2009 Gussow Geoscience Conference 1

Integrated 3D Reservoir Characterization for Oil Sands Evaluation, Development and Monitoring

Laurie Weston Bellman*

Oil Sands Imaging Inc., 100, 1100 - 8 Avenue SW Calgary, Alberta T2P 3T8

laurie@oilsandsimaging.com

Introduction

In a complex formation like the McMurray, it goes without saying that an accurate reservoir characterization is useful in all stages of oil sands management. Resulting improvements in efficiency of resource evaluation, field development, and production monitoring provide immediate and long-term economic and environmental advantages which in turn support sustainability. There is little professional dispute over the previous statements however diversity of opinion and technical challenge come with the realities of attempting to reveal the true nature of the reservoir. This is where methods and results diverge often as a consequence of inadequate data and discipline integration. Effective integration involves harmonization of disparate data sources with a wide range of scale, integrity and uncertainty. These can include (but are not limited to) seismic and other geophysical data, core, wireline logs (including dipmeter and/or image logs), observation well measurements (temperature and pressure), surface measurements, and production parameters.

I will be presenting a process that integrates all available data to produce a volume of deterministically derived reservoir properties. The workflow is illustrated using 3d seismic and well data from the Nexen/Opti Long Lake SAGD Phase 2 area to create a detailed volume of facies and fluids. The subsequent drilling results were compared with the predicted properties and showed very high average correlation of greater than 80%. Additional examples of 2d and 3d projects will also be shown.

Method

As the conceptual flow-chart in Figure 1 shows, rock physics attributes are first determined from seismic data and then classified in terms of facies and fluids using the wireline log and core data from wells. The seismic process involves the use of AVO (amplitude vs. offset) analysis to separate the compressional (P-wave) and shear (S-wave) components of the seismic data. The resulting components are then used to calculate physical rock properties such as shear rigidity (mu) and incompressibility (lambda). It is common knowledge among oil sands geoscientists that the density log through the McMurray Formation shows a strong correlation to the gamma ray log and is therefore a good lithology indicator. In this process I incorporate an estimate of density obtained from seismic using a multi-attribute analysis approach.

2009 Gussow Geoscience Conference 2

Figure 1: Conceptual flow-chart for the Seismic Transformation and Classification (STAC) process.

Wireline logs directly (or indirectly) measure P-wave velocity, S-wave velocity and density. Integrating this data with core and log analysis, the lambda and mu properties are calculated and assigned lithologies and fluid properties. Detailed quality assessment and cross-plot analysis is carried out to assign empirical limits and guidelines for lithology and fluid discrimination based on the measured rock physics properties. The determined relationships are then used to calibrate and classify the seismically-derived properties. The result is a seismic volume transformed to a detailed lithological characterization of the reservoir within the zone of interest. Drilling results are shown to validate and quantify the success of the method.

Applying this technique over a project area allows more confident identification of the geological features and associated reservoir quality and continuity. A few of the potential benefits in oil sands development include fewer vertical wells required to define the resource area, more effectively placed horizontal wells for optimal production and reduced steam/oil ratios, as well as flow simulations based on deterministic facies models.

References Goodway, W., Chen, T., and Downton, J., 1997, Improved AVO fluid detection and lithology discrimination using Lame petrophysical parameters; “Lambda*rho”, “mu*rho” and “lambda/mu fluid stack”, from P and S inversions: 1997, 67

th Annual International Meeting.,

SEG Expanded Abstracts, p183-186.

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DEPTHMETRES

LMR.RHO_RP_1K/M31750 2750

LMR.RHO_1K/M31750 2750

dt4pUS/M500 100

298wbsk_c_g_tp

299

mcmr_ch_tp300

top_g_bs5

305top_wat_tp

2307

top_wat_bs3

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max_pay_tp42

353

max_pay_bs3

355

TOPS.TOPS

WIRE.GR_1GAPI0 200

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DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

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DEPTHMETRES

LMR.RHO_RP_1K/M31750 2750

LMR.RHO_1K/M31750 2750

dt4pUS/M500 100

298wbsk_c_g_tp

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mcmr_ch_tp300

top_g_bs5

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top_wat_bs3

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max_pay_tp42

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max_pay_bs3

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TOPS.TOPS

WIRE.GR_1GAPI0 200

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DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

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WIRE.DT_1US/M500 100

298wbsk_c_g_tp

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mcmr_ch_tp300

top_g_bs5

305top_wat_tp

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top_wat_bs3

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max_pay_tp42

353

max_pay_bs3

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TOPS.TOPS

WIRE.GR_1GAPI0 200

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DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

ρ Vs Vp

Geophysics

λρ μρ ρ

STAC Workflow

SSeeiissmmiicc DDaattaa

λρ = (Vp*ρ)2 – 2(Vs*ρ)

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DEPTHMETRES

LMR.RHO_RP_1K/M31750 2750

LMR.RHO_1K/M31750 2750

dt4pUS/M500 100

298wbsk_c_g_tp

299

mcmr_ch_tp300

top_g_bs5

305top_wat_tp

2307

top_wat_bs3

310

max_pay_tp42

353

max_pay_bs3

355

TOPS.TOPS

WIRE.GR_1GAPI0 200

300

325

350

DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

300

325

350

DEPTHMETRES

LMR.RHO_RP_1K/M31750 2750

LMR.RHO_1K/M31750 2750

dt4pUS/M500 100

298wbsk_c_g_tp

299

mcmr_ch_tp300

top_g_bs5

305top_wat_tp

2307

top_wat_bs3

310

max_pay_tp42

353

max_pay_bs3

355

TOPS.TOPS

WIRE.GR_1GAPI0 200

300

325

350

DEPTHMETRES

LMR.LAMBDA_RHO_1GPA-K/M38000 25000

LMR.MU_RHO_1GPA1000 10000

λρ μρ

Filter: FAC_LMR<7&FAC_LMR<>4&SHEAR_QUAL>0.6&BB_FLAG==1Range: All of Well

Well: 47 Wells

LMR.MU_RHO_1 vs. LMR.LAMBDA_RHO_1 Crossplot

0.1 13.4

Color: Maximum of FAC_LMR

Wells:102062507708W400 102110807707W400 1AA011207708W4001AA012107707W400 1AA020807707W400 1AA023507707W4001AA030207708W400 1AA033407707W400 1AA042107707W4001AA052907707W400 1AA053207707W400 1AA061707707W4001AA062307708W400 1AA063407707W400 1AA070207807W4001AA071807707W400 1AA081507708W400 1AA081707707W4001AA082007707W400 1AA082607707W400 1AA082707707W4001AA082807707W400 1AA082907707W400 1AA083007707W4001AA091807608W400 1AA091807707W400 1AA092007608W4001AA092707608W400 1AA092907608W400 1AA101607608W4001AA101607707W400 1AA102807707W400 1AA103407608W4001AA113507608W400 1AA121707708W400 1AA122007707W4001AA122607708W400 1AA142207707W400 1AA143007707W4001AA151807707W400 1AA152807707W400 1AA152907707W4001AB042107708W400 1AB072207708W400 1AB081407708W4001AB123107707W400 1AB142307708W400

Functions:kinosis_mudcurve_cubic: Regression from curve kinosis_mudcurve

MU = (6657.917 - 1.24686*(x) + 8.84e-05*(x)**2- 1.43184e-09*(x)**3)

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λρ

μρ

Filter: FAC_LMR<7&SHEAR_QUAL>0.6Range: All of Well

Well: 47 Wells

LMR.MU_RHO_1 vs. LMR.LAMBDA_RHO_1 Crossplot

Wells:102062507708W400 102110807707W400 1AA011207708W4001AA012107707W400 1AA020807707W400 1AA023507707W4001AA030207708W400 1AA033407707W400 1AA042107707W4001AA052907707W400 1AA053207707W400 1AA061707707W4001AA062307708W400 1AA063407707W400 1AA070207807W4001AA071807707W400 1AA081507708W400 1AA081707707W4001AA082007707W400 1AA082607707W400 1AA082707707W4001AA082807707W400 1AA082907707W400 1AA083007707W4001AA091807608W400 1AA091807707W400 1AA092007608W4001AA092707608W400 1AA092907608W400 1AA101607608W4001AA101607707W400 1AA102807707W400 1AA103407608W4001AA113507608W400 1AA121707708W400 1AA122007707W4001AA122607708W400 1AA142207707W400 1AA143007707W4001AA151807707W400 1AA152807707W400 1AA152907707W4001AB042107708W400 1AB072207708W400 1AB081407708W4001AB123107707W400 1AB142307708W400

Functions:kinosis_mudcurve_cubic: Regression from curve kinosis_mudcurve

MU = (6657.917 - 1.24686*(x) + 8.84e-05*(x)**2- 1.43184e-09*(x)**3)

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LMR.LAMBDA_RHO_1 ()

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μρ

λρ

Core Facies Geology!