INTRODUCTION
Seismic acquisition since its early days had and continues to have a substantial impact on hydrocarbon exploration and field development [1]. Accordingly, acquisition design could be considered the most vital factor in meeting project objectives by utilizing sound hypotheses and state-of-the-art technology to deliver the expected product, on time and within budget.
The rudimentary supposition of designing a 3D survey based on a single plane layer over a half space [2] with a suitable bin size. The bin is a square or rectangular area, which contains all midpoints that correspond to the same CMP while bin size corresponds to the
length and to the width of the bin [3]. These are the most critical parameters in a 3D survey design, which determines the sampled maximum wavenumber and subsequently the spatial aliasing threshold [4,5].
The bin size is computed as:
Δx ≤ v
4 f sin θ (1)
Where v is the average velocity up to the target depth, f is the dominant frequency at the target depth and θ is the dip of the target [6]. Basically, (1) infers that the bin size should not be larger than one-fourth of the dominant wavenumber. Theoretically, only higher- than-dominant frequencies are at risk of spatial
INNOVATIVE WORKFLOW FOR SEISMIC ACQUISITION DESIGN
Mohamed Mahgoub1, Deva Prasad Ghosh2, Abdul Halim Abdul Latiff3, Fernando A Neves4
1,4 Abu Dhabi National Oil Company (ADNOC), Abu Dhabi, UAE.
2, 3 Department of Geosciences,
Universiti Teknologi PETRONAS Email: [email protected]
ABSTRACT
Any new survey geometry design must be based on thorough integrated analysis and modeling of project goals and objectives, exploration and/or field development targets’ requirements, available technologies, operational constraints, terrain conditions and project budgets. A critical matrix for a good quality seismic acquisition geometry design is dense and uniform distribution of seismic traces in areal extent and within CDP, bins with sufficiently high trace density, high fold, wide azimuth, long inline/crossline/maximum offsets and small largest minimum offset. That said, operational considerations could be a hindrance for such quality as in heavily congested fields, extensive network of wells, platforms, production facilities and terrain obstacles leading to gaps in coverage and severe irregularities of fold, offset and azimuth distributions. This work demonstrates the essential value of using all available data and information about the project area. Such as well logs, RMS and interval velocities, legacy 2D and 3D seismic acquisition and processing reports, satellite and positioning maps, scouting reports, multi-physics maps and data and near surface environmental reports are all relevant. Thus, detailed, accurate and realistic estimates of the design’s attributes are generated including desired temporal and spatial resolution, focused target illumination parameters, signal penetration, recording requirements and noise minimization processes.
Keywords: Fold, Offset, Azimuth, Acquisition footprints, Well logs, Interval velocities, spatial resolution.
aliasing. In fact, setting bin size according to the highest frequency impacts data redundancy [7]. For stratigraphy with rapidly changing velocity, gradients [8] proposed an alternate form of (1):
Δx ≤ 1
4 f p (2)
where, p is the ray parameter (horizontal slowness) which stays constant along the ray path.
Other important design parameters are minimum and maximum offsets Xmin and Xmax respectively, they are needed for adequate offsets, move out analysis and multiple attenuation for shallow and deep targets. The smaller the Xmin the more adequate is the illumination of the shallowest horizon that may needed for building near surface velocity models of pre-stack depth imaging (PSDM) and for horizon based reflection statics computation as well. Contrary to 2D seismic geometries, Xmin is not constant throughout the survey since it changes with azimuth [7]. Xmin is the largest in orthogonal geometries where receiver and source lines are perpendicular forming a box bounded by adjacent receiver and source lines [9].
Xmin =
√
RLI 2 + SLI 2 (3)where RLI is receiver line interval and SLI is a source line interval.
As a rule of thumb, maximum offset Xmax should be equal to target depth multiplied by the tangent of the deepest horizon dip angle assuming curved rays.
This is essential for proper imaging of deep targets whereby the larger Xmax, the higher the fold and the Signal-to Noise Ratio (S/N). However, excessively large offsets exaggerate normal move out (NMO) stretching leading to the need for stretch mute (Xmute) which is a key design parameter controlling the selection of the maximum offsets.
While the maximum offset (Xmax), which is the distance between source and geophones, has to be planned according the geology and target depth [10]. Xmax is one of the most significant field parameters and the
maximum offset equal to the maximum depth of the deepest target. However, it is generally chosen after optimizing many different factors; velocity resolution, normal move out stretch (the distortion in waveform by normal move out-correction especially at the larger offsets) and multiple attenuation. For a velocity resolution Δv/v required to distinguish velocities at the time (T),
Xmax =
√
2Tv2 + Δf(
Δvv)
(4)where ∆f = fmax-fmin or the seismic bandwidth.
As Xmax increases, the ∆v/v, the velocity resolution increases and the multiple cancellation will be improved if the longer offset stretches will decrease.
In 3D seismic design, there is a trade-off between desired design parameterization and ground related practicalities along with budget constraints. One example is when regularly and densely sampled geometries are not feasible due to severe obstacles, terrain conditions or limited budgets, where multi- dimensional trace interpolation filling the gaps in preparation for Pre-Stack Time Migration (PSTM) or Pre-Stack Depth Migration (PSDM) is an alternative.
The challenge here is that interpolation cannot fill up large gaps in coverage and could cause suboptimal seismic imaging (smearing).
These geometry-related data quality issues must be well understood and continually monitored by seismic interpreters via close awareness of processing plans and results throughout the lifetime of the project.
Orthogonal designs are common in 3D land and marine OBS surveys as well as recently in wide-azimuth marine streamer surveys. These geometries provide multi-azimuth, full-azimuth or wide-azimuth data volumes, highly critical for reservoir characterization, azimuthal anisotropy, fracture analysis and 4D projects. Open fractures are the main corridors of permeability (secondary porosity) particularly in tight rocks, such as dense carbonate reservoirs. Therefore, reliable and geologically meaningful picture of the subsurface requires sufficient spatial sampling of the full wavefield [11].
Legacy seismic data acquisition and processing remaining concerns
The case study of this research is a 3D seismic OBC (Ocean Bottom Cable) offshore survey in which the survey was acquired in orthogonal overlapping patches with six receiver lines roll three lines while the shotline/receiver line spacing is 400/225 m with 200 m shotline interleaving and 18.75x25 m shot/
receiver station spacing. Symmetric sampling is the optimal acquisition design to reduce significantly the acquisition footprints [12] In the 3D OBC case survey, which has a sparse asymmetrical sampling either in line spacing or in station spacing that has led to fold stripping, stronger at 4.5 km inline offsets than at 3 km inline offsets. In addition, there was not enough wide azimuth distribution (Figure 1a, b and c).
Irregular offset distribution is clearly visible on the bin attribute (Figure 1d) and it has also reinforced the acquisition footprints artefacts due to combined fold and offset variability. Zippering and overlapping patches are the common way to build up the fold
between consecutive swaths, in the case of shortage of the seismic acquisition equipment. A multi receiver lines roll is an option to have higher fold with redundancy due acquiring the seismic patches with many repeated shots.
All shortfalls in the acquisition parameters that have previously mentioned has adversely affected the seismic signal by heavily polluted the seismic signal by seismic acquisition footprints. Due to this sparseness of the acquisition parameters, there are two main issues of the final migrated seismic data quality. Firstly, the acquisition footprints signature was stronger not only at the shallow data but penetrated the data even deeply. Secondly, the coarser sampling lead to aliased noise leak into the migration input and resulted in the cross hatching noise as is identified as the second type of seismic acquisition footprints (Figure 1g). All of those residual drawbacks of the seismic data quality was the motivation to come up with seismic design of any coming seismic activity of that survey in the future that shall overcome on the shortfall of the legacy seismic data quality.
Figure 1 Legacy seismic data concerns (a) fold stripes at 3 km (b) fold stripes at 4.5 km, (c) narrow azimuth survey, (d) non-uniform offset distribution, (e) fold stripes at near offsets, (f) fold stripes at far offsets and (g) final migrated stack of the legacy seismic showing strong seismic acquisition footprints due to suboptimum
seismic acquisition design
SEISMIC ACQUISITION DESIGN WORKFLOW
The innovative seismic acquisition design workflow consists of five basic steps (Figure 2). This workflow aims to generate a fit-for-purpose seismic acquisition geometry design utilizing the existing information in any area of study such as seismic data and well velocity information. Each single step of this seismic acquisition design workflow detailed in this section.
THE WEDGE MODEL
The first step is a wedge model creation to investigate and analyze the effects of spatially varying rock properties and formation thickness on the seismic signal response. Changes in bed thickness are introduced into the model along with changes in acoustic properties as determined from rock physics modeling. Ricker and Widess criteria of resolving thin beds are well established and widely accepted [13]. A zero-offset elastic synthetic seismic model has been generated from the legacy dataset (using logs and seismic parameters). The synthetic model was
computed with formation thickness ranging from 0 to 1500 feet. There was a strong seismic tuning effect from 40 feet and below of the first three layers while the other two lower layers do not show seismic tuning in the synthetic wedge model (Figure 3).
In order to investigate exactly the minimum thickness of the seismic tuning at those upper three members, the wedge model convolved with a Ricker zero-phase wavelet with different dominant frequency, i.e., 60, 80 and 100 Hz respectively. The wedge model convolved with the Ricker 100 Hz has improved the horizon picking with less tuning effect from 40 feet to less than 10 feet seismic tuning. Moreover, the seismic amplitude-tuning curve depicts that the length of tuning effect has been shorten compares to the tuning length of the input synthetic model computed from the legacy seismic data. Furthermore, the 100 Hz Ricker has strongly improved the other two lower Members resolution and leads to a proper matching with the well sonic logs (Figure 4).
1 - Wedge model creation
2 - Seismic acquisition
design
5 - Testing the processing filters prior
to seismic processing project
3 - Verification of the seismic acquisition design options
4 - Simulation of the seismic acquisition footprint
Figure 2 The seismic acquisition design workflow
Figure 3 The synthetic seismic elastic model from the 3D legacy seismic volume of the OBC seismic case shows seismic tuning at 40 feet and thinner
The statistical wavelet of the legacy seismic data was extracted using a time range from 900 ms to 1300 ms for the whole 3D cube every 10 inlines and every 10 crosslines and shows zero phase wavelet with 100 ms wavelet length with 2 ms sample interval. The wavelet is ideally zero phase with residual side lobes
with the dominant seismic frequency of the zone of reservoir is from 40-50 Hz (Figure 5a and b). On the other hand, the deterministic Ricker wavelet of 100 Hz shows ideally zero phase response with wide seismic bandwidth with almost no side lobes (Figure 5c and d).
Figure 4 (a) The synthetic wedge model from seismic (b) the synthetic model with 100 Hz Ricker wavelet and its corresponding seismic tuning curve synthetic seismic section
(note that the picked seismic event is a trough (negative amplitude)
Figure 5 (a) Extracted seismic statistical wavelet at the reservoir level, (b) its amplitude spectrum, while the wedge model convolved with Ricker 100 Hz
(c) extracted seismic statistical wavelet of the reservoir level and (d) its amplitude spectrum (dominant frequency pointed out by yellow arrows) The synthetic seismic traces of the Ricker 100 Hz
wedge model have a better match with well logs vertical variability of the different members of the reservoir formation and sharp wavelet compare to the seismic legacy one. (Figure 6).
Therefore, the resolvable limits of top and bottom of the target layer considering the bandwidth, peak
and maximum frequency of seismic data have been identified. Once these required resolution limits are determined from this modeling, relevant shot/receiver station intervals, source and receiver configurations and equipment types are chosen.
Figure 6 The well logs & synthetic seismic of legacy seismic and Ricker 100 Hz synthetic seismogram
SEISMIC ACQUISITION DESIGN
The second step is to determine the optimum bin size, source/receiver line spacing and inline/crossline offsets using the information providing by extracting the wedge model with analyzing RMS and interval velocities from well data as for example done by using Vermeer’s [14] design optimization spread sheets wizard. This is based on the concise description of the one-line roll orthogonal geometry with two equal station spacing and four integers corresponding to shot line interval, receiver line interval, maximum inline offset, and maximum crossline offset. This exercise reduces the number of possible parameter choices considerably and allows the Excel Solver to quickly produce satisfactory solutions. Time-RMS velocity pairs of a well has the target levels of any survey as in the 3D seismic OBC case of this study (Figure 7a).
Once the velocity file is loaded, the program displays time and RMS and interval velocity curves (Figure 7b and c) and computes the important maximum NMO stretch. Maximum offsets can be optimized by using well velocity information whereby actual requested offsets may be shorter than theoretical prediction, potentially saving considerable cost. Additionally, testing different stretch factors allows more optimized design options and verification of design accuracy. The required seismic frequencies for proper target temporal resolution, migration aperture and required fold at different target depths are all entered in the design wizard program. The program based on the provided parameters from extracted from the wedge model step one in this design workflow, it will compute the relevant parameters for station spacing, and maximum line spacing with the predicted large minimum offsets (LMOS) (Figure 7d). Then, four different design options (Figure 7e) are generated accordingly considering the offshore operation complexity and the financial constraints.
Figure 7 (a) Loading time/depth velocity pairs in design wizard with loaded velocity curves:
(b) RMS velocity and (c) interval velocity curve (d) Desired frequency with target depth intervals with the corresponding parameters for different depths of the targets are
pointed out by blue arrows and (e) the 4 design options with highlighting the predictable achievable proposed design
ACQUISITION DESIGN VERIFICATION
The third step in the process is to verify the design by performing quality control of different bin attributes as in fold at near/far offsets, bin offset distribution and rose diagrams. This shows which inline or crossline offset range has full azimuth distribution. Verification was made by using the OMNI 3D seismic design wizard 2015’s release software (Figure 8 a-d).
Figure 8 Acquisition parameters verification through OMNI 3D seismic survey design. (a) Rose diagram, (b) trace count versus offsets, (c) trace count versus azimuth and (d) a time slice at 500 ms over the 3D stackcube shows acquisition footprint (e) a time slice at 3000 ms shows no seismic acquisition footprints
If there is a legacy seismic volume in the project area, as in this case, it should be fully analyzed and compared to the proposed new design. The previous 3D OBC seismic survey acquired using overlapping orthogonal patches with 400m/225m receiver/
source line spacing with 200 m interleaving shot- line, and 18.50m/25m source/receiver station spacing
in a spread of six receiver lines and 3-line roll. The legacy data fold is relatively low (Figure 9) due to the sparseness and shorter offsets which compromised proper imaging of the deeper targets.
Accordingly, the new proposed design aimed at filling the gaps of the legacy and generating less footprints the design was made using one full active patch, 200m/200m source/receiver line spacing and
25m/25m shot and receiver station giving nominal fold of 500 (Figure 9b). In this new design, each swath has 40 receiver lines per shot, 400 channels per receiver line, 5000 m inline offsets and 4000 m cross line offsets.
Figure 9 Nominal fold of (a) 3D OBC legacy seismic and (b) the proposed design fold of one full active patch.
The Kx- KY spectrum of offset distribution is depicting the acquisition footprint pattern (c) legacy acquisition geometry has strong complex acquisition footprints aligned as straight lineaments and (d) is the new
proposed acquisition design has regular point periodic pattern. Yellow arrows point out Kx-Ky spikes of the seismic acquisition footprint
SIMULATION OF THE SEISMIC ACQUISITION FOOTPRINT
The forth step is a simulation of seismic acquisition footprint through creating 3D synthetic seismic cube to verify the severity of the acquisition footprints of any acquisition design. Acquisition footprints should appear as spikes in the Kx-Ky wavenumber domain. The sparseness of the legacy seismic data shows continuous straight lines pattern footprints (Figure 9c), while new design footprints appear as regular spikes (Figure 9d) which can be removed efficiently in processing. Stacking of the asymmetrical acquisition design of the legacy seismic survey is clearly showing the short wavelength jitters of the
acquisition footprints and lateral amplitude stripping at different time levels (Figure 10a) while the one of the symmetrical proposed acquisition design is showing acquisition footprints only at the very shallow levels while the footprint does heal with depth (Figure 10b).
TESTING THE SEISMIC PROCESSING FILTERS PRIOR TO SEISMIC PROCESSING PROJECT
Ultimately, the fifth and final step in this design workflow is the application of seismic cascaded processing filters workflow [15] on the sparse 3D synthetic seismic volumes. This is to verify if processing will optimally treat the sparseness consequences
of the seismic acquisition design and filter out the seismic acquisition footprints, in anticipation of the seismic acquisition footprints filtering in the seismic processing phase. The cascaded processing workflow manages to clean up the seismic volume from the severe acquisition footprint signatures from the very shallow data to the last time sample of the generated 3D synthetic seismic cube (Figure 10c & d).
Figure 10 A vertical section over the Synthetic 3D stack synthetic seismic cube (a) stack with of the legacy 3D seismic survey of asymmetrical sampling acquisition design while (b) the stack with symmetrical proposed
design has a far better illumination at all levels with less acquisition footprints as pointed out by blue arrows. View of the 3D Synthetic seismic cube of a sparser seismic acquisition design (c) before
the cascaded seismic processing workflow and (d) after the workflow application
CONCLUSIONS
Sensible seismic acquisition geometry designs should be based on denser sampling to reduce footprints,
increase S/N and improve seismic imaging. Suboptimal spread layout offsets, fold, azimuth and NMO variability can lead to severe artefacts. Conducting the integrated 5-steps design workflow of this work leads to optimized parameterization and superior data quality for best resolution of geologic features.
The key success factor in this design approach is the integration of all relevant data information. Wedge
modelling was instrumental to define an optimum design that will help to suppress strong seismic tuning effects of the legacy seismic data. Frequency and thickness of the reservoir layers are the main
controllers of the design parameters. This innovative design workflow will help the industry, especially the seismic young professionals, to understand the design workflow and utilize every single information of the area of study to conduct optimum design. This is crucial to achieve the objective and to overcome all residual concerns of any sparse legacy seismic surveys.
ACKNOWLEDGEMENTS
The authors are grateful to UTP (University Technology of PETRONAS and ADNOC (Abu Dhabi National Oil Company) management for permission to publish this work. We extend the thanks to the administrators of OMNI 3D 2015 Seismic survey design (mark of Schlumberger) for their continuous online support.
REFERENCES
[1] Y. Chingyong, ‘Full 3D seismic interpretation’, Progress in Geophysics, vol. 3, article 014, 1999.
[2] C. P. Ashton, B. Bacon, C. Deplante, T. Sinclair, and G. Redekop, ‘3D seismic survey design’, The Leading Edge, vol. 12, no. 11, pp. 1094–1117, 1993.
[3] A. Chaouch, and J. L. Mari, ‘3-D Land Seismic Surveys: Definition of Geophysical Parameters’, Oil & Gas Science and Technology – Rev. IFP, vol. 61 (2006), no. 5, pp. 611-630, 2006.
[4] J. F. Claerbout, Imaging the Earth's Interior. New Jersey: Blackwell Scientific Publications, 1985.
[5] Ö. Yilmaz, and S. M. Doherty, Seismic Data Processing. Tulsa: Society of Exploration Geophysicists, 1987.
[6] M. T. Reblin, G. G. Chapel, S. L. Roche, and C. Keller,
‘Seismic interpretation, 28, A 3-D reflection seismic survey over the Dollarhide Field, Andrews County, Texas’, The Leading Edge, vol. 10, no. 8, pp. 11–15.
1991
[7] K. Shukla, P. Jaiswal and S. C. Singh, ‘Recovering Uniform Coverage in a 3D Survey: Case Study from Onshore Southern India’, International Journal of Geophysics, http://dx.doi.org/10.1155/2014/987605, vol. 2014.
[8] C. L. Liner, W. D. Underwood and R. Gobeli, ‘3-D seismic survey design as an optimization problem’, The Leading Edge, vol. 18, no. 9, pp. 1054–1060, 1999.
[9] D. G. Stone, ‘Designing Seismic Surveys in Two and Three Dimensions’, Geophysical Reference, Society of Exploration Geophysicists, vol. 5, http://dx.doi.
org/10.1190/1.9781560802730, 1994.
[10] S. Bauer, A. Donat, and H. Reutter, ‘Geothermal Exploration Best Practices, geophysical methods, seismic, data acquisition’, IGA Academy Report 0107, 2013.
[11] M. Williams and S. Hoenmans, ‘Moving towards full-sampling in land 3D Acquisition’, First Break, vol. 24, February 2006.
[12] J. K Cooper, G. F. Margrave, and D.C. Lawton,
‘Simulation of seismic acquisition footprint’, CREWES Research Report, vol. 19, 2007.
[13] L. M. Gochioco, ‘Tuning effect and interference reflections from thin beds and coal seams’, Geophysics, vol. 56, no. 8, pp. 1288-95, 1991.
[14] G.J.O. Vermeer, 2003, ‘3D seismic survey design optimization’, The Leading Edge, 22, pp. 934-41, doi: 10 – 1190/1.1623633, 2003.
[15] M. Mahgoub, A.H. A. Latiff, D. P. Ghosh and F.Neves, 2017, Taking seismic acquisition artefacts beyond mitigation, FB, volume 35, doi: 10.3997/1365- 2397.2017014, June 2017
AUTHORS' INFORMATION
Mohamed Mahgoub has been working in Abu Dhabi National Oil Company (ADNOC) since 2006 as a Geophysics Specialist. He is following up all seismic data processing projects within the ADNOC Group and leading the In-house processing group.
Mohamed has presented and published my technical papers and his last publication was in June 2017 in the EAGE, First Break Magazine. Mohamed is a student in the UTP Petroleum Geoscience post graduate Master Program.
Prof. Dr. Deva Prasad Ghosh obtained his Bachelor’s Degree in Physics and Geology and Master’s Degree in Geophysics in 1958 from Banaras Hindu University at Varnasi, India. Later on in his career, he pursued his PhD in geophysics at Delft University of Technology that he finalized in 1971. Prof. Dr. Deva Ghosh launched his career in 1974 as Research Advisor for KSEPL.
He continued his journey as Research Manager and Advisor for Shell as well as Chief Geophysicist and Research Advisor for Shell International B.V. He also worked as Chief Geophysicist for Shell Nigeria and Adviser, Custodian, Geophysical Research Advisor for Petronas. He currently serves as principal Investigator at the Centre for Subsurface Seismic Imaging & Hydrocarbon Prediction (CSI) at the Universiti Teknologi PETRONAS (UTP) in Malaysia. He assumed his present role in 2011 when he also became a Professor of Geophysics at the Geoscience Department of the University. In the course of his duties as a professor, he guides master’s degree geosciences students and conducts research with several of them for their master’s degree and PhDs in geophysics. Furthermore, Prof. Dr. Deva Ghosh is a valuable consultant to multiple oil and gas companies.
Abdul Halim Abdul Latiff is currently a lecturer with Geosciences Department, Universiti Teknologi Petronas (UTP), Malaysia. He obtained M. Sc.in Petroleum Geoscience from UTP in 2014. He received the M.Eng. in electrical & electronic engineering from Imperial College London in 2008.
He had worked for CGG from April 2009 to March 2012, before joining UTP. His research includes earthquake seismology, optimization algorithms and finding new solutions for subsurface seismic image data. He is a member of Board of Geologist, Board of Engineer, SEG, EAGE and GSM.
Dr. Fernando A. Neves is a Geophysical Specialist at the Abu Dhabi National Oil Company (ADNOC). Previously he was the Exploration Director of Petra Energia in Brazil. He received his Ph.D.
in Geophysics from The University of Cambridge-England and a Post- Doctorate from The Colorado School of Mines-USA. Fernando worked for Petroleum Geo-Services (USA and Norway), Saudi Aramco (Saudi Arabia) and Shell International (Oman). Since 2013, he has been working at the Exploration Unit of ADNOC Upstream Directorate, where he leads the interpretation and reservoir characterization team.
