Characterization of gas hydrate systems on the Hikurangi margin (New Zealand) through geostatistical seismic and petrophysical inversion

Geophysics ◽  
2021 ◽  
pp. 1-60
Author(s):  
Francesco Turco ◽  
Leonardo Azevedo ◽  
Dario Grana ◽  
Gareth J. Crutchley ◽  
Andrew R. Gorman
Keyword(s):  
New Zealand ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Effective Means ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Seismic Reflection Data ◽  
Reflection Data

Quantitative characterization of gas hydrate systems on continental margins from seismic data is challenging, especially in regions where no well logs are available. However, probabilistical seismic inversion provides an effective means for constraining the physical properties of subsurface strata in such settings and analyzing the variability related to the results. We apply a workflow for the characterization of two deep-water gas hydrate reservoirs east of New Zealand, where high concentrations of gas hydrate have been inferred previously. We estimate porosity and gas hydrate saturation in the reservoirs from multi-channel seismic data through a two-step procedure based on geostatistical seismic and Bayesian petrophysical inversion built on a rock physics model for gas hydrate-bearing marine sediments. We found that the two reservoirs together host between 2.45 × 105 m3 and 1.72 × 106 m3 of gas hydrate, with the best estimate at 9.68 × 105 m3. This estimate provides a first-order assessment for further gas hydrate evaluations in the region. The two-step statistically based seismic inversion method is an effective approach for characterizing gas hydrate systems from long-offset seismic reflection data.

Potentially Large Subsurface Gas Hydrate Bodies in the Mexican Ridges, Southwestern Gulf of Mexico

2021 ◽  
pp. 1-29
Author(s):  
Papia Nandi ◽  
Patrick Fulton ◽  
James Dale
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Poor Quality ◽  
Reflection Point ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
High Impedance ◽  
Acoustic Velocities ◽  
Seismic Images

As rising ocean temperatures can destabilize gas hydrate, identifying and characterizing large shallow hydrate bodies is increasingly important in order to understand their hazard potential. In the southwestern Gulf of Mexico, reanalysis of 3D seismic reflection data reveals evidence for the presence of six potentially large gas hydrate bodies located at shallow depths below the seafloor. We originally interpreted these bodies as salt, as they share common visual characteristics on seismic data with shallow allochthonous salt bodies, including high-impedance boundaries and homogenous interiors with very little acoustic reflectivity. However, when seismic images are constructed using acoustic velocities associated with salt, the resulting images were of poor quality containing excessive moveout in common reflection point (CRP) offset image gathers. Further investigation reveals that using lower-valued acoustic velocities results in higher quality images with little or no moveout. We believe that these lower acoustic values are representative of gas hydrate and not of salt. Directly underneath these bodies lies a zone of poor reflectivity, which is both typical and expected under hydrate. Observations of gas in a nearby well, other indicators of hydrate in the vicinity, and regional geologic context, all support the interpretation that these large bodies are composed of hydrate. The total equivalent volume of gas within these bodies is estimated to potentially be as large as 1.5 gigatons or 10.5 TCF, considering uncertainty for estimates of porosity and saturation, comparable to the entire proven natural gas reserves of Trinidad and Tobago in 2019.

scholarly journals Application of model-based inversion technique in a field in the coastal swamp depobelt, Niger delta

2018 ◽  
Vol 6 (1) ◽  
pp. 122
Author(s):  
Okoli Austin ◽  
Onyekuru Samuel I. ◽  
Okechukwu Agbasi ◽  
Zaidoon Taha Abdulrazzaq
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Seismic Reflection Data ◽  
Model Based ◽  
Reflection Data ◽  
X 32 ◽  
Niger Delta Basin ◽  
Bypassed Oil

Considering the heterogeneity of the reservoir sands in the Niger Delta basin which are primary causes of low hydrocarbon recovery efficiency, poor sweep, early breakthrough and pockets of bypassed oil there arises a need for in-depth quantitative interpretation and more analysis to be done on seismic data to achieve a reliable reservoir characterization to improve recovery, plan future development wells within field and achieve deeper prospecting for depths not penetrated by the wells and areas far away from well locations. An effective tool towards de-risking prospects is seismic inversion which transforms a seismic reflection data to a quantitative rock-property description of a reservoir. The choice of model-based inversion in this study was due to well control, again considering the heterogeneity of the sands in the field. X-26, X-30, and X-32 were used to generate an initial impedance log which is used to update the estimated reflectivity from which we would obtain our inverted volumes. Acoustic impedance volumes were generated and observations made were consistent with depth trends established for the Niger Delta basin, inverted slices of Poisson impedances validated the expected responses considering the effect of compaction. This justifies the use of inversion method in further characterizing the plays identified in the region.

Using instantaneous frequency and colored inversion attributes to distinguish and determine the sandstones facies of the Late Ordovician Mamuniyat reservoir, R-field in Murzuq Basin, Libya

2016 ◽  
Vol 4 (4) ◽  
pp. T507-T519 ◽  
Author(s):  
Yousf Abushalah ◽  
Laura Serpa
Keyword(s):  
Instantaneous Frequency ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Rock Physics ◽  
Side Lobe ◽  
Petroleum Reservoir ◽  
Seismic Reflection Data ◽  
Frequency Bands ◽  
Reflection Data ◽  
Sandstone Facies

The Mamuniyat petroleum reservoir in southwestern Libya is comprised of clean sandstones and intercalated shale and sand facies that are characterized by spatial porosity variations. Seismic reflection data from the field exhibit relatively low vertical seismic resolution, side lobes of reflection wavelets, reflection interference, and low acoustic impedance contrast between the reservoir and the units underneath the reservoir, which make mapping those facies a difficult task. In the absence of broadband seismic data, optimizing frequency bands of bandlimited data can be used to suppress pseudoreflectors resulting from side-lobe effects and help to separate the clean sandstone facies of the reservoir. We have optimized the data based on our investigation of seismic frequency bands and used instantaneous frequency analysis to reveal the reflection discontinuity that is mainly associated with the reservoir boundary of the sandstone facies of the clean Mamuniyat reservoir. We also preformed rock-physics diagnostic modeling and inverted the seismic data using spectral-based colored inversion into relative acoustic impedance. The inverted impedance matches the up-scaled impedance from the well data and the inversion of relative acoustic impedance confirms the conclusion that was drawn from the instantaneous frequency results. The interpretation of facies distributions based on the instantaneous frequency was supported by the inversion results and the rock-physics models.

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 attribute enhancement of weak and discontinuous gas hydrate bottom-simulating reflectors in the Pegasus Basin, New Zealand

2019 ◽  
Vol 7 (3) ◽  
pp. SG11-SG22 ◽  
Author(s):  
Heather Bedle
Keyword(s):  
New Zealand ◽  
Gas Hydrates ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Rock Physics ◽  
Stability Zone ◽  
Seismic Attribute ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Hydrate Stability

Gas hydrates in the oceanic subsurface are often difficult to image with reflection seismic data, particularly when the strata run parallel to the seafloor and in regions that lack the presence of a bottom-simulating reflector (BSR). To address and understand these imaging complications, rock-physics modeling and seismic attribute analysis are performed on modern 2D lines in the Pegasus Basin in New Zealand, where the BSR is not continuously imaged. Based on rock-physics and seismic analyses, several seismic attribute methods identify weak BSR reflections, with the far-angle stack data being particularly effective. Rock modeling results demonstrate that far-offset seismic data are critical in improving the imaging and interpretation of the base of the gas hydrate stability zone. The rock-physics modeling results are applied to the Pegasus 2009 2D data set that reveals a very weak seismic reflection at the base of the hydrates in the far-angle stack. This often-discontinuous reflection is significantly weaker in amplitude than typical BSRs associated with hydrates. These weak far-angle stack BSRs often do not appear clearly in full stack data, the most commonly interpreted seismic data type. Additional amplitude variation with angle (AVA) attribute analyses provide insight into identifying the presence of gas hydrates in regions lacking a strong BSR. Although dozens of seismic attributes were investigated for their ability to reveal weak reflections at the base of the gas hydrate stability zone, those that enhance class 2 AVA anomalies were most effective, particularly the seismic fluid factor attribute.

Stochastic perturbation optimization for discrete-continuous inverse problems

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. M73-M83 ◽  
Author(s):  
Leonardo Azevedo ◽  
Dario Grana ◽  
Leandro de Figueiredo
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Stochastic Perturbation ◽  
Rock Physics ◽  
Rock Properties ◽  
Petrophysical Properties ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Physics Model ◽  
Rock Physics Model

Accurate subsurface modeling and characterization require the prediction of facies and rock properties within the reservoir model. This is commonly achieved by inverting geophysical data, such as seismic reflection data, using a two-step approach either in the discrete or the continuous domain. We have adopted an iterative simultaneous method, namely, stochastic perturbation optimization, to invert seismic reflection data jointly for facies and rock properties. Facies first are simulated according to a Markov chain model, and then rock properties are generated with stochastic sequential simulation and cosimulation conditioned to each facies. Elastic and seismic data are computed by applying a rock-physics model to the realizations of petrophysical properties and a seismic convolutional model. The similarity between observed and synthetic seismic data is used to update the solution by perturbing facies and rock properties until convergence. Coupling the discrete and continuous domains ensures a consistent perturbation of the reservoir models throughout the iterations. We have evaluated the method in a 1D synthetic example for the estimation of facies and porosity from zero-offset seismic data assuming a linear rock-physics model to demonstrate the validity of the method. Then, we apply the method to a real 3D data set from the North Sea for the joint estimation of facies and petrophysical properties from prestack seismic data. The results show spatially consistent rock and fluid inverted models in which the predicted facies reproduce the vertical ordering as observed in the well 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.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 1: Theory and synthetic test

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. C177-C191 ◽  
Author(s):  
Yunyue Li ◽  
Biondo Biondi ◽  
Robert Clapp ◽  
Dave Nichols
Keyword(s):  
Seismic Data ◽  
Seismic Anisotropy ◽  
Model Building ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Additional Information ◽  
Synthetic Test ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Geologic Information

Seismic anisotropy plays an important role in structural imaging and lithologic interpretation. However, anisotropic model building is a challenging underdetermined inverse problem. It is well-understood that single component pressure wave seismic data recorded on the upper surface are insufficient to resolve a unique solution for velocity and anisotropy parameters. To overcome the limitations of seismic data, we have developed an integrated model building scheme based on Bayesian inference to consider seismic data, geologic information, and rock-physics knowledge simultaneously. We have performed the prestack seismic inversion using wave-equation migration velocity analysis (WEMVA) for vertical transverse isotropic (VTI) models. This image-space method enabled automatic geologic interpretation. We have integrated the geologic information as spatial model correlations, applied on each parameter individually. We integrate the rock-physics information as lithologic model correlations, bringing additional information, so that the parameters weakly constrained by seismic are updated as well as the strongly constrained parameters. The constraints provided by the additional information help the inversion converge faster, mitigate the ambiguities among the parameters, and yield VTI models that were consistent with the underlying geologic and lithologic assumptions. We have developed the theoretical framework for the proposed integrated WEMVA for VTI models and determined the added information contained in the regularization terms, especially the rock-physics constraints.

Prediction of Petrophysical Properties from Seismic Inversion and Neural Network: A case study

2021 ◽  
Author(s):  
Siddharth Garia ◽  
Arnab Kumar Pal ◽  
Karangat Ravi ◽  
Archana M Nair
Keyword(s):  
Neural Network ◽  
Bulk Density ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Petrophysical Properties ◽  
Seismic Section ◽  
Frequency Model ◽  
Seismic Reflection Data ◽  
Reflection Data

<p>Seismic inversion method is widely used to characterize reservoirs and detect zones of interest, i.e., hydrocarbon-bearing zone in the subsurface by transforming seismic reflection data into quantitative subsurface rock properties. The primary aim of seismic inversion is to transform the 3D seismic section/cube into an acoustic impedance (AI) cube. The integration of this elastic attribute, i.e., AI cube with well log data, can thereafter help to establish correlations between AI and different petrophysical properties. The seismic inversion algorithm interpolates and spatially populates data/parameters of wells to the entire seismic section/cube based on the well log information. The case study presented here uses machine learning-neural network based algorithm to extract the different petrophysical properties such as porosity and bulk density from the seismic data of the Upper Assam basin, India. We analyzed three different stratigraphic  units that are established to be producing zones in this basin.</p><p> AI model is generated from the seismic reflection data with the help of colored inversion operator. Subsequently, low-frequency model is generated from the impedance data extracted from the well log information. To compensate for the band limited nature of the seismic data, this low-frequency model is added to the existing acoustic model. Thereafter, a feed-forward neural network (NN) is trained with AI as input and porosity/bulk density as target, validated with NN generated porosity/bulk density with actual porosity/bulk density from well log data. The trained network is thus tested over the entire region of interest to populate these petrophysical properties.</p><p>Three seismic zones were identified from the seismic section ranging from 681 to 1333 ms, 1528 to 1575 ms and 1771 to 1814 ms. The range of AI, porosity and bulk density were observed to be 1738 to 6000 (g/cc) * (m/s), 26 to 38% and 1.95 to 2.46 g/cc respectively. Studies conducted by researchers in the same basin yielded porosity results in the range of 10-36%. The changes in acoustic impedance, porosity and bulk density may be attributed to the changes in lithology. NN method was prioritized over other traditional statistical methods due to its ability to model any arbitrary dependency (non-linear relationships between input and target values) and also overfitting can be avoided. Hence, the workflow presented here provides an estimation of reservoir properties and is considered useful in predicting petrophysical properties for reservoir characterization, thus helping to estimate reservoir productivity.</p>

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