Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History

2021 ◽  
pp. 1-18
Author(s):  
Gisela Vanegas ◽  
John Nejedlik ◽  
Pascale Neff ◽  
Torsten Clemens
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
Numerical Models ◽  
Operating Conditions ◽  
Model Parameters ◽  
Large Set ◽  
Model Parameter ◽  
Production History ◽  
Hydrocarbon Fields ◽  
Parameter Distributions

Summary Forecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data. Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells. The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

scholarly journals Machine Learning Model for GSM BSC Control Plane Units

2019 ◽  
Vol 8 (6S) ◽  
pp. 219-223
Keyword(s):  
Machine Learning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Model Parameters ◽  
Large Set ◽  
Data Set ◽  
Wide Acceptance ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Accuracy Of Prediction

At maximum traffic intensity i.e. during the busy hour, the GSM BSC signalling units (BSU) measured CPU load will be at its peak. The BSUs CPU load is a function of the number of transceivers (TRXs) mapped to it and hence the volume of offered traffic being handled by the unit. The unit CPU load is also a function of the nature of the offered load, i.e. with the same volume of offered load, the CPU load with the nominal traffic profile would be different as compared to some other arbitrary traffic profile. To manage future traffic growth, a model to estimate the BSU unit CPU load is an essential need. In recent times, using Machine Learning (ML) to develop such a model is an approach that has gained wide acceptance. Since, the estimation of CPU load is difficult as it depends on large set of parameters, machine learning approach is more scalable. In this paper, we describe a back-propagation neural network model that was developed to estimate the BSU unit CPU load. We describe the model parameters and choices and implementation architecture, and estimate its accuracy of prediction, based on an evaluation data set. We also discuss alternative ML architectures and compare their relative prediction accuracies, to the primary ML model

scholarly journals Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

2019 ◽  
Author(s):  
Aynom T. Teweldebrhan ◽  
John F. Burkhart ◽  
Thomas V. Schuler ◽  
Morten Hjorth-Jensen
Keyword(s):  
Machine Learning ◽  
Parameter Identification ◽  
Hydrological Model ◽  
Response Surfaces ◽  
Model Parameters ◽  
Large Set ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Main Challenge ◽  
Mc Simulation

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods.

scholarly journals Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

2020 ◽  
Vol 24 (9) ◽  
pp. 4641-4658 ◽  
Author(s):  
Aynom T. Teweldebrhan ◽  
Thomas V. Schuler ◽  
John F. Burkhart ◽  
Morten Hjorth-Jensen
Keyword(s):  
Machine Learning ◽  
Parameter Identification ◽  
Hydrological Model ◽  
Response Surfaces ◽  
Model Parameters ◽  
Large Set ◽  
Distributed Hydrological Model ◽  
Streamflow Prediction ◽  
Main Challenge ◽  
Mc Simulation

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to acquire an adequate sample size, which may take from days to months – especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual-based response surfaces. Here, we apply emulators of an MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time-relaxed limits of acceptability concept (pLoA). Three machine-learning models (MLMs) were built using model parameter sets and response surfaces with a limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time-relaxed limits of acceptability approach, based on the predicted pLoA values, and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations with an R2 value of 0.7 to 0.92. Similarly, the models identified using the coupled machine-learning (ML) emulators and limits of acceptability approach have performed very well in reproducing the median streamflow prediction during the calibration and validation periods, with an average Nash–Sutcliffe efficiency value of 0.89 and 0.83, respectively.

Use of Machine Learning algorithms in evaluating the WRF model parameter sensitivity for the simulation of tropical cyclones

2021 ◽  
Author(s):  
Baki Harish ◽  
Sandeep Chinta ◽  
Chakravarthy Balaji ◽  
Balaji Srinivasan
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Tropical Cyclones ◽  
Surrogate Model ◽  
Learning Algorithms ◽  
Wrf Model ◽  
Machine Learning Algorithms ◽  
Model Parameters ◽  
Model Parameter ◽  
Parameter Values

&lt;p&gt;The Indian subcontinent is prone to tropical cyclones that originate in the Indian Ocean and cause widespread destruction to life and property. Accurate prediction of cyclone track, landfall, wind, and precipitation are critical in minimizing damage. The Weather Research and Forecast (WRF) model is widely used to predict tropical cyclones. The accuracy of the model prediction depends on initial conditions, physics schemes, and model parameters. The parameter values are selected empirically by scheme developers using the trial and error method, implying that the parameter values are sensitive to climatological conditions and regions. The number of tunable parameters in the WRF model is about several hundred, and calibrating all of them is highly impossible since it requires thousands of simulations. Therefore, sensitivity analysis is critical to screen out the parameters that significantly impact the meteorological variables. The Sobol&amp;#8217; sensitivity analysis method is used to identify the sensitive WRF model parameters. As this method requires a considerable amount of samples to evaluate the sensitivity adequately, machine learning algorithms are used to construct surrogate models trained using a limited number of samples. They could help generate a vast number of required pseudo-samples. Five machine learning algorithms, namely, Gaussian Process Regression (GPR), Support Vector Machine, Regression Tree, Random Forest, and K-Nearest Neighbor, are considered in this study. Ten-fold cross-validation is used to evaluate the surrogate models constructed using the five algorithms and identify the robust surrogate model among them. The samples generated from this surrogate model are then used by the Sobol&amp;#8217; method to evaluate the WRF model parameter sensitivity.&lt;/p&gt;

scholarly journals Model parameter estimation using coherent structure colouring

2018 ◽  
Vol 861 ◽  
pp. 886-900 ◽  
Author(s):  
Kristy L. Schlueter-Kuck ◽  
John O. Dabiri
Keyword(s):  
Parameter Estimation ◽  
Data Assimilation ◽  
Coherent Structure ◽  
Large Scale ◽  
Numerical Models ◽  
Spatial Uncertainty ◽  
Chaotic Advection ◽  
Model Parameters ◽  
Model Parameter ◽  
Lagrangian Data

Lagrangian data assimilation is a complex problem in oceanic and atmospheric modelling. Tracking drifters in large-scale geophysical flows can involve uncertainty in drifter location, complex inertial effects and other factors which make comparing them to simulated Lagrangian trajectories from numerical models extremely challenging. Temporal and spatial discretisation, factors necessary in modelling large scale flows, also contribute to separation between real and simulated drifter trajectories. The chaotic advection inherent in these turbulent flows tends to separate even closely spaced tracer particles, making error metrics based solely on drifter displacements unsuitable for estimating model parameters. We propose to instead use error in the coherent structure colouring (CSC) field to assess model skill. The CSC field provides a spatial representation of the underlying coherent patterns in the flow, and we show that it is a more robust metric for assessing model accuracy. Through the use of two test cases, one considering spatial uncertainty in particle initialisation, and one examining the influence of stochastic error along a trajectory and temporal discretisation, we show that error in the coherent structure colouring field can be used to accurately determine single or multiple simultaneously unknown model parameters, whereas a conventional error metric based on error in drifter displacement fails. Because the CSC field enhances the difference in error between correct and incorrect model parameters, error minima in model parameter sweeps become more distinct. The effectiveness and robustness of this method for single and multi-parameter estimation in analytical flows suggest that Lagrangian data assimilation for real oceanic and atmospheric models would benefit from a similar approach.

scholarly journals Reliable Delay Based Algorithm to Boost PUF Security Against Modeling Attacks

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 224 ◽  
Author(s):  
Fathi Amsaad ◽  
Mohammed Niamat ◽  
Amer Dawoud ◽  
Selcuk Kose
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Cyber Attacks ◽  
Operating Conditions ◽  
Ring Oscillator ◽  
Optimal Time ◽  
Large Set ◽  
Physical Unclonable Functions ◽  
Secret Keys ◽  
Security Algorithm

Silicon Physical Unclonable Functions (sPUFs) are one of the security primitives and state-of-the-art topics in hardware-oriented security and trust research. This paper presents an efficient and dynamic ring oscillator PUFs (d-ROPUFs) technique to improve sPUFs security against modeling attacks. In addition to enhancing the Entropy of weak ROPUF design, experimental results show that the proposed d-ROPUF technique allows the generation of larger and updated challenge-response pairs (CRP space) compared with simple ROPUF. Additionally, an innovative hardware-oriented security algorithm, namely, the Optimal Time Delay Algorithm (OTDA), is proposed. It is demonstrated that the OTDA algorithm significantly improves PUF reliability under varying operating conditions. Further, it is shown that the OTDA further efficiently enhances the d-ROPUF capability to generate a considerably large set of reliable secret keys to protect the PUF structure from new cyber-attacks, including machine learning and modeling attacks.

Navigating through extreme flood simulations with intelligently chosen parameter sets

2020 ◽  
Author(s):  
Anna E. Sikorska-Senoner ◽  
Bettina Schaefli ◽  
Jan Seibert
Keyword(s):  
Climate Variability ◽  
Hydrological Model ◽  
Weather Generator ◽  
Model Parameters ◽  
Large Set ◽  
Return Periods ◽  
Simulation Framework ◽  
Model Parameter ◽  
Extreme Floods ◽  
Extreme Flood

&lt;p&gt;The quantification of extreme floods and associated return periods remains to be a challenge for flood hazard management and is particularly important for applications where the full hydrograph shape is required (e.g., for reservoir management). One way of deriving such estimates is by employing a comprehensive hydrological simulation framework, including a weather generator, to simulate a large set of flood hydrographs. In such a setting, the estimation uncertainties originate from the hydrological model, but also from the climate variability. While the uncertainty from the hydrological model can be described with common methods of uncertainty estimation in hydrology (in particular related to model parameters), the uncertainties from climate variability can only be represented with repeated realizations of meteorological scenarios. These scenarios can be generated with the help of the selected weather generator(s), which are capable of providing numerous and continuous long time series. Such generated meteorological scenarios are then used as input for a hydrological model to simulate a large sample of extreme floods, from which return periods can be computed based on ranking.&lt;/p&gt;&lt;p&gt;In such a simulation framework, many thousands of possible combinations of meteorological scenarios and of hydrological model parameter sets may be generated. However, these simulations are required at a high temporal resolution (hourly), needed for the simulation of extreme floods and for determining infrequent floods of a return period equal to or lower than 1000 years. Accordingly, due to computational constraints related to any hydrological model, one often needs to preselect meteorological scenarios and representative model parameter sets to be used within the simulation framework. Thus, some kind of an intelligent parameter selection for deriving the uncertainty ranges of extreme model simulations for such rare events would be very beneficial but is currently missing.&lt;/p&gt;&lt;p&gt;Here we present results from an experimental study where we tested three different methods of selecting a small number of representative parameter sets for a Swiss catchment. We used 100 realizations of 100 years of synthetic precipitation-streamflow data. We particularly explored the reliability of the extreme flood uncertainty intervals derived from the reduced parameter set ensemble (consisting of only three representative parameter sets) compared to the full range of 100 parameter sets available. Our results demonstrated that the proposed methods are efficient for deriving uncertainty intervals for extreme floods. These findings are promising for the simulation of extreme floods in comparable simulation frameworks for hydrological risk assessment.&lt;/p&gt;

Accelerating Mature Field EOR Evaluation Using Machine Learning Uncertainty Workflows Integrating Subsurface And Economics

2021 ◽  
Author(s):  
Mathias Bayerl ◽  
Pascale Neff ◽  
Torsten Clemens ◽  
Martin Sieberer ◽  
Barbara Stummer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Economic Evaluation ◽  
Dynamic Modeling ◽  
Oil Recovery ◽  
History Matching ◽  
Development Planning ◽  
Pattern Generation ◽  
Oil Field ◽  
Production History ◽  
Roll Out

Abstract Field re-development planning for tertiary recovery projects in mature fields traditionally involves a comprehensive subsurface evaluation circle, including static/dynamic modeling, scenario assessment and candidate selection based on economic models. The aforementioned sequential approach is time-consuming and includes the risk of delaying project maturation. This work introduces a novel approach which integrates subsurface geological and dynamic modeling as well as economics and uses machine learning augmented uncertainty workflows to achieve project acceleration. In the elaborated enhanced oil recovery (EOR) evaluation process, a machine learning assisted approach is used in order to narrow geological and dynamic parameter ranges both for model initialization and subsequent history matching. The resulting posterior parameter distributions are used to create the input models for scenario evaluation under uncertainty. This scenario screening comprises not only an investigation of qualified EOR roll-out areas, but also includes detailed engineering such as well spacing optimization and pattern generation. Eventually, a fully stochastic economic evaluation approach is performed in order to rank and select scenarios for EOR implementation. The presented workflow has been applied successfully for a mature oil field in Central/Eastern Europe with 60+ years of production history. It is shown that by using a fully stochastic approach, integrating subsurface engineering and economic evaluation, a considerable acceleration of up to 75% in project maturation time is achieved. Moreover, the applied workflow stands out due to its flexibility and adaptability based on changes in the project scope. In the course of this case study, a sector roll-out of chemical EOR is elaborated, including a proposal for 27 new well candidates and 17 well conversions, resulting in an incremental oil production of 4.7MM bbl. The key findings were: A workflow is introduced that delivers a fully stochastic economic evaluation while honoring the input and measured data.The delivered scenarios are conditioned to the geological information and the production history in a Bayesian Framework to ensure full consistency of the selected subsurface model advanced to forecasting.The applied process results in substantial time reduction for an EOR re-development project evaluation cycle.

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