Data-Driven Prediction of Unconventional Shale-Reservoir Dynamics

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2564-2581 ◽  
Author(s):  
Hector Klie ◽  
Horacio Florez
Keyword(s):  
Linear Operators ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Production Data ◽  
Dynamic Mode ◽  
State Variables ◽  
Coupled Flow ◽  
Two Phase ◽  
Data Set ◽  
Well Production

Summary The present work introduces extended dynamic mode decomposition (EDMD) as a suitable data-driven framework for learning the reservoir dynamics entailed by flow/fracture interactions in unconventional shales. The proposed EDMD approach builds on the approximation of infinite-dimensional linear operators combined with the power of deep learning autoencoder networks to extract salient transient features from pressure/stress fields and bulks of production data. The data-driven model is demonstrated on three illustrative examples involving single- and two-phase coupled flow/geomechanics simulations and a real production data set from the Vaca Muerta unconventional shale formation in Argentina. We demonstrated that we could attain a high level of predictability from unseen field-state variables and well-production data given relatively moderate input requirements. As the main conclusion of this work, EDMD stands as a promising data-driven choice for efficiently reconstructing flow/fracture dynamics that are either partially or entirely unknown, or that are too complex to formulate using known simulation tools on unconventional plays.

scholarly journals An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

2020 ◽  
Author(s):  
Nicola Demo ◽  
Giulio Ortali ◽  
Gianluca Gustin ◽  
Gianluigi Rozza ◽  
Gianpiero Lavini
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Free Form ◽  
Dynamic Mode ◽  
Two Phase ◽  
Initial Shape ◽  
Reduced Order

Abstract This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

scholarly journals A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

2015 ◽  
Vol 25 (6) ◽  
pp. 1307-1346 ◽  
Author(s):  
Matthew O. Williams ◽  
Ioannis G. Kevrekidis ◽  
Clarence W. Rowley
Keyword(s):  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
Koopman Operator ◽  
Mode Decomposition

Data-Driven Model Development and Feedback Control Design for PZT Bimorph Actuators

2017 ◽  
Author(s):  
Nikolas Bravo ◽  
Ralph C. Smith ◽  
John Crews
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Micro Air Vehicles ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
High Fidelity ◽  
Actuator Dynamics ◽  
Rate Dependent ◽  
Homogenized Energy Model

In the paper, we discuss the development of a high-fidelity and surrogate model for a PZT bimorph used as an actuator for micro-air vehicles including Robobee. The models must quantify the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in dynamic operating regimes. The actuator dynamics are initially modeled using the homogenized energy model (HEM) framework. This provides a comprehensive high-fidelity model, which can be inverted and implemented in real time for certain control regimes. To improve efficiency, we additionally discuss the development of data-driven models and focus on the implementation of a surrogate model based on a dynamic mode decomposition (DMD). Finally, we detail the design and implementation of a PI controller on the surrogate and high-fidelity models.

scholarly journals Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 138
Author(s):  
Anqi Bao ◽  
Eduardo Gildin ◽  
Abhinav Narasingam ◽  
Joseph S. Kwon
Keyword(s):  
Hydraulic Fracturing ◽  
Moving Boundary ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Flow In Porous Media ◽  
Computational Time ◽  
Dynamic Mode ◽  
Reservoir Models ◽  
Trade Offs ◽  
Fracturing Process

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.

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