Facies and the Architecture of Estuarine Tidal Bar in the Lower Cretaceous Mcmurray Formation, Central Athabasca Oil Sands, Alberta, Canada

Tang, Mingming; Zhang, Kexin; Huang, Jixin; Lu, Shuangfang

doi:10.3390/en12091769

Cited by 17 publications

(3 citation statements)

References 42 publications

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“…In general, the McMurray–Wabiskaw interval is composed primarily of a deepening-upward complex system of sediments controlled by a sub-Cretaceous unconformity configuration 37 . These deposits are primarily composed of four facies associations: fluvial, tidal flat, tidal bar complex, and tidal bar cap 38 . The McMurray and Wabiskaw reservoirs have a thickness of up to 40 m and a porosity of up to 30% 39 .…”

Section: Dataset and Methodologymentioning

confidence: 99%

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization

Mubarak,

Koeshidayatullah

2023

Sci Rep

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Recent advances in machine learning (ML) have transformed the landscape of energy exploration, including hydrocarbon, CO2 storage, and hydrogen. However, building competent ML models for reservoir characterization necessitates specific in-depth knowledge in order to fine-tune the models and achieve the best predictions, limiting the accessibility of machine learning in geosciences. To mitigate this issue, we implemented the recently emerged automated machine learning (AutoML) approach to perform an algorithm search for conducting an unconventional reservoir characterization with a more optimized and accessible workflow than traditional ML approaches. In this study, over 1000 wells from Alberta’s Athabasca Oil Sands were analyzed to predict various key reservoir properties such as lithofacies, porosity, volume of shale, and bitumen mass percentage. Our proposed workflow consists of two stages of AutoML predictions, including (1) the first stage focuses on predicting the volume of shale and porosity by using conventional well log data, and (2) the second stage combines the predicted outputs with well log data to predict the lithofacies and bitumen percentage. The findings show that out of the ten different models tested for predicting the porosity (78% in accuracy), the volume of shale (80.5%), bitumen percentage (67.3%), and lithofacies classification (98%), distributed random forest, and gradient boosting machine emerged as the best models. When compared to the manually fine-tuned conventional machine learning algorithms, the AutoML-based algorithms provide a notable improvement on reservoir property predictions, with higher weighted average f1-scores of up to 15–20% in the classification problem and 5–10% in the adjusted-R2 score for the regression problems in the blind test dataset, and it is achieved only after ~ 400 s of training and testing processes. In addition, from the feature ranking extraction technique, there is a good agreement with domain experts regarding the most significant input parameters in each prediction. Therefore, it is evidence that the AutoML workflow has proven powerful in performing advanced petrophysical analysis and reservoir characterization with minimal time and human intervention, allowing more accessibility to domain experts while maintaining the model’s explainability. Integration of AutoML and subject matter experts could advance artificial intelligence technology implementation in optimizing data-driven energy geosciences.

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Section: Dataset and Methodologymentioning

confidence: 99%

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization

Mubarak,

Koeshidayatullah

2023

Sci Rep

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“…The middle and upper McMurray Formation in the Mackay River Project, CNPC, is dominated by six estuaryoriginated microfacies (Figure 2). The tidal channel, salt marsh, sand bar, and tidal bar are deposited in the middle McMurray Formation [23]. Meanwhile, the mixed flat, sand bar, and tidal bar are dominated in the upper McMurray Formation.…”

Section: Geologic Backgroundmentioning

confidence: 99%

Quantitative Characterization of Tidal Couplets in Oil Sands Reservoir, the Upper McMurray Formation, Northeastern Alberta, Canada

et al. 2021

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The McMurray Formation, NE Alberta, Canada, is one of the most significant bitumen bearing deposits worldwide. This formation deposited and reworked in fluvial, tidal, or estuarine environments results in a huge number of tidal couplets (TCs) which is consisted of mm-cm scale sandy and muddy interlayers. These couplets not only increase the geologic heterogeneity of the oil sand reservoir but also make it hard to predict the performance of in situ thermal processes. In this paper, based on literatures, lab analysis, core photos, logging, and drilling data, a quantitative characterization procedure for mm-cm scale tidal couplets was proposed. This procedure, which includes identification, classification, quantitative description, and spatial distribution prediction, was presented. Five parameters, thickness, mud volume, laminae frequency, spatial scale, and effective petrophysical properties, were selected to describe the TCs quantitatively. To show the procedure practically, TCs in the oil sand reservoir of McMurray Formation, Mackay River Project, and CNPC, were selected to demonstrate this procedure. The results indicate that the TCs are in mm-cm thickness, densely clustered, and in a variety of geometries. Based on geologic origins, these couplets were divided into four types: tidal bar couplets (TBCs), sand bar couplets (SBCs), mix flat couplets (MFCs), and tidal channel couplets (TCCs). The thickness, mud volume, and frequency were calculated by mathematical morphological processed core photos. The spatial scale of TCs was estimated by high-density well correlations. The effective petrophysical properties were estimated by bedding scale modeling and property modeling via REV. Finally, the spatial distribution of TCs was predicted by object-based modeling.

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“…Lower Cretaceous McMurray Formation, Canada: e.g. Mossop & Flach, 1983; Flach & Mossop, 1985; Smith, 1987a; Fustic et al ., 2008, 2018; Hubbard et al ., 2011; LaBrecque et al ., 2011; Musial et al ., 2012; Nardin et al ., 2012; Jablonski & Dalrymple, 2016; Martinius et al ., 2017; Durkin et al ., 2018; Tang et al ., 2019. Upper Cretaceous Horseshoe Canyon Formation, Canada: Durkin et al ., 2015.…”

Section: Introductionmentioning

confidence: 99%

Hydrodynamic controls on sedimentary facies of tidal point bars: A case study in the Georgia coastal plain, USA

et al. 2022

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Tidal point bars are commonly developed in coastal plain meandering channels. They form by the same basic processes as fluvial point bars but are further modified by tidal action. This study analyses the sedimentary facies of six active tidal point bars and their relationships. The bars are situated along the Georgia coastal plain, USA, in the lower and upper reaches of the fluvial marine transition and in tidal channels without upstream river input. Based on millimetric logging of 31 cores, nine sedimentary facies were identified, which are proposed to be used for the characterization of tidal point bars in other depositional settings. These bars share common sedimentary facies; however, the internal organization of these facies and their associations differ appreciably in response to complex local tidal processes and fluvial-tidal interactions. The increase in tidal influence results in prominent heterogeneous architectures, with greater concentration of heterolithic stratification (mostly inclined), bioturbation and evidence of flow reversals. In contrast, when freshwater river discharge overcomes tidal stages in the fluvial marine transition bars, structureless coarse-grained facies with attendant erosional surfaces and decreased bioturbation are predominantly developed. In the lower reach of the fluvial marine transition, along the estuarine turbidity maximum, alternations of fluvial and tidal sedimentation are evidenced by a bimodal facies association, where alternations of gravel lag and muddy facies (massive to heterolithic) dominate. Moreover, despite differences in local hydrodynamics, coarsening-upward sequences, mostly composed of structureless coarser facies, are consistently distributed along the upper parts of the bars, which may indicate overprinting of regional events such as storm surge-related floodings in the sedimentological record. This study provides a basis for better identifying the hydrodynamic processes that shape coastal systems, such as estuarine and backbarrier environments. Additionally, those deposits are analogues for certain hydrocarbon reservoirs, thereby increasing knowledge of their facies distribution and aiding production optimization.

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Facies and the Architecture of Estuarine Tidal Bar in the Lower Cretaceous Mcmurray Formation, Central Athabasca Oil Sands, Alberta, Canada

Cited by 17 publications

References 42 publications

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization

Hierarchical automated machine learning (AutoML) for advanced unconventional reservoir characterization

Quantitative Characterization of Tidal Couplets in Oil Sands Reservoir, the Upper McMurray Formation, Northeastern Alberta, Canada

Hydrodynamic controls on sedimentary facies of tidal point bars: A case study in the Georgia coastal plain, USA

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