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THEPROBLEM Horizontal Well Completion Effectiveness 2
SPE SHALEGASCONFERENCESURVEY From Zoback, 2012 3
REASONS TOINNOVATE Limitations of Current Methodology Acoustics: Shear Anisotropy, Chevrons Use of OBM Failure of Imaging Limitations of Alford Valid only for vertical or near-vertical(20 deg max) Greater understanding of natural fracture distribution in reservoir is necessary to optimally place hydraulic fractures in naturally fractured reservoirs. AdityaVyasfrom Model Calibration and Effective Reservoir Imaging Group at Texas A&M University 4
ACOUSTICLOGGING VS. SEISMIC Seismic Signal Attenuation Noise Research/Improvements Acoustic Logging Similarities to Seismic Smaller Scale Good Enough Last Major Innovation was in 2005 (SonicScanner ) 5
RESEARCH Fracture Identification Limitations of Shear Commonplace Seismic Techniques Gathers Normalization Uses of compressional Azimuth Papers showing limitations of Alford Options for Alternatives P1Az reference point 6
Stacking FRACTUREIDENTIFICATION Seeking Fine Perturbations in the Data Smaller No. of Gathers than Seismic Normalization Accounting for receiver or tool abnormalities Compressional Theory Based on Seismic Research Adapted to Smaller Scale 7
FRACTUREIDENTIFICATION Theory:Compressionalwaves drop in energy when traversing a fracture. Raw, sectored compressionalwaveforms are processed, combined, rotated, & mapped Across open fractures or fault zones, RMS energy differential is calculated Location and Azimuth of fractures computed Published patent available through USPTO site 8
WORKFLOW Input: Raw Waveforms Mute Raw Waveforms in the Time Domain Filter Raw Waveforms in the Frequency Domain Target & Isolate Frequency Spectrum Rotate RMS Arrays Compute RMS Energy Normalize Arrays Stack Data Arrays Compute RMS Differential & Azimuth Plot RMS Differential and Azimuth Rose Diagrams Deliver Electronic or Paper Plots with LAS file 9
FRACTUREIDENTIFICATION CASE STUDIES Tested and Used in Bakken, Permian Basin, and Eagle Ford Used in Laterals Can use cross-dipole waveform data in laterals on a case-by-case basis Leaky Mode Fractured zones Individual fractures vs. fractured zones Eg. Induced Fractures 80-85% correlation with core over fractured AND unfractured zones 10
Blind Test EAGLEFORD GeoBizTechnology Fracture Identification in middle track. X s demonstrate fracture density in Core Shear Anisotropy result in far left track. Processed by third party 11
EAGLE FORD EXAMPLE (AGAINST CORE) 12
FRACTUREIDENTIFICATION CASE STUDIES No distinction for induced and natural fractures It s Math! It doesn t discriminate! Vertical fractures Manifested as a series of peaks and apparent in plot Planar Nature of Method Identified Limitations Vugs may mask azimuth calculations 13
Blind test PERMIANBASIN Middle track -GeoBizTechnology Fracture Identification Result Vertical Fractures Highly fractured vs. Unfracturedvs. Hairline Fracture 14
PERMIAN BASIN: UNFRACTURED Fracture Density Index 15
PERMIAN BASIN: HAIRLINE FRACTURE Fracture Density Fracture 16
PERMIAN BASIN: HEAVILY FRACTURED Fracture Density 17
FASTSHEARAZIMUTHMODELING Theory:Above 20 degrees from vertical, error is introduced into Alford-based azimuth calculations Trigonometric premise limits calculations in horizontal wells Quick-and-dirty Model provides a calibrated correction factor (Japanese Research) Robust model uses pre-stack inversion modeling to find fracture azimuth Found in testing:errors of around 20 degrees in azimuth calculation in wells with greater than 50 degree deviation 18
IMAGE BREAKOUT VS. CORRECTED AZIMUTH Image Stress Direction Image (11 o ) Stress Direction Alford (34 o ) Stress Direction GeoBizTech (14 o ) 19
WHAT SNEW FORGEOBIZTECHNOLOGY? In Partnership with Dr. AkhilDattaguptaof Texas A&M University, GeoBizTechnology can now run production and flow simulations using natural fracture patterns in the wellbore. Dr. Dattaguptacan compare simulated production using a traditional, non-engineered completions design to a program optimized to account for natural fracture patterns Allows comparison of NPV Further azimuth research is being conducted to create a more complete model 20
GEOBIZTECHNOLOGY Unique Capabilities: Fracture Identification Fast Shear Azimuth Calculations (lateral-specific) Standard Capabilities Shear Anisotropy Slowness Calculations Rock Properties Specialization in Petrophysics w/understanding of total industry needs 21
CONTACT INFORMATION: info@geobiztechnology.com (281) 661-8305 22
REFERENCES Shearer, P.M.; Orcutt, J.A.; Compressionaland Shear Wave Anisotropy in the Oceanic Lithosphere the NgendeiSeismic Refraction Experiment; Geophys.J. R. astr.soc.(1986)87,967-1003. YI, Y Y et al: On P-Wave Seismic Detection Methods for Fractured Reservoirs: V. 29, n0. 4 pp. VI, Aug 2007, Journal of Oil and Gas Technology Neves, F.A. et al: P-Wave Anisotropy from AzimuthalAVO and Velocity Estimates Using 3-D Seismic Data from Saudi Arabia, Geophysics v.71, no.2, pp E7-E11, March-April, 2006 Dellinger J.A., Nolte B., Etgen J.T., 2001. Alford rotation, ray theory, and crossed-dipole geometry, Geophysics, 66(2), 637 647. Mueller M.C., 1991. Prediction of lateral variability in fracture intensity using multicomponentshear-wave surface seismic as a precursor to horizontal drilling in the Austin Chalk, Geophys. J. Int., 107, 409 415 WintersteinD.F., GopaS.D., Meadows M.A., 2001. Twelve years of vertical birefringence in nine-component VSP data, Geophysics, 66(2), 582 597. WintersteinD.F., Meadows M.A., 1991. Changes in shear-wave polarisationazimuth with depth in Cymricand Railroad Gap oil fields, Geophysics, 56(9),1349 1364. Hou, A. and K. J. Marfurt, 2002, Multicomponentprestackdepth migration by scalar wavefieldextrapolation: Geophysics, 67, 1886-1894. Michaud, G. and Snieder, R. (2004), Error in shear-wave polarization and time splitting. Geophysical Prospecting, 52: 123 132. doi: 10.1046/j.1365-2478.2003.00404.x ZengX., MacBethC., 1993. Algebraic processing techniques for estimating S-wave splitting in near-offset VSP data: theory, Geophys. Prospect., 41, 1033 1066. ZengX., MacBethC., 1993. Accuracy of shear-wave splitting estimations from near-offset VSP data, Can. J. Explor. Geophys., 29, 246 265. 23