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Seismic and Well Log Reprocessing, Re-interpretation and Geostatistical InversionYields More Detailed View of Yuzhno Khilchuyu FieldBret Fossum, ConocoPhillips; John Snow, NaryanMarNefteGaz; Yoann Guilloux, Fugro-Jason and Inga Khromova,Andrey Chernitskiy and Aleksey Glebov, LUKoil

Copyright 2006, Society of Petroleum Engineers

This paper was prepared for presentation at the 2006 SPE Russian Oil and Gas TechnicalConference and Exhibition held in Moscow, Russia, 36 October 2006.

This paper was selected for presentation by an SPE Program Committee following review ofinformation contained in an abstract submitted by the author(s). Contents of the paper, aspresented, have not been reviewed by the Society of Petroleum Engineers and are subject tocorrection by the author(s). The material, as presented, does not necessarily reflect anyposition of the Society of Petroleum Engineers, its officers, or members. Papers presented atSPE meetings are subject to publication review by Editorial Committees of the Society ofPetroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paperfor commercial purposes without the written consent of the Society of Petroleum Engineers isprohibited. Permission to reproduce in print is restricted to an abstract of not more than300 words; illustrations may not be copied. The abstract must contain conspicuousacknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O.Box 833836, Richardson, TX 75083-3836 U.S.A., fax 01-972-952-9435.

AbstractLUKoil and ConocoPhillips formed the joint venture companyNaryanMarNefteGaz (NMNG) to develop jointly ownedlicenses in the Timan-Pechora Basin. The Yuzhno Khilchuyulicense lies in this province and, is expected to be one of the

largest and most prolific fields in the region. Development ofthe Yuzhno Khilchuyu Field requires a huge initial investment

in infrastructure, drilling and transportation. Successfullyachieving acceptable reserves and production levels from thefield will be critical to offset these investments. To meet thischallenge a more detailed understanding of the reservoir is

needed to optimize well placement.

In 2004, a large multi-disciplinary subsurface project teamwas formed with members from LUKoil, ConocoPhillips andFugro-Jason to develop updated high-resolution geologic andreservoir simulation models. The seismic and well log data

were completely reprocessed, resulting in a significantimprovement in the overall data quality. All log, core, and

production test data were incorporated into a new, fully

integrated, interpretation. A sophisticated Markov ChainMonte Carlo (MCMC) geostatistical inversion methodologywas applied, and the resulting high-resolution geologic model

yields a dramatic increase in reservoir detail. The new modelenabled the team to define the aerial extent of different

reservoirs and the distribution of internal barriers. It alsoprovided insight into porosity and permeability distributionwithin each reservoir, enabling better decisions on the locationof production and water injection wells. Development drillingis in progress.

IntroductionThe NMNG joint venture agreement between LUKoil (70%)

and ConocoPhillips (30%) was consummated in 2005 and is

comprised of two exploration and eleven production licenses.The NMNG assets are located in the Nenets AutonomousOkrug, situated in the northern Timan-Pechora Basin onshore

region (Figure 1). The hydrocarbon bearing sequences arepredominantly carbonates and secondarily siliciclasticsranging from the Silurian to the Triassic. The YuzhnoKhilchuyu Field is considered to be the most prolific of theNMNG joint venture portfolio.

The Yuzhno Khilchuyu Field reservoir characterization re-interpretation project that commenced in late 2004 and endedin early 2006, was carried out by an integrated technical teamfrom Fugro-Jason, NMNG, ConocoPhillips, and LUKoil(Guilloux, et. al., 2006). Although Fugro-Jason had theaccountability of executing and completing the project,

ConocoPhillips, LUKoil and NMNG collaborated with Fugro-Jason on a regular basis to facilitate knowledge sharing,

validate and understand interim results and to ensure the

resulting high-resolution geological model would initialize inthe selected reservoir simulation software. Collaborationactivities included participation in interim work sessions and

implementation of milestone-related peer reviews and projectreviews with pertinent company specialists and management.

Additionally, ConocoPhillips prepared a secondary shadowmodel utilizing Petrel to aid in validation of the project as itprogressed. The three companies contributed to the projectmanagement and all major technical project tasks including

data discovery and collection, seismic reprocessing, seismicinversion, petrophysics, rock physics, interpretation (seismic,

geology, biostratigraphy), petrophysical cluster analysis,stochastic porosity simulations, high-resolution geologic

modeling and analysis. The ongoing and successfulcollaboration between the three companies and Fugro-Jason

contributed significantly to the success of the project andensured alignment of interim and final results with each

company and defendable project results. Although a technicalinterpretation of the entire hydrocarbon-bearing sequence ofthe Yuzhno Khilchuyu Field was carried out during theproject, this paper will focus on the reservoir characterizationof the principal oil-bearing deposits in the Lower PermianAsselian-Sakmarian carbonate sequence.

Geological SettingThe Timan-Pechora Basin Province, located in the Arctic

coastal region of northwestern Russia, ranks high in

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Figure 1. NaryanMarNefteGaz (NMNG) Timan-Pechora Nenets Okrug Region Basemap. Annotated fields depict theeleven production licenses in the NMNG portfolio.

discovered oil and gas volumes (Lindquist, 1999). The basin is

characterized by one total petroleum system, where the mainsource rocks are basinal facies equivalents to shelfal carbonate

systems. The source rocks range in age from Ordovician toearliest Carboniferous. The onshore area of the province is315,100 km

2; the offshore area is 131,700 km

2, including

5,400 km2 of islands.

The basin overall consists of 55-60% carbonates, 35-40%

siliciclastics, and 5% evaporates (Dedeev et. al, 1993). Thepresence of oil has been known since at least 1595, and thefirst wells were drilled between 1869 and 1917 (Meyerhoff,1980). Known ultimate recoverable reserves are nearly 20BBOE, distributed as 66% oil, 30% gas and 4% condensate.Province wide, oil gravity ranges from 11-62 API, with an

average API of 35 degrees (Lindquist, 1999). Nearly 90% ofTiman-Pechora known reserves are associated with structuraltraps, however in the Kolva Swell region, Middle Devonianunconformities provide stratigraphic trapping mechanisms aswell.

Tectonics and Structure

The Timan-Pechora Basin is located on the eastern edge of theEast European Plate and was a passive margin basin during the

Paleozoic. Approximately 610 km of Paleozoic andMesozoic sediments overlies a thick upper Precambriangranitic-metamorphic basement as well as an intermediateRiphean-Vendian igneous-sedimentary layer overlying themetamorphic basement. Multiple phases of local inversionassociated with compression and transpression occurred along

a northwest-southeast trend during the OrdovicianDevonian.Past grabens are now structural highs, whereas past structuralhighs are now depressions.

The Yuzhno Khilchuyu field is located on the 350 km-longKolva swell, a broad regional structural high located partly

over an inverted lower PaleozoicMiddle Devonian basin(Figures 2 and 3 below). Immediately to the west lie theDenisov trough and the Lay swell, and to the east, theKhoreyver depression. The Khoreyver depression hasundergone little faulting or folding, whereas the westernmargin of the Timan-Pechora Basin, comprised of the Kolva

swell, Denisov trough, and Lay swell, has been much more

mobile.

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thbuildups hold large oil accumulations at Yuzhno Khilchuyu

Field Summ

Th e

re ede sited in response to sea

ower Zone C primarily present in the perimeterisplays for Zones A and

istribution based on theversion volume. In general, enhanced intraparticle and

terature). Oil accumulations have been identified througho

e Kolva swell in these carbonate buildups, and these

and Yareiyu fields (Swirydczuk, 2003).

ary

e principal Lower Permian Asselian-Sakmarian carbonat

servoir is a stacked succession of shallow-water carbonatposits, with individual cycles depo

level fluctuations (Figure 5). Each cycle consists of basal

wackestone or packstone overlain by Palaeoplysinidgrainstones or boundstones. Two main reservoir intervals exist

in the Asselian-Sakmarian sequence the upper Zone Asweet spot is primarily present in the middle portion of thefiel and the ldof the field. Figure 6, total amplitude d

, illustrate the horizontal reservoir dCin

Figure 4. Timan-Pechora Basin generalized stratigrap ction (Fossum et. al, 2001). AAPG 2001, repr inted byhic seher use.permiss ion of the AAPG who se permiss ion is requi red for fut

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interp

response to subaerial exposure (Swirydczuk, 2003).

The field has been appraised by twenty-four wells (two

additional offset wells were utilized in the present study) andhas an average reservoir depth of 2,200 m. The trap is a

articlewith microcodium (ancient soil profiles) are exhibited as a

combination structural stratigraphic (Figure 7) as convincing

evidence exists for an oil column present outside of the

structural closure. The gross reservoir thickness ranges from80 to 130 meters; permeability ranges dramatically from lessthan 1 to 4,520 md, with higher permeability values associatedwith dissolution. Maximum measured porosity based on core

plug data is 30% and the average API of the oil is 35. Fluidanalysis indicates the Asselian-Sakmarian H2S values range

from 2-3%.

Development and Transportation Programlopment program, comprised of five pads with 46

producers, 44 injectors and 18 water source wells will beimplemented in two phases. Phase 1 production plan calls for

acity of780 MBO, will be capable of handling up to 250 MBWPD,

and the produced gas will be sweetened to less than 5 ppmH2S prior to re-injection. Yuzhno Khilchuyu hydrocarbons

porosity and permeability commonly associated The deve

Figure 5. uzhno Khilchuyu Field type log utilizing tworepresentative wells. Display illustrates a stackedsuccession of shallow-water carbonate deposits withindividual cycles deposited in response to sea levelfluctuations. Arrows indicate upward coarsening sequences.

Figure 6. Total amplitude displays for Zones A and C. Totalamplitude = integrated inversion porosity. Yellow, red, greenand light blue colors indicate porous regions, dark blue andpurple colors indicate low porosity.

Figure 7. Lower Permian Asselian-Sakmarian top reservoirdepth structure with well control. Blue dashed line illustratesapproximate area of Yuzhno Khilchuyu 3D seismic survey.White wavy line illustrates Zone A sweet spot regiondefined by mapped porosity trends (see Figure 6). Black lineindicates a south to north line-of-section depicted in Figures10 and 15 below. Coutour interval is 20 meters and north istowards top of page.

60 MBOPD output by 4thquarter 2007 and Phase 2 production

plan calls for an increase to 160 MBOPD by 4thquarter 2008.The central production facility will have a storage cap

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Figure 9. Simplified cross-section illustrating variability of water and oil fluid contact surfaces based on interpretation ofproduction test data (Strauss, 2005).

Well log interpretation

A comprehensive petrophysical evaluation of each well wascarried out using

Followplug deand water saturation were interpreted. Following this process,

ater saturation was initially estimated based on two mainty data and unfocused

induction and lateral devices. Unfortunately, all but one wellwas drilled with fresh water, which generated uncertainty in

n of thewithdeep

penetrating, focused induction log that allowed a calibration of

defined in theA (primary

wireline and core plug analysis data.

ing a lengthy data quality control, log splicing and corepth matching process, matrix porosity, vug porosity

the BKZ resistivity measurements due to deep invasiofresh water drilling fluids. However one well was drilledan oil-based mud system and was logged with a

a cluster analysis was carried out to characterize petrophysicalrock types related to lithology and complex pore types(Sokolova and Klyazhnikov, 2005).

The well log porosity estimation was performed usingRussian-style single detector neutron-gamma (compensatedneutron available in four of the newer wells), P-sonic, densitydata and calibrated to core plug data. Total porosity from theneutron-gamma log data was estimated by the traditionaltechnique of utilizing two marking beds with high and low

readings of neutron gamma log with a correction for gammaactivity according to the neutron log tool. Based on priorknowledge in the area and results of special core analysis data,a 9% porosity cut-off was utilized to determine net and non-

net reservoir rock.

Wsets of data focused induction resistiviBKZ (Russian-style) resistivity data. The BKZ data, whichwere the most plentiful, were interpreted using an iso-

resistivity technique that used focused single-detector

the BKZ data and an accurate reading of true resistivity in theoil column. Due to the uncertainty in the water saturationvalues as stated above, a J-function approach, based on

available capillary pressure and results of the petrophysicalcluster analysis was utilized. Additional details may be found

below.

Correlation Framework and Geological Facies Model

A fully integrated correlation framework based on seismicdata, synthetic well ties, wireline character and

biostratigraphic data was carried out. As the correlationframework was developed, the geological facies model as

discussed above was defined and optimized throughout theinterpretation process. Five main intervals werecorrelation framework caprock (top seal), Zonereservoir), Zone B (tertiary reservoir), Zone C (secondaryreservoir) and basal Zone C (primarily non-reservoir).Additionally, secondary internal surfaces were correlated

whenever the data allowed. See Figure 10 for a representativecross-section which illustrates the main intervals and porositybearing regions.

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The caprock interval is easily mapped throughout the fieldarea and represents the top seal for the Yuzhno Khilchuyuhy sra nbe its a

ounding morphology (see porosity distribution depicted in

ickness of a few meters on the perimeter

bearing region to greater than 70 meters on the

primarily to the subtle

p/Vs fluid effects typical in carbonates and secondarily to

Figure 10. South to north stratigraphic cross-section illustrating YGamma ray (left) and interpreted porosity (right) logs are dispCarboniferous. See Figure 7 for line-of-section.

drocarbon accumulation. The caprock interval thicknesnges from 15 to 40 meters over the main hydrocarboaring portion of the field. Zone A, which exhib

m

Figure 6), ranges in thto over 60 meters in the central portion of the field and sweetspot region. Zone B ranges in thickness from 10 to 30 metersand exhibits sporadic porosity development. Zone C, which

exhibits a tectonically influenced orthogonal mound pattern(see porosity distribution depicted in Figure 6) ranges in

thickness of 10 meters in the central portion of thehydrocarbonperimeter. Generally, thicker sequences correlate to higherporosity.

Constrained Sparse Spike Inversion.

A constrained sparse spike acoustic inversion was carried out

on the newly reprocessed full-fold stacked seismic data toinvestigate the porosity signature in the Asselian-Sakmarian

reservoir interval. A relationship between well log derived

acoustic impedance and porosity estimated from the well logcalculations was derived. Using that relationship, the acousticimpedance volume was converted to porosity. The expected

variations in porosity in the reservoir zones are apparent at acoarse scale (see Figure 11 for an example). The seismic

derived porosity was very successful as the correlationbetween the inversion seismic porosities and the well logderived porosity were quite good, the best correlation beingporosity times thickness. See Figure 12 for a cross-plot of total

amplitude (integrated seismic porosity thickness or Phi-Hwithin the reservoir interval) versus well log average porosity

times Phi-H. The correlation coefficient was calculated at 96%while the standard error based on the Students T-Distributionis 75%, given limitations of the data population.

Offset Acoustic Impedance and Vp/Vs Analysis

Additionally, offset acoustic impedance and Vp/Vs(compressional vs. shear velocity) volumes were calculated toaddress fluid characterization. Due

Vthe very limited shear velocity data, the predictive capabilitiesof this method was considered to be weak (Foster, 2006).

uzhno Khilchuyu stratigraphic architecture and zonation.layed for each well. Cross-section flattened on the Top

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Stochastic Inversion

The stochastic inversion process utilized a Markov chain

Monte Carlo (MCMC) approach. The reprocessed seismic data(for lateral detail) and well log porosity (for vertical detail)was stochastically processed to produce a high resolution viewof the reservoir that is statistically correct and geologicallyreasonable.

Initially a rigorous probability distribution matrix was definedbased on well log and acoustic impedance porosity data thatlinked the petrophysical cluster-based rock type volume with

e pre-stack time migration seismic, well log porosity,nd geostatistics. The geostatistical

describe the characteristics of continuity and variance for eachzone.

Subsequently, MCMC methods were used to generate a

statistically correct set of output samples from the validatedprobability distribution function. Numerous iterations were

atistical inputs with the intentogically reasonable and

consistent with the facies models generated from thecorrelation framework task discussed above. Once input data

were validated, numerous realizations were run to explore thesimulation space, and to ensure the input data variance wasreflected in the results.

The MCMC methods have recently emerged as the main toolsfor solving problems involving a large number of random

variables. As the input probability distribution function can bevery complex, analytical methods cannot be used (Chen andHoversten, 2003). In the case of a stochastic inversionapplication, the input data can be numerous including welllogs, seismic attribute volumes, variography and mostimportantly, geological insight. The resulting simulation is

computed by selecting values from a Markov Chain that has a

given probability distribution, and using these values toco gpr ewe puted. Ifthe difference is reduced, the iteration is retained and starts

d for the

generated to test alternative geostof generating a product that was geol

Figure 11. Representative acoustic impedance display showing strong response to porosity.

thgeological facies model ainput data were comprised of expected data distributions in the

form of histograms and lateral and vertical variograms to

over with new values. If the difference is larger, it may be

rejected or accepted by the next iteration. The simulation isexpected to converge to the best solution which results in onerealization, and is determined using convergence diagnosismethods (Gelman and Rubin, 1992).

The stochastic inversion process yielded highly detailed

information on porosity throughout the Asselian-Sakmarian

reservoir and non-reservoir interval, and was utilize

Figure 12. Total amplitude versus porplot for Zones A, B and C. Illus

osity-thickness cross-trates the strong correlation

between the acoustic inversion and porosity in the wells.

mpute other properties. In each iteration, the resultinoperties are compared to the expected distributions for thighted input data, and a residual difference is com

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porosity property in the high-resolution geological model. See

Figure 13 for the generalized workflow of the stochasticporosity process. Following generation of the simulatedpermeability logs and J-function based water saturationfunctions discussed below, the MCMC algorithm co-simulation approach was utilized to produce final high-resolution property models.

Petrophysical Cluster Analysis

The primary goal of the cluster analysis was to differentiateprincipal rock types based on porosity, pore throat structure

and the occurrence of fracturing or vugs. The aim was to usethe petrophysical cluster based rock type classes as anadditional conditioning control in the geological modeling co-simulation process. Rocks were divided into six clusters using

blocked gamma-ray, total porosity and P-sonic logs as inputdata. As many of the cluster populations were subtle, two main

clusters were interpreted and carried forward in the fine-scalegeological and reservoir simulation model. The two maincluster population rock types were utilized in the fine-scalegeological model to populate water saturation based on resultsof the J-function analysis. See Figure 14 for a summary of thesix clusters as a relationship of porosity and permeability.

Permeability simulation

Well log permeability was simulated by utilizing a densified

P-C ydat sity

rossplot was densified by blocking the porosity andermeability point data and carrying out an editing process to

n. As a first pass editing process,

Figure 13. Stochastic inversion generalized workflow. The phase "*n" refers to the number of realizations.

was plotted against core plug permeability. The resulting P-Cloud cp

create a reasonable distributiodiscretizing (blocking) the core plug point data naturallyremoved the outliers. The well log calculated porosity wassimulated against the densified P-Cloud distribution using abivariate application. The resulting permeability log, necessary

for the MCMC co-simulation input, was created by

Figure 14. Porosity vs. permeability by cluster cross-plotbased on petrophysical analysis of the Yuzhno Khilchuyu

well log database (Sokolova and Klyazhnikov, 2005).

loud transform of the core plug porosity and permeabilita and calculated well log porosity. First, core plug poro

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geometrically averaging the resulting realizations. See Figure

f well test results and water saturation estimatedfro BKZ resistivity log data, specific areas and zones within

free water levels. Following a review

The MCMC approach was utilized to generate multiplefinal high-resolution rock type, permeability

resolabov lution stochastic

Figure 15. Simulated permeability log workflow based on P-Cloud transform of core plug porosity, core plug permeability and

well log porosity.

15 for a summary of the simulated well log permeabilitysimulation workflow.

Water Saturation

As discussed above, a J-function approach was utilized topopulate the water saturation property model. Based on a

combination om

the field were assignedof the available capillary pressure information, the data wereconverted to J-functions based on laboratory data consistentwith the approach utilized in Harrison (2001). Two J-functions

were created, one for the rock type 2 (best porosities andpermeabilities) and one for the remaining rock types (Figure

16). The resulting J-functions were utilized in the rockproperty simulation discussed below. Input for the J-functionbased simulated water saturation calculations were modeledrock type, porosity, permeability and interpreted free water

levels.

Rock Property Simulation

realizations ofand water saturation property models to complement the high-

ution stochastic porosity property models discussede. Input data included the high-reso

Figure 16. Summary J-functions utilized in the MCMCsimulation. Two curves represent calculated J-functionsfor each summarized rock type illustrated in Figure 14.

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porosity, petrophysical well log calculations, simulatedeability data, derived petrophysical relationships, J-ions by rock type, and geologic inferences about

rvoir tren

permfunctese ds, shape, thickness, and lateral extent captured

alid sults to well

moddata See Figure7 for examples of modeled rock type, porosity, permeability

utili

aria volumetrics and streamline simulation as input

utili

onclusionsct yielded many insights to the Yuzhno Khilchuyu

f the final high-

solution geological models. It was very clear from thebeginning that reprocessing the seismic data would have a

he modeling process was critical to the ultimateccess. Also, utilizing the MCMC based geostatistical

tanding the variance and related

ncertainties.

itchell, Gary

yers, Claude Scheepens, Sindre Soerensen, Stephen Strauss,

otruk, Tatyana Pilosova, Boris Rapoportnd Alexander Rusalin from NMNG; Denis Antonov, Nataliya

enis Saussus,atiana Sokolova, Paul Tijink and Olga Zhuravliova from

eferences

rin the input geostatistics (variograms and histograms).Following multiple iterations of varying input data and a

ation process that included comparing revdata, sector model history matching and a drop-out analysis

using four key wells, the resulting high-resolution propertyels were proved to be statistically consistent with the inputand considered to be geologically reasonable.

1

and water saturation. The multiple realizations were rankedzing property model statistics (mean, standard deviation,

nce, etc),vcriteria. Following the ranking process, the best technical casemodel and variance analysis were calculated and are being

zed to optimize the drilling program.

CThis projeField and for the project team in terms of the workflow. Inputfrom the multi-discipline team contributed greatly tomaximizing the integrity and reliability o

re

large impact, and that understanding the geology prior tocommencing tsuinversion provided direction input into the high-resolutiongeological model and was critical for capturing finer detail inthe model and unders

u

AcknowledgementsThe authors would like to thank the individuals contributing tothe project team. These individuals are Andre Bouchard, ChipFeazel, Gordon Fielder, Douglas Foster, Ray M

MKrys Swirydczuk and Mark Wuensher from ConocoPhillips;

Andy Haas, Valery MaChernoglazova, Dmitry Daudin, Mikhail Ercenkov, PavelErshov, Dmitriy Klyazhnikov, Konstantin Kunin, Elena

Malysheva, Beth Rees, Alexander Rykov, DT

Fugro-Jason and Vasily Duzin from Pomor-Gers.

RBonner, F. M., E. J. Bergamo, C. Gonzalez, W. King, S. R. Strauss

and P. L. Wilson, 1995, Yuzhno Khilchuyu well test report,

ConocoPhillips internal report, p. 1-22.

Figure 17. South to north transect (see Figure 7 for line-of-section) final rock type, porosity, permeability and watersaturation property models. Vertical lines indicate well control points.

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Chen, J. and G. M. Hoversten, 2003, Joint Stochastic Inversion of

. Musafirova, and K. Swirydczuk, 1996, Lower

ecial Publication

8, p. 75.

no. 9-10, p. 324-335 (translated from Aktual nyye problemytektoniki SSSR, Nauka, p. 124-138, 1988).

Fossum, B. J., W. J. Schmidt, D. A. Jenkins,V. I. Bogatsky, and B. I.

Rappoport, 2001, New frontiers for hydrocarbon production inthe Timan-Pechora Basin, Russia, in M.W. Downey, J. C.

Threet, and W. A. Morgan, eds., Petroleum provinces of thetwenty-first century: AAPG Memoir 74, p. 259279.

Foster, D. 2006, Summary of the Yuzhno Khilchuyu SeismicInversion Project, ConocoPhillips Internal Report, p. 1-3.

Gelman, A. and Rubin, D., 1992, Inference from iterative simulation

using multiple sequences: Statistical Science, 7, p. 457-511.

Guilloux, Y. C., E. O. Maleshova, T. F. Cokolova and P. H. Ershov,2006, Yuzhno Khilchuyu Field reservoir characterization and

geological modeling final report, NaryanMarNefteGaz internalreport.

Harrison, B., 2001, Saturation Height Methods and Their Impact onVolumetric Hydrocarbon in Place Estimates, SPE 71326, p. 1-

12.

Lindquist, Sandra J., 1999, The Timan-Pechora Basin Province of

Northwest Arctic Russia: DomanikPaleozoic Total PetroleumSystem, U.S. Department of the Interior, U.S. Geological

Survey.

Meyerhoff, A. A., 1980, Petroleum basins of the Soviet Arctic:Geological Magazine, v. 117, No. 2, p. 101-186.

Sokolova T.F., Klyazhnikov D.V. et al., 2005, Saturation

determination of complex carbonate reservoirs using well logdata with application of cluster analysis, 7th International

Conference Geomodel-2005, Gelendjik, Russia.

Strauss, S., 2005, Yuzhno Khilchuyu Field interpretation of well testdata and fluid contacts, ConocoPhillips Internal ProprietaryReport.

Swirydczuk, K., B. I. Rapoport, V. F. Lesnichy, and J. A. Quadir,

2003, Yuzhno Khilchuyu field, Timan-Pechora Basin, Russia, inM. T. Halbouty, ed., Giant oil and gas fields of the decade1990 1999, AAPG Memoir 78, p. 251 274.

Figures1. NaryanMarNefteGaz (NMNG) Timan-Pechora NenetsOkrug Region Basemap. Annotated fields depict the elevenproduction licenses in the NMNG portfolio.

2. Generalized tectonic map of the Timan-Pechora Basin.Cross-section A-A (Figure 3) illustrates structural setting ofYuzhno Khilchuyu Field. (Fossum et. al., 2001).

3. West-east cross section across the northern part of the

Timan-Pechora Basin showing the location of YuzhnoKhilchuyu Field on the Kolva Swell. Yuzhno Khilchuyu has

trapped hydrocarbons in the Lower Permian (Asselian-Sakmarian and Kungarian) and also in the overlying Upper

Permian Sands (Fossum et. al., 2001).

4. Timan-Pechora Basin generalized stratigraphic section(Fossum et. al, 2001).

5. Yuzhno Khilchuyu Field type log utilizing two key wells.

Display illustrates a stacked succession of shallow-watercarbonate deposits with individual cycles deposited in

response to sea level fluctuations. Arrows indicate upwardcoarsening sequences.

6. Total amplitude displays for Zones A and C. Totalamplitude = integrated inversion porosity. Yellow, red, green

and light blue colors indicate porous regions, dark blue andpurple colors indicate low porosity.

7. Lower Permian Asselian-Sakmarian top reservoir depthstructure with well control. Blue dashed line illustratesapproximate area of Yuzhno Khilchuyu 3D seismic survey.

White wavy line illustrates Zone A sweet spot region definedby mapped porosity trends (see Figure 6). Black line indicatesa south to north line-of-section transect depicted in Figures 10and 15 below. Contour interval is 20 meters and north istowards top of page.

8. Representative seismic line, original vs. currentreprocessing.

9. Simplified cross-section illustrating variability of water andoil fluid contact surfaces based on interpretation of productiontest data (Strauss, 2005).

10. South to north stratigraphic cross-section illustrating

Yuzhno Khilchuyu stratigraphic architecture and zonation.Gamma ray (left) and interpreted porosity (right) logs aredisplayed for each well. Cross-section flattened on the TopCarboniferous. See Figure 7 for line-of-section.

11. Representative acoustic impedance display showing strong

response to porosity.

12. Total amplitude versus porosity-thickness cross-plot forZones A, B and C. Illustrates the strong correlation between

the acoustic inversion and porosity in the wells.

13. Stochastic inversion generalized workflow. Note: *nrefers to number of realizations.

14. Porosity vs. permeability by cluster cross-plot based on

petrophysical analysis of the Yuzhno Khilchuyu well logdatabase (Sokolova and Klyazhnikov, 2005).

Geophysical Data for Reservoir Parameter Estimation, Society

of Exploration Geophysicists 73rdAnnual Meeting Proceedings.

lopine, W. W., E. MCPermian fusulinid biostratigraphy and graphic correlation inYuzhno Khilchuyu field, Timan Pechora basin, northern Russia

(abs.): Sixth North American Paleontological ConventionAbstracts of Papers, Paleontological Society Sp

Dedeev, V., L. Aminov, V. A. Molin and V. V. Yudin, 1993,

Tectonics and systematic distribution of deposits of energyresources of the Pechora platform: Petroleum Geology, v. 27,

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15. Simulated permeability log workflow based on P-Cloudtransform of core plug porosity, core plug permeability and

well log porosity.

16. Summary J-functions utilized in the MCMC simulation.Two curves represent calculated J-functions for each

summarized rock type illustrated in Figure 14.

17. South to north transect (see Figure 7 for line-of-section) final rock type, porosity, permeability and water saturation

property models. Vertical lines indicate well control points.