Using neural networks and the Markov Chain approach for facies analysis and prediction from well logs in the Precipice Sandstone and Evergreen Formation, Surat Basin, Australia

Marine and Petroleum Geology 101 (2019) 410–427

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Research paper

Using neural networks and the Markov Chain approach for facies analysis and prediction from well logs in the Precipice Sandstone and Evergreen Formation, Surat Basin, Australia

T

Jianhua Hea,b,c,∗, Andrew D. La Croixc, Jiahao Wangc,d, Wenlong Dinga,b, J.R. Underschultze a

School of Energy Resources, China University of Geosciences, Beijing, 100083, China Key Laboratory of Marine Reservoir Evolution and Hydrocarbon Enrichment Mechanism, Ministry of Education, China University of Geosciences, Beijing, 100083, China c Energy Initiative, University of Queensland, Brisbane, 4072, Australia d Faculty of Earth Resource, China University of Geosciences, Wuhan, 430074, China e Centre for Coal Seam Gas, Brisbane, University of Queensland, 4072, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Surat Basin Precipice Sandstone Evergreen Formation Facies analysis Neural networks Markov chain analysis

Facies analysis is crucial for reservoir evaluation because the distribution of facies has significant impact on reservoir properties. Artificial Neural Networks (ANN) are a powerful way to use facies interpretations from core to determine equivalent facies from wireline logs in uncored wells. However, ANN do not incorporate information that relates to facies successions. This has limited the ability to effectively use facies information derived from logs alone in reservoir modelling, especially at the regional scale where data is often sparse, clustered, or incomplete. In this study, based on observations of 8 cored wells with a total thickness of ∼2000 m, 20 core facies were defined that range from 0.22 m to 11.56 m thick. Facies were based on grain size, sedimentary structures, and ichnological characteristics; Each facies corresponds to a distinct depositional sub-environment within the broader context of a large nearshore to shallow marine system. It was essential that these facies were incorporated into reservoir models to accurately map the distribution of reservoir and seal geobodies for CO2 storage assessment in the Surat Basin, Australia. However, core data are few and far between in the Surat Basin. To use core-defined facies in the absence of core, six wireline log parameters – gamma ray, density, sonic, neutron, photoelectric factor, and deep resistivity were plotted in multidimensional space and examined using Linear Discriminator Analysis. Combined with model recognition and Fisher Canonical Discriminance, the 20 core facies were simplified into 10 representative wireline log facies (WLF) with unique petrophysical parameters. We then used the Markov Chains Approach (MCA) to determine the significance of vertical facies transitions, which supported the interpretation that facies group into 5 distinct associations: (1) channel-levee complex; (2) lower delta plain; (3) subaqueous delta; (4) shoreface and; (5) tidal flats and channels. Based on the facies analysis and statistical classification, Multilayer Perceptron Classifier, a type of neural network method was applied using a training set of three cored wells that had all 6 wireline log data and using the facies successions determined from the MCA. Results show that the accuracy of WLF prediction ranges from 66% to 99% (ca. 83%). The accuracy of facies recognition decreased step wise with a decreasing number of logs as input data, such that when only gamma ray, density, deep resistivity, and sonic were used to train neural networks the accuracy dropped to between 45 and 98% (ca. 67%), depending upon the facies. This was considered the lowest acceptable threshold of accuracy for facies determination for input into reservoir models for carbon capture and storage. The results of this study show that sedimentary facies can be accurately predicted for uncored intervals in the Precipice Sandstone and Evergreen Formation to improve facies mapping and static reservoir modelling. Additionally, wireline log facies are helpful for interpreting Lower Jurassic stratigraphy, depositional setting, and basin evolution in the Mesozoic of Eastern Australia.

∗

Corresponding author. School of Energy Resources, China University of Geosciences, Beijing, 100083, China. E-mail address: [email protected] (J. He).

https://doi.org/10.1016/j.marpetgeo.2018.12.022 Received 2 July 2018; Received in revised form 2 December 2018; Accepted 10 December 2018 Available online 11 December 2018 0264-8172/ Crown Copyright © 2018 Published by Elsevier Ltd. All rights reserved.

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1. Introduction

learning algorithms, determining network architecture, selecting sensitive input variables, and adapting codes for special issues (Wang and Carr, 2012a, b). MLPC is a useful research tool because of its ability to solve complex nonlinear problems stochastically, especially in shale lithofacies application (Wang and Carr, 2012a, b). Most previous facies from wireline logs methods have determined facies at each data point in the well, but failed to account for vertical continuity in the facies profile (Lindberg and Grana, 2015). Each sample in the well log was recognized independently from the adjacent samples. Therefore, unrealistic facies successions tend to occur in facies profiles determined this way. Markov Chain Analysis (MCA) has long been applied to determine whether the occurrence facies in a stratigraphic succession are dependent on the underlying facies (Gingerich, 1969; Le Roux, 1994; Xu and MacCarthy, 1998; Bohling and Dubois, 2003). MCA results reveal the presence of preferred vertical occurrences of facies in a sedimentary succession and therefore, can serve as independent evidence to support interpretations of facies associations (Miall, 1973; Powers and Easterling, 1982; Wells, 1989; Carle, 1999). This improves facies associations and facies succession prediction in complex and variable sedimentary systems (Weissmann, 2005). To use the most effective and reliable facies prediction method and include information about the vertical relationships between facies, MCA are applied in this study. The main objectives of this paper are to (1) use detailed interpretations of the sedimentary-facies and facies classification to identify electrofacies from conventional well logs and relate these to facies observed in core; (2) apply statistical methods to predict and analyse electrofacies based on MLPC and MCA; and, (3) use the electrofacies and electrofacies associations to map the distribution of sedimentary environments in a small case-study area to provide guidance for carbon capture and storage.

The Lower Jurassic Precipice Sandstone and Evergreen Formation are an important prospective reservoir and seal target for potential future carbon capture and storage (CCS) in the Surat Basin (Bradshaw, 2010). The geological context in terms of depositional environment is poorly constrained especially in the basin centre because of the fact that the strata are not thought to be hydrocarbon bearing and therefore data is sparse. However, the basin centre is also where carbon storage potential is highest. Depositional interpretations and facies analysis have not been examined in detail, and this hinders the predictive accuracy of reservoir performance and sealing potential. The level of detail of the interpretation of depositional-facies and facies associations in the Precipice-Evergreen succession is in need of an overhaul, and this will help establish more realistic reservoir models for carbon-geostorage-site evaluation (Hodgkinson and Grigorescu, 2013). This is because the sedimentary fabric, as well as grain size of different facies will influence the hydraulic behaviour of strata in dynamic reservoir simulation of CO2 injection. Capturing this detail may reduce uncertainty in the prediction of plume migration and sealing potential of the top seal. Sedimentary facies analysis is used to classify and map sedimentary bodies, each which formed under unique depositional conditions. Facies are typically assigned based on their physical or paleontogical characteristics (Middleton, 1978; Dalrymple, 2010). However, facies differ in their intrinsic textures and rock properties and this can greatly affect hydraulic and mechanical properties (Chang et al., 2000, 2002; Burton and Wood, 2013; La Croix et al., 2013, 2017; Baniak et al., 2014; He et al., 2016). Identification of sedimentary facies is based on both qualitative and quantitative parameters, including mineral composition, texture and fabric, stratification, sedimentary structures, bioturbation, grain-size distribution, and can be applied in outcrop or core (Borer and Harris, 1991; Dill et al., 2005; Khalifa, 2005; Qi and Carr, 2006; Qing and Nimegeers, 2008). However, geological datasets are commonly limited in breath (e.g. outcrop) or due to cost (e.g. core), and thus establishing facies relationships with regional perspective with limited control data is often a challenge. Therefore, facies distributions based on well log data are highly sought after (Berteig et al., 1985; Li and Anderson-Sprecher, 2006; Dubois et al., 2007), as they represent the most abundant and widespread dataset in subsurface studies. The prediction of facies from conventional wireline logs has the potential to extend observations from the core scale (centimetres to metres) to the well scale (meters or tens of metres), and ultimately to the regional scale (> kilometers), allowing facies to be mapped. Nonetheless, the process of quantitatively determining facies from well logs is currently being refined such that it can be applied in a variety of sedimentary basins and in deposits from different depositional environments (Tang et al., 2011; Wang and Timothy, 2013). High precision sedimentary facies prediction is absolutely essential to build large-scale, geologically reasonable, static reservoir models. Past studies have focused on using statistical methods to analyse facies from well logs such as discriminant analysis (Sakurai and Melvin, 1988; Avseth et al., 2001; Tang et al., 2004), naïve Bayes classifier (Li and Anderson-Sprecher, 2006; He et al., 2016), fuzzy logic (Cuddy, 2000; Saggaf and Nebrija, 2003), and support vector machines (El-Sebakhy et al., 2010; Wang et al., 2014; Deng et al., 2017). The past decade has also seen successful application of Artificial Neural Networks (ANN) (Derek et al., 1990; Wong et al., 1995; Siripitayananon et al., 2001; Bhatt and Helle, 2002; Wang and Timothy, 2012) in the prediction of sandstone and carbonate lithofacies because of the ability to unravel non-linear relationships, quantify learning from training data, and work in conjunction with other kinds of artificial intelligence (Bohling and Dubois, 2003; Kordon, 2010). Multilayer perceptron classifier (MLPC) is a classifier based on feedforward ANNs. MLPC is not a unique classifier for pattern recognition; however, the merits of MLPC result in its broad application within various scientific and academic fields (Micheli- Tzanakou, 2000). MLPC is very flexible in the design of

2. Geological setting The Surat Basin is a large Early Jurassic to Early Cretaceous intracratonic basin in eastern Australia and covers some 327, 000 km2 of Queensland and New South Wales, Australia, from latitudes 25°–33° S, and from longitudes 147°–152° E (Fig. 1A). The basin is filled with ∼2500 m of clastic sedimentary rocks and coal. The Eromanga and Clarence-Moreton basins (Fig. 1) are broadly time equivalent to the Surat Basin, connected on the western and southeastern parts of the basin, respectively (Power and Devine, 1970; Exon, 1976; Green et al., 1997). The Surat Basin developed as a shallow platform depression following 30 Ma of uplift, exposure, and non-deposition that eroded the top of the Bowen and Gunnedah basins (Exon, 1976; Green et al., 1997). The major stages of basin development and driving processes are not well studied, however, thermal subsidence (Korsch et al., 1989), dynamic platform tilting (Gallagher et al., 1994; Korsch and Totterdell, 2009; Waschbusch et al., 2009), and intraplate rifting (Fielding, 1996) have been suggested as possible mechanisms. The Surat Basin has several important structural features, the most important of which is the Mimosa Syncline that forms the north-south axis of the basin (Fig. 1; Exon, 1976; Fielding et al., 1990; Hoffmann et al., 2009; Fielding et al., 1990; Raza et al., 2009). The Surat Basin was filled with sediment in six major fining upward pulses/cycles (Exon and Burger, 1981). The first cycle encompasses the Precipice Sandstone and Evergreen Formation (Fig. 2). The Precipice Sandstone represents braided river deposits characterized by thick cross-bedding with only a few thin muddy intervals that lack marine palynoflora (Sell et al., 1972; Exon, 1976; Exon and Burger, 1981; Martin, 1981). However recent evidence has shown that the deposition of the Lower Precipice Sandstone also could be influenced by marine processes due to flaser and wavy bedding, clay drapes, rare marine trace fossils and ‘brackish’ palynomorphs (Martin et al., 2018). In contrast, the Evergreen Formation has been interpreted to represent 411

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Fig. 1. Structure contour map of the base-Surat unconformity within the study area.

more widespread than the Precipice Sandstone. The unit is dominated by carbonaceous siltstone with some horizons of sandstone, carbonaceous mudstone, oolitic ironstone, and coal (Green et al., 1997). This study will help elucidate the palaeoenvironments recorded by the Precipice Sandstone and Evergreen Formation, because at present these remain poorly constrained in a regional context. The study area is located in the northern part of the Surat Basin. It covers more than 21, 000 km2 (Fig. 1). 2D seismic data coverage across most of study area helps constrain the lateral distribution of the Precipice Sandstone beyond well control. A total of 192 wells across the basin had stratigraphic tops picked based on well log signatures tied to core. Additionally, there are 8 cored wells located within or in close proximity to the study area. Therefore, the study area is an ideal casestudy in which to test our facies prediction and mapping capabilities. We chose two important intervals within the study area to showcase the prediction and mapping of facies from logs. These are the lowstand systems tract- LST which is defined by the sequence stractigraphic surface ‘J10’ and ‘TS1‘, and the overlying transgressive surface defined by the surface ‘TS1’ and ‘MFS1’ (Wang et al., 2018). These represent the main reservoir intervals being investigated for CCS, and the overlying seal.

Fig. 2. Stratigraphic column for the Lower Jurassic in the Surat Basin (after Hoffmann et al., 2009).

3. Data set and methods

deposits laid down in meandering rivers and freshwater lakes. Though the upper parts of the Evergreen Formation, including the Westgrove Ironstone Member and the Boxvale Sandstone Member, show possible indications of marine influence on deposition (Mollan et al., 1972; Exon, 1976). The Precipice Sandstone has a maximum thickness of ∼150 m and dominantly consists of quartzose, fine-to coarse-grained sandstone with common siltstone and shale laminae in the upper part (Exon, 1976; Martin et al., 2013). The finer-grained Evergreen Formation can be as thick as 300 m in addition to being geographically

3.1. Database This study utilizes sedimentological and ichnological core observations in addition to wireline log data. The cumulative cored section that this study is based on is approximately 2000 m from 8 wells (Fig. 1C). Core facies were defined based on their sedimentological and ichnological characteristics, including grain size, the nature of bedding contacts, physical structures, biogenic structures, bioturbation intensity and the distribution of burrowing. Wireline logs that were used for 412

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sequentially from one to another in succession. Let the indicator variable, Ij(x), for facies j be defined as Ij(x) = {1, if j occurs at x; 0, otherwise}, where x is a location in one vertical facies succession. In terms of indicator variables, the marginal (initial) probability, Pj, can be defined by

facies identification and prediction included gamma ray (GR), bulk density (DEN), compressional slowness (SONIC), deep resistivity (LLD), neutron porosity (NEUTRON), and photoelectric factor (PDPE) from 31 wells. The logs have a resolution of 0.15 m. Dataset selection and calibration are important for obtaining successful facies identification and prediction results. Several quality-assurance steps were performed on wireline logs to improve the outputs including depth correction, normalization, and the removal of outliers. Outliers were defined using the following criteria (Wong et al., 1998; Tang et al., 2011): (1) Intervals with null or missing values (core missing); (2) Intervals with obvious post-depositional overprints (fractures observed in core or image logs; hot sandstone influenced by hydrothermal fluids input); (3) Intervals characterized by caliper-indicated washouts or bad-wellbore conditions; (4) Intervals with facies thickness less than 1.0 m; (5) An interval surrounding the contact between different logging facies to remove the “averaging” of properties between two adjacent facies. After establishing a robust and representative training set, neural-network training can be conducted on the key core wells.

pj = E{Ij(x)}

(1)

The joint probability, pjk(h), can be also define by pjk(h) = E{Ij(x)Ik(x + h)}

(2)

Where h represents the lag distance in one direction (Fig. 3). Fundamentally, the joint probability is the purest bivariate measure of spatial variability. However, the transition probability of facies 1 passing into facies 2, tjk(h) is the most interpretable, defined here with respect to indicator variables as tjk(h) = E{Ij(x)Ik(x + h)}/E{Ij(x)}

(3)

It can also be defined in terms of a conditional probability as tjk(h) = Pr(k at x + h |j at x}

3.2. Discriminant and principal component analysis

(4)

Probability law requires that the row sums of the transition probability matrix,T(h), sum to one and that the column sums obey

To develop a more accurate facies prediction model, pre-processing of the training dataset was undertaken to identify a representative set of wireline logging facies (WLF) from the set of core facies (CF). The representative input database is the most important factor for controlling the quality of classifiers, because successful application of neural networks generally requires clear petrophysical and geological classification (Wong et al., 1998). Linear discriminant analysis (LDA) and principal component analysis (PCA) are two methods we used to extract and determine the main components of variation within our dataset. These improve the accuracy of classifiers by removing non-distinctive and interrelated features (Jungmann et al., 2011). LDA is a multivariate method for finding a linear combination of features that characterizes or separates two or more classes of samples (i.e., grouping samples into major categories). Fisher canonical discriminant (FCD) is a slightly different discriminant method from LDA that does not make some of the assumptions that LDA does, such as normally distributed classes or equal class covariance. FCD was used to double check the CF classification results by LDA. PCA was used to determine the sensitivity of different types of well logs to WLFs and also to determine the contribution of each log type to facies differentiation. All types of CF could not be identified using conventional well logs alone because core scale observations of structure and texture do not necessarily translate to petrophysical properties. Therefore, LDA, PCA, and FCD methods were all applied in this study to collectively achieve a useful set of WLF to be predicted in wells lacking core.

∑j pjtjk(h)= pk

(5)

In this study, the probability of each facies transitioning to another was calculated using PAleontological Statistics Software (PAST Version 3.17; Hammer, 1999). Vertical facies succession analysis used an interactive algorithm for Embedded Markov Chains (EMC) (Davis, 1986) based on the PAST platform. The algorithm calculates a transition count matrix, a transition probability matrix, an independent trials probability matrix, and a difference matrix. In the transition probability matrix, the self-transition curves start at a probability of 1 (100%) and decrease with increasing lag distances, whereas the off-diagonal curves start at a probability of 0.0 (0%) and increase with lag distance (Fig. 3; Carle, 1999). In the difference matrix, high positive entries serve to emphasize the Markov property by suggesting which transitions have occurred with greater than random frequency. The Powers-Easterling method in this paper was used to test the matrix as a whole for nonrandomness. It determines the significance level of each facies transition and produces preferred facies trends. The Powers-Easterling method yields the chi-square value, degrees of freedom and critical value (Powers and Easterling, 1982). 3.4. Multi-Layer Perceptron Classifiers We used a Multi-Layer Perceptron Classifier (MLPC), a form of feedforward ANN (Haykin, 1998), to take the conventional wireline log input data and determine the most probable WLF. MLPC consists of multiple layers of nodes. Each layer is fully connected to the next layer in the neural network. Nodes in the input layer represent input data; in this case wireline log data consisting of DEN, SONIC, LLD, GR, NEUTRON, and PDPE. All other nodes map inputs to outputs by a linear combination of the inputs with the node's weights w and bias b and applying an activation function (Fig. 4). This can be written in matrix form for the MLPC with k + 1 layers as follows:

3.3. The Markov Chain Analysis The Markov Chain Analysis (MCA) is a statistical means of determining the probability of transition between two states that are not controlled by the previous state – i.e., they are “memoryless” (Grinstead and Snell. 1997). The transition probability (Markov Chain) method is a modified form of indicator kriging. In geological application, the method assumes the type of sediment that will be deposited in a stratigraphic succession depends solely upon what is currently being deposited in the present environment and not on the rock types deposited in past environments (Jones et al., 2002). For example, in a prograding shoreface environment, a gradual upward-coarsening succession of facies will occur if no significant depositional hiatus exists (Fig. 3). In terms of a vertical facies distribution, the probability of the occurrence of one facies is dependent on the nearest occurrence of another facies over a lag interval. The Markov chain can be mathematically described as follows: There is a set of facies, F = {F1, F2, …, Fr}, which pass

y(x) = fk (…f2 (w2Tf1 (w1Tx + b1) + b2) … + bK)

(6)

Nodes in recurrent layers use sigmoid (logistic) function:

f (Zi ) =

1 1 + e−zi

(7)

Nodes in the output layer use softmax function:

f (z i ) =

413

e zi N ∑k = 1

e zk

(8)

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Fig. 3. An example of a Markov chain transiogram (modified from Carle, 1999 and Hsieh at al., 2015). The transition probability of F1 passing upward into F2. The point at which the Markov chain levels out is the “sill” and the lag distance at which the Markov chain reaches the sill is the “range”. The transition rate is defined by the slope of the tangent line, and the mean lens length is the lag distance at which the tangent line.

The recurrent layer included two parts. The first was the computed distance from the input vector to the training vector. The second layer summed these contributions for each class of inputs to produce a vector probability. The number of nodes N in the output layer corresponds to the number of classes; in this case the 10 different types of wireline logging facies. The MCA helped us understand the relationship between different logging facies and helped limit the probability of a facies determinations that were not supported by the transition probability analysis.

ε= Ti − Tj

The variable t is the number of lithofacies types. Solving this equation with a grid size of 10 × 10 gives the number of state variables, KMAX, 100. The number (i, j) under each lithofacies indicates the position of classes in the error matrix. The value of Ti and Tj are equal to the code of the lithofacies in the MLPC output. Small convergence error values represent higher prediction accuracy. After confirming adequate and accurate neural network results, prediction was undertaken on 38 uncored wells across the northern portion of the study area. Log facies were flagged according to confidence levels with different prediction accuracy; yellow for high confidence (wells with 6 logs), green for medium confidence (5 logs), and red for low confidence predictions (4 logs). The proportion of each WLF occurring in the same stratigraphic interval was calculated. We also calculated the dominant WLF occurring in each well. The facies were then grouped into their corresponding associations, allocated to their respective stratigraphic position in the succession and mapped with reference to the shale content distribution of wells (calculated by using GR logs) and seismic interpretation.

3.5. Neural network performance evaluation and application We cross-validated our neural network results to determine the degree to which outcomes were consistent and reliable. This was done by withholding wireline log data from the control set, log by log, and then assessing the ratio of times that the neural network made correct facies predictions as determined by core facies in the training set. In addition, to decrease uncertainty in our results, a convergence error was calculated to test the accuracy of facies prediction by MLPC (Eyi, 2012). The convergence error (ɛc) is defined as follows:

⎛ 1 ε c = ‖ε‖t = ⎜ KMAX ⎝

KMAX

∑ k=1

(10)

1 t

⎞ ε t⎟ , ⎠

‖ε‖ = max( ε1 , ..., εKMAX ) (9) Fig. 4. Schematic diagram showing the architecture of the Multilayer perceptron classifier. Facies MA, MB, SMB, SMA, SD, SC, SB, SA, OA and OB are the different types of logging facies; MA, MB: Mudstone facies; SMB, SMA: Heterolithic facies; SD, SC, SB, SA: Sandstone facies; OA, OB: Organics and Miscellaneous facies. See Table 1 for facies descriptions.

414

415

Organic and Miscellaneous Facies

SM1

Heterolithics Facies

O1 O2 O3

SM5

SM4

SM3

SM2

M1 M2 M3 M4

S4 S5 S6

S2 S3

S1

Mudstone Facies

Sandstone Facies

G2

> 10% and < 90% sand

> 90% mud; Silt and clay

> 90% sand

> 30%; granules And coarser

Conglomerate and Breccia Facies

G1

Compositon

Facies Name Dark-grey coloured, structrureless interbedded conglomerate and sandstone with a grain size of medium to very coarse sand and rarely exceeds granules Breccia with structrureless, medium to very coarse-grained sandstone. The nature of the lower contact is sharp or scouring Coarse-grained planar-tabular to trough cross-strtified sandstone, and some Angular rip-up clasts, pebbles and pebble lags occur in the bottom Fine-to-medium-grained, planar-tabular to current ripple laminated sandstone Wave to combined-flow ripple laminated sandstone with grain size ranging from very fine sand to fine sand, and accessories consists of rootlets, carbonaceous detritus and siderite horizons Light grey-coloured, planar-parallel laminated sandstone with grain size ranging from fine sand to medium sand Dark-grey, bioturbated sandstone and muddy sandstone with grain size ranging from very fine sand to fine sand Interbedded bioturbated muddy sandstone displaying wave-ripple lamination to hummocky cross-stratification with a grain size of fine to medium sand Dark grey or black plannar-laminated mudstone with thin sandstone laminae Dark coloured structureless mudstone with a grain size of fine to coarse silt Black coloured, rare horizontal planar parallel laminated, wavy or lenticular bedded, bioturbated sandy mudstone Coarse silt containing interstitial very fine to fine grained sand interbedded with rare wave-ripple laminated to hummocky cross-stratification fine grained sandstone Light-grey to dark-grey coloured, medium to coarse silt interbedded with very fine to fine grained sand and described as sand-diminated heterolithics Less sand than mud (70% > sand > 30%), heterolithics with current to combined flow ripple lamination, wave ripple lamination and synaereses cracks A black or dark-grey colour, wave-influenced mud-dominated heterolithics with medium to coarse silt and very fine to fine grained sand Tide-influenced mixed heterolithics with more intense bioturbation accessories consist of carbonaceous detritus, rootlets and rare sideritized horizons Grey colour, inclined heterolithics stratification with current ripples flashers, wavy, and lenticular bedding and rare synaereses cracks Bituminous to sub-bituminous coal Grey colour, very fine silt to fine-grained sand, carbonaceous sandstone and siltstone Reddish brown colour ironstone (oolithic or cemented)

Facies Description

Table 1 aDescription of the 20 core facies observed in the Precipice Sandstone and Evergreen Formation.

Peat mire or Interdistributary bay Floodplain or Interdistributary bay Lagoon or Restricted embayment

Tide-fluvial channel

Tidal flats

Prosimal prodelta

Distal delta front

Proximal delta front

Prodelta Floodplain Brackish-bayfill or Lagoon Offshore/Shelf

Channel levee Marine bay deposits Shoreface

Braided channel or Braided delta distribution channel Meandering channel or Distributary channel Mouth bar

Channel base or Channel bank collapse

Lag deposit or Channel base

Interpretation

J. He et al.

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Fig. 5. Comparison between the core facies and gamma ray log signature for the 20 core facies we observed in the Precipice Sandstone and Evergreen Formation.

4. Results

information to facies differentiation, demonstrated by a high eigen absolute value (an indication of how well that variable differentiates the groups), followed by PDPE and DEN. SONIC and NEUTRON have the same contribution rate, while the log that provides the least information relation to discriminating WLF is LLD (Fig. 6B and C). The FDA results are consistent with the CF classification results from LDA and this demonstrates that WLF determination from LDA is effective and credible.

4.1. Core-scale facies definition From core observations, twenty CF were defined based on their sedimentological and ichnological characteristics. The CF types included conglomerates and breccias (Facies G1 and G2), sandstones (Facies S1, S2, S3, S4, S5, and S6), mudstones (Facies M1, M2, M3, and M4), heterolithics (Facies SM1, SM2, SM3, SM4, and SM5), as well as organic and miscellaneous facies (Facies O1, O2 and O3; Table 1 and Fig. 5).

4.2.2. Facies succession analysis using MCA The transition frequency matrix, transition probability matrix, independent trials probability matrix, and difference matrix for cored well data by MCA are presented in Table 4 and Fig. 8. Only three facies transitions were determined to be highly significant, larger than predicted for a random sequence at the 0.20 level of significance. Six facies transitions are moderately significant at levels between 0.15 and 0.20. Four transitions were significant at levels from 0.10 to 0.15. Finally, nineteen facies transitions were slightly significant between the levels of 0.01–0.10 (Fig. 8 and Table 4C). The Powers-Easterling method was used to test the matrix results with a chi-square value of 186.37, 68 degrees of freedom, and a critical value of 118.57. This means that facies transitions are significant with a 96% confidence level indicating a strong rejection of the null hypothesis which was random deposition. The most significant facies transitions support our facies association interpretations (Fig. 9). For example, the shoreface facies association is supported by the transition from bioturbated sandy mudstone (MB) passing upward into bioturbated muddy sandstone with wave-ripple to

4.2. Wireline log facies determination and analysis 4.2.1. Wireline log facies determination The twenty CF were simplified into ten representative wireline log facies (WLF) using the LDA method (Fig. 6A). Some CFs were not recognized because they did not have discrete petrophysical properties that allowed their differentiation, such as G1, G2, SM5 and O2. Other CFs were grouped together into a single WLF (Fig. 6A; Table 2) because of their similar log response characteristics (Table 3). For example, CFs S5 and S6, both have high neutron porosity (> 35%) and sonic values (> 98 us/f), moderate GR (avg. 106.6 API) and PDPE (avg. 2.98 B/E) values, and low LLD (< 4 ohmm) and DEN (< 2.42 g/cm3) (Fig. 7). These can only be differentiated in core based on their sedimentological differences, but plot together based on their petrophysical properties (Table 3). The results of PCA show that GR contributes the most 416

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Fig. 6. (A) Results of liner discriminant analysis in key cored wells with all six well logs that comprise a “full suite”. This shows how the twenty core facies were grouped into ten wireline log facies (indicated by the coloured circles). (B and C) Principal component analysis showing the relative importance of the various input logs to facies differentiation (e.g. GR, PDPE and SONIC). Note: LLD: deep resistivity; PDPE: Photoelectric factor; GR: gamma ray; DEN: bulk density; SONIC: compressional slowness; NEUTRON: neutron porosity.

Table 2 The relationship between core facies and wireline log facies from the five key core wells. Core facies

Key well

thickness

Porosity/%

Permeability/mD

N

Logging Facies

Feature of GR curve shape

G1 G2 S1 S2 S3 S4 S5 S6 M1 M2 M3 M4 SM1 SM2 SM3 SM4 SM5 O1 O2 O3

3 2 5 5 5 2 3 1 5 4 3 1 5 4 5 5 1 4 4 5

0.25–1.2/0.83 0.17–0.8/0.41 0.2–77.2/12.59 0.35–18.7/4.54 0.2–10.33/2.26 0.18–4.85/1.30 0.1–3.2/0.98 1.30–7.12/3.29 0.14–3.65/1.43 0.12–7.60/1.15 0.23–1.90/0.80 0.53–4.13/2.33 0.18–4.84/1.53 0.25–5.70/2.08 0.16–10.6/1.49 0.11–5.50/1.54 0.4–0.6/0.5 0.05–0.91/0.22 0.1–1.3/0.32 0.05–1.35/0.44

– – 17.7–21.3/19.89 6.4–12.2/10.7 5.9–11.2/9.05 – 8.1–9.1/8.5 – – – – – 4.6–11.4/9.3 5.2–9.4/8.15 5.1–7.2/7.38 2.8–6.8/5.7 – – 6.3–6.9/6.4 6.8–7.9/7.2

– – 3.18–2500/2100 0.004–0.8/0.61 0.002–0.23/0.038 – 0.085–0.26/0.18 – – – – – 0.013–0.069/0.03 0.002–0.051/0.028 0.001–0.032/0.023 0.001–0.029/0.021 – – 0.001–0.005/0.003 < 0.001

6 5 19 32 64 19 11 4 29 50 10 2 53 31 70 94 2 21 15 33

Not Find Not Find SA SB SC

Smooth concave bell shape Smooth concave bell shape Smooth cylindrical shape Smooth concave bell shape Smooth concave funnel shape Erratic concave funnel shape Smooth concave funnel shape Erratic concave funnel shape Erratic line shape Smooth line shape Erratic line shape Smooth line shape Smooth concave egg shape Erratic concave egg shape Erratic line shape Erratic line shape Erratic concave egg shape Smooth convex egg shape Erratic concave funnel shape Smooth convex egg shape

SD MA MB SMA SMB Not Find OA Not Find OB

Note: 0.25–1.2/0.83: the minimum –maximum thickness/the average thickness; N: the frequency of each core facies; 17.7–21.3/19.89, 3.18–2500/2100: the value reach the 25 th and 75 th percentiles of cumulative percentage/average value. 417

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Table 3 A summary of wireline log characteristics for the ten wireline log facies. Logging facies

SA SB SC SD SMA SMB MA MB OA OB

Core Description

> 90 sand > 90 sand > 90 sand > 90 sand 70% > sand > 30% 30% > sand > 10% > 90% mud; Silt and clay > 90% mud; Silt and clay Coal Oolitic ironstone

Features of conventional logs GR(API)

DEN(g/cm3)

NEU(%)

SONIC(us/f)

LLD(ohmm)

PDPE(B/E)

19.4–27.4/26.4 75.3–97.6/85.9 96.4–118.6/113.7 88.4–116.4/106.6 91.9–118.8/98.4 120.4–144.4/129.1 136.1–154.1/142.7

2.26–2.34/2.32 2.33–2.41/2.37 2.45–2.53/2.50 2.39–2.45/2.42 2.40–2.48/2.46 2.39–2.49/2.43 2.45–2.52/2.47

0.26–0.32/0.30 0.25–0.29/0.26 0.20–0.27/0.23 0.39–0.48/0.42 0.26–0.32/0.26 0.29–0.36-0.33 0.24–0.31/0.28

76.1–79.1/78.2 77.3–81.2/78.9 78.8–85.8/82.8 97.8–107.4/101.2 77.1–91.2/79.32 89.5–97.3/93.1 79.2–86.4/85.3

5.3–89.3/23.5 16.6–27.6/17.6 2.8–16.1/10.7 2.28–3.06/2.58 2.28–24.7/6.13 1.96–7.36/3.63 3.26–6.26/4.15

1.69–2.04/1.78 2.08–2.48/2.29 2.45–2.83/2.68 2.80–3.36/2.98 2.39–2.84/2.41 2.23–2.83/2.56 2.58–2.89/2.76

112.4–127.2/120.4

2.40–2.45/2.44

0.41–0.49/0.45

99.6–107.7/103.4

2.36–3.02/2.65

2.68–3.12/2.89

88.9–119.9/108.7 30.4–87.4/66.5

2.12–2.28/2.19 2.71–2.98/2.91

0.31–0.48/0.40 0.04–0.24/0.13

87.5–104.3/95.9 68.8–87.8/84.2

3.26–25.5/11.26 3.06–29.06/7.86

2.03–2.18/2.11 5.72–8.72/6.98

Note: 19.4–27.4/26.4: the value reach the 25th and 75th percentiles of cumulative percentage/average value.

grained planar-parallel laminated sandstone (SC). This vertical transition sequence indicates a transition from braided channel complex deposits to a lower-energy meandering channel environment (Fig. 9). Wireline Log Facies AssociationⅡ is dominated by muddy and organic facies: OA, MA, and less commonly MB. This association shows frequent transitions from thicker massive mudstone (MA) to coal (OA), capped with coarse silt bioturbated sandy mudstone (MB). This muddominated succession is interpreted to have mainly been deposited on a low-energy floodplain or delta plain. Wireline Log Facies Association Ⅲ consists of the WLF MA, SMB, SMA, SC, SB, and less commonly OB (Fig. 9). These transitions form a coarsening-upward succession, indicating deposition that grades from

HCS interbeds (SD). This transition is supported by a significance value of 0.17. Similarly, all five major facies associations are identified from the embedded Markov Chains method: channel-levee complex (Wireline Log Facies AssociationⅠ), floodplain/lower delta plain (Wireline Log Facies AssociationⅡ), subaqueous delta (Wireline Log Facies Association Ⅲ), shoreface (Wireline Log Facies Association Ⅳ), and tidal flats and channels (Wireline Log Facies Association Ⅴ; Fig. 9). Wireline Log Facies AssociationⅠis characterized by a sandy facies succession that includes facies SA, SB, and SC. This association consists of two major facies transitions: coarse-grained planar-tabular to trough cross-stratified sandstone (SA), transitioning into fine-grained planartabular grading into current-ripple laminated sandstone (SB), to fine-

Fig. 7. Rider chart showing the recognition models for the ten types of wireline log facies. The average value of each log in the different wireline log facies is displayed. 418

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Table 4 Statistical data from the Markvo Chain Analysis: (A) Transition count matrix; (B) Transition probability matrix; and, (C) Difference matrix. MA

MB

SMB

SMA

SD

SC

SB

SA

OA

OB

2 0 7 0 3 1 0 0 2 4

36 5 0 30 5 53 13 6 21 12

5 0 45 0 2 11 13 9 1 2

3 4 2 3 0 1 1 0 0 3

11 1 49 17 0 0 5 3 9 3

4 0 13 10 0 5 0 3 2 2

2 0 4 8 0 4 1 0 1 0

10 2 17 5 3 6 1 0 0 0

15 3 9 3 3 6 2 0 1 0

0.02273 0 0.03867 0 0.1765 0.01031 0 0 0.04545 0.09524

0.4091 0.1632 0 0.3409 0.2941 0.5464 0.3333 0.2857 0.4773 0.2857

0.05682 0 0.2486 0 0.0176 0.1134 0.3333 0.4286 0.02273 0.04762

0.03409 0.2105 0.01105 0.03409 0 0.01031 0.02564 0 0 0.07143

0.125 0.01263 0.2707 0.1932 0 0 0.1282 0.1429 0.2045 0.04143

0.04545 0 0.07182 0.1136 0 0.05155 0 0.1229 0.04545 0.04762

0.02273 0 0.0221 0.09091 0 0.04124 0.02564 0 0.02273 0

0.1136 0.1053 0.09392 0.05682 0.1765 0.06186 0.02564 0 0 0

0.1705 0.1579 0.04972 0.03409 0.1765 0.06186 0.02128 0 0.02273 0

−0.0119 0 −0.0031 −0.0347 0.1758 −0.0249 −0.0318 −0.0309 0.0134 0.0633

0.0788 −0.0302 0 0.0106 0.0017 0.2106 0.0301 −0.0086 0.1716 0.0191

−0.1038 −0.1426 0.0552 0 −0.0246 0.0099 0.1858 0.2855 −0.1259 −0.1005

0.0031 0.1829 −0.0263 0.0031 0 −0.0212 −0.0028 −0.0276 −0.0287 0.0428

−0.0538 −0.1062 0.0553 0.0144 −0.1583 0 0.0359 −0.0165 0.0389 −0.0936

−0.0257 −0.0632 −0.0139 0.0424 −0.0630 0.1208 0 −0.0795 −0.0204 −0.0180

−0.0138 −0.0324 −0.0219 0.0544 −0.0323 0.0141 0.0786 0 −0.0111 −0.0337

0.0333 0.0339 −0.0028 −0.0235 0.1054 −0.0198 −0.0481 −0.0715 0 −0.0741

0.0939 0.0898 −0.0426 −0.0425 0.1086 −0.0161 −0.0191 −0.0683 −0.0482 0

(A) Transition count matrix MA MB SMB SMA SD SC SB SA OA OB

0 4 35 12 1 10 3 0 7 16

(B) Transition probability matrix MA MB SMB SMA SD SC SB SA OA OB

0 0.2105 0.1934 0.1364 0.03882 0.1031 0.07692 0 0.1591 0.381

(C) difference matrix MA MB SMB SMA SD SC SB SA OA OB

0 0.0687 0.0335 −0.0241 −0.0833 −0.0602 −0.0705 −0.1431 0.0105 0.2329

Fig. 8. Results of the Markov Chain Analysis of the various wireline log facies: (A) Transition frequency matrix; (B) transition probability matrix; (C) independent trials probability matrix; and, (D) difference matrix. Statistically significant transitions have been marked with a black rectangle. 419

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Fig. 9. Conceptual models of the five facies associations determined by MCA. An approximate scale indicates the thickness of individual facies. The arrow shows the transitions from one facies to another. The number on the arrow represents the level of significance of facies transitions (based on the difference matrix). (A) Channel Complex Association. (B) Delta Plain Association. (C) Subaqeous Delta Association. (D) Shoreface Association. (E) Tidal Flats and Channels Association.

convergence error of prediction decreased sharply and became asymptotic at 0.53, corresponding to 1300 cycles. At 1300 cycles the model was stable and had the best match with the core-defined facies. The accuracy of facies prediction (Rs means the ratio of the count of correctly identified facies sample to the number of core-defined facies samples) to CF ranges from 66.48% to 99.14% with an average value of 83.05% (Fig. 10A; Table 5). Moreover, the WLFs SA, SB and OB – the most representative siliclastic facies – are for the most part correctly classified with a prediction accuracy of > 92%. By contrast, the WLFs SC and SMA were less accurately predicted by the MLPC method with Rs values of less than 75%. These WLFs were commonly misidentified as one another or SMB (Table 5). This is because the rescaled distance between petrophysical properties among these facies was very small (Fig. 10B). We also preferentially chose WLF predictions that resulted in thick intervals rather than those with thinner intervals due to their improved mapping potential (Fig. 11). For instance, the thickness intervals of SA, SB, SMB seemed to be predicted better than other facies, because thicker facies intervals are less influenced by the petrophyscial signature of their neighbouring facies.

low-energy muddy prodelta deposits to a wave-influenced sandy delta front environment. Wireline Log Facies Association Ⅳis composed of only a single transition: bioturbated sandy mudstone (MB) grading into bioturbated muddy sandstone with wave-ripple and HCS interbeds (SD), arranged as a coarsening-upward succession. This association is interpreted to represent the upper offshore to shoreface transition (Fig. 9). Wireline Log Facies Association Ⅴ comprises facies SD, OB, SMB, and MB. These transitions construct a fining-upward succession and are interpreted to reflect sandy lower tidal flats to mixed sandy and muddy tidal flats, capped with mud dominated upper tidal flats and lagoons (Fig. 9).

4.3. Wireline log facies prediction using MLPC and facies associations Three WLF training wells were selected - Condabri MB9-H, Woleebee creek GW4 and Reedy Creek MB3-H - on the basis of their geographic location within the basin and the availability of appropriate well logs and core data (Table 5). From these, 12194 data points from the six logging parameters, DEN, SONIC, LLD, GR, NEUTRON and PDPE, were used as inputs to the MLPC model (Fig. 4). The MLPC method shows geologically reasonable facies prediction results (Fig. 10A). By increasing the number of training cycles, the

4.4. Mapping of WLF in the study area We applied the results from the robust MLPC model to the uncored 420

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Table 5 Plot of the wireline log facies from the MLPC, compared with the core-defined facies, showing the proportion of correctly predicted facies on the diagonal and missclassified facies in the off diagonal. Core-defiend facies

MA MB SMB SMA SD SC SB SA OA OB Predicted total Predicted/core

Predicted litofacies MA

MB

SMB

SMA

SD

SC

SB

SA

OA

OB

690 8 197 – – – – – – – 895 0.980

– 171 – – 12 – – – – – 183 0.897

212 18 2531 292 35 73 – – 5 – 3282 1.179

3 – 4 710 – 84 33 35 – – 869 0.814

– 7 – – 143 – – – – – 150 0.789

8 – 50 66 – 468 114 – – 2 708 1.081

– – – – – 30 1922 1 – 1 1954 0.942

– – – – – – 5 4152 – – 4157 0.993

– – – – – – – – 18 – 18 0.783

– – – – – – – – – 94 94 0.969

Core total

Proportion of correct predictions

913 204 2782 1068 190 655 2074 4188 23 97 12194 –

75.57503 83.82353 90.97771 66.47940 75.26316 71.45038 92.67117 99.14040 78.26087 96.90722 83.0549 –

the basin with secondary provenance being located on the northeastern and eastern margins.

intervals in the wells with at least 4 wireline logs (Fig. 12 A) to develop a better understanding of the facies distribution and depositional setting. In the lowstand systems tract (LST; i.e., the Precipice Sandstone) (Fig. 12B), the WLFs were dominated by SA and the proportion of WLFs other than SA were not volumetrically (or spatially) important. However, from the analysis we determined that the thickness of SA is greatest in Woleebee Creek GW4, with the thickness decreasing sharply towards the margins of the basin. In the southwestern part of the study area, SA is not widespread. In contrast, within the transgressive systems tract (TST; broadly equivalent to the lower Evergreen Formation) facies zonation is more prominent (Fig. 12 C). The TST shows channel sandstone facies mainly located in the southwestern and northern part of study area, whereas the central portion of the region is mudstonedominated (Fig. 12 D). This suggests that at these stratigraphic levels sediment input was mainly from the southwest and northern portion of

5. Discussion 5.1. Effects of the scale of observation on WLF prediction The accuracy of the MLPC prediction strongly depends on the input provided by the training data. To ensure highly reliable WLF prediction adequate training data are needed. However, more data does not always equate with better prediction results. It is far more important to acquire representative training samples and remove outliers on the basis of geological insights from core. The mismatch between the resolution of core logging and wireline log data makes it challenging to obtain facies identification at an

Fig. 10. (A) Relative proportion of the ten wireline log facies predicted by MLPC method with the prediction accuracy. (B) Rescaled distance between different wireline log facies calculated from input variable space. 421

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Fig. 11. An example of the wireline log facies prediction using the Multilayer Perceptron Classification method compared to core-defined lithofacies in the Condabri MB9-H well. J10: base-Surat unconformity; TS1: the transgressive surface at the top of the Precipice Sandstone; MFS1: maximum flooding surface; SB2: sequence boundary within the Evergreen Formation; J20: unconformity at the approximate base of the Boxvale Sandstone Member; TS3: transgressive surface underlying the Westgrove Ironstone Member; MFS3: maximum flooding surface; J30: top of the Evergreen Formation (Wang et al., in press). Wireline log facies colour codes are the same as Fig. 4. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

422

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Fig. 12. Wireline log facies prediction results for wells within the study area. (A) Well location map of study area with colours representing the number of logs used in the neural network facies prediction; (B) Pie chart map showing wireline log facies proportions for the interval between J10 and TS1. The number in the pie charts indicates the cumulative thickness of sandstone in meters; (C) Pie chart map showing wireline log facies proportions for the interval between TS1 and MFS1. The number in the pie charts indicates the cumulative thickness of sandstone in meters; (D) The dominant (thickest) wireline log facies in their interval from TS1 to MFS1, showing different facies zones and potential sediment provenance regions. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Therefore, we had to determine the minimum number of logs needed to produce an acceptable accuracy of facies determination that would be useful for mapping depositional environments on a regional scale. The accuracy of facies recognition decreases step wise with decreasing log input data, such that when only gamma ray, density, deep resistivity, and sonic are used to establish the MLPC structure the accuracy drops to between 45% and 98% depending on the facies (avg. 67%; Fig. 13) with a convergence error of 0.75. We considered this to be the bottom threshold for facies prediction appropriate for regional paleogeography determination and reservoir modelling. In addition to the number of logs, the choice of wireline log input affects the accuracy of WLF prediction results. For example, when using NEU and DEN, the accuracy is quite high; however, the additional input of SONIC does not greatly improve the accuracy. The reason for this is that different logs add different levels of “new” information to the neural network. New independent information will increase the identification ability significantly, while redundant or even conflicting information may reduce the neural network recognition ability. Different WLF have different sensitivity to the input log parameters. In our case example, SA, SB and SMB facies consistently have a high prediction accuracy no matter which type of well logging data are used for prediction. However, a decrease of input log data exerts great influence on

appropriate scale for study. Wireline logs are recorded at the cm-scale, whereas facies are typically assigned at the m-scale for utility in mapping of depositional environments. Therefore, some balance is necessary and WLF may have to be upscaled to a useful thickness for reservoir or regional studies. An example of this in our dataset was the differentiation of the facies SMA and SMB. SMA and SMB are relatively easily differentiated on the basis of sedimentological features observed in core. However, in wireline logs, the distinction between these facies by MLPC methods is more obscure. For example, in Condabri MB9-H between the depths of 1475–1484.72 m a bad match between the CF and predicted WLF occurs (Fig. 11). This shows how SMA can be misidentified as SMB, resulting in a low prediction accuracy overall for these two particular WLFs. Therefore, it is necessary to have a geologist ensure that the predicted WLF are geologically sensible and not to rely solely on numerical results from the neural network.

5.2. Effects of well-log input The use of a full suite of wireline logs as input greatly increases the prediction accuracy of WLF, as manifest in a decreased convergence error. However, the full suite of logs seldom occurs in wells within the study area; only 37.62% of wells had all six types of logs available. 423

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Fig. 13. (A) Final convergence error. (B) Prediction accuracy based on the input log types used for wireline log facies prediction.

5.4. Geographic and stratigraphic distribution of facies: implications for the depositional history of the Precipice-Evergreen succession in the northern Surat Basin Facies interpretation of uncored wells gives detailed information about the regional depositional environment especially when used in conjunction with Vshale maps. The Precipice Sandstone was dominated by SA facies deposited in braided fluvial systems. The coarse-grained sandstone was deposited over an increasingly large area through time as paleovalleys in the underlying unconformity were filled (Exon, 1976). But the southwestern part of the study area lacks these thick sandstone deposits (Fig. 15A) because elevated basement blocks to the west and southwest provided the main sediment input source for the area and this was an area of sediment bypass or non-deposition (Exon, 1976; Green et al., 1997). During deposition of the Lower Evergreen Formation, sea level rise occurred rapidly (Wang et al., 2018). Many deltas building out into the central basin (near wells West Wandoan 1 and Trelinga 1) from the west and southwestern margin extending into the basin for a distance of at least 53 km. West Wandoan 1, Woleebee Creek GW4, and Trelinga 1 are interpreted to be located near the locus of deposition– representing the “basin centre”. Younger fluvio-deltaic systems cut into older strata with complex cross-cutting relationships (Fig. 15B). It is also possible that minor delta complexes could have been sourced from the north and eastern parts of the basin, but did not extended as far from their provenance areas (Auburn Arch and Yarraman Block) potentially due to the fact that accommodation space was being filled by these nearshore and shallow marine systems (Bianchi et al., 2018). The stratal stacking patterns are indicative of progressive backstepping of depositional environments up-section and towards the basin margins within the Precipice Sandstone and Lower Evergreen Formation.

Fig. 14. Cross-validation results of the ten wireline log facies from the three key wells: Condabri MB9-H, Reedy Creek MB3-H, and Woleebee Creek GW4.

the identification of MB, SD and SC. The lack of DEN strongly affects the accuracy of OB facies prediction.

5.3. Cross validation by withholding input log data Cross validation is useful to evaluate the performance of MLPC. In this study, the training dataset in the four key wells was divided into two groups: a training group and a validation group. The training dataset accounted for approximately 75% of the entire dataset and was used to calculate errors and adjust connection weights and bias. The residual validation group was used to avoid over-training or over-fitting by detecting the predicted results in the validation group. In practice, the training dataset was randomly collected from three-quarters of the dataset and the remaining part was applied as a validation group. During validation the jackknife statistical approach was run ten times using subsets of available data. From cross-validation we were able to understand the range of prediction accuracy for each facies (Fig. 14). The cross-validation results suggest the prediction accuracy ranges from 48% to 97% depending on the facies with an average value of 71%. Additionally, there is 70–97% prediction accuracy for common facies but significantly lower accuracy for less-common and improminent facies. It also means that the established MLPC mode actually gives some satisfying performance.

5.5. Application of WLF prediction to CCS in the Surat Basin The WLF distribution is essential for modelling reservoir flow units in the Precipice Sandstone, as well as evaluating the sealing capacity of the overlying Evergreen Formation. From the facies maps of the LST and TST, the greatest stratigraphic sealing potential occurs in the central and northwestern part of study area because of more laterally continuous and thicker muddy intervals. By contrast, in the southwestern part, channel sandstones in the Lower Evergreen Formation are widely distributed. As porosity and permeability are influenced by the stacking patterns of facies, a realistic 3-dimensional facies distribution has the potential to significantly improve modelling of facies and reservoir properties in static reservoir models. 424

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Fig. 15. Facies distribution maps based on wireline log facies determined from neural networks. (A) The Precipice Sandstone. The zero-thickness boundary shown in red is based on seismic interpretation (B) The Lower Evergreen Formation. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

6. Conclusion

2) Using statistical means, the 20 core facies were simplified into 10 recurring wireline log facies with common wireline log responses and petrophysical distributions. Using MCA methods, 44 significant facies transitions were observed, and these were used to group the WLF into five wireline log facies associations – fluvial channel belt, lower delta plain, subaqueous delta, shoreface, and tidal flats and channels. 3) After establishing a representative training set of WLF artificial neural networks were trained using key cored wells to predict WLF in wells where core was not present. The average overall prediction

In this paper, a robust workflow is introduced to predict siliciclastic sedimentary facies from wireline logs by integrating Multi-Layer Perceptron Classifier (MLPC) and Markov Chain Analysis (MCA) techniques. To summarize the key finding of this research: 1) Twenty core facies were defined from the Precipice Sandstone and Evergreen Formation that were distinguished based on sedimentary texture, physical sedimentary structures, and bioturbation. 425

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accuracy was > 83% for the most common facies. 4) We used the WLF to map depositional environments across a large area in the north-central portion of the Surat Basin to better understand the paleogeography and improve reservoir modelling efforts for CO2 storage application. This is one of the most detailed attempts at understanding the paleogeography in this stratigraphic interval and should be investigated further for the rest of the basin.

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Acknowledgments We thank the Australian Government, through the CCS RD&D programme, ACA Low Emissions Technology (ACALET), and the University of Queensland for financial support of this project. We also thank APLNG, CTSCo, and QGC for data access and Schlumberger for the use of their Petrel and Techlog softwares for research purposed. We also appreciate Ahmed Harfoush and Iain Rodger for providing constructive suggestions on facies prediction and petrophysical analysis. Staff at the DNRM Exploration Data Centre in Zillmere are acknowledged for access to core data. JH's research was supported by the China Scholarship Council (201706400014), Fundamental Research Funds for the Central Universities (2652017309), National Science and Technology Major Project of China (2016ZX05046-003-001 and 2016ZX05034-004-003), and the National Natural Science Foundation Projects (GrantNos 41372139 and 41072098). We thank Associate Editor Sergio G. Longhitano and two anonymous reviewers for their constructive revisions and comments that greatly improve this manuscript. References Avseth, P., Mukerji, T., Jorstad, A., Mavko, G., Veggeland, T., 2001. Seismic reservoir mapping from 3-D AVO in a North Sea turbidite system. Geophysics 66 (4), 1157–1176. Baniak, G.M., La Croix, A.D., Polo, C.A., Playter, T., Pemberton, S.G., Gingras, M.K., 2014. Associating x-ray microtomography with permeability contrasts in bioturbated media. Ichnos 4, 234–250. Berteig, V., Helgeland, J., Mohn, E., 1985. Lithofacies prediction from well data. In: Proceedings of SPWLA Twenty-sixth Annual Logging Symposium. Bhatt, A., Helle, H., 2002. Determination of facies from well logs using modular neural networks. Petrol. Geosci. 8 (3), 217–228. Bianchi, V., Zhou, F., Pistellato, D., Martin, M., Boccardo, S., Esterle, J., 2018. Mapping a coastal transition in braided systems: an example from the Precipice Sandstone, Surat Basin. Aust. J. Earth Sci. 65 (4), 483–502. Bohling, G.C., Dubois, M.K., 2003. An integrated application of neural network and Markov chain techniques to prediction of lithofacies from well logs. Kansas geological survey open-file report. 50, 6. Borer, J.M., Harris, P.M., 1991. Lithofacies and cyclicity of the yates formation; permian basin: implications for reservoir heterogeneity. AAPG (Am. Assoc. Pet. Geol.) Bull. 75 (4), 726–779. Bradshaw, J., 2010. Regional scale Assessment results & methodology Queensland CO2 storage atlas. In: 2nd EAGE Workshop on CO2 Geological Storage, Berlin, Germany. Burton, D., Wood, L.J., 2013. Geologically-based permeability anisotropy estimates for tidally-influenced reservoirs using quantitative shale data. Petrol. Geosci. 19, 3–20. Carle, S.F., 1999. T-PROGS: Transition Probability Geostatistical Software. Version 2.1 User's Guide. University of California, Davis, CA. Chang, H.C., Kopaska-Merkel, D.C., Chen, H.C., 2002. Identification of lithofacies using Kohonen self organizing maps. Comput. Geosci. 28, 223–229. Chang, H.C., Kopaska-Merkel, D.C., Chen, H.C., Durrans, S.R., 2000. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system. Comput. Geosci. 26, 591–601. Cuddy, S., 2000. Litho-facies and permeability prediction from electrical logs using fuzzy logic. SPE Reservoir Eval. Eng. 3 (4), 319–324. Dalrymple, R.W., 2010. Interpreting sedimentary successions. In: James, N.P., Dalrymple, R.W. (Eds.), Facies Models 4: St. Johns, Newfoundland. Geological Association of Canada, pp. 3–18. Davis, J.C., 1986. Statistics and Data Analysis in Geology, second ed. Wiley, New York, pp. 646. Deng, C.X., Pan, H.P., Fang, S.N., Konaté, A.A., Qin, R.D., 2017. Support vector machine as an alternative method for lithology classification of crystalline rocks. J. Geophys. Eng. 14 (2), 341. Derek, H., Johns, R., Pasternack, E., 1990. Comparative study of a back propagation neural network and statistical pattern recognition techniques in identifying sandstone lithofacies. In: Proceedings 1990 Conference on Artificial Intelligence in Petroleum Exploration and Production. Texas A and M University, College Station, TX, pp. 41–49. Dill, H., Ludwig, R.R., Kathewera, A., Mwenelupembe, J., 2005. A lithofacies terrain model for the Blantyre Region: implications for the interpretation of palaeosavanna depositional systems and for environmental geology and economic geology in

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