Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems

Mudunuru, Maruti K.; Karra, Satish; Harp, Dylan R.; Guthrie, George D.; Viswanathan, Hari

doi:10.1016/j.geothermics.2017.06.013

Cited by 23 publications

(13 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to significant costs involved in field trials of HDR reservoirs [7], computational modelling is deemed to be an affordable option to enable researchers to investigate possible ways to extend the use of EGS particularly during the preliminary stages to assess the thermal power and the economic feasibility of EGS sites [7]. A comprehensive study conducted by [16] on numerical modelling of EGS reservoirs, indicated that, in general, modelling can be divided into three different categories in terms of EGS reservoir performance. The first category relates to improving the efficiency of heat extraction technologies for different rock deposits considering a wide range of contributing temperatures.…”

Section: Introductionmentioning

confidence: 99%

A hybrid optimisation approach to improve long-term performance of enhanced geothermal system (EGS) reservoirs

et al. 2019

View full text Add to dashboard Cite

Improving the long-term performance of deep geothermal reservoirs, as an energy source, can lead to a significant increase in efficiency of heat extractions from these assets. This will assist designers, energy firms, managers, and government decision makers to plan and maintain the use of limited available energy resources and hence enhance key sustainable development goals. Enhanced geothermal reservoirs possess a multi-phase behaviour with complex interrelationship between several parameters that makes the analysis and design of these systems challenging. Often, this challenge is increased when taking into consideration the optimum use of the available resources and induced costs during both creation and exploitation phases. This research presents a novel design approach developed to achieve efficiency and improved long-term performance in doublet enhanced geothermal systems (EGS). The proposed approach is based on an optimisation procedure using a numerical hybrid methodology integrating a multi-objective genetic algorithm with finite element analysis of fully coupled thermal hydraulic processes of reservoirs. The results of the optimisation process are discussed in comparison with data available from a benchmark case study. The results demonstrate a significant improvement in the long-term performance of EGS reservoir, both in terms of thermal power and costs when optimised using the proposed methodology.

show abstract

Section: Introductionmentioning

confidence: 99%

A hybrid optimisation approach to improve long-term performance of enhanced geothermal system (EGS) reservoirs

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Machine learning (ML) is a widely used tool for extracting features [16,17] and classifying seismic signals. In addition to earthquake detection and prediction [10,13], ML is shown to be successful in various other subsurface applications [18][19][20][21][22][23]. Within the context of ML, there are many available techniques that are able to predict the evolution of time-series and distinguish seismic signals, within a certain tolerance [24,25].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study Using CO2‐Driven Cold‐Water Geyser in Chimayó, New Mexico

Yuan

Tan

Mudunuru

et al. 2019

Seismological Research Letters

View full text Add to dashboard Cite

We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimayó geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of CO 2 intrusion into shallow water aquifers. By studying this geyser, we can understand upwelling of CO 2 -rich fluids from depth, which has relevance to leak monitoring in a CO 2 sequestration project. ML methods such as Random Forests (RF) are known to be robust multi-class classifiers and perform well under unfavorable noisy conditions. However, the extent of the RF method's accuracy is poorly understood for this CO 2 -driven geysering application. The current study aims to quantify the performance of RF-classifiers to discern the geyser state. Towards this goal, we first present the data collected from the seismometer that is installed near the Chimayó geyser. The seismic signals collected at this site contain different types of noises such as daily temperature variations, seasonal trends, animal movement near the geyser, and human activity. First, we filter the signals from these noises by combining the Butterworth-Highpass filter and an Autoregressive method in a multi-level fashion. We show that by combining these filtering techniques, in a hierarchical fashion, leads to reduction in the noise in the seismic data without removing the precursors and eruption event signals. We then use RF on the filtered data to classify the state of geyser into three classes -remnant noise, precursor, and eruption states. We show that the classification accuracy using RF on the filtered data is greater than 90%. We also evaluate the accuracy of other classical time-series methods such as Dynamic Time Warping (DTW) on filtered data along with RF on partially-filtered data where we remove the seasonal trends. Classification accuracy shows that DTW performs poorly (44%) and RF on partially-filtered data performs decently (87%). Denoising seismic signals from both seasonal trends and human activity enhances RF classifier performance by 7%. These aspects make the proposed ML framework attractive for event discrimination and signal enhancement under noisy conditions, with strong potential for application to monitoring leaks in CO 2 sequestration.

show abstract

“…Due to the prevalence of brittle materials in several applications in geosciences (e.g., hydraulic fracturing, geothermal, etc.) [7][8][9], infrastructure [5] and aerospace industry [10], fast and accurate models of brittle fracturing are needed.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

et al. 2019

Self Cite

View full text Add to dashboard Cite

In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a k-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity fracture mechanics model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective moduli at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are ≈ O ( 10 6 ) times faster compared to high-fidelity fracture simulations by HOSS. These aspects make the proposed surrogate model attractive for upscaling damage from micro-scale models to continuum models. We would like to emphasize that the surrogate models are not a replacement of physical understanding of fracture propagation. The proposed method in this paper is limited to tensile loading conditions at low-strain rates. This loading condition corresponds to a dominant fracture perpendicular to tensile direction. The proposed method is not applicable for in-plane shear, out-of-plane shear, and higher strain rate loading conditions.

show abstract

Regression-based reduced-order models to predict transient thermal output for enhanced geothermal systems

Cited by 23 publications

References 34 publications

A hybrid optimisation approach to improve long-term performance of enhanced geothermal system (EGS) reservoirs

A hybrid optimisation approach to improve long-term performance of enhanced geothermal system (EGS) reservoirs

Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study Using CO2‐Driven Cold‐Water Geyser in Chimayó, New Mexico

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

Contact Info

Product

Resources

About