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Each simulation algorithm, including Truncated Gaussian Simulation, Sequential Indicator Simulation and Indicator Kriging is characterized by different operating modes, which variably influence the facies proportion, distribution and association of digital outcrop models, as shown in clastic sediments. A detailed study of carbonate heterogeneity is then crucial to understanding these differences and providing rules for carbonate modelling. Through a continuous exposure of Bajocian carbonate strata, a study window (320 m long, 190 m wide and 30 m thick) was investigated and metre-scale lithofacies heterogeneity was captured and modelled using closely-spaced sections. Ten lithofacies, deposited in a shallow-water carbonate-dominated ramp, were recognized and their dimensions and associations were documented. Field data, including height sections, were georeferenced and input into the model. Four models were built in the present study. Model A used all sections and Truncated Gaussian Simulation during the stochastic simulation. For the three other models, Model B was generated using Truncated Gaussian Simulation as for Model A, Model C was generated using Sequential Indicator Simulation and Model D was generated using Indicator Kriging. These three additional models were built by removing two out of eight sections from data input. The removal of sections allows direct insights on geological uncertainties at inter-well spacings by comparing modelled and described sections. Other quantitative and qualitative comparisons were carried out between models to understand the advantages/disadvantages of each algorithm. Model A is used as the base case. Indicator Kriging (Model D) simplifies the facies distribution by assigning continuous geological bodies of the most abundant lithofacies to each zone. Sequential Indicator Simulation (Model C) is confident to conserve facies proportion when geological heterogeneity is complex. The use of trend with Truncated Gaussian Simulation is a powerful tool for modelling well-defined spatial facies relationships. However, in shallow-water carbonate, facies can coexist and their association can change through time and space. The present study shows that the scale of modelling (depositional environment or lithofacies) involves specific simulation constraints on shallow-water carbonate modelling methods.
Considerable effort has been devoted to the development of simulation algorithms for facies modeling, whereas a discussion of how to combine those techniques has not existed. The integration of multiple geologic data into a three-dimensional model, which requires the combination of simulation techniques, is yet a current challenge for reservoir modeling. This article presents a thought process that guides the acquisition and modeling of geologic data at various scales. Our work is based on outcrop data collected from a Jurassic carbonate ramp located in the High Atlas mountain range of Morocco. The study window is 1 km (0.6 mi) wide and 100 m (328.1 ft) thick. We describe and model the spatial and hierarchical arrangement of carbonate bodies spanning from largest to smallest: (1) stacking pattern of high-frequency depositional sequences, (2) facies association, and (3) lithofacies. Five sequence boundaries were modeled using differential global position system mapping and light detection and ranging data. The surface-based model shows a low-angle profile with modest paleotopographic relief at the inner-to-middle ramp transition. Facies associations were populated using truncated Gaussian simulation to preserve ordered trends between the inner, middle, and outer ramps. At the lithofacies scale, field observations and statistical analysis show a mosaiclike distribution that was simulated using a fully stochastic approach with sequential indicator simulation.
This study observes that the use of one single simulation technique is unlikely to correctly model the natural patterns and variability of carbonate rocks. The selection and implementation of different techniques customized for each level of the stratigraphic hierarchy will provide the essential computing flexibility to model carbonate settings. This study demonstrates that a scale-dependent modeling approach should be a common procedure when building subsurface and outcrop models.
During the second phase of the Alpine Fault, Deep Fault Drilling Project (DFDP) in the Whataroa River, South Westland, New Zealand, bedrock was encountered in the DFDP-2B borehole from 238.5–893.2 m Measured Depth (MD). Continuous sampling and meso- to microscale characterisation of whole rock cuttings established that, in sequence, the borehole sampled amphibolite facies, Torlesse Composite Terrane-derived schists, protomylonites and mylonites, terminating 200–400 m above an Alpine Fault Principal Slip Zone (PSZ) with a maximum dip of 62°. The most diagnostic structural features of increasing PSZ proximity were the occurrence of shear bands and reduction in mean quartz grain sizes. A change in composition to greater mica:quartz + feldspar, most markedly below c. 700 m MD, is inferred to result from either heterogeneous sampling or a change in lithology related to alteration. Major oxide variations suggest the fault-proximal Alpine Fault alteration zone, as previously defined in DFDP-1 core, was not sampled.