Improved characterization of fault zones by quantitative integration of seismic and production data

Ali, Aamir; Shahraini, A.; Jakobsen, Morten

doi:10.1088/1742-2132/8/2/011

Cited by 7 publications

(5 citation statements)

References 28 publications

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“…To quantify the prediction uncertainty in the estimates of model parameters, an assessment of the full posterior distribution q(m|d) is required. Since the number of parameters to be inverted in this paper are small, we have used numerical integration method (Ali et al 2011(Ali et al , 2015 for full exploration of posterior distribution q(m|d), which yields the marginal PDFs for small dimensional problems (Tarantola 2005). Subsequently, we present an adaptation of the numerical integration method to the particular inverse problem used in this study (inverting for V shale and S w ).…”

Section: The Inverse Problemmentioning

confidence: 99%

“…However, as shales are very heterogeneous and strongly anisotropic, core and well data alone may not be sufficient to map the subsurface shale sequences (Kumar and Hoversten 2012). Remote seismic measurements may play a central role in helping to characterize sand-shale media, but this will require a good understanding of relevant rock physics and scaling issues (Ali et al 2011;Ali and Jakobsen 2011a, b;Kumar and Hoversten 2012). For instance, Takahashi (2000) have predicted sand-shale ratio based on statistical rock physics simulations of various bedding scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian inversion of synthetic AVO data to assess fluid and shale content in sand-shale media

2017

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Reservoir characterization of sand-shale sequences has always challenged geoscientists due to the presence of anisotropy in the form of shale lenses or shale layers. Water saturation and volume of shale are among the fundamental reservoir properties of interest for sand-shale intervals, and relate to the amount of fluid content and accumulating potentials of such media. This paper suggests an integrated workflow using synthetic data for the characterization of shaley-sand media based on anisotropic rock physics (T-matrix approximation) and seismic reflectivity modelling. A Bayesian inversion scheme for estimating reservoir parameters from amplitude vs. offset (AVO) data was used to obtain the information about uncertainties as well as their most likely values. The results from our workflow give reliable estimates of water saturation from AVO data at small uncertainties, provided background sand porosity values and isotropic overburden properties are known. For volume of shale, the proposed workflow provides reasonable estimates even when larger uncertainties are present in AVO data.

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Section: The Inverse Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian inversion of synthetic AVO data to assess fluid and shale content in sand-shale media

2017

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“…Therefore, the best available choice for rock physics modeling of shales in this study is using a laminar/layered model performed through the Backus averaging. Shale layers within the sand matrix often show horizontally aligned orientation, thus making a transversely isotropic (TI) medium with a vertical axis of symmetry also known as VTI medium characterized either in terms of five independent elastic/stiffness constants (C 11 , C 13 , C 33 , C 55 , and C 66 ) or of two vertical velocities (V P and V S ) and three Thomsen anisotropic parameters (Ȗ, į, İ) (Thomsen 1986, Tsvankin 1997aAli et al 2011).…”

Section: Anisotropic Rock Physics Based Ava Modelling For Basal Sandmentioning

confidence: 99%

“…In order to calculate the effect of fluid saturation on the effective properties of alternating sand shale layers, one can use the (anisotropic Gassmann) relations of Brown and Korringa (1975) (also see Ali et al 2011. The values of porosity, water saturation, and volume of shale have been used in anisotropic Gassmann relation from well data of Siraj South-01 at Basal sand level given in Table 6.…”

Section: Anisotropic Rock Physics Based Ava Modelling For Basal Sandmentioning

confidence: 99%

Effect of Shale Distribution on Hydrocarbon Sands Integrated with Anisotropic Rock Physics for AVA Modelling: A Case Study

et al. 2016

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Shales can be distributed in sand through four different ways; laminated, structural, dispersed and any combination of these aforementioned styles. A careful analysis of well log data is required for the determination of shale distribution in sand affecting its reservoir quality. The objective of this study is to characterize the effect of shale distribution on reservoir quality of sands using well log data. The correlation of well data in terms of lithology has revealed four sand and three shale layers in Lower Goru Formation acting as a major reservoir in the study area. Our results indicate that the laminated type of shale distribution prevails at the Basal sand level, which does not affect its reservoir quality greatly. The remaining layers of variable vertical extent show a variety of shale distribution models affecting their reservoir quality adversely. We also present anisotropic rock physics modelling for AVA analysis at Basal sand level.

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“…Weatherley & Henley 2013) and the flow of fluids in hydrocarbon reservoirs (e.g. Ali et al 2011). Fault zones contain information on future rupture dynamics including the style of earthquake rupture, slow slip or creep (Peng & Gomberg 2010).…”

Section: Introductionmentioning

confidence: 99%

The effect of gradational velocities and anisotropy on fault-zone trapped waves

Gulley

Eccles

Kaipio

et al. 2017

Geophysical Journal International

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Improved characterization of fault zones by quantitative integration of seismic and production data

Cited by 7 publications

References 28 publications

Bayesian inversion of synthetic AVO data to assess fluid and shale content in sand-shale media

Bayesian inversion of synthetic AVO data to assess fluid and shale content in sand-shale media

Effect of Shale Distribution on Hydrocarbon Sands Integrated with Anisotropic Rock Physics for AVA Modelling: A Case Study

The effect of gradational velocities and anisotropy on fault-zone trapped waves

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