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Modeling Channel Forms and Related Sedimentary Objects Using a Boundary Representation Based on Non-uniform Rational B-Splines

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Abstract

This paper aims at providing a flexible and compact volumetric object model capable of representing many sedimentary structures at different scales. Geobodies are defined by a boundary representation; each bounding surface is constructed as a parametric deformable surface. A three-dimensional sedimentary object with a compact parametrization which allows for representing various geometries and provides a curvilinear framework for modeling internal heterogeneities is proposed. This representation is based on non-uniform rational basis splineswhich smoothly interpolate between a set of points. The three-dimensional models of geobodies are generated using a small number of parameters, and hence can be easily modified. This can be done by a point and click user interaction for manual editing or by a Monte-Carlo sampling for stochastic simulation. Each elementary shape is controlled by deformation rules and has connection constraints with associated objects to maintain geometric consistency through editing. The boundary representations of the different sedimentary structures are used to construct hexahedral conformal grids to perform petrophysical property simulations following the particular three-dimensional parametric space of each object. Finally these properties can be upscaled, according to erosion rules, to a global grid that represents the global depositional environment.

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References

  • Abrahamsen P, Fjellvoll B, Hauge R (2007) Process based stochastic modelling of deep marine reservoirs. In: EAGE petroleum geostatistics

  • Abreu V, Sullivan M, Pirmez C, Mohrig D (2003) Lateral accretion packages (laps): an important reservoir element in deep water sinuous channels. Mar Pet Geol 20(6):631–648

    Article  Google Scholar 

  • Allen JR (1963) The classification of cross-stratified units. With notes on their origin. Sedimentology 2(2):93–114

    Article  Google Scholar 

  • Alpak FO, Barton MD, Naruk SJ (2013) The impact of fine-scale turbidite channel architecture on deep-water reservoir performance. AAPG Bull 97(2):251–284. doi:10.1306/04021211067

    Article  Google Scholar 

  • Arnott R (2010) Deep-marine sediments and sedimentary systems. In: James N, Dalrymple R (eds) Facies model 4. St. John’s: Geological Association of Canada, pp 295–322

  • Bertoncello A, Caers JK, Biver P, Caumon G (2008) Geostatistics on stratigraphic grids. In: Ortiz J, Emery X (eds) Proc. eighth geostatistical geostatistics congress, vol 2. Gecamin ltd, pp 677–686

  • Bertoncello A, Sun T, Li H, Mariethoz G, Caers J (2013) Conditioning surface-based geological models to well and thickness data. Math Geosci 45(7):873–893

    Article  Google Scholar 

  • Bhattacharya J (2010) Deltas. In: James N, Dalrymple R (eds) Facies model 4. St. John’s: Geological Association of Canada, pp 233–264

  • Boisvert JB, Pyrcz MJ, Deutsch CV (2007) Multiple-point statistics for training image selection. Nat Resour Res 16(4):313–321

    Article  Google Scholar 

  • Comunian A, Jha SK, Giambastiani BM, Mariethoz G, Kelly BF (2014) Training images from process-imitating methods. Math Geosci 46(2):241–260

    Article  Google Scholar 

  • Desbarats A (1987) Numerical estimation of effective permeability in sand–shale formations. Water Resour Res 23(2):273–286

    Article  Google Scholar 

  • Deutsch CV, Wang L (1996) Hierarchical object-based stochastic modeling of fluvial reservoirs. Math Geol 28(7):857–880

    Article  Google Scholar 

  • Deutsch C, Tran T (2002) Fluvsim: a program for object-based stochastic modeling of fluvial depositional systems. Comput Geosci 28(4):525–535

    Article  Google Scholar 

  • Deutsch C, Tran T (2004) Simulation of deepwater lobe geometries with object based modelling: Lobesim. Tech. rep., Tech. rep., University of Alberta. http://www.uofaweb.ualberta.ca/ccg//pdfs/1999%2004-LobeModeling1.pdf. Accessed 7 Dec 2015

  • Durlofsky LJ (2005) Upscaling and gridding of fine scale geological models for flow simulation. In: 8th international forum on reservoir simulation, Borromees Island, pp 20–24

  • Fisher T, Wales R (1992) Rational splines and multidimensional geologic modeling. In: Pflug R, Harbaugh J (eds) Computer graphics in geology, vol 41., Lecture notes in earth sciencesSpringer, Berlin, pp 17–28

    Chapter  Google Scholar 

  • Gai X, Wu Xh, Branets L, Sementelli K, Robertson G (2012) Concept-based geologic modeling using function form representation. In: Abu Dhabi international petroleum conference and exhibition

  • Google Earth (2012) Atchafalaya delta 29\(^\circ \)O

  • Graham GH, Jackson MD, Hampson GJ (2015) Three-dimensional modeling of clinoforms in shallow-marine reservoirs: part 1. Concepts and application. AAPG Bull 99(06):1013–1047. doi:10.1306/01191513190

    Article  Google Scholar 

  • Haldorsen H, Lake L (1984) A new approach to shale management in field-scale models. Old SPE J 24(4):447–457

    Google Scholar 

  • Hassanpour MM, Pyrcz MJ, Deutsch CV (2013) Improved geostatistical models of inclined heterolithic strata for McMurray formation, Alberta, Canada. AAPG Bull 97(7):1209–1224. doi:10.1306/01021312054

    Article  Google Scholar 

  • Holden L, Hauge R, Skare Ø, Skorstad A (1998) Modeling of fluvial reservoirs with object models. Math Geol 30(5):473–496

    Article  Google Scholar 

  • Howard A (1996) Modelling channel evolution and floodplain morphology. In: Floodplain processes, pp 15–62

  • Howard A, Knutson T (1984) Sufficient conditions for river meandering: a simulation approach. Wat Resour Res 20(11):1659–1667

    Article  Google Scholar 

  • Issautier B, Fillacier S, Gallo YL, Audigane P, Chiaberge C, Viseur S (2013) Modelling of \({\rm CO}_2\) storage capacity and performance. Energy Procedia 37:5181–5190

  • Jackson M, Muggeridge A (2000) Effect of discontinuous shales on reservoir performance during horizontal waterflooding. SPE J 5(4):446–455

    Article  Google Scholar 

  • Jackson MD, Hampson GJ, Sech RP (2009) Three-dimensional modeling of a shoreface-shelf parasequence reservoir analog: part 2. Geologic controls on fluid flow and hydrocarbon recovery. AAPG Bull 93(9):1183–1208. doi:10.1306/05110908145

    Article  Google Scholar 

  • Journel A (1996) Conditional simulation of geologically averaged block permeabilities. J Hydrol 183(1): 23–35

    Article  Google Scholar 

  • Journel A, Gundeso R, Gringarten E, Yao T (1998) Stochastic modelling of a fluvial reservoir: a comparative review of algorithms. J Pet Sci Eng 21(1):95–121

    Article  Google Scholar 

  • Knighton D (2014) Fluvial forms and processes: a new perspective, 2nd edn. Routledge, New York

    Google Scholar 

  • Li H, Caers J (2011) Geological modelling and history matching of multi-scale flow barriers in channelized reservoirs: methodology and application. Pet Geosci 17(1):17–34. doi:10.1144/1354-079309-825

    Article  Google Scholar 

  • Lopez S (2003) Modélisation de réservoirs chenalisés méandriformes: une approche génétique et stochastique. PhD thesis, Mines Paris Tech

  • Manchuk JG, Deutsch CV (2012) Implementation aspects of sequential gaussian simulation on irregular points. Comput Geosci 16(3):625–637

    Article  Google Scholar 

  • Manchuk J, Leuangthong O, Deutsch CV (2005) Direct geostatistical simulation on unstructured grids. In: Geostatistics Banff 2004. Springer, New York, pp 85–94

  • Mariethoz G, Caers J (2014) Multiple-point geostatistics: stochastic modeling with training images. Wiley-Blackwell, New York

  • Mariethoz G, Comunian A, Irarrazaval I, Renard P (2014) Analog-based meandering channel simulation. Water Resour Res 50(2):836–854. doi:10.1002/2013WR013730

    Article  Google Scholar 

  • McKee ED (1957) Flume experiments on the production of stratification and cross-stratification. J Sediment Res 27(2):129–134. doi:10.1306/74D70678-2B21-11D7-8648000102C1865D

    Google Scholar 

  • Miall A (1985) Architectural-element analysis: a new method of facies analysis applied to fluvial deposits. Earth-Sci Rev 22(4):261–308

    Article  Google Scholar 

  • Miall A (1996) Geology of fluvial deposits: sedimentary facies. Springer, New York

  • Miall A (2010) Alluvial deposits. In: James N, Dalrymple R (eds) Facies model 4. St. John’s: Geological Association of Canada, pp 105–138

  • Mirowski PW, Tetzlaff DM, Davies RC, McCormick DS, Williams N, Signer C (2009) Stationarity scores on training images for multipoint geostatistics. Math Geosci 41(4):447–474

    Article  Google Scholar 

  • Nordahl K, Ringrose PS, Wen R (2005) Petrophysical characterization of a heterolithic tidal reservoir interval using a process-based modelling tool. Pet Geosci 11(1):17–28

    Article  Google Scholar 

  • Novakovic D, White C, Corbeanu R, Hammon Iii W, Bhattacharya J, McMechan G (2002) Hydraulic effects of shales in fluvial–deltaic deposits: Ground-penetrating radar, outcrop observations, geostatistics, and three-dimensional flow modeling for the Ferron sandstone, Utah. Math Geol 34(7):857–893

    Article  Google Scholar 

  • Piegl L, Tiller W (1995) The NURBS book. Springer, London

    Book  Google Scholar 

  • Pyrcz MJ, Catuneanu O, Deutsch CV (2005) Stochastic surface-based modeling of turbidite lobes. AAPG Bull 89(2):177–191. doi:10.1306/09220403112

  • Pyrcz M, Boisvert J, Deutsch C (2009) Alluvsim: a program for event-based stochastic modeling of fluvial depositional systems. Comput Geosci 35(8):1671–1685. doi:10.1016/j.cageo.2008.09.012

    Article  Google Scholar 

  • Reineck HE, Singh IB (1980) Depositional sedimentary environments. Springer, Berlin

    Book  Google Scholar 

  • Renard P, Mariethoz G (2014) Special issue on 20 years of multiple-point statistics: part 1. Math Geosci 46(2):129–131

    Article  Google Scholar 

  • Rongier G, Collon P, Renard P, Ruiu J (2015) Channel simulation using L-system, potential fields and NURBS. In: Petroleum geostatistics 2015, EAGE. doi:10.3997/2214-4609.201413604

  • Ruiu J, Caumon G, Viseur S, Antoine C (2014) Modeling channel forms using a boundary representation based on non-uniform rational b-splines. In: Mathematics of planet earth. Springer, New York, pp 581–584

  • Ruiu J, Caumon G, Viseur S (2015) Semiautomatic interpretation of 3D sedimentological structures on geologic images: an object-based approach. Interpretation 3(3):SX63–SX74

  • Sech RP, Jackson MD, Hampson GJ (2009) Three-dimensional modeling of a shoreface-shelf parasequence reservoir analog: part 1. Surface-based modeling to capture high-resolution facies architecture. AAPG Bull 93(9):1155–1181. doi:10.1306/05110908144

    Article  Google Scholar 

  • Shtuka A, Samson P, Mallet JL (1996) Petrophysical simulation within an object-based reservoir model. In: Proc. European 3D reservoir modelling conference (SPE 35480)

  • Viseur S (2004) Turbidite reservoir characterization: object-based stochastic simulation meandering channels. Bull Soc Geol Fr 175(1):11–20. doi:10.2113/175.1.11

    Article  Google Scholar 

  • Wen R, Martinius A, Næss A, Ringrose P (1998) Three-dimensional simulation of small-scale heterogeneity in tidal deposits—a process-based stochastic simulation method. In: Proceedings of the 4th annual conference of the international association of mathematical geology (IAMG). De Frede editore, Ischia, pp 129–134

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Acknowledgments

This research was performed in the frame of the Research for Integrative Numerical Geology (RING) project. We would like to thank our colleagues from RING for their help during the development of the project. The companies and universities members of the GOCAD consortium managed by ASGA (http://www.ring-team.org/index.php/consortium/) are acknowledged for their support. We thank Paradigm for providing the SKUA-GOCAD software and development kit.

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Correspondence to Jeremy Ruiu.

Appendix: Construction of an Oxbow Lake

Appendix: Construction of an Oxbow Lake

Oxbow lakes are formed during the lateral migration of a channel when a meander becomes much curved and the two concave banks at the extremity of the meander become very close. The meander cutoff occurs when the neck of land between the two concave banks is eroded due to the channel migration or due to strong current during a flooding event. The meander is then abandoned and a shortcut in the channel path is created where two concave banks are connected (Fig. 19).

Fig. 19
figure 19

Principle of the formation of an oxbow lake during the lateral migration of a meander

In the proposed approach, this process is taken into account during the lateral channel migration by detecting the self-intersections in the control point net of the top surface of the channel.

1.1 Segment Intersection

Considering two non-parallel segments belonging, for instance, to the control mesh of a channel top surface. Their intersections can be computed by determining two scalar values a and b such that

(8)

where \(\mathbf {p}_1\) is the origin of the first segment, \(\mathbf {p}_2\) the origin of the second segment, and \(\mathbf {v}_1\) and \(\mathbf {v}_2\) the vectors corresponding to the segments.

Taking the cross-product of Eq. (8) with \(\mathbf {v}_1\) and \(\mathbf {v}_2\), respectively, the following expressions come for a and b

$$\begin{aligned} \left\{ \begin{array}{l} a \; (\mathbf {v}_1\times \mathbf {v}_2) = (\mathbf {p}_2 - \mathbf {p}_1)\times \mathbf {v}_2 \\ b \; (\mathbf {v}_2\times \mathbf {v}_1) = (\mathbf {p}_1 - \mathbf {p}_2)\times \mathbf {v}_1. \\ \end{array} \right. \end{aligned}$$
(9)

If \(0\le a \le 1\) and \(0\le b \le 1\), the two segments intersect and the coordinates of the intersection points are given by \(\mathbf {p}_{\mathrm{inter}} = \mathbf {p}_1 + a \mathbf {v}_1\).

1.2 Construction of an Oxbow Lake

The construction of an oxbow lake from a NURBS channel is divided in five steps:

  1. 1.

    Detection of the self-intersection for each side of the channel top surface (Fig. 20a). The indexes of the channel sections behind and ahead of the intersecting segments are memorized.

  2. 2.

    Sorting and reduction of the number of intersections. This operation may be needed to keep only one intersection, see below.

  3. 3.

    Increasing knot multiplicity at the parametric coordinate corresponding to the projection of the intersection point on the NURBS. This operation is used to decrease the continuity of the NURBS while preserving its shape.

  4. 4.

    Selection of the sections before and after the intersection point (Fig. 20b).

  5. 5.

    Lateral closure of the oxbow lake sections and connection of the cutoff sections to maintain channel continuity (Fig. 20c)

Fig. 20
figure 20

Construction of an oxbow. During the process of a channel lateral migration, self-intersections can occur and are associated with the formation of an oxbow lake. a Self-intersections are detected (red points). b Sections before and after the self-intersection are detected (red lines). c Control point between sections are detected and suppressed to form the oxbow lake and the new channel path

Fig. 21
figure 21

Sorting of the different cases of self-intersections during the NURBS channel migration. a Unique intersection. b Sections associated with an intersection (in green) are included between the sections corresponding to another intersection (in red). c The first section associated with an intersection (in red) is included between the sections corresponding to another intersections (in green). d The second section associated with an intersection (in red) is included between the sections corresponding to another intersections (in green)

Fig. 22
figure 22

Result of the oxbow lake formation during the channel lateral migration

The proposed intersection sorting is essential to obtain consistent final geometry. For each intersection, a couple of channel section indexes are determined. Their number has to be reduced so that only one intersection by meander remains. Four possible configurations have been determined. If there is only one intersection in the meander, then it is directly stored in the final intersection list (Fig. 21a). If two sections (in green in Fig. 21b) are included into another one (in red in Fig. 21b), only the largest one is kept in the final list. If the first element of a couple of sections linked with a self-intersection (in red in Fig. 21c) is included in the space defined by another couple (in green in Fig. 21c), the second couple of intersections in kept in the final list of intersections (Fig. 21c). If the second element of a couple of sections linked with a self-intersection (in red in Fig. 21d) is included in the space defined by another couple (in green in Fig. 21d), the first couple of intersections is kept in the final list of intersections (Fig. 21d).

Once the final intersection list has been determined, the control points between two sections corresponding to an intersection are removed from the channel (Fig. 20c). The result of the channel lateral migration process associated with the construction of oxbow lakes is shown in Fig. 22.

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Ruiu, J., Caumon, G. & Viseur, S. Modeling Channel Forms and Related Sedimentary Objects Using a Boundary Representation Based on Non-uniform Rational B-Splines. Math Geosci 48, 259–284 (2016). https://doi.org/10.1007/s11004-015-9629-3

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