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>

Geostatistical seismic inversion for frontier exploration

2017 ◽  
Vol 5 (4) ◽  
pp. T477-T485 ◽  
Author(s):  
Ângela Pereira ◽  
Rúben Nunes ◽  
Leonardo Azevedo ◽  
Luís Guerreiro ◽  
Amílcar Soares
Keyword(s):  
Acoustic Impedance ◽  
Seismic Reflection ◽  
Seismic Inversion ◽  
Well Log ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Log Data ◽  
The Real ◽  
Reflection Data ◽  
Impedance Models

Numerical 3D high-resolution models of subsurface petroelastic properties are key tools for exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas, the available data set is often composed exclusively of seismic reflection data due to the lack of drilled wells and are therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations, we have developed a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology simultaneously integrates the available seismic reflection data and information from geologic analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by a stochastic sequential simulation methodology that handles the nonstationary probability distribution function. Convergence from iteration to iteration is ensured by a genetic algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin offshore southwest Europe, where no well has been drilled yet. Geologic information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. The resulting acoustic impedance models are geologically consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions.

Seismic amplitude variation with offset inversion in a vertical transverse isotropy medium

2019 ◽  
Vol 7 (3) ◽  
pp. T581-T593 ◽  
Author(s):  
Mark Sams ◽  
Annushia Annamalai ◽  
Jeremy Gallop
Keyword(s):  
Transverse Isotropy ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Well Log ◽  
Amplitude Variation ◽  
Frequency Model ◽  
Isotropic Solution ◽  
Amplitude Variation With Offset ◽  
Vertical Transverse Isotropy ◽  
The Impact

Vertical transverse isotropy (VTI) will affect seismic inversion, but it is not possible to solve for the full set of anisotropic elastic parameters from amplitude variation with offset inversion because there exists an isotropic solution to every VTI problem. We can easily approximate the pseudoisotropic properties that result from the isotropic solution to the anisotropic problem for well-log data. We can then use these well-log properties to provide a low-frequency model for inversion and/or a framework for interpreting either absolute or relative inversion results. This, however, requires prior knowledge of the anisotropic properties, which are often unavailable or poorly constrained. If we ignore anisotropy and assume that the amplitude variations caused by VTI are going to be accounted for by effective wavelets, the inversion results would be in error: The impact of anisotropy is not merely a case of linear scaling of seismic amplitudes for any particular angle range. Ignoring VTI does not affect the prediction of acoustic impedance, but it does affect predictions of [Formula: see text] and density. For realistic values of anisotropy, these errors can be significant, such as predicting oil instead of brine. If the anisotropy of the rocks is known, then we can invert for the true vertical elastic properties using the known anisotropy coefficients through a facies-based inversion. This can produce a more accurate result than solving for pseudoelastic properties, and it can take advantage of the sometimes increased separation of isotropic and anisotropic rocks in the pseudoisotropic elastic domain. Because the effect of anisotropy will vary depending on the strength of the anisotropy and the distribution of the rocks, we strongly recommend forward modeling for each case prior to inversion to understand the potential impact on the study objectives.

scholarly journals Strategies for integrating uncertainty in iterative geostatistical seismic inversion

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R207-R219 ◽  
Author(s):  
Pedro Pereira ◽  
Fernando Bordignon ◽  
Leonardo Azevedo ◽  
Ruben Nunes ◽  
Amílcar Soares
Keyword(s):  
Elastic Properties ◽  
Correlation Coefficients ◽  
Seismic Inversion ◽  
Distribution Functions ◽  
Spatial Uncertainty ◽  
Well Log ◽  
Seismic Reflection Data ◽  
Sources Of Uncertainty ◽  
Reflection Data ◽  
Global Correlation

Iterative geostatistical seismic inversion integrates seismic and well data to infer the spatial distribution of subsurface elastic properties. These methods provide limited assessment to the spatial uncertainty of the inverted elastic properties, overlooking alternative sources of uncertainty such as those associated with poor well-log data, upscaling, and noise within the seismic data. We have expressed uncertain well-log samples, due to bad logging reads and upscaling, in terms of local probability distribution functions (PDFs). Local PDFs are used as conditioning data to a stochastic sequential simulation algorithm, included as the model perturbation within the inversion. The problem of noisy seismic and narrow exploration of the model parameter space, particularly in the early steps of the inversion, is tackled by the introduction of a cap on local correlation coefficients (CCs) responsible for the generation of the new set of models during the next iteration. We evaluate a single geostatistical framework with application to a real case study. When compared against a conventional iterative geostatistical seismic inversion, the integration of additional sources of uncertainty increases the match between real and inverted seismic traces and the variability within the ensemble of models inverted at the last iteration. The selection of the local PDFs plays a central role in the reliability of the inverted elastic models. Avoiding high local CCs at early stages of the inversion increases convergence in terms of global correlation between synthetic and real seismic reflection data at the end of the inversion.

A fast automatic multichannel blind seismic inversion for high-resolution impedance recovery

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. V357-V364 ◽  
Author(s):  
Ali Gholami
Keyword(s):  
High Resolution ◽  
Seismic Reflection ◽  
Mathematical Problem ◽  
A Priori ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Split Bregman ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Tv Regularization

The inversion of seismic reflection data for acoustic impedance (AI) is a common and accepted method for the interpretation of poststack seismic data. The original mathematical problem is nonlinear due to the nonlinearity of relation between the AI and the earth reflectivity series and also the insufficiency of information about the source wavelet. Furthermore, the problem is ill posed due to the lack of low and high frequencies in the data. We have developed a multichannel blind inversion and solved it for obtaining the AI model and the wavelet directly from seismic reflection data. We found a solution to the overall problem by alternating between two subproblems, corresponding to the AI and wavelet recovery. Having an estimation of the wavelet, the algorithm directly inverts multichannel data for a high-resolution AI model, having blocky structures in the sense of total variation (TV) regularization, while satisfying a priori low-frequency information. The advantages of the split Bregman technique and the discrete cosine transform are used to build a fast and efficient algorithm for solving the nonlinear impedance inversion with TV regularization. Having an estimate of the AI model, the wavelet is updated by restricting it to have a sparse representation in a wavelet transformation domain while predicting the observed data. Numerical tests using simulated 2D data obtained from the benchmark Marmousi model and also 2D field data confirm that the proposed algorithm stably generates accurate estimates of complex AI models and complicated wavelets, simultaneously.

Shallow seismic reflection study of a glaciated valley

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1395-1407 ◽  
Author(s):  
Frank Büker ◽  
Alan G. Green ◽  
Heinrich Horstmeyer
Keyword(s):  
Seismic Reflection ◽  
High Amplitude ◽  
Data Sets ◽  
Seismic Section ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Northern Switzerland ◽  
Reflection Data ◽  
Shallow Seismic ◽  
Glaciated Valley

Shallow seismic reflection data were recorded along two long (>1.6 km) intersecting profiles in the glaciated Suhre Valley of northern Switzerland. Appropriate choice of source and receiver parameters resulted in a high‐fold (36–48) data set with common midpoints every 1.25 m. As for many shallow seismic reflection data sets, upper portions of the shot gathers were contaminated with high‐amplitude, source‐generated noise (e.g., direct, refracted, guided, surface, and airwaves). Spectral balancing was effective in significantly increasing the strength of the reflected signals relative to the source‐generated noise, and application of carefully selected top mutes ensured guided phases were not misprocessed and misinterpreted as reflections. Resultant processed sections were characterized by distributions of distinct seismic reflection patterns or facies that were bounded by quasi‐continuous reflection zones. The uppermost reflection zone at 20 to 50 ms (∼15 to ∼40 m depth) originated from a boundary between glaciolacustrine clays/silts and underlying glacial sands/gravels (till) deposits. Of particular importance was the discovery that the deepest part of the valley floor appeared on the seismic section at traveltimes >180 ms (∼200 m), approximately twice as deep as expected. Constrained by information from boreholes adjacent to the profiles, the various seismic units were interpreted in terms of unconsolidated glacial, glaciofluvial, and glaciolacustrine sediments deposited during two principal phases of glaciation (Riss at >100 000 and Würm at ∼18 000 years before present).

Assessment of gas hydrate using pre-stack seismic inversion in Mahanadi Basin, offshore eastern India

2021 ◽  
pp. 1-42
Author(s):  
Maheswar Ojha ◽  
Ranjana Ghosh
Keyword(s):  
Gas Hydrate ◽  
Rock Physics ◽  
Type Equation ◽  
Seismic Inversion ◽  
Log Analysis ◽  
Well Log ◽  
Seismic Section ◽  
Bottom Simulating Reflector ◽  
Hydrate Saturation ◽  
Well Log Analysis

The Indian National Gas Hydrate Program Expedition-01 in 2006 has discovered gas hydrate in Mahanadi offshore basin along the eastern Indian margin. However, well log analysis, pressure core measurements and Infra-Red (IR) anomalies reveal that gas hydrates are distributed as disseminated within the fine-grained sediment, unlike massive gas hydrate deposits in the Krishna-Godavari basin. 2D multi-channel seismic section, which crosses the Holes NGHP-01-9A and 19B located at about 24 km apart shows a continuous bottom-simulating reflector (BSR) along it. We aim to investigate the prospect of gas hydrate accumulation in this area by integrating well log analysis and seismic methods with rock physics modeling. First, we estimate gas hydrate saturation at these two Holes from the observed impedance using the three-phase Biot-type equation (TPBE). Then we establish a linear relationship between gas hydrate saturation and impedance contrast with respect to the water-saturated sediment. Using this established relation and impedance obtained from pre-stack inversion of seismic data, we produce a 2D gas hydrate-distribution image over the entire seismic section. Gas hydrate saturation estimated from resistivity and sonic data at well locations varies within 0-15%, which agrees well with the available pressure core measurements at Hole 19. However, the 2D map of gas hydrate distribution obtained from our method shows maximum gas hydrate saturation is about 40% just above the BSR between the CDP (common depth point) 1450 and 2850. The presence of gas-charged sediments below the BSR is one of the reasons for the strong BSR observed in the seismic section, which is depicted as low impedance in the inverted impedance section. Closed sedimentary structures above the BSR are probably obstructing the movements of free-gas upslope, for which we do not see the presence of gas hydrate throughout the seismic section above the BSR.

scholarly journals Structural controls on the location, geometry and longevity of an intraplate volcanic system: the Tuatara Volcanic Field, Great South Basin, New Zealand

2020 ◽  
Vol 177 (5) ◽  
pp. 1039-1056
Author(s):  
Thomas B. Phillips ◽  
Craig Magee
Keyword(s):  
Volcanic Field ◽  
Auckland Volcanic Field ◽  
Seismic Section ◽  
Volcanic System ◽  
Seismic Reflection Data ◽  
Content Type ◽  
Reflection Data ◽  
3D Geometry ◽  
Plumbing Systems ◽  
Supplementary Material

Intraplate volcanism is widely distributed across the continents, but the controls on the 3D geometry and longevity of individual volcanic systems remain poorly understood. Geophysical data provide insights into magma plumbing systems, but, as a result of the relatively low resolution of these techniques, it is difficult to evaluate how magma transits highly heterogeneous continental interiors. We use borehole-constrained 2D seismic reflection data to characterize the 3D geometry of the Tuatara Volcanic Field located offshore New Zealand's South Island and investigate its relationship with the pre-existing structure. This c. 270 km2 field is dominated by a dome-shaped lava edifice, surrounded and overlain by c. 69 volcanoes and >70 sills emplaced over 40 myr from the Late Cretaceous to Early Eocene (c. 85–45 Ma). The Tuatara Volcanic Field is located above a basement terrane boundary represented by the Livingstone Fault; the recently active Auckland Volcanic Field is similarly located along-strike on North Island. We suggest that the Livingstone Fault controlled the location of the Tuatara Volcanic Field by producing relief at the base of the lithosphere, thereby focussing lithospheric detachment over c. 40 myr, and provided a pathway that facilitated the ascent of magma. We highlight how observations from ancient intraplate volcanic systems may inform our understanding of active intraplate volcanic systems, including the Auckland Volcanic Field.Supplementary material: Interpreted seismic section showing well control on stratigraphic interpretation is available at https://doi.org/10.6084/m9.figshare.c.5004464

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.

Reservoir Prediction Under Control of Sedimentary Facies

2017 ◽  
Vol 25 (03) ◽  
pp. 1750022
Author(s):  
Xiuwei Yang ◽  
Peimin Zhu
Keyword(s):  
Seismic Data ◽  
Low Frequency ◽  
Prediction Method ◽  
Rock Physics ◽  
Real Data ◽  
Sedimentary Facies ◽  
Seismic Inversion ◽  
Rock Properties ◽  
Reservoir Prediction ◽  
Frequency Model

Acoustic impedance (AI) from seismic inversion can indicate rock properties and can be used, when combined with rock physics, to predict reservoir parameters, such as porosity. Solutions to seismic inversion problem are almost nonunique due to the limited bandwidth of seismic data. Additional constraints from well log data and geology are needed to arrive at a reasonable solution. In this paper, sedimentary facies is used to reduce the uncertainty in inversion and rock physics modeling; the results not only agree with seismic data, but also conform to geology. A reservoir prediction method, which incorporates seismic data, well logs, rock physics and sedimentary facies, is proposed. AI was first derived by constrained sparse spike inversion (CSSI) using a sedimentary facies dependent low-frequency model, and then was transformed to reservoir parameters by sequential simulation, statistical rock physics and [Formula: see text]-model. Two numerical experiments using synthetic model and real data indicated that the sedimentary facies information may help to obtain a more reasonable prediction.

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