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Reservoir Characterization Using Expert Knowledge, Data and Statistics
Prepared with assistance from:
Peter Corvi, Head of Effective PropertiesKes Heffer, Head of Reservoir DescriptionPeter King, Senior Reservoir Engineer,
Effective PropertiesStephen Tyson, Computing ConsultantGeorges Verly, Senior Geostatistician,
Effective PropertiesBP Research Sunbury-on-Thames, England
Christine Ehlig-Economides, ReservoirDynamics Program Leader
Isabelle Le Nir, GeologistShuki Ronen, Geophysics Integration
Program LeaderPhil Schultz, Department Head Etudes et Productions SchlumbergerReservoir Characterization DepartmentClamart, France
Patrick Corbett, Research AssociateJonathan Lewis, Conoco Lecturer in GeologyGillian Pickup, Research AssociatePhillip Ringrose, Senior Research AssociateHeriot-Watt UniversityDepartment of Petroleum EngineeringEdinburgh, Scotland
Dominique Gurillot, Sr. Research Engineer, Reservoir Engineering Division
Lucien Montadert, Director of Exploration and Production
Christian Ravenne, Principal Research Associate,Geology and Chemistry Division
Institut Franais du Ptrole Rueil-Malmaison, France
Helge Haldorsen, Assistant Director, InternationalField Developments
Norsk Hydro A.S. Oslo, Norway
Thomas Hewett, Professor of Petroleum Engineering
Stanford University California, USA
Accurately simulating field performance requires knowing properties
like porosity and permeability throughout the reservoir. Yet wells provid-
ing the majority of data may occupy only a billionth of the total reser-
voir volume. Transforming this paucity of data into a geologic model for
simulating fluid flow remains an industry priority.
The simplest representation of a reservoir isa homogeneous tank with uniform proper-ties throughout. Characterizing such a sim-plified model requires merely enough datato define its volume and basic petrophysicalproperties. This information can then beused to help forecast reservoir behavior andcompare possible production scenarios.
But efficient exploitation of reservesrequires a more sophisticated approach.Reservoirs have been created by complexsedimentary and diagenetic processes, andmodified by a history of tectonic change.Rather than resembling simple tanks, reser-voirs are heterogeneous structures at everyscale. The need for accurate characteriza-tion is vital for oil company economic plan-ning (next page, top).
In the past, geologists have used expertknowledge to interpolate between wells andproduce basic reservoir models. However,during the last ten years, this effort has beentransformed by statistical modeling offeringinsight into the effects of heterogeneity.
This article details how reservoir descrip-tions are developed and is divided intothree stages (page 27):Defining the reservoirs large-scale struc-
ture using deterministic dataDefining the small-scale structure using
statistical techniquesgeostatistics Rescaling the detailed geologic model to
be suitable input for a fluid-flow simulator.
Cray is a mark of Cray Research Inc. MicroVAX is a markof Digital Equipment Corp.1. Weber KJ: How Heterogeneity Affects Oil Recovery,
in Lake LW and Carroll HB Jr (eds): Reservoir Charac-terization. Orlando, Florida, USA: Academic PressInc. (1986): 487-544.
Defining the Large-Scale StructureThe first stage aims to construct as detailed ageologic description of the reservoir as mea-surements will allow. The available informa-tion includes a structural interpretation fromseismic data, static data from cores and logsand dynamic production data from testing.To these, the geologist adds expert knowl-edge on geologic structures, obtained fromfield experience and outcrop studies (seeGathering Outcrop Data, page 28).
The first step is to recognize depositionalunits and then, if possible, correlate thembetween wells. It helps to know the generalshape and order of deposition of the units,and ideally the relationships between width,thickness and length for each unit.1 Well-to-well correlation is made using traditionalhard-copy maps and logs or, today, interac-tive computer workstations. Workstationsallowing simultaneous visualization of seis-mic, log and other data enable easier recog-nition of subtle correlations.
In most cases, this large-scale characteri-zation results in a conventional layer-cakemodela stack of subhomogeneous layers.There is no doubt that many reservoirsshould be characterized by more complexarrangements of depositional unitssuch asthe jigsaw and labyrinth models (nextpage, bottom).2 The ability to use thesemore complex models routinely remains anindustry goal (see Trends in Reservoir Man-agement, page 8).
2. Weber KJ and van Geuns LC: Framework For Con-structing Clastic Reservoir Simulation Models, Jour-nal of Petroleum Technology 42 (October 1990):1248-1253, 1296-1297.
nHow heterogeneity affects productivity. In this example developed at BP Research,Sunbury-on-Thames, England, two different models have been interpolated between apair of hypothetical wells. The lithologies are identical: only their spatial correlationhas been altered. The graph shows how the pore volume recovered versus pore volumeinjected varies for the two models. After one pore volume has been injected, the differ-ence in recovery between the two models is about 0.1 of a pore volume.
nDefining reservoir type. Current large-scale characterization tends to result in a sim-ple layer-cake model. However, efforts are being made to use more complex modelslike the jigsaw and labyrinth models shown.
Heterogeneity case A
Hypothetical well data
Heterogeneity case B
Pore volume (PV) injected
00 0.2 0.4 0.6 0.8 1.0
Case BCase A
Predicted oil recovery
Layer cake Jigsaw Labyrinth
Barrier footTransgressive deposit Barrier barBarrier bar Crevasse splay
Distributary channel fill
Defining the Small-Scale Structure UsingGeostatistical ModelingOnce the shape of the reservoir and itslarge-scale structure have been described,the next stage focuses on defining hetero-geneity within each depositional unit. Thisrequires more detailed interwell measure-ments than are currently available. Boreholeseismic data typically cannot span wells.Although surface and cross-well seismicdata do reach everywhere, there is insuffi-cient knowledge relating seismic data tophysical rock properties, and explorationseismic data have insufficient resolution. Inaddition, well testing data often lack direc-tional information. Therefore nondetermin-istic, or geostatistical, methods are required.3
Large-scale reservoir models typicallycomprise a few thousand grid blocks, eachmeasuring about 100 m square and morethan 1-m thick [about 1000 ft2 by 3 ft] Butmodels that take into account small-scaleheterogeneity use smaller grid blocks1million or more are often needed. The sheerquantity of input data required to fill somany blocks also favors geostatistics.
Geostatistical techniques designed to pro-vide missing data can be classified in twoways: by the allowable variation of theproperty at a given point and by the waydata are organized. There are two classes ofproperty variation, continuous and discrete.Continuous models are suited to propertieslike permeability, porosity, residual satura-tion and seismic velocity that can take anyvalue. Discrete models are suited to geo-logic properties, like lithology that can berepresented by one of a few possibilities.4
Data organization has developed alongtwo paths depending on how the modelsare built up. One is grid based. All theproperties are represented as numbers on agrid, which is then used for fluid-flow simu-lation. The second is object based. Reser-voir features such as shales or sands aregenerated in space and a grid then superim-posed on them.
Grid-Based ModelingOne of the key geostatistical tools used ingrid-based modeling is krigingnamed aftera pioneer of rock-property estimation, D. G.Krige, who made a series of empirical stud-ies in the South African goldfields.5 Krigingis designed to honor measured data andproduce spatially correlated heterogeneity.The heart of the technique is a two-pointstatistical function called a variogram thatdescribes the increasing difference (ordecreasing correlation) between sample val-ues as separation between them increases.
Stage 2: Defining small-scale structure
Stage 1: Defining large-scale structure
Core plugs Whole core Well logs Well testing
Borehole geophysics Surface seismicsOutcrop studies
Geologists expert knowledge
Kriging minimizes the error between theinterpolated value and the actual (butunknown) value of the property.6
For a given property, a variogram is con-structed from pairs of data generally mea-sured in wells. In the oilfield, criticism ofkriging centers on the low density of welldata when compared to mining. To over-come this problem, variograms can alterna-tively be constructed from data measuredon outcrops or in mature fields where bettersampling is available.
Although variograms are commonly usedto infer the spatial continuity of a singlevariable, the same approach can be used tostudy the cross-continuity of several differ-ent variablesfor example, the porosity atone location can be compared to seismictransit time. Once constructed, this cross-correlation can be used in a multivariateregression known as cokriging. In this way,a fieldwide map of porosity can be com-puted using not only porosity data, but alsothe more abundant seismic data.7
Kriging and cokriging deal with quantita-tive, or hard, data. A third method calledsoft kriging combines expert information,also called soft data, with the quantitativedata. The expert data are encoded in theform of inequalities or probability distribu-tions. For example, in mapping a gas/oilcontact (GOC), if the contact is not reachedin a certain well, soft kriging uses theinequality: GOC is greater than well totaldepth. At any given point where there is nowell, an expert may define the probability offinding the GOC within a certain depthinterval. Soft kriging will use this probability.8
(continued on page 30)
nBuilding a reservoir model in three stages with measured data, expert knowledgeand statistics.
3. Haldorsen HH and Damsleth E: Stochastic model-ing, Journal of Petroleum Technology 42 (April1990): 404 -412. Haldorsen HH and Damsleth E:Challenges in Reservoir Characterization Research,presented at Advances in Reservoir Technology, Char-acterization, Modelling & Management, organized bythe Petroleum Science and Technology Institute, Edin-burgh, Scotland, February 21-22, 1991.
4. Journel AG and Alabert GF: New Method For Reser-voir Mapping, Journal of Petroleum Technology 42(February 1990): 212-218.
5. Krige DG: A Statistical Analysis of Some of the Bore-hole Values of the Orange Free State Goldfield, Jour-nal of the Chemical, Metallurgical and Mining Societyof South Africa 53 (1952): 47-70.
6. Journel AG: Geostatistics for Reservoir Characteriza-tion, paper SPE 20750, presented at the 65th SPEAnnual Technical Conference and Exhibition, NewOrleans, Louisiana, USA, September 23-26, 1990.
7. Doyen PM: Porosity From Seismic Data: A Geostatis-tical Approach, Geophysics 53 (October 1988):1263-1275.
8. Kostov C and Journel AG: Coding and ExtrapolatingExpert Information for Reservoir Description, in LakeLW and Carroll HB (eds): Reservoir Characterization.Orlando, Florida, USA: Academic Press Inc. (1986):249-264.
Stage 3: Scaling up
30,000 grid blocks
1 million grid blocks
nPhotomosaic of the Tensleep Sandstone in the Big Horn basin, Wyoming, USA. ThisPennsylvanian-age sandstone is being studied because of its similarity to the Rotliegen-des Sandstone, an important gas reservoir unit found in the Southern Basin of the NorthSea. Like most outcrops, it is irregularly shaped. Therefore, to obtain the mosaic withconstant scale and no distortion, overlapping photographs were taken from points in aplane parallel to a major depositional surface on the outcrop. With a helicopter posi-tioned in this plane, laser range finders were used to maintain a fixed distance fromthe outcrop to ensure constant scale. This was double-checked by positioning scalebars at regular intervals on the outcrop. The mosaic was prepared during studies byJon Lewis and Kjell Rosvoll at Imperial College, University of London, England andHeriot-Watt University, Edinburgh, Scotland with the support of Conoco (UK) Ltd.
Every reservoir is unique. Yet recurring deposi-
tional conditions create families of reservoirs
that are broadly similar. If reservoir rock outcrops
at the surface, it presents a golden opportunity to
gather data which can be used to help character-
ize related subsurface formations.1
Outcrop studies allow sampling at scales that
match interwell spacing. Small-scale permeabil-
ity can also be measured and compared with
depositional characteristics. Then the statistical
and depositional information measured at the
outcrop can be incorporated into the stochastic
modeling of the subsurface analog.2
Generally, there are two kinds of outcrop
study.3 Bed or bed-set scale genetic models
(GEMs) concentrate on reservoir heterogeneity as
a function of the sedimentary process. To date,
most outcrop studies have been of this type. The
second type, a field analog model (FAM), looks at
heterogeneity caused not only by depositional
processes but also by diagenesis. FAMs present
two difficulties. First, it is hard, given the paucity
of reservoir data, to decide which diagenetic pro-
cesses are relevant. Second, finding outcrops
that have undergone specific diagenetic pro-
cesses can be difficult.
Although the study of outcrops is a traditional
skill learned by most geologists, in the past they
have concentrated on such goals as describing
the sedimentological environment and the
geometries of the major depositional units. For
reservoir characterization, a much more detailed
and quantitative picture of the heterogeneity
within depositional sequences is required.
Therefore, in addition to the geologists tradi-
tional mapping skills, new analytical techniques
have been devised. Analyzing photographs of the
outcrop helps clarify the lithofacies distribution.4
Clearly, more than one photograph is needed,
and this technique can succeed only if all the
photographs are at the same scale and taken at
the same distance from the rock. A helicopter-
mounted camera with a laser range finder is one
way of achieving this. Successive photographs
are taken with a 30 to 40% overlap and then com-
bined to construct a photomosaic of the whole
Gathering Outcrop Data
outcrop (above). The mosaic can be digitized to
quantify lithofacies patterns. It can also be used
to plan and execute small-scale petrophysical
measurements on the outcrop.
Gamma ray logging of outcrops is used to help
describe the composition of the lithofacies. A
standard truck-mounted sonde can be lowered
down the face of an outcrop and g...