Academic literature on the topic 'Multi-variate observations'
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Journal articles on the topic "Multi-variate observations"
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Alazman, Ibtehal, Badr Saad T. Alkahtani, and Shahid Ahmad Wani. "Certain Properties of Δh Multi-Variate Hermite Polynomials." Symmetry 15, no. 4 (March 31, 2023): 839. http://dx.doi.org/10.3390/sym15040839.
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The research described in this paper follows the hypothesis that the monomiality principle leads to novel results that are consistent with past knowledge. Thus, in line with prior facts, our aim is to introduce the Δh multi-variate Hermite polynomials ΔhHm(q1,q2,⋯,qr;h). We obtain their recurrence relations by using difference operators. Furthermore, symmetric identities satisfied by these polynomials are established. The operational rules are helpful in demonstrating the novel characteristics of the polynomial families, and thus the operational principles satisfied by these polynomials are derived and will prove beneficial for future observations.
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FRITZNER, SINDRE M., RUNE G. GRAVERSEN, KEGUANG WANG, and KAI H. CHRISTENSEN. "Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation." Journal of Glaciology 64, no. 245 (April 25, 2018): 387–96. http://dx.doi.org/10.1017/jog.2018.33.
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ABSTRACTIncreasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method for assimilation of sea-ice variables is introduced. The MVN assimilation method includes procedures for multivariate update of sea-ice volume and concentration, and for extrapolation of observational information spatially. The MVN assimilation scheme is compared with the Ensemble Kalman Filter (EnKF) using the Los Alamos Sea Ice Model. Two multi-variate experiments are conducted: in the first experiment, sea-ice thickness from the European Space Agency's Soil Moisture and Ocean Salinity mission is assimilated, and in the second experiment, sea-ice concentration from the ocean and Sea Ice Satellite Application Facility is assimilated. The multivariate effects are cross-validated by comparing the model with non-assimilated observations. It is found that the simple and computationally cheap MVN method shows comparable skills to the more complicated and expensive EnKF method for multivariate update. In addition, we show that when few observations are available, the MVN method is a significant model improvement compared to the version based on one-dimensional sea-ice concentration assimilation.
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Rannik, Ü., N. Altimir, I. Mammarella, J. Bäck, J. Rinne, T. M. Ruuskanen, P. Hari, T. Vesala, and M. Kulmala. "Ozone deposition into a boreal forest over a decade of observations: evaluating deposition partitioning and driving variables." Atmospheric Chemistry and Physics Discussions 12, no. 5 (May 22, 2012): 12715–58. http://dx.doi.org/10.5194/acpd-12-12715-2012.
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Abstract. This study scrutinizes a decade-long series of ozone deposition measurements in a boreal forest in search for the signature and relevance of the different deposition processes. Canopy-level ozone flux measurements were analysed for deposition characteristics and partitioning into stomatal and non-stomatal fractions, focusing on growing season day-time data. Ten years of measurements enabled the analysis of ozone deposition variation at different time- scales, including daily to inter-annual variation as well as the dependence on environmental variables and concentration of biogenic volatile organic compounds (BVOC-s). Stomatal deposition was estimated by using multi-layer canopy dispersion and optimal stomatal control modelling from simultaneous carbon dioxide and water vapour flux measurements, non-stomatal was inferred as residual. Also, utilising big-leaf assumption stomatal conductance was inferred from water vapour fluxes for dry canopy conditions. The total ozone deposition was highest during the peak growing season (4 mm s−1) and lowest during winter dormancy (1 mm s−1). During the course of the growing season the fraction of the non-stomatal deposition of ozone was determined to vary from 26 to 44% during day time, increasing from the start of the season until the end of the growing season. By using multi-variate analysis it was determined that day-time total ozone deposition was mainly driven by photosynthetic capacity of the canopy, vapour pressure deficit (VPD), photosynthetically active radiation and monoterpene concentration. The multi-variate linear model explained high portion of ozone deposition variance on daily average level (R2 = 0.79). The explanatory power of the multi-variate model for ozone non-stomatal deposition was much lower (R2 = 0.38). Model calculation was performed to evaluate the potential sink strength of the chemical reactions of ozone with sesquiterpenes in the canopy air space, which revealed that sesquiterpenes in typical amounts at the site were unlikely to cause significant ozone loss in canopy air space. This was also confirmed by the statistical analysis that did not link measured sesquiterpene concentration with ozone deposition. It was concluded that chemical reactions with monoterpenes, or other removal mechanisms such as surface reactions, play a role as ozone non-stomatal sink inside canopy.
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Rannik, Ü., N. Altimir, I. Mammarella, J. Bäck, J. Rinne, T. M. Ruuskanen, P. Hari, T. Vesala, and M. Kulmala. "Ozone deposition into a boreal forest over a decade of observations: evaluating deposition partitioning and driving variables." Atmospheric Chemistry and Physics 12, no. 24 (December 21, 2012): 12165–82. http://dx.doi.org/10.5194/acp-12-12165-2012.
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Abstract. This study scrutinizes a decade-long series of ozone deposition measurements in a boreal forest in search for the signature and relevance of the different deposition processes. The canopy-level ozone flux measurements were analysed for deposition characteristics and partitioning into stomatal and non-stomatal fractions, with the main focus on growing season day-time data. Ten years of measurements enabled the analysis of ozone deposition variation at different time-scales, including daily to inter-annual variation as well as the dependence on environmental variables and concentration of biogenic volatile organic compounds (BVOC-s). Stomatal deposition was estimated by using multi-layer canopy dispersion and optimal stomatal control modelling from simultaneous carbon dioxide and water vapour flux measurements, non-stomatal was inferred as residual. Also, utilising the big-leaf assumption stomatal conductance was inferred from water vapour fluxes for dry canopy conditions. The total ozone deposition was highest during the peak growing season (4 mm s−1) and lowest during winter dormancy (1 mm s−1). During the course of the growing season the fraction of the non-stomatal deposition of ozone was determined to vary from 26 to 44% during day time, increasing from the start of the season until the end of the growing season. By using multi-variate analysis it was determined that day-time total ozone deposition was mainly driven by photosynthetic capacity of the canopy, vapour pressure deficit (VPD), photosynthetically active radiation and monoterpene concentration. The multi-variate linear model explained the high portion of ozone deposition variance on daily average level (R2 = 0.79). The explanatory power of the multi-variate model for ozone non-stomatal deposition was much lower (R2 = 0.38). The set of common environmental variables and terpene concentrations used in multivariate analysis were able to predict the observed average seasonal variation in total and non-stomatal deposition but failed to explain the inter-annual differences, suggesting that some still unknown mechanisms might be involved in determining the inter-annual variability. Model calculation was performed to evaluate the potential sink strength of the chemical reactions of ozone with sesquiterpenes in the canopy air space, which revealed that sesquiterpenes in typical amounts at the site were unlikely to cause significant ozone loss in canopy air space. The results clearly showed the importance of several non-stomatal removal mechanisms. Unknown chemical compounds or processes correlating with monoterpene concentrations, including potentially reactions at the surfaces, contribute to non-stomatal sink term.
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Wani, Shahid Ahmad, Ibtehal Alazman, and Badr Saad T. Alkahtani. "Certain Properties and Applications of Convoluted Δh Multi-Variate Hermite and Appell Sequences." Symmetry 15, no. 4 (March 29, 2023): 828. http://dx.doi.org/10.3390/sym15040828.
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This study follows the line of research that by employing the monomiality principle, new outcomes are produced. Thus, in line with prior facts, our aim is to introduce the Δh multi-variate Hermite Appell polynomials ΔhHAm[r](q1,q2,⋯,qr;h). Further, we obtain their recurrence sort of relations by using difference operators. Furthermore, symmetric identities satisfied by these polynomials are established. The operational rules are helpful in demonstrating the novel characteristics of the polynomial families and thus operational principle satisfied by these polynomials is derived and will prove beneficial for future observations. Further, a few members of the Δh Appell polynomial family are considered and their corresponding results are derived accordingly.
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Brassington, Gary B., and Prasanth Divakaran. "The theoretical impact of remotely sensed sea surface salinity observations in a multi-variate assimilation system." Ocean Modelling 27, no. 1-2 (January 2009): 70–81. http://dx.doi.org/10.1016/j.ocemod.2008.12.005.
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Ciani, Daniele, Sarah Asdar, and Bruno Buongiorno Nardelli. "Improved Surface Currents from Altimeter-Derived and Sea Surface Temperature Observations: Application to the North Atlantic Ocean." Remote Sensing 16, no. 4 (February 8, 2024): 640. http://dx.doi.org/10.3390/rs16040640.
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We present a study on the ocean surface currents reconstruction by merging Level-4 (L4, gap-free) altimeter-derived geostrophic currents and satellite sea surface temperature. Building upon past studies on the multi-variate reconstruction of geostrophic currents from satellite observations, we regionalized and optimized an algorithm to improve the altimeter-derived surface circulation estimates in the North Atlantic Ocean. A ten-year-long time series (2010–2019) is presented and validated by means of in situ observations. The newly optimized algorithm allowed us to improve the currents estimate along the main axis of the Gulf Stream and in correspondence of well-known upwelling areas in the North Eastern Atlantic, with percentage improvements of around 15% compared to standard operational altimetry products.
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Mereu, Luigi, Simona Scollo, Antonella Boselli, Giuseppe Leto, Ricardo Zanmar Sanchez, Costanza Bonadonna, and Frank Silvio Marzano. "Dual-Wavelength Polarimetric Lidar Observations of the Volcanic Ash Cloud Produced during the 2016 Etna Eruption." Remote Sensing 13, no. 9 (April 29, 2021): 1728. http://dx.doi.org/10.3390/rs13091728.
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Lidar observations are very useful to analyse dispersed volcanic clouds in the troposphere mainly because of their high range resolution, providing morphological as well as microphysical (size and mass) properties. In this work, we analyse the volcanic cloud of 18 May 2016 at Mt. Etna, in Italy, retrieved by polarimetric dual-wavelength Lidar measurements. We use the AMPLE (Aerosol Multi-Wavelength Polarization Lidar Experiment) system, located in Catania, about 25 km from the Etna summit craters, pointing at a thin volcanic cloud layer, clearly visible and dispersed from the summit craters at the altitude between 2 and 4 km and 6 and 7 km above the sea level. Both the backscattering and linear depolarization profiles at 355 nm (UV, ultraviolet) and 532 nm (VIS, visible) wavelengths, respectively, were obtained using different angles at 20°, 30°, 40° and 90°. The proposed approach inverts the Lidar measurements with a physically based inversion methodology named Volcanic Ash Lidar Retrieval (VALR), based on Maximum-Likelihood (ML). VALRML can provide estimates of volcanic ash mean size and mass concentration at a resolution of few tens of meters. We also compared those results with two methods: Single-variate Regression (SR) and Multi-variate Regression (MR). SR uses the backscattering coefficient or backscattering and depolarization coefficients of one wavelength (UV or VIS in our cases). The MR method uses the backscattering coefficient of both wavelengths (UV and VIS). In absence of in situ airborne validation data, the discrepancy among the different retrieval techniques is estimated with respect to the VALR ML algorithm. The VALR ML analysis provides ash concentrations between about 0.1 μg/m3 and 1 mg/m3 and particle mean sizes of 0.1 μm and 6 μm, respectively. Results show that, for the SR method differences are less than <10%, using the backscattering coefficient only and backscattering and depolarization coefficients. Moreover, we find differences of 20–30% respect to VALR ML, considering well-known parametric retrieval methods. VALR algorithms show how a physics-based inversion approaches can effectively exploit the spectral-polarimetric Lidar AMPLE capability.
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Grinsted, A. "An estimate of global glacier volume." Cryosphere Discussions 6, no. 5 (September 3, 2012): 3647–66. http://dx.doi.org/10.5194/tcd-6-3647-2012.
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Abstract. I asses the feasibility of multi-variate scaling relationships to estimate glacier volume from glacier inventory data. I calibrate scaling laws against volume observations of optimized towards the purpose of estimating the total global ice volume. This is applied individually to each record in the Randolph Glacier Inventory which is the first globally complete inventory of glaciers and ice caps. I estimate that the total volume of all glaciers in the world is 0.35 ± 0.07 m sea level equivalent. This is substantially less than a recent state-of-the-art estimate. Area volume scaling bias issues for large ice masses, and incomplete inventory data are offered as explanations for the difference.
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Pujol, Léo, Pierre-André Garambois, and Jérôme Monnier. "Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains." Geoscientific Model Development 15, no. 15 (August 3, 2022): 6085–113. http://dx.doi.org/10.5194/gmd-15-6085-2022.
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Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic–hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydrometeorology and satellite data. The multi-D hydraulic model relies on the 2D shallow-water equations solved with a 1D–2D adapted single finite-volume solver. One-dimensional-like reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1D-like–2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on virtual and real cases of increasing complexity, including using the second-order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry friction, upstream and lateral hydrographs and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is, backward hydrology. A 1D-like model of part of the Garonne River is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, with inflow from the semi-distributed hydrological model, is built. High-resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1D-like reaches. The current work constitutes an upgrade of the DassFlow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.
Dissertations / Theses on the topic "Multi-variate observations"
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Archambault, Théo. "Deep learning for sea surface height reconstruction from multi-variate satellite observations." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS253.
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Cette thèse de doctorat porte sur la reconstruction d'images satellites de la surface de l'océan à partir de mesures éparses et bruitées. Son objectif est l'estimation de la hauteur de la mer (SSH), une variable importante pour approximer les courants de surface. Elle est actuellement mesurée par des altimètres pointant au nadir, laissant de nombreuses zones non observées. Les cartes complètes de SSH sont produites en utilisant des interpolations optimales linéaires présentant une faible résolution effective.D'autre part, la température de surface de la mer (SST) est observée sur des zones plus étendues et est physiquement liée aux courants géostrophiques à travers l'advection.Cette thèse explore les algorithmes d'apprentissage profond pour estimer les champs de SSH. En s'appuyant sur des années de données de simulation et d'observations, les réseaux neuronaux profonds sont capables d'apprendre des relations complexes entre les variables SSH et SST. Nous utilisons ces algorithmes ainsi que les observations de température, pour reconstruire la SSH d'abord dans une perspective de réduction d'échelle sur une simulation physique. Ensuite, nous considèrerons le problème de son interpolation sur des données de simulation et d'observation, en nous concentrant particulièrement sur la manière de transférer l'apprentissage dans des contextes opérationnels. Enfin, nous adaptons notre méthode pour produire des estimations en temps réel et des prévisionsThis Ph.D. thesis focuses on reconstructing satellite images of the ocean surface from sparse and noisy measurements. Our objective is the Sea Surface Height (SSH), an important variable to estimate surface currents. It is retrieved through nadir-pointing altimeters, leaving important observation gaps due to their remote sensing technology. Complete SSH maps are produced using linear Optimal Interpolations with low effective resolution.On the other hand, Sea Surface Temperature (SST) products have much higher data coverage, and SST is physically linked to geostrophic currents through advection.This thesis explores deep learning algorithms to estimate SSH fields. Relying on years of data from simulation and observations, deep neural networks are able to learn complex relationships between SSH and SST variables. Using these algorithms and SST observations, we first enhance SSH mapping from a downscaling perspective on a physical simulation. Then, we tackle the SSH interpolation problem on simulation and observation data, with a particular focus on how to transfer the learning in operational settings. Finally, we adapt our method to produce near real-time and forecast estimations
Book chapters on the topic "Multi-variate observations"
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Musolino, Giuseppe, Antonio Cartisano, and Giuseppe Fortugno. "Methodologies for Sustainable Development of TEN-T/RFC Corridors and Core Ports: Estimation of Time-Series Economic Impact." In Computational Science and Its Applications – ICCSA 2023 Workshops, 551–62. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37123-3_38.
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AbstractThe container revolution in the last decades of the 20th century determined the arise of container ports. They further evolved in the so-called third-generation ports, becoming generators of value added due to the manipulation of goods in transit. The increase of value added in third-generation ports is amplified in core ports connected to TEN-T/RFC corridors, where Special Economic Zones (SEZs) and the three pillars of smartness (ICT, Transport and Energy) are present. The paper deals with time-series models for the estimation of economic impact of a SEZ in an underdeveloped region of EU. The test case is the Calabria region (Italy). SEZ in Calabria has its fulcrum in the industrial area close to the transhipment hub port of Gioia Tauro. The economic impacts of the SEZ were quantified through two variables: exports and employment of industrial firms settled in Calabria. The comparison of two scenarios (Do-Nothing scenario and SEZ) shows relevant positive impacts in the SEZ one. Future developments concern the calibration and validation of multi-variate time-series models from observations provided by worldwide SEZs.
Conference papers on the topic "Multi-variate observations"
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Mackay, E. B. L., C. J. R. Murphy-Barltrop, and P. Jonathan. "The SPAR Model: A New Paradigm for Multivariate Extremes. Application to Joint Distributions of Metocean Variables." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-130932.
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Abstract This paper presents the application of a new multivariate extreme value model for the estimation of metocean variables. The model requires fewer assumptions about the forms of the margins and dependence structure compared to existing approaches, and provides a flexible and rigorous framework for modelling multi-variate extremes. The method involves a transformation of variables to polar coordinates. The tail of the radial variable is then modelled using the generalised Pareto distribution, with parameters conditional on angle, providing a natural extension of uni-variate theory to multivariate problems. The resulting model is referred to as the semi-parametric angular-radial (SPAR) model. We consider the estimation of the joint distributions of (1) wave height and wave period, and (2) wave height and wind speed. We show that the SPAR model provides a good fit to the observations in terms of both the marginal distributions and dependence structures. The use of the SPAR model for estimating long-term extreme responses of offshore structures is discussed, using some simple response functions for floating structures and an offshore wind turbine with monopile foundation. We show that the SPAR model is able to accurately reproduce response distributions, and provides a realistic quantification of uncertainty.
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Chun, Sejong. "Asymptotic Expansion Technique for Evaluating the Uncertainty of Moist-Air Density Formula." In ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/ajkfluids2019-4635.
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Abstract Asymptotic expansion technique can evaluate the measurement uncertainty by classifying an output quantity into a measured value and its correction values. The asymptotic expansion technique combines simultaneous observations of input quantities into the output measured value. The asymptotic expansion technique is useful in evaluating a multi-variate output quantity such as the moist-air density formula (CIPM-2007), in which covariances among input quantities could complicate the evaluation of measurement uncertainty. This study demonstrates that both the Taylor’s series expansion and the chain rule of differentiation are enough to calculate the sensitivity coefficients for the CIPM-2007 air density formula. The measurement uncertainty is found to be greater than the original CIPM-2007 formula by two orders of magnitude. It is because the uncertainty of correction values come from a commercial instrument for monitoring laboratory environments. Nevertheless, the asymptotic expansion technique is useful for measurement uncertainty evaluation to avoid subtle problems of ignoring covariance of input quantities in the literature.
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Farahat, Waleed A., and H. Harry Asada. "Identification of Phenotypic State Transition Probabilities in Living Cells." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2705.
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Living cells stochastically switch their phenotypic states in response to environmental cues to maintain persistence and viability. Estimating the state transition probabilities from biological observations of cell populations gives valuable insight to the underlying processes, and gives insights as to how the transition statistics are influenced by external factors. In this work, we present two Bayesian estimation approaches. The first is applicable when individual cell state trajectories are observed. The second approach is applicable when only aggregate population statistics are available. Estimation of transition probabilities when individual cell state trajectories are available is a straightforward problem, whereas estimation from only aggregate statistics can be computationally expensive. In the latter case, we present an algorithm that relies on three key ideas to cut down computational time: i) approximating high-dimensional multinomial distributions with multi-variate Gaussians, ii) employing Monte-Carlo techniques to efficiently integrate over high dimensional spaces, and iii) explicitly incorporating sampling constraints by computing lower dimensional distributions over the constrained variable. Simulation results demonstrate the viability of the algorithm.
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Goodwin, Nigel H. "Bridging the Gap Between Material Balance and Reservoir Simulation for History Matching and Probabilistic Forecasting Using Machine Learning." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203941-ms.
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Abstract Objectives/Scope Methods for efficient probabilistic history matching and forecasting have been available for complex reservoir studies for nearly 20 years. These require a surprisingly small number of reservoir simulation runs (typically less than 200). Nowadays, the bottleneck for reservoir decision support is building and maintaining a reservoir simulation model. This paper describes an approach which does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance. It can be useful where a full simulation model is not economically justified, or where rapid decisions need to be made. Methods, Procedures, Process Previous work has described the use of proxy models and Hamiltonian Markov Chain Monte Carlo to produce valid probabilistic forecasts. To generate a data driven model, we take historical measurements of rates and pressures at each well, and apply multi-variate time series to generate a set of differential-algebraic equations (DAE) which can be integrated over time using a fully implicit solver. We combine the time series models with material balance equations, including a simple PVT and Z factor model. The parameters are adjusted in a fully Bayesian manner to generate an ensemble of models and a probabilistic forecast. The use of a DAE distinguishes the approach from normal time-series analysis, where an ARIMA model or state space model is used, and is normally only reliable for short term forecasting. Results, Observations, Conclusions We apply these techniques to the Volve reservoir model, and obtain a good history match. Moreover, the effort to build a reservoir model has been removed. We demonstrate the feasibility of simple physics models, and open up the possibility of combinations of physics models and machine learning models, so that the most appropriate approach can be used depending on resources and reservoir complexity. We have bridged the gap between pure machine learning models and full reservoir simulation. Novel/Additive Information The approach to use multi-variate time series analysis to generate a set of ordinary differential equations is novel. The extension of previously described probabilistic forecasting to a generalised model has many possible applications within and outside the oil and gas industry, and is not restricted to reservoir simulation.
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Suboyin, Abhijith, Morgan Eldred, Jimmy Thatcher, Abdul Rehman, Ivan Gee, and Hassaan Anjum. "Environomics Framework for Sustainable Business Practices: Industrial Case Studies on True Impact Reduction and Process Optimization Through AI." In SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry. SPE, 2023. http://dx.doi.org/10.2118/214459-ms.
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Abstract Artificial Intelligence (AI) has significant potential to optimize practices, processes, and energy consumption along with maximizing yield, quality, and uptime. This has substantial impact on putting organizations on the path to net-zero, as such optimizations can reduce greenhouse gas emissions by 20% with minimal capital investments. This comprehensive study presents proven industrial case studies that delivered economically strong strategies coupled with sustainability practice and providing strategic insights to identify, manage and/or attenuate the associated impacts. Environomics presented in this study is a novel framework which deals with unifying economic strategies with sustainability practices (through artificial intelligence) for optimal business performance in terms of finances but also environmental impact. This is achieved through a track, trace, and optimize approach for resources (particularly emissions, energy, water, waste, materials,, and safety) This was achieved through a combination of AI methods such as unsupervised machine learning, multi-variate optimization, and the implementation of similarity measures. A few of the inputs included well data (including production data, drilling data, completion data etc.), logistics/supply chain data (scheduling data, production inventory, mobilization data etc.), safety data (near-miss, observations, hazards, disciplines and insights etc.) with associated costs and emission data. Multiple industrial case studies are presented where sustainability metrics are identified through validated AI models to optimize productivity while reducing emissions and inventory. For instance, well profiling can be used to identify historical parameters that have maximized production potential while optimizing for aspects such as cost or emissions. Furthermore, we can identify the optimal completion parameters for a new well which satisfies carbon targets, use well profiles to build an optimized drilling schedule that meets budget or production criteria while still achieving production targets and optimizing drilling rig routes. Thus, the approach can quickly (within run time) solve interrelated environomic challenges in the reservoir studies space and the field development space. Further case studies indicate that the supply chain can have immense optimization impact on scope 3 aspects with results indicating 30-50% asset utilization improvement with respect to fleets (Vessel, Truck, Rigs). With respect to materials, a 10-20% reduction of material inventory levels all improved through AI. As the workforce are also part of the environment it has been observed that identifying unsafe behaviors within a large operation, also leads to enhanced sustainability behaviors. The models indicate potential of overall emission reduction ranging from 12-20%. This led to the comprehensive framework presented in this study to support sustainable practices that are also economically feasible and deployable. The real-time sustainability metrics generated has immense values in terms of decision-making processes and scenario generation in a fraction of the time that is required using traditional approaches. In addition to assessing the scope of impact, a novel multidisciplinary study and framework is presented to analyze environomic strategies to propose a market-oriented approach through the application of artificial intelligence. Furthermore, industrial, and academic case studies have been evaluated to identify, predict, and optimize the crucial parameters within such workflows that are effective in reducing resources utilized and associated emissions.
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Alrumaih, Abdulrahman W., Kingsley Kanu, and Zahra Sakhin. "Identifying Optimal Operating Envelope in Horizontal Wells Producing from Dual Permeability Media." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22777-ea.
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Abstract In this paper, we present an integrated workflow utilizing a numerical model to identify optimal operating envelope for horizontal wells producing from dual permeability media. A synthetic dual permeability numerical model comprised of critical components namely: fracture and matrix permeabilities, matrix-fracture conductivity (shape factor), and fracture distribution based on Discrete Fracture Network (DFN) scheme was built. In addition, two horizontal producers completed with Inflow-Control – Devices (ICDs) and as open-hole respectively, are also connected to the model. Rigorous sensitivity analysis is implemented on these key parameters using the dynamic model under Equalspacing sampling scheme with well oil rate as the objective function. Range of values on each variable are typical for naturally fractured reservoirs. In addition, detailed sensitivity on ICD parameters such as flow resistance ratings are performed. Results from the analysis are presented in cumulative probability function identifying most likely, pessimistic and optimistic values. Among all the variables, Matrix-fracture transmissibility has most impact. Unfortunately, it is often neglected when designing horizontal wells in dual permeability reservoirs. Current practice pays most attention on permeability contrast lack of consensus in the industry on best technique for its estimation. Most common methodologies applied are Kazemi and Warren-Roots. Nevertheless, it plays a critical role in describing dual permeability subsurface flow mechanism. Most significantly, it is widely acknowledged that beaning up choke sizes enhances production performance of wells connected to dual permeability. However, in this study, we have been able to establish operating limits for this phenomenon. Matrix-fracture transmissibility could significantly influence a producer, which has not intersected high permeability streaks. Our study clearly demonstrates this observation using high-resolution dual permeability dynamic model. In addition, by performing multi-variate characterization, we establish operating envelope for optimizing well production performance in dual media systems through choke size settings.