Log Interpretation Parameters Determined by Analysis of Green River Oil Shale Samples: Initial Steps Michael M. Herron Susan L. Herron Malka Machlus Schlumberger-Doll Research
Log Interpretation in Green River is Not Usual Oilfield Practice Not simple sand-shale or carbonate Unusual minerals: Dawsonite, nahcolite, trona, buddingtonite, Solid hydrocarbon Pre-conversion, intermediate and post-conversion end-points
Traditional Log Interpretation Model Tool readings = Log response parameters Formation volumes T = R v Solve for V from measured T and lookup R Optimize R values for trace elements If # of volumes >> # tool readings Or if R values are not diagnostic
An Alternate Approach Build data table Quantitative mineralogy, chemistry, matrix density, computed log parameters Compute local algorithms of log interpretation parameters as functions of measurable log data Use local algorithms to compute formation properties This has worked for total clay, matrix density,
Geochemical Lithology 1 Clay Carbonate Sand Measured wt% 5 5 1 5 1 5 1 Estimated Estimated Estimated 5 Initials 3/12/29
Measured vs Computed Density Estimated from elements 3 2.8 2.6 3 2.8 Non-arkose aad =.12 aad =.26 aad =.74 aad =.9 Subarkose aad =.1 aad =.5 2.6 2.6 2.8 3 6 Initials 3/12/29 2.6 2.8 3 2.6 2.8 3 Matrix density from minerals
Scope of the Study, So Far >1 Outcrop Samples All R- and L- zones Geographic Variability
Scope of the Study, So Far >16 Core Samples All R- and L- Zones Geographic Variability
Data Being Collected Inorganic chemistry Quantitative mineralogy Matrix density Computed log response parameters Kerogen-rich and kerogen-free bases Other parameters measured
Data Being Collected: Quantitative Mineralogy 35 Minerals (76 Mineral Varieties) Quartz, Na-feldspar, K-feldspar, Ca-feldspar Muscovite, biotite, pyrite Calcite, dolomite, Hi-Mg calcite, aragonite, ankerite, siderite, magnesite Illite, kaolinite, smectite, chlorite, glauconite Opal-A, opal-ct, chert Anhydrite, gypsum, barite, hematite, fluorite, celestite, analcime, apatite Dawsonite, nahcolite, trona, buddingtonite, halite, 1 Initials 3/12/29
Data Being Collected: Inorganic Chemistry Log-Sensitive Chemical Elements: Si, Al, Ca, Mg, Fe, Na, K, P, Ti, S, Th, U Ag, As, B, Ba, Be, Bi, Cd, Ce, Cl, Co, Cr, Cs, Cu, Dy, Er, Eu, F, Ga, Gd, Ge, Hf, Ho, In, La, Li, Lu, Mn, Mo, Nb, Nd, Ni, Pb, Pr, Rb, Sm, Sc, Sn, Sr, Ta, Tb, Tl, Tm, V, W, Y, Zn, Zr Organic and inorganic C, CO 2 11 Initials 3/12/29
Data Being Collected: Computed Log Response Parameters Gamma ray (from Th, U, K) Matrix Density Thermal neutron capture cross section (sigma) Photoelectric cross section (PEF) Hydrogen index Thermal neutron porosity response Epithermal neutron porosity response
Why DRFT-IR Quantitative Mineralogy? Earth science standard is X-ray diffraction Excellent for mineral identification Poor for quantification Dual Range Fourier Transform Infrared Spectroscopy (DRFT-IR) Excellent for quantification
Commercial XRD and Actual Mineralogy Sample A Qtz Ksp Plag Cal Mg-Cal Dol Sid Py Kaol 2:1 Al clay 2:1 Fe clay Fe-Chl Actual 25 5 5 5 15 2 2 5 Texaco 26 4 3 5 16 19 22 5 Vendor 1 68 3 4 5 8 11 Vendor 2 44 1 5 2.1.1 23 12 11 Vendor 3 41 2 3 9 36 9 Sample B Qtz Ksp Plag Cal Mg-Cal Dol Sid Py Kaol 2:1 Al clay 2:1 Fe clay Fe-Chl Actual 3 5 1 5 5 5 2 1 1 Texaco 31 2 8 4 4 3 21 13 1 Vendor 1 56 7 3 6 14 12 2 1 Vendor 2 34 1 8 3 6 4 24 2 18 Vendor 3 52 2 3 13 2 24 5 Sample C Qtz Ksp Plag Cal Mg-Cal Dol Sid Py Kaol 2:1 Al clay 2:1 Fe clay Fe-Chl Actual 3 1 5 5 5 5 1 1 15 5 Texaco 3 8 4 5 5 4 1 1 16 5 Vendor 1 63 3 14 3 5 6 5 1 Vendor 2 39 2 6 4 7 14.2 15 4 1 Vendor 3 37 1 2 8 9 19 19 5 14 Initials 3/12/29 Srodon et al., Clays and Clay Minerals, 49, 21)
True and Estimated Mineral Concentrations True Composition Estimated from FT-IR Quartz Opal-A Ortho. Olig. Calcite Dolomite Pyrite 5 1 Sample 15 2 25 3 35 4 45 1.23 1.1.57 1.6.95 1.73.7 5 1 5 1 5 5 5 1 5 1 5 15 Initials
True and Estimated Mineral Concentrations True Composition Estimated from FT-IR Illite Smec. Kaol. Chlor. Biotite Musc. Glauc. 5 1 Sample 15 2 25 3 35 4 1.6 1.8.57.97.13.57.66 45 5 5 5 5 5 5 5 16 Initials
Mineralogy Validation From Reconstructed Chemistry Green River Core Samples Measured Chemistry, wt % 3 Silicon 3 1 Aluminum 1 1 1 Calcium 1 Sulfur 1 1 1 Iron 1 Sodium 1 1 1 Potassium 1 Magnesium Reconstructed From Mineralogy, wt % 1
Burnham Sample and Illite 1 Absorbance, au Quartz Carbonate Kerogen 5 25 5 Wavenumber, cm -1
Dawsonite 1 1 Absorbancs, au.9.8.7.6.5.4 Monticolo, Italy.9.8.7 Francon Quarry, Quebec.3.2.1 Absorbancs, au.6.5.4.3.2.1 5 1 15 2 25 3 35 4 45 5 Wavenumber, cm-1 5 1 15 2 25 3 35 4 45 5 Wavenumber, cm-1 NaAl(CO 3 )(OH) 2 A low-temperature hydrothermal mineral (www.mindat.org)
Very Far Infrared Halite Usual lower FT-IR range 1.6 Absorbance, au.8 1 2 3 4 Wavenumber, cm -1
Nahcolite and Trona 2.5 2 Absorbances, au 1.5 1 www.webmineral.com.5 Nahcolite: NaHCO 3 Trona: Na 3 (CO 3 )(HCO 3 ) 2(H 2 O) 5 1 15 2 25 3 35 4 45 5 Wavenumber, cm-1 Continental evaporite deposits (www.webmineral.com)
Two Buddingtonite Standards 1.9.8 Absorbancs, au.7.6.5.4.3.2.1 5 1 15 2 25 3 35 4 45 5 Wavenumber, cm-1 www.mindat.org www.webmineral.com (NH 4 )Al(Si 3 O 8 ).5(H 2 O) Plagioclase altered by ammonium-bearing waters (www.webmineral.com)
Two Kerogen Spectra.5 Absorbance, au.25 5 25 5 Wavenumber, cm-1
Organic Carbon and Single Wavelength Absorbance 3 Organic carbon, wt % 2 1.6.12 Absorbance, au
Estimating Clay From Gamma Ray? Gamma ray computed from Th, U, and K measured on core samples Total Clay, wt% 1 8 6 4 2 5 1 15 2 25 3 Gamma Ray, API Not a good idea from these data!
Our Approach Evaluate End-Member R values Build data table Quantitative mineralogy, chemistry, matrix density, computed log parameters Compute local algorithms of log interpretation parameters as functions of measurable log data Use local algorithms to compute formation properties This has worked for total clay, matrix density,