschuur, 2017). Therefore, the noise suppression of seismic data plays an important role in seismic exploration, solid earth research and underground fracture monitoring.…”

Section: Introductionmentioning

confidence: 99%

Robust fast dictionary learning for seismic noise attenuation

Feng

2022

Geophysical Prospecting

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Dictionary learning has been intensively applied to process multi-channel seismic data due to its adaptively learned basis atoms that are data driven. Traditionally, dictionary learning is mostly used to attenuate random noise in the literature since the dictionary update operation is not sensitive to Gaussian noise. However, when dictionary learning is applied to seismic data containing strong erratic noise, which does not follow the Gaussian distribution, its performance greatly deteriorates. In this paper, we propose a novel robust dictionary learning method for dealing with both random and erratic noise. We formulate the dictionary-learning-sbased denoising problem as an iterative process. During each iteration, we gradually diminish the effect of the erratic noise and make the denoising problem more Gaussian type. Considering the computational overburden of the classic K-singular value decomposition algorithm due to many iterations, we substitute the K-singular value decomposition algorithm with an efficient algorithm, which does not require the singular value decomposition operation. We apply the proposed method to several synthetic and field datasets and obtain good performance, which demonstrates its potential for wide application.

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Time‐lapse impedance inversion of post‐stack seismic data

Cited by 12 publications

References 2 publications

Study on attribute characterization for reservoir dynamic monitoring by seismic

Study on attribute characterization for reservoir dynamic monitoring by seismic

Seismic Inversion Methods and some of their constraints

Robust fast dictionary learning for seismic noise attenuation

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