Quantifying and communicating uncertainty from
reservoir models is a big topic, and is one which is not
covered in any detail here. It is important to understand and communicate to the decision makers (for
example of a field development or an IOR initiative)
that the deterministic outcomes of a modelling exercise (such as volumes, production profiles or well
positions) are only one valid solution in an often
large outcome space. If some of the input parameters
are skewed (e.g. having a large probability of a fluid
contact being deeper rather than shallower) this will
affect the uncertainty analysis, and the statistical mean
value may deviate significantly from the deterministic
base case model.
22.6 Summary
The purpose of this chapter is to introduce some of the
key elements and success factors for 3D reservoir
modelling, which now is a major activity in most oil
and gas companies. Proper planning with clearly
stated business objectives is a good starting point for
successful reservoir modelling. Constructing a high
quality database of modelling input data can be time
consuming, however it will ultimately save time for
the project. The construction of reservoir models is an
integrated and multidisciplinary process, involving
static and dynamic data. Many disciplines provide
input to the geomodel, and the geomodel is most
often handed over to reservoir engineers for dynamic
simulations. Keeping all project members actively
involved and the ability to communicate clearly
between all the involved disciplines is a key factor
for achieving a good result. Through modelling alternative conceptual scenarios, and by making multiple
stochastic realisations, the uncertainty related to
estimated static and dynamic volumes can be
quantified.
Acknowledgement John Thurmond and Sarah Riordan
provided many useful comments that improved the manuscript
significantly.
References and Further Reading
Bjørlykke, K. 2010. Petroleum Geoscience: From Sedimentary
Environments to Rock Physics. Springer, 508 pp.
Christie, M. 1996. Upscaling for reservoir simulation. Journal of
Petroleum Technology 48(11), 1004–1010. ISSN: 01492136
Damsleth, E., Tjolsen, C.B., Omre, H. and Haldorsen, H.H.
1992. A two-stage stochastic model applied to a North Sea
reservoir. SPE (Society of Petroleum Engineers) Reservoir
Engineering 44(4), 402–408.
Deutsch, C.V. 2002. Geostatistical Reservoir Modeling. Oxford
University Press, 376 pp.
Deutsch, C.V. and Meehan, D.N. 1996. Geostatistical
techniques improve reservoir management. Petroleum Engineer International 999, 21–27.
Haldorsen, H.H. and Damsleth, E. 1990. Stochastic modeling.
Journal of Petroleum Technology 42, 404–412.
Liu, K., Paterson, L., Wong, P. and Qi, D. 2002. A sedimentological approach to upscaling. Transport in Porous Media
46(2), 285–310.
Lun, L., Dunn, P., Stern, D., Oyerinde, A., Chorneyko, D.,
Stewart, J., et al. 2012. A procedure for integrating geologic
concepts into history matching. SPE Annual Technical Conference and Exhibition. SPE-159985.
Pickup, G.E., Stephen, K.D., Ma, J., Zhang, P. and Clark, J.D.
2005. Multi-stage upscaling: Selection of suitable methods.
Transport in Porous Media 58(555), 191–216.
Scaglioni, P., Ruvo, L. and Cozzi, M. 2006. Implicit net-to-gross
in the petrophysical characterization of thin-layered
reservoirs. Petroleum Geoscience 12(4), 325–333.
Thurmond, J.B., Løseth, T.M., Rivenæs, J.C., Martinsen, O.J.,
Xu, X. and Aiken, C. 2005. Using outcrop data in the 21st
century–New methods and applications, with example from
the Ainsa Turbidite System, Ainsa, Spain. Ch. 6 in Deepwater outcrops of the world atlas: Tulsa, Oklahoma: American Association of Petroleum Geologists Special Publication
CD-ROM.
van Wagoner, J.C., Mitchum, R.M. Jr., Campion, K.M. and
Rahmanian, V.D. 1990. Siliciclastic sequence stratigraphy
in well logs, cores and outcrops: Concepts for highresolution correlation of time and facies. Tulsa: AAPG
Methods in Exploration Series, No. 7. The American Association of Petroleum Geologists, 55 pp.
Worthington, P.F. and Cosentino, L. 2005. The role of cutoffs in
integrated reservoir studies. SPE (Society of Petroleum
Engineers) Reservoir Evaluation and Engineering 8(4),
276–290. SPE-84387.
Zakrevsky, K.E. 2011. Geological 3D Modelling. EAGE
Publications, 261 pp.
580
J.C. Rivenæs et al.
reservoir models is a big topic, and is one which is not
covered in any detail here. It is important to understand and communicate to the decision makers (for
example of a field development or an IOR initiative)
that the deterministic outcomes of a modelling exercise (such as volumes, production profiles or well
positions) are only one valid solution in an often
large outcome space. If some of the input parameters
are skewed (e.g. having a large probability of a fluid
contact being deeper rather than shallower) this will
affect the uncertainty analysis, and the statistical mean
value may deviate significantly from the deterministic
base case model.
22.6 Summary
The purpose of this chapter is to introduce some of the
key elements and success factors for 3D reservoir
modelling, which now is a major activity in most oil
and gas companies. Proper planning with clearly
stated business objectives is a good starting point for
successful reservoir modelling. Constructing a high
quality database of modelling input data can be time
consuming, however it will ultimately save time for
the project. The construction of reservoir models is an
integrated and multidisciplinary process, involving
static and dynamic data. Many disciplines provide
input to the geomodel, and the geomodel is most
often handed over to reservoir engineers for dynamic
simulations. Keeping all project members actively
involved and the ability to communicate clearly
between all the involved disciplines is a key factor
for achieving a good result. Through modelling alternative conceptual scenarios, and by making multiple
stochastic realisations, the uncertainty related to
estimated static and dynamic volumes can be
quantified.
Acknowledgement John Thurmond and Sarah Riordan
provided many useful comments that improved the manuscript
significantly.
References and Further Reading
Bjørlykke, K. 2010. Petroleum Geoscience: From Sedimentary
Environments to Rock Physics. Springer, 508 pp.
Christie, M. 1996. Upscaling for reservoir simulation. Journal of
Petroleum Technology 48(11), 1004–1010. ISSN: 01492136
Damsleth, E., Tjolsen, C.B., Omre, H. and Haldorsen, H.H.
1992. A two-stage stochastic model applied to a North Sea
reservoir. SPE (Society of Petroleum Engineers) Reservoir
Engineering 44(4), 402–408.
Deutsch, C.V. 2002. Geostatistical Reservoir Modeling. Oxford
University Press, 376 pp.
Deutsch, C.V. and Meehan, D.N. 1996. Geostatistical
techniques improve reservoir management. Petroleum Engineer International 999, 21–27.
Haldorsen, H.H. and Damsleth, E. 1990. Stochastic modeling.
Journal of Petroleum Technology 42, 404–412.
Liu, K., Paterson, L., Wong, P. and Qi, D. 2002. A sedimentological approach to upscaling. Transport in Porous Media
46(2), 285–310.
Lun, L., Dunn, P., Stern, D., Oyerinde, A., Chorneyko, D.,
Stewart, J., et al. 2012. A procedure for integrating geologic
concepts into history matching. SPE Annual Technical Conference and Exhibition. SPE-159985.
Pickup, G.E., Stephen, K.D., Ma, J., Zhang, P. and Clark, J.D.
2005. Multi-stage upscaling: Selection of suitable methods.
Transport in Porous Media 58(555), 191–216.
Scaglioni, P., Ruvo, L. and Cozzi, M. 2006. Implicit net-to-gross
in the petrophysical characterization of thin-layered
reservoirs. Petroleum Geoscience 12(4), 325–333.
Thurmond, J.B., Løseth, T.M., Rivenæs, J.C., Martinsen, O.J.,
Xu, X. and Aiken, C. 2005. Using outcrop data in the 21st
century–New methods and applications, with example from
the Ainsa Turbidite System, Ainsa, Spain. Ch. 6 in Deepwater outcrops of the world atlas: Tulsa, Oklahoma: American Association of Petroleum Geologists Special Publication
CD-ROM.
van Wagoner, J.C., Mitchum, R.M. Jr., Campion, K.M. and
Rahmanian, V.D. 1990. Siliciclastic sequence stratigraphy
in well logs, cores and outcrops: Concepts for highresolution correlation of time and facies. Tulsa: AAPG
Methods in Exploration Series, No. 7. The American Association of Petroleum Geologists, 55 pp.
Worthington, P.F. and Cosentino, L. 2005. The role of cutoffs in
integrated reservoir studies. SPE (Society of Petroleum
Engineers) Reservoir Evaluation and Engineering 8(4),
276–290. SPE-84387.
Zakrevsky, K.E. 2011. Geological 3D Modelling. EAGE
Publications, 261 pp.
580
J.C. Rivenæs et al.