Calibration of two rock-frame models using deterministic and probabilistic approaches: Application to a carbonate reservoir in south-west Iran

Heidari, Akbar; Amini, Navid; Amini, Hamed; Niri, Mohammad Emami; Zunino, Andrea; Hansen, Thomas Mejer

doi:10.1016/j.petrol.2020.107266

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…To generate the synthetic seismic data (Figure 4b), we use the rock‐physics model proposed by Heidari et al. (2020) and the seismic wavelet. By convolving the seismic wavelet with a 2D Gaussian random reflectivity field

\mathcal{N}( {0,0.0324} )

, we simulate a realistic correlated (band‐limited) Gaussian noise (Figure 4c).…”

Section: The Synthetic Seismic Data Setmentioning

confidence: 99%

“…For the reservoir understudy, different rockphysics templates were proposed (Emami Niri et al, 2021;Rahmani et al, 2022). To generate the synthetic seismic data (Figure 4b), we use the rock-physics model proposed by Heidari et al (2020) and the seismic wavelet. By convolving the seismic wavelet with a 2D Gaussian random reflectivity field  (0, 0.0324), we simulate a realistic correlated (bandlimited) Gaussian noise (Figure 4c).…”

Section: The Synthetic Seismic Data Setmentioning

confidence: 99%

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Assessment of the impact of noise magnitude and bandwidth variations on a probabilistic inversion of reflection seismic data

Heidari

Madsen

Amini

et al. 2023

Geophysical Prospecting

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Accounting for an accurate noise model is essential when dealing with real data, which are noisy due to the effect of environmental noise, failures and limitations in data acquisition and processing. Quantifying the noise model is a challenge for practitioners in formulating an inverse problem, and usually, a simple Gaussian noise model is assumed as a white noise model. Here we propose a pragmatic approach to use an estimated seismic wavelet to capture the correlated noise model (coloured noise) for the processed reflection seismic data. We assess the proposed method through a direct inversion of post‐stack seismic data associated with a carbonate reservoir of an oil field in southwest Iran to porosity, using a probabilistic sampling‐based inversion algorithm. In the probabilistic formulation of the inverse problem, we assume eight different noise models with varying bandwidth and magnitude and investigate the corresponding posterior statistics. The results indicate that if the correlated nature of the noise samples is ignored in the noise covariance matrix, some unrealistic features are generated in porosity realizations. In addition, if the noise magnitude is underestimated, the inversion algorithm overfits the data and generates a biased model with low uncertainty. Furthermore, by considering an imperfect bandwidth for the noise model, the error is propagated to the posterior realizations. Assuming the correlated noise in a probabilistic inversion resolves these issues significantly. Therefore, for inverting real seismic data where the estimation of the magnitude and correlations of the noise is not straightforward, the wavelet, which is estimated from the real seismic data, provides a good proxy for describing the correlation of the noise samples or equivalently the bandwidth of the noise model. In addition, it might be better to overestimate the noise magnitude than to underestimate it. This is true especially for an uncorrelated noise model and to a lesser degree also for the correlated noise model.

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\mathcal{N}( {0,0.0324} )

, we simulate a realistic correlated (band‐limited) Gaussian noise (Figure 4c).…”

Section: The Synthetic Seismic Data Setmentioning

confidence: 99%

Section: The Synthetic Seismic Data Setmentioning

confidence: 99%

Assessment of the impact of noise magnitude and bandwidth variations on a probabilistic inversion of reflection seismic data

Heidari

Madsen

Amini

et al. 2023

Geophysical Prospecting

Self Cite

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“…( 18) and ( 19). To calculate ρ m , the volume percentages of calcite and clay were first obtained, and then their respective density values were given as ρ calcit = 2.75 g/cc and ρ clay = 2.31 g/cc (Based on the previous studies of Heidari et al 2020). Next, for the studied depth interval of Sarvak formation, the average solid matrix density was calculated: Well A: ρ m = 2.503 g/cc, and Well B: ρ m = 2.82 g/cc.…”

Section: • Step 3: Calculation Of the Fluid-saturated Rock Density An...mentioning

confidence: 99%

“…This new model is used to characterize the elastic properties of the rocks with multiplex pore types. Heidari et al (2020) investigated applying empirical and semi-empirical rock-frame models for a carbonate reservoir in one of the oilfields in southwest Iran. They used deterministic and probabilistic optimization techniques to calibrate the free parameter of each model, effective elastic moduli, and densities of the minerals and saturating fluids.…”

Section: Introductionmentioning

confidence: 99%

“…sat case study, calcite and clay were the two main mineral components of the Sarvak reservoir. Based on the previous studies ofHeidari et al (2020), the elastic moduli of calcite and clay of the Sarvak carbonate reservoir of field Y were chosen as K calcit = 61.41 Gpa, K clay = 18.21 Gpa, μ calcit = 22.77 Gpa, and μ clay = 5.19 Gpa. Calcite and clay volume percentages were obtained, and the bulk (K m ) and shear modulus (μ m ) of the solid matrix were then computed at each depth.…”

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Construction of 2D and 3D rock physics templates for quantitative prediction of physical properties of a carbonate reservoir in SW of Iran

Rahmani

Niri

Jozanikohan

2022

J Petrol Explor Prod Technol

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The characterization of carbonate rocks is not straightforward, as they often experience complex diagenetic processes causing them to expose wide variations in pore types. This research aims to characterize the properties of a carbonate reservoir with a complicated porous structure through rock physics principles and tools. Two representative wells from an oil field located in SW of Iran were selected, and two-dimensional (2D) and three-dimensional (3D) rock physics templates (RPTs) were constructed by employing the appropriate rock physics models. The porosity, water saturation, and pore type are considered reservoir parameters affecting carbonate rock's elastic properties and indicating the reservoir quality. The 2D RPTs described variations in two reservoir parameters in terms of elastic properties. However, they were not able to simultaneously characterize all three reservoir parameters. The proposed 3D RPTs revealed the underlying relationship of elastic properties with pore aspect ratio, water saturation, and porosity. To validate the constructed RPTs, well logging data, scanning electron microscope images, and thin section images were utilized. The RPTs were also employed to predict the reservoir properties quantitatively, and these predictions were compared with the petrophysical data. The average errors of the predicted porosity and water saturation by 3D RPT were, respectively, 1.22% and 6.66% for well A, and 2.65% and 8.18% for well B. The 2D RPTs provided three sets of predictions for porosity and water saturation (considering three specific pore aspect ratios of 0.03, 0.1, and 0.5), all with higher average errors compared to the predictions by 3D RPT for both wells. The obtained results proved that 3D RPT could predict reservoir properties more accurately. Finally, based on the estimated values of pore aspect ratio, water saturation, and porosity using 3D RPTs, the reservoir under study was divided into distinct depth intervals, and a quality level was assigned to each interval. The introduced rock physics-based procedure for a carbonate reservoir characterization could increase the reliability in predicting the reservoir properties, enhance the ability to detect the reservoir fluid, and thereby reduce the interpretation risk.

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Pore-type identification of a heterogeneous carbonate reservoir using rock physics principles: a case study from south-west Iran

2021

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Calibration of two rock-frame models using deterministic and probabilistic approaches: Application to a carbonate reservoir in south-west Iran

Cited by 6 publications

References 33 publications

Assessment of the impact of noise magnitude and bandwidth variations on a probabilistic inversion of reflection seismic data

Assessment of the impact of noise magnitude and bandwidth variations on a probabilistic inversion of reflection seismic data

Construction of 2D and 3D rock physics templates for quantitative prediction of physical properties of a carbonate reservoir in SW of Iran

Pore-type identification of a heterogeneous carbonate reservoir using rock physics principles: a case study from south-west Iran

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