MRCSP Phase I Geologic Characterization Report - Midwest ...

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18 CHARACTERIZATION OF GEOLOGIC SEQUESTRATION OPPORTUNITIES IN THE <strong>MRCSP</strong> REGION Table 2.—Summary of data and error statistics (5,000-foot grid resolution) for the major regional maps of this project Mapping Unit Number Square Cross Validation Grid Error Range of of Wells Miles/ Well Error (RMSE ft) (RMSE ft) Values (ft) Basal Cambrian Injection Targets Structure 510 323 595 361 17,210 Basal Cambrian Injection Targets Isopach 373 441 123 100 2,022 Copper Ridge Structure 641 321 385 390 15,691 Copper Ridge Isopach 337 610 658 567 9,751 Rose Run Structure 1,786 40 236 259 17,933 Rose Run Isopach 1,756 41 32 27 611 St Peter Structure 502 162 362 474 10,709 St Peter Isopach 254 321 60 84 1,156 Knox Structure 2,424 77 161 183 13,806 Knox-Silurian Isopach 2,051 90 86 47 5,202 Queenston Structure 11,327 15 74 55 10,431 Medina Structure 6519 13 58 78 9,299 Medina Isopach 6976 12 23 25 627 Oriskany Structure 11724 5 216 154 7,907 Oriskany Isopach 11024 6 21 10 386 lated and are provided as a useful guide when using the maps of this project. Rigorous measures of map accuracies have been obtained for most of the major regional-scale maps in this study. Uncertainty was estimated using the two approaches described below. How good are the interpolations at unsampled locations This question was evaluated using geostatistical cross-validation based on ordinary kriging. Grids that obey well points exactly may provide a poor prediction at unsampled locations (which is the majority of the area being considered). Consequently, the surfaces were estimated by kriging at each point location, but without using the value at that point. Summary statistics (RMSE) were generated using the differences between the actual data value at a known point versus the interpolated (kriged) value at that same point (Table 2). The resultant RMSE value provide a general estimate of the systematic and random error of interpolation at unsampled locations. The value is an average error for the map; actual error at any specific location on the map can be smaller or larger than the RMSE value. Not all final maps were created using geostatistics (Table 1); however, cross validation was calculated for all layers as a method to compare the strength of geostatistical interpolation between mapped layers. The results of this analysis are provided in the column labeled “Cross-validation error” in Table 2. How accurately do the fi nal grids obey the well values This question was evaluated by calculating the difference between the value at the control point (well) and the value of the nearest calculated grid cell. The result was summarized using the RMSE method. Faithfulness of the grid (Table 2) was partly a function of grid cell size, as finer grids were more able to accurately model complex trends. Analysis found that a cell resolution of 5,000 feet provided a reasonable compromise between grid accuracy and computational efficiency (Venteris and others, 2005). However, increasing the cell resolution to 2,000 feet further reduced grid errors for many of the map layers; yet there was little to be gained from using resolutions greater than 2,000 feet. The results of this analysis are provided in the column labeled “Grid error” in table 2. Accuracy Discussion There were considerable differences between the accuracy of the various structure and isopach surfaces. RMSE values ranged from 10 to 658 feet. Several factors contributed to the uncertainty of the maps. 1. Accuracy is expected to increase with the number of wells per unit area. Ultimately, the value and geometry of the point data have the biggest influence on the final surface produced by computer interpolation, regardless of the method used. Increased numbers of data points lead to more robust statistical prediction. 2. Increased range of data values can have negative and positive influences on spatial modeling. Large trends in areas of sparse data result in errors for non-exact interpolators, such as kriging, that relies heavily on neighboring values. Large trends can also increase the strength of the prediction model (variogram) by decreasing the signal to noise ratio. 3. The shape of the surface and the amount of faulting affect accuracy. Surfaces that are smooth and predictable are easier to model than those with abrupt discontinuities (faults, breaks in slope). These discontinuities violate the basic assumptions of geostatistical interpolation. Also, spatial data often have a component of spatial variability below the scale of sampling. The greater this variability (the micro variance component of the nugget effect), the less the interpolated values will agree with the proximal data values. 4. The well data set is also a source of error. Individual data points should be very accurate (within 10 1 feet). However, misidentified horizons are common and can result in errors greater than 100 feet. Such cases are usually detected by the screening method and removed. 5. Gridding (ANUDEM) in areas of intense faulting may introduce additional errors. In areas of a very steep slope (as found in the Rome trough) small errors in gridding can result in a large difference between well and grid values. For this data set, error sources one and two had the most influ-
GEOLOGIC MAPPING PROCEDURES, DATA SOURCES AND METHODOLOGY 19 EXPLANATION Known Basement Faults Top of Precambrian surface Elevation in feet (relative to sealevel) -2000 -49000 Contours Pittsburgh-Washington Lineament Lexington Fault R o m e T r o u g h 50 25 0 50 100 150 Miles 50 25 0 50 100 150 200 Kilometers ³ Figure 11.—Structure map on the top of the Precambrian unconformity illustrating the complexity within the Rome trough area. Hand-contouring was used within the heavily faulted portions of the Rome trough and computer-based contouring was employed in less faulted areas.
Page 1 and 2: Characterization of Geologic Seques
Page 3 and 4: ABOUT THE MRCSP The Midwest Regiona
Page 5 and 6: CONTENTS About the MRCSP ..........
Page 7 and 8: CONTENTS Figure A14-2.—Structure
Page 9 and 10: 1 CHARACTERIZATION OF GEOLOGIC SEQU
Page 11 and 12: BACKGROUND INFORMATION 3 (a minimum
Page 13 and 14: INTRODUCTION TO THE MRCSP REGION’
Page 21 and 22: GEOLOGIC MAPPING PROCEDURES, DATA S
Page 25: GEOLOGIC MAPPING PROCEDURES, DATA S
Page 35 and 36: OIL, GAS, AND GAS STORAGE FIELDS 27
Page 41 and 42: CO 2-SEQUESTRATION STORAGE CAPACITY
Page 53 and 54: CONCLUSIONS AND REGIONAL ASSESSMENT
Page 55 and 56: REFERENCES CITED 47 National Confer
Page 57 and 58: 49 APPENDIX A Geologic Summaries of
Page 59 and 60: APPENDIX A: PRECAMBRIAN UNCONFORMIT
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