Ebb tidal deltas (ETDs) are highly dynamic features of sandy coastal systems, and coastal management concerns (e.g., nourishment and navigation) present a pressing need to better describe and quantify their evolution. Here we propose two techniques for leveraging the availability of high-resolution bathymetric surveys to generate new insights into the dynamics and preservation potential of ebb-tidal deltas. The first technique is conformal mapping to polar coordinates, using Ameland ebb-tidal delta in the Netherlands as a case study. Since the delta tends to evolve in a clockwise direction around the inlet, this approach provides an improved quantification and visualization of the morphodynamic behaviour as a timestack. We clearly illustrate the sediment bypassing process and repeated rotational migration of channels and shoals across the inlet from updrift to downdrift coasts. Secondly, we generate a decadal scale (1975–2021) stratigraphic model from the differences between successive bathymetries. This stratigraphy showcases the delta's depositional behaviour through space and time, and provides a modern analogue for prehistoric ebb-tidal deltas. During the surveyed period, inlet fills form the largest and most stable deposits, while the downdrift swash platform is the most stable structure over longer periods. Together, these approaches provide new perspectives on ebb-tidal delta dynamics and preservation potential which are readily applicable to other sites with detailed bathymetric data. These findings are valuable at annual to decadal timescales for coastal management (e.g., for planning sand nourishments) and also at much longer timescales for interpreting stratigraphy in ancient rock records.@en

The construction of subsurface reservoir models is typically aided by the use of outcrops and modern analogue systems. We show how process-based models of depositional systems help to develop and substantiate reservoir architectural concepts. Process-based models can simulate assumptions relating to the physical processes influencing sedimentary deposition, accumulation and erosion on the resultant 3D sediment distribution. In this manner, a complete suite of analogue geometries can be produced by implementing different sets of boundary conditions based on hypotheses of depositional controls. Simulations are therefore not driven by a desired/defined outcome in the depositional patterns, but their application to date in reservoir modelling workflows has been limited because they cannot be conditioned to data such as well logs or seismic information. In this study a reservoir modelling methodology is presented that addresses this problem using a two-step approach: process-based models producing 3D sediment distributions that are subsequently used to generate training images for multi-point geostatistics. The approach has been tested on a dataset derived from a well-exposed outcrop from central Utah. The Ferron Sandstone Member includes a shallow-marine deltaic interval that has been digitally mapped using a high-resolution unmanned aerial vehicle (UAV) survey in 3D to produce a virtual outcrop (VO). The VO was used as the basis to build a semi-deterministic outcrop reference model (ORM) against which to compare the results of the combined process/multiple-point statistics (MPS) geostatistical realizations. Models were compared statically and dynamically through flow simulation. When used with a dense well dataset, the MPS realizations struggle to account for the high levels of non-stationarity inherent in the depositional system that are captured in the process-based training image. When trends are extracted from the outcrop analogue and used to condition the simulation, the geologically realistic geometries and spatial relationships from the process-based models are directly imparted onto the modelling domain, whilst simultaneously allowing the facies models to be conditioned to subsurface data. When sense-checked against preserved analogues, this approach reproduces more realistic architectures than traditional, more stochastic techniques.@en

Modern and ancient analogues are often consulted by geologists to help understand subsurface systems. While modern analogues provide information on the areal relationship between facies, ancient systems provide detailed data on the vertical facies variations, typically along a two-dimensional outcrop. Combining data from modern and ancient systems effectively requires translating areal morphology, which is often still evolving, to the related sediments preserved in three dimensions. Process-based models simulate both depositional processes while preserving stratigraphy. These models can be employed to unravel the relationship between sediment supply and preserved deposits in natural systems and to help integrate field data. Four synthetic deltas were modelled using different sediment supply compositions, from coarse to very fine sand systems. The resultant sedimentary deposits are classified into architectural elements, and the grain size composition of each architectural element is studied over time. Facies that are extensive in their horizontal dimensions are often less abundant in three-dimensional preserved deposits. Between deltas, grain size compositions of a specific architectural element type (e.g. mouth bars) are more similar than their corresponding sediment supply compositions. This is due to selective deposition of grain size classes across each architectural element type. This selective deposition causes overrepresentation of the same range of grain sizes, even for systems with different sediment supply compositions. When a particular supply composition does not contain enough of the overrepresented grain size class for a particular architectural element, that element will be under-supplied and constitute a smaller proportion of the overall delta deposits. It is imperative to account for over-representation of grain size classes in particular architectural elements when estimating palaeo-sediment supply, delta architecture and morphology from field data. Even when data availability/accessibility does not allow the inclusion of distal deposits in field studies, process-based simulations can contribute valuable information on sediment sorting patterns in three dimensions.

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Reservoir models can be improved through the incorporation of process-based models as training images for simulation with MPS. Process-based models in this study are reported to be excellent sources of training images. When combined with knowledge of depositional stratigraphic information, delta complex architectural trends can be accurately reproduced. This approach is applicable to systems that are well constrained by knowledge of depositional processes that can be efficiently synthetized to generate an appropriate process model-derived training image.@en

Geological reservoir models, created based on sparse core and seismic data, inform hydrocarbon production, geothermal applications and aquafer management. Important factors contributing to reservoir quality in these applications include the heterogeneities within and connectivity between the relevant geo‐bodies constituting the reservoir. The transport and preservation of sediment at the time of deposition impacts these factors. Therefore, a better understanding of sediment delivery, transport and deposition can be used to better quantify reservoir properties. This same computational methodology can also be applied test hypotheses concerning the depositional processes responsible for preservation of ancient deposits. Constraining such hypotheses improves our understanding of the paleo‐sediment dynamics and the accuracy of future geological models.@en

In deltaic deposits, the largest volumes of sandy deposits occur at the delta top (as channel accretion, overbank deposits) and as distinct or amalgamated sandy mouth bars in the upper delta front. We use process-based models to study the sediment distribution in four evolving deltas, each with a different input sediment profile. We show how the mouthbar deposits can preserve a large proportion of the sand preserved in the sedimentary record of deltaic systems.@en

Geological models are generated by interpretation and interpolation of sparse data. To limit uncertainty, relevant analogues are used to extrapolate knowledge of previously studied, well understood systems. However, these analogues only provide a snapshot of deposition. During delta progradation, sediment will not only be deposited, but is also reworked resulting in unique preserved sediment distribution patterns for each delta. We show how process-based models can be used to study the evolution of deltaic sediment distribution in four dimensions. Grain-size distribution trends are extracted from preserved deposits in synthetic analogues of prograding deltas.@en

Abstract Understanding the processes and conditions at the time of deposition is key to the development of robust geological models which adequately approximate the heterogeneous delta morphology and stratigraphy they represent. We show how the mechanism of sediment transport (the proportion of the sediment supply transported as bed load vs. suspended load) impacts channel kinematics, delta morphology and stratigraphy, to at least the same extent as the proportion of cohesive sediment supply. This finding is derived from 15 synthetic delta analogues generated by processes-based simulations in Delft3D. The model parameter space varies sediment transport mechanism against proportions of cohesive sediment whilst keeping the total sediment mass input constant. Proximal morphology and kinematics previously associated with sediment cohesivity are also produced by decreasing the proportion of bed load sediment transport. However, distal depositional patterns are different for changes in sediment transport and sediment load cohesivity. Changes in sediment transport mechanisms are also shown to impact clinoform geometry as well as the spatiotemporal scale of autogenic reorganisation through channel avulsions. We conclude that improving insight into the ratio of bed load to suspended load is crucial to predicting the geometric evolution of a delta.@en

The process of constructing geological models is used on various scales in mining, oil and gas exploration, hydrology as well as in large construction projects. Development of geological models is a complex process consisting of various phases. A large degree of uncertainty is introduced from the interpretation of the data to the construction of the geological model. To arrive at the best approximation of the subsurface, relevant analogues are identified and consulted. Therefore, uncertainties originate from unknown depositional processes, but also from uncertain correlation between the study area and the analogues. We developed a set of tools to quantify the variability in deltaic geological models resulting from these uncertainties. These tools were applied to an ensemble of simulations generated in Delft3D by processed-based forward modelling. We show how a set of analyses can be used to quantify the differences in the resultant delta deposits. Analyses investigated channel networks, topographic profiles and sediment distribution in the delta. The tools make use of the unique advantages of numerical forward models, allowing single variables to be studied at high spacial and temporal resolution. @en

Exploitation of unconventional resources could prolong the gas production in the North Sea. Low-net-to-gross fluvial intervals may have tough-gas reservoir potential in thin-bedded crevasse splays.To assess economic risks associated to the development of these reservoirs, a numerical model can help to predict the sediment distribution. To this end, simulations were conducted with Delft3D process-based modelling software. Input parameters and the validation data sets for these models are derived from outcrop studies in the present-day Río Colorado fluvial system in the Altiplano Basin, Bolivia. The grain-size trends of the simulated surface sediments for a single flood event show a trend which is consistent with the validation data. For example, grain-size decreases with increasing distance from the channel, which is in line with the physical concept of decreasing sediment size for decreasing flow energy. This shows that numerical models can be used to support sediment trends and depositional mechanisms of a crevasse splay. The combination of numerical models and discrete field data provides a solid case for sediment distribution predications. However, simulations still have a limited accuracy. @en

Exploitation of unconventional resources could prolong the gas production in the North Sea. Low-net-togross fluvial intervals have tough-gas reservoir potential in thin-bedded crevasse splays. To assess economic risks associated to the development of these reservoirs, a reliable grain-size distribution model is required. Sparse areal data availability for reservoir models commonly results in the use of stochastic interpolation. Numerical models offer the possibility to support these methods with proven physical concepts.To this end, simulations were conducted with Delft3D process-based modelling software. Input parameters and the validation data sets for these models are derived from outcrop studies in the presentday Río Colorado fluvial system in the Altiplano Basin, Bolivia. The grain-size trends of the simulated surface sediments for a single flood event are consistent with the validation data. These trends were used to populate individual crevasses splays within a static model. This shows that process-based models are able to support sediment trends and depositional mechanisms of a crevasse splay. The combination of numerical models and discrete field data provides a solid case for sediment distribution predications. However, simulations still have a limited accuracy. @en

New developments are currently being undertaken to develop a new open source web-based modelling system based on the process-based model Delft3D. This modelling systems (Delft3D-GeoTool) aims to provide non-modeling specialists (as wel as specialists) in the field of sedimentary geology and reservoir geology with tools to easily set up their own model simulations and scenarios, perform post-processing analyses and store the results in a database. @en