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.

Elastic mineral facies: Selecting site-specific elastic moduli of clay

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. MR111-MR119
Author(s):  
Uri Wollner ◽  
Jack P. Dvorkin
Keyword(s):  
Elastic Moduli ◽  
Rock Physics ◽  
Data Sets ◽  
Depth Range ◽  
Chemical Formula ◽  
Rock Matrix ◽  
Well Control ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model

The elastic moduli of the mineral constituents of the rock matrix are among the principal inputs in all rock-physics velocity-porosity-mineralogy models. Published experimental data indicate that the elastic moduli for essentially any mineral vary. The ranges of these variations are especially wide for clay. The question addressed here is how to select, based on well data, concrete values for clay’s elastic constants where those for other minerals are fixed. The approach is to find a rock-physics model for zero-clay intervals and then adjust the clay’s constants to describe the intervals dominated by clay using the same model. We examine three data sets from clastic environments, each represented by three wells, where the selected constants for clay were different between the fields but stable within each field. These constants can then be used for seismic forward modeling and interpretation in a specific field away from well control and within a depth range represented in the wells. In essence, we introduce the concept of elastic mineral facies where we identify clay as a mineral with certain elastic moduli rather than by its chemical formula.

scholarly journals Rock Physics Modeling Assisted Reservoir Properties Prediction: Case Study in Malay Basin

2018 ◽  
Vol 7 (3.32) ◽  
pp. 24 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Deva Ghosh ◽  
Ahmed M. A. Salim ◽  
S Y. Moussavi Alashloo
Keyword(s):  
Oil And Gas ◽  
Rock Physics ◽  
Shear Velocity ◽  
Reservoir Modeling ◽  
Reservoir Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Well Log Analysis ◽  
Physics Model ◽  
Rock Physics Model

Shear velocity log is not measured at all wells in oil and gas fields, thus rock physics modeling plays an important role to predict this type of log. Therefore, seismic pre stack inversion is performed and elastic properties are estimated more accurately. Subsequently, a robust Petro-Elastic relationship arising from rock physics model leads to far more precise prediction of petrophysical properties. The more accurate rock physics modeling results in less uncertainty of reservoir modeling. Therefore, a valid rock physics model is intended to be built. For a better understanding of reservoir properties prediction, first of all rock physics modeling for each identified litho-facies classes should be performed separately through well log analysis.  

Seismic Reflections of Rock Properties in a Clastic Environment

2021 ◽  
Author(s):  
Vagif Suleymanov ◽  
Abdulhamid Almumtin ◽  
Guenther Glatz ◽  
Jack Dvorkin
Keyword(s):  
Rock Physics ◽  
Pore Fluid ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Sound Waves ◽  
Reservoir Properties ◽  
Field Guide ◽  
Physics Model ◽  
Rock Physics Model ◽  
Seismic Reflections

Abstract Generated by the propagation of sound waves, seismic reflections are essentially the reflections at the interface between various subsurface formations. Traditionally, these reflections are interpreted in a qualitative way by mapping subsurface geology without quantifying the rock properties inside the strata, namely the porosity, mineralogy, and pore fluid. This study aims to conduct the needed quantitative interpretation by the means of rock physics to establish the relation between rock elastic and petrophysical properties for reservoir characterization. We conduct rock physics diagnostics to find a theoretical rock physics model relevant to the data by examining the wireline data from a clastic depositional environment associated with a tight gas sandstone in the Continental US. First, we conduct the rock physics diagnostics by using theoretical fluid substitution to establish the relevant rock physics models. Once these models are determined, we theoretically vary the thickness of the intervals, the pore fluid, as well as the porosity and mineralogy to generate geologically plausible pseudo-scenarios. Finally, Zoeppritz (1919) equations are exploited to obtain the expected amplitude versus offset (AVO) and the gradient versus intercept curves of these scenarios. The relationship between elastic and petrophysical properties was established using forward seismic modeling. Several theoretical rock physics models, namely Raymer-Dvorkin, soft-sand, stiff-sand, and constant-cement models were applied to the wireline data under examination. The modeling assumes that only two minerals are present: quartz and clay. The appropriate rock physics model appears to be constant-cement model with a high coordination number. The result is a seismic reflection catalogue that can serve as a field guide for interpreting real seismic reflections, as well as to determine the seismic visibility of the variations in the reservoir geometry, the pore fluid, and the porosity. The obtained reservoir properties may be extrapolated to prospects away from the well control to consider certain what-if scenarios like plausible lithology or fluid variations. This enables building of a catalogue of synthetic seismic reflections of rock properties to be used by the interpreter as a field guide relating seismic data to volumetric reservoir properties.

scholarly journals Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands

2022 ◽  
Vol 9 ◽  
Author(s):  
Kyle T. Spikes ◽  
Mrinal K. Sen
Keyword(s):  
Reservoir Characterization ◽  
Rock Physics ◽  
Model Parameters ◽  
Data Sets ◽  
Unconsolidated Sands ◽  
Bayesian Formulation ◽  
Input Parameters ◽  
Physics Model ◽  
Rock Physics Model ◽  
Prior Models

Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that framework, we use velocity and porosity measurements on unconsolidated, dry, and clean sands. Three pressure- and three porosity-dependent rock-physics models are applied to the data to examine relationships among the inputs. As with any Bayesian formulation, we define a prior model and calculate the likelihood in order to evaluate the posterior. With relatively few inputs to consider for each rock-physics model, we found that sampling the posterior exhaustively to be convenient. The results of the Bayesian analyses are multivariate histograms that indicate most likely values of the input parameters given the data to which the rock-physics model was fit. When the Bayesian procedure is repeated many times for the same data, but with different prior models, correlations emerged among the input parameters in a rock-physics model. These correlations were not known previously. Implications, for the pressure- and porosity-dependent models examined here, are that these correlations should be utilized when applying these models to other relevant data sets. Furthermore, additional rock-physics models should be examined similarly to determine any potential correlations in their inputs. These correlations can then be taken advantage of in forward and inverse problems posed in reservoir characterization.

scholarly journals Rational Rock Physics for Improved Velocity Prediction and Reservoir Properties Estimation for Granite Wash (Tight Sands) in Anadarko Basin, Texas

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Muhammad Z. A. Durrani ◽  
Keith Willson ◽  
Jingyi Chen ◽  
Bryan Tapp ◽  
Jubran Akram
Keyword(s):  
Rock Physics ◽  
Accurate Estimation ◽  
Complex Nature ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Anadarko Basin ◽  
S Waves ◽  
Velocity Prediction ◽  
Physics Model ◽  
Rock Physics Model

Due to the complex nature, deriving elastic properties from seismic data for the prolific Granite Wash reservoir (Pennsylvanian age) in the western Anadarko Basin Wheeler County (Texas) is quite a challenge. In this paper, we used rock physics tool to describe the diagenesis and accurate estimation of seismic velocities of P and S waves in Granite Wash reservoir. Hertz-Mindlin and Cementation (Dvorkin’s) theories are applied to analyze the nature of the reservoir rocks (uncemented and cemented). In the implementation of rock physics diagnostics, three classical rock physics (empirical relations, Kuster-Toksöz, and Berryman) models are comparatively analyzed for velocity prediction taking into account the pore shape geometry. An empirical (VP-VS) relationship is also generated calibrated with core data for shear wave velocity prediction. Finally, we discussed the advantages of each rock physics model in detail. In addition, cross-plots of unconventional attributes help us in the clear separation of anomalous zone and lithologic properties of sand and shale facies over conventional attributes.

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.

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.

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