<i>L<sub>p</sub></i>‐norm deconvolution

Debeye, H.; Riel, P. van

doi:10.1190/1.1889540

Cited by 17 publications

(16 citation statements)

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“…A numerical rock model is built to predict the elastic properties of the rock from the petrophysical properties [Sams 2007]. AVA deterministic simultaneous inversion is based on amplitude-versus-angle-constrained sparse spike inversion algorithm [Contreras 2006] which is an extension of the original constrained sparse spike inversion algorithm [Debeye 1990]. The inversion is a global, traced-based process that converts amplitudes from multiple partial angle stacks into P-impedance, Vp/Vs and Density volumes (elastic properties) which have higher resolution than the input seismic data.…”

Section: Methodsmentioning

confidence: 99%

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Sulistiono

Vaughan²,

Ali³

et al. 2015

Day 3 Wed, November 11, 2015

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Building a reservoir model by integrating seismic angle stacks and well data has been challenging for oil and gas companies. Seismic data contain very detailed information of reservoir properties lateraly but lack vertical resolution while well data provide very detailed information vertically but lack horizontal information. One established method of integrating these two kinds of data is deterministic seismic inversion. Deterministic seismic inversion has proven to be a good method in delineating the reservoirs at seismic resolution but it has limitation in analyzing thin reservoirs and it only pruduced one result. Geostatistical seismic inversion method has the ability to integrate many types of data and produce multiple relaizations of the results. The realizations are at much higher details than seismic data and are able to capture thin reservoirs.An AVA geostatistical seismic inversion workflow was successfully implemented to produce highly detailed reservoir models of Abu Madi reservoir sands in Nile delta, Egypt. Abu Madi Formation is composed of lacustrine turbidite deposits in semi-isolated basin and can be subdivided into Upper and Lower reservoirs. Intraformational shale baffles occur quite commonly within Abu Madi Formation which acted as barriers of pressure depletion between the Upper and Lower reservoirs. Well data analysis has demonstrated the complex pattern of the stacked reservoir sand zones of heterogeneous reservoir parameters and pressure trends. The main objective of the study was to produce reservoir models that could be used to understand the observed pressure depletion trends within the Upper and Lower Abu Madi reservoirs which have great significance for effective field management.Seismic angle stacks and well data were integrated through AVA geostatistical inversion to produce highly detailed lithotype and elastic property results at 0.5 ms vertical sampling. Thesse models have successfully captured the shale baffles. Lithotype and elastic property realizations were used to cosimulate for reservoir (engineering) properties of Effective Porosity and Volume of Clay. The Effective Porosity and Volume of Clay realizations were then ranked to provide the P10, P50 and P90 models to be used as input for dynamic flow simulation. Five permeability rock types were derived based on extensive SCAL database and used to define the permeability and saturation models. These detailed engineering models were used for dynamic flow simulation and successfully predicted the pressure depletion trends in the Abu Madi reservoirs.

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Section: Methodsmentioning

confidence: 99%

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Sulistiono

Vaughan²,

Ali³

et al. 2015

Day 3 Wed, November 11, 2015

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“…O algoritmo Constrained Sparse Spike da Jason (empresa responsável pelos softwares de inversão sísmica utilizados) tem por objetivo minimizar uma função objetivo que contém no mínimo dois termos: o primeiro minimiza a diferença entre o dado sísmico e o sintético, enquanto o outro controla a energia das refletividades (Debeye & Van Riel, 1990). Este algoritmo gera um modelo de impedância acústica determinística a partir do dado sísmico de reflexão empilhado totalmente, onde a sísmica é modelada através da convolução de uma série de coeficientes de reflexão com uma wavelet.…”

Section: Inversão Determinísticaunclassified

Comparison of Deterministic and Geostatistical Inversion Results from a Petroleum Field

2016

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ResumoA maneira mais efetiva de se integrar o dado sísmico no processo de caracterização de reservatórios é por meio da geração de modelos de impedância derivados do processo de inversão sísmica. Neste trabalho foram comparados os resultados das inversões sísmicas determinística e geoestatística de um campo de petróleo, no intuito de melhorar a caracterização do campo e de gerar um modelo mais preciso, onde as previsões do comportamento do campo possam ser feitas de maneira mais efetiva. A inversão acústica determinística é uma técnica bastante utilizada, que gera um único resultado, invertendo o dado sísmico disponível para parâmetros acústicos (impedância P). Já a inversão acústica geoestatística gera múltiplos modelos de propriedades de reservatório (parâmetros acústicos, petrofísicos e litologia), todos equiprováveis, o que leva à possibilidade de se quantificar a incerteza em torno do modelo de reservatório que está sendo criado. Palavras-chave: inversão; caracterização de reservatório; incerteza AbstractThe most effective way to integrate seismic data in the reservoir characterization process is through the generation of impedance models derived from seismic inversion. In this work we compared the deterministic and geostatistical seismic inversion results of an oil field in order to improve the characterization of the field and generate a more accurate model, where the field behavior predictions could be done in a more effective way. The deterministic acoustic inversion is a widely used technique, which yields a single result, inverting the seismic data available for acoustic parameters (P-impedance). Geostatistical acoustic inversion generates multiple models of reservoir properties (elastic, lithology and petrophysical parameters), all of them equally probable, leading to the possibility of quantifying the uncertainty surrounding the reservoir model being created.

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“…The technique applied to the inversion process is constrain sparse spike inversion (Debeye and van Reil, 1990;Helgesen et al, 2000). All available impedance logs near the gas fields were integrated with the incision surfaces of the submarine canyons and the PlioPleistocene boundaries to build the background impedance model for inversion.…”

Section: Inverted Impedancementioning

confidence: 99%

The biogenic gas potential of the submarine canyon systems of Plio-Peistocene foreland Basin, southwestern Taiwan

Fuh¹,

Chern²,

Liang³

et al. 2009

Marine and Petroleum Geology

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L_p‐norm deconvolution

Cited by 17 publications

References 0 publications

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Comparison of Deterministic and Geostatistical Inversion Results from a Petroleum Field

The biogenic gas potential of the submarine canyon systems of Plio-Peistocene foreland Basin, southwestern Taiwan

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Lp‐norm deconvolution

Cited by 17 publications

References 0 publications

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Integrating Seismic and Well Data into Highly Detailed Reservoir Model through AVA Geostatistical Inversion

Comparison of Deterministic and Geostatistical Inversion Results from a Petroleum Field

The biogenic gas potential of the submarine canyon systems of Plio-Peistocene foreland Basin, southwestern Taiwan

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L_p‐norm deconvolution