Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application

Carbonate reservoirs have significant reserves globally, but the substantial heterogeneity brings intractable difficulties to exploration. In the work area, the thick salt rock reduces the resolution of pre-salt seismic signals and increases the difficulty of reservoir characterization. Therefore, t...

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Main Authors: Xingda Tian, Handong Huang, Jun Gao, Yaneng Luo, Jing Zeng, Gang Cui, Tong Zhu
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Minerals
Subjects:
Online Access:https://www.mdpi.com/2075-163X/11/9/973
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author Xingda Tian
Handong Huang
Jun Gao
Yaneng Luo
Jing Zeng
Gang Cui
Tong Zhu
author_facet Xingda Tian
Handong Huang
Jun Gao
Yaneng Luo
Jing Zeng
Gang Cui
Tong Zhu
author_sort Xingda Tian
collection DOAJ
description Carbonate reservoirs have significant reserves globally, but the substantial heterogeneity brings intractable difficulties to exploration. In the work area, the thick salt rock reduces the resolution of pre-salt seismic signals and increases the difficulty of reservoir characterization. Therefore, this paper proposes to utilize wavelet frequency decomposition technology to depict the seismic blank reflection area’s signal and improve the pre-salt signal’s resolution. The high-precision pre-stack inversion based on Bayesian theory makes full use of information from various angles and simultaneously inverts multiple elastic parameters, effectively depicting reservoirs with substantial heterogeneity. Integrating the high-precision inversion results and the Kuster-Toksöz model, a porosity prediction method is proposed. The inversion results are consistent with the drilling rock samples and well-logging porosity results. Moreover, the reef’s accumulation and growth, which conform to the geological information, proves the accuracy of the above methods. This paper also discusses the seismic reflection characteristics of reefs and the influence of different lithological reservoirs on the seismic waveform response characteristics through forward modeling, which better proves the rationality of porosity inversion results. It provides a new set of ideas for future pre-salt carbonate reef reservoirs’ prediction and characterization methods.
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spelling doaj.art-f44d13bbb0224aaea743f95109b6746e2023-11-22T14:21:21ZengMDPI AGMinerals2075-163X2021-09-0111997310.3390/min11090973Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and ApplicationXingda Tian0Handong Huang1Jun Gao2Yaneng Luo3Jing Zeng4Gang Cui5Tong Zhu6State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, ChinaState Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, ChinaSINOPEC Exploration and Production Research Institute, Beijing 100083, China BGP Inc., China National Petroleum Corporation, Zhuozhou 072751, China State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing 102249, ChinaThe First Oil Production Plant, Huabei Oilfield Company, PetroChina, Renqiu 062550, China Sulige Exploration and Development Company of CNPC Huabei Petroleum Administration Bureau, Ordos 017300, China Carbonate reservoirs have significant reserves globally, but the substantial heterogeneity brings intractable difficulties to exploration. In the work area, the thick salt rock reduces the resolution of pre-salt seismic signals and increases the difficulty of reservoir characterization. Therefore, this paper proposes to utilize wavelet frequency decomposition technology to depict the seismic blank reflection area’s signal and improve the pre-salt signal’s resolution. The high-precision pre-stack inversion based on Bayesian theory makes full use of information from various angles and simultaneously inverts multiple elastic parameters, effectively depicting reservoirs with substantial heterogeneity. Integrating the high-precision inversion results and the Kuster-Toksöz model, a porosity prediction method is proposed. The inversion results are consistent with the drilling rock samples and well-logging porosity results. Moreover, the reef’s accumulation and growth, which conform to the geological information, proves the accuracy of the above methods. This paper also discusses the seismic reflection characteristics of reefs and the influence of different lithological reservoirs on the seismic waveform response characteristics through forward modeling, which better proves the rationality of porosity inversion results. It provides a new set of ideas for future pre-salt carbonate reef reservoirs’ prediction and characterization methods.https://www.mdpi.com/2075-163X/11/9/973reef reservoirpre-salt carbonateseismic reservoir characterizationwavelet frequency decompositionmulti-parameter inversionporosity prediction
spellingShingle Xingda Tian
Handong Huang
Jun Gao
Yaneng Luo
Jing Zeng
Gang Cui
Tong Zhu
Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application
Minerals
reef reservoir
pre-salt carbonate
seismic reservoir characterization
wavelet frequency decomposition
multi-parameter inversion
porosity prediction
title Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application
title_full Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application
title_fullStr Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application
title_full_unstemmed Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application
title_short Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application
title_sort pre stack seismic data driven pre salt carbonate reef reservoirs characterization methods and application
topic reef reservoir
pre-salt carbonate
seismic reservoir characterization
wavelet frequency decomposition
multi-parameter inversion
porosity prediction
url https://www.mdpi.com/2075-163X/11/9/973
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