Anoxia Modelling and TOC Analysis in Wolfcamp Formation
Explore an in-depth analysis of Total Organic Carbon (TOC) in the Wolfcamp Formation through elemental data modeling. Discover the significance of proxies like Uranium (U) and Molybdenum (Mo), the impact of lithology and facies on TOC burial, and the relationship between Nickel (Ni) and depositional environments. Uncover the role of redox control in TOC burial and the unique trends in mineralogy. Gain insights into the depositional settings of the Wolfcamp Formation and the limitations of certain proxies in TOC modeling.
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Modelling TOC and Anoxia From Elemental Data in the Wolfcamp Fm: A Reality Check Milly Wright, Eliza Mathia, Ken Ratcliffe Chemostrat Inc.
Why Model TOC ? TOC in shale plays is critical Gathering elemental data has become commonplace with the trace elements U or Mo widely used as a proxy for TOC The Wolfcamp is different ! 200 14 5 180 4.5 12 160 4 10 140 3.5 Mo (ppm) Ni (ppm) U (ppm) 120 3 8 100 2.5 6 80 2 60 1.5 4 40 1 2 20 0.5 0 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 TOC (wt.%) TOC (wt.%) TOC (wt.%) Why some proxies don t work? What does this tell us about the depositional setting of the Wolfcamp ?
Presentation Outline Lithology Controls on TOC Burial How do we define lithology from elemental data Do the facies provide a means to understand TOC burial Carbonates dilute TOC Redox Control on TOC Burial Once Carbonate Dilution is discarded, Redox is the primary control Only Nickel (Ni) shows a consistent positive linear association to TOC what does that tell us about depositional environments in the Wolfcamp ? At least partial restriction Fluctuating chemocline associated with carbonate deposition Periodic disoxia-anoxia during shale deposition Modelling TOC from trace elements Nickel (Ni) is the only trace element that can be used as a proxy for TOC
Lithology (Facies ?) and TOC Mn Ca 15 Al Si 3 TOC 16 Si/Al EF Mo Fe/Al 0.01 0 0.02 10 0.03 0.04 0.05 0.06 60 20 30 40 50 0 0 5 10 10 20 15 30 20 40 0 1 2 4 5 6 7 8 4 10 22 28 34 0 1 2 3 4 5 6 0 5 10 15 20 25 30 35 5 25 35 -9220 -9220 -9220 -9220 -9220 -9220 -9220 -9220 40 ft -9260 -9260 -9260 -9260 -9260 -9260 -9260 -9260 -9300 -9300 -9300 -9300 -9300 -9300 -9300 -9300 -9340 -9340 -9340 -9340 -9340 -9340 -9340 -9340 -9380 -9380 -9380 -9380 -9380 -9380 -9380 -9380 -9420 -9420 -9420 -9420 -9420 -9420 -9420 -9420 -9460 -9460 -9460 -9460 -9460 -9460 -9460 -9460 -9500 -9500 -9500 -9500 -9500 -9500 -9500 -9500 -9540 -9540 -9540 -9540 -9540 -9540 -9540 -9540 -9580 -9580 -9580 -9580 -9580 -9580 -9580 -9580 -9620 -9620 -9620 -9620 -9620 -9620 -9620 -9620
Lithology (Facies ?) and TOC TOC < 2% TOC > 2%
Mineralogy variation reveals 3 distinct trends Qtz+Fd Qtz+Fd : Clays variation Carbonate trend along the Si:Al illite line Carbonate trend with the Si: Al ratio > illite Clays Carbonates How lithology controls TOC?
Facies assignment helps to predict TOC values 12 Qtz+Fd 10 No. of samples 20 8 No. of samples 6 15 4 10 2 0 5 0 TOC (%) 20 TOC (%) No. of samples 15 10 5 0 TOC (%) Clays Carbonates
Authigenic enrichment Mo auth U auth 6 Clays Carbonates 3 TOC Qtz+Fd 0 3 9 12 15 0 20 40 60 80 100 0 10 20 30 40 50 0 1 2 4 5 6 7 8 0 20 40 60 80 0.0 0.5 1.0 1.5 2.0 2.5 3.0 -9220 -9220 -9220 -9220 -9220 -9220 -9260 -9260 -9260 -9260 -9260 -9260 -9300 -9300 -9300 -9300 -9300 -9300 -9340 -9340 -9340 -9340 -9340 -9340 -9380 -9380 -9380 -9380 -9380 -9380 -9420 -9420 -9420 -9420 -9420 -9420 -9460 -9460 -9460 -9460 -9460 -9460 -9500 -9500 -9500 -9500 -9500 -9500 -9540 -9540 -9540 -9540 -9540 -9540 -9580 -9580 -9580 -9580 -9580 -9580 -9620 -9620 -9620 -9620 -9620 -9620 How redox controls TOC?
TOC: Redox or Dilution? Facies Carbonate mud. / Limestone Siliceous mudstone Mixed mudstone Argillaceous
TOC: Redox or Dilution? Facies Carbonate mud. / Limestone Siliceous mudstone Mixed mudstone Argillaceous TOC < 2% TOC > 2%
Summary so far 60 80 70 50 Al2O3 and SiO2 (%) 60 Lithology control 40 CaO (%) 50 40 30 30 20 20 10 Redox control 10 0 0 1 5 7 3 0 2 4 6 0 1 2 3 4 5 6 7 TOC (wt.%) TOC (wt.%)
What elements are suitable as TOC proxies? 400 200 200 a = 38.5 180 180 350 a = 27.9 Pearson s r = 0.85 Pearson s r = 0.97 160 160 300 140 140 Cu (ppm) Cr (ppm) Ni (ppm) 250 120 120 a = 8.8 Pearson s r = 0.76 200 100 100 TM 80 80 150 W TM 60 60 TM W 100 W 40 40 a = 68.5 50 20 20 a = 9.0 Pearson s r = 0.83 Pearson s r = 0.51 0 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 TOC (wt.%) TOC (wt.%) TOC (wt.%) 10 20 400 9 18 350 8 16 300 7 14 Mo (ppm) a = 24.6 Pearson s r = 0.69 U (ppm) V (ppm) 250 6 12 5 10 200 a = 0.3 a = 0.4 Pearson s r = 0.24 4 8 Pearson s r = 0.58 150 TM a = 1.6 3 a = -0.02 6 W Pearson s r = 0.34 100 Pearson s r = -0.04 2 4 W W 50 2 1 TM a = 58.1 TM Pearson s r = 0.82 0 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 TOC (wt.%) TOC (wt.%) TOC (wt.%)
Enrichment Factors (EF) & Interpretation EF Element = ( ( ) ) Element Measured Aluminum Measured Element Standard Aluminum Standard The standard often used is Post Archian Australian Shale (PAAS). Care must be used, if Al is very low the calculation can be misleading. Algeo et al., 2009
Enrichment Factors (EF) & Interpretation H2S/HS- MoS4 The Particulate Shuttle (PS) requires free hydrogen sulfide. EFMo This is easier to accomplish in clay poor environments, because there is less Fe to scavenge sulfur compounds. EFU Algeo et al., 2009
Enrichment Factors (EF) & Interpretation Eagle Ford Shale EFMo EFMo EFU EFU Lwr Eagle Ford samples show strong evidence of persistent Euxinic conditions and with particulate shuttle controlling high Mo values in these sequences. Algeo et al., 2009
Redox conditions & water chemistry 0.3 x SW 3 x SW 1 x SW 1000 0.1 x SW 100 EFMo 10 1 0.1 0.1 1 10 100 1000 EFU
Redox conditions & water chemistry 0.3 x SW 3 x SW 1 x SW 1000 0.1 x SW 3 redox states identified: At least intermittent H2S associated with the carbonate deposition and Fe-Mn cycling Low TOC Fe-Mn cycling 100 EFMo 10 HighTOC Anoxic Suboxic with little OM burial and EFU > EFMo 1 Anoxic with enhanced OM burial and higher Mo accumulation TOC 2% Suboxic-anoxic 0.1 0.1 1 10 100 1000 EFU
Residence time in seawater of redox elements 400 200 200 a = 38.5 180 180 350 a = 27.9 Pearson s r = 0.85 Pearson s r = 0.97 160 160 300 140 140 Cu (ppm) Cr (ppm) Ni (ppm) 250 120 120 a = 8.8 Pearson s r = 0.76 200 100 100 TM 80 80 150 W TM 60 60 TM W 100 W 40 40 a = 68.5 50 6kyr 8kyr 5kyr 20 20 a = 9.0 Pearson s r = 0.83 Pearson s r = 0.51 0 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 TOC (wt.%) TOC (wt.%) TOC (wt.%) 10 20 400 9 18 350 8 16 300 7 14 Mo (ppm) a = 24.6 Pearson s r = 0.69 U (ppm) V (ppm) 250 6 12 5 10 200 a = 0.3 a = 0.4 Pearson s r = 0.24 4 8 Pearson s r = 0.58 150 TM a = 1.6 3 a = -0.02 6 W Pearson s r = 0.34 100 Pearson s r = -0.04 2 4 W W 50 400 kyr 800 kyr 50 kyr 2 1 TM a = 58.1 TM Pearson s r = 0.82 0 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 TOC (wt.%) TOC (wt.%) TOC (wt.%)
Optimum proxy for the TOC abundance Ni is the best proxy for TOC abundance in the investigated Wolfcamp strata: 7 y = 1.0403x - 0.1068 R = 0.9361 p < 0.001 6 It met the following conditions: Predicted TOC (wt.%) 5 Similarly to TOC, it is affected by the carbonate matrix 4 3 Ni burial controlled by redox with no effect of Fe-Mn cycling 2 1 Short residence time in seawater 0 0 1 2 Measured TOC (wt.%) 3 4 5 6 7
Summary Carbonate content (dilution) is a first-order proxy for the TOC abundance in lithologies with carbonate content > 50% In the carbonate-poor shale (< 10%), TOC varies primarily as a function of palaeo-redox conditions The concentrations of redox-sensitive elements were affected by the water mass restriction, with elements of high residence time in seawater having the lowest abundances High enrichment in Mo, Cu, and Fe in the carbonate lithology suggests operation of the Fe-Mn cycling and at least intermittent H2S despite very low TOC concentrations (high dilution) Understanding mechanisms operating during deposition of shales (water chemistry, basin restriction, redox) is essential for establishing correct relationships between TOC, elemental data, mineralogy and rock facies. Why do we care? & how can we use this information?
Facies Differentiation Siliceous mudstone Qtz+Fd > 50%, Carbonates > 10% Clay-bearing siliceous mudstone Qtz+Fd > 50%, Carbonates < 10% Argillaceous mudstone Clays > 50%, Carbonates > 10% Silica-bearing argillaceous mudstone Clays > 50%, Carbonates < 10% Carbonate dominated mudstone Carbonates > 80% Carbonate mudstone Carbonates > 50%, Clays > 10% Silica-bearing carbonate mudstone Carbonates > 50%, Clays < 10% Argillaceous-siliceous mudstone Qtz+Fd >30%, Clays < 50%, Carbonates < 20% Carbonate-siliceous mudstone Qtz+Fd, Carbonates < 50%, Clays < 20% Mixed siliceous mudstone Qtz+Fd > 40%, Carbonates, Clays > 20% Mixed argillaceous mudstone Clays > 40%, Qtz+Fd, Carbonates > 20% Mixed carbonate mudstone Carbonates > 40%, Qtz+Fd, Clays > 20% Mixed mudstone < 20% Qtz+Fd, Clays, Carbonates < 40% A2 A3 B2 B3 C1 C2 C3 D1 D2 D4 D5 D6 D7
Facies from cuttings Qtz+Fspar c. 8,000 ft MD A1 Well 1 -wellbore pathway A3 A4 A2 c. 50 ft D1 D2 D4 D3 C4 B3 D5 D6 TVD B2 C2 D3 B1 C1 B4 C3 Carbonates Clay Qtz+Fspar A1 Highcount A3 A4 Low count A2 c. 50 ft D1 D2 D4 D3 C4 B3 D5 D6 B2 C2 TVD D3 B1 B4 C1 C3 Well 2 -wellbore pathway Carbonates Clay
Modelling in laterals Carbonate depleted clay-bearing siliceous mudstone Carbonate mudstone Argillaceous mudstone Siliceous mudstone 50 Lithofacies - Midland AC/AF FEE 5HB 95 50 ChemSonic us/ft 95 100 Stacked Curves 0 MD (ft) Depth 10000 10050 10100 10150 10200 10250 10300 10350 10400 10450 10500 10550 10600 10650 10700 10750 10800 10850 10900 10950 11000 11050 11100 11150 11200 11250 11300 11350 11400 11450 11500 11550 11600 11650 11700 11750 11800 11850 11900 11950 12000 12050 12100 12150 12200 12250 12300 12350 12400 12450 12500 12550 12600 12650 12700 12750 12800 12850 12900 12950 13000 13050 13100 13150 13200 13250 13300 13350 13400 13450 13500 13550 13600 13650 13700 13750 13800 13850 13900 13950 14000 14050 14100 14150 14200 14250 14300 14350 14400 14450 14500 14550 14600 14650 14700 14750 14800 14850 14900 14950 15000 15050 15100 15150 15200 15250 15300 15350 15400 15450 15500 15550 15600 15650 15700 15750 15800 15850 15900 15950 16000 16050 16100 16150 16200 16250 16300 16350 16400 16450 16500 16550 16600 16650 16700 16750 16800 16850 16900 16950 17000 17050 17100 17150 17200 17250 17300 17350 17400 17450 17500 17550 17600 17650 17700 17750 17800 17850 9300 9350 9400 9450 9500 9550 9600 9650 9700 9750 9800 9850 9900 9950 Can you link facies to predicted TOC contents? Can you take the next step and link facies to production?
