Analysis of factors influencing the interpretation of a digitally examined fluvial meanderbelt system: Joggins Formation, Nova Scotia

Clastic reservoir exploration, development, and exploitation are inherently complex with recovery depending largely on the understanding of sand body architecture and interlayered clayey/silty baffles and barriers. Numerous data collection techniques and methods are now widely available for helping to enrich reservoir outcrop analogue data extraction from the well scale to the larger seismic scale. This integrated study uses the inherited, combined data from a localized light detection and ranging survey, measurements taken from a portable handheld spectrometer and air permeameter, in addition to total (or absolute) porosity measurements from thin sections to assist with the analysis of components influencing the interpretation of a digitally analyzed fluvial meanderbelt system outcrop. The purpose is not to perform a detailed reservoir characterization or to model a potential reservoir, but rather to study a section of a reservoir analogue and apply reservoir geology with integrated data collection techniques to highlight potential benefits and shortcomings of this type of approach. A point cloud survey generated from light detection and ranging, coupled with other tools including a portable handheld spectrometer and permeameter, supplements data from the light detection and ranging scan and increases the confidence of interpretations. Spectrometer measurements recorded at the outcrop are used to generate a pseudo-gamma log. Handheld air permeameter measurements give a sense of the permeability of corresponding lithologies, as well as the variability in permeability of the reservoir both laterally and vertically. Light detection and ranging also provides important information regarding rock properties. The high detail of the outcrop images is used for the assessment of reservoir characteristics. The reservoir data leads to an increased understanding of subsurface reservoirs, particularly of the fluvial meanderbelt type. This study shows the importance and drawbacks of a combined digital data collection approach for the analysis of a sedimentary outcrop.


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The open-water (marine) facies contain organic-rich, well-cemented limestone or "clam coals" as 152 they are locally known due to their dark grey appearance and presence of bivalve fossils (Davies et al.     The total gamma radiation for the studied section (Table 3 and  (Table 4).

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The gamma-ray data set for the measured section (Fig. 10) indicates that values lower than 93 cps 392 are 100 % sandstone, whereas gamma-ray values greater than 255 cps are indicative of 100 % mudstone. The average net-to-gross ratio for the measured section (study area) by Rygel (2005) (Fig. 11 Table 5 and plotted on D r a f t 19 416 Porosity was measured at numerous areas of each thin section as total porosity. The porosity exhibited 417 by the samples is intergranular as it exists between grains. The total porosity analysis shows the 418 percentage of cement is greater than initially predicted visually and the cement is primarily ankerite. A 419 summary of the cement area, grain area, and porosity of each of the six sandstone lithologies is 420 presented ( Table 6). Fig. 13 and Fig. 14 (Fig. 15).

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The lidar images show the lateral variations in sandstone body thickness observed at the outcrop.

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Lithologies can be recognized by displaying the point cloud using a colour scheme where the 435 dark brown colours (Fig. 16, 17, and 18)  The quantitative data are presented in Table 7. These data could be useful for creating a low- 463 The pseudo-gamma ray curve helps to distinguish between sandstone and mudstone. The porosity and 464 permeability data can be used to populate the simple model to allow for basic fluid flow simulations to 465 be run using oil, gas, water, or a combination.

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Additionally, carbon dioxide can be used as a simulation fluid since carbon capture and storage 467 are becoming more prevalent. These simulations would help show the flow dynamics within a fluvial 468 meanderbelt system but could potentially be useful for other analogues. Furthermore, the data provide 469 insight into sandstone/mudstone contact architecture and permeability/porosity distributions.

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Numerous benefits and problems are recognized in this study. One obvious benefit is how well 471 the lidar intensity correlates with lithology. Also, the study provides a sense of the requirements for 472 data collection versus the size of the outcrop study area. Using the current data to create a geomodel 473 would produce a low-resolution example. A higher density of data is needed for high-resolution models.

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One of the problems is the study area was too large for the amount of data collected. Another 475 issue relates to the measurement of permeability and porosity. There is no laboratory-measured 476 permeability to compare with air permeameter measurements of permeability, and there is no 477 laboratory-measured porosity to compare with thin section measurements of porosity.

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To improve upon similar studies in the future, it is recommended that the study area be decreased    (maximum value)  GW101-2013TK  sandstone  115  155  GW102-2013TK  sandstone  116  151  GW103-2013TK  sandstone  118  149  GW104-2013TK  coal  111  154  GW105-2013TK  limestone  113  143  GW106-2013TK  sandstone  120  143  GW107-2013TK  sandstone  112  145  GW108-2013TK  sandstone  112  142  10  11 Table 5. Air permeameter values with their corresponding permeability value; samples 1 to 8 were collected from random, 12 fallen blocks near the base of Coal Mine Point; samples 9 to 24 are described in more detail, consisting of fallen blocks and 13 direct measurements made on the cliff face near Coal Mine Point. Results are plotted in Fig. 11