A feasibility study of CO2 geologic sequestration integrating reservoir simulation, rock-physics theory, and seismic modeling

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. M71-M82 ◽  
Author(s):  
Yanjun Hao ◽  
Dinghui Yang ◽  
Yanjie Zhou
Keyword(s):  
Seismic Response ◽  
Reservoir Simulation ◽  
Rock Physics ◽  
Reservoir Rock ◽  
Seismic Modeling ◽  
Geologic Model ◽  
Simulation Results ◽  
Physics Model ◽  
Rock Physics Model ◽  
Wave Properties

[Formula: see text] capture and sequestration is a promising approach to reduce carbon emission and mitigate the greenhouse effect. We have developed a methodology combining reservoir simulation, rock-physics theory, and seismic modeling to simulate the [Formula: see text] sequestration and monitoring process, based on an idealized geologic model of the Sleipner field. First, we simulated a constant-rate [Formula: see text] injection into the idealized geologic model to study the basics of the two-phase flow involved in [Formula: see text] sequestration. The main features of the [Formula: see text] plume evolution and pressure build-up are captured in the simulation results. In any [Formula: see text] sequestration project, an important part is monitoring [Formula: see text] distribution using seismic methods. The seismic response of the injected [Formula: see text] is controlled by its effect on elastic properties of the reservoir rock. We built a rock-physics model to assess the effect of [Formula: see text] on wave properties. For unconsolidated sand, a sensitivity study found that [Formula: see text] saturation and effective pressure can strongly affect wave properties. Based on the reservoir simulation results and the rock-physics model, seismic modeling is performed at different stages of the injection using the symplectic stereomodeling method. The synthetic seismograms found that the seismic responses of the reservoir are strongly affected by the saturation and pressure change induced by the injection of [Formula: see text], and the seismic response of [Formula: see text] is strong enough to be resolved from seismic data.

Rock-physics diagnostics of a turbidite oil reservoir offshore northwest Australia

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. MR1-MR13 ◽  
Author(s):  
Humberto S. Arévalo-López ◽  
Jack P. Dvorkin
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Rock Properties ◽  
Oil Reservoir ◽  
Controlled Experiments ◽  
Seismic Modeling ◽  
Physics Model ◽  
Rock Physics Model ◽  
Offshore Oil ◽  
Northwest Australia

Interpreting seismic data for petrophysical rock properties requires a rock-physics model that links the petrophysical rock properties to the elastic properties, such as velocity and impedance. Such a model can only be established from controlled experiments in which both groups of rock properties are measured on the same samples. A prolific source of such data is wellbore measurements. We use data from four wells drilled through a clastic offshore oil reservoir to perform rock-physics diagnostics, i.e., to find a theoretical rock-physics model that quantitatively explains the measurements. Using the model, we correct questionable well curves. Moreover, a crucial purpose of rock-physics diagnostics is to go beyond the settings represented in the wells and understand the seismic signatures of rock properties varying in a wider range via forward seismic modeling. With this goal in mind, we use our model to generate synthetic seismic gathers from perturbational modeling to address “what-if” scenarios not present in the wells.

scholarly journals CBM Reservoir Rock Physics Model and Its Response Characteristic Study

IEEE Access ◽  
2017 ◽  
pp. 1-1 ◽  
Author(s):  
Yaping Huang ◽  
Mingdi Wei ◽  
Reza Malekian ◽  
Xiaopeng Zhen
Keyword(s):  
Rock Physics ◽  
Response Characteristic ◽  
Reservoir Rock ◽  
Physics Model ◽  
Rock Physics Model

Rock-physics-based double-difference inversion for CO2 saturation and porosity at the Cranfield CO2 injection site

2015 ◽  
Vol 3 (2) ◽  
pp. SM23-SM35
Author(s):  
Russell W. Carter ◽  
Kyle T. Spikes
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Time Lapse ◽  
Reservoir Rock ◽  
Well Logs ◽  
Double Difference ◽  
Co2 Injection ◽  
Text Data ◽  
Physics Model ◽  
Rock Physics Model

Large-scale subsurface injection of [Formula: see text] has the potential to reduce emissions of atmospheric [Formula: see text] and improve oil recovery. Studying the effects of injected [Formula: see text] on the elastic properties of the saturated reservoir rock can help to improve long-term monitoring effectiveness and accuracy at locations undergoing [Formula: see text] injection. We used two vintages of existing 3D surface seismic data and well logs to probabilistically invert for the [Formula: see text] saturation and porosity at the Cranfield reservoir using a double-difference approach. The first step of this work was to calibrate the rock-physics model to the well-log data. Next, the baseline and time-lapse seismic data sets were inverted for acoustic impedance [Formula: see text] using a high-resolution basis pursuit inversion technique. The reservoir porosity was derived statistically from the rock-physics model based on the [Formula: see text] estimates inverted from the baseline survey. The porosity estimates were used in the double-difference routine as the fixed initial model from which [Formula: see text] saturation was then estimated from the time-lapse [Formula: see text] data. Porosity was assumed to remain constant between survey vintages; therefore, the changes between the baseline and time-lapse [Formula: see text] data may be inverted for [Formula: see text] saturation from the injection activities using the calibrated rock-physics model. Comparisons of inverted and measured porosity from well logs indicated quite accurate results. Estimates of [Formula: see text] saturation found less accuracy than the porosity estimates.

Multivariate probabilistic rock physics models using Kumaraswamy distributions

Geophysics ◽  
2021 ◽  
pp. 1-43
Author(s):  
Dario Grana
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Random Variables ◽  
Rock Physics ◽  
Petrophysical Properties ◽  
Kumaraswamy Distribution ◽  
Model Predictions ◽  
Physics Model ◽  
Rock Physics Model

Rock physics models are physical equations that map petrophysical properties into geophysical variables, such as elastic properties and density. These equations are generally used in quantitative log and seismic interpretation to estimate the properties of interest from measured well logs and seismic data. Such models are generally calibrated using core samples and well log data and result in accurate predictions of the unknown properties. Because the input data are often affected by measurement errors, the model predictions are often uncertain. Instead of applying rock physics models to deterministic measurements, I propose to apply the models to the probability density function of the measurements. This approach has been previously adopted in literature using Gaussian distributions, but for petrophysical properties of porous rocks, such as volumetric fractions of solid and fluid components, the standard probabilistic formulation based on Gaussian assumptions is not applicable due to the bounded nature of the properties, the multimodality, and the non-symmetric behavior. The proposed approach is based on the Kumaraswamy probability density function for continuous random variables, which allows modeling double bounded non-symmetric distributions and is analytically tractable, unlike the Beta or Dirichtlet distributions. I present a probabilistic rock physics model applied to double bounded continuous random variables distributed according to a Kumaraswamy distribution and derive the analytical solution of the posterior distribution of the rock physics model predictions. The method is illustrated for three rock physics models: Raymer’s equation, Dvorkin’s stiff sand model, and Kuster-Toksoz inclusion model.

Porosity estimation based on rock skeleton general formula and comprehensive pore structure parameter – An application to a tight-sand reservoir

2021 ◽  
pp. 1-59
Author(s):  
Kai Lin ◽  
Xilei He ◽  
Bo Zhang ◽  
Xiaotao Wen ◽  
Zhenhua He ◽  
...  
Keyword(s):  
Aspect Ratio ◽  
Pore Structure ◽  
Seismic Data ◽  
Rock Physics ◽  
Structure Parameters ◽  
Pore Structure Parameter ◽  
Elastic Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Porosity Estimation

Most of current 3D reservoir’s porosity estimation methods are based on analyzing the elastic parameters inverted from seismic data. It is well-known that elastic parameters vary with pore structure parameters such as pore aspect ratio, consolidate coefficient, critical porosity, etc. Thus, we may obtain inaccurate 3D porosity estimation if the chosen rock physics model fails properly address the effects of pore structure parameters on the elastic parameters. However, most of current rock physics models only consider one pore structure parameter such as pore aspect ratio or consolidation coefficient. To consider the effect of multiple pore structure parameters on the elastic parameters, we propose a comprehensive pore structure (CPS) parameter set that is generalized from the current popular rock physics models. The new CPS set is based on the first order approximation of current rock physics models that consider the effect of pore aspect ratio on elastic parameters. The new CPS set can accurately simulate the behavior of current rock physics models that consider the effect of pore structure parameters on elastic parameters. To demonstrate the effectiveness of proposed parameters in porosity estimation, we use a theoretical model to demonstrate that the proposed CPS parameter set properly addresses the effect of pore aspect ratio on elastic parameters such as velocity and porosity. Then, we obtain a 3D porosity estimation for a tight sand reservoir by applying it seismic data. We also predict the porosity of the tight sand reservoir by using neural network algorithm and a rock physics model that is commonly used in porosity estimation. The comparison demonstrates that predicted porosity has higher correlation with the porosity logs at the blind well locations.

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