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.

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.

Seismic reservoir characterization of the Bone Spring and Wolfcamp Formations in the Delaware Basin: Challenges and uncertainty in characterization using rock physics — A case study: Part 2

2020 ◽  
Vol 8 (4) ◽  
pp. T1057-T1069
Author(s):  
Ritesh Kumar Sharma ◽  
Satinder Chopra ◽  
Larry Lines
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Well Log ◽  
Data Set ◽  
Frequency Model ◽  
Log Data ◽  
Delaware Basin ◽  
Seismic Reservoir

The discrimination of fluid content and lithology in a reservoir is important because it has a bearing on reservoir development and its management. Among other things, rock-physics analysis is usually carried out to distinguish between the lithology and fluid components of a reservoir by way of estimating the volume of clay, water saturation, and porosity using seismic data. Although these rock-physics parameters are easy to compute for conventional plays, there are many uncertainties in their estimation for unconventional plays, especially where multiple zones need to be characterized simultaneously. We have evaluated such uncertainties with reference to a data set from the Delaware Basin where the Bone Spring, Wolfcamp, Barnett, and Mississippian Formations are the prospective zones. Attempts at seismic reservoir characterization of these formations have been developed in Part 1 of this paper, where the geologic background of the area of study, the preconditioning of prestack seismic data, well-log correlation, accounting for the temporal and lateral variation in the seismic wavelets, and building of robust low-frequency model for prestack simultaneous impedance inversion were determined. We determine the challenges and the uncertainty in the characterization of the Bone Spring, Wolfcamp, Barnett, and Mississippian sections and explain how we overcame those. In the light of these uncertainties, we decide that any deterministic approach for characterization of the target formations of interest may not be appropriate and we build a case for adopting a robust statistical approach. Making use of neutron porosity and density porosity well-log data in the formations of interest, we determine how the type of shale, volume of shale, effective porosity, and lithoclassification can be carried out. Using the available log data, multimineral analysis was also carried out using a nonlinear optimization approach, which lent support to our facies classification. We then extend this exercise to derived seismic attributes for determination of the lithofacies volumes and their probabilities, together with their correlations with the facies information derived from mud log data.

Simultaneous inversion of PP and PS seismic data

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. R1-R10 ◽  
Author(s):  
Helene Hafslund Veire ◽  
Martin Landrø
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Model Building ◽  
Joint Inversion ◽  
P Wave ◽  
Inversion Method ◽  
System Of Equations ◽  
S Wave ◽  
Data Set ◽  
Simultaneous Inversion

Elastic parameters derived from seismic data are valuable input for reservoir characterization because they can be related to lithology and fluid content of the reservoir through empirical relationships. The relationship between physical properties of rocks and fluids and P-wave seismic data is nonunique. This leads to large uncertainties in reservoir models derived from P-wave seismic data. Because S- waves do not propagate through fluids, the combined use of P-and S-wave seismic data might increase our ability to derive fluid and lithology effects from seismic data, reducing the uncertainty in reservoir characterization and thereby improving 3D reservoir model-building. We present a joint inversion method for PP and PS seismic data by solving approximated linear expressions of PP and PS reflection coefficients simultaneously using a least-squares estimation algorithm. The resulting system of equations is solved by singular-value decomposition (SVD). By combining the two independent measurements (PP and PS seismic data), we stabilize the system of equations for PP and PS seismic data separately, leading to more robust parameter estimation. The method does not require any knowledge of PP and PS wavelets. We tested the stability of this joint inversion method on a 1D synthetic data set. We also applied the methodology to North Sea multicomponent field data to identify sand layers in a shallow formation. The identified sand layers from our inverted sections are consistent with observations from nearby well logs.

Reservoir properties prediction integrating controlled-source electromagnetic, prestack seismic, and well-log data using a rock-physics framework: Case study in the Hoop Area, Barents Sea, Norway

2017 ◽  
Vol 5 (2) ◽  
pp. SE43-SE60 ◽  
Author(s):  
Pedro Alvarez ◽  
Amanda Alvarez ◽  
Lucy MacGregor ◽  
Francisco Bolivar ◽  
Robert Keirstead ◽  
...  
Keyword(s):  
Seismic Data ◽  
Barents Sea ◽  
Rock Physics ◽  
Positive Outcome ◽  
Rock Properties ◽  
Integrated Analysis ◽  
Reservoir Properties ◽  
Data Set ◽  
Controlled Source ◽  
Rotation Scheme

We have developed an example from the Hoop Area of the Barents Sea showing a sequential quantitative integration approach to integrate seismic and controlled-source electromagnetic (CSEM) attributes using a rock-physics framework. The example illustrates a workflow to address the challenges of multiphysics and multiscale data integration for reservoir characterization purposes. A data set consisting of 2D GeoStreamer seismic and towed streamer electromagnetic data that were acquired concurrently in 2015 by PGS provide the surface geophysical measurements that we used. Two wells in the area — Wisting Central (7324/8-1) and Wisting Alternative (7324/7-1S) — provide calibration for the rock-physics modeling and the quantitative integrated analysis. In the first stage of the analysis, we invert prestack seismic and CSEM data separately for impedance and anisotropic resistivity, respectively. We then apply the multi-attribute rotation scheme (MARS) to estimate rock properties from seismic data. This analysis verified that the seismic data alone cannot distinguish between commercial and noncommercial hydrocarbon saturation. Therefore, in the final stage of the analysis, we invert the seismic and CSEM-derived properties within a rock-physics framework. The inclusion of the CSEM-derived resistivity information within the inversion approach allows for the separation of these two possible scenarios. Results reveal excellent correlation with known well outcomes. The integration of seismic, CSEM, and well data predicts very high hydrocarbon saturations at Wisting Central and no significant saturation at Wisting Alternative, consistent with the findings of each well. Two further wells were drilled in the area and used as blind tests in this case: The slightly lower saturation predicted at Hanssen (7324/7-2) is related to 3D effects in the CSEM data, but the positive outcome of the well is correctly predicted. At Bjaaland (7324/8-2), although the seismic indications are good, the integrated interpretation result predicts correctly that this well was unsuccessful.

scholarly journals Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. O1-O19 ◽  
Author(s):  
Mohammad S. Shahraeeni ◽  
Andrew Curtis ◽  
Gabriel Chao
Keyword(s):  
Seismic Data ◽  
Water Saturation ◽  
Random Noise ◽  
Clay Content ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Desktop Computer ◽  
Data Set ◽  
Mixture Density

A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.

scholarly journals Reservoir characterization based upon the onset of time-lapse amplitude changes

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. M1-M14 ◽  
Author(s):  
Donald W. Vasco ◽  
Andrey Bakulin ◽  
Hyoungsu Baek ◽  
Lane R. Johnson
Keyword(s):  
Large Scale ◽  
Reservoir Characterization ◽  
Reference Model ◽  
Rock Physics ◽  
Time Lapse ◽  
Seismic Velocities ◽  
Reservoir Properties ◽  
Fluid Saturation ◽  
Seismic Amplitudes ◽  
Physics Model

Time-lapse geophysical monitoring has potential as a tool for reservoir characterization, that is, for determining reservoir properties such as permeability. Onset times, the calendar times at which geophysical observations begin to deviate from their initial or background values, provide a useful basis for such characterization. We found that, in contrast to time-lapse amplitude changes, onset times were not sensitive to the exact method used to related changes in fluid saturation to changes in seismic velocities. As a consequence of this, we found that an inversion for effective permeability based upon onset times was robust with respect to variations in the rock-physics model. In particular, inversions of synthetic onset times calculated using Voigt and Reuss averaging techniques, but inverted using sensitivities from Hill’s averaging method, resulted in almost identical misfit reductions and similar permeability models. All solutions based on onset times recovered the large-scale, resolvable features of the reference model. Synthetic tests indicated that reliable onset times can be obtained from noisy seismic amplitudes. Testing also indicated that large-scale permeability variations can be recovered even if we used onset times from seismic surveys that were spaced as much as 300 days apart.

Integrated simulation to seismic and seismic reservoir characterization in a CO2 EOR monitoring application

2020 ◽  
Vol 39 (9) ◽  
pp. 668-678
Author(s):  
Alan Mur ◽  
César Barajas-Olalde ◽  
Donald C. Adams ◽  
Lu Jin ◽  
Jun He ◽  
...  
Keyword(s):  
Oil Recovery ◽  
Reservoir Simulation ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Time Lapse ◽  
Seismic Signal ◽  
Inversion Method ◽  
Data Sets ◽  
Production Effects

Understanding the behavior of CO2 injected into a reservoir and delineating its spatial distribution are fundamentally important in enhanced oil recovery (EOR) and CO2 capture and sequestration activities. Interdisciplinary geoscience collaboration and well-defined workflows, from data acquisition to reservoir simulation, are needed to effectively handle the challenges of EOR fields and envisioned future commercial-scale sites for planned and incidental geologic CO2 storage. Success of operations depends on decisions that are based on good understanding of geologic formation heterogeneities and fluid and pressure movements in the reservoir over large areas over time. We present a series of workflow steps that optimize the use of available data to improve and integrate the interpretation of facies, injection, and production effects in an EOR application. First, we construct a simulation-to-seismic model supported by rock physics to model the seismic signal and signal quality needed for 4D monitoring of fluid and pressure changes. Then we use Bayesian techniques to invert the baseline and monitor seismic data sets for facies and impedances. To achieve a balance between prior understanding of the reservoir and the recorded time-lapse seismic data, we invert the seismic data sets by using multiple approaches. We first invert the seismic data sets independently, exploring sensible parameter scenarios. With the resulting realizations, we develop a shared prior model to link the reservoir facies geometry between seismic vintages upon inversion. Then we utilize multirealization analysis methods to quantify the uncertainties of our predictions. Next, we show how data may be more deeply interrogated by using the facies inversion method to invert prestack seismic differences directly for production effects. Finally, we show and discuss the feedback loop for updating the static and dynamic reservoir simulation model to highlight the integration of geophysical and engineering data within a single model.

Integrated Stochastic Inversion Approach Assessing Thin Reservoirs Full Potential in the Prolific Oil-Prone Periphery of Hassi Messaoud Field, Algeria

2021 ◽  
Author(s):  
Mohamed Samy Tawfik ◽  
Medjdouba Nasrine ◽  
Sabiha Annou ◽  
Aiouna Ahcene ◽  
Abderaouf Haddouche ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Rock Physics ◽  
Full Potential ◽  
Reservoir Properties ◽  
Stochastic Inversion ◽  
Area Of Interest ◽  
Characterization Study ◽  
Reservoir Potentiality ◽  
Saharan Platform

Abstract Nowadays it become harder, risker and more expensive understanding the reservoir potentiality and design the optimum development plan for challenging thin reservoirs. In a geological complex area, integrated seismic reservoir characterization approach was crucial to unlock the potentiality for the study area, which is located at Oued Mya basin, SE Saharan platform Algeria. Seismic data analysis is one of the key procedures for characterizing reservoirs and monitoring subsurface properties. Integration of seismic stochastic inversion and geological model will help to demonstrate the link between seismic and reservoir properties more quantitative. The seismic data of region were challenging, with around 30 wells drilled over 400 Km2. To overcome the challenges, the available geological and geophysical data were integrated to construct the reservoir characterization study and reduce drilling uncertainty. Ensure the reservoir characterization process was constrained by a robust workflow and consistent with the available geophysical, geological, and petrophysical data. Petrophysical interpretation, seismic interpretation, rock physics analysis and Stochastic Inversion processes were carried out. These processes were integrated to characterize the lateral and vertical extent of the lithofacies in five stacked reservoirs across the area of interest to identify the potential delineation of thin reservoirs of nine-meter thickness.

Bayesian lithology and fluid prediction from seismic prestack data

Geophysics ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. C13-C21 ◽  
Author(s):  
Arild Buland ◽  
Odd Kolbjørnsen ◽  
Ragnar Hauge ◽  
Øyvind Skjæveland ◽  
Kenneth Duffaut
Keyword(s):  
Seismic Data ◽  
Rock Physics ◽  
Real Data ◽  
Feasibility Analysis ◽  
Bayesian Inversion ◽  
Inversion Method ◽  
Elastic Parameters ◽  
Data Set ◽  
Prior Probabilities ◽  
Simultaneous Inversion

A fast Bayesian inversion method for 3D lithology and fluid prediction from prestack seismic data, and a corresponding feasibility analysis were developed and tested on a real data set. The objective of the inversion is to find the probabilities for different lithology-fluid classes from seismic data and geologic knowledge. The method combines stochastic rock physics relations between the elastic parameters and the different lithology-fluid classes with the results from a fast Bayesian seismic simultaneous inversion from seismic data to elastic parameters. A method for feasibility analysis predicts the expected modification of the prior probabilities to posterior probabilities for the different lithology-fluid classes. The feasibility analysis can be carried out before the seismic data are analyzed. Both the feasibility method and the seismic lithology-fluid probability inversion were applied to a prospect offshore Norway. The analysis improves the probability for gas sand from 0.1 to about 0.2–0.4 with seismic data.

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.

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