Supplementary information for Inadequate regulation of the geological aspects of shale exploitation in the UK David Smythe Contents Page 1 Figure S1 2 Figure S2 3 Figure S3 4 Figure S4 5 1. Aminzadeh et al. 2013 extract 6 2. A note on licensing regimes outwith the UK 14 3. Smythe comments 2015 on letter from DECC to LCC 15 4. Case histories of relationship between BGS and government 20 5. Cuadrilla at Balcombe 21 6. Canonbie 23 7. Airth 2014 24 8. Micrites in the Kimmeridge Clay Formation 28 9. Europa at Leith Hill 30 10. Celtique Energie in the Weald 42 11. IGas at Springs Road, Misson 49 12. Angus Energy at Brockham 55 13. UKOG at Broadford Bridge 53 14. UKOG at Horse Hill 61 15. Lancashire 2009-2012 66 16. Correspondence between the EA and local resident 2014 68 17. Sherwood Sandstone aquifer below the western Fylde 71 18. Manchester Marls as a seal 75 19. Smythe objection 2017 to PNR variation of permit for microseismic monitoring 77 20. Smythe objection to approval of PNR-2 hydraulic fracture plan 83 21. Meeting Balcombe PC and Cuadrilla 2013 100 22. Minutes of SEPA Enzygo Greenpark Meeting 2011 103 23. Selley 2014 response to SDNPA letter of 24 June 2014 105 24. Smythe comments upon report to SDNPA by Prof. Selley 113 25. OGA response to Sutcliffe letter 7 July 2016 120 26. BGS comments 2013 to scoping opinion, Grange Rd 124 27. Charles Hendry (DECC) - Cuadrilla correspondence 2012 125 28. EA response to Estelle Dehon 22 July 2019 129 29. IGas letter 2017 to MPA Ellesmere Port-1 well 134 Page 1 Figure S1. Who does what at the exploration stage. From: Potts, C. 2013. Oil and gas development: planning perspective. South Downs National Park Authority presentation, 15 October 2013. Page 2 Figure S2. Scientific definition of an unconventional hydrocarbon resource as <0.1 millidarcies. The UK legal definition (red outline) of conventional resources encompasses rock type reservoirs falling into the unconventional category. Page 3 Figure S3. Slide from talk by Dr James Verdon at Glastonbury, 2013, wrongly illustrating Wytch Farm as an example of fracking with horizontal drilling. In fact the sandstone reservoir was drilled horizontally, but there has never been any fracking at Wytch Farm. Page 4 Figure S4. Diagram of environmental risks from fracking. From: Davey, I. Water environment risks from shale gas exploitation. EA submission to RR2012, 23 March 2012. Page 5 Hydrocarbon seepage examples Selected pages from: Aminzadeh, F. et al. (eds.) 2013. Hydrocarbon Seepage: From Source to Surface Society of Exploration Geophysicists and American Association of Petroleum Geologists, 244 pp. ISBN 978-1-560-80-310-2. Seven pages have been reproduced under the US doctrine on fair use (https://www.copyright.gov/fair-use/more-info.html). Page 6 Chapter 10: A Southeastern Louisiana Case History 163 ZEUS sw PROSPECT NE WORKFLOW 1) Data analysis-select attribute set 2) Select representative train locations 3) Calculate seismic attributes at train locations 4) Feed calculated data to neural network and train 5) Apply neural network to data Figure 2. Gas-chimney processing workflow and results displayed on arbitrary line A-A'. When the chimney processing results are overlain on an arbitrary southwest-northeast-trending line (Figure 2), we notice three things: 1) The chimneys originate at or below the salt weld; this corresponds to the Eocene and Cretaceous source-rock intervals. 2) The chimneys are often associated with shallow ampli­ tude anomalies, which are usually pay. 3) The chimneys are often (but not always) associated with low-velocity zones. To assess the quality of the chimney volume and to check for false positives and false negatives, we performed a quality-control and validation step. Initial validation was done by visual inspection. An in-depth validation was then done as part of the second phase of the project, including Figure 3. Chimney probability overlain on similarity attribute validation based on a set of criteria such as surface seepage, 2.628-s TWT time slice. mud-logging shows, and association with direct hydrocar­ bon indicators. Mud-log and core data were available for well A-60 and were used during the validation step. more interestingly, distinguish leaking faults from those that The next step was to interpret the chimney data. One may be sealing faults. When we overlay the chimney data on way of doing this is to overlay the chimney time or depth a time slice of the similarity data (Figure 3), we note that the slices (yellow to green) on fault time slices (gray scale). In chimneys have characteristic pockmark morphology. Major this way, we can observe the major gas chimneys but also, chimneys tend to occur at major fault intersections. Page 7 • - .... - •• - ••• - ---~------ ... • _.__ .... ........... _ ..I-~ Chapter 13: Detection ofSubsurface Hydrocarbon Seepage in Seismic Data 205 c) OfnlrGrHlO.-4OI 0-........... .120) ..~ .. 0 .....,... o ,..~*""* .. I20.-4OI'liIPOIIt·1 o ...... ~..-aI-4O.~. I IIIJlOII· l O"'~I6O. I 2OJJI.tP08t. 1 o ......, .. I20.040I{·',2)e(1 ..2)f'SMIn • -.....nty .-..0,401 (-1,.2)(1,-2) I"S htn .....,.,. i"O.1l01 (.1.2)(1.-2) P'$ "'" o a-IInl1fol20.-4Ol( IP)l(O.o)tS .... e _1-<0.... '".0)0(0.0',._ o ~t40 . I .HI'O)l(O'O)P'.Mn e -"", 0-­ O ......fQMI .. 120••.tOt 0--1-<0·... 0-""'­0--1'0."'" -..- .. ......,.. .... .... Oa.­0-""'''' 0""""'­00.-... ........ a.. b) '•. _ -'­ Figure 3. Chimney-detection methodology. (a) Chimneys are detected on key lines. (b) Examples of chimneys (green) and non­ chimneys (blue) are picked on key lines. (c) A set of attributes is selected and calculated at picked sites. Graphs show training performance of MLP network and the percent error for the training and test sets. Input attributes with gray scale coding reveal the importance of each attribute (dark nodes are most important). (d) Results are displayed on key seismic lines. High-probability chimneys are yellow to green; low-probability chimneys are transparent. The output of the neural network is a set of numbers depth slices. In addition, the results can be displayed in 3D between approximately zero and one, giving the probabili­ form (Figure 5a) or as geobodies (Figure 5b). ty of any point in the data volume to be or not be a chim­ ney. The closer the value is to one, the more probable it is that the location belongs to the gas chimney. Results are Validation of gas-chimney processing evaluated interactively on key lines and slices to assess the results performance of the neural network (Figure 3d), with pos­ sible iterations of the workflow updating the seismic attri­ After a chimney-probability volume is generated, the butes and example picks to increase the quality of the neu­ results must be interpreted to validate them as true verti­ ral-network map. When the interpreter is satisfied with the cal hydrocarbon migration and to determine if they origi­ performance of the neural network, the algorithm is ap­ nate from a biogenic or thermogenic source. Vertically plied to the entire volume and stored for more rapid visual­ aligned chaotic data can occur for reasons other than gas ization and analysis. These chimney-probability results chimneys. Certain geologic features and poor seismic im­ can also be displayed on mapped horizons (Figure 4a), aging can also cause a low-amplitude chaotic seismic sig­ along mapped fault surfaces (Figure 4b), or on time or nature that looks like a gas chimney or gas cloud. It is Page 8 Chapter 13: Detection ofSubsurface Hydrocarbon Seepage in Seismic Data 209 Visualizing gas-chimney-processing results The method consists of combining chimney- and fault-detection volumes to determine which faults, or A major benefit of chimney processing is the potential portions of faults, have had associated active vertical to visualize the results in three dimensions. We can show hydrocarbon migration. Faults can be highlighted using the areal extent and origin of hydrocarbon charge into a several approaches. Single multi trace attributes such as prospect. We can also show the areal extent, vertical extent, coherency or similarity can be utilized. Also, faults can and morphology of leakage from that reservoir. be enhanced using a neural-network supervised training Chimney-processing results can be visualized in a approach, similar to chimney processing (Meldahl et aI., number of ways. One way is as semitransparent overlays 2001) or the neural-network approach, whereby examples on seismic secliolts. Chimney results are displayed on key of faults and nonfaults are picked on representative lines. dip or strike seismic lines (Figure 3). Seismic lines should Then a set of multitrace attributes is chosen which high­ be shown uninterpreted and overlain with chimney-proba­ lights the picked faults most clearly. These attributes are bility results. This type of display is critical to show the calculated at the picked locations, and the results are fed seismic character of the detected chimney. Key seismic into a neural network. The resultant output algorithm is lines need to be oriented such that they transect the crest of applied to the entire seismic volume to create a probabili­ the suspected trap as well as stay within the reservoir. ty-of-fault volume. Another method is chimney results displayed on hori­ The fault-attribute or neural-network volume can be zon suifaces or time (or depth) slices (Figure 4). Interpreted displayed on seismic sections but is more useful when dis­ sUlfaces should be extensive enough to show any fault-relat­ played on seismic time slices or key horizons (Ligtenberg, ed chimneys. Time or depth slices often show the chimneys 2005). If the chimney-probability results are displayed over more clearly than horizon slices because horizons are often the fault-probability results, we can get an indication of mapped on strong, continuous seismic events that may ob­ which faults have been migration pathways for hydrocar­ scure more subtle chimneys.