Density Inversion and Porosity Estimation Using Seismic Data

Shi, Yue; Yao, Fengchang; Sun, Hu-Sheng; Li, Qi

doi:10.1002/cjg2.1481

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…According to the discussion of parameter m in Table 2, we can get the range of m : 0.72 < m < 1.2. When α > 0.12, C = m α > 0.1, the parameter C can satisfy the presumption of expression (16), which can verify the rationality of expression (18) we developed in this paper, and expression (18) can be used to the prediction of porosity in reef-flat reservoir. When α < 0.072, there is C = m α < 0.1 and the expression 1…”

Section: Discussion Of Parameter Csupporting

confidence: 61%

“…On the other hand, we can get v p , v s and ρ from the prestack inversion directly. Thus, according to the practical geological case, we can compute precisely the porosity of the region of interest based on expression (18).…”

Section: Fig 1 the Compression Coefficient Of Rocks Under Drainage Smentioning

confidence: 99%

“…Expression (18) describes the relationship between η and C, v p , v s , ρ. How to precisely compute these parameters is the key of predicting successfully porosity.…”

Section: Petrophysical Test and Logging Analysismentioning

confidence: 99%

“…Firstly, we set α = 0.535, m = 0.86, and get the average value of compressibility coefficient of solid rock matrix in the region of interest by expression (2) using the actual measurement data of rock samples. And then, we substituted the term of expression (18) using the test data of every rock sample. Finally, we figure out the porosity of the 21 rock samples.…”

Section: The Theoretic Speculation Of the Test Data And The Analysis mentioning

confidence: 99%

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Pore Structure Model Simulation and Porosity Prediction in Reef‐Flat Reservoirs

Jiang¹,

Xu²,

He³

et al. 2011

Chinese J of Geophysics

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Rock in the subsurface composed of rock matrix and pore fluid is a two‐phase medium. So the effective elastic parameters of rock are influenced by rock matrix, pore structure and pore fluid. Therefore, in order to get the relationship between porosity and effective elastic parameters of rock, the influence of the other factors must be removed. Based on the basic theory of rock physics and test data, we propose the concept of equivalent volume and introduce the Eshelby ellipsoid inclusion fracture theory. With reasonable assumption, the Gassmann equation is applied to deduce and establish the equation between porosity and related factors including the compressibility of saturated rock (no drainage), the compressibility of rock matrix, pore aspect ratio describing pore shape and parameter m, and simulate properly pore structure in reef‐flat reservoirs which are of strong heterogeneity. The test on real data proves that the formula between porosity and elastic parameters we build can be used to predict porosity of reef‐flat reservoirs effectively.

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Section: Discussion Of Parameter Csupporting

confidence: 61%

Section: Fig 1 the Compression Coefficient Of Rocks Under Drainage Smentioning

confidence: 99%

“…Expression (18) describes the relationship between η and C, v p , v s , ρ. How to precisely compute these parameters is the key of predicting successfully porosity.…”

Section: Petrophysical Test and Logging Analysismentioning

confidence: 99%

Section: The Theoretic Speculation Of the Test Data And The Analysis mentioning

confidence: 99%

See 2 more Smart Citations

Pore Structure Model Simulation and Porosity Prediction in Reef‐Flat Reservoirs

Jiang¹,

Xu²,

He³

et al. 2011

Chinese J of Geophysics

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“…Seismic data has a low signal/noise when registered at high angles. Shi et al (2010) points out that the near offset seismic amplitudes are mainly controlled by velocity, and that only the far offset amplitudes may record r information.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous prestack seismic inversion in a carbonate reservoir

Cataldo

Leite

2018

REM, Int. Eng. J.

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We applied a method of Simultaneous Prestack Seismic Inversion (SPSI) in a hydrocarbon field located in the Campos Basin, Brazil. The goal was to study the application of this method to Albian carbonate rocks of the Quissamã Formation. Based on what is found in nearby fields, this Formation potentially contains hydrocarbon accumulations that can be revealed with the inverted models. P-wave velocity (Vp), S-wave velocity (Vs) and density (r) curves along seven wells were used as inputs to construct 3-D initial models of acoustic impedance (Zp), shear impedance (Zs) and r. Wells without Vs and r curves had those values calculated from well-established equations. Final 3-D models were calculated from deviations in linear relationships between the logarithms of Zs and Zp, as well as between r and Zp, which were merged with the initial models. Interpretations of the inversion results were conducted based on patterns found along well logs such as resistivity (ILD), gamma ray (GR), density (RHOB), neutron porosity (NPHI) and sonic (DT). These analyses provided criteria to select the best inverted models. These models show low impedance anomalies that are consistent with previous studies performed with the well logs. One well in particular was interpreted as having high potential to contain hydrocarbons. This well shows an impedance pattern that allowed us to highlight other areas with the same pattern throughout the entire seismic volume.

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Numerical Calculation Method for Critical Point, Pore Fluid and Framework Parameters of Gas‐Bearing Media

Bin-hua¹,

Sun²,

Liu³

et al. 2010

Chinese J of Geophysics

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The mostly used methods for studying critical porosity and porous media are experimental measurement and analysis of testing data, and how to determine the porosity at the critical point and the constituents' properties are topics of great concern. In this article, we propose a method for calculation of these elastic parameters. Firstly, we derive three linear equations for numerical calculation. By combining regression coefficients of the equations, we propose equations for calculating constituents' parameters. Then we present the steps for calculation and give some notes. We use this method to analyze gas‐bearing sandstone samples. Using the entire densities, P‐velocities and S‐velocities as input data, we calculate related properties about porous fluid, framework and critical point. In the end, by comparing our results with test data of experimental measurements, we test our method and prove that this method is correct and effective.

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Density Inversion and Porosity Estimation Using Seismic Data

Cited by 8 publications

References 14 publications

Pore Structure Model Simulation and Porosity Prediction in Reef‐Flat Reservoirs

Pore Structure Model Simulation and Porosity Prediction in Reef‐Flat Reservoirs

Simultaneous prestack seismic inversion in a carbonate reservoir

Numerical Calculation Method for Critical Point, Pore Fluid and Framework Parameters of Gas‐Bearing Media

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