WELLSITE FULL WAVEFORM SONIC INTERPRETATION

2021 ◽  
Author(s):  
J. Adam Donald ◽  
◽  
Erik Wielemaker ◽  
Chris Holmes ◽  
Tom Neville ◽  
...  
Keyword(s):  
Transverse Isotropy ◽  
Rock Physics ◽  
Measured Signal ◽  
Inversion Algorithm ◽  
Fracture Detection ◽  
Industry Standard ◽  
Eastern Australia ◽  
Physics Model ◽  
Rock Physics Model ◽  
Vertical Transverse Isotropy

Sonic data are now acquired in most wellbores for a variety of applications including seismic tie, porosity evaluation, lithology determination, fracture detection, gas detection, and geomechanics modeling. The industry is also more aware of the impacts of intrinsic (fractures, layering), extrinsic (stress), and borehole effects that may affect the basic measurements of compressional and shear slownesses. Any advanced interpretation of sonic data has historically been done days to weeks after the acquisition, and the value of the measurement can be diminished due to the time of delivery of the final product. An updated data-driven inversion algorithm applied while logging can provide robust shear and compressional slownesses with associated quality control indicators. The updated algorithm has fewer user parameters and is more reliable in layered, stressed, or damaged formations. Processing quality is determined using the coherency of the measured signal and an industry-standard rock physics model for theoretical validation. With the updated dipole shear inversion and more flexible dipole anisotropy frequency filters, the dipole shear anisotropy processing can deliver reliable results at the wellsite. A byproduct of the new dipole shear inversion algorithm is the environmental slowness that is used to optimally fit the dipole dispersion signal. The interpretation of the environmental slowness parameter can indicate the anisotropy mechanism in addition to zones of near-wellbore alteration to provide further insight immediately. The wellsite dipole shear inversion and anisotropy processing were run on a vertical well in eastern Australia, within a stacked tight gas sand reservoir that requires hydraulic fracturing. The main application of the sonic data was reliable slownesses as input to stress modeling for designing the stimulation, but the direction of the maximum horizontal stresses within the clastic gas-filled zones was also required. The dipole shear inversion results were able to handle various lithologies and hole conditions, as well as identify vertical transverse isotropy (VTI) anisotropic shale intervals between the horizontally stressed sand zones.

Stochastic inversion by matching to large numbers of pseudo-wells

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. M7-M22 ◽  
Author(s):  
Patrick A. Connolly ◽  
Matthew J. Hughes
Keyword(s):  
Rock Physics ◽  
Large Data ◽  
Data Sets ◽  
Stochastic Algorithm ◽  
Inversion Algorithm ◽  
Reservoir Properties ◽  
Geological Interpretation ◽  
Large Numbers ◽  
Physics Model ◽  
Rock Physics Model

We have developed a 1D stochastic algorithm for estimating reservoir properties, based on matching large numbers of pseudo-wells to seismic angle stacks. The pseudo-wells are part deterministic and part stochastic 1D stratigraphic profiles with consistent elastic and reservoir properties. Pseudo-wells are sampled from a prior distribution defined by the geological interpretation, a rock physics model and a model for the vertical statistics that provides close control of the lithofacies proportions. A new set of pseudo-wells, typically [Formula: see text] tied to the local stratigraphy, is constructed for each seismic trace. Synthetics, derived from the pseudo-wells using extended elastic impedance, are matched to either one or two seismic angle stacks, and the best matches are selected and averaged to provide a joint estimate of reservoir properties and impedances and the associated uncertainties. The algorithm has been tested on a number of data sets and validated by blind well ties. The algorithm is 1D with no additional constraints on spatial correlation beyond that provided by the seismic data. This restricts the maximum frequency to that of the seismic; however, it makes the algorithm highly parallelizable, allowing for large data sets to be inverted in a few hours given adequate computing resources. We envisage that this inversion algorithm could form the first part of a two-step process with the output used to constrain subsequent geostatistical modeling.

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.

Rock physics model‐based seismic inversion

2006 ◽  
Author(s):  
Kyle Spikes ◽  
Jack Dvorkin ◽  
Gary Mavko
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Model Based ◽  
Physics Model ◽  
Rock Physics Model
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