An extra 10% - Improving the productivity of vibroseis surveys through advanced fleet management Timothy Dean*, WesternGeco

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1 An extra 10% - Improving the productivity of vibroseis surveys through advanced fleet management Timothy Dean*, WesternGeco Summary Improving the productivity of vibroseis surveys has been the focus of much activity over the last five years. The assumption that the limit of conventional flip-flop shooting has been reached has led to the improvement of existing, and the creation of several new, high-productivity acquisition techniques. This paper shows how the productivity of flip-flop surveys can actually be improved by up to 10%. This improvement is obtained by planning the most efficient acquisition sequence and then ensuring that this sequence is achieved by automatically managing the relative progress of the vibrator fleets within the recording system. Acquisition algorithms also have the ability to improve productivity when other, more complicated, techniques are applied. Introduction In the last five years much attention has been paid to improving the productivity of vibroseis surveys. This has included improvements made to existing techniques, for example harmonic filtering applied to data acquired using the slip-sweep technique (Rozemond, 1996) and improving the productivity of the High Fidelity Vibratory Seismic (HFVS) technique (Krohn & Johnson, 2003) by removing the listen times, and the invention of several new techniques, such as Distance Separated Simultaneous Source (DS 3 ) (Bouska, 2010) and Independent Simultaneous Sweeping (ISS) (Howe et al., 2008). Little attention has been paid to improving the productivity of conventional flip-flop shooting as there has been an inherent assumption that its limit has been reached. In what follows, I show that by carefully controlling the order of acquisition, significant productivity improvements of up to 10% can be made when acquiring data using the flip-flop technique. I also demonstrate how the method can be applied to improve productivity in more complex acquisition techniques. Method Vibroseis surveys are typically considered within a geographical context, i.e. fleets of vibrators moving along source lines sweeping on a series of source points. A survey conducted on flat, clear terrain with a sufficient number of vibrator fleets will involve a fleet completing cycles of: sweeping, moving up to their next position, reporting ready, and then waiting for the next available sweep time. However, if we look at the survey procedure within the context of mathematical queuing theory (Gross et al., 2008) we then have a queue of fleets that have reported ready and are waiting to sweep, a fleet that is being served, i.e. sweeping for a fixed service time, and fleets that are moving. The flip-flop survey model is shown in Figure 1; within the sweep step there is a single fleet being served and there is a queue of fleets waiting. The length of the queue is limited to the total number of fleets, N. The next stage of the survey is the move stage; this has a variable service time, i.e. the move-up time varies for each source point, and the number of fleets moving is again limited to N. The line connecting the two steps shows that we have a fixed set of fleets returning to be served within the sweep stage after moving. This means that we have a cycling customer model queuing system (Campbell, 1991). Inherent in the theoretical description of a queuing system is the definition of the queue discipline; this refers to the way in which customers are selected from the queue to be serviced. Currently acquisition systems operate using a simple first-in-first-out (FIFO) discipline. As fleets report ready they appear at the back of the queue before working their way forward to be served. Postel et al. (2008) described a different type of discipline where fleets had predefined slots in a shooting schedule; if a slot was missed it had to wait for the fleet s next slot. This led to a 30% difference in acquisition rates between the fastest and slowest of the 12 fleets being used. Variations between fleets progress creates inefficiencies and reduces productivity. For example, if one fleet falls behind the others, we can either wait for the fleet to finish, thus delaying spread rolling, or we can move other fleets to help it finish its points. While the fleets move, sometimes large distances, productivity is reduced, fuel wasted, and noise created on the spread. Modern wide-azimuth land acquisition geometries typically involve acquiring source points in small salvos of between 8 and 16 vibrator points (VPs). After completing a salvo the fleet moves to the next salvo; this results in a large number of crossline move-ups. To ensure maximum efficiency we must ensure that only one fleet is moving at a time, as illustrated by the simple schematic in Figure 2. A stagger between each fleets position in a salvo ensures that only one fleet makes the extended crossline move at a time. To maximise productivity we have introduced a proprietary system of planning and fleet control called Advanced Fleet Management (AFM). During survey planning the optimum SEG Las Vegas 2012 Annual Meeting Page 1

2 Improving the productivity of vibroseis surveys source-point acquisition order is established (Figure 2) and then tested by realistic modelling. During acquisition the recording system applies a priority-based queue discipline to ensure that the source points are acquired in the order that most closely follows the plan. Examples The examples in this section were all obtained using a specially developed survey simulator. To ensure an accurate representation of the real-world move-up times were extracted from actual survey data. An example of the extracted move-up times is shown in Figure 3. Consistent with other reports (Pecholcs et al., 2010) distributions typically show an extended tail. If running simulations using the same geometry from which the move-up times were derived then they could be used directly. If not, the real move-up times were either adjusted or random distributions were generated using models based on real results. Initially simulations were run using standard FIFO discipline. Despite the move-up times differing between groups by less than 10%, significant differences in the progress of the fleets resulted. Figure 4a shows the result when the FIFO discipline was applied. The time taken for each of the three groups to complete 1,000 source points varied by a maximum of 135 minutes (14% of the total acquisition time). When AFM was applied (Figure 4b) this time was reduced to just three minutes (0.3%). If this was a real survey, at several instances during the acquisition fleets would have been required to move and help Fleet 1 (the slowest) complete its VPs thereby wasting considerable time. The equalisation of acquisition rates through the use of AFM clearly removes this requirement. The geometry for this survey involved acquiring salvos of eight VPs. Using AFM we can apply an optimal stagger between groups preventing more than one group moving between source lines at the same time. Applying the optimal stagger shown in Figure 2 increased productivity by 6% and, combined with avoiding the need to move fleets between source lines, gives an expected total productivity gain of over 10%. More advanced implementations of AFM can also be applied to increase productivity when using other acquisition methods. The next example given here relates to a combination of the DS3 and slip-sweep methods. DS3 allows multiple fleets to sweep simultaneously while the slip-sweep method reduces the time between sweeps. The AFM algorithm is based around improving efficiency by carefully selecting which fleet(s) sweeps when more than one fleet is available. In this DS3 slip-sweep example, when multiple combinations of fleets can be swept simultaneously, the algorithm will select those that conform best to the acquisition plan. Simulations show that as the slip-time decreases the simulated productivity value diverges further from the theoretical productivity. This is due to the decrease in sliptime making it less likely that more than one fleet will be ready at any time (Figure 6a). Simulation results show that often the fleet/fleets report ready just after the start of a sweep and are then forced to wait for the slip-time to expire. If the acquisition system had the ability to look ahead and see that a fleet/fleets was going to become ready (e.g. if the fleets sent a nearly-ready tone ) then it could delay the start of the sweep and then sweep multiple fleets simultaneously. We refer to this capability as clairvoyant AFM or C-AFM. An example of this situation is shown in Figure 6a; when the fourth slip-time expires only one fleet is ready. The other fleet, however, reports ready just after the start of the previous (single) sweep (the ready tone is circled in blue). When C-AFM is used (Figure 6b) the system receives a nearly-ready tone prior to that sweep starting so it knows to wait for the next fleet to become ready so they can be swept simultaneously. This results in the seven sweeps being acquired in less overall time (Figure 6b shows the dashed orange line that marks the completion of the seven sweeps for Figure 6a). By running a series of different realistic simulations the impact of C-AFM can be modelled. Some example results are shown in Figure 5. The use of C-AFM increases the proportion from 20% to more than 40% for a slip-time of 6 s. The use of C-AFM reduces cycle times by about 5%. The lack of improvement for AFM on its own at short sliptimes is due to the inability of the system to re-order the queue as there is never more than a single fleet, or pair of sweeps, available to sweep. Conclusions Increasing the productivity of vibroseis surveys has become increasingly important. Previously the only way to improve productivity was thought to be through the application of high-productivity acquisition methods, with their resulting complexity and possible noise problems. The work presented here shows that we can improve productivity of flip-flop surveys by up to 10% by planning the most efficient acquisition sequence, and ensuring that this sequence is achieved by automatically managing the relative progress of the vibrator fleets within the recording system. These acquisition sequencing algorithms also have the ability to improve productivity when combined with other, more complicated techniques. SEG Las Vegas 2012 Annual Meeting Page 2

3 Sweep 1 served N Queue length Fixed service time N vibrators Move N served Variable service time Figure 1: Schematic diagram of a flip-flop Vibroseis survey. N vibrators, or groups of vibrators, are used that sweep for a fixed time with only one sweeping at a time before moving. There is no limit on the number of vibrators that can move at a single time and thus no queue develops within this stage. The service time refers to the time required to service a fleet; in this case, the sweep length + listen time. (c) (d) Figure 2: Simple schematic showing the most efficient way to acquire short salvos of source points. Each color shows the VPs for a fleet with the box indicating the current position of the fleet with the arrows indicating the direction of movement. to (d) show the sequential progress of the fleets at four different moments in time. Note that only one group moves between salvos at any time, maximizing productivity. Figure 3: Histograms of the crossline and inline move-up times extracted from a survey. The inline and crossline move-up distances were 50 m and 350 m respectively. SEG Las Vegas 2012 Annual Meeting Page 3

4 Figure 4: Plots of the time at which each VP (of 1,000) was acquired by each of three fleets without AFM, and with AFM. The dotted black line indicates the number of fleets operating. Figure 5: Results from a series of simulated surveys showing the percentage of sweeps acquired simultaneously (left) and the resulting cycle times (right) for DS 3 slip-sweep acquisition without AFM, with AFM, and with C-AFM. Time Time Figure 6: Simplified examples of DS 3 slip-sweep acquisition. The red diamonds are ready tones, the blue diamonds nearly-ready tones and the green bars are the sweeps. The start time of each sweep is indicated by the vertical dashed line. The yellow dashed line in is the sweep start time of the 6 th sweep for. Example is without any form of fleet management and shows the use of clairvoyant-afm. SEG Las Vegas 2012 Annual Meeting Page 4

5 EDITED REFERENCES Note: This reference list is a copy-edited version of the reference list submitted by the author. Reference lists for the 2012 SEG Technical Program Expanded Abstracts have been copy edited so t hat references provided with the online metadata for each paper will achieve a high degree of linking to cited sources that appear on the Web. REFERENCES Bouska, J., 2010, Distance separated simultaneous sweeping for fast, clean, vibroseis acquisition: Geophysical Prospecting, 58, Campbell, G. M., 1991, Cyclical queueing systems: European Journal of Operational Research, 51, Gross, D., J. Shortle, J. Thompson, and C. Harris, 2008, Fundamentals of queuing theory: Wiley. Howe, D., M. Foster, T. Allen, B. Taylor, and I. Jack, 2008, Independent simultaneous sweeping A method to increase the productivity of land seismic crews: 78th Annual International Meeting, SEG, Expanded Abstracts, Krohn, C. E., and M. L. Johnson, 2003, High fidelity vibratory seismic (HFVS): 1 Enhanced data quality: 73rd Annual International Meeting, SEG, Expanded Abstracts, Pecholcs, P. I., S. K. Lafon, T. Al-Ghamdi, H. Al-Shammery, P. G. Kelamis, S. X. Huo, O. Winter, J. Kerboul, and T. Klein, 2010, Over 40,000 vibrator points per day with real-time quality control: Opportunities and challenges: 80th Annual International Meeting, SEG, Expanded Abstracts, Postel, J.-J., J. Meunier, T. Bianchi, and R. Taylor, 2008, V1: Implementation and applic ation of single - vibrator acquisition: The Leading Edge, 27, Rozemond, H. J., 1996, Slip-sweep acquisition: 66th Annual International Meeting, SEG, Expanded Abstracts, SEG Las Vegas 2012 Annual Meeting Page 5