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.

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.

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>

Low velocity layer characterization in the Niger Delta: Implications for seismic reflection data quality

2017 ◽  
Vol 90 (2) ◽  
pp. 187-195
Author(s):  
A. I. Opara ◽  
C. C. Agoha ◽  
C. N. Okereke ◽  
U. P. Adiela ◽  
C. N. Onwubuariri ◽  
...  
Keyword(s):  
Data Quality ◽  
Niger Delta ◽  
Seismic Reflection ◽  
Low Velocity ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Low Velocity Layer ◽  
Velocity Layer

Accuracy of wavelets, seismic inversion, and thin-bed resolution

2017 ◽  
Vol 5 (4) ◽  
pp. T523-T530
Author(s):  
Ehsan Zabihi Naeini ◽  
Mark Sams
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Low Frequency ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Frequency Content ◽  
Frequency Model ◽  
North West ◽  
The North ◽  
Well Data

Broadband reprocessed seismic data from the North West Shelf of Australia were inverted using wavelets estimated with a conventional approach. The inversion method applied was a facies-based inversion, in which the low-frequency model is a product of the inversion process itself, constrained by facies-dependent input trends, the resultant facies distribution, and the match to the seismic. The results identified the presence of a gas reservoir that had recently been confirmed through drilling. The reservoir is thin, with up to 15 ms of maximum thickness. The bandwidth of the seismic data is approximately 5–70 Hz, and the well data used to extract the wavelet used in the inversion are only 400 ms long. As such, there was little control on the lowest frequencies of the wavelet. Different wavelets were subsequently estimated using a variety of new techniques that attempt to address the limitations of short well-log segments and low-frequency seismic. The revised inversion showed greater gas-sand continuity and an extension of the reservoir at one flank. Noise-free synthetic examples indicate that thin-bed delineation can depend on the accuracy of the low-frequency content of the wavelets used for inversion. Underestimation of the low-frequency contents can result in missing thin beds, whereas underestimation of high frequencies can introduce false thin beds. Therefore, it is very important to correctly capture the full frequency content of the seismic data in terms of the amplitude and phase spectra of the estimated wavelets, which subsequently leads to a more accurate thin-bed reservoir characterization through inversion.

Band-limited scattered wavefield reconstruction beneath complex overburdens using the Marchenko method

2021 ◽  
Author(s):  
David Vargas ◽  
Ivan Vasconcelos ◽  
Matteo Ravasi
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Accurate Estimation ◽  
Target Area ◽  
Complex Media ◽  
Depth Level ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Band Limited ◽  
Internal Multiples

<p>Structural imaging beneath complex overburdens, such as sub-salt or sub-basalt, typically characterized by high-impedance contrasts represents a major challenge for state-of-the-art seismic methods. Reconstructing complex geological structures in the vicinity of and below salt bodies is challenging not only due to uneven, single-sided illumination of the target area but also because of the imperfect removal of surface and internal multiples from the recorded data, as required by traditional migration algorithms. In such tectonic setups, most of the downgoing seismic wavefield is reflected toward the surface when interacting with the overburden's top layer. Similarly, the sub-salt upcoming energy is backscattered at the salt's base. Consequently, the actual energy illuminating the sub-salt reflectors, recorded at the surface, is around the noise level. In diapiric trap systems, conventional seismic extrapolation techniques do not guarantee sufficient quality to reduce exploration and production risks; likewise, seismic-based reservoir characterization and monitoring are also compromised. In this regard, accurate wavefield extrapolation techniques based on the Marchenko method may open up new ways to exploit seismic data.</p><p>The Marchenko redatuming technique retrieves reliable full-wavefield information in the presence of geologic intrusions, which can be subsequently used to produce artefact-free images by naturally including all orders of multiples present in seismic reflection data. To achieve such a goal, the method relies on the estimation of focusing operators allowing the synthesis of virtual surveys at a given depth level. Still, current Marchenko implementations do not fully incorporate available subsurface models with sharp contrasts, due to the requirements regarding the initialization of the focusing functions. Most importantly, in complex media, even a fairly accurate estimation of a direct wave as a proxy for the required initial focusing functions may not be enough to guarantee sufficiently accurate wavefield reconstruction.</p><p>In this talk, we will discuss a scattering-based Marchenko redatuming framework which improves the redatuming of seismic surface data in highly complex media when compared to other Marchenko-based schemes. This extended version is designed to accommodate for band-limited, multi-component, and possibly unevenly sampled seismic data, which contain both free-surface and internal multiples, whilst requiring minimum pre-processing steps. The performance of our scattering Marchenko method will be evaluated using a comprehensive set of numerical tests on a complex 2D subsalt model.</p>

Data-driven multichannel seismic impedance inversion with anisotropic total variation regularization

2018 ◽  
Vol 26 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Dehua Wang ◽  
Jinghuai Gao ◽  
Hongan Zhou
Keyword(s):  
Total Variation ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Data Driven ◽  
Total Variation Regularization ◽  
Split Bregman ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Impedance Inversion ◽  
Anisotropic Total Variation

AbstractAcoustic impedance (AI) inversion is a desirable tool to extract rock-physical properties from recorded seismic data. It plays an important role in seismic interpretation and reservoir characterization. When one of recursive inversion schemes is employed to obtain the AI, the spatial coherency of the estimated reflectivity section may be damaged through the trace-by-trace processing. Meanwhile, the results are sensitive to noise in the data or inaccuracies in the generated reflectivity function. To overcome the above disadvantages, in this paper, we propose a data-driven inversion scheme to directly invert the AI from seismic reflection data. We first explain in principle that the anisotropic total variation (ATV) regularization is more suitable for inverting the impedance with sharp interfaces than the total variation (TV) regularization, and then establish the nonlinear objective function of the AI model by using anisotropic total variation (ATV) regularization. Next, we solve the nonlinear impedance inversion problem via the alternating split Bregman iterative algorithm. Finally, we illustrate the performance of the proposed method and its robustness to noise with synthetic and real seismic data examples by comparing with the conventional methods.

Depth-domain seismic reflectivity inversion with compressed sensing technique

2017 ◽  
Vol 5 (1) ◽  
pp. T1-T9 ◽  
Author(s):  
Rui Zhang ◽  
Kui Zhang ◽  
Jude E. Alekhue
Keyword(s):  
Compressed Sensing ◽  
Time Domain ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Synthetic Data ◽  
Seismic Inversion ◽  
Inversion Method ◽  
Depth Migration ◽  
Band Limited ◽  
The Time Domain

More and more seismic surveys produce 3D seismic images in the depth domain by using prestack depth migration methods, which can present a direct subsurface structure in the depth domain rather than in the time domain. This leads to the increasing need for applications of seismic inversion on the depth-imaged seismic data for reservoir characterization. To address this issue, we have developed a depth-domain seismic inversion method by using the compressed sensing technique with output of reflectivity and band-limited impedance without conversion to the time domain. The formulations of the seismic inversion in the depth domain are similar to time-domain methods, but they implement all the elements in depth domain, for example, a depth-domain seismic well tie. The developed method was first tested on synthetic data, showing great improvement of the resolution on inverted reflectivity. We later applied the method on a depth-migrated field data with well-log data validated, showing a great fit between them and also improved resolution on the inversion results, which demonstrates the feasibility and reliability of the proposed method on depth-domain seismic data.

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