skip to main content

Title: Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods
Learning reservoir flow dynamics is of primary importance in creating robust predictive models for reservoir management including hydraulic fracturing processes. Physics-based models are to a certain extent exact, but they entail heavy computational infrastructure for simulating a wide variety of parameters and production scenarios. Reduced-order models offer computational advantages without compromising solution accuracy, especially if they can assimilate large volumes of production data without having to reconstruct the original model (data-driven models). Dynamic mode decomposition (DMD) entails the extraction of relevant spatial structure (modes) based on data (snapshots) that can be used to predict the behavior of reservoir fluid flow in porous media. In this paper, we will further enhance the application of the DMD, by introducing sparse DMD and local DMD. The former is particularly useful when there is a limited number of sparse measurements as in the case of reservoir simulation, and the latter can improve the accuracy of developed DMD models when the process dynamics show a moving boundary behavior like hydraulic fracturing. For demonstration purposes, we first show the methodology applied to (flow only) single- and two-phase reservoir models using the SPE10 benchmark. Both online and offline processes will be used for evaluation. We observe that we only require a few DMD modes, which are determined by the sparse DMD structure, to capture the behavior of the reservoir models. Then, we applied the local DMDc for creating a proxy for application in a hydraulic fracturing process. We also assessed the trade-offs between problem size and computational time for each reservoir model. The novelty of our method is the application of sparse DMD and local DMDc, which is a data-driven technique for fast and accurate simulations.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    To understand the governing mechanisms of bio-inspired swimming has always been challenging due to intense interactions between flexible bodies of natural aquatic species and water around them. Advanced modal decomposition techniques provide us with tools to develop more in-depth understating about these complex dynamical systems. In this paper, we employ proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) techniques to extract energetically strongest spatio-temporal orthonormal components of complex kinematics of a Crevalle jack (Caranx hippos) fish. Then, we present a computational framework for handling fluid–structure interaction related problems in order to investigate their contributions towards the overall dynamics of highly nonlinear systems. We find that the undulating motion of this fish can be described by only two standing-wave like spatially orthonormal modes. Constructing the data set from our numerical simulations for flows over the membranous caudal fin of the jack fish, our modal analyses reveal that only the first few modes receive energy from both the fluid and structure, but the contribution of the structure in the remaining modes is minimal. For the viscous and transitional flow conditions considered here, both spatially and temporally orthonormal modes show strikingly similar coherent flow structures. Our investigations are expected to assist in developing data-driven reduced-order mathematical models to examine the dynamics of bio-inspired swimming robots and develop new and effective control strategies to bring their performance closer to real fish species.

    more » « less
  2. Turbulence is a major source of momentum, heat, moisture, and aerosol transport in the atmosphere. Hence, it is crucial to understand and accurately characterize turbulence mechanisms in atmospheric flows. Many complex factors in the atmosphere influence the turbulence structures including stratification and background shear. However, our understanding of the interacting effects of these factors on coherent turbulence structure evolutions is still limited. In this talk, we aim to bridge this knowledge gap by using mode decomposition techniques and a wide range of large-eddy simulation (LES) data. By developing a data-driven technique, we will characterize unique features of atmospheric boundary layer (ABL) turbulence under different forcing scenarios. We will present 3D LES wind speed snapshots of different ABL flows that will be used as dynamic mode decomposition (DMD) input data. Then, the obtained modes and eigenvalues will be employed to gain insights into coherent turbulence structures in ABLs. We will explain the physical meaning of dominant modes and how each mode relates to the physical cause of turbulence structures. The dominant modes, which are selected based on the mode amplitude, contain the most important spatial and temporal characteristics of the flow. We will evaluate the accuracy of the performance of this method by reconstructing the flow field with only a small number of modes, and then calculate the mean average error between the real flow and the reconstructed flow fields. We will present different data frequencies, wind speeds, and surface heat fluxes. This allows us to elucidate the modes and determine the conditions in which the mode decomposition provides more accurate results for the ABL flows. Our findings can be used to identify the major causes of turbulence in real atmospheric flows and could provide a deeper insight into the dynamics of turbulence in ABLs. Our results will also be useful for developing reduced-order models that can rapidly predict the turbulent ABL flow fields. 
    more » « less
  3. Abstract

    Long‐lead forecasting for spatio‐temporal systems can entail complex nonlinear dynamics that are difficult to specify a priori. Current statistical methodologies for modeling these processes are often highly parameterized and, thus, challenging to implement from a computational perspective. One potential parsimonious solution to this problem is a method from the dynamical systems and engineering literature referred to as an echo state network (ESN). ESN models usereservoir computingto efficiently compute recurrent neural network forecasts. Moreover, multilevel (deep) hierarchical models have recently been shown to be successful at predicting high‐dimensional complex nonlinear processes, particularly those with multiple spatial and temporal scales of variability (such as those we often find in spatio‐temporal environmental data). Here, we introduce a deep ensemble ESN (D‐EESN) model. Despite the incorporation of a deep structure, the presented model is computationally efficient. We present two versions of this model for spatio‐temporal processes that produce forecasts and associated measures of uncertainty. The first approach utilizes a bootstrap ensemble framework, and the second is developed within a hierarchical Bayesian framework (BD‐EESN). This more general hierarchical Bayesian framework naturally accommodates non‐Gaussian data types and multiple levels of uncertainties. The methodology is first applied to a data set simulated from a novel non‐Gaussian multiscale Lorenz‐96 dynamical system simulation model and, then, to a long‐lead United States (U.S.) soil moisture forecasting application. Across both applications, the proposed methodology improves upon existing methods in terms of both forecast accuracy and quantifying uncertainty.

    more » « less
  4. Hydraulic fracturing arises as a method to enhance oil and gas production, and also as a way to recover geothermal energy. It is, therefore, essential to understand how injecting a fluid inside a rock reservoir will affect its surroundings. Hydraulic fracturing processes can be strongly affected by the interaction between two mechanisms: the elastic effects caused by the hydraulic pressure applied inside fractures and the poro-mechanical effects caused by the fluid infiltration inside the porous media (i.e. fluid diffusivity); this, in turn, is affected by the injection rate used. The interaction between poro-elastic mechanisms, particularly the effect of the fluid diffusivity, in the hydraulic fracturing processes is not well-understood and is investigated in this paper. This study aims to experimentally and theoretically comprehend the effects of the injection rate on crack propagation and on pore pressures, when flaws pre-fabricated in prismatic gypsum specimens are hydraulically pressurized. In order to accomplish this, laboratory experiments were performed using two injection rates (2 and 20 ml/min), applied by an apparatus consisting of a pressure enclosure with an impermeable membrane in both faces of the specimen, which allowed one to observe the growth of a fluid front from the pre-fabricated flaws to the unsaturated porous media (i.e. rock), before fracturing took place. It was observed that the fracturing pressures and patterns are injection-rate-dependent. This was interpreted to be caused by the different pore pressures that developed in the rock matrix, which resulted from the significantly distinct fluid fronts observed for the two injection rates tested. 
    more » « less

    The chemo-mechanical loading of rocks causes the dissolution and precipitation of multiple phases in the rock. This dissolution and precipitation of load-bearing mineral phases lead to the stress redistribution in neighboring phases, which in turn results in deformational changes of the sample composite. The aim of this study is to investigate the link between microstructural evolution and creep behavior of shale rocks subjected to chemo-mechanical loading through modeling time-dependent deformation induced by the dissolution-precipitation process. The model couples the microstructural evolution of the shale rocks with the stress/strain fields inside the material as a function of time. The modeling effort is supplemented with an experimental study where shale rocks were exposed to CO2-rich brine under high temperature and pressure conditions. 3D snapshots of the sample microstructure were generated using segmented micro-CT images of the shale sample. The time-evolving microstructures were then integrated with the Finite element-based mechanical model to simulate the creep induced by dissolution and precipitation processes independent of the intrinsic viscoelasticity/viscoplasticity of the mineral phases. After computation of the time-dependent viscoelastic properties of the shale composite, the combined microstructure model and finite element model were utilized to predict the time-dependent stress and strain fields in different zones of reacted shale.


    Determination of viscous behavior of shale rocks is key in wide range of applications such as stability of reservoirs, stability of geo-structures subjected to environmental forcing, underground storage of hazardous materials and hydraulic fracturing. Short-term creep strains in hydraulic fracturing can change stress fields and in turn can impact the hydraulic fracturing procedures(H. Sone & Zoback, 2010; Hiroki Sone & Zoback, 2013). While long-term creep strains can hamper the reservoir performance due to the reduction in permeability of the reservoir by closing of fractures and fissures(Du, Hu, Meegoda, & Zhang, 2018; Rybacki, Meier, & Dresen, 2016; Sharma, Prakash, & Abedi, 2019; Hiroki Sone & Zoback, 2014). Owing to these significance of creep strain, it is important to understand the viscoelastic/viscoplastic behavior of shales.

    more » « less