Uncertainty quantification of overpressure buildup through inverse modeling of compaction processes in sedimentary basins

Abstract

This study illustrates a procedure conducive to a preliminary risk analysis of overpressure development in sedimentary basins characterized by alternating depositional events of sandstone and shale layers. The approach rests on two key elements: (1) forward modeling of fluid flow and compaction, and (2) application of a model-complexity reduction technique based on a generalized polynomial chaos expansion (gPCE). The forward model considers a one-dimensional vertical compaction processes. The gPCE model is then used in an inverse modeling context to obtain efficient model parameter estimation and uncertainty quantification. The methodology is applied to two field settings considered in previous literature works, i.e. the Venture Field (Scotian Shelf, Canada) and the Navarin Basin (Bering Sea, Alaska, USA), relying on available porosity and pressure information for model calibration. It is found that the best result is obtained when porosity and pressure data are considered jointly in the model calibration procedure. Uncertainty propagation from unknown input parameters to model outputs, such as pore pressure vertical distribution, is investigated and quantified. This modeling strategy enables one to quantify the relative importance of key phenomena governing the feedback between sediment compaction and fluid flow processes and driving the buildup of fluid overpressure in stratified sedimentary basins characterized by the presence of low-permeability layers. The results here illustrated (1) allow for diagnosis of the critical role played by the parameters of quantitative formulations linking porosity and permeability in compacted shales and (2) provide an explicit and detailed quantification of the effects of their uncertainty in field settings

Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins

“…Following previous approaches [11,13,15], we rely on the assumptions that (a) the most relevant phenomena take place mainly along the vertical direction and (b) the rock domain is assumed to be fully saturated by a single fluid characterized by uniform properties. Note that the first assumption enables us to consider a one-dimensional system described by the domain Ω(t) = [z bot (t), z top (t)], where z bot and z top denote the bottom and the top of the rock domain respectively, which can both vary with time.…”

Multiscale Modeling of Glacial Loading by a 3D Thermo-Hydro-Mechanical Approach Including Erosion and Isostasy

“…We assume that information about sedimentary units thicknesses, porosity and/or mineral composition are available at selected locations across a sedimentary system, typically from well logs, i.e., along the vertical direction (as sketched in Figure 1). In this framework we employ the stochastic inverse modelling procedure implemented in [7] to interpret vertical distributions of system properties with a one-dimensional model, which was developed in [5] starting from classical approaches to vertical compaction modeling (e.g., [2]). At each location such one-dimensional model provides an approximation of layers interface locations, whose characterization under uncertainty is investigated in detail in [27].…”

“…In this context a number of approaches have considered the geochemical and mechanical compaction problem from a one-dimensional perspective, i.e., by considering mass, momentum and energy balances along the vertical direction, applied to fluid and solid phases [3][4][5][6]. These simplified one-dimensional approaches may be effective in interpreting qualitatively well data (e.g., [7]), however, they cannot capture inherently three dimensional processes that may arise due to the coupling of mechanical deformations and fluid mechanics in geological bodies that play an important role in the presence of glaciations [8].…”

Predicting fluid pressure in sedimentary basins from seismic tomography

“…2). The importance of these processes vary with chemical composition, depth of burial, temperature and fluid flow regime (Marín-Moreno et al 2013b;Colombo et al 2017). For example, the breakdown of feldspar into clay minerals is likely to be important in arkosic quartz-sand sediments and is sensitive to temperature and fluid flow.…”

“…Numerical models that involve a complete inclusion and description of chemical and physical phenomena, such as coupled thermal and fluid flow processes have been developed and calibrated with exploration well and seismic data (Marín-Moreno et al, 2013b;Colombo et al 2017). These models use empirical methods applied to wireline-logs such as Eaton's Method (Eaton 1975;van Ruth et al 2004;Ramdhan & Goulty 2018) to test model predictions, using advanced geophysical inversion techniques.…”