Facies modelling of Ngimbang Clastics interval of Ngimbang formation with the integration of rock physics analysis and simultaneous inversion, North East Java Basin

Although shales constitute about 75% of most sedimentary basins, the studies dealing with their seismic response are relatively few, particularly for the organic rich shale gas. Mapping distribution of shale gas and identifying their maturation level and organic carbon richness is critically important for unconventional gas field exploration and development. This study analyses the sensitivity of acoustic and elastic parameters of shales to variations in pore fluid content. Based on the effective medium theory a rock physics model has been made by inversion of the shale stiffness tensor from sonic, density, porosity and clay content logs. Due to the lack of a generally agreed upon fluid substitution model for shale, a statistical approach to Gassmann’s Model using effective porosity in the near boundary conditions, has been developed to account for shale. Fluid substituted logs—for a variety of maturation levels—and gas saturations were generated and used to make the layered earth models. AVO and seismic forward modelling were performed using the rock physics modelled and the fluid substituted logs on layered models. As part of seismic forward modelling, simultaneous inversion is performed for each model to generate P-impedance, S-impedance and density volumes. The sensitivity of the models were analysed by histogram, cross plotting, cross section highlighting, and body checking techniques. This study showed a dramatic hydrocarbon content effect—specifically gas—in the seismic response of shales.

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Analysis of the Hidrocarbon Sandstone Distribution Using Rock Physics and Petrophysic Calculations, Simultaneous Inversion, Extended Elastic Impedance (EEI) and Curved Pseudo-Elastic Impedance (CPEI) in X Field Bonaparte Basin

Proc. Indon. Petrol. Assoc., Digital Technical Conference, 2020 ◽

10.29118/ipa20-g-171 ◽

2020 ◽

Author(s):

A., H. Kusuma

Keyword(s):

Gamma Ray ◽

Water Saturation ◽

Rock Physics ◽

Fluid Distribution ◽

Elastic Parameters ◽

Angular Rotation ◽

Simultaneous Inversion ◽

A Value ◽

Seismic Volumes ◽

Elastic Impedance

To analyze the distribution of hydrocarbon reservoirs in an area, appropriate methods and parameters can be used to map sediments in the area. In this study, the research was conducted on the X Field located in the Bonaparte Basin. The EEI and CPEI methods are used. The Extended Elastic Impedance (EEI) method is a method that can be used to connect seismic data with elastic parameters by applying the principle of angular rotation. From this method, seismic volumes of various elastic parameters and reservoir parameters will be obtained. The Curved Pseudo-elastic Impedance (CPEI which is able to answer the problem in describing the distribution of fluid in the study area) is also carried out. Gamma Ray (GR) is used to detect lithology and fluid distribution, while from the results of CPEI inversion, the water saturation volume is obtained to see the hydrocarbon distribution in the study area. The results show that the two inversions are able to differentiate the distribution of tight sand lithology, shale, wet sand, gas sand and porous sand. The presence of gas sand distribution can be identified by the value of GR = 20-40 API, λρ = 0-50 GPA*gr/cc, μρ > 80 GPA*g/cc, σ = 0.05-0.24 unitless and the value of Sw = 0-40%, lithology tight sand has a value of GR = 40-70 API, λρ = 70-80 GPA*gr/cc, σ>0.4 unitless, shale lithology has a value of GR > 90 API , μρ <30 GPA*gr/cc, and wet sand is shown with the value GR = 20-40 API, λρ = 50-80 GPA*gr/cc, σ = 0.25-0.35 unitless and Sw> 70%. Based on the results of these interpretations, a sandstone distribution map in X Field was generated and it consists of 2 reservoir layers in the research target zone.

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Seismic inversion of a carbonate buildup: A case study

Interpretation ◽

10.1190/int-2017-0081.1 ◽

2017 ◽

Vol 5 (4) ◽

pp. T641-T652 ◽

Cited By ~ 1

Author(s):

Mark Sams ◽

Paul Begg ◽

Timur Manapov

Keyword(s):

Seismic Data ◽

Probability Distributions ◽

Low Frequency ◽

Rock Physics ◽

Seismic Inversion ◽

Accurate Estimation ◽

Amplitude Analysis ◽

Simultaneous Inversion ◽

Band Limited ◽

Using Data

The information within seismic data is band limited and angle limited. Together with the particular physics and geology of carbonate rocks, this imposes limitations on how accurately we can predict the presence of hydrocarbons in carbonates, map the top carbonate, and characterize the porosity distribution through seismic amplitude analysis. Using data for a carbonate reef from the Nam Con Son Basin, Vietnam, the expectations based on rock-physics analysis are that the presence of gas can be predicted only when the porosity at the top of the carbonate is extremely high ([Formula: see text]), but that a fluid contact is unlikely to be observed in the background of significant porosity variations. Mapping the top of the carbonate (except when the top carbonate porosities are low) or a fluid contact requires accurate estimates of changes in [Formula: see text]. The seismic data do not independently support such an accurate estimation of sharp changes in [Formula: see text]. The standard approach of introducing low-frequency models and applying rock-physics constraints during a simultaneous inversion does not resolve the problems: The results are heavily biased by the well control and the initial interpretation of the top carbonate and fluid contact. A facies-based inversion in which the elastic properties are restricted to values consistent with the facies predicted to be present removes the well bias, but it does not completely obviate the need for a reasonably accurate initial interpretation in terms of prior facies probability distributions. Prestack inversion improves the quality of the facies predictions compared with a poststack inversion.

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Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Geophysics ◽

10.1190/geo2015-0239.1 ◽

2016 ◽

Vol 81 (3) ◽

pp. M35-M53 ◽

Cited By ~ 17

Author(s):

Bastien Dupuy ◽

Stéphane Garambois ◽

Jean Virieux

Keyword(s):

Input Data ◽

Reservoir Characterization ◽

Quantitative Estimation ◽

A Priori ◽

Rock Physics ◽

Fluid Phase ◽

Quality Factors ◽

Simultaneous Inversion ◽

Fluid Saturation ◽

Wide Range

The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

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