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.

Improving hydrocarbon exploration with pore pressure assisted earth-model building

2018 ◽  
Vol 6 (3) ◽  
pp. SG41-SG47
Author(s):  
Yangjun (Kevin) Liu ◽  
Michael O’Briain ◽  
Cara Hunter ◽  
Laura Jones ◽  
Emmanuel Saragoussi
Keyword(s):  
Pore Pressure ◽  
Initial Velocity ◽  
Model Building ◽  
Rock Physics ◽  
Velocity Model ◽  
Hydrocarbon Exploration ◽  
Earth Model ◽  
Well Control ◽  
Physics Model ◽  
Rock Physics Model

In shale-dominated clastic lithology environments, a rock-physics model relating velocity and pore pressure (PP) can be calibrated and used to convert velocity to PP properties. The crossvalidation between velocity and overpressure, which follows the geology, can be used to better understand the model, help to build an initial velocity model, and allow selecting tomography solutions with more confidence. The velocity model developed using this approach is more plausible and more suitable for subsequent PP analysis. We highlight the application of this method in areas with poor seismic illumination and insufficient well control.

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.

Rock-physics diagnostics of an offshore gas field

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. MR121-MR132 ◽  
Author(s):  
Uri Wollner ◽  
Yunfei Yang ◽  
Jack P. Dvorkin
Keyword(s):  
Rock Physics ◽  
Rock Properties ◽  
Gas Field ◽  
Amplitude Variation With Offset ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Using Data ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model

Seismic reflections depend on the contrasts of the elastic properties of the subsurface and their 3D geometry. As a result, interpreting seismic data for petrophysical rock properties requires a theoretical rock-physics model that links the seismic response to a rock’s velocity and density. Such a model is based on controlled experiments in which the petrophysical and elastic rock properties are measured on the same samples, such as in the wellbore. Using data from three wells drilled through a clastic offshore gas reservoir, we establish a theoretical rock-physics model that quantitatively explains these data. The modeling is based on the assumption that only three minerals are present: quartz, clay, and feldspar. To have a single rock-physics transform to quantify the well data in the entire intervals under examination in all three wells, we introduced field-specific elastic moduli for the clay. We then used the model to correct the measured shear-wave velocity because it appeared to be unreasonably low. The resulting model-derived Poisson’s ratio is much smaller than the measured ratio, especially in the reservoir. The associated synthetic amplitude variation with offset response appears to be consistent with the recorded seismic angle stacks. We have shown how rock-physics modeling not only helps us to correct the well data, but also allows us to go beyond the settings represented in the wells and quantify the seismic signatures of rock properties and conditions varying in a wider range using forward seismic modeling.

Rock-physics transforms and scale of investigation

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. MR75-MR88 ◽  
Author(s):  
Jack Dvorkin ◽  
Uri Wollner
Keyword(s):  
Elastic Properties ◽  
Rock Physics ◽  
Clay Content ◽  
Pore Fluid ◽  
Petrophysical Properties ◽  
Reflection Seismology ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model ◽  
Seismic Scale

Rock-physics “velocity-porosity” transforms are usually established on sets of laboratory and/or well data with the latter data source being dominant in recent practice. The purpose of establishing such transforms is to (1) conduct forward modeling of the seismic response for various geologically plausible “what if” scenarios in the subsurface and (2) interpret seismic data for petrophysical properties and conditions, such as porosity, clay content, and pore fluid. Because the scale of investigation in the well is considerably smaller than that in reflection seismology, an important question is whether the rock-physics model established in the well can be used at the seismic scale. We use synthetic examples and well data to show that a rock-physics model established at the well approximately holds at the seismic scale, suggest a reason for this scale independence, and explore where it may be violated. The same question can be addressed as an inverse problem: Assume that we have a rock-physics transform and know that it works at the scale of investigation at which the elastic properties are seismically measured. What are the upscaled (smeared) petrophysical properties and conditions that these elastic properties point to? It appears that they are approximately the arithmetically volume-averaged porosity and clay content (in a simple quartz/clay setting) and are close to the arithmetically volume-averaged bulk modulus of the pore fluid (rather than averaged saturation).

Simultaneous impedance inversion and interpretation for an offshore turbiditic reservoir

2017 ◽  
Vol 5 (3) ◽  
pp. SL9-SL23 ◽  
Author(s):  
Humberto S. Arévalo-López ◽  
Jack P. Dvorkin
Keyword(s):  
Rock Physics ◽  
Clay Content ◽  
Wave Impedance ◽  
New Paradigm ◽  
S Wave ◽  
Impedance Inversion ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model ◽  
Amplitude Variation With Angle

By using simultaneous impedance inversion, we obtained P- and S-wave impedance ([Formula: see text] and [Formula: see text]) volumes from angle stacks at a siliciclastic turbidite oil reservoir offshore northwest Australia. The ultimate goal was to interpret these elastic variables for fluid, porosity, and mineralogy. This is why an essential part of our workflow was finding the appropriate rock-physics model based on well data. The model-corrected S-wave velocity [Formula: see text] in the wells was used as an input to impedance inversion. The inversion parameters were optimized in small vertical sections around two wells to obtain the best possible match between the seismic impedances and the upscaled impedances measured at the wells. Special attention was paid to the seismically derived [Formula: see text] ratio because we relied on this parameter for hydrocarbon identification. Even after performing crosscorrelation between the angle stacks to correct for two-way traveltime shifts to align the stacks, these stacks did not indicate a coherent amplitude variation with angle (AVA) dependence. To deal with this common problem, we corrected the mid and far stacks by using the near and ultrafar stacks as anchoring points for fitting a [Formula: see text] AVA curve. This choice allowed us to match the seismically derived [Formula: see text] ratio with that predicted by the rock-physics model in the reservoir. Finally, the rock-physics model was used to interpret these [Formula: see text] and [Formula: see text] for the fluid, porosity, and mineralogy. The new paradigm in our inversion/interpretation workflow is that the ultimate quality control of the inversion is in an accurate deterministic match between the seismically derived petrophysical variables and the corresponding upscaled depth curves at the wells. Our interpretation is very sensitive to the inversion results, especially the [Formula: see text] ratio. Despite this fact, we were able to obtain accurate estimates of porosity and clay content in the reservoir and around it.

Using cuttings to extract geomechanical properties along lateral wells in unconventional reservoirs

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. MR167-MR185 ◽  
Author(s):  
Romain Prioul ◽  
Richard Nolen-Hoeksema ◽  
MaryEllen Loan ◽  
Michael Herron ◽  
Ridvan Akkurt ◽  
...  
Keyword(s):  
Organic Matter ◽  
Elastic Moduli ◽  
Rock Physics ◽  
In Situ Stress ◽  
Vertical Control ◽  
Petrophysical Model ◽  
Minimum Stress ◽  
Physics Model ◽  
Rock Physics Model

We have developed a method using measurements on drill cuttings as well as calibrated models to estimate anisotropic mechanical properties and stresses in unconventional reservoirs, when logs are not available in lateral wells. We measured mineralogy and organic matter on cuttings using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). We described the methodology and illustrated it using two vertical control wells in the Vaca Muerta Formation, Argentina, and one lateral well drilled in the low-maturity oil-bearing reservoir. The method has two steps. First, using a vertical control well containing measurements from cuttings, a comprehensive logging suite, cores, and in situ stress tests, we define and calibrate four models: petrophysical, rock physics, dynamic-static elastic, and geomechanical. The petrophysical model provides petrophysical constituent volumes (mineralogy, organic matter, and fluids) from logs or DRIFTS inputs to the rock-physics model for calculating the dynamic anisotropic elastic moduli. The dynamic-static elastic and geomechanics models provide the relationships for computing static elastic properties and the minimum stress. Second, we acquire DRIFTS data on cuttings in the target lateral well and apply the four models for calculating stresses. We find that the method is successful for two reasons. First, the sonic-log-derived elastic moduli could be reconstructed accurately from the rock-physics model using input from petrophysical volumes from logs and DRIFTS data. A striking observation is that the elastic-property heterogeneity in those wells is explainable almost solely by compositional variations. Second, petrophysical volumes can be reconstructed by the petrophysical model and DRIFTS data. In the lateral well, we observed horizontal variations of mineralogy and organic matter, which controlled variations of elastic moduli and its anisotropy, and, in turn, affected partitioning of the gravitational and tectonic components in the minimum stress. This methodology promises accurate in situ stress estimates using cutting-based measurements and assessments of unconventional-reservoir heterogeneity.

scholarly journals Estimation of elastic moduli of mixed porous clay composites

Geophysics ◽  
2011 ◽  
Vol 76 (1) ◽  
pp. E9-E20 ◽  
Author(s):  
Erling Hugo Jensen ◽  
Charlotte Faust Andersen ◽  
Tor Arne Johansen
Keyword(s):  
Experimental Data ◽  
Elastic Properties ◽  
Elastic Moduli ◽  
Rock Physics ◽  
Common Procedure ◽  
Effective Elastic Properties ◽  
Confining Pressures ◽  
Physics Model ◽  
Rock Physics Model ◽  
The Individual

We have developed a procedure for estimating the effective elastic properties of various mixtures of smectite and kaolinite over a range of confining pressures, based on the individual effective elastic properties of pure porous smectite and kaolinite. Experimental data for the pure samples are used as input to various rock physics models, and the predictions are compared with experimental data for the mixed samples. We have evaluated three strategies for choosing the initial properties in various rock physics models: (1) input values have the same porosity, (2) input values have the same pressure, and (3) an average of (1) and (2). The best results are obtained when the elastic moduli of the two porous constituents are defined at the same pressure and when their volumetric fractions are adjusted based on different compaction rates with pressure. Furthermore, our strategy makes the modeling results less sensitive to the actual rock physics model. The method can help obtain the elastic properties of mixed unconsolidated clays as a function of mechanical compaction. The more common procedure for estimating effective elastic properties requires knowledge about volume fractions, elastic properties of individual constituents, and geometric details of the composition. However, these data are often uncertain, e.g., large variations in the mineral elastic properties of clays have been reported in the literature, which makes our procedure a viable alternative.

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.

Sign in / Sign up

Export Citation Format

Share Document