GRAY, F. DAVID *, PAUL F. ANDERSON, and JAY A. GUNDERSON *, * Veritas DGC, Calgary, AB, Canada, Apache Canada Ltd., Calgary, AB, Canada Prediction of Shale Plugs between Wells in Heavy Oil Sands using Seismic Attributes Abstract A fundamental geologic problem in the Steam-Assisted Gravity Drainage (SAGD) heavy oil developments in the McMurray formation of Northern Alberta is to determine the location of shales in the reservoirs which may interfere with the steaming and/or recovery process. Petrophysical analysis shows that a key acoustic indicator of the presence of shale is bulk density. In theory, density can be derived from seismic data using Amplitude Versus Offset (AVO) analysis of conventional and/or multi-component seismic data, but this is not widely accepted in practice. However, with billions of dollars slated for SAGD developments in the upcoming years, this technology warrants further investigation. In addition, many attributes can be investigated using modern tools like neural networks; so, the density extracted from seismic using AVO can be compared and combined with more conventional attributes in solving this problem. Density AVO attributes are extracted and correlated with density synthetics created from the logs just as the seismic stack correlates to conventional synthetics. However, multi-attribute tests show that more than density is required to best predict the volume proportion of shale (Vsh). Vsh estimates are generated by passing seismic attributes derived from conventional PP, and multicomponent PS seismic, AVO and inversion from an arbitrary line following the pilot SAGD wells through a neural network. This estimate shows very good correlation to shale proportions estimated from core. The results have encouraged the application of the method to the entire 3D.
2 Introduction Premium synthetic crude is being upgraded from the bitumen sands of the McMurray Formation in Northern Alberta, Canada. The McMurray reservoir contains bitumen-supported sands and shales situated in the Cretaceous McMurray Formation at a depth of 10-m. The Lower McMurray consists of a fluvial lowstand systems tract of valleys incised into a paleo-karsted carbonate terrain. Braided channel sands were deposited in these valleys, with laterally discontinuous mudstones and shale-plugs occurring as overbank deposits and abandoned channel fills. The Upper McMurray is a transgressive system tract consisting of estuarine channel and point bar complexes in a shoreface environment (Hein et al, 01). The sands throughout the McMurray are saturated with bitumen and SAGD technology (Edmunds et al, 12) is used to efficiently extract it. The SAGD process uses two horizontal wells, one about m above the other, to extract the oil. The upper well injects steam into the formation. This steam creates a chamber around the upper well where the oil is heated lowering its viscosity. This less viscous oil drains, with the condensed steam, to the bottom of the chamber, where it is collected by the lower horizontal well (Figure 1). The SAGD process works very well in a high net-to-gross sand package. However, problems may occur with the SAGD method if either of the wells encounter thick, continuous shales. If the upper well encounters a relatively thick shale the formation of the steam chamber may be impeded; if the lower well encounters shale, production from that well may be impeded. Therefore, the geological problem in this reservoir is to be able to determine, prior to drilling, where the shales are. The initial phase of development of one reservoir will use SAGD well pairs averaging approximately 00 m in length and expected to produce 00-0 b/d of bitumen each. A total of more than 0 such SAGD well pairs are expected to be required in order to fully develop the bitumen reservoir (Long Lake Project, 04). The gross capital cost of such a project is expected to be $3.4 billion (Nexen, 04). Therefore, a
3 reasonably cost-effective solution to the geologic problem of determining the location of the shales is to use 3D seismic over the entire project area. For that reason, a three-component 3D (3C-3D) seismic survey was acquired over a pilot SAGD area of the reservoir in 02 to test the concept. That survey is examined here to test the concept of predicting Vsh from seismic data. Subsequently, a second 3D-3C survey has been acquired over the rest of the project area. Gray et al (04) proposed a method of determining the presence of shales by using a neural network (Russell et al, 1) to examine all the various attributes that show some correlation to the presence of shale in the McMurray (Dumitrescu et al, 03 and Gray, 03). Dumitrescu et al (03) indicate that there is a correlation between the multi-component PS AVO attributes and Vsh in this reservoir. Our test involves using various conventional, AVO and multicomponent attributes generated during the earlier work as predictors of Vsh in a neural network. A petrophysical analysis indicates that the seismic property most related to the shale content is density (Figure 2). Therefore, methods of extracting density from seismic data are used. The density estimations are done using 3-term AVO driven by the Gidlow et al (12) AVO equation and multicomponent PS converted-wave AVO as suggested by Gray (03). Gidlow s equation also estimates normal-incidence P-wave and S-wave impedance reflectivities. Gray s converted-wave AVO equation also estimates S-wave impedance reflectivity. The neural network test is performed on an arbitrary line from the 3D volume that follows the path of three horizontal wells used in a pilot study to test the effectiveness of the SAGD extraction process in this reservoir. Extensive analyses have been done in this area of this test, including core studies that can be used to test the veracity of the Vsh prediction. Density estimation from seismic data is starting to be done with AVO (e.g. Roberts, 00, Kelly and Skidmore, 01, and Downton and Ursenbach, 0) but it is not yet widely accepted. Density estimation requires the measured seismic response to have wide angles of incidence (> degrees) with the reservoir. The data used in this study does have sufficient angles to do this (>0 degrees) because the reservoir is shallow. Typical PS multicomponent seismic data will have sufficient angles to extract density because the
4 emergent seismic S-wave propogates much closer to vertical incidence than the associated P-wave. The validity of the density prediction is checked by comparing the prediction to the density logs and by estimating density using the neural network. The latter also serves as a check on the neural network because the density prediction should be linear and therefore it should be easily detected by the neural network. This density estimated by the neural network is added to the other attributes and they are all used in a second pass of the neural network to predict Vsh. Method Since density shows a strong correlation to the presence of shale in this reservoir, our strategy is to use seismic data to determine attributes that directly determine density or values that are related to density (such as impedance) in the reservoir. Since extracting density using AVO methods for conventional and multicomponent seismic data is not yet well accepted by the geophysical community, the density extractions are compared to the density logs to ensure that they are valid. Wherever possible, the other extracted values are compared to the corresponding logs. These attributes, in combination with more conventional attributes such as stacks, are used to predict a measurement of the shale content in the reservoir; in this case, the Vsh log calculated by the petrophysicist. The ability of the attributes to predict the Vsh log is assessed quantitatively at several well locations along a test line using a ranking algorithm (Russell et al, 1). The best predictors are used to predict the Vsh log and therefore the shale content between the wells. Results An arbitrary line from the original 3D seismic survey that passes along the course of the three pilot SAGD horizontal well pairs is used for testing. The first step is to extract the attributes from the seismic data. A least-squares fit of the pre-stack P-wave data to the Gidlow et al (12) three-term AVO equation produces
estimates of the reflectivities of the P-impedance, S-impedance and density. A least-squares fit of the prestack PS converted-wave data produces estimates of the reflectivities of S-impedance and density. These attributes are compared to the corresponding attributes derived from logs. Eight vertical wells are on the test line and are used for comparisons. Their Vsh logs will also later be used as desired outputs of the neural network. Examples of typical correlations of some of the attributes are shown in Figure 3. The P-impedance and density reflectivities extracted using AVO methods applied to Gidlow et al s (12) equation show good correlation to the well control. They also appear to be correlated to each other in the reservoir zone (-3 ms). This is expected since there is little change in sonic velocity within the McMurray formation; therefore the density is driving the impedance contrasts in the McMurray zone. Outside of this zone, significant differences between these two attributes exist, which is also consistent with expected petrophysical properties, indicating that the three-term AVO extraction is seeing geologic effects. Dumitrescu et al (03) indicated that there is lateral correlation between the multi-component AVO attributes and the lateral extent of the shales. The density reflectivity derived from the multi-component PS pre-stack data, shows strong reflections in the reservoir, again consistent with the petrophysical analysis. There appears to be a weak vertical correlation between this attribute and the well information. This may be due to the lower frequency content of the PS data. The inversion for density from the three-term AVO is shown in Figure 4c. It shows more character than any of the other seismic attributes (Gray et al, 04). This probably results from the correlation of density to Vsh, the physical property that is changing most significantly in the reservoir. Figure 4d shows the estimate of Vsh derived from the use of the neural network on the various seismic attributes. The attributes used for the Vsh prediction are the density inversion, the P-impedance inversion and Goodway et al s (1) Lambda-Rho, all of which show correlation to the Vsh in petrophysical analyses (Gray et al, 04). This Vsh estimate shows good correlation with the core studies done in this area with all significant shale bodies detected and a known increase in the quality of the sand from the top to the bottom of the reservoir detected.
One well was purposely left out of the analysis as a blind test and the Vsh estimate is a good match to the core study at this location (CMP 3). Discussion Gray s (04) method of determining Vsh using a neural network in the bitumen reservoir appears to be able to indicate the presence of shale bodies both at the wells and between them based on this test. The shales observed in the core at all eight well locations are well reproduced and the neural network is able to predict the shales present at a well that was purposefully left out of the analysis. The shale bodies and sand proportions predicted by the neural network make sense from a geologic standpoint. At the top of the McMurray, the shales are associated with a marine encroachment and so are more continuous than the shale bodies deeper in the McMurray which are associated with channel fill. The sand content also increases with depth in the McMurray which is consistent with what is known about the reservoir. A most interesting observation is that neural network Vsh estimate suggests that these channel-fill shales may be much smaller in lateral extent than is expected from previous work. This could mean that there is more bitumen sand in this area than has been predicted to date. The success of this test on an arbitrary seismic line from the 3D indicates that this method should be pursued on the entire 3D. The Vsh estimated from the 3D should have geometries that are consistent with the depositional systems in place during the deposition of the McMurray, i.e. channel-fill in the early McMurray changing to a marine environment at the end of the McMurray. Observation of such geometries in the 3D will increase confidence that the Vsh estimates achieved by the neural network are accurate. The 3D work has been completed and it has demonstrated valuable results. Thirty wells were used in the 3D analysis versus eight used in the earlier test. The primary driver remained the same, the density estimate from 3-term AVO. The secondary drivers for the Vsh prediction changed somewhat, with the average frequency
attribute of the PS AVO estimate of the S-wave reflectivity ranking second. The change in the drivers may be a result of including shalier parts of the reservoir in the 3D analysis than in the test area. The 3D distribution of sands in the reservoir as predicted by the neural network is revealing, showing shalefilled channels at the base of the McMurray, changing to preferential sand deposition in the north and east of the 3D survey area once the channels are filled. There is a gradual increase in the average sand content throughout the survey from 3% (Figure a) in the Upper McMurray to % in the Lower McMurray (Figure b). In the lower McMurray, there are patches of shale predicted between the wells that can now be avoided by horizontal wells. In the upper McMurray, there are patches of sand that are potential reservoir. These results could have implications for the depositional model of the McMurray in this area. Acknowledgements The authors thank the owners of the data and of the project for permission to show these results and Veritas for permission to publish them. References Downton, J.E. and Ursenbach, C., 0, Linearized AVO inversion with supercritical angles, th Annual Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts,. Dumitrescu, C., Gray, D, Bellman, L. and Williams, A., 03, PS and PP AVO Analysis: A Multi-component Seismic Case Study for the Long Lake Oil Sands Project, 03 CSEG/CSPG Joint Conference Abstracts. Edmunds, N. R., Cordell, G. M., and Haston, J. A., 12, Steaming process, involving a pair of horizontal wells, for use in heavy oil reservoir, Canadian Patent #CA 0 EnCana, 04, Steam Assisted Gravity Drainage, http://www.encana.com/operations_and_projects/sagd.shtml Gidlow, P.M., Smith, G.C. and Vail, P.J., 12, Hydrocarbon detection using fluid factor traces: A case history, Expanded Abstracts of the Joint SEG/EAEG Summer Research Workshop on "How Useful is Amplitude-Versus-Offset (AVO) Analysis?", pp. -.
Goodway, W., Chen, T., and Downton, J., 1, Improved AVO fluid detection and lithology discrimination using Lamé petrophysical parameters; "Lambda-Rho, Mu-Rho", & "Lambda/Mu fluid stack", from P and S inversions: th Annual Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, -1. Gray, D., 03, PS converted-wave AVO, 3rd Ann. Internat. Mtg.: Soc. of Expl. Geophys., 1-1. Gray, D., Anderson, P. and Gunderson, J., 04, Examination of wide-angle, multi-component, AVO attributes for prediction of shale in heavy oil sands: A case study from the Long Lake Project, Alberta, Canada, 4 th Ann. Internat. Mtg: Soc. of Expl. Geophys., RC P1.2. Gray, F.D., Anderson, P. and Gunderson, J., 0, Prediction of shale plugs between wells in heavy oil sands using seismic attributes, AAPG 0 Annual Meeting, Technical Abstracts. Gray, F.D., Anderson, P.F. and Gunderson, J.A., 0, Prediction of Shale Plugs between Wells in Heavy Oil Sands using Seismic Attributes, Natural Resources Research, Springer Netherlands, ISSN 0- (Print) -1 (Online), http://www.springerlink.com/content/010qh/?p=dc03c4ca1cfa&pi= 0 Hein, F.J., Cotterill, D.K. and Rottenfusser, B.A., 01, The Good, the Bad and the Ugly: Reservoir Heterogeneity in the Athabasca Oil Sands, Northeast Alberta, 01 CSPG Rock the Foundations Convention Abstracts, pp. 04-1 04-3. Kelly, M. and Skidmore, C., 01, Non-linear AVO equations and their use in 3-parameter inversions, 1st Ann. Internat. Mtg: Soc. of Expl. Geophys., 2-2. Long Lake Project, 04, http://www.longlake.ca/project/technology.asp Nexen, 04, Year-end conference call, http://www.nexeninc.com/files/conference_calls/yearendccanalyst.pdf Roberts, G., 00, Wide-angle AVO, 0th Ann. Internat. Mtg: Soc. of Expl. Geophys., 4-. Russell, B., Hampson, D., Schuelke, J. and Quirein, J., 1, Multiattribute seismic analysis: The Leading Edge, 1, no., 3-.
Figures Figure 1: A typical SAGD horizontal well pair showing the steam chamber around the well (EnCana, 04).
GR vs. RHO Crossplot Well: Wells Intervals: MCM, CHAN Filter: 1 4 2 0 0 0 0 4 1 0 10 10 0 2 2 0 0 3 GR (GAPI) 1 1 1 1 2 2 1 1 2 1 2 2 1 1 3 4 2 2 2 1 2 4 3 3 4 4 4 1 1 3 1 4 2 2 1 2 3 3 4 1 2 4 3 3 2 1 4 3 2 4 1 1 1 1 1 0 2 0 2 3 3 2 1 3 3 3 2 4 0 3 3 2 2 1 1 1 1 1 3 1 1 2 4 2 2 2 1 2 3 1 2 3 0 4 4 4 4 2 1 2 2 1 3 1 1 0 4 0 2 4 1 1 1 0 0 2 1 1 1 3 1 1 1 3 1 1 1 1 1 2 2 2 2 3 3 2 2 3 1 1 1 2 3 3 3 4 4 2 2 1 1 3 4 1 2 1 1 2 0 4 2 4 1 2 4 1 1 3 3 3 3 2 2 1 1 2 2 2 2 2 1 1 1 1 1 1 1 1 2 4 0 0 2 4 3 1 0 3 4 3 3 2 1 1 2 4 0 4 4 0 0 4 2 0 4 1 3 0 2 1 2 1 1 1 1 4 2 4 3 4 3 3 3 1 4 4 4 0 4 4 3 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 2 2 3 2 3 3 3 4 3 4 4 4 4 4 4 3 3 2 1 2 1 1 1 1 1 2 3 3 3 1 1 0 4 2 2 1 1 1 1 1 1 2 1 1 1 1 2 2 2 3 2 1 3 3 2 1 2 1 1 1 2 2 3 3 3 4 2 3 4 4 4 2 2 1 1 1 1 2 1 1 2 3 4 2 3 1 1 2 3 3 3 4 3 2 3 2 2 1 1 1 1 2 2 3 3 3 3 2 1 1 2 1 1 1 1 2 3 2 4 3 3 2 3 1 1 2 2 2 3 4 3 3 2 2 1 1 1 1 1 1 2 2 2 2 4 4 2 3 2 1 2 1 1 1 1 1 1 2 2 2 3 2 4 3 4 2 3 2 1 2 1 1 1 1 1 1 1 1 1 1 2 3 0 2 3 2 1 1 1 2 1 1 2 3 2 2 2 1 1 1 2 3 3 2 2 2 2 1 1 1 1 2 2 2 4 2 4 2 2 3 2 4 3 3 3 3 3 2 3 2 3 1 1 1 1 1 2 3 4 2 2 2 1 2 1 2 1 1 1 RHO () -0 0 Color: FREQUENCY* Symbol: FREQUENCY Functions: rhob_gr : Regression Logs: RHO, GR, CC: 0.0 y = (-4. + 0.*(x)) rhob_gr3 : Regression Logs: RHO, GR, CC: 0. y = (-0. + 0.*(x)) Figure 2: Crossplot of gamma ray (GR) versus density (RHO, in kg/m3) showing a strong correlation of 0.3. The color shows the sample frequency.
Figure 3: Typical correlation of P-impedance reflectivity (left) and density reflectivity (right) derived from wells to the corresponding attributes derived from P-wave seismic using 3-term AVO. The correlation is measured from just above the Wabiska (Wab) to the Devonian (Dev) formations.
a) c) b) d) Figure 4: a) Density reflectivity calculated using 3-term PP. b) Amplitude inversion of density reflectivity for density. c) Neural network estimate of density section. d)neural network Vsh estimation. Yellow indicates sand and green, shale. Negative values are outside of the zone of interest and are associated with carbonates below the unconformity at the base of the reservoir and so should be disregarded.
2 a) b) Figure : Maps showing a) Upper McMurray Vsh estimate, b) Lower McMurray Vsh estimate, output by the neural network.