Oil & Gas - Maximising Recovery Programme (formerly SHARP) IOR Views

Introduction

The industries’ overall acceptance of 4D is evidenced by the number of papers that have appeared in the past few years and the breadth of topics they cover. At the 2003 EAGE and SEG conferences more than 50 papers were presented whose title included 4D or ‘time-lapse’. They came from a variety of sources, including operating oil companies, service companies and universities. A selection of these is listed in the references section for further reading. 4D has gained acceptance over the past 5 years, and in that time a total of 35,000 km² of 3D seismic has been acquired for time lapse analysis.

Perhaps the bulk of 4D work has been carried out in the North Sea, but ‘globalisation’ is taking place (Figure 1). In BP’s case, eleven surveys for 4D analysis have been acquired to date outside the North Sea either as operator or as a partner.

Figure 1: The Growth of 4D Acquisition

4D has given an important new data source on which to base understanding of reservoir dynamics. This has allowed un-accessed reserves to be located, and thereby drilling options to be identified, leading to the creation of value through increased production rate and/or recovery.

In this article I summarise some of the recent successes and remaining challenges pertaining to 4D. For further information, there is a selection of recent papers included in the reference list. The list is by no means exhaustive, but is intended to illustrate the range of work being carried out in the 4D arena.

Some Successes

Figure 2 ranks some BP fields in terms of oil-brine reflection coefficient, and thereby indicates the potential for interfaces between these fluids, and in particular their movement, to be identified by 4D. The normal incidence reflection coefficient (RC₀) is a function of the difference in acoustic impedance of the rocks with different fluid fills. The value RC₀ is about half the fractional change in impedance that could be detected due to the fluid change, so the diagram spans an impedance change from ~3% to ~20%. The ability to detect the change will also depend on the level of noise present. This depends on a number of factors, including the reflectivity of the geology; however, impedance changes of as little as 3-4% are often detectable in the North Sea.

Valuable 4D results have been obtained across the range. As examples, I will mention three cases:

Harding Central is a high porosity (35%) and permeability (2-15 D) reservoir at a depth of ~1650 mTVDss. Its 72m saturated oil column is sandwiched between primary gas cap and aquifer. 4D has been used to monitor fluid contact movement and thereby position new wells into the thickest parts of the remaining oil column to avoid gas and water encroachment for as long as possible.

Moving down the list, Forties was one of the first of BP’s ‘4D fields’. A combination of lithology and fluid imaging rescued the infill drilling programme by locating un-accessed oil and thereby reducing uncertainty in selecting infill options. The remaining development potential revealed by 4D helped BP to sell the Forties Field as a going concern.

Andrew was not ranked highly as a 4D candidate, yet 4D has yielded excellent results, revitalising a drilling and workover programme. Working in the S/N region of 1, the availability of PLT and repeat RST logs in two horizontal producers gave the necessary confidence to 4D interpretation.

Figure 2: 4D Success in the North Sea.

In spite of the successful use so far – and the number of reported cases is ever growing, there remain some significant challenges for 4D. These include challenges of getting more out of the 4D in situations where it has already been useful, and pushing the technical boundaries to make it work in situations previously thought to be out of bounds.

Many other case studies have been published. Further reading may be consulted in references 1, 3, 4, 5, 6, 7, 8, 9, 12, 13, 17, 21, 26, 27, 30, and 34.

The Challenges

We are continually challenged to both to do our current job better, and to push the technical boundaries of the subject. The challenges can be grouped under the following headings.

Visualisation - Much of the interpretation and integration done to date has used visual comparison of data as its core. Recognition of patterns within the 4D data which are explainable in the context of expected reservoir changes provides an accessible method which requires limited specialist knowledge to apply, and can gain immediate results. Although much successful work has been achieved by co-visualising seismic, simulation, surveillance, well log and other pertinent data [e.g. 13, 26, 27], there is still a lack of software tools by which it can be done easily. Also, only a limited number of properties (or dimensions) can be compared which may lead to a bias in interpretation.

Discipline Integration - Use of data from many branches of subsurface technology requires teamwork and mutual technical understanding. Better results can be achieved more easily with teams that communicate well and understand each other. Yet this has its problems. Technical languages differ. Geophysicists speak of amplitudes and velocities; reservoir engineers consider pressure, saturation, production and injection rates. Geologists think of structure, faulting, fracturing etc. Rock physics relates the acoustic behaviour of rocks to their physical properties, including pressure and fluid saturation, but in doing so brings in a further terminology, bulk modulus, frame modulus, Poisson’s ratio etc. Even within these major subject areas there are specialisms. This means that team members must be able to understand and express the value and uncertainty of their own, and others, data.

Discrimination and Quantification of Pressure and Fluid Saturation - A change in pressure or saturation within a rock gives rise to a change both in its bulk density and sonic velocity. The magnitude of the changes is controlled by the physical properties of the rock frame and the filling pore fluids. Figure 3 illustrates the effect of these changes on acoustic impedance (AI) which is a function of density and p-wave velocity and therefore has a combined response to pressure and saturation change. The polarity of the response depends on whether the pressure is increasing or decreasing and the difference between the fluid properties at the start and end of the period.

Figure 3: Change of Acoustic Impedance (AI) in Response to Production and Injection.

The effect of the changes on amplitude seen at the top of a reservoir depends on the contrast between the AI of the reservoir and the overlying caprock, so an increase in reservoir impedance will lead to an increase in the seismic amplitude at the top of the zone if the overlying zone is acoustically softer. Most caprocks in the North Sea are harder than the reservoir, so an increase in impedance leads to a dimming of amplitude.

More advanced seismic imaging techniques such as amplitude versus offset (AVO) can be used to differentiate the combined effect of pressure and saturation. Where good quality data are available, this can be achieved, but often the quality and/or quantity of data may be sufficient to give only an indication of the competing reservoir effects. In this case understanding from the simulation model can be used to help to differentiate these effects. The challenge of improving these techniques remains [16, 18, 22, 32, 35].

Computation

Quantitative analysis for reservoir management is commonly linked to reservoir simulation; however, this may not always be the case. Use of computers enables multi-attribute correlation, thus removing the constraints of visual methods. Use of many attributes tends to highlight anomalies, while reducing noise. Techniques such as neural network analysis have been used in this field [25].

4D provides scope to improve predictive capacity and uncertainty estimation by its use in constraining the history matching of simulators. This is already a broad subject and impossible to do justice to in an article of this nature. A stochastic approach is usually taken by parameterising models in a geologically sensible way. Parameters are assigned supportable ranges and a statistical algorithm is used to create multiple model realisations. The quality of the match of each realisation to reference data is quantified using an objective function. Matches are grouped and optimised. Optimal match groupings have to be scrutinised for their geological and physical quality in order to prevent the process becoming a ‘black box’. Because multiple equiprobable match types can be found using this process, the opportunity for bias as a result of pre-selecting a match is reduced.

Both individual companies and research consortia are tackling the challenge of accelerating history matching, driven by the need to take advantage of our 4D data by incorporating its learning quickly into predictions. With the potential for more frequent repeat surveys, this is most important to avoid ‘data indigestion’.

Complexity

In spite of the challenges described above, the industry is pursuing 4D in increasingly challenging reservoir situations. I have summarised some of these below, and made reference to recent studies which can be consulted for further information.

Hard, Low Porosity Reservoirs: High rock frame hardness reduces the 4D signal associated with pressure change, and low porosity reduces the fluid volume fraction, thus limiting the 4D signal associated with fluid change.

Hard Carbonates: As above, the high rock hardness limits the effect of pressure change on the seismic signal. Variability of pore shapes can also have a significant effect, especially at lower porosity.

High Dip Reservoirs: High dips present challenges to 3D seismic processing and interpretation and in turn to 4D.

Compacting Reservoirs: In the North Sea, significant compaction is seen in chalk reservoirs such as Ekofisk and Valhall. Interpretation of reservoir changes normally can assume that changes in porosity are negligible; however, in compacting reservoirs, this change must be taken into account. [2, 24, 31]

Dry Gas Reservoirs: Both pressure depletion and water contact movement are indicative of the performance of gas reservoirs; however, both can be challenging to observe using 4D. The residual gas saturation remaining when water encroaches into the gas zone can be sufficient to mask the effect of the contact movement. Large pressure change may be required to produce a detectable 4D signal, owing to the compressibility of gas. [10, 14, 15, 20, 28].

Gas Condensates: Changes in the composition of the pore fluid is common during production of gas condensate reservoirs. This may be large enough to require special consideration in analysing the effect on seismic response. [33]

Stress Arching: Effective reservoir stress is a first order effect on seismic response; however, there is uncertainty in quantifying it. Evidence of 4D effects in both overburden and ‘under’ burden indicates that the change of reservoir stress as a result of production may be less than expected due to an increase in stress in the bounding rocks. [11]

Value

Is it worth it? On the basis of BP’s own experience, and the evidence of the published case studies, it is clear that it undoubtedly is. The benefits come in many ways, which are often not easily quantified as there is always a judgement required as to what part of the benefit is directly attributable to 4D. In BP, data have been collected by well to enable an estimate of the value delivered by the technology in terms of production additions. Such an exercise has an element of subjectivity, but this is minimised by involving the entire integrated subsurface team in providing data, rather than relying on an individual who may introduce a particular bias. The initial value assessment that was made towards the end of 2002 indicated that use of 4D across BP’s fields had made available an additional 24 Mstb/d production and 95 MMstb of reserves (Figure 4). These statistics are dominated by 4D activity in the North Sea. Savings made by avoiding drilling unsuccessful wells, also a product of 4D deployment, are not taken into account in these numbers. It is important to note that the production and reserve prize that were identified by the field teams is consistently and substantially in excess of the prize originally anticipated.

Figure 4: Production Increment Assigned to the Use of 4D.

Note that Figure 4 contains information on 4D data shot up to the Summer of 2003. Estimates are for currently producing fields, and do not cover new fields about to come into production. While we are in the ‘early days’ of 4D, it is necessary to make this quantification to indicate the kind of return on investment that it makes. But will this always be so? How often do we have to do this for 3D surveys, or logging suites etc?

Achnowledgements

The author would like to acknowledge the contribution made by his many BP colleagues to the North Sea 4D effort, in particular, D Whitcombe, M Dyce, C McKenzie, and J Fletcher.

The author thanks BP for permission to publish this article, and acknowledges the contribution of partners, CNR, ENI, Kerr-McGee, Murphy, and NOEX to the examples cited in this article. The views expressed are the opinions of the author and do not necessarily represent the official position of BP or any of its partners.

References/Further Reading on Reservoir Monitoring

1. Alsos, T., Tøndel, R., Aanvik, F. & Solheim, O.A. Quantifying Rise In Gas Water Contact From Time-Lapse Seismic On The Sleipner Øst Field. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A08.
2. Askim, O.J. Seismic Forward Modeling In A Chalk Reservoir With Permanent Monitoring. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A16.
3. Barkved, O. K., Buer, K., Halleland, K.B., Kjelstadli, R., Kleppan, T., & Kristiansen, T. 4d Seismic Response Of Primary Production And Waste Injection At The Valhall Field. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A22.
4. Bogan, C., Johnson, D., Litvak, M. & Stauber, D. Building Reservoir Models Based On 4D Seismic & Well Data In Gulf Of Mexico Oil Fields. SPE 84370
5. Clifford, P.J., Trythall, R., Parr, R.S., Moulds, T.P., Cook, T., Allan, P.M. & Sutcliffe, P. 'Integration of 4D Seismic Data into the Management of Oil Reservoirs with Horizontal Wells between Fluid Contacts. SPE 83956, 2003.
6. Dubucq, D., Lefeuvre, F. & Bertini, F. Deep offshore seismic monitoring: the Girassol field, a West Africa textbook example. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT1.3
7. Furre, A-K., Munkvold, F.R. & Nordby, L.H. Improving Reservoir Understanding Using Time-Lapse Seismic At The Heidrun Field. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A20.
8. Guderian, K., Kleemeyer, M., Kjeldstad, A., Pettersson, S.E. & Rehling, J. Draugen Field – Successful Reservoir Management Using 4d Seismic. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A01.
9. Hague, P., Staples, R., Weisenborn, T. & Ashton, P. 4d Seismic For Oil Rim Monitoring. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A03.
10. Hall, S.A. & MacBeth, C., Stammeijer, J. & Omerod, M. Time-lapse seismic analysis of pressure depletion in the Southern Gas Basin. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT2.4
11. Hatchell, P.J., van den Beukel, A., Molenaar, M.M., Maron, K.P., Kenter, C.J., Stammeijer, J.G.F., van der Velde, J.J. & Sayers, C.M. Whole earth 4D: reservoir monitoring geomechanics. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT1.1
12. Johnston, D.E., Eastwood, J.E., Shyeh, J.J., Vauthrin, R., Khan, M., & Stanley, L.R. Using legacy data in an integrated time-lapse study: Lena Field, Gulf of Mexico. 2000. The Leading Edge 19 pp. 294-302
13. Johnston, D.H. & Gouveia, W.P., Solberg, A. & Lauritzen, M. Integration of Time-Lapse Seismic and Production Logging Data: Jotun Field, Norway. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT1.5
14. Kloosterman, H.J., Kelly, R.S., Stammeijer, J., Hartung, M., van Waarde, J. & Chajecki, C. Successful application of time-lapse seismic data in Shell Expro's Gannet Fields, Central North Sea, UKCS. Petroleum Geoscience, Vol 9 2003, pp. 25-34.
15. Kovacic, L. & Poggialiomi, E. Integrated time lapse reservoir monitoring and characterisation of the Cervia Field - a case study. Petroleum Geoscience, Vol 9 2003, pp. 43-52.
16. Landrø, M., 2001, Discrimination between pressure and fluid saturation changes from time lapse seismic data. Geophysics, Soc. of Expl. Geophys., 66, pp. 836-844.
17. Lefeuvre, F., Medina, S., Charrier, P., L’Houtellier, R. & Dubucq, D. Improved Reservoir Understanding through, Rapid and Effective 4D: Girassol field, Angola, West Africa. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT1.2
18. Lumley, D., Meadows, M., Cole, S. & Adams, D. Estimation of reservoir pressure and saturations by crossplot inversion of 4D seismic attributes. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT6.7
19. Lygren, M. et al. A method for performing history matching of reservoir flow models using 4D seismic data. Petroleum Geoscience, Vol 9 2003, pp. 85-90.
20. MacBeth, C., Stammeijer, J. & Ormerod, M. A petroelastic-based feasibility study of monitoring pressure depletion in a UKCS gas reservoir. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT4.1
21. Marsh, J.M., Whitcombe, D.N., Raikes, S.A., Parr, R.S. and Nash, T. BP's Increasing Systematic Use Of Time-Lapse Seismic Technology. Petroleum Geoscience, Vol 9 2003, pp. 7-13.
22. McInally, A.T. et al. Optimising 4D fluid imaging. Petroleum Geoscience, Vol 9 2003, pp. 91-101.
23. Najjar, N.F., Stronen, L.K. & Alsos, T. Time lapse seismic programme at Gullfaks: value and the road ahead. Petroleum Geoscience, Vol 9 2003, pp. 35-41.
24. Nickel, M., Schlaf, J. & Sonneland, L. New tools for 4D seismic analysis in compacting reservoirs. Petroleum Geoscience, Vol 9, pp. 53-59
25. Oldenziel, T. Time-lapse Seismic Within Reservoir Engineering. PhD Thesis, Delft University of Technology, 2003.
26. Parr, R.S. & Marsh, J.M. Development of 4D Reservoir Management West of Shetland. World Oil. September, 2000.
27. Parr, R.S., Trythall, R., Wreford, J. & Smout, A. Andrew Seismic Reservoir Surveillance. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A17.
28. Riviere, M. C. , McKenzie, C. J. & Zhou, J., 2003, The Evaluation of 4D Seismic over the Yacheng Gas Field, South China Sea, South-East Asia Petroleum Exploration Society Press, Volume 6, Issue 6, December 2003, pp 57-63.
29. Santos, R.A. et al. 4D Integrated technologies for deep water turbidite reservoirs - from petrophysics to fluid flow simulation. Petroleum Geoscience, Vol 9 2003, pp. 73-84.
30. Slater, C.P., Fletcher, J., Walder, D., Marsh, J.M., MacGregor, J. 4D Monitoring of Schiehallion Field, UKCS. 64th Conference & Exhibition, EAGE, Florence, 2002. Expanded abstract Z99.
31. Stammeijer, J. & Landrø, M. Quantitative Estimation Of Compaction And Velocity Changes Using 4d Impedance And Travel Time Changes. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract A10.
32. Veire, H.H., Borgos, H.G. & Landrø, M. Stochastic inversion of pressure and saturation changes from time-lapse seismic data. 73rd Ann. Internat. Mtg., Soc. Expl. Geophys., Dallas. 2003, Expanded abstract RCT2.5
33. Waggoner, J.R., Cominelli, A., Seymour, R.H. & Stradiotti, A. Improved reservoir modelling with time lapse seismic data in a Gulf od Mexico gas condensate reservoir. Petroleum Geoscience, Vol 9 2003, pp. 61-72.
34. Watts, G., Jizba, D.,Gawith, D., Gutteridge, P. 1996: Reservoir Monitoring of the Magnus Field through 4D-seismic analysis. Petroleum Geoscience, 2, 361-372.
35. Whitcombe, D., Connolly, P.A., Reagan, R., L., and Redshaw, T., C., 2002, Extended elastic impedance for fluid and lithology prediction. Geophysics, Vol 67, No 1, P 63-67.
36. Zhang, F., Skjervheim, J.A. & Reynolds, A.C. An Automatic History Matching Example. 65th Conference & Exhibition, EAGE, Stavanger, 2003. Expanded abstract D45.