Correlation-based Localization Research Articles

Characterization of conductivity fields through iterative ensemble smoother and improved correlation-based adaptive localization

Localization is critical to the effective use of an (iterative) ensemble Kalman filter or ensemble smoother to estimate uncertain quantities of interest. Here, we propose a novel, fully adaptive, correlation-based localization method (termed FBadap). We embed our FBadap approach within an iterative ensemble smoother to estimate three-dimensional spatially heterogeneous log-conductivity (Y) fields. The latter are characterized through a Generalized sub-Gaussian model, which includes the Gaussian distribution as a particular case. They constitute random fields within which head and concentration observations are collected at monitoring wells screened at multiple depths. To ensure transparent comparisons, we study and analyze the performance of our approach through a wide range of synthetic test cases. These comprise diverse configurations, including (a) various ensemble sizes, (b) various degrees of departure of the description of the spatial heterogeneity from a Gaussian model, as well as (c) different values of the mean and variance of the initial ensemble of Y. Our results show that (i) FBadap is robust adaptive approach enabling one to tackle a variety of settings; (ii) FBadap exhibits stronger adaptivity to cope with diverse ensemble sizes than FBconst, and can provide improved accuracy of conductivity estimates in comparison with traditional methods; and (iii) the quality of conductivity estimates is jointly impacted by the degree of departures of the reference Y field and of the initial ensemble of Y from a description based on a Gaussian model.

Complexity analysis for recent ALOHA random access techniques in satellite communications

SummaryIn this paper, we study the the complexity of packet localization at reception, for recent synchronous Random Access (RA) techniques based on the protocol ALOHA for satellite communications. The promising CRDSA (Contention Resolution Diversity Slotted ALOHA) offers better throughput, in comparison to the traditional slotted ALOHA protocols, thanks to the use of Successive Interference Cancellation (SIC) along with multireplica transmission. MARSALA (Multi‐replicA decoding using corRelation baSed locALizAtion) is one of the many variants and enhancement schemes of CRDSA that have been proposed in the literature. It is applied to CRDSA each time a decoding deadlock situation is reached (when no packets can be retrieved by CRDSA). MARSALA first localizes the replicas of collided packets on a chosen reference time slot using correlations. Then it performs coherent signal combination of packet replicas prior to decoding. However, despite the good performance offered by MARSALA, its localization process adds a significant complexity to the receiver in terms of correlation operations. R‐SPOTiT (Random Shared POsition Technique for Interfered random Transmissions) mitigates this complexity by introducing a shared information between the receiver and each of the transmitters, about all potential packets' locations on the frame, without any additional signaling overhead. We focus in this paper on the analysis of the total number of correlations which are needed to localize packets' replicas for both MARSALA and R‐SPOTiT, with a single or with multiple Gold preambles. This should include preamble detection operations that are performed at CRDSA with a coarse and fine tracking. The results show that the most suitable system to use is the multi‐preamble R‐SPOTiT with two preambles.

Simultaneous assimilation of production and seismic data: application to the Norne field

Automatic history matching using production and seismic data is still challenging due to the size of seismic datasets. The most severe problem, when applying ensemble-based methods for assimilating large datasets, is that the uncertainty is usually underestimated due to the limited number of models in the ensemble compared with the dimension of the data, which inevitably leads to an ensemble collapse. Localization and data reduction methods are promising approaches mitigating this problem. In this paper, we present a new robust and flexible workflow for assimilating seismic attributes and production data. The methodology is based on sparse representation of the seismic data, using methods developed for image denoising. We propose to assimilate production and seismic data simultaneously, and to ensure equal weight on these data types, we apply scaling based on the initial data match. Further, a newly developed flexible correlation-based localization technique is used for both data types. The workflow is successfully implemented for the released Norne benchmark dataset, and an iterative ensemble smoother is used for the simultaneous assimilation of production and seismic data. We show that the methodology is robust and ensemble collapse is avoided. Furthermore, the proposed workflow is flexible, as it can be applied to seismic data or inverted seismic properties, and the methodology requires only moderate computer memory. The results show that through this method, we can successfully reduce the data mismatch for both production data and seismic data.

Automatic and adaptive localization for ensemble-based history matching

Ensemble-based history matching methods are among the state-of-the-art approaches to reservoir characterization. In practice, however, they often suffer from ensemble collapse, a phenomenon that deteriorates history matching performance. To prevent ensemble collapse, it is customary to equip an ensemble history matching algorithm with a certain localization scheme.In a previous study, the authors propose an adaptive localization scheme that exploits the correlations between model variables and simulated observations. Correlation-based adaptive localization not only overcomes some longstanding issues (e.g., the challenges in handling non-local or time-lapse observations) arising in the conventional distance-based localization, but also is more convenient and flexible to use in real field case studies.In applications, however, correlation-based localization is also found to be subject to two problems. One is that, it requires to run a relatively large ensemble in order to achieve decent performance in an automatic manner, which becomes computationally expensive in large-scale problems. As a result, certain empirical tuning factors are introduced to reduce the computational costs. The other problem is that, the way used to compute the tapering coefficients in the previous study may induce discontinuities, and neglect the information of certain still-influential observations for model updates.The main objective of this work is to improve the efficiency and accuracy of correlation-based adaptive localization, making it run in an automatic manner but without incurring substantial extra computational costs. To this end, we introduce two enhancements that aim to avoid the aforementioned two problems, namely, empirical tuning and discontinuities. We apply the resulting automatic and adaptive correlation-based localization with these two enhancements to a 2D and a 3D cases investigated in the previous study, and show that it leads to better history matching performance (in terms of efficiency and/or estimation accuracy) than that is achieved in the previous work.

Correlation-Based Localization Effective in Ensemble-Based History Matching

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 191305, “Correlation-Based Adaptive Localization for Ensemble-Based History Matching Applied to the Norne Field Case Study,” by Xiaodong Luo, SPE, Rolf Lorentzen, SPE, Randi Valestrand, SPE, and Geir Evensen, SPE, International Research Institute of Stavanger, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has been peer reviewed and published in the October 2018 SPE Journal. Ensemble-based methods are considered to be state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. An ensemble history-matching algorithm is equipped customarily with a localization scheme to prevent ensemble collapse. To enhance the applicability of localization to various history-matching problems, the authors adopt an adaptive localization scheme that exploits the correlations between model variables and observations. Introduction In the current work, the authors focus on adopting an efficient adaptive localization scheme, previously established in the literature, for the full Norne Field case study. The adaptive localization scheme exploits the information of sample correlation coefficients between an ensemble of model variables and the corresponding realizations of simulated observations. The adaptive localization scheme uses a data-selection procedure; however, instead of physical distances between the locations of model variables and observations being used for data selection, the magnitudes of the sample correlation coefficients are used for data selection through a hard-thresholding strategy (i.e., keep or kill). To conduct data selection in the adaptive localization scheme, one specifies a positive correlation-threshold value. For a given observation, if the magnitude of the sample correlation coefficient between a model variable and the simulated observation is greater than the threshold value, then the observation will be used to update that model variable. Otherwise, one discards the observation in the update of that model variable. This described data-selection procedure essentially means that a given model variable is updated using only the observations that have significant correlations with the model variable. The rationale behind this hard-thresholding strategy is the interpretation of the magnitude of the correlation coefficient as a measure to detect the causal relation between a model variable and an observation, and the effect is the suppression of spurious correlations caused by a finite sample size. Correlation-based localization can overcome or alleviate the issues arising in distance-based localization, such as the use of nonlocal and time-lapse observations, the need of physical locations for model variables and measurements, and the different degrees of correlations or sensitivities of model variables to observations. Because data selection depends on the magnitudes of sample correlation coefficients between model variables and the corresponding simulated observations, the measurements need not have associated physical locations. Model variables are thus selected by using those observations that exhibit strong-enough correlations, regardless of the physical distances between observations and model variables. As a result, correlation-based localization can be used to localize nonlocal observations. The changes of correlations caused by the effect of time-lapse observations or different types of model variables will be taken into account automatically in correlation-based localization, and this makes the proposed localization scheme more-adaptive and more-flexible in various situations.

Correlation-Based Adaptive Localization for Ensemble-Based History Matching: Applied To the Norne Field Case Study

Summary Ensemble-based methods are among the state-of-the-art history-matching algorithms. However, in practice, they often suffer from ensemble collapse, a phenomenon that deteriorates history-matching performance. It is customary to equip an ensemble history-matching algorithm with a localization scheme to prevent ensemble collapse. Conventional localization methods use distances between the physical locations of model variables and observations to modify the degree of the observations’ influence on model updates. Distance-based localization methods work well in many problems, but they also suffer from dependence on the physical locations of both model variables and observations, the challenges in dealing with nonlocal and time-lapse measurements, and the nonadaptivity to handling different types of model variables. To enhance the applicability of localization to various history-matching problems, we adopt an adaptive localization scheme that exploits the correlations between model variables and simulated observations. We elaborate how correlation-based adaptive localization can overcome or mitigate issues arising in conventional distance-based localization. To demonstrate the efficacy of correlation-based adaptive localization, we adopt an iterative ensemble smoother (iES) with the proposed localization scheme to history match the real production data of the Norne Field model, and we compare the history-matching results with those obtained by using the iES with distance-based localization. Our study indicates that when compared with distance-based localization, correlation-based localization not only achieves close or better performance in terms of data mismatch, but also is more convenient to use in practical history-matching problems. As a result, the proposed correlation-based localization scheme might serve as a viable alternative to conventional distance-based localization.

Correlation-Based Adaptive Localization With Applications to Ensemble-Based 4D-Seismic History Matching

Summary Ensemble-based history-matching methods have received much attention in reservoir engineering. In real applications, small ensembles are often used in reservoir simulations to reduce the computational costs. A small ensemble size may lead to ensemble collapse, a phenomenon in which the spread of the ensemble of history-matched reservoir models becomes artificially small. Ensemble collapse is not desired for an ensemble-based history-matching method because it not only deteriorates the capacity in uncertainty quantification, but also forces the ensemble-based method to later stop updating reservoir models. In practice, distance-based localization is thus introduced to tackle ensemble collapse. Distance-based localization works well in many problems. However, one prerequisite in using distance-based localization is that the observations have associated physical locations. In certain circumstances with complex observations, this may not be true, and it thus becomes challenging to apply distance-based localization. In this work, we propose a correlation-based adaptive localization scheme that does not rely on the physical locations of the observations. Instead, we use the spatial distributions of the correlations between model variables and the corresponding simulated observations. In the course of history matching, we update model variables by only using the observations that have relatively high correlations with them, while excluding those that have relatively low correlations. This is equivalent to introducing a data-selection procedure to the history-matching algorithm. As a result, the threshold values for data selection play an essential role in the proposed adaptive localization scheme, and we develop both ideal and practical approaches to the choices of the threshold values. We demonstrate the efficacy of the proposed localization scheme using seismic history-matching problems—one 2D and one 3D—in which ensemble collapse is severe in the presence of large amounts of observational data, but distance-based localization may not be applicable because of the lack of physical locations of the seismic data in use. In contrast, correlation-based localization works well to prevent ensemble collapse and also renders good history-matching results. We also note some practical conveniences of the proposed localization scheme, including the applicability to nonlocal observations, the relative simplicity in implementation, the transferability of the same codes among different (either 2D or 3D) case studies, and the adaptivity to different types of observations and petrophysical parameters.

Studies on Damage Detection Using Frequency Change Correlation Approach for Health Assessment

Abstract Damage Location Assurance Criteria (DLAC) which is a correlation based approach between vectors of experimental natural frequency change ratios with vectors of analytical natural frequency change ratios, is adopted in this study for damage assessment. Here, the focus is on damage detection and correlation-based localization. For this study, numerical models of cantilever beam with three different damage locations have been modelled using finite element tool. Decreased mass is considered in this study to incorporate as damage. Natural frequencies for the first four modes are arrived for both undamaged and the damaged state of the structures. The damage incorporated is in the range of 10 percent of mass. From the correlation analysis carried out, the locations of the damage are matching with that of the damage considered. Further, to automate the correlation based algorithm, coding is developed using MATLab.