impact of the cdp gathers and pre-stack seismic inversion...

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J Geol Geosci Volume 1(1): 20181

ReseaRch aRticle

Journal of Geology and Geoscience

Impact of the CDP Gathers and Pre-Stack Seismic Inversion in the Prospect Evaluation, Onshore Nile Delta, EgyptAhmed S Abu El Ata¹, Mohamed G El Behiry², Mohamed I Hussein³*¹Prof. of Geophysics, Department of Geophysics, Faculty of Science, Ain Shams University, Cairo, Egypt ²Prof. of Geophysics, Department of Geophysics, Faculty of Science, Cairo University, Cairo, Egypt ³Ass. Lecturer of Geology, Department of Petroleum Engineering, Faculty of Engineering and Technology, Future University, Cairo, Egypt

AbstractThis study focuses on the role of the CDP (common depth point) gathers in the evaluation stage of the geological prospects using the AVO (amplitude versus offset) analysis of the 3D seismic data and the Pre-stack inversion to differentiate between both gas bearing and dry sands and to understand the reservoir configuration and its relation to the different amplitude response through analyzing two encountered similar amplitude response in two different locations but with different drilling results, although they have the same stratigraphic sequences and structural setting.

The results led to a high success of exploration ratio as the positives vastly outweigh the negatives. Several lessons have been learned from the negative results-which how to differentiate between the similar amplitude responses for two dry and gas discovery wells-by understanding the relation between the near traces (near offset) and the far traces (far offset) from the CDP gathers in order to reduce the amplitude anomalies to their right justification. Consequently, a variation in the reflectivity strength is observed, which is controlled by the elastic properties of the rocks: Compressional wave (Vp), Shear wave (Vs) and density.

These properties are affected by the lithology and fluid contents. The analysis of the CDP gathers and the inversion results help in validating the prospects before drilling and in determining the response of the seismic amplitude variation with the offset, so it is possible to confirm the amplitude anomaly if it is related to hydrocarbon or not.

Keywords: CDP Gathers; AVO Analysis; Seismic Inversion; Compressional Wave; Shear Wave

IntroductionThe Nile Delta has a complex stratigraphic and structural framework and affected by the Hinge Zone which separates between the southern and northern basins, the northern basin is considered a part of the Nile Delta region and contains thick Neogene sediments while the southern basin is considered a combination between the Western Desert and the Nile Delta areas [1].

The study area is one of the most promising areas for gas and oil approximately 130 km NNW of Cairo and considered a part of the unstable shelf structural regime of the Nile Delta basin (Figure1). The hydrocarbon potential of the Nile Delta is believed to be limited to the Neogene-Quaternary succession [2, 3]. As a result of the thick Neogene overburden, pre-Miocene sediments are rarely penetrated and their maturity and hydrocarbon potential are poorly known [4]. The Neogene-Quaternary sequence is separated to main three sedimentary successions: Miocene, Pliocene, and Holocene [1, 5-7]. The cycles are dominated by shales and sandstones and rest on a pre-Miocene succession which extends down to the Jurassic (Figure 2).

At El Mansoura concession, the Neogene succession consists of the Messinian sandstones and shales of the Qawasim

Correspondence to: Mohamed I Hussein, Ass. Lecturer of Geology, Department of Petroleum Engineering, Faculty of Engineering and Technology, Future University, Cairo, Egypt, Email: hessein8[AT]Hotmail[DOT]com, Tel: +201284332008

Received: Oct 09, 2018; Accepted: Oct 11, 2018; Published: Oct 15, 2018

formation which are unconformably overlain by Pliocene shales with minor sandstones of the Kafr El-Sheikh and El-Wastani formations. Above this are Pleistocene sand and claystones of the Mit Ghamr formation. The Qawasim formation [6] consists of poorly-sorted sandstones and conglomerates with thin clay intervals; the clastic material is reworked from the underlying Cretaceous and Eocene section.

There are three identified source rock intervals in the Nile Delta: Oligocene (Tineh), Lower Cretaceous (Alam El Buieb) and Mid Jurassic (Khatatba). All of these mentioned source rocks are good to fair, with total organic carbon content values more than 2% average. Kerogen type is predominantly terrigenous Type III but there is sufficient marine oil-prone kerogen in the richest sections of the source intervals to warrant the Type II/III kerogen. Oligocene Tineh formation source rock is restricted to the northern basin of Nile Delta province. It is considered as a potential source rock within the

Hussein MI (2018) Impact of the CDP Gathers and Pre-Stack Seismic Inversion in the Prospect Evaluation, Onshore Nile Delta, Egypt


Figure 1: Location map of the study area.

Figure 2: Nile Delta generalized stratigraphic sequence.



mature phase with maturity level (~ .7 to 1.3 Ro %) and in the phase of gas/oil generation and expulsion. In the concerned area, Qawasim formation sets unconformably underlain by the Sidi Salem formation (Middle Miocene) and is overlaid by the Kafr El-Sheikh formation (Lower Pliocene). Shales of the Kafr El-Sheikh formation provide an effective seal for the oil and gas trapped in the different sandstone reservoir.

Materials and MethodsFigure 3 highlights two striking seismic amplitude anomalies located in two different locations but have the same geologic and stratigraphic sequence. After drilling both anomalies, it has been found that the first well (to the left of Figure 3) was a gas well and the other one (to the right of Figure 3) was a dry well. So, why is there a difference in the results of the two wells even though the seismic character of the amplitude anomalies in the stacked sections are similar to a great extent although the two anomalies have the same geologic setting and stratigraphic sequence? This question cannot be answered before deep studying for the CDP gathers of both wells, which will be discussed in the next section in details. (Figure 3)

Before the analysis of the CDP gathers, it’s also worth to mention briefly summary of the acquisition parameters used in the area to delineate the 3D seismic data, as follows:

Spread Geometry

Spread: Symmetrical split-spread

Shooting Geometry: Brick Pattern, Orthogonal patch

Receiver Orientation: North-South

Source Orientation: East-West

Receiver Spacing: 50m

Receiver Line Spacing: 300m

Receiver Lines/Swath: 10

Active channels/Line: 180

Source Point Interval: 50m

Source Line Spacing: 500m, 250m stagger between adjacent swath (Brick Pattern)

Bin Size: 25m X 25m

Recording Parameters:

Recorder: I/O System II

Channel Recorded: 1800 data channels, 3 auxiliary channels

Record Length: 6 Sec.

Sample Rate: 2 ms

Low-Cut Filter: Out

Hi-Cut Filter: ¾ Nyquist, minimum Phase

Recording Gain: 48 dB.

Geophone Polarity: Normal SEG Standard-downward motion of Seismometer (Tap

On TOP results in positive Number on tape.

Receiver Parameters:

Geophone Type: SM - 4 with Spikes.

Natural Frequency: 10 Hz.

Receiver array: Linear, Inline

Geophones per Pattern: 24 - 2 strings of 12

Element Spacing: 2.0833 m. between Phones

Pattern length: 47.92 m.

Figure 3: Two different amplitude anomalies displayed in both seismic lines and amplitude maps.



Source Parameters:

Source: Dynamite

Hole Depth: 12m

Charge Per Hole: 2kg

Source Array: Single hole

The present research study of the CDP gathers covers the three main following subjects:

i. Wavelet and seismic tie

ii. AVO Theory, Classification and Processing

iii. AVO Reflectivity Attributes, Inversion and Interpretation

Wavelet and seismic tie

AVO modeling workflow starting with loading and checking the data of the Electric logs (GR, Sonic, Density, VSP), loading the seismic data (CDP gathers), wavelet extraction (in-house zero- phase 25 Hz wavelet was extracted from the

3D seismic data, gives much better matching results), and synthetic generation. In order to make a good AVO modeling and invert seismic data for rock properties, the seismic data need to be calibrated to the geology encountered in a well. This calibration process (often referred to as “making a well tie”) involves the comparison of a synthetic (or modeled) seismic trace with the real seismic (Figure 4).

If the calibration results give good correlation, then the seismic can be taken in terms of the geology. If the calibration results were bad, there will stay major uncertainty in the interpretation of seismic data.

AVO Theory, Classification and Processing

AVO Theory

When P-wave incident the boundary between two different rock properties on both sides, the energy of the ray is reflected and transmitted as P-waves and converted S-waves. The angle of incidence, the angle of reflection, and angle of transmission, together with P and S-wave velocities on both sides of the boundary follow Snell’s law (Figure 5):

Figure 4: Synthetic generation showing best matching results.

Figure 5: Reflection and transmission at an interface for an incident P-wave.



p is the ray parameter, θ1 is the incident/reflected P-wave angle, θ2 is the transmitted P-wave angle, 1and 2 are the reflected and transmitted S-wave angle correspondingly, α1 and β1 are the P- and S-wave velocities of medium 1, and α2 and β2 are the P and S-wave velocities for the second medium.

However, the complex mathematical expression developed by these authors, of plane-wave reflection coefficients makes it difficult to prove how reflection amplitude would change if a rock property is changed slightly. Simplifying Zoeppritz’s equation by [8-10] among others shed the light on how the amplitudes relate to the various physical parameters.

The Aki, Richards’s approximation is interesting because it is described in three terms, the first is P-wave velocity, the second is the density, and the third is the S-wave velocity. Their approximation can be described as follows:

where α is the two P-wave average velocities on the two sides of the boundary, β is the two S- wave average velocities on two sides of the boundary, is the average of the two densities on two sides of the reflector, and is the average of the angles of transmission and incidence of the P-wave Δα = α2 – α1, Δβ = β2 – β1, and Δρ =ρ2 – ρ1.

Ostrander [11] was the first who established the reflection coefficients of gas bearing sands can be contrasted in an anomalous way by increasing the offset. Since a number of P-wave AVO methods have been derived to use as a lithology and fluid analysis discrimination, including the gradient and intercept analysis [12] and the weighted stacking method [13]. Another presentation of the reflection coefficient of the Zoeppritz equations was achieved by Shuey where he changed the variables in equation (2) from β to α to show the variation in Poisson’s ratio. The new simplification of the Zoeppritz equations is:

Shuey observed that for small angles tan2θ − sin2θ ≈ 0 (equation 3) and suggested an approximation which is valid up to 30°. Therefore, this simplification includes all the relations between R pp (θ) and elastic properties. This simplification requires a fixed Poisson’s ratio, so a smooth velocity model is also required. Equation (4) can also be simplified to

Where Rp is the Normal Incidence Reflectivity (intercept) and G is often called the AVO gradient (slope) affected by larger

angles and can be obtained by performing a linear regression analysis on the seismic amplitudes.

Assumptions and limitations

-3rdterm is truncated and

-for angles ≤ 30 degrees

-change in gradient could indicate the change in fluid content, but could also be caused by a change in lithology.

AVO Classification

According to the AVO classification for gas sand by [14 & 15], there are four AVO classes for clastic rocks (Figure 6):

Class I gas bearing sands characterized by high impedance than the overlying layer. The intercept A is relatively large and positive, and the reflection magnitude decreases with the offset faster than the background trend {which are the result of summing the displacements related to the definite rock properties differences}.

The product of intercept A and gradient B is negative at the top. These sands lie in quadrant four of the intercept versus gradient cross-plot (Figure 7). Class II gas sands have nearly the same impedance as that of the overlying layer: the normal- incidence coefficient a value is lower than 0.02 in magnitude. The reflection magnitude may increase or decrease with offset and may reverse polarity, the product A*B is indeterminate. This type of sand is usually moderately compacted and consolidated, and can lie in any of quadrants two, three, or four. Class II sands may or may not correspond to amplitude anomalies on stacked data.

Class III gas sands have the lower impedance than that of the overlying unit (classical bright spots). The A value is negative and large, the reflection amplitude increases with offset,

Figure 6: Classification of AVO responses (Rutherford and Williams, 1989) with the addition of Class 4 by Castagna and Swan (1997) (plot modified by Feng and Bancroft, 2006).



the product A*B is positive at the top. Class IV gas sands characterized by lower impedance than the overlying unit. The A value is negative and large, but reflection amplitude decreases with the increasing offset, the product A*B is negative at the top.

Class IV gas bearing sands occur when porous sand is overlaid by a high-velocity layer, such as hard shale, siltstone, tightly cemented sand, or a carbonate. Since the AVO gradient from class IV brine sand may be almost identical to the gradient from class IV gas bearing sand, these gas sands may be problematic to detect by the conventional approach of comparing partial offset stacks.

AVO Processing

The purpose of data conditioning and processing seismic data for AVO analysis is to be able to extract the rock properties from the seismic data along with the structural image enhancement. To do an AVO analysis, the true amplitude (TAR) must be preserved for proper gradient change over a range of offsets. Therefore, extraordinary attention must be careful to preserve this variation to amplitude due to the variation of the lithology and fluid contents. Agreeing to [16], there are three significant processing steps:

1. The amplitudes of the seismic data must be preserved throughout the analysis to identify the variation of amplitude with the offset.

2. The broadband signal should be retained in the data with a flat spectrum.

3. Pre-stack amplitude inversion must be applied to common-depth-point (CDP) gather to obtain the AVO attributes.

The following steps summarize the processing sequence

applied to the 3D seismic data until generating the final stack and AVO attribute, as follows:

1. Reformat of field tapes.

2. Geometry assignment.

3. Geometry QC/Refraction statics analysis

4. Spherical divergence and amplitude compensation

5. Model-based noise attenuation

6. Deconvolution and Surface consistent gain

7. Velocity analysis every 2km

8. Surface consistent residual statics

9. Velocity analysis every 1km

10. Surface consistent residual statics

11. PSTM velocity analysis (every 0.5km)

12. Pre-Stack time migration

13. 4thorder residual velocity analysis

14. Final stacks and AVO attributes

Super gather (common offset stack)

The simple explanation of the common offset stack is to gather traces within a box which is well- defined by the range of the offset, and a CMP range or it is the procedure of creating average CDPs (the averaging was done by collecting the neighboring CDPs and add them together) to improve the signal to noise (S/N) ratio while maintaining the AVO amplitude information, at the same time, the offset dimension is preserved (Figure 8).

The risks included in the super gather are twofold:

a. Too many traces may be used in the CMP range, and therefore, smear over the structure.

b. Too many traces may be used with the offset range and therefore mislead the amplitude response. Therefore, it is very important to generate different common offset stacks before confirming the geometry to the model.

Transforming from the offset domain to the angle domainAs mentioned, both Zoeppritz and Shuey’s equations dependent on the angle of incidence at which the seismic ray strikes the horizon of interest. While offset and angle are similar, there is a nonlinear relationship between them, which must first be included in processing and analysis sequences which require using angle in place of offset. We called this type of transformation AVA (amplitude versus angle) quite than AVO. Figure 9(a) shows the offset gather while in Figure 9(b) shows the equivalent angle gather (Western Geophysical). At the top of each one is a diagram of the ray path geometry supposed for the reflected events of each gather. Still fixed with depth for a fixed angle trace. The conversion from a constant offset to the constant angle necessitates the identification of the relation between X and θ. For a complete solution, a full ray tracing must be done. (Figure 9a & 9b)

Figure 7: AVO intercept (A) versus gradient (B) crossplot showing four possible quadrants. For a limited time window, brine-saturated sandstones and shales tend to fall along a well-defined background trend. Top sand reflectors tend to fall below the background trend, whereas bottom gas-sand reflections tend to fall above the trend. (Castagna et al., 1998)



Where θ: angle of incidence, X: offset and Z: depth. If we know the velocity to the target layer, we can write:

Figure 8: Super gather (common offset stack) generated from the 3D seismic data

Figure 9: (a) shows AVO response and (b) shows transform of (a) in AVA (amplitude versus angle) (Western Geophysical)

Where V: velocity (RMS or average) and to: total zero offset travel time. Substituting equation (7) in (6) gives:

This gives the mapping from offset to angle. By inverting equation (8), we can get the mapping from angle to offset:



From equation (9) let us to draw the amplitude on the offset gather to amplitude on the angle gather. All these previous equations can apply to a single layer only. Another approximation can be used for the multi-layer involves the ray parameters p and total travel time t, where (Figure 10):

Angle gathers

In this process, each input sample is mapped to its corresponding incident angle. For this process, the super gather volume was used as an input. The velocity data necessary for this process can be derived from well data or from the stacking velocity. Another benefit that could be taken from the generation

of super and angle gathers is to plot the offset against the incidence angles shown in order to verify the limit of the far offset or far angles that can be trusted regarding the data quality (Figure 11).

Range limited stacks (far and near offsets)

Those are the CDP stacks averaged over specified offset or angle ranges. The input for this process is the super gather. This step has to do with dividing the data into both near and far angle stacks, as shown in (Figure 12). Therefore, the amplitude anomaly can be monitored in both stacks (near and far), hence helping in providing information about the amplitude behavior with offset. As a result, it is recommended to generate those stacks, using the velocity derived from the well data, after making a good seismic matching to well tie in order to make quality control for the output of the processing center.

AVO Reflectivity Attributes, Inversion and Interpretation

The AVO attributes represent the output, which can be obtained from the AVO analysis. The AVO response of a reflector is described by two parameters: the intercept or reflectivity (amplitude) at the zero-offset and the gradient of the amplitude variation with offset.

Figure 13 illustrates the AVO response derived from the intercept and gradient volumes. In this figure, the trace data shows the intercept, while the color display represents the product of the intercept and gradient, which indicates the AVO anomaly. By calibration of this AVO anomaly with both the gamma-ray log and the top pay of gas well, it has been found that there is compatibility between the AVO response,

Figure 10: Ray path geometry for a single shot-receiver pair in a constant velocity medium.

Figure 11: Angle gather generated from the 3D seismic data after transform offset to angle



Figure 12: Range limited stacks extracted from the own 3D seismic data (near and far angle stacks) to monitor the amplitude variation with different angles

Figure 13: Product of intercept and gradient (A*B) showing the AVO anomaly at the gas bearing sand



the sand reservoir and the top-pay zone of the well. Poisson’s ratio is defined as “the ratio between longitudinal strain and axial strain”. Figure 14 shows this attribute in which the trace data show the intercept, while the color data shows the scaled Poisson’s ratio change. Again, it has been found that there is compatibility between the AVO response, the sand reservoir and the top pay for the gas well. (Figure 13&14)

Cross plot

The latest step in the cross plots analysis was carried out by [17, 18]. One of the challenges with the cross plots was that it is hard to know where the points are coming from on the cross plots. Of course, you could color code them. Cross plots work for a single trace however, it will be hard for the seismic data in which the window is defined in both CDP and time. The principle of Verm and Hilterman’s is to determine the anomalous values on the cross plot and then redisplay these points on the seismic section. Bear in mind that they use a slightly altered form of equation (1):

where: RP: Normal Incidence Reflectivity and :Poisson ratio

A more practical approach that has been done is to cross-plot the intercept and gradient for the all-time samples at all trace locations within an aerial window. This has the significant advantage of providing the ability to consider more than just the sample of the seismic event that has been picked. Information about an interface is contained in the whole wavelet, not just the peak or trough. Deviation from this may be a hydrocarbon indicator. The intercept and gradient pairs move more away

from the background trend with a decrease in the fluid density so that gas sands will be the most well-separated (Figure 15).

The degree of shift is/is to be controlled by the stiffness of the rock, its porosity, and its fluid content as well as the AVO interpretation using this technique which was done in this study by:

1. Defining the background trend around the origin (yellow color);

2. The two points, which lie outside this trend, and which have been highlighted (blue indicates top-gas zone and grey indicates base-gas zone; and

3. These anomalies which are dropped to the seismic trace and calibrated with the well results (Figure 16).

Pre-Stack Inversion (AVO inversion)

The seismic inversion means determining how we can obtain the physical characteristics of rocks and fluids from the seismic record i.e., converting a processed and noisy seismic trace to a sonic or density log is the reverse of converting these two logs to a synthetic seismogram, so the name “inversion” (Figure 17).

Conventionally, the inversion has been applied to the post-stack seismic data in order to extract the acoustic impedance cubes. In recent times, inversion can be applied to the pre-stack seismic data to extract both the shear and the acoustic impedance cubes. This type of inversion can be identified as elastic impedance (Figure 18).

The workflow is summarized in (Figure 19). After estimating of Rp and Rs from the AVO analysis, as discussed before, we

Figure 14: Change in Poisson’s ratio indicates AVO anomaly confirm the A*B response



Figure 15: Cross plot for the intercept vs. gradient

Figure 16: Overlying different fluid zones with the seismic data



can proceed to invert Rp, which will give the p-wave acoustic impedance, Zp = pVp, and inverting Rs will give the S- wave impedance, Zs = pVs. In the Pre-stack inversion, we analyze the fully processed CDP gathers to generate volumes of Zp, Zs and density cubes (Figure 20).

DiscussionThe results of this research study and after thorough investigation and analysis of the seismic CDP gathers of the two drilled wells, it has been concluded that there is a large

difference in the results of the two wells, even though the seismic character of the amplitude anomalies in the stacked sections are similar to a great extent and have the same structure and stratigraphic sequence.

The authors have found out that the successful well (to the left of Figure 21) has a full offset coverage. In other words, the near offset is completely recorded as well as the far offset. Therefore, in this case, there is a true amplitude anomaly, easily recognized by a full offset coverage. Also, the variation of seismic amplitude with the offset can be observed (i.e. the

Figure 17: Acoustic impedance inversion workflow

Figure 18: Elastic impedance inversion workflow

Figure 19: workflow Chart for the inversion procedures



increase of gradient with offset, while this amplitude appears in the near traces and increases gradually to the far traces), which may reveal a hydrocarbon fluid effect. On the other hand, the CDP gather of the dry well (to the right of Figure 21), the complete absence of the amplitude response in the near offset can be noticed and it’s starting from the mid offset,

Figure 20: Final output for the inversion (Acoustic Imped. P-wave, S-wave and density cubes).

Figure 21: CDP gathers for both gas and dry wells.

causing the pseudo bright anomaly confirmed with the failure drilling well results (Figure 21).

In other cases, it can be found that the near offset does not exist at all (Figure 22). This is mainly due to some surface obstacles in the acquisition as an example in the urban areas or agriculture areas, especially when the acquisition commenced in a highly



Figure 22: CDP gather shows the missing of near traces.

Figure 23: Arbitrary line between the proposed locations of two gas wells from the inverted cube.



cultivated, mountains and populated areas, hence, it cannot be considered at all. Therefore, studying the CDP gathers in near and far offsets and monitoring the amplitude variation with different angles should be taken in consideration in the prospect evaluation stage (helps in validating the prospects before drilling) and in determining the response of the seismic amplitude variation with the offset, so it is possible to confirm the amplitude anomaly if it is related to hydrocarbon or not (i.e, delineation less risky prospects).

Concerning the results of the inversion, by determining the density, the fizz water problem can be solved. Figure 23 below illustrates the final P-wave inversion result where the low P-wave impedance only represents the gas sand. The results of the inversion around a certain prospect can be tolerated with the closest wells in the area (Figure 23).

Also, the results of the pre-stack inversion can help in the evaluation

Figure 24: Vertical sections from the inverted seismic data cube.

of the reservoir quality (Figure 24). Accordingly, there are three cases, the first of which is the upper one for a non-economic gas well where the slight decrease in the acoustic impedance can be seen, due to the weak contrast between the overlying shale and the gas sand, which may indicate a bad reservoir quality in this well. Second is the middle case for a gas well where a sharp contrast between the shale and the gas sand can be seen, which is confirmed by the sharp decrease in acoustic impedance in the pay zone. The third is the lower case for a prospect which resembles, to a great extent, the middle case. Therefore, from this display, the reservoir quality in gas wells can be evaluated which will also help in the prospect evaluation.

ConclusionsThe pre-stack modeling (single interface modeling, single CDP gather modeling, 2D stratigraphic modeling, and 2D elastic wave equation modeling) allows the interpreter to:



-Understand the seismic signature due to the wave propagation.

-Define the reservoir rock physical properties.

-Integrate seismic, well logs, lab testing, and VSP information to verify the reservoir conditions.

Pre-stack modeling is effective to:

-Predict the usefulness of the AVO analysis before investing time and effort in acquiring seismic

-Examine the seismic response due to lithology’s physical properties such as porosity, fluid content, and reservoir and pay thickness.

-Substitute the pore fluid and model the seismic response.

-Vary the reservoir properties and model the seismic response.

-Explore the uniqueness of possible seismic interpretation. -Evaluate the exploration potential recognize the exploration risk.

-Process the synthetic gather to extract attributes to understand which may be useful. -Investigate the sensitivity of seismic to noise, bandwidth, processing, acquisition parameters, and geology.

-Investigate the effects of acquisition parameters.

Therefore, studying the CDP gathers, helps in validating the prospects and in determining the response of the seismic amplitude variation with the offset, so it is possible to confirm the amplitude anomaly if it is related to hydrocarbon presence or not.

AcknowledgmentThe authors would like to extend their sincere appreciation and great thanks to the Egyptian Natural Gas Holding Company (EGAS), Petroceltic-International Plc and El Mansoura Petroleum Company for all their support during the production of this work.

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Citation: El Ata ASA, El Behiry MG, Hussein MI (2018) Impact of the CDP Gathers and Pre-Stack Seismic Inversion in the Prospect Evaluation, Onshore Nile Delta, Egypt. J Geol Geosci 1: 001-003.

Copyright: © 2018 Mohamed I Hussein et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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