3D modeling of deepwater turbidite lobes: a review of the research status and progress

Abstract

Deepwater turbidite lobe reservoirs have massive hydrocarbon potential and represent one of the most promising exploration targets for hydrocarbon industry. Key elements of turbidite lobes internal heterogeneity include the architectural hierarchy and complex amalgamations at each hierarchical level leading to the complex distribution of shale drapes. Due to limitation of data, to build models realistically honoring the reservoir architecture provides an effective way to reduce risk and improve hydrocarbon recovery. A variety of modeling techniques on turbidite lobes exist and can be broadly grouped into pixel-based, process-based, process-oriented, surface-based, object-based and a hybrid approach of two or more of these methods. The rationale and working process of methods is reviewed, along with their pros and cons. In terms of geological realism, object-based models can capture the most realistic architectures, including the multiple hierarchy and the amalgamations at different hierarchical levels. In terms of data conditioning, pixel-based and multiple-point statistics methods could honor the input data to the best degree. In practical, different methods should be adopted depending on the goal of the project. Such a review could improve the understanding of existing modeling methods on turbidite lobes and could benefit the hydrocarbon exploration activities of such reservoirs in offshore China.

Facies analysis and distribution of Late Palaeogene deep‐water massive sandstones in submarine‐fan lobes, NW Borneo

“…It is important to classify the deep‐marine deposition into channel, proximal lobe, medial lobe, and distal lobe settings (Figure 12a) (Kane et al, 2017; L.‐F. Zhang et al, 2020). The proximal lobes comprised of more thickness of sandstones than the distal ones, because more sediments are available in the vicinity of feeder channel in the proximal lobe settings (Fildani et al, 2018; L.‐F.…”

Centimeter-Scale Lithology and Facies Prediction in Cored Wells Using Machine Learning

“…Deep TEN (Zhang et al, 2020) is a Convolutional Neural Network (CNN) model architecture that adapts dictionary learning, a popular and successful approach to image texture classification (Liu et al, 2019), as a trainable component of the neural network. We adapted the network to take depth-aligned core images as input (see Figure 5), and predict sequences of labels as output (rather than a single classification per image) (Figure 7).…”

“…One way to extend the prediction capability is by incorporating spatial information and metrics. For example, utilizing forward stratigraphic models to generate stratal surfaces, geometries, and scales could be incorporated into spatial prediction of lithology and facies (Mulder et al, 1997;Tinterri et al, 2003;Pyrcz et al, 2005;McHargue et al, 2011;Albertão and Athayde, 2015;Burgess et al, 2019;Zhang et al, 2020). The model results might also provide another metric to score the results from machine-learning outputs.…”

Recognizing key sedimentary facies and their distribution in mixed turbidite–contourite depositional systems: The case of the Pacific margin of the Antarctic Peninsula

“…Furthermore, past sedimentological studies of turbidite deposits, in ancient records and in modern environments, have called into question the previously established criteria for turbidites (Shanmugam, 1997(Shanmugam, , 2016. These studies have proposed new conceptual models (Lowe, 1982;Haughton et al, 2009;Stow & Smillie, 2020;Zhang et al, 2020) and highlighted the significance of interaction or alternation in sedimentary processes (Mutti, 2011;de Castro et al, 2020. Mulder et al (2008) suggested that interactions between turbidity and bottom currents can generate three distinct types of deposits: (i) alternations of turbidites and contourites (Stow et al, 1998;Michels et al, 2002;Brackenridge et al, 2013); (ii) redistributed or reworked gravity-driven deposits (Mutti, 1992;Stow et al, 2002;Hanquiez et al, 2010;Gong et al, 2013); and (iii) synchronous (mixed or hybrid) deposits (Sansom, 2018;Fonnesu et al, 2020;Fuhrmann et al, 2020).…”

“…Furthermore, past sedimentological studies of turbidite deposits, in ancient records and in modern environments, have called into question the previously established criteria for turbidites (Shanmugam, 1997, 2016). These studies have proposed new conceptual models (Lowe, 1982; Haughton et al ., 2009; Stow & Smillie, 2020; Zhang et al ., 2020) and highlighted the significance of interaction or alternation in sedimentary processes (Mutti, 2011; de Castro et al ., 2020, 2021). Mulder et al .…”