![](images/pdficon.gif)
GCAttribute Analysis
of Mid-Continent Washes*
Ryan Fairfield¹ and Renjun Wen²
Search and Discovery Article #41616 (2015)
Posted April 20, 2015
*Adapted from the Geophysical Corner column, prepared by the authors, in AAPG Explorer, April, 2015. Editor of Geophysical Corner is Satinder Chopra ([email protected]). Managing Editor of AAPG Explorer is Vern Stefanic. AAPG © 2015
¹SM Energy, Tulsa, Oklahoma, USA ([email protected])
²Geomodeling Technology Corp, Calgary, Canada
The Anadarko Basin granite wash play has been considered difficult to interpret both geologically and geophysically. Consequently, the application of seismic attribute analysis
to Mid-Continent washes has not been widely published.
SM Energy's Mid-Continent granite wash asset covered a portion of a prolific Missourian age wash trend. In 2010, a competitor drilled a successful horizontal well
,
Well
#1, in the interval with excellent initial production rates (Figure 1). Adequate
well
control existed to generally map the trend across the acreage – however, some uncertainty remained regarding the extent of the sand trend in areas that lacked
well
control. In order to gain insight into these areas, a seismic attribute study was conducted over a 14-square-mile portion of SM Energy's 3-D seismic holdings.
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment
♦General statement ♦Figures ♦Background ♦Method ♦Workflow ♦Conclusions ♦Acknowledgment |
The mature, oil and gas-producing Anadarko Basin granite wash play is a series of stacked fanglomerate reservoirs with sands of varying mineralogies derived from the erosion of the Wichita-Amarillo Mountain Front. The reservoir, which is the focus for this study, is a Missourian age wash. Petrophysical properties from local For the purpose of this study, low acoustic impedance and low gamma-ray are assumed to indicate clean, porous sand within the reservoir. Thicknesses of the sand trend range from seven feet at its thinnest recorded location to over 90 feet in the heart of the trend. Average porosities of the target sand (estimated from neutron/density porosity logs) are 10-12 percent. Here is a summary of the workflow used to predict critical rock properties through the application of statistical 1) Generate post-stack seismic attributes from 3-D seismic. 2) Interpret top and base of target, tied by synthetic seismic traces. 3) Build strata-grid from interpreted time horizons conformable to target area and extract each attribute to the strata-grid. 4) Upscale 5) Correlate attributes to upscaled 6) Apply neural network prediction to combine the statistically significant attributes and generate a predicted property grid representing the property of the correlated 7) Use cross-plots and define the target sand. 8) Generate geobodies to compare seismically-derived sand thickness estimation to 9) Visually compare morphology of geobodies to 1) Generation of Post Stack Seismic Attributes The 3-D seismic data used for the attribute 2) Attain an Accurate Synthetic to Seismic Tie Thirty-eight vertical It was from these wells that the location of the target sand was identified within the seismic trace, allowing time horizons that bracket the sand to be mapped in the seismic section. Four of these eight wells contained excellent examples of the target sand and would serve as the calibration wells. 3) Generate a Strata-Grid, Extract Attributes and Upscale Subject A "strata-grid" was created using the interpreted horizons to encompass the target sand. Each of the post stack attributes (in addition to the original stacked amplitude volume) was extracted to the strata-grid. Several An appropriate upscaling window size was finally adopted after testing the sensitivity of the size to the correlation coefficient between seismic attributes and the upscaled 4) Correlate Attributes to Upscaled Logs, Perform Neural-Network Prediction and Generate Predicted Property Grids Generated seismic attributes were correlated to each subject 5) Cross-Plot of Predicted Property Grids, Refinement of Cross-Plot Polygon and Geobody Extraction First, the target zone within the predicted gamma-ray and impedance grids were cross-plotted with samples from the calibration wells highlighted (Figure 4). Upper-range sand cutoff values from the upscaled gamma-ray and impedance logs were estimated, allowing for a polygon to be drawn around the sand points. The polygon shape and size was further refined by plotting extracted predicted property grid values at blind 6) Attempts to Avoid Spurious Correlations Spurious correlations are a constant concern in attribute First, at least two logs need to be chosen that can discriminate porous sand in cross-plot space by themselves. Second, the morphological shapes of these chosen logs should be different to avoid redundancy in correlated attributes for each predicted property grid. Third, as many wells as feasible should be used in calibration in order to sufficiently sample the reservoir and capture some measure of reservoir variability. Criteria 1 and 2 were addressed in the discussion on upscaling window size selection. Rather than using one It also is important that the target reservoir be sufficiently sampled to make the correlation coefficients statistically significant. A statistical sample of points was able to be attained from within the sand zone itself. A minimum of 15 is necessary, while greater than 50 were attained in this study. 7) Validating the Results through Reliability Recall, the primary focus of this study was determining morphology more so than the absolute thickness of the reservoir. However, a blind A sand isochron was extracted at each Of the 38 wells used in the study, 23 wells had a 60 percent or better prediction to the 8) Activity and Results SM Energy completed its first horizontal producer in the play, Five additional horizontal wells were drilled by other operators with outcomes that appear to correlate to the predicted sand map generated by this workflow. Four wells were drilled to the north with mixed results – however, the best Using commercially available seismic attribute Furthermore, when doing a simple visual comparison of the attribute-derived porous sand map to the The authors thank CGG for permission to use its data in this study, as |