appendix 1: photogrammetry techniques, fieldwork...

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Appendix 1: Photogrammetry Techniques, Fieldwork Planning and Outcrop Models

Contents GIS analysis on planning fieldtrip ..................................................................................................................................... 1

Terrestrial digital photogrammetry (TDP) ................................................................................................................... 4

Fieldwork planning ................................................................................................................................................................. 6

Ground resolution .............................................................................................................................................................. 6

Accuracy and precision .................................................................................................................................................... 7

Mapping area and physical/topographic constraints ......................................................................................... 8

Registration approaches .................................................................................................................................................. 9

Differential global positioning system (DGPS) ..................................................................................................... 10

Geodetic principles .......................................................................................................................................................... 11

Differential positioning .................................................................................................................................................. 12

Image processing ................................................................................................................................................................... 13

Features extraction ............................................................................................................................................................... 18

Complex surfaces .............................................................................................................................................................. 19

Joints structures ................................................................................................................................................................ 20

Outcrops overview ................................................................................................................................................................ 22

Carnarvon Highway road cut ....................................................................................................................................... 22

Isla Gorge 1 .......................................................................................................................................................................... 22

Isla Gorge 2 .......................................................................................................................................................................... 22

Cabbagetree Creek ........................................................................................................................................................... 22

Other methods considered: drone and laser scanner ............................................................................................ 22

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GIS analysis on planning fieldtripDue to the large extent of Precipice Sandstone formation, a preliminary GIS analysis was carried out to develop a targeted field plan for photogrammetric survey in the eastern part of the outcrop line. Several data, available for free at http://qldspatial.information.qld.gov.au/catalogue/, have been used. In particular they are:

� 1 sec Digital Elevation Model (DEM)� Baseline roads and tracks Queensland shapefile � Surface geology 1974 - Surat Basin shapefile � Exploration and production permits shapefile � Queensland Boreholes Web Map Service � Queensland Topographic Web Map Service

The study area has been chosen on the basis of: known exploration permits boundaries, known position of CTSCo lease area and regional geology (distribution of Precipice Sandstone outcrops and regional geological structures). An area placed between Taroom Trough (Mimosa Syncline) and Burunga – Leichhardt Fault System was considered to be the most suitable region to conduct our analysis. It isn`t significantly deformed and geological and geotechnical features can be extrapolated to CTSCo's West Wandoan 1 well site.

In order to find out the steepest areas and locate possible outcrops, slope analysis and contour line generation (with 5m spacing from each other) were performed (Figure 1).

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Since DEM (digital elevation model) resolution was too low (approximately 30m per pixel), it proved to be not detailed enough for our terrain analysis and, so, unsuitable for recognition of scarps and cliffs. In fact, these topographic features are generally 30/40 meters high and tend to be smoothed by automatic interpolation (Figure 2).

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Because no orthophotos were accessible in the public domain (except for a fee), research for adequate areas was conducted using available Web Map Services (WMS). At this stage, several survey locations have been selected especially taking into account ease of access.

Also a literature review helped us to close off different sites on the basis of formation thickness. Martin`s thesis on “Sedimentology of the Precipice Sandstone, Surat Basin, Queensland” (Martin, 1976) provided an excellent documentation of outcrops. A few images have been captured from this paper and georeferenced to improve our knowledge on the outcrop’s line (Figure 3 and Figure 4).

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Initially, three different sites that expose the change in palaeocurrent direction on the eastern margin were selected for reconnaissance work. Later, three short fieldtrips were performed (March, April, June 2015) to explore the outcropping belt and extend the facies mapping eastwards (Figure 5).

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Terrestrial digital photogrammetry (TDP)One of the main aims of this project is the application of 3D ground based photogrammetric models to capture digital images that can be archived and analysed for sedimentary and structural features that will impact on reservoir properties. The term Photogrammetry, meaning measuring on photographs, has been defined as the science and technique of interpreting and evaluating the form, dimension and position of objects by analysing and measuring images of them. The result of photogrammetric procedures is a precise three-dimensional geometric reconstruction of the object that can be orthogonally projected onto a plane (normally, horizontally or vertically) at a certain scale or visualized in a perspective static or dynamic representation in a computer monitor for further evaluation.

In photogrammetry, images are georeferenced through the determination of the six exterior orientation parameters that describe the original spatial relation existing between the photo and the object coordinate systems at the moment the image was captured. This set of parameters (Figure 6) is called the exterior orientation of the image and consists of three object

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coordinates of the projection center (X0, Y0, Z0) and three rotation angles around the object coordinates axes defining the spatial orientation of the photographic axis in the object space.

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A process called resection is used to calculate the camera exterior orientation based on relative or absolute coordinates of a number of object points (the more there are the more the redundancy). A crucial assumption in this process is the principle of collinearity, which states that an object point, the perspective center and an image point on the focal plane of the camera are aligned in a straight line. In order to respect this principle, the interior geometry of the camera during exposure must be known. Indeed, if correction for distortion and other internal parameters is not applied, collinearity will not be respected. The three main elements of interior orientation are the focal length and the x0 and y0 coordinates of the principle point (offset). Other parameters include lens radial (K1, K2, K3… Kn) and tangential (P1, P2) distortion. The interior orientation is determined during the process called “calibration”. To address the aim of the project, the research team used a photographic system accurately calibrated by CSIRO in their laboratories and formed by a Nikon camera body and two fixed lenses. The calibration file (Figure 7) has been provided in order to process correctly the images acquired and obtain in this way the most reliable 3D models.

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Photogrammetric software uses an automated matching process to find corresponding image points on pairs of photographs. In order to optimize the matching process, it is important to respect a number of recommendations when taking photographs (Figure 8).

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Fieldwork planningBefore undertaking a field campaign using terrestrial remote-sensing techniques, some essential planning components should be considered. These include:

� specifying the resolution (ground point spacing) necessary for the purpose of a project,� specifying the required accuracy and precision,� defining the area to be mapped, taking into account physical/topographic constraints.

Careful planning allows a better understanding of the effect of these components on the subsequent geotechnical discontinuity measurements from 3D models in terms of potential orientation bias, truncation level and observation scale effect.

Ground resolutionIn a photogrammetric project, the focal length of a lens, the pixel size and the distance to an object determine the instantaneous angular field of view (IFOV), i.e. the area covered by one pixel on the ground. The ground point spacing (ground resolution) is slightly different, being

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dependent on the step size. Step size quantifies the number of pixels used, both horizontally and vertically, to generate one spatial point (typical step sizes range between 4 and 8 pixels). In other words, the ground pixel size (IFOV) should be multiplied by the step size to obtain the ground point spacing.

It should be emphasized that the ground point spacing used in digital photogrammetry surveysis an average value. Because of the object topography, areas closer to the scanner have a closer ground point spacing than the average value whereas distant objects have a wider ground point spacing.

As a general rule, it is suggested that a slightly higher ground resolution than required is used,so that there is enough redundancy to guarantee adequate interpolation of a 3D model surface.

SirovisionTM was the available and recommended system during the early stages of the project. It is a geology/geotechnical mapping and analysis tool that can generate accurate, scaled 3D images, georeferenced using a wide range of surveying equipment. SirovisionTM

provides also a very useful “fieldwork planning tool” that enables the user to estimate the results based on several different criteria (Figure 9).

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Accuracy and precisionPhotogrammetric stereomodel accuracy and precision depend on calibration, automated stereomatching, ground resolution and network geometry. The expected planimetric accuracy is typically 0.3 pixels ([0.05-0.5]) on the ground assuming an adequate calibration and orientation. The depth accuracy depends on the geometric relationship between the camera positions and the object being photographed according to the following equation:

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This equation suggests that a large distance/baseline ratio provides a better depth accuracy. However, a large distance/baseline ratio results in difficulties for the image matching process (Figure 10 and Figure 11) causing errors that may show up as “spikes”. Consequently, a ratio situated between 5/1 and 8/1 is recommended.

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Mapping area and physical/topographic constraintsDepending on the size of the area to be mapped, several sets of photographs will need to be taken. When there are major changes in perspective, such as around a corner, the area should be split into smaller windows. When there is a potential for occlusion and/or orientation bias, separated pairs of photographs should be taken from different angles. Occlusion occurs when parts of a rock face cannot be sampled because it is obscured by protruding features. It may create holes (shadow zones) with missing spatial points in a 3D model (Figure 12). Orientation bias occurs when the camera line-of-sight is sub-parallel to a discontinuity, resulting in a linear trace if viewed from the camera position. Furthermore, obstructions (like tall trees or shrubbery) and occlusions in front of the outcrop spoil the construction of a correct and reliable

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model. For this reason, future work may require laser scanning technology, in combination with photogrammetry.

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Registration approachesTDP 3D models can be registered in a variety of coordinate systems, including the Universal Transverse Mercator (UTM) Geographic Coordinate System and relative (local) systems oriented with respect to North. The process of projecting 3D models into one of these systems is called “registration” and it can quickly become the most time-consuming part of a terrestrial remote sensing field survey. Depending on the accuracy/precision required for a specific project several approaches can be adopted.

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These approaches (Figure 13A-D) can be used to register 3D models varying in terms of setup (time), and equipment required (cost). The simplest one (approach A) uses compass clinometer readings allowing registration in a relative reference system oriented with respect to North. The setup is quick, easy and inexpensive. On the other hand, approaches B, C and D use a total station and/or DGPS, which require a longer and more expensive survey. When access to a rock cut is limited, approaches A and B are very convenient.

In order to obtain the required detail for the project, method B was used to observe the outcrops at a distance from the exposure, due to topographic constraints and inaccessibility.Basics on DGPS theory, technique used to record the camera stations coordinates, andchallenges faced during the field operations will be shortly described in the next chapters.

Differential global positioning system (DGPS)By tracking the microwave radio signal that GPS satellites are transmitting continuously, a GPS (Global Positioning System) can calculate the latitude, longitude and height of a receiver. The principle is illustrated in Figure 14. On this figure in two dimensions, three distances from three satellite positions are required to determine a unique receiver position, the equal-distance trace to a fixed point (satellite) being a circle. In three dimensions, to find a unique receiver position, four satellites and four distances are required, the equal-distance trace to a fixed point being a sphere. There is an additional unknown as the receiver clock is usuallydifferent from the GPS clock, consequently, an additional satellite is required, i.e. five in total. However, by using only four satellites, two possible solutions can be obtained, one being in space which can thus be neglected. Consequently, four satellites are required to satisfy four unknowns, the X, Y and Z coordinates and the receiver clock delay. Additional satellites allow redundancy.

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Geodetic principlesThe fact that the topographic surface of the Earth is highly irregular makes it difficult for geodetic calculations (Figure 15). To overcome this problem, geodesists adopt a smooth mathematical surface called an “ellipsoid”, to approximate the Earth surface. In Australia, a common ellipsoid (or horizontal datum) is the Geocentric Datum of Australia (GDA94).The World Geodetic System from 1984 (WGS 84) is another datum usable worldwide. The vertical datum, i.e. the surface of zero height, is often the geoid (equipotential surface, along which the gravity potential is constant). On a global basis, it represents the mean sea level. Across Australia, mean sea level and its onshore realisation, the Australian Height Datum (AHD), correspond to within approximately ±0.5m of the geoid.

The height above or below the geoid is called “orthometric height”. GPS obtained heights are referred to the ellipsoid and are called the “ellipsoidal heights”. AUSGeoid09 is Australia's geoid model for converting ellipsoidal heights to AHD heights and is accurate to 0.03m across most of Australia. Many GPS receivers and software packages have built-in models for automatic conversion between orthometric height and ellipsoidal height.

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GPS data are measured in a three-dimensional geodetic coordinate system (latitude, longitude, height). Map projection is the transformation of the geodetic coordinates into rectangular grid coordinates (northing, easting, height) or Cartesian coordinates (x, y, z). The projection should minimize the distortion due to transforming an ellipsoidal shape to a flat surface. The Universal Transverse Mercator (UTM) is the more common map projection. The earth ellipsoid is divided into 60 zones, which are projected separately, using a secant cylinder. It is not suitable for the polar areas, where the projection results in significant distortion.

The photogrammetric software solution used for this project needs coordinates to be imported as grid values, i.e. metric values, (Figure 16). Accordingly, GPS 3D geodetic data (latitude, longitude and ellipsoidal height) have been transformed in grid coordinates (MGA 94 zone 55 and 56).

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Differential positioningPositioning can be both absolute and relative (differential). Using absolute positioning, i.e. using a single receiver (like commercial handheld GPS, integrated or external camera GPS), the accuracy is in the order of meters or tens of meters, due to the large distance between the satellites and the antenna, the small magnitude of the time increments and other systematic errors (Figure 17). The accuracy also depends on whether the receiver is a single L1 frequency or a dual L1 and L2 frequency GPS. Indeed, the data coming down to the ground are transmitted by the satellites as a complex coded signal modulated by the L1 and L2

frequencies, which, once they`re combined together, permit the correction of many errors and delays enhancing in this way the accuracy.

Using relative (or differential) positioning, i.e. two or more receivers, an accuracy of a few meters to millimetres can be obtained. Usually, one receiver, called the base, is positioned at a known point or left on a point for a long time, so that its position is recorded with accuracy. Another receiver, called the rover, is placed at the position, which is to be surveyed. By simultaneously tracking the same satellites, the base and rover are subject to the same errors and bias. The known position of the base is used to calculate corrections to the GPS derived position and these corrections are subsequently applied to the rover. The shorter the distance between base and rover receivers, the more similar the errors.

Various field measurements methods exist for differential surveys, but, according to technical requirements and budget available, just one has been chosen to perform surveys needed. This is called RTK (Real Time Kinematic) and it merges the information of code and carrier phase observables received at both base and rover receivers and instantaneously compute the precise position on the spot. The RTK method calculates new positions from the old ones, through continuous tracking of the satellites in real time. Consequently, although post-processing is not required, the system and direct line-of-sight between rovers and the base must be initialized.

The survey carried out allowed us to get a series of geographic points with an average 3D quality of about 3 cm.

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Survey time at individual outcrops usually spans over a few hours to a day, which precludes from surveying a base station for a longer time. Consequently, the position accuracy of the base station can be limited. To overcome this problem, the base receiver is located at an unknown point and logged for as long as possible period. The shift of the measured values with respect to the true position will be approximately constant for all the receivers: base and rover receivers may not thus be accurately positioned, although correctly oriented. In many cases, such a setting is adequate for geological applications.

Also, it is extremely important try to avoid and remove every possible obstruction (foliage, rock wall closeness), identify easy accesses to set DGPS stations, check for satellite (number of satellites, cutoff angle, GDOP, visibility) and weather forecast (geomagnetic activity) status, take care of baselines layout. All these precautions have significantly reduced the number of sites potentially surveyable for this study.

Image processing Photogrammetry has been captured in three main sites, one along the Leichhardt Highway in Isla Gorge National Park, the second one near Cabbagetree Creek and the last one along Carnarvon Highway.

To completely cover the first outcrop length, 88 pictures have been taken from 31 positions for a total of 44 stereo couples (Figure 18). The average distance from the outcrop is 14 meters. To correctly georeference the model using only camera positions, three other stations have been arranged closer to the outcrop. In this way everything is more spatially constrained. Accuracy on the outcrop surface (ground pixel size) is 1.4 mm.

To survey the second outcrop it has been necessary to take 44 pictures from 15 different positions for a total of 22 stereo couples (Figure 19). The average distance from the outcrop is 20 meters. The geographical constraint represented by the presence of a large creek just in front of the outcrop forced us capturing the images from different distances to the rock face.

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Proceeding in this way allowed us to obtain an adequate 3D spatial configuration for georeferencing the model. Accuracy on the outcrop surface (ground pixel size) is 1.8 mm.

3D models have been created for every stereo couple thanks to the powerful SirovisionTM

algorithm (Figure 21). If necessary, spikes generated automatically because of perspective errors (outliers) or deformations at image borders have been cleaned (Figure 22). All single models have been then merged together by manually digitizing anchor points between them to form a large unique mosaic.

Since Carnarvon Highway road cut was surveyed using a different approach and processed by means of an alternative software called Agisoft Photoscan. Although it belongs to the family of programs known as “Structure from Motion” (SfM), Photoscan is not a strictly photogrammetric package. SfM relies on algorithms that detect and describe local features for each image and then match those 2D points throughout the multiple images. Using this set of correspondences as input, SfM computes the locations of those interest points in a local coordinate frame (also called model space) and produces a sparse 3D point cloud that represents the geometry/structure of the scene. The camera position and internal camera parameters are also retrieved.

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Features extraction The extraction of both geological and geotechnical features provided a real challenge for the research team (Figure 23). Indeed, SirovisionTM is only able to follow planar properties such as joints, faults and beddings planes for instance. On the contrary ripples, planar stratification (PPS), cross-planar stratification (CPS) are “unfortunately” wavy surfaces, so it`s not possible to interpolate them through complex surfaces for their entire length but only to discretise them in different parts. This workflow has no sense and no scientific validity in order to describe the spatial trend and predict their influence underground.

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Complex surfaces The software was not able to extract curvilinear surfaces, making difficult to use to pull out inclined beddings. Hence, non-linear geometries were described as 3D polylines in which every node was accurately snapped to the model surface (Figure 24).

Figure 24: Sedimentary features described spatially by 3D polylines. Outcrop is 70m long.

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Joints structures Due to the 3 dimensions attributes of the point cloud, photogrammetry allowed to create and extract joint sets out of the models by means of best fitting planes detection.The joint sets have been extrapolated from Cabbagetree Creek model because it represents the most clear and thick outcrop surveyed with photogrammetry, in this case the joint sets result immediate to recognition (Figure 25). The extrapolation and elaboration have been performed with SiroJointTM, plugin of SirovisionTM. In the Cabbagetree Creek model 3 families of joint sets have been recognized. In the summary table these families of joint sets are categorized with colours (Figure 27).The green family is most frequent compared to the other two families (Figure 28). The direction of the three families is consistent with the general structural geology of the area (Figure 26).

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Outcrops overview Carnarvon Highway road cut Carnarvon HWY road cut outcrop (Figure 20) has been surveyed from both sides of the road, providing full coverage and spatial continuity. Pictures were taken from 117 positions with a 4 meters long baseline (distance between two following camera positions). The acquisition was performed from every camera station according to a fan schema in which every photo has both 50% of horizontal and vertical overlap. The 3D model was georeferenced and scaled by means of 16 markers whose positionswere recorded using a Differential Global Positioning System (DGPS) in RTK (Real Time Kinematic) mode. The model has been generated using Agisoft Photoscan, a SfM (structure from motion) software which exploits the most recent computer vision algorithms in order to reconstruct faithful 3D duplicates.

Isla Gorge 1CTSCo, made available one 3D model for the major road cut outcrop located in proximity of Isla Gorge National Park. The model was not considered suitable for this project due to the poor resolution, low photographic quality and unknown coordinate system used. Every attempt to generate a new model was unsuccessful because of the lack of the original frames. Furthermore, it wasn't possible to acquire new pictures since lush vegetation now covers the outcrop surface.

Isla Gorge 2A second and minor model was built in order to provide a record coming from the same area. Pictures were taken according to a stereo couple configuration, to satisfy SirovisionTM requirements. In particular, just two images with 50% horizontal overlap were acquired from every camera positions. The model, shown in Figure 18, has proved to be suitable for facies analysis and sedimentary mapping, although any geotechnical element was found.

Cabbagetree Creek in Nathan GorgeCabbagetree Creek model has been built according to the geometric configuration required by SirovisionTM. 22 stereo couples have been acquired from 23 camera stations and surveyed with a DGPS in RTK mode. The model itself is 55 meters wide and 11 meters tall at its highest point and clearly exhibits two families of joints trending according the regional structural setting.

Other methods considered: drone and laser scanner

Other methods considered to enhance the high-resolution outcrop imagery include laser scanner and drone photography and photogrammetry.

� The laser scanner or Lidar technique considers the controlled steering of laser beams followed by a distance measurement at every pointing direction. The power of the Laser scanner technique allows a large degree of freedom during the management of resulted point clouds, removing for example vegetation coverage.

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� A drone was initially planned to be used for mapping large and vertical cliffs.Aerial photogrammetry represents the only way to survey, for example,Carnarvon Gorge National Park because of the presence of high cliffs. Unfortunately the drone was been used due to National Park regulations that limit their use in proximity to the public area (Isla Gorge camping ground).

Appendix 1A: Approach to Facies Analysis and Modelling Contents Facies analysis and stratigraphy adopted .................................................................................. 1

Nested approach (FAKTS database) ............................................................................................ 2

Facies analysis and stratigraphy adoptedTo help in the construction of reservoir flow units in this project, the facies model approach has been used. Although facies models, which provide an overall summary of a particular sedimentary environment (Walker, 1984), are a dated concept, they still form the backbone of reservoir modelling and become more effective with continuous innovation (Howell et al., 2008; Martinius et al, 2014).

Deposits were subdivided into sedimentary units or allounits, namely stratigraphic units bounded by both unconformities and correlative-conformity surfaces (North American Commission on Stratigraphic Nomenclature, 1983). Sedimentary units represent different sedimentary environments (i.e. facies associations). Facies association analysis was adopted since it is a powerful tool to define the depositional history and basin-scale geometry of sedimentary successions (Bianchi et al., 2014; Ghinassi, et al., 2009; Martini et al., 2011, 2013). Allounits are preferred to depositional sequences (Vail et al., 1977), UBSU (Unconformity Bounded Stratigraphic Units, Salvador, 1987) or synthems (Chang, 1975; International Subcommission on Stratigraphic Classification, 1987; Salvador, 1994) because of their wide applicability in heterogeneous, confined basins characterized by erosional, non–depositional and correlative-conformity surfaces, either in marginal or depocentral areas.

The integration of elements as facies units, architectural elements, sub-environments and depositional environments provides the workflow that can be codified into a quantitative database for application to reservoir modelling (for example the Fluvial Architecture Knowledge Transfer System (FAKTS): a database of fluvial-reservoir analogues described in Colombera et al, 2012). The use of these types of database increases information transfer between the real and virtual, and provides a range of scenarios for application within a reservoir, that can be verified through model validation. An overview of FAKTS follows, as it is an option for this style of project, and its different elements can also be used in stratigraphic forward modelling.

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In this report, the authors propose a schema (Table 1: facies schemaTable 1, Table 2, Table 3) that groups all the facies recognised in outcrops into a table that shows the relative dimensions as well as spatial and genetic associations that allow them to be grouped or upscaled into adepositional environment (Bianchi et al., 2014; Ghinassi, et al., 2009; Martini et al., 2011, 2013). As an example, a lower and upper allounit subdivision was proposed for the Precipice (and follows general convention), but the upper allounit is characterised by three different sub-environments, all grouped into the coastal depositional environment.

Nested approach (FAKTS database)With the enhancing of the sedimentological knowledge the relation of facies unit to architectural element is no longer one-to-one, but rather different facies units describe one architectural element, in turn different architectural elements define one depositional element. This database comprises data from literature or case study of modern and ancient fluvial successions described in a qualitative and quantitative way, for a total of 111 case studies (Colombera et al, 2012, 2013). The power of this approach is the inclusion of genetic units recognised in stratigraphic and geomorphological realms (�� ). Following the sedimentological approach and the concepts of Miall (1996), the classification has a hierarchy, which starts with the classification of bounding surfaces even if the ranking could increase the difficulties. It follows the classification of depositional elements that in large scale are classified as channel complexes and floodplain (�� ). Architectural element classification corresponds to sub-environments representing the architecture variability. Finally, the facies unit classification is the broadest list of feature and concerns all of the set containing same lithofacies and paleocurrents. The database provides also statistical parameters, as percentage of units and range of dimensions (��). The geometrical parameters in the database are given along strike downstream. Moreover the catalogue provides filters based on fluvial style, climate and hydraulic regime, in this way the research for the most suitable case is cleverly driven.

Our aim is to use the same nested hierarchical approach in the subdivision of sedimentary features for the Precipice Sandstone. For our purpose we limit the genetic units to be recognised in the stratigraphic record and not only in the fluvial realm. We want to test the extension of this approach to transitional coastal environments since in the literature there is

no evidence of a quantitative facies database for this type of setting. Unfortunately, in the facies schema authors cannot guarantee in dimension parameters presented in the FAKTS database in detail, due to the reduced number of outcrops and the difficulty of access to many vertical cliff faces. For this reason, photogrammetry was used in this analysis.

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Facies code

Facies association code Facies Facies association

Depositional Environment

1 FA1 Channel lag Channel belt

Fluvial environment (FAbr 1/2a/2b/3/4/6/7 FAme 1/4/5/9/8a/8b )

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1 FA4 Channel lag Gilbert Delta

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Table 3: table showing the occurrence of facies in each outcrop and core considered in this project.

FFacies code OOutcrops CCore Cn FH Fl IG1 IG2 Cr Cn Hgw WW1 GW4 11 X X X X X

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111 X X

112 X X X

114 X X

115 X X

116 X X X

117 X X X

118a X X X X X X

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220 X X X X X X

221 X X X

Appendix 2: Static Geomodels of the Outcrops

Contents The workflow ............................................................................................................ 2

Resulting Models...................................................................................................... 4

Modelling the outcrop Cabbagetree Creek ..................................................................... 4

Geometries ...................................................................................................................... 4

Sedimentary Fabric Modelling ......................................................................................... 5

Grain Size distribution.................................................................................................... 14

Modelling the outcrop Isla Gorge#2 .............................................................................. 18

Geometries .................................................................................................................... 18

Sedimentary Fabric Modelling ....................................................................................... 18

Grain Size Distribution ................................................................................................... 23

References ....................................................................................................................... 27

The workflow The Precipice Sandstone outcrop modelling has been conducted following the workflow represented below (Figure 1). After a sedimentological characterisation, the outcrop has been imaged by photogrammetry techniques in order to allow the extrapolation of surfaces detected through the facies analysis. This leads to populatinga static model with geometries, fabrics and grain size (Table 1).

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Different properties have been modelled to achieve simulated features. Modelling the outcrop uses just observation data; no interpretation is taken into account at this stage.

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Properties Features1. Geometry Units2. Fabric Internal Bedding3. Grain size Sedimentary Facies

The jump to up-scaling the outcrop into the subsurface required an understanding of the effective traceability of outcrop facies and the recognition of these facies in core

and wireline (Table 2). This required the interpretation of observational data, and the extrapolation of geobody surfaces based on their dip, since they were commonly only partially exposed (e.g. lobes, channel belt, etc.). Based on core, the Precipice Sandstone in the Glenhaven area is made of vertical and lateral stacking of geobodiesthat change character. This required a composite interpretation based on observations from all outcrops which variably exposed different parts of the Precipice section. To understand the geometry of the stacked channel belts, the area of dense open file drilling within the APLNG area along strike to the Glenhaven area was used to test the continuity of the geobodies and geobodies groups also called storeys.

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This workflow provides a conceptual framework within which the spatial dimensions of different scale sedimentary features can be measured in outcrop, as well as the nature of their bounding surfaces and internal variability in grain size and bedding that influence reservoir flow units. Ideally, outcrops should have two cuts running parallel to each other (such as the unpublished in-house CTSCo model of the Carnarvon Highway) to achieve the best measurements, but the remoteness and character of the eastern flank outcrops did not have this feature because they are shown only in one cut instead of two cuts. The workflow for the outcrop modelling encompassed three methodologies:

1. Outcrop analogue mapping. This study used outcrop mapping, through an allostratigraphic-sedimentological approach based on facies-analysis principles (more information in the appendix: Facies Analysis). Facies associations and bounding surfaces were characterized via bed by bed logging and outcrop architectural panels, with the aim of identifying associations of spatially and genetically related facies. Two approaches were used to define the bounding surfaces of architectural elements: hand drawn line tracing on scaled 2D photomosaics and georeferenced 3D photogrammetry using Datamine Sirovision and later Maptek I-SiteTM. Within these bounding surfaces, variation in grain size, fabric and sedimentary structures were recorded and used to develop a detailed schema.

2. Photogrammetry. The application of 3D ground based photogrammetric models captures digital images that can be archived and analysed for sedimentary and structural features that can be related to reservoir properties. The result of photogrammetric procedures provides a precise three-dimensional geometric reconstruction of an object at a certain scale, which can then be visualized in perspective either as a static or dynamic (e.g. animation) representation. The assemblage of photogrammetric stereo-couples and the development of photogrammetric models have been completed in Datamine Sirovision, although

Outcrop modelling Regional modellingUnits GeobodiesInternal bedding Internal beddingSedimentary facies Depositional elements

alternative platforms are also being evaluated. For more information regarding the acquisition technique and processing please visit Appendix 1.

� Static geological modelling. Static geological models of the outcrops were created using the bounding surfaces mapped in outcrop to create subunits that were populated by field data such as grain-size and fabric (internal bedding). The approach undertaken for nested geometry and fabric modelling includes building geometries for different units using outcrop measured boundary lines; building a fabric model by multiple-point statistical modelling (MPS); and building grain size distribution by a hierarchical Gaussian random function simulation with fabric distribution as a constraint.

The resulting models are detailed below.

Resulting Models

Modelling the outcrop Cabbagetree Creek

Geometries Models were developed for each of the outcrops. The model size is constrained by the outcrop, rather than being extrapolated to the interpreted size of the lobe geobodies.Deposits cropping out in the Cabbagetree Creek area are interpreted as compensational stacked lobes belonging to a shoal-water delta, based on its low gradient and the presence of bioturbation, wave ripples and climbing ripples. In our modelling, we tried to remain consistent for geometry (geobodies with bounding surfaces), internal sedimentary structures that control bedding (fabric) and grain size distributions (texture).

The boundary lines of units were interpreted based on the surveyed outcrop at Cabbagetree Creek (Figure 2A). This outcrop description is an update of the model already presented by Bianchi et al. (2015b), considering the real dip calculated by photogrammetry. The bounding lines were exported from Datamine Sirovision™ and imported into Petrel2014TM as shown in Figure 2B. The bounding lines are georeferenced and the structural dip was also measured by compass in the field. In order to control the generation of horizons within the units model, the bounding lines were projected 200 m northward away from the outcrop (Figure 2C) with the structural dip angle and azimuth (constraints which are listed in Table 3). All the measured points for the outcrop section are projected using the following equations:

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Unit Boundaries �� A 2.4 181.9B 4.0 184.4C 3.6 206.3D 3.1 153.0E 18.7 158.3F 5.8 311.0Bottom 5.7 197.0

Surfaces lines were first generated using the measured points at the outcrop; the surface lines were then projected away from the outcrop according to the measured dip and dip direction. Based on these surfaces, geometries were constructed with x- and y- range of 54 m and 45 m respectively. Results show that units D and F pinch out toward the West and North (Figure 3).

The grid size in the x- and y-direction is 0.1 m and in the vertical is varied. The top eroded surface and units A through D, are layered in proportion according to unit thickness. Units E, F and Bottom are layered with a constant thickness of 0.1 m reflecting their lateral continuity. Figure 4 shows the cell vertical size distribution for each unit. The average cell thickness is 0.09 m with standard deviation of 0.02 m. The number of layers for the Top, A to F and Bottom are 17, 17, 17, 22, 15, 53, 18 and 22, respectively. Finally, the total cell number is 539×452×181=44,096,668.

Sedimentary Fabric Modelling Different interpolation options are available when assigning properties to the grid models created from the bounding surfaces. Sequential indicator simulation (SIS), object modelling (ObjM) and multiple-point statistics (MPS) are used widely for models with categorical variables, such as sedimentary facies, lithology, and flow unit.

SIS is based on spatial variation in a given data property as described by the variogram and is known as two-point geostatistics (Journel, 1983; Journel and Isaaks, 1984; Journel and Alabert, 1988; Deutsch, 2006). The modelled properties have an equiprobable chance to be assigned to a grid, but are guided by the variogram.

ObjM, also known as a marked point process (Holden et al., 1998), is object-based instead of two-point geostatistics. ObjM forces the grid assignment to follow a preordained pattern interpreted from knowledge of the object or unit such as a channel

belt, mouth bar, shoreline, etc. The facies fraction, orientation, amplitude, wavelength, width and thickness for a given object, for example a channel belt, are important inputs for ObjM (Holden et al., 1998; Petrel, 2014).

MPS, a pixel-based sequential simulation algorithm, is capable of producing models with more geological complexity (Strebelle, 2000; Strebelle and Journel, 2001) than SIS and it is also easier for conditioning to hard data than ObjM. In outcrop modelling, MPS was used to guide the assignment of grain size into patterns that reflected the fabrics, e.g. parallel, planar cross and trough cross etc (see Figure 5).

In this study, three relative grain sizes, e.g. coarse, medium and fine, are used to simulate the internal bedding fabric. Then the interactive modelling (hand drawing) method was used to generate three fabric types, e.g. trough cross, parallel and planar cross, which are used as training images in MPS modelling. Those fabric types are a combination of the above-mentioned three relative grain sizes (Figure 5). In these models, grid size is 0.1 m and cell number is 100-10-100 in the x-y-z direction. The grid size and grid number was selected to reflect the average internal bedding size observed in outcrop, and the promote definition in the lateral variation of cross beds and larger scale bounding surfaces. The grid size in the fabric modelling is also considered in selecting the grid size for the training image of internal bedding.

An ellipsoid search mask with radius of 10-3-10 in x-y-z direction and a multi-grid (Gómez-Hernández, 1991; Tran, 1994; Petrel, 2014) with number 3 is used to create training image patterns. The ellipsoid search mask with radius of 10-3-10 in x-y-z- direction will cover 80 cells (=2(3-1)×10×2) in x and z direction and 24 cells (=2(3-

1)×1×2) in y direction (Okabe and Blunt, 2005; Petrel, 2014) which meets the requirement of covering 80% of cells of the training image when creating patterns. Figure 6a is a training image with a channel areal ratio of 26%. This area is gridded into 400 cells. According to the events’ template with four neighbour conditioning data (Figure 6b), the data event number is 24=16; then we can account for the probability of each event globally as shown in Table 4. For example, there are 168 grids are cross connected with four neighbouring grids at event #1 (EV1); 5 of those 168 grids are channel. Figure 7 presents the steps to conduct a MPS with one realisation for a simple case without soft data. This is also the process to take the training images from Figure 5 and populate them throughout each of the depositional elements into Figure 8 which is the generated fabric model.

Based on the bedding inspected from the outcrop, Table 5 lists all parameters used in modelling for the different units. Figure 9 shows that the modelled percentages of relative grain sizes, coarse sandstone, medium sandstone and fine sandstone are 12.8%, 76.3%, and 10.9%, respectively. Note that these values are calculated from the modelled fabric, not from outcrop.

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Grain Size distribution The grain size and its sorting are related to reservoir properties of sandstone, especially porosity and permeability (Chapuis and Aubertin, 2003). In this study, the grain size distributions for different units are listed in Table 6. These values are estimated based on the outcrop observed grain size.

Based on Table 6 listed parameters, hierarchical Gaussian random function simulation is used to generate the distribution of grain size by using fabric as constraints. An omnidirectional variogram with horizontal range of 23 m which is half size of the model and vertical range of 0.2 m was used (for minor sedimentary fabrics, there are only several cells have the same fabric in vertical direction as shown in Figure 5); standard deviations of grain size were assumed as 0.07 mm which is nearly equal to the minimum average grain size of the fabric (0.05 mm) and the rough estimation of standard deviation by the experience equation of (max-min)/4 because of the shortage of statistical data.

Figure 10 shows one realisation of real grain size distribution and Figure 11 shows the histogram of real grain size in the model. Results show that the real grain size distributions for all units except unit C have two peaks.

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A Coarse grain 0.5 1 0.52Medium grain 0.25 0.5 0.3Fine grain 0.125 0.25 0.23

B Coarse grain 0.5 1 0.55Medium grain 0.25 0.5 0.3Fine grain 0.125 0.25 0.23

C Coarse grain 0.125 0.25 0.13Medium grain 0.062 0.125 0.065Fine grain 0.0078 0.062 0.05

D Coarse grain 0.5 1 0.55Medium grain 0.25 0.5 0.27Fine grain 0.125 0.25 0.13

E Coarse grain 0.5 1 0.55Medium grain 0.25 0.5 0.3Fine grain 0.125 0.25 0.23

F Coarse grain 0.5 1 0.9Medium grain 0.25 0.5 0.45Fine grain 0.125 0.25 0.22

Bottom Coarse grain 1 0.5 0.6Medium grain 0.5 0.25 0.26Fine grain 0.25 0.125 0.2

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Modelling the outcrop Isla Gorge#2

Geometries

Deposits present in the Isla Gorge 2 area are interpreted to be fluvial, presenting as a multichannel non-sinuous belt. The style is consistent with a braided ephemeral stream (Bianchi et al., 2015, Milestone 1 report).

The Isla Gorge #2 section is about 5 m in height and 62 m in length. The process for geometry modelling for Isla Gorge #2 is similar to that for Cabbagetree Creek. Figure 12 shows the surveyed outcrop for Isla Gorge #2, the interpolated boundary lines of depositional units and parallel extended boundary lines.

Based on these boundary lines, horizons on top of units #Top, A, B, C and D were constructed first; then sub-horizons are generated on tops for units # B2 to B7, and D2 to D10. The grid size in x- and y-direction is 0.5 m and in the vertical is varied. The grid size is selected by considering the computing time, the modelling area and the heterogeneity/ geometry of the fabric. Figure 13 shows the generated units with ranges of 62 m and 55 m in x- and y- direction.

Figure 14 shows the cell vertical size distribution for each unit. The average cell height is about 0.08 m with standard deviation of 0.04 m. Finally, the total cell number is 129×117×132=1,992,276.

Sedimentary Fabric Modelling For Isla Gorge #2, the types of fabric are same as Cabbagetree Creek. Three fabrics, coarse grain, medium grain and fine grain, are used as basic fabric elements. Training images including those three fabrics are shown in Figure 5. Based on these image patterns, MPS is used to generate the fabric models. According to the bedding inspected from the outcrop, Table 7 lists all the parameters used in modelling for the different units. Figure 15 presents the vertical fabricproportions in the model. Results show that the lower section has more fine grains than the upper section. Figure 16 shows the modelled fabric distribution. In this model, the percentages of coarse grain, medium grain and fine grain are 14.5%, 69.0% and 16.4% respectively based on the geological model.

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The grain size distributions for different units of Isla Gorge #2 are listed in Table 8.These values are measured based on the outcrop observed grain size and the Wentworth Scale of siliciclastic grain-size (Wentworth, 1922). Based on the parameters listed in Table 8, a hierarchical Gaussian random function simulation is used to generate the distribution of grain size by using measurement of outcrop fabric as a constraint. An omnidirectional variogram with a horizontal range of 23 m and avertical range of 0.2 m was used; standard deviations of grain size were assumed as 0.07 mm because of the shortage of data. Figure 17 shows one generated realisation of grain size distribution and Figure 18 shows the histogram of grain size in the model.

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Top Coarse grain (ms) 0.25 0.5 0.3Medium grain (fs) 0.125 0.25 0.13Fine grain (vfs) 0.0625 0.125 0.12

A Medium grain (vcs) 1 2 1.5Fine grain (cs) 0.5 1 0.8Coarse grain (sPb) 2 64 3

B Coarse grain (vcs) 1 2 1.8Medium grain (cs) 0.5 1 0.8Fine grain (ms) 0.25 0.5 0.4

C Medium grain (vcs) 1 2 1.5Fine grain (cs) 0.5 1 0.8Coarse grain (sPb) 2 64 3

D1-D8 Coarse grain (vfs) 0.0625 0.125 0.1Medium grain (silt) 0.0156 0.031 0.02Fine grain (mud) 0 0.0039 0.003

D9-D10 Coarse grain (vfs) 0.0625 0.125 0.12Medium grain (silt) 0.0156 0.031 0.029Fine grain (mud) 0 0.0039 0.003

Compared to Cabbagetree Creek results of simpler design due to the characteristic downstream dipping facies, Isla Gorge 2 has more architectural complexity, exhibitingthe intense “cut and fill” behavior of a braided plain. Nonetheless, mouth bar deposits of Cabbagetree Creek belong to a fluvial-dominated deltaic shallow marine system, which means that mostly of the deposits can be similar to the pure fluvial facies. The difference in grain size distribution between Cabbagetree Creek and Isla Gorge 2 is because the grid size and input grain size are different though they are built with the

same training images. The grid size Cabbagetree Creek is 0.1 m and Isla Gorge is 0.5 m which makes the beddings in Isla Gorge coarser than Cabbagetree Creek. The input coarser grain size for units A, B and C of Isla Gorge 2 leads a coarser grain size distribution for those units.

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References Ashraf, M., 2014. Geological storage of CO2: Heterogeneity impact on the behavior of pressure. International Journal of Greenhouse Gas Control 28, 356-368.

Bachu, S., 2008. CO2 storage in geological media: Role, means, status and barriers to deployment. Prog Energ Combust 34, 254-273.

Cavanagh, A.J. and Haszeldine, R.S., 2014. The Sleipner storage site: Capillary flow modeling of a layered CO2 plume requires fractured shale barriers within the Utsira Formation. International Journal of Greenhouse Gas Control 21, 101-112.

Deutsch, C.V. and Journel, A.G., 1998. GSLIB Geostatistical Software Library and User's Guide. Second Edition.

Georgi, D.T., Bergren, P.A. and Devier, C.A., 1997. Plug gamma ray: Key to formation evaluation. Poster presentation at the 1997 SCA International Symposium, Calgary, 8-10 September. SCA-9732.

Martin, K., 1976. Sedimentology Of The Precipice Sandstone, Surat Basin, Queensland. Unpublished PhD Thesis, The University of Queensland. 224 pp plus appendices.

Martinsen, O.J., Ryseth, A., Helland-Hansen, W., Flesche, H., Torkildsen, G. and Idil, S., 1999. Stratigraphic base level and fluvial architecture, Ericson Sandstone (Campanian), Rock Springs Uplift, W. Wyoming, USA. Sedimentology, 46, 235–260.

OGIA model, 2014. Office of Groundwater Impact Assessment, Queensland, 2014.

Petrel, 2014. Schlumberger, user manual.

Chapuis, R.P., Aubertin, M., 2003. Predicting the coefficient of permeability of soils using the Kozeny-Carman equation. EPM-RT-2003-03, 1-30. http://www.polymtl.ca/biblio/epmrt/rapports/rt2003-03.pdf.

Deutsch, C.V., 2006. A sequential indicator simulation program for categorical variables with point and block data: BlockSIS. Computers & Geosciences 32, 1669-1681.

Gómez-Hernández, J., 1991. A stochastic approach to the simulation of block fields conductivity upon data measured at a smaller scale. Ph.D. thesis, Stanford University, Stanford.

Journel, A.G., 1983. Nonparametric estimation of spatial distribution. Mathematical Geology 15 (3), 445-468.

Journel, A.G. and Alabert, F.G., 1988. Focusing on spatial connectivity of extreme valued attributes: stochastic indicator models of reservoir heterogeneities, SPE Paper 18324.

28

Journel, A.G. and Isaaks, E.H., 1984. Conditional indicator simulation: application to a Saskatchewan uranium deposit. Mathematical Geology 16 (7), 685-718.

Holden, L., Hauge, R., Skare, O. and Skorstad, A., 1998. Modeling of fluvial reservoirs with object models. Mathematical Geology 30, 473-496.

Okabe, H. and Blunt, M.J., 2005. Pore space reconstruction using multiple-point statistics. Journal of Petroleum Science and Engineering, 46, 121-137.

Strebelle, S.B., 2000. Conditional simulation of complex geological structures using multiple-point statistics. Mathematical Geology 34, 1–22.

Strebelle, S.B. and Journel, A.G., 2001. Reservoir modeling using multiple-point statistics. SPE paper 71324 presented at the SPE Annual Technical Conference and Exhibition held in New Orleans, Louisiana, 1–11.

Tran, T., 1994. Improving variogram reproduction on dense simulation grids. Computers & Geosciences 20, 1161–1168.

Wentworth, C.K., 1922. A scale of grade and class terms for clastic sediments. The Journal of Geology, 30(5): 377-392.

Zhou, F., Shields, D., Tyson, S. and Esterle J., 2016. Nested approaches to modelling swamp and fluvial channel distribution in the Upper Juandah Member of the Walloon Coal Measures, Surat Basin. APPEA Journal, in press.

0

Appendix 3: Subsurface Modelling Of Lithologies Within The Precipice Sandstone, A Case Study From Densely Drilled Well Data

Contents

Background .............................................................................................................. 1

Data ........................................................................................................................... 1

Comparison of wireline and core............................................................................ 2

Modelling .................................................................................................................. 5

Structural framework ...................................................................................................... 5

Correlation approach and relation to outcrop observations........................................ 6

Petrophysical upscaling ................................................................................................. 7

Variogram Analysis ......................................................................................................... 9

Modelled Distribution of GR ......................................................................................... 11

Volume rendering of the different lithologies...................................................... 17

Comparison of subsurface model with outcrop models .................................... 23

Discussion and future work .................................................................................. 24

References.............................................................................................................. 25

1

SUBSURFACE MODELLING OF LITHOLOGIES WITHIN THE PRECIPICE SANDSTONE, A CASE STUDY FROM DENSELY DRILLED WELL DATA

Background

The ultimate goal in subsurface static reservoir modelling is an accurate realisation of the heterogeneity likely to be encountered that will influence flow unit behaviour during the injection of CO2. The spatial heterogeneity of porous and permeable versus impervious lithologies, along with pressure and stress, will affect the flow pathways for CO2 (Dance, 2013; Issautier et al., 2013; Cavanagh and Haszeldine, 2014). These reservoir properties are related to the internal architecture of a reservoir, in particular the lateral continuity of bounding surfaces, sedimentary bedding, grain texture and fabric, often referred to as lithofacies. In the outcrop studies, the channel and lobe stacking patterns were complex and the actual size and extent of a complete geobody was difficult to determine because their exposure is limited compared to their actual size. In the absence of 3D seismic survey, a densely drilled area can be used to correlate bounding surfaces and model the distribution of lithologies that comprise the different geobodies.

This report presents a case study for modelling the distribution of lithologies within the Lower Precipice Sandstone (considered the main reservoir) using Gamma Ray (GR) wireline logs as a proxy. This wireline log was used as it was available in all wells and able to be normalized for the interpretation of lithologies by simple cut off values. Correlation of the wireline cut off values to lithofacies interpretations were validated from previous work on core in the Precipice Sandstone and overlying units (Bianchi et al, 2015b and c). Although simple in its approach, this subsurface model was used to test the insights gained from outcrop facies studies within the Precipice Sandstone (Bianchi et al, 2015b and c), to develop the geostatistics that influence the assignment of rock and reservoir properties into a grid model, and to determine the gaps needed to improve conceptualization and subsurface modelling of sedimentary geobodies for the Glenhaven site.

Data

A wireline dataset from the densely drilled Durham Ranch and Spring Gully field area (Asia Pacific Liquified Natural Gas or APLNG project) was used to develop a preliminary workflow for lithofacies modelling in the Precipice Sandstone. This area is along depositional strike from the less densely drilled EQP7, the exploration license area where injection for a CO2 storage project in Precipice Sandstone is proposed (Figure 1). In the absence of a large scale 3D seismic volume, the densely drilled APLNG area, with an approximate well spacing of 1 km, can provide insight to the

2

distribution and connectivity of lithologies, in particular the fine grained siltstone and mudstone (high gamma) units that potentially form baffles.

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Figure 2 shows the comparison of GR and Density logs (DEN) with core observed lithology and measured porosity and permeability for the West Wandoan 1 well. Porosity (67 points), permeability (51 points), and overburden permeability (27 points) for well West Wandoan 1 were collected from the milestone report by Golab et al. (2015). If the comparisons between these data sets are good, then the wireline logs can be used to estimate the lithologies, but more importantly their reservoir character that is important to dynamic modelling. The more detailed sedimentary facies are also shown to remind the reader of the variability in the architectural elements or geobodies (i.e. large scale bars and sand sheets within the main belt of a braided river, point bars within a meandering river, lobate mouth bars and so on) interpreted within the Precipice Sandstone. In future subsurface models, these geobodies can be modelled as objects or controlled through the application of multi point statistical methods, but for this model, the intent was to see how well probabilistic modelling within different subunits could, if at all, define the position and size of the trunk channel and the distribution of the finer grained units within them.

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From core observation, there are mainly three basic lithologies in the Lower Precipice Sandstone that are easily discriminated: quartzose coarse sand, quartzose fine sand and fine lithic sandstone with mudstone interlaminations (dirty sand+mud). Results show that it is easy to discriminate the different lithotypes using GR for the Lower Precipice Sandstone. However, the thin clean fine sand at the top of the Lower Precipice Sandstone, which is interpreted to have some tidal reworking from outcrop observations, is too thin to identify in wireline. Similarly, the quartzose conglomeratelayers are also not recognisable in wireline. Hence the preliminary model for the LowerPrecipice Sandstone was kept simple and modelled using gamma cut off values.

Figure 3 shows a good relationship of porosity with GR and DEN. If GR less than 40 API, then the porosity of most samples except for one is greater than 16.5%; whereas the porosity of most samples is less than 11% for GR greater than 80 API. If porosity is less than 11%, the permeability is less than 0.1 mD as shown in Figure 4. These correlations suggest that simple wireline logs can be used as a first pass estimation of reservoir character, and that discrimination of different lithologies and their geometry (which can also reflect geobodies) within a reservoir unit can be used to model spatial heterogeneity of permeability.

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Six well correlation sections (example section in Figure 5 and full suite in Appendix; Locations are shown in Figure 1) were used to quality control (QC) stratigraphic well tops provided through the Queensland Petroleum Exploration Database (QPED July 2015) and the Office of Groundwater Impact Assessment (OGIA unpublished 2014), and to correlate three allostratigraphic subdivisions within the Lower Precipice Sandstone: the-Meandering unit, the Blocky unit and the Basal Blocky unit. Note that the Basal unit is not always present in every well. These well tops were used in building an unfaulted static geological model (Figure 6). Well spacing varied and the surfaces were modelled using a 100 m by 100 m x- and y- grid. Normally, maximum cell dimensions are dictated by the minimum sizes of the features to be resolved. In this study, the selection of cells size of 100 m in x- and y- directions is based on well spacing and computing time because of the 250 layers in the vertical direction. After gridding, the convergent gridder method (Petrel, 2014) was used firstly to build the surfaces on the tops of the Lower Precipice Sandstone and the Bowen Basin Unconformity (Buncon) constrained by the picks from the well logs of 89 wells as shown in Figure 1b. Then the allostratigraphic surfaces were modelled proportionally into 120 (meandering), 100 (blocky) and 30 (basal blocky) vertical layers, within which yields an average cell height of 0.3 m for each of the units.

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The three subunits were recognized – the Meandering, Blocky and Basal Blocky - onthe basis of different GR trends bounded by higher GR layers (Figure 5). The high GR layers correspond to fine-grained layers that can be widely recognized in the area, associated with overbank/floodplain deposits. The subdivisions, that define a multi-story channel system, record the stratigraphic evolution in fluvial style, which reflects the variation in accommodation space (Martinsen et al., 1999). The lateral continuity of the fine grained units in outcrop was only observable in Carnarvon Gorge, where they could be traced for a distance of at least 2 kilometres.

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The Basal Blocky subunit is characterized by a blocky low GR pattern which extensively floors most of the model area with a maximum thickness of 13 m; locally it appears to be eroded by the overlying Blocky subunit. The peculiar feature of this subunit is its widespread spatial extent; these deposits could be interpreted as unconfined sand sheets, representing the first big supply of sediment across the basin,perhaps coming from erosion of the strata exposed by the late Triassic to early Jurassic uplift in the Bowen Basin.

Between the Basal Blocky and the Blocky subunits, a widespread fine grained zone divides the two, except where it is eroded by the overlying Blocky subunit. The Blocky subunit is characterized by an average of 35 m to 40 m of blocky low GR signature, divided into smaller intervals up to 10 m thick. Locally, if the GR log has high enough resolution it is possible to appreciate small scale (a few metres), stacked, upwards

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Litho

59764 [SSTVD]MD

1.1 g/cm3 3.1

DEN

-10 260gAPI

GR

Color fill

Color fill

Litho

60875 [SSTVD]MD

1.0 g/cm3 2.8

DEN

-24 264gAPI

GR

Color fill

Color fill

Litho

60937 [SSTVD]MD

0.9 g/cm3 2.8

DEN

-21 263gAPI

GR

Color fill

Color fill

Litho

60948 [SSTVD]MD

0.9 g/cm3 3.0

DEN

-17 263gAPI

GR

Color fill

Color fill

Litho

59583 [SSTVD]MD

1.1 g/cm3 2.8

DEN

-2 183gAPI

GR

Color fill

Color fill

Litho

Basal

Blocky

Meandering?

7

coarsening packages (sort of a zig-zag of the GR). This could be the GR signature of single-story braided bars.

The Meandering subunit has a heterogeneous pattern and grain-size mostly resembling fining-upward packages capped by mudstones, and in places coal (where observed in Woleebee Creek GW4 and West Wandoan 1 cores; locations of these two wells are shown in Figure 1a). Coals were not observed in the wireline density logs. The GR signature is highly serrated, recording the heterolithic character of the fine-grained lithologies. Some mudstone zones in this subunit may not be as extensive as those in the lower subunits because they can represent the mud plug formed during the channel abandonment phase; this is a frequent process in meandering environments. From the Basal Blocky to the Meandering subunit it is clear that the accommodation space is increasing from highly amalgamated sandstone dominated channels upwards to more isolated channels in the floodplain. Thus the Basal Blockyand Blocky subunits record a low accommodation space period transitioning to higher accommodation in the Meandering subunit (Martinsen et al., 1999), with a rise in base level.

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Petrophysical upscaling

Before petrophysical modelling, the GR logs from the 83 wells were upscaled by using the Arithmetic averaging method because GR is continuous and non-directional. Figure 7 shows the upscaled GR and the comparison of histograms of GRs before

8

and after the upscaling. Results show that the average GR is about 28 API with standard deviation of 19 API and the histogram of upscaled GR is nearly the same as that from logs. This suggests that the upscaling cell height is small enough to capture thin intervals with high GR, which are also observed in the core descriptions.

Figure 8 show the histogram of upscaled GR for different units. Results show that the Blocky unit has a lower average GR of 20.5 API with standard deviation of 13.7 API while the Basal Blocky unit has higher average GR of 26.4 API with standard deviation of 17.7 API. The slightly higher GR could reflect the inclusion of lithic clasts in the conglomeratic sandstone that forms this unit.

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GR, API

Mean=28 APIStd.=19 API

(a)(b)

High GR mudstone

100

2030

4050

200 40 60 80 100 120 140 160 180 200

Meandering unit(Mean=24.4; Std.=17.7)

Blocky unit(Mean=20.5; Std.=13.7)

Basal unit(Mean=26.4; Std.=17.7)

Prob

abili

ty, %

GR, API

9

Variogram Analysis

Kriging required the analysis of the horizontal variogram based on the upscaled GR. Considering the average well spacing which is about 1 km, and the size of modelling area is about 17 km in x- and y-direction, a search distance in x- and y- direction of 10km and lag numbers of 10 were used to generate the horizontal variogram which were used to identify the orientation of the variogram range. Figure 9 shows the generated horizontal variogram map which indicates a major orientation of about 135� within the study area. This figure also shows that the major and minor ranges are 3.9 km and 2.5 km respectively at a normalized variance of 0.75. This orientation is consistent with regional isochore maps and palaeoflow directions that define the main channel system within the Precipice Sandstone (Bianchi et al, 2015b; Martin, 1976).

Based on the horizontal variogram, a directional variogram was generated along major and minor orientations, respectively. Table 1 lists the parameters used in calculating the experimental variogram. The lag distance is 800 m and the number of lags is 20along both major and minor orientations. The band width is 800 m; tolerance angle is �� In vertical direction, the number of lags is 20 and lag distance is 0.4 m. Figure 10 shows the vertical experimental semivariance and its regressions for each of the Meandering, Blocky and Basal Blocky units. Results show that the type variogram is exponential for GR in the vertical direction. With same vertical distance, e.g. 2.6 m, the semivariance is least for the Blocky unit while it is largest for the Basal Blocky unit. Note that the steep increase of semivariance in Figure 10c may be because the thickness of the Basal Blocky subunit is commonly less than 7 m in most wells. However, the horizontal experimental semivariance points are very scattered and it is difficult to get a good variogram at the current well spacing for this study area, which suggests it is even more difficult in areas of sparse drilling without us of an analogue model. Geostatistics derived from this study might assist in modelling the different units within the Glenhaven area, where drilling is less dense.

10

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C�!��#��(��

Direction Azimuth from north,

�

Number lags

Lagdistance,

m

Band width, m

Tolerance ��

Lagtolerance,

%

Vertical // 20 0.4 // 45 50

Major 135 20 800 800 45 50

minor 45 20 800 800 45 50

0.75

0.75

0.75

0.75

0.750.75

0.75

0.75

0.875

0.875

0.875

0.875

0.875

0.875

-8000 -6000 -4000 -2000 0 2000 4000 6000 8000

-8000 -6000 -4000 -2000 0 2000 4000 6000 8000

-800

0-6

000

-400

0-2

000

020

0040

0060

0080

00

-8000-6000

-4000-2000

02000

40006000

8000

0 1000 2000 3000 4000 5000m

1:129119 0.650.680.700.730.750.780.800.830.850.880.900.930.950.981.001.031.051.081.10

Variance

3.9 km

2.5 km

Distance, m

Dist

ance

, m

N

11

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Modelled Distribution of GR

Simple Kriging (SK; Deutsch and Journel, 1998) was used to interpolate the distribution of GR values (without ��#� �� interpretation of lithologies) with different variogram ranges the in x-y-z direction. Table 2 lists three scenarios; the models are shown in Figure 11. The choice of a vertical variogram range compared well to outcrop observations for sandstone, where cosets of bedforms varied between 3 to 5 m, although individual beds were much thinner. The fine grained units observed in outcrop, which would form the lower permeability zones, varied in thickness from about 1 to 3 m, which is below the range applied in our modelling.

The variograms in the x and y directions were investigated as the sedimentary geobodies, such as channels or mouthbars, were larger than the outcrop. Even at the Carnarvon Gorge, which is an extensive outcrop, fine grained units that bound the subdivisions could only be tracked for a few kilometers before disappearing behind vegetation. Therefore, different arbitrary variogram ranges were trialled in the subsurface modelling to determine which captured the spatial variation. With a simple interpretation that low gamma signatures represent sandstone and higher gamma signatures the finer grained units, a central sinuous pattern bordered by fine grained

(a) Meandering unit (b) Blocky unit

(c) Basal unit

(a) sill=0.54; vertical range=3.1 m;

(b) sill=0.40; vertical range=5.0 m;

(c) sill=0.85; vertical range=6.0 m;

12

lithologies emerges, regardless of variogram range. As expected, the areas of fine grained lithologies merge with a greater variogram range, but define the margins of a main channel.

Figure 12 to Figure 14 present the comparison for histograms of GR from modelled cells, upscaled cells and well logs from scenarios S1 to S3 respectively. Results show that the distribution of GR in those models is not the same as those from well logs.

� For the Meandering and Basal Blocky units in scenarios S1 and S2, the percentage of GR values for range from 20 to 30 API in models is higher than those from upscaled and well log values. It is lower in models for other ranges.

� For the Blocky unit, the percentage of GR values for range from 10 to 20 API in models is higher than those from upscaled and well log values; while it is lower in models for other ranges.

� For the Meandering and Basal Blocky units in scenario S3 that was modelled using a long variogram range of 10 km in x- and y- direction, the percentage of GR values for ranges from 20 to 30 API and 30 to 40 API in models is higher than those from upscaled and well log values; while it is lower in models for other ranges.

� For the Blocky unit, the percentage of GR values for ranges from 10 to 20 API and 20 to 30 API in models is higher than those from upscaled and well log values; while it is lower in models for other ranges. This yields a lower percentage of cells with high GR values in the models compared with that from logs (i.e. the models do not always capture the distribution of the thinner fine grained units).

Nevertheless, the model captured units less than 1 m because the average cell height in these models is about 0.3 m. Results also show that with an increasing variogram range, the modelled GR distribution is closer to the upscaled and logging GR.

13

C�!��%�D��

Scenarios Units Exponential variogram range in x-y-z direction

Nugget/Sill

S1 Meandering 3 km× 3 km×3.1 m 0/0.54

Blocky 3 km× 3 km×5.0 m 0/0.40

Basal Blocky

3 km× 3 km×6.0 m 0/0.85


Blocky 5 km× 5 km×5.0 m 0/0.40

Basal Blocky

5 km× 5 km×6.0 m 0/0.85


Blocky 10 km× 10 km×5.0 m 0/0.40

Basal Blocky

10 km× 10 km×6.0 m 0/0.85

14

(a) S1

(b) S2

15

��*+�+��!��./��(��C�!��%��:��(��

��%�&��#��./�� '��#� ��

(c) S3


(c) Basal unit

0 40 80 120 160 200 2400

10

20

30

40

50

60

10

20

30

40

50

60

00 40 80 120 160

Perc

enta

ge, %

Perc

enta

ge, %

Perc

enta

ge, %

GR, API0

10

20

30

40

50

60

70

0 40 80 120 160 200GR, API

GR, API

16

��*�&��#��./�� '��#� �� %�

��6�&��#��./�� '��#� �� *�


(c) Basal unit

0

8

16

24

32

40

48

0 40 80 120 160 200 240

Perc

enta

ge, %

GR, API

0 40 80 120 160

0 40 80 120 160 2000

10

20

30

40

50

60

Perc

enta

ge, %

GR, API

8

16

24

32

40

48

0

Perc

enta

ge, %

GR, API


(c) Basal unit

8

16

24

32

40

00 40 80 120 160 200 240

0 40 80 120 160

0 40 80 120 160 200

Perc

enta

ge, %

8

16

24

32

40

0

Perc

enta

ge, %

48

56

GR, API GR, API

GR, API

Perc

enta

ge, %

8

16

24

32

40

0

17

Volume rendering of the different lithologies

The volumetric percentages of the different lithologies and their spatial distributions allow the visualisation of potential reservoir and baffle units within the different allounits of the Precipice Sandstone. In some software packages these are also called “geobodies”, but should not be confused with the sedimentary elements described in the main report, although in this instance they define the main trunk of the channel system. In this exercise, the lithologies were kept simple: Type 1 was defined by cells with GR<=40 API (coarse sand), Type 2 (dirty fine sand) was defined by cells with GR>40 and <=80 API, and Type 3 (dirty fine sand + mud) with GR>80 API (Figure 15).These were assigned to the grid models using different variogram ranges.

Table 3 lists the volumetric percentage for different lithologies within the different modelling scenarios using different variograms. Results shows that with horizontal variogram range increasing from 3 to 10 km (from S1 to S3), the percentage of Type2 increases from 4.6% to 10.6%; from 1.1% to 2.6% and from 3.1% to 8.2% for the Meandering, the Blocky and the Basal Blocky units, respectively; the percentage of Type 3 increases from 0.2% to 0.9%; from 0.2% to 0.6% and from 0.2% to 0.8% for the Meandering, Blocky and Basal Blocky units, respectively.

C�!��*��(�� #�� #��

ScenariosLithology Type Volumetric percentage, %

Meandering Blocky Basal BlockyS1 Type 1 95.2 98.8 96.8

Type 2 4.6 1.1 3.1Type 3 0.2 0.2 0.2

S2 Type 1 92.5 98 94.5Type 2 7 1.7 5.1Type 3 0.5 0.3 0.4

S3 Type 1 88.6 96.8 90.9 Type 2 10.6 2.6 8.2Type 3 0.9 0.6 0.8

There is commonly a question of “how thick does a fine grained impermeable unit have to be before it forms a baffle to flow”. Although that question cannot be answered by this study, Figure 16 shows that the thickness of the fine grained Type 3 occurrences are less than 2.5 m and more than 90% are less than 1 m across the wells. The thickness ratio of Type 3 from borehole data is about 3.1%, which is higher than the percentage of high gamma classes from models showing in Figure 11 which is about 0.2%, 0.4% and 0.8% from S1 to S3 respectively over the study volume. This suggests

18

that stochastic modelling of GR alone would underestimate the distribution of the lithotypes.

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In order to map the distribution of the ply number of the fine grained units (Type 3), a code was used to count the quantity of Type 3 within the 83 wells. Figure 17 shows the number distribution of Type 3, which was generated by using the borehole data and an isochore interpolation method. Results show that the number varies from 0 to

GR, A

PI

(1=mud; 2=dirty fine sand+mud; 3=dirty fine sand; 4=clean fine sand; 5=clean coarse sand; 6=clean conglomerate.)

10points

50points

10points

121points

556points10points

Litholoies in Lower Precipice Sandstone

Max

75th percentilesMedium

25th percentiles

Min

Lithologies of the Lower Precipice Sandstone

GR, A

PI

Thickness, m0 1 2

Perc

enta

ge, %

0

10

20

30

40

50

60

19

11 with an average of 3; the west, middle and south areas have more fine grained units. These finer grained units potentially mark the boundaries of the high porosity and permeability reservoir.

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Figure 18 shows the distribution of filtered Type 3 for different variogram scenarios S1 to S3, or increasing the chance for the fine grain units to amalgamate. Results in each scenario show a “baffle free” low sinuosity belt with a maximum width of about 2.7 km trending southeast. From scenarios S1 to S3, the extent of the fine grained layers witha potential to baffle flow increases with the increase of horizontal variogram range (as expected).

Figure 19 shows the distribution of reservoir and potential baffle volumes from scenario S1, S2 and S3 along a well section. Results show well Spring Gully 37 has 11 layers of Type 3 with length from 0.2 km to 1.3 km. These high GR layers may represent low permeability intervals which would assist in delaying the arriving time of a CO2 plume to the top of Lower Precipice Sandstone. Such a pathway would increase the contact volume of reservoir fluid with the injected CO2 and hence increase the CO2

storage. This could be modelled in a dynamic flow simulation.

0

7

5

10

5

9

106

0

1

73

0

0

1

3

3

20

3

7 010

25

7

22

0 5

3

0

3

5

1

2

6

21

66 2

143

010

0

30

1

2

1 23

3

6

1

4

0

2

0

0

0

3

7

1

110

2

116

6

0

25

6

4

3

6

4

1

149°02'E 149°04'E 149°06'E 149°08'E 149°10'E 149°12'E

149°02'E 149°04'E 149°06'E 149°08'E 149°10'E 149°12'E

26°0

4'S

26°0

2'S

26°0

0'S

25°5

8'S

25°5

6'S

25°5

4'S

26°04'S26°02'S

26°00'S25°58'S

25°56'S25°54'S

0 1 2km

012345678910111213

N

Number of Geobody 3

+ Well location and layer numbers

20

(a) S1

2.7 km

(b) S2

2.5 km

21

��<��./��!�� F��*�:��(��

(c) S3

2.3 km

22

��?�� !�� '��!�� %'�� *'�� C�#��*G�� C��

DURHAM RANCH 23

SPRING GULLY 37

SPRING GULLY 43

DURHAM RANCH 64

SPRING GULLY 39SPRING GULLY 38

DURHAM RANCH 40DURHAM RANCH 50

DURHAM RANCH 51


DURHAM RANCH 15

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

-160

-80

080

160

-160-80

080

160

0 0.5 1 1.5 2 2.5km

Body with GR<=40Body with GR(>40,<=80)Body with GR(>80)

(c) S3

DURHAM RANCH 23

SPRING GULLY 37

SPRING GULLY 43

DURHAM RANCH 64



DURHAM RANCH 51


DURHAM RANCH 15

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

-160

-80

080

160

-160-80

080

160

0 0.5 1 1.5 2 2.5km


(b) S2

DURHAM RANCH 23

SPRING GULLY 37

SPRING GULLY 43

DURHAM RANCH 64



DURHAM RANCH 51


DURHAM RANCH 15

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

-160

-80

080

160

-160-80

080

160

0 0.5 1 1.5 2 2.5km


(a) S2

Basal unit

Meandering unit

Blocky unit

23

Comparison of subsurface model with outcrop models

In this study, the observed lithology in core from West Wandoan 1 was used to relate GR with lithology as shown in Figure 2. The vertical channel filling pattern and lateral continuity from outcrop was also compared. However, for further comparison of this preliminary model with outcrop observations, the grain size has to be related with GR. Georgi et al. (1997) found that the core-plug-derived grain size and plug GR can be used to infer GR cutoffs for differentiating fine-grained sands from silty-shaley sands as shown in Figure 20. However, to quantitatively calibrate the GR with grain size is still a challenge. Another issue is that the grid sizes in outcrop models are about 0.5 m in horizontal and 0.1 m in vertical while the local model is 100 m×100 m× 0.3 m as shown in Figure 21. Upscaling is needed before this comparison can be made.

In the future work, we will develop the relationship between the GR wireline log and the sedimentary facies using West Wandoan 1 and Woleebee Creek GW4 logs, potentially with a core from the APLNG area, and use these to develop a facies distribution model. The geometry of the geobodies in which the facies occur, will be constrained by parameters obtained in outcrop. The static geological grid model is this way populated by the facies, using either (or both) object modelling or multiple-point simulation. Based on facies models, we will generate distributions for grain size or petrophysical properties which can be used for dynamic reservoir modelling. A pictorial workflow is shown in Figure 21.

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24

��%��@�� #�� #��.��@��!�� #��5��.��% ��!��./��!��!�� -��5��.��H��!'�!��!��#��(��

Discussion and future work

This preliminary model was set up to develop a workflow for further, more sophisticated subsurface facies modelling for the Precipice Sandstone, and to test the influence of the variograms on the interpretation of lateral connectivity of the lithologies, in particular the finer grained baffle units within the Lower Precipice Sandstone allostratigraphic unit. The dense drilling in the APLNG area was selected for modelling, as the jump from outcrop scale to an area with sparse drilling, such as in the Glenhaven area doesn’t allow a good test of architectural element or geobody modelling. Observations of the lower braided fluvial facies are best exposed at the Carnarvon Gorge, and here discrete, albeit thin, finer grained units could be tracked for about 2 km. This suggests that one can correlate, or at least model, the finer grained units and facies in the braided facies for variogram correlation lengths in the range of 3-10 km. In the detailed model, regardless of variogram used, a “baffle free” belt trending southeast was present and, coupled with a southerly marching, cross

(a)

(b) (c)

25

bedded bar forms observed in core and outcrop in the lower unit, could act to control flow pathways in the reservoir. Based on the geometry of the baffle free zone, we’d interpret the trunk channel belt of the Precipice to average 2.5km in width, although smaller scale macroforms and channels would be modelled within this.

Regarding the vertical distribution of baffles in Basal Blocky and Blocky subunits they are thinner but more laterally extensive, whereas in the Meandering subunit they appear locally developed and thicker, suggesting the latter originated as abandoned channel fill in meandering systems in response to the base level rise. It is recommended that available core from this APLNG area be logged to test the interpretation of facies in comparison to those from the Glenhaven area.

Future work, should apply a more sophisticated approach to facies modelling, utilizing the geobody dimensions obtained in the outcrop studies, constrained by further subdivision within the wireline logs according to the three allostratigraphic units- the lower braid plain, meandering facies transitioning into more deltaic facies as the sealing unit of the Evergreen is approached. A test of the geometry of the different sedimentary facies and their geobodies might be to object model their distribution as interpreted for a given outcrop, then “cookie cut” that outcrop and compare to the measured data. This is outside the scope of this project. Included in the appendix are the preliminary correlations that can be used for further modelling and/or extended to other areas. Knowledge gained from this study, e.g. the lateral extent of subunit bounding surfaces, the statistics for assignment of lithofacies properties to the grid, the relationship between the wireline, core and porosity and permeability, and thedistribution and extent of the potential baffles and the baffle-free reservoir within the Precipice Sandstone; can be applied to the more sparsely drilled EPQ7, to improve confidence in the subsurface static geological model.

References

Ashraf, M., 2014. Geological storage of CO2: Heterogeneity impact on the behavior of pressure. International Journal of Greenhouse Gas Control 28, 356-368.

Bachu, S., 2008. CO2 storage in geological media: Role, means, status and barriers to deployment. Prog Energ Combust 34, 254-273.

Begg, S., Carter, R., and Dranfield, P., 1989. Assigning effective values to simulator gridblock parameters for heterogeneous reservoirs. SPE reservoir engineering, 4(04), 455-463.

Bianchi, V., Pistellato, D., Boccardo, S., Zhou, F. and Esterle, J. (2015a) Outcrop mapping and photogrammetry of the Precipice Sandstone ANLEC Milestone 2 Report: Photogrammetry and preliminary mapping results.

26

Bianchi, V., Pistellato, D., Boccardo, S., Zhou, F. and Esterle, J. (2015b) Outcrop mapping and photogrammetry of the Precipice Sandstone ANLEC Milestone 3 Report: Detailed facies schema and associated trial models.

Bianchi, V., Pistellato, D., Boccardo, S., Zhou, F. and Esterle, J. (2015c) Outcrop mapping and photogrammetry of the Precipice Sandstone ANLEC Milestone 4 Report: Geobody dimensions database and associated models.

Bowman, M., McClure, N., and Wilkinson, D., 1993. Wytch Farm oilfield: deterministic reservoir description of the Triassic Sherwood Sandstone. Paper presented at the Geological Society, London, Petroleum Geology Conference series.

Cavanagh, A.J., Haszeldine, R.S., 2014. The Sleipner storage site: Capillary flow modeling of a layered CO2 plume requires fractured shale barriers within the Utsira Formation. International Journal of Greenhouse Gas Control 21, 101-112.

Dance, T., 2013. Assessment and geological characterisation of the CO2CRC Otway project CO2

storage demonstration site: from prefeasibility to injection. Marine and Petroleum Geology, 46, 251-269.

Davies, D.K., Williams, B.P.J., and Vessell, R.K., 1993, Dimensions and quality of reservoirs originating in low and high sinuosity channel systems, Lower Cretaceous Travis Peak Formation, east Texas, U.S.A., in C. P. North and D. J. Prosser, eds., 1993, Characterization of fluvial and eolian reservoirs: Geological Society Special Publication 73, 95-121.

Deutsch, C.V. and Journel, A.G., 1998. GSLIB Geostatistical Software Library and User's Guide. Second Edition.

Geehan, G. W., Lawton, T.F., Sakurai, S. Klob, H., Clifton, T.R., Inman, K.F., and Nitzberg, K.E., 1986. Geologic prediction of shale continuity, Prudhoe Bay field, in L. W. Lake and H. B. Carroll, eds., Reservoir characterization: Orlando, Florida, Academic Press, 63-82.

Georgi, D.T., Bergren, P.A., and Devier, C.A. 1997. Plug gamma ray: Key to formation evaluation. Poster presentation at the 1997 SCA International Symposium, Calgary, 8-10 September. SCA-9732.

Golab, A., Arena, A., Khor, J., Goodwin, C., Young, B., Carnerup, A., Hussain, F., 2015. Milestone 1.4. Final report of RCA and SCAL data on plugs from West Wandoan-1 Well. Technical report for Project 7-0311-0128, Sub-project 1: Derive a full suite of special core analysis (capillary pressure, supercritical CO2: brine relative permeability) data sets on plugs from Surat Basin reservoir and seal rock samples.

Issautier, B., Fillacier, S., Gallo, Y.L., Audigane, P., Chiaberge, C., and Viseur, S., 2013. Modelling of CO2 injection in fluvial sedimentary heterogeneous reservoirs to assess the impact of geological heterogeneities on CO2 storage capacity and performance. Energy Procedia, 37, 5181-5190.

Ma, J., Couples, G.D., and Gardiner, A.R., 2008. Numerical modeling of the fluid flow impact of thin baffle laminae in cross bedding. Water Resources Research, 44, 1-6.

27

Martin, K. (1976). Sedimentology Of The Precipice Sandstone, Surat Basin, Queensland. Unpublished PhD Thesis, The University of Queensland. 224 pp plus appendices.

Martinsen, O.J., Ryseth, A., Helland-Hansen, W., Flesche, H., Torkildsen, G. and Idil, S., (1999). Stratigraphic base level and fluvial architecture, Ericson Sandstone (Campanian), Rock Springs Uplift, W. Wyoming, USA. Sedimentology, 46, 235–260.OGIA model, 2014. Office of Groundwater Impact Assessment, Queensland, 2014.

Petrel, 2014. Schlumberger, user manual.

Shepherd, M., 2016. Braided fluvial reservoirs. AAPG Memoirs. http://wiki.aapg.org/Braided_fluvial_reservoirs.

28

APPENDIX

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PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

Sand

ston

e

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

415.7

554.2

421.

143

1.1

441.

145

1.1

461.

147

1.1

481.

149

1.1

501.

151

1.1

521.

153

1.1

541.

1

Sand

ston

eSa

ndst

one

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

395.8

534.3

407.

641

7.6

427.

643

7.6

447.

645

7.6

467.

647

7.6

487.

649

7.6

507.

651

7.6

527.

6

Sand

ston

eSa

ndst

one

e

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

404.9

543.4

415.

442

5.4

435.

444

5.4

455.

446

5.4

475.

448

5.4

495.

450

5.4

515.

452

5.4

535.

4

Silts

tone

Silts

tone

Sand

ston

eSa

ndst

one

Sand

ston

eSi

ltsto

nee

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

419.5

558

428.

343

8.3

448.

345

8.3

468.

347

8.3

488.

349

8.3

508.

351

8.3

528.

353

8.3

548.

3

Sand

ston

eSa

ndst

one

Sand

ston

e

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

431.6

570.1

439.

244

9.2

459.

246

9.2

479.

248

9.2

499.

250

9.2

519.

252

9.2

539.

254

9.2

559.

2

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

414.1

552.6

422

432

442

452

462

472

482

492

502

512

522

532

542

Sand

ston

eSa

ndst

one

Sand

ston

e

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

404.4

542.9

411.

942

1.9

431.

944

1.9

451.

946

1.9

471.

948

1.9

491.

950

1.9

511.

952

1.9

531.

9

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

413.2

551.7

422.

343

2.3

442.

345

2.3

462.

347

2.3

482.

349

2.3

502.

351

2.3

522.

353

2.3

542.

3

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

400.3

538.7

406.

541

6.5

426.

543

6.5

446.

545

6.5

466.

547

6.5

486.

549

6.5

506.

551

6.5

526.

5

Sand

ston

eSa

ndst

one

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

398.1

536.6

408.

841

8.8

428.

843

8.8

448.

845

8.8

468.

847

8.8

488.

849

8.8

508.

851

8.8

528.

8

Sand

ston

eSa

ndst

one

ston

e

PRECLOW

LP1

BUNCON

LP2

438.7

577.2

451.

546

1.5

471.

548

1.5

491.

550

1.5

511.

552

1.5

531.

554

1.5

551.

556

1.5

571.

5

PRECLOW

LP1

BUNCON

LP2

1096 m 1063 m 1022 m 896 m 1016 m 1281 m 839 m 1851 m 2522 m 1405 m58593 [SSTVD]MD

0.7 g/cm3 3.7

DEN

-12 218gAPI

GR

Color fill

Color fill

Litho

59300 [SSTVD]MD

1.0 g/cm3 2.9

DEN

-16 261gAPI

GR

Color fill

Color fill

Litho

59007 [SSTVD]MD

1.1 g/cm3 3.1

DEN

-6 211gAPI

GR

Color fill

Color fill

Litho

1 ohm.m 100

RT

59028 [SSTVD]MD

1.1 g/cm3 2.9

DEN

-6 255gAPI

GR

Color fill

Color fill

Litho

1 ohm.m 100

RT

59000 [SSTVD]MD

0.8 g/cm3 3.0

DEN

-12 212gAPI

GR

Color fill

Color fill

Litho

59005 [SSTVD]MD

1.0 g/cm3 2.8

DEN

-9 184gAPI

GR

Color fill

Color fill

Litho

58583 [SSTVD]MD

1.2 g/cm3 3.3

DEN

-24 264gAPI

GR

Color fill

Color fill

Litho

58585 [SSTVD]MD

1.2 g/cm3 3.3

DEN

-24 263gAPI

GR

Color fill

Color fill

Litho

59058 [SSTVD]MD

1.0 g/cm3 3.0

DEN

-7 259gAPI

GR

Color fill

Color fill

Litho

1 ohm.m 100

RT

59546 [SSTVD]MD

0.6 g/cm3 2.9

DEN

-12 178gAPI

GR

Color fill

Color fill

Litho

61102 [SSTVD]MD

0.3 g/cm3 3.3

DEN

-15 256gAPI

GR

Color fill

Color fill

Litho

Basal

Blocky

Meandering?

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

Sand

ston

eSa

ndst

one

Silts

tone

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

379.2

532.4

385.

439

5.4

405.

441

5.4

425.

443

5.4

445.

445

5.4

465.

447

5.4

485.

449

5.4

505.

451

5.4

525.

4

Sand

ston

eSa

ndst

one

Sand

ston

e

PRECLOW

LP1BUNCON

LP2

411.2

569.2

422.

143

2.1

442.

145

2.1

462.

147

2.1

482.

149

2.1

502.

151

2.1

522.

153

2.1

542.

155

2.1

562.

1

Sand

ston

e

PRECLOW

LP1

BUNCON

LP2

407.6

565.4

413.

542

3.5

433.

544

3.5

453.

546

3.5

473.

548

3.5

493.

550

3.5

513.

552

3.5

533.

554

3.5

553.

5

Sand

ston

eM

udst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

406.5

575.4

413.

442

3.4

433.

444

3.4

453.

446

3.4

473.

448

3.4

493.

450

3.4

513.

452

3.4

533.

454

3.4

553.

456

3.4

Silts

tone

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

406.5

584.9

416.

742

6.7

436.

744

6.7

456.

746

6.7

476.

748

6.7

496.

750

6.7

516.

752

6.7

536.

754

6.7

556.

756

6.7

576.

7

Sand

ston

eSa

ndst

one

Sand

ston

eSi

ltsto

nePRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

410.1

590.2

423.

443

3.4

443.

445

3.4

463.

447

3.4

483.

449

3.4

503.

451

3.4

523.

453

3.4

543.

455

3.4

563.

457

3.4

583.

4

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

393

554

407.

241

7.2

427.

243

7.2

447.

245

7.2

467.

247

7.2

487.

249

7.2

507.

251

7.2

527.

253

7.2

547.

2

Sand

ston

eSa

ndst

one

Sand

ston

eM

udst

one

PRECLOW

LP1

BUNCON

LP2

405.7

588.6

419.

742

9.7

439.

744

9.7

459.

746

9.7

479.

748

9.7

499.

750

9.7

519.

752

9.7

539.

754

9.7

559.

756

9.7

579.

7

Sand

ston

eSa

ndst

one

Sand

ston

eSa

ndst

one

Silts

tone

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

397.4

578.6

404.

341

4.3

424.

343

4.3

444.

345

4.3

464.

347

4.3

484.

349

4.3

504.

351

4.3

524.

353

4.3

544.

355

4.3

564.

3

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

1355 m 996 m 1889 m 1579 m 2623 m 1057 m 1186 m 2985 m58590 [SSTVD]MD

0.7 g/cm3 3.4

DEN

-7 193gAPI

GR

Color fill

Color fill

Litho

58591 [SSTVD]MD

1.1 g/cm3 2.9

DEN

-22 261gAPI

GR

Color fill

Color fill

Litho

59033 [SSTVD]MD

0.7 g/cm3 3.2

DEN

-13 206gAPI

GR

Color fill

Color fill

Litho

59738 [SSTVD]MD

0.9 g/cm3 3.0

DEN

-19 263gAPI

GR

Color fill

Color fill

Litho

59721 [SSTVD]MD

1.2 g/cm3 2.9

DEN

-16 263gAPI

GR

Color fill

Color fill

Litho

59804 [SSTVD]MD

1.2 g/cm3 3.0

DEN

-15 262gAPI

GR

Color fill

Color fill

Litho

60841 [SSTVD]MD

0.6 g/cm3 3.3

DEN

-12 215gAPI

GR

Color fill

Color fill

Litho

60824 [SSTVD]MD

1.1 g/cm3 2.9

DEN

-22 264gAPI

GR

Color fill

Color fill

Litho

61247 [SSTVD]MD

0.9 g/cm3 3.0

DEN

-22 263gAPI

GR

Color fill

Color fill

Litho

Basal

Blocky

Meandering?

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

433.3

548.8

437.

544

7.5

457.

546

7.5

477.

548

7.5

497.

550

7.5

517.

552

7.5

537.

5

Sand

ston

eSa

ndst

one

Sand

ston

eto

ne

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

402.5

518

410.

642

0.6

430.

644

0.6

450.

646

0.6

470.

648

0.6

490.

650

0.6

510.

6

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

403.9

519.4

411.

842

1.8

431.

844

1.8

451.

846

1.8

471.

848

1.8

491.

850

1.8

511.

8

Mud

ston

eSa

ndst

one

Sand

ston

eSa

ndst

one

PRECLOW

LP1

PRECUP

BUNCON

LP2

406.4

521.9

414.

842

4.8

434.

844

4.8

454.

846

4.8

474.

848

4.8

494.

850

4.8

514.

8

Sand

ston

eSa

ndst

one

Sand

ston

eSa

ndst

one

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

391.9

507.4

398.

240

8.2

418.

242

8.2

438.

244

8.2

458.

246

8.2

478.

248

8.2

498.

2

Sand

ston

eSa

ndst

one

Sand

ston

e

PRECMID

PRECLOW

LP1

PRECUP

BUNCON

LP2

394.3

509.8

404.

541

4.5

424.

543

4.5

444.

545

4.5

464.

547

4.5

484.

549

4.5

504.

5

Sand

ston

eSa

ndst

one

PRECLOW

LP1

BUNCON

LP2

403.5

519

407.

641

7.6

427.

643

7.6

447.

645

7.6

467.

647

7.6

487.

649

7.6

507.

6

Mud

ston

eSa

ndst

one

Sand

ston

e

PRECLOW

LP1

BUNCON

LP2

398.7

514.2

403.

941

3.9

423.

943

3.9

443.

945

3.9

463.

947

3.9

483.

949

3.9

503.

9

PRECLOW

LP1

BUNCON

LP2

1118 m 1780 m 1276 m 1018 m 1233 m 1991 m 2508 m59739 [SSTVD]MD

-9 213gAPI

GR

Color fill

Color fill

Litho

59805 [SSTVD]MD

1.1 g/cm3 2.9

DEN

-14 263gAPI

GR

Color fill

Color fill

Litho

59617 [SSTVD]MD

1.1 g/cm3 3.0

DEN

-18 263gAPI

GR

Color fill

Color fill

Litho

59764 [SSTVD]MD

1.1 g/cm3 3.1

DEN

-10 260gAPI

GR

Color fill

Color fill

Litho

60875 [SSTVD]MD

1.0 g/cm3 2.8

DEN

-24 264gAPI

GR

Color fill

Color fill

Litho

60937 [SSTVD]MD

0.9 g/cm3 2.8

DEN

-21 263gAPI

GR

Color fill

Color fill

Litho

60948 [SSTVD]MD

0.9 g/cm3 3.0

DEN

-17 263gAPI

GR

Color fill

Color fill

Litho

59583 [SSTVD]MD

1.1 g/cm3 2.8

DEN

-2 183gAPI

GR

Color fill

Color fill

Litho

Basal

Blocky

Meandering?

appendix 1: photogrammetry techniques, fieldwork...

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