How variable can shales be?

How variable can shales be? Lessons learned of mudrock heterogeneity Bryan W. Turner *, Daniela Becerra-Rondon, and Roger M. Slatt PhD Candidate ConocoPhillips School of Geology and Geophysics University of Oklahoma - Norman, OK 23 September 2015

Homogenous Black Shale?

Purpose of this Talk: • Document geochemical changes within stratigraphic successions of mudrocks. • Determine if these geochemical changes predict mechanical properties. • Evaluate how these changes affect the stratigraphic framework. – Demonstrate how this can by applied to target specific horizons for production.

Core Location

Methods - Using HHXRF • Clean the sample to remove any drilling mud, brine, etc. • Make sure you have a flat surface – Cut with a rock saw is sufficient

• Label the positions where you will be scanning. • Lighter elements require a longer scan time than heavier elements to improve the signal.

Methods – Rebound Hammer Rock Saw

Flat surface

Hardness Profile

Methods – Lateral Extrapolation Regional Framework •

Four sites selected spanning 70 miles • Local, cumulative shoreline trajectories used to – Two outcrops within a quarter mile • To confirm the signal is locally reproducible

– Three additional subsurface cores

• •

Outcrops sampled every stratigraphic foot Core sample spacing varied – 6” to 2” depending on time with the core

•

Gamma ray data used when present (core gamma, down-hole gamma, outcrop gamma)

build the regional chemosequence stratigraphic framework based on individual chemostratigraphic profiles

Hand-Held X-Ray Fluorescence (HHXRF)

CHEMOSTRATIGRAPHIC DATA

Geochemical Models • Chemostratigraphic variation is contingent upon changing environmental conditions within the sedimentary basin. • By applying geochemical models, we are capable of interpreting these changing conditions within a geological framework. • These models are oversimplified, but can still provide useful insights into changing depositional systems. – “All models are wrong, some models are useful”. -George E.P. Box

Elemental Proxies

• Certain elements have been found to be commonly found in association with different types of sediment: Element

Proxy

Reference

Titanium (Ti)

Continental source and dust fraction

Sageman and Lyons 2004

Zirconium (Zr)

Continental source

Bhatia and Crook 1986

Silicon:Aluminum ratio (Si/Al)

Quartz (biogenic and detrital)

Pearce and Jarvis 1992; Pearce et al. 1999; Sageman and Lyons 2004

Calcium (Ca)

Carbonate source and phosphate

Banner 1995; Tribovillard et al. 2006

Strontium (Sr)

Carbonate source and phosphate

Banner 1995; Tribovillard et al. 2006

Phosphorous (P)

Phosphate accumulation

Tribovillard et al. 2006

Aluminum (Al)

Clay and feldspar

Pearce and Jarvis 1992; Tribovillard et al. 2006

Potassium (K)

Clay and feldspar

Tribovillard et al. 2006

Molybdenum (Mo)

Bottom water euxinia, redox sensitive

Tribovillard et al. 2006; Algeo and Rowe 2012

Vanadium (V)

Bottom water anoxia, redox sensitive

Tribovillard et al. 2006

• These elements are generally considered “immobile”, but it is best to utilize multiple proxies to make interpretations.

Quartz proxy • Si: Quartz ->SiO2 Clay minerals (e.g. illite) ->KAl2Si4O10(OH)2 Radiolarians -> amorphous SiO2 (may recrystallize during diagenesis) • To estimate quartz component – Divide Si by Al or Ti to remove the clay and feldspar components

• To estimate biogenic input – Compare Si/Al to other continental influx proxies (such as Zr)

Rebound Hammer

HARDNESS DATA

“LITHOLOGICAL PROXY” USING THE HARDNESS TEST

807 HLD Chert

551 HLD Mudrock

Foot # 7

Silicic 784 HLD Mudrock 572 HLD Clay-rich? 263 HLD Clay-rich?

Foot # 29

FRACTURE ANALYSIS: METHODS • Counting fractures every single bed (Scan line method 3 ft length) and recording the following data per bed (About 600 beds): • Is it Hard or Soft (fissile)? • Bed thickness • Number of fractures • Filling Material • Fractures orientation when was clear to measure. • Fractures Spacing

Hard Soft Hard Soft Hard Soft

Hooker, et al 2013

FRACTURE ANALYSIS:

Thickn. (ft)

Preliminary Results

80 70

Natural

Lithology Weathering profile

# of beds per

Radioactivity

0 cps

1200

0

foot

# of Fractures 2 0

0

per foot

# of Fractures

800

500

4

60 50

3

40 30 20 10 0

2 1

Hard vs Soft 0

500

Fracture Density

Hard vs Soft 0

Ratio

1

0

per bed

40

HARDNESS TEST: Preliminary Results Natural Radioactivity (cps) 90

Lithology (Weathering profile)

Hardness HLD 80

80 70

70 60

60

50

50

40

40

30

30

0

0 1200

10

800

10

400

20

0

20

450

550

650

750

850

Hardness vs. Chemostratigraphy

Multivariate Statistics

HIERARCHICAL CLUSTERING ANALYSIS

Losing the Forest for the Trees • A single elemental profile is backed by 100’s of data points. – The HHXRF collects data for 23 elements.

• Hierarchical Clustering Analysis (HCA) provides a technique that allows users to quickly group data based on similarities within a group. – The objective is to sort your data so the intra-group variability is less than the inter-group variability.

Chemofacies vs. Lithofacies

Modified from Turner et al., 2015

Application to Field and Core

STRATIGRAPHIC FRAMEWORK

Chemosequence Stratigraphic Predictions • LST: A general increasing trend in detrital signals (Ti, Al, Zr, K) – Al and K are more likely to be associated with feldspars – Isolated mini-basins may show high degrees of restriction (high Mo and V)

• TST: A general decreasing trend in continental proxies (Ti and Zr) – Al and K become more associated with the clay fraction, and may remain high with respect to Ti and Zr – General decline in levels of restriction within the basins

• HST: A general increasing trend in detrital signals – Capped by a surface of erosion (non-waltherian facies shift) – Bottom waters should be well circulated (low Mo and V) – In a carbonate system, HST will be dominated by increases in Ca and Sr.

Wyche Farm Quarry – Type Well

Modified from Turner et al., 2015

Chemosequence Stratigraphic Framework (Regional Scale) Proximal N

Distal S

HST MFS

TST

Modified from Turner et al., 2014

Woodford Incised Valleys Valley fill is thin where underlying Hunton is thick and vice versa 0

Hunton Group

0

Woodford Shale A C’ C

Brent McCullough OU Master's Thesis 2013 2014

12 mi.

Brent McCullough OU Master's Thesis 2013 2014

RAMER 1-13

VICTORY 41X-12

Rel WOOD TOM 4A Depth(ft) 5 -130

A’

MAYES [BM]

SMITH JOE TRUST PENNY WILLIAM R WISE A Rel Depth(ft)28-1 1 1 -130

ELLIS 1-21

WILLMOTT CLINKENBEARD 1 1

C’

C

-105 -80 -55 MAYES [BM] -30 -5 WOODFORD [BM] WOODFORD LOW_GAMMA_UPPER_TOP [BM] LOW_GAMMA_BASE [BM] 20 MARKER_5 [BM] 45 MARKER_7 [BM] 70 MARKER_2 [BM] MARKER_4 HUNTON_GROUP 95 120 120 145 170 195 220 HUNTON_GROUP [BM] 245 Hunton 270 SYLVAN_SHALE [BM] [BM] 295 SYLVAN_SHALE -2750

A

MCCLOUD JONES ELLIS 1-12 1-9A 1-21

12 mi.

380

A’ 285

YES [BM]

Horizontal Distance is approx. 150mi.

-3250 -3000

-3000

100 ft

-3000 -3000

-3500

-3250 -3250 -3250

370

-3250

320 345 370

-3250

-3750

-3500

-3250

-3000

-4500 -4750

-3500

-4500 -4750

OUP [BM]

Woodf ord

-3250

100 ft

ORD [BM]

-3000

-3000

Mayes

Horizontal Distance is approx. 22mi.

McCullough, 2014

Rel Dep -13 -10 MAYES [BM] -80 -55 -30 -5 WOODFORD [BM] LOW_GAMMA_UP LOW_GAMMA_BA 20 MARKER_5 [BM] [BM] MARKER_4 MARKER_7 MARKER_2 MARKER_1 MARKER_6 HUNTON_GROUP 45 70 95 120 145 170 SYLVAN_SHALE [B 195 220 245 270 295 320 345 370

Model for Lowermost Woodford

Local Sea Level vs Global Sea Level

Example from Ray 1-13

• The trends in local sea level for the Arkoma Basin are in good agreement with previous work interpreting the global sea level trends at this time.

(Johnson et al., 1985)

Conclusions • Mudrock variability can be subtle, contributing to the idea that they are homogenous, but that variability has significant implications. • Mudrock facies shifts reflect changing environmental conditions and mechanical properties, and can happen on the scale of inches. • Chemostratigraphic proxies can be used to develop stratigraphic frameworks that can be used to identify potential targets for hydrocarbon exploration.

Acknowledgements • • • •

Roger Slatt • Brian Cardott Harry Rowe • Jessica Tréanton Matt Pranter • Daniel Sigward Andrew Elwood-Madden • Christopher Toth • OU’s Woodford Shale Consortium • AAPG Grants-in-Aid

Works Cited: • • • • • • • • • • • • • • • •

Algeo, T.J. and Maynard, J.B. (2004) Trace-element behavior and redox facies in core shales of Upper Pennsylvanian Kansas-type cyclothems. Chem. Geo., 206, p.289-318. Algeo, T.J. and Rowe, H. (2012) Paleoceanographic applications of trace-metal concentration data: Chemical Geology, 324-325, p. 6-18. Banner, J.L., 1995: Application of the trace element and isotope geochemistry of strontium to studies of carbonate diagenesis: Sedimentology, 42, p.805-824. Blakey (2011) Paleogeography and Geologic Evolution of North America. http://www2.nau.edu/rcb7/namD360.html. Accessed 10-16-2012 Brumsack, H.J. (2006) Trace-element behavior and redox facies in core shales of Upper Pennsylvanian Kansas-type cyclothems. Palaeogeog. Palaeoclim. Palaleoecol., 232, p.344-361. Harris, N.B., C.A. Mnich, D. Selby, and D. Korn, 2013, 2013, Minor and trace element and Re-Os chemistry of the Upper Devonian Woodford Shale, Permian Basin, West Texas: Insights into metal abundance and basin processes: Chemical Geology, 356, 76-93. Molinares-Blanco, C.E., 2013, Stratigraphy and palynomorphs composition of the Woodford Shale in the Wyche Farm Shale Pit, Pontotoc County, Oklahoma: M.S. thesis, University of Oklahoma. Pearce, T.J., Besly, B.M., Wray, D.S., Wright, D.K., 1999: Chemostratigraphy: a method to improve interwell correlation in barren sequences – a case study using onshore Duckmantian/Stephanian sequences (West Midlands, U.K.): Sedimentary Geology,124, p.197-220. Pearce, T.J. and Jarvis, I., 1992: Applications of geochemical data to modelling sediment dispersal patterns in distal turbidites: Late Quaternary of the Madeira Abyssal Plain: Journal of Sedimentary Petrology, 62, p.1112-1129. Rowe, H., Hughes, N., and Robinson, K. (2012) The quantification and application of handheld energy-dispersive x-ray fluorescence (ED-XRF) in mudrock chemostratigraphy and geochemistry. Chem. Geo., 324-325, p. 122-131. Sageman, B. and T. Lyons (2004) Geochemistry of Fine-grained Sediments and Sedimentary Rocks in MacKenszie, F. ed. Sediments, Diagenesis, and Sedimentary Rocks, Treatise on Geochemistry, 7, p.115-158. Slatt, R.M. and Rodriguez, N.D. (2012) Comparative sequence stratigraphy and organic geochemistry of gas shales: Commonality or coincidence?: Journal of Natural Gas Science and Engineering, 8, p.68-84 Tréanton, J.A. (2014) Outcrop-derived chemostratigraphy of the Woodford Shale, Murray County, Oklahoma: M.S. thesis, University of Oklahoma. Tréanton, J.A., Turner, B.W., and Slatt, R.M. (2014) Outcrop Derived Inorganic Geochemistry of the Woodford Shale, Murray County, Oklahoma. Houston Geological Society Applied Geoscience Conference (Mudrocks), Houston, TX. Tribovillard, N., Algeo, T.J., Lyons, T., and Riboulleau A. (2006) Trace metals as paleoredox and paleoproductivity proxies: An update. Chem. Geo., 232, p. 12-32. Turner, B.W., Molinares-Blanco, C.E., and Slatt, R.M. (2015) Chemostratigraphic, palynostratigraphic, and sequence stratigraphic analysis of the Woodford Shale, Wyche Farm Quarry, Pontotoc County, Oklahoma: Interpretation, 3, p.SH1-SH9