scholarly journals Biot's equations-based reservoir parameter inversion using deep neural networks

2021 ◽  
Vol 18 (6) ◽  
pp. 862-874
Author(s):  
Fansheng Xiong ◽  
Heng Yong ◽  
Hua Chen ◽  
Han Wang ◽  
Weidong Shen
Keyword(s):  
High Efficiency ◽  
Synthetic Data ◽  
Rock Physics ◽  
Seismic Attributes ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Shear Moduli ◽  
Parameter Inversion ◽  
Reservoir Parameter ◽  
Biot’S Equations

Abstract Reservoir parameter inversion from seismic data is an important issue in rock physics. The traditional optimisation-based inversion method requires high computational expense, and the process exhibits subjectivity due to the nonuniqueness of generated solutions. This study proposes a deep neural network (DNN)-based approach as a new means to analyse the sensitivity of seismic attributes to basic rock-physics parameters and then realise fast parameter inversion. First, synthetic data of inputs (reservoir properties) and outputs (seismic attributes) are generated using Biot's equations. Then, a forward DNN model is trained to carry out a sensitivity analysis. One can in turn investigate the influence of each rock-physics parameter on the seismic attributes calculated by Biot's equations, and the method can also be used to estimate and evaluate the accuracy of parameter inversion. Finally, DNNs are applied to parameter inversion. Different scenarios are designed to study the inversion accuracy of porosity, bulk and shear moduli of a rock matrix considering that the input quantities are different. It is found that the inversion of porosity is relatively easy and accurate, while more information is needed to make the inversion more accurate for bulk and shear moduli. From the presented results, the new approach makes it possible to realise accurate and pointwise inverse modelling with high efficiency for actual data interpretation and analysis.

Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

Geophysics ◽  
2010 ◽  
Vol 75 (3) ◽  
pp. O21-O37 ◽  
Author(s):  
Dario Grana ◽  
Ernesto Della Rossa
Keyword(s):  
Seismic Data ◽  
Measurement Errors ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Joint Estimation ◽  
Physical Models ◽  
Inversion Method ◽  
Petrophysical Properties ◽  
Reservoir Properties ◽  
Statistical Rock Physics

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and linearized seismic modeling connects velocities and density to seismic amplitude. A full Bayesian approach was adopted to propagate uncertainty from seismics to petrophysics in an integrated framework that takes into account different sources of uncertainty: heterogeneity of the real data, approximation of physical models, measurement errors, and scale changes. The method has been tested, as a feasibility step, on real well data and synthetic seismic data to show reliable propagation of the uncertainty through the three different steps and to compare two statistical approaches: parametric and nonparametric. Application to a real reservoir study (including data from two wells and partially stacked seismic volumes) has provided as a main result the probability densities of petrophysical properties and litho-fluid classes. It demonstrated the applicability of the proposed inversion method.

scholarly journals Spectral-ratio tomography for seismic attenuation estimation

2021 ◽  
Vol 18 (3) ◽  
pp. 392-405
Author(s):  
Ziqi Jin ◽  
Ying Shi ◽  
Qiqi Ma ◽  
Deguang Tian ◽  
Qi'an Meng ◽  
...  
Keyword(s):  
Seismic Waves ◽  
Estimation Method ◽  
Synthetic Data ◽  
Spectral Ratio ◽  
Seismic Attenuation ◽  
Estimation Methods ◽  
Inversion Method ◽  
Target Layer ◽  
Reservoir Parameter ◽  
Attenuation Estimation

Abstract When measuring surface seismic data, an accurate attenuation estimation method is necessary to compensate for the energy loss and phase distortion of seismic waves, and is also beneficial for further quantitative amplitude analyses and reservoir parameter predictions. For conventional Q-estimation methods (such as the log spectral-ratio (LSR) method and attenuated traveltime tomography), accuracy may be affected by the differences between the overburden ray paths of two selected reflections (we call it the overburden effect). In this study, we design a more accurate Q-tomography method to estimate Q-values (both in the overburden and target layer simultaneously) without overburden assumptions. We address the overburden effect by using an inversion method, which allows us to separate attenuation effects from the overburden through the traveltime differences in the tomography grid cells. We test the method on synthetic data and prove its feasibility and effectiveness by applying it to field data.

Data-driven elastic modeling of organic-rich marl during maturation

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. MR11-MR23 ◽  
Author(s):  
Krongrath Suwannasri ◽  
Tiziana Vanorio ◽  
Anthony C. Clark
Keyword(s):  
Rock Physics ◽  
Peak Oil ◽  
S Wave ◽  
Reservoir Properties ◽  
Shear Moduli ◽  
The Matrix ◽  
Early Peak ◽  
Organic Porosity ◽  
Physics Model ◽  
Kerogen Porosity

The elastic modeling of source-rock reservoirs during maturation must incorporate microstructural and geochemical alterations. The common challenge is calibrating the volumetric proportion between each form of organic porosity along with the changes in the bulk and shear moduli of kerogen as a function of maturity. Two forms of organic porosity have generally been observed: (1) spongy and bubble pores inside kerogen and (2) low-aspect-ratio pores or gaps at the interface between shrinking kerogen bodies and the matrix. We have constructed a rock-physics model of organic-rich marl during maturation and calibrated it using rock-physics data sets from controlled pyrolysis experiments of organic-rich marl under stress. We chose these pyrolysis data because the samples provide subsequent changes in porosity and P- and S-wave velocities as a function of maturity, while evidencing minimal grain sliding and mechanical compaction due to their stiff matrix. Our calibration results indicate that spongy and bubble pores should be used as the predominant form in the model regardless of maturity. Our results also indicate a competing effect between increasing kerogen porosity and the increasing moduli of solid kerogen. Kerogen porosity mainly develops throughout the oil windows. Whereas the moduli of solid kerogen increase by a factor of two in the early-peak oil window and remain relatively constant afterward. Consequently, the effective moduli of kerogen experience minimal changes in the early-peak oil window and rapidly decrease to half of the immature values in the late oil window. These calibration results are consistent with several petrophysical and nanoindentation studies on kerogen. Finally, we used the calibrated model to build a rock-physics template of organic-rich marl during maturation. The template was tested with pyrolyzed and naturally matured samples, which showed that our model can be used to characterize reservoir properties across different maturity windows.

scholarly journals Seismic attribute and petrophysics-assisted interpretation of the Nanushuk and Torok Formations on the North Slope, Alaska

2020 ◽  
Vol 8 (2) ◽  
pp. SJ17-SJ34 ◽  
Author(s):  
Shuvajit Bhattacharya ◽  
Sumit Verma
Keyword(s):  
Oil And Gas ◽  
Gamma Ray ◽  
Regional Scale ◽  
Rock Physics ◽  
Seismic Attributes ◽  
North Slope ◽  
Reservoir Properties ◽  
Seismic Attribute ◽  
Seismic Reflection Data ◽  
The North

Exploration of the Brookian sandstone reservoirs in the Nanushuk and Torok Formations on the North Slope of Alaska is a hot topic and presents opportunities to the oil and gas community because of their shallow depth, vast extent, and scope of development. The consecutive hydrocarbon discoveries announced by Repsol-Armstrong, Caelus Energy, and ConocoPhillips in 2015, 2016, and 2017 have indicated the presence of the vast recoverable resources on the North Slope in the Nanushuk and Torok reservoirs. We have investigated the detailed geophysical and petrophysical characteristics of these reservoirs. Our goal is to detect dominant geologic features in these formations using a combination of seismic attributes at the regional scale and analyze critical petrophysical and rock physics properties to evaluate formation heterogeneities and identify the reservoir targets by integrating well log and core data at the well scale. The Nanushuk Formation is expressed as topset reflections, whereas the Torok and gamma-ray zone formations are expressed as foresets and bottomsets on the seismic reflection data. Using seismic attributes, we mapped the extent of different geomorphological features, including shelf edges, channels, slides, and basin-floor fans, all with significant amplitude anomalies. The shelf edges continue for tens to hundreds of miles along the north/northwest and east–west directions, depending on the areas. The internal characters of these formations delineated by conventional well logs and advanced petrophysical analysis reveal their vertical heterogeneities and complexities, in terms of reservoir properties. We conclude that the reservoirs are vertically and laterally heterogeneous. These are thin-bedded low-resistivity reservoirs. Only a few zones in the parasequences are oil-saturated. We find that a combination of low [Formula: see text] ratio and acoustic impedance can be a useful proxy to detect the hydrocarbon-bearing sand intervals in these formations.

Reservoir property mapping and monitoring from joint inversion of time-lapse seismic, electromagnetic, and production data

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. ID73-ID84 ◽  
Author(s):  
Lin Liang ◽  
Aria Abubakar ◽  
Tarek M. Habashy
Keyword(s):  
Prior Information ◽  
Joint Inversion ◽  
Synthetic Data ◽  
Rock Physics ◽  
Rock Properties ◽  
Production Data ◽  
Inversion Algorithm ◽  
Reservoir Properties ◽  
Temporal And Spatial Distribution ◽  
Archie’S Law

We have developed a deterministic multiphysics joint inversion approach integrating seismic, electromagnetic (EM), and production data to map relevant reservoir properties, such as permeability and porosity, and the time evolution of the flooding front movement, i.e., saturation changes with time. These measurements are complementary in terms of their sensitivity to individual reservoir properties and their coverage of reservoir volumes. As a consequence, integration reduces ambiguities in the interpretation. In the workflow, a reservoir model is first built based on prior information. The production data are simulated by evolving the model in time based on the known well-control strategy. Simultaneously, the temporal and spatial distribution of fluid properties, such as saturation, salt concentration, density, and pressure are also obtained from the forward modeling. These properties, together with in situ rock properties, are transformed to formation resistivity and elastic properties using prescribed petrophysical relationships, such as Archie’s law and effective medium rock-physics models. From the transformation results, synthetic EM and full-waveform seismic data can be subsequently simulated. A Gauss-Newton optimization scheme is used to iteratively update the reservoir permeability and porosity fields until the mismatch between the synthetic data and the observed data becomes less than a predefined threshold. This inverse problem is usually highly underdetermined; hence, it is necessary to bring in prior information to further constrain the inversion. Different regularization approaches are investigated to facilitate incorporation of prior information into the joint inversion algorithm.

Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. M25-M39 ◽  
Author(s):  
Mingliang Liu ◽  
Dario Grana
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Computational Cost ◽  
Rock Physics ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Data Set ◽  
Ensemble Smoother

We have developed a new stochastic nonlinear inversion method for seismic reservoir characterization studies to jointly estimate elastic and petrophysical properties and to quantify their uncertainty. Our method aims to estimate multiple reservoir realizations of the entire set of reservoir properties, including seismic velocities, density, porosity, mineralogy, and saturation, by iteratively updating the initial ensemble of models based on the mismatch between their seismic response and the measured seismic data. The initial models are generated using geostatistical methods and the geophysical forward operators include rock-physics relations and a seismic forward model. The optimization is achieved using an iterative ensemble-based algorithm, namely, the ensemble smoother with multiple data assimilation, in which each iteration is based on a Bayesian updating step. The advantages of the proposed method are that it can be applied to nonlinear inverse problems and it can provide an ensemble of solutions from which we can quantify the uncertainty of the model properties of interest. To reduce the computational cost of the inversion, we perform the optimization in a lower dimensional data space reparameterized by singular value decomposition. The proposed methodology is validated on a synthetic case in which the set of petroelastic properties is recovered with satisfactory accuracy. Then, we applied the inversion method to a real seismic data set from the Norne field in the Norwegian Sea.

Incorporation of geology with rock physics enables subsalt poststack inversion: A case study in the Gulf of Mexico

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. WB53-WB65 ◽  
Author(s):  
Huyen Bui ◽  
Jennifer Graham ◽  
Shantanu Kumar Singh ◽  
Fred Snyder ◽  
Martiris Smith
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Low Frequency ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Reservoir Properties ◽  
Blind Test ◽  
Frequency Model ◽  
Layer Sequence

One of the main goals of seismic inversion is to obtain high-resolution relative and absolute impedance for reservoir properties prediction. We aim to study whether the results from seismic inversion of subsalt data are sufficiently robust for reliable reservoir characterization. Approximately [Formula: see text] of poststack, wide-azimuth, anisotropic (vertical transverse isotropic) wave-equation migration seismic data from 50 Outer Continental Shelf blocks in the Green Canyon area of the Gulf of Mexico were inverted in this study. A total of four subsalt wells and four subsalt seismic interpreted horizons were used in the inversion process, and one of the wells was used for a blind test. Our poststack inversion method used an iterative discrete spike inversion method, based on the combination of space-adaptive wavelet processing to invert for relative acoustic impedance. Next, the dips were estimated from seismic data and converted to a horizon-like layer sequence field that was used as one of the inputs into the low-frequency model. The background model was generated by incorporating the well velocities, seismic velocity, seismic interpreted horizons, and the previously derived layer sequence field in the low-frequency model. Then, the relative acoustic impedance volume was scaled by adding the low-frequency model to match the calculated acoustic impedance logs from the wells for absolute acoustic impedance. Finally, the geological information and rock physics data were incorporated into the reservoir properties assessment for sand/shale prediction in two main target reservoirs in the Miocene and Wilcox formations. Overall, the poststack inversion results and the sand/shale prediction showed good ties at the well locations. This was clearly demonstrated in the blind test well. Hence, incorporating rock physics and geology enables poststack inversion in subsalt areas.

Simultaneous multitrace impedance inversion with transform-domain sparsity promotion

Geophysics ◽  
2015 ◽  
Vol 80 (2) ◽  
pp. R71-R80 ◽  
Author(s):  
Sanyi Yuan ◽  
Shangxu Wang ◽  
Chunmei Luo ◽  
Yanxiao He
Keyword(s):  
Structural Information ◽  
Synthetic Data ◽  
Real Data ◽  
Inversion Method ◽  
Transform Domain ◽  
Reservoir Properties ◽  
Simultaneous Inversion ◽  
Inversion Technique ◽  
Impedance Inversion ◽  
Sparsity Constraint

The impedance inversion technique plays a crucial role in seismic reservoir properties prediction. However, most existing impedance inversion methods often suffer from spatial discontinuities and instability because each vertical profile is processed independently in the inversion. We tested a transform-domain sparsity promotion simultaneous multitrace impedance inversion method to address this issue. The approach was implemented through minimizing a data misfit term and a transform-domain sparsity constraint term that incorporates the (2D or 3D) structural information into the inversion processing. A 2D synthetic data example was applied to mainly explain the roles of the transform-domain sparsity constraint. We determined that the transform-domain sparsity constraint can help in stabilizing the inversion, reducing the influence of high-wavenumber noise on the inverted result, and exploring spatial continuities of structures. Furthermore, a 3D field data example was used to examine the effectiveness of the proposed method for dealing with the real data and to reveal the difference between the results from the 3D simultaneous inversion and the section-by-section inversion. We found that the inverted results roughly matched a low-pass-filtered version of impedance curves derived from well log data. Also, it has been demonstrated that the 3D simultaneous inversion technique provided a better estimation than the section-by-section inversion technique in terms of guaranteeing more spatial continuities of geologic features in the impedance model.

scholarly journals Validation of Digital Rock Physics Algorithms

Minerals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 669
Author(s):  
Rongrong Lin ◽  
Leon Thomsen
Keyword(s):  
Rock Physics ◽  
Image Resolution ◽  
Staggered Grid ◽  
Static Properties ◽  
Shear Moduli ◽  
Digital Rock Physics ◽  
Digital Rock ◽  
Saturated Rock ◽  
Theoretical Predictions ◽  
Theoretical Test

With a detailed microscopic image of a rock sample, one can determine the corresponding 3-D grain geometry, forming a basis to calculate the elastic properties numerically. The issues which arise in such a calculation include those associated with image resolution, the registration of the digital numerical grid with the digital image, and grain anisotropy. Further, there is a need to validate the numerical calculation via experiment or theory. Because of the geometrical complexity of the rock, the best theoretical test employs the Hashin–Shtrikman result that, for an aggregate of two isotropic components with equal shear moduli, the bulk modulus is uniquely determined, independent of the micro-geometry. Similarly, for an aggregate of two isotropic components with a certain combination of elastic moduli defined herein, the Hashin–Shtrikman formulae give a unique result for the shear modulus, independent of the micro-geometry. For a porous, saturated rock, the solid incompressibility may be calculated via an “unjacketed” test, independent of the micro-geometry. Any numerical algorithm proposed for digital rock physics computation should be validated by successfully confirming these theoretical predictions. Using these tests, we validate a previously published staggered-grid finite difference damped time-stepping algorithm to calculate the static properties of digital rock models.

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