Porosity and permeability prediction through forward stratigraphic simulations using GPM™ and Petrel™: application in shallow marine depositional settings

Otoo, Daniel; Hodgetts, David

doi:10.5194/gmd-14-2075-2021

Cited by 5 publications

(2 citation statements)

References 34 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method used in this study to estimate host rock permeability magnitude from porosity and sediment type is just one possible approach for deriving permeability from a SFM. An alternative approach was adopted by Otoo and Hodgetts (2021), who used the grain size distribution and deposition depth to determine the lithofacies of each cell in a SFM, which in turn was used to calculate porosity and permeability based on characteristics of the lithofacies determined in previous studies. Their approach is suited to scenarios where the petrographic characteristics of the target formation are well known, whereas the more generic approach used in this study has broader applicability in the absence of extensive petrographic data.…”

Section: Discussionmentioning

confidence: 99%

Realistic permeability distributions in faults and sediments: The key to predicting fluid flow in sedimentary basins

et al. 2023

View full text Add to dashboard Cite

The permeability of faults and their sedimentary host rocks is a critical input for models of fluid flow in sedimentary basins. Permeability of sedimentary rocks can vary by orders of magnitude over short distances due to variations in sedimentary facies, as well as being strongly anisotropic. Structural features also affect permeability, with faults acting as fluid conduits or barriers depending on the nature of the sedimentary host rock. Constraining these variations in permeability is challenging where outcrops are lacking and drillhole data are sparse. This study describes a workflow using stratigraphic forward modelling to estimate the permeability distribution (both magnitude and anisotropy) in sedimentary rocks and associated faults, which is then used in fluid flow simulations. Permeability is represented as a tensor in the global coordinate system, enabling the use of an unstructured mesh that is independent of stratigraphic layering. The workflow is demonstrated in a sub‐basin of the Proterozoic McArthur Basin of northern Australia. A range of fault permeability scenarios are explored, where fault permeability is a function of host rock properties. The simulation results demonstrate the importance of capturing the direction of permeability anisotropy in dipping strata, as well as highlighting the effect of different fault permeability scenarios. The workflow is particularly well‐suited to scenarios where there are sufficient boreholes to constrain a stratigraphic forward model, but insufficient to determine the permeability distribution by interpolation. Potential applications include mineral and hydrocarbon exploration, groundwater studies, contaminant dispersal modelling, and CO2 sequestration.

show abstract

Section: Discussionmentioning

confidence: 99%

Realistic permeability distributions in faults and sediments: The key to predicting fluid flow in sedimentary basins

et al. 2023

View full text Add to dashboard Cite

show abstract

“…SedSimple simulates sedimentary deposition, erosion and transport over geological timescales given a base topography and relative sea level curve, to create a three‐dimensional geological conceptual model simulation of the subsurface. Compared to other, often commercially available process models, such as SLB's GPM (Courtade et al, 2021; Otoo & Hodgetts, 2021), DionisosFlow™ (Al‐Wazzan et al, 2021; Borgomano et al, 2020; Hamon et al, 2021), SedsimX (Snieder et al, 2021), or CarboCAT (Masiero et al, 2021), SedSimple requires less computational resource making it possible to run a large number of simulations, but at the cost of reduced complexity in modelled processes and hence in the produced simulations. We train neural networks to represent the information in a large set of geometries obtained from SedSimple simulations, to produce networks for shallow marine environments that mirror the fluvial networks of Laloy et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Introducing conceptual geological information into Bayesian tomographic imaging

Bloem

Curtis

Tetzlaff³

2023

Basin Research

View full text Add to dashboard Cite

Geological process models typically simulate a range of dynamic processes to evolve a base topography into a final two‐dimensional cross section or three‐dimensional geological scenario. In principle, process parameters may be updated to better align with observed geophysical or geological data. However, it is hard to find any process model realisations that fit all observations if data sets are complex and sparse in space or time because the simulations typically depend highly non‐linearly on base topography and dynamic parameters. As an alternative, geophysical probabilistic tomographic methods may be used to estimate the family of models of a target subsurface structure that are consistent both with information obtained from previous experiments and with new data (the Bayesian posterior probability distribution). However, this family seldom embodies geologically reasonable images. Here we show that the posterior distribution of tomographic images obtained from travel time data can be fully geological by injecting geological prior information into Bayesian inference and that we can do this near‐instantaneously by using trained mixture density networks (MDNs). We invoke two geological concepts as prior information about the possible depositional environment of an imaged target structure: a braided river system and a set of marine parasequences. Each concept is parameterised by the latent parameters of a generative adversarial network. Data from a target structure can then be used to infer the family of compatible latent parameter values using either geological concept using MDNs. Our near‐instantaneous MDN solutions closely resemble those found using relatively expensive Monte Carlo methods. We show that while the use of incorrect geological conceptual models provides significantly less accurate results, a classifier neural network can infer which geological conceptual model is most consistent with the data. It is thus demonstrated that even apparently barely related geophysical data may contain information about abstract geological concepts, and that geological conceptual models are key to creating reasonable images from geophysical data.

show abstract