Impact of phase rotation on reservoir characterization and implementation of seismic well tie technique for calibration offshore Nile Delta, Egypt

2020 ◽  
Vol 39 (5) ◽  
pp. 346-352
Author(s):  
Mohamed G. El-Behiry ◽  
Mohamed S. Al Araby ◽  
Ramy Z. Ragab
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Wavelet ◽  
Well Location ◽  
Formation Behavior ◽  
Dynamic Components ◽  
Well Data ◽  
Phase Amplitude ◽  
Seismic Anomalies

Seismic wavelets are dynamic components that result in a seismic trace when convolved with reflectivity series. The seismic wavelet is described by three components: amplitude, frequency, and phase. Amplitude and frequency are considered static because they mainly affect the appearance of a seismic event. Phase can have a large effect on seismic appearance by changing the way it describes the subsurface. Knowing the wavelet properties of certain seismic data facilitates the process of interpretation by providing an understanding of the appearance of regional geologic markers and hydrocarbon-bearing formation behavior. The process through which seismic data wavelets are understood is called seismic well tie. Seismic well tie is the first step in calibrating seismic data in terms of polarity and phase. It ensures that the seismic data are descriptive to regional markers, well markers, and discoveries (if they exist). The step connects well data to seismic data to ensure that the seismic correctly describes well results at the well location. It then extends the understanding of seismic behavior to the rest of the area covered by the seismic data. Good seismic well tie will greatly reduce uncertainties accompanying seismic interpretation. One important outcome of the seismic well tie process is understanding the phase of seismic data, which affects how seismic data will reflect a known geologic marker or hydrocarbon-bearing zone. This understanding can be useful in quantifying discoveries attached to seismic anomalies and extending knowledge from the well location to the rest of the area covered by seismic data.

Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt

2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar
Keyword(s):  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Connectivity Analysis ◽  
Gas Field ◽  
Geostatistical Inversion ◽  
Well Data ◽  
Reservoir Connectivity ◽  
Inversion Techniques

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.

Seismic-to-well tie of a field of the Nigerian Delta

2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed
Keyword(s):  
Seismic Data ◽  
Processing Parameters ◽  
Seismic Inversion ◽  
Seismic Wavelet ◽  
Statistical Measures ◽  
Convolution Model ◽  
Data Driven Approach ◽  
Well Data ◽  
Seismic Reflectors ◽  
Matching Techniques

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,

High-resolution reservoir characterization by an acoustic impedance inversion of a Tertiary deltaic clinoform system in the North Sea

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. O57-O67 ◽  
Author(s):  
Daria Tetyukhina ◽  
Lucas J. van Vliet ◽  
Stefan M. Luthi ◽  
Kees Wapenaar
Keyword(s):  
North Sea ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Structural Information ◽  
Sampling Rate ◽  
Seismic Inversion ◽  
Acoustic Properties ◽  
The North ◽  
The North Sea ◽  
Well Data

Fluvio-deltaic sedimentary systems are of great interest for explorationists because they can form prolific hydrocarbon plays. However, they are also among the most complex and heterogeneous ones encountered in the subsurface, and potential reservoir units are often close to or below seismic resolution. For seismic inversion, it is therefore important to integrate the seismic data with higher resolution constraints obtained from well logs, whereby not only the acoustic properties are used but also the detailed layering characteristics. We have applied two inversion approaches for poststack, time-migrated seismic data to a clinoform sequence in the North Sea. Both methods are recursive trace-based techniques that use well data as a priori constraints but differ in the way they incorporate structural information. One method uses a discrete layer model from the well that is propagated laterally along the clinoform layers, which are modeled as sigmoids. The second method uses a constant sampling rate from the well data and uses horizontal and vertical regularization parameters for lateral propagation. The first method has a low level of parameterization embedded in a geologic framework and is computationally fast. The second method has a much higher degree of parameterization but is flexible enough to detect deviations in the geologic settings of the reservoir; however, there is no explicit geologic significance and the method is computationally much less efficient. Forward seismic modeling of the two inversion results indicates a good match of both methods with the actual seismic data.

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.

Pre-Messinian Petroleum Systems and Trap Style in the Offshore Western of Nile Delta; An Integrated Geological and Geophysical Approach

2016 ◽  
Author(s):  
Mostafa Monir ◽  
Omar Shenkar
Keyword(s):  
Seismic Data ◽  
Nile Delta ◽  
Lower Pressure ◽  
Hydrocarbon Exploration ◽  
Source Rocks ◽  
Geochemical Data ◽  
Peak Oil ◽  
West Nile ◽  
Petroleum Systems ◽  
Well Data

ABSTRACT Exploration in the offshore Nile Delta province has revealed several hydrocarbon plays. Deep marine Turbidites is considered one of the most important plays for hydrocarbon exploration in the Nile Delta. These turbidites vary from submarine turbidite channels to submarine basin floor fans. An integrated exploration approach was applied for a selected area within West Delta Deep Marine (WDDM) Concession offshore western Nile Delta using a variety of geophysical, geological and geochemical data to assess the prospectivity of the Pre-Messinian sequences. This paper relies on the integration of several seismic data sets for a new detailed interpretation and characterization of the sub-Messinian structure and stratigraphy based on regional correlation of seismic markers and honoured the well data. The interpretation focused mainly on the Oligocene and Miocene mega-sequences. The seismic expression of stratigraphic sequences shows a variety of turbidite channel/canyon systems having examples from West Nile delta basin discoveries and failures. The approach is seismically based focusing on seismic stratigraphic analysis, combination of structure and stratigraphic traps and channels interpretation. Linking the geological and geophysical data together enabled the generation of different sets of geological models to reflect the spatial distribution of the reservoir units. The variety of tectonic styles and depositional patterns in the West Nile delta provide favourable trapping conditions for hydrocarbon generations and accumulations. The shallow oil and gas discoveries in the Pliocene sands and the high-grade oils in the Oligo-Miocene and Mesozoic reservoirs indicate the presence of multiple source rocks and an appropriate conditions for hydrocarbon accumulations in both biogenic and thermogenic petroleum systems. The presence of multi-overpressurized intervals in the Pliocene and Oligo-Miocene Nile delta stratigraphic column increase the depth oil window and the peak oil generation due to decrease of the effective stress. Fluids have the tendency to migrate from high pressure zones toward a lower pressure zones, either laterally or vertically. Also, hydrocarbons might migrate downward if there is a lower pressure in the deeper layers. Well data and the available geochemical database have been integrated with the interpreted seismic data to identify potential areas of future prospectivity in the study area.

Estimation of the depth-domain seismic wavelet based on velocity substitution and a generalized seismic wavelet model

Geophysics ◽  
2021 ◽  
pp. 1-50
Author(s):  
Jie Zhang ◽  
Xuehua Chen ◽  
Wei Jiang ◽  
Yunfei Liu ◽  
He Xu
Keyword(s):  
Least Squares ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Regularization Parameter ◽  
Estimation Method ◽  
Model Parameters ◽  
Wavelet Estimation ◽  
Seismic Wavelet ◽  
Hydrocarbon Reservoir ◽  
Convolution Model

Depth-domain seismic wavelet estimation is the essential foundation for depth-imaged data inversion, which is increasingly used for hydrocarbon reservoir characterization in geophysical prospecting. The seismic wavelet in the depth domain stretches with the medium velocity increase and compresses with the medium velocity decrease. The commonly used convolution model cannot be directly used to estimate depth-domain seismic wavelets due to velocity-dependent wavelet variations. We develop a separate parameter estimation method for estimating depth-domain seismic wavelets from poststack depth-domain seismic and well log data. This method is based on the velocity substitution and depth-domain generalized seismic wavelet model defined by the fractional derivative and reference wavenumber. Velocity substitution allows wavelet estimation with the convolution model in the constant-velocity depth domain. The depth-domain generalized seismic wavelet model allows for a simple workflow that estimates the depth-domain wavelet by estimating two wavelet model parameters. Additionally, this simple workflow does not need to perform searches for the optimal regularization parameter and wavelet length, which are time-consuming in least-squares-based methods. The limited numerical search ranges of the two wavelet model parameters can easily be calculated using the constant phase and peak wavenumber of the depth-domain seismic data. Our method is verified using synthetic and real seismic data and further compared with least-squares-based methods. The results indicate that the proposed method is effective and stable even for data with a low S/N.

scholarly journals Application of Artificial Neural Network in seismic reservoir characterization: a case study from Offshore Nile Delta

2021 ◽  
Author(s):  
Adel Othman ◽  
Mohamed Fathy ◽  
Islam A. Mohamed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Seismic Data ◽  
Reservoir Characterization ◽  
Nile Delta ◽  
Reservoir Properties ◽  
Seismic Amplitude ◽  
Inversion Methods ◽  
Amplitude Data ◽  
Seismic Reservoir

AbstractThe Prediction of the reservoir characteristics from seismic amplitude data is a main challenge. Especially in the Nile Delta Basin, where the subsurface geology is complex and the reservoirs are highly heterogeneous. Modern seismic reservoir characterization methodologies are spanning around attributes analysis, deterministic and stochastic inversion methods, Amplitude Variation with Offset (AVO) interpretations, and stack rotations. These methodologies proved good outcomes in detecting the gas sand reservoirs and quantifying the reservoir properties. However, when the pre-stack seismic data is not available, most of the AVO-related inversion methods cannot be implemented. Moreover, there is no direct link between the seismic amplitude data and most of the reservoir properties, such as hydrocarbon saturation, many assumptions are imbedded and the results are questionable. Application of Artificial Neural Network (ANN) algorithms to predict the reservoir characteristics is a new emerging trend. The main advantage of the ANN algorithm over the other seismic reservoir characterization methodologies is the ability to build nonlinear relationships between the petrophysical logs and seismic data. Hence, it can be used to predict various reservoir properties in a 3D space with a reasonable amount of accuracy. We implemented the ANN method on the Sequoia gas field, Offshore Nile Delta, to predict the reservoir petrophysical properties from the seismic amplitude data. The chosen algorithm was the Probabilistic Neural Network (PNN). One well was kept apart from the analysis and used later as blind quality control to test the results.

On the seismic wavelet estimative and reflectivity recovering based on linear inversion: Well-to-seismic tie on a real data set from Viking Graben, North Sea

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. D157-D165
Author(s):  
Isadora A. S. de Macedo ◽  
José Jadsom S. de Figueiredo
Keyword(s):  
Reservoir Characterization ◽  
Gamma Ray ◽  
Real Data ◽  
Estimation Methods ◽  
Linear Inversion ◽  
Seismic Section ◽  
Data Set ◽  
Well Location ◽  
Well Data ◽  
Depth Relationship

Tying seismic data to well data is critical in reservoir characterization. In general, the main factors controlling a successful seismic well tie are an accurate time-depth relationship and a coherent wavelet estimate. Wavelet estimation methods are divided into two major groups: statistical and deterministic. Deterministic methods are based on using the seismic trace and the well data to estimate the wavelet. Statistical methods use only the seismic trace and generally require assumptions about the wavelet’s phase or a random process reflectivity series. We have compared the estimation of the wavelet for seismic well tie purposes through least-squares minimization and zero-order quadratic regularization with the results obtained from homomorphic deconvolution. Both methods make no assumption regarding the wavelet’s phase or the reflectivity. The best-estimated wavelet is used as the input to sparse-spike deconvolution to recover the reflectivity near the well location. The results show that the wavelets estimated from both deconvolutions are similar, which builds our confidence in their accuracy. The reflectivity of the seismic section is recovered according to known stratigraphic markers (from gamma-ray logs) present in the real data set from the Viking Graben field, Norway.

Quantitative uncertainty evaluation of seismic facies classification: A case study from northeast China

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. B87-B99 ◽  
Author(s):  
Cheng Yuan ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Ying Rao
Keyword(s):  
Seismic Data ◽  
Northeast China ◽  
Reservoir Characterization ◽  
Early Stage ◽  
Uncertainty Propagation ◽  
Seismic Facies ◽  
Uncertainty Evaluation ◽  
Computational Time ◽  
Facies Classification ◽  
Well Data

Reservoir characterization in the early stage of oilfield exploration generally has enormous uncertainty because few geophysical and well data are typically available. The uncertainty when classifying the facies with seismic data propagates throughout the processes of seismic facies classification, causing errors in the final evaluation of geologic features in an area. To quantitatively evaluate the uncertainty in seismic facies classification, we have analyzed prestack seismic data and well observations in a tight reservoir from northeast China and calculated the uncertainties throughout the process. To achieve this, the facies probabilities conditioned on different properties in each step of seismic facies classification were first derived using a probabilistic multistep inversion. Second, the associated uncertainty and maximum a posterior (MAP) of facies probabilities were evaluated by means of entropy and reconstruction rate, which assessed the degree of similarity between MAP and facies sequence within the range [0, 1]. This enabled us to investigate the influence of the uncertainty propagation on seismic facies classification. The uncertainty of the inversion results for the target reservoir was finally characterized by the calculated entropy and its indicator transform. Additionally, parameter spaces of well-log and upscaled elastic properties were restricted according to the data distribution characteristics in the crossplot. Parameter vectors that were outside the restricted scopes were excluded, reducing the computational time and uncertainty. We determined that quantitative uncertainty evaluation by entropy with a probabilistic multistep approach enabled us to explore much more details of the uncertainty propagation in the processes of seismic reservoir characterization. It should be the method of choice for risk of management and decision making in reservoir assessment.

scholarly journals STUDI KOMPARASI METODE MIGRASI SEISMIK DALAM MENGKARAKTERISASI RESERVOIR MIGAS DI BLOK KANGEAN, LAUT BALI MENGGUNAKAN INVERSI IMPEDANSI AKUSTIK BERBASIS MODEL

2019 ◽  
Vol 11 (1) ◽  
pp. 205-219
Author(s):  
Aditya P. Sidiq ◽  
Henry M. Manik ◽  
Tumpal B. Nainggolan
Keyword(s):  
Seismic Data ◽  
Reservoir Characterization ◽  
Inversion Method ◽  
Seismic Migration ◽  
Kirchhoff Migration ◽  
Model Based ◽  
Seismic Image ◽  
Diffracted Waves ◽  
Regression Error ◽  
Well Data

ABSTRAK Karakterisasi reservoir menjadi penting dalam tahapan eksplorasi minyak dan gas bumi. Salah satu hal yang dibutuhkan untuk mencapai keakuratan dalam mengkarakterisasi reservoir adalah penampang seismik yang sesuai dengan penampang aslinya. Struktur lapisan bumi yang kompleks mengakibatkan gelombang terdifraksi, sehingga penampang seismik mengalami pembelokan dari posisi sebenarnya. Penelitian ini menerapkan metode migrasi seismik Kirchhoff dan Stolt (F-K) untuk mengembalikan posisi reflektor pada waktu dan kedalaman yang sebenarnya pada data seismik 2D di Perairan Utara Bali. Data seismik diintegrasikan dengan data sumur APS-1 sebagai kontrol untuk diinversikan dengan teknik inversi berbasis model sehingga dapat mengkarakterisasi reservoir.  Penelitian ini bertujuan membandingkan hasil migrasi seismik yaitu migrasi Stolt dan migrasi Kirchhoff untuk diinversikan menggunakan metode inversi berbasis model sehingga dapat diketahui sejauh mana kualitas data seismik mempengaruhi proses karakterisasi reservoir. Nilai korelasi dari hasil analisis regresi antara log impedansi inversi dengan log impedansi data sumur pada migrasi Kirchhoff sebesar 0,739 dan galat regresi sebesar 873,54, sedangkan pada migrasi Stolt memiliki nilai korelasi sebesar 0,698 dan nilai galat sebesar 1236,17. Hal ini menunjukkan bahwa migrasi Kirchhoff lebih baik dari migrasi Stolt baik secara kualitatif maupun kuantitatif dalam mengkarakterisasi reservoir hidrokarbon. ABSTRACTReservoir characterization is an important method in gas and oil exploration. In order to obtain accuracy for defining reservoir, required seismic image that similar to the actual seismic image. The complexity of earth structure could cause diffracted waves, therefore, seismic image was diffracted from its actual position. This study applies Kirchhoff and Stolt (F-K) seismic migration methods to restore the position of the reflector at the actual time and depth  seismic data in North Bali. Seismic data is integrated with APS-1 well data as controls to be converted with model-based inversion techniques so as to characterize the reservoir. This study aims to compare the results of seismic migration namely Stolt and Kirchhoff migration to be converted using a model-based inversion method so that it can be seen to what extent the quality of seismic data influences the reservoir characterization process. Correlation value from the results of regression analysis between inversion log impedance and well impedance log data in Kirchhoff migration is 0.739 and regression error is 873.54, while the Stolt migration has a correlation value of 0.698 and an error value of 1236.17. This shows that Kirchhoff's migration is better than Stolt migration both qualitatively and quantitatively in characterizing hydrocarbon reservoirs.

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