Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

Z., Y.

doi:10.1007/978-3-030-17860-4

Cited by 42 publications

(20 citation statements)

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“…It is apparent that in the years 1990-2011 subsidence values consistently increase from -234.0 mm in 1990, through -589.6 mm in 2003 (at the maximum number (35) of deep-seated BMs on the structure) to -845.5 mm in 2011 (the largest subsidence in the geodetic monitoring history). A similar tendency as in the case of total vertical displacements uz c of the BMs on the structure, is also observed for average values, which changed from -98.0 to -275.9 mm over the years 1990 ÷ 2011.…”

Section: Methodsmentioning

confidence: 95%

“…They are also used in thematic cartography, e.g. to mapping hydrocarbon reservoirs, and in the oil and natural gas extraction industry [35]. In hydrogeology, they are used to model the properties (permeability and porosity), geometry and hydraulic parameters of aquifers and to assess the pollution of the soil-water environment, soils and groundwater, f.g. by heavy metals content [18, [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Analysis and Short-Term Forecasting of Hydrotechnical Facility Displacements

Namysłowska-Wilczyńska¹,

Janusz²

2020

Int J Earth Sci Geophys

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Methods of applied (spatial) statistics enable the analysis of measurement data. This paper presents research methods aimed at applying geostatistics, i.e., anisotropic (directional) variogram, ordinary (block) kriging and point ordinary (point) kriging to the spatial analysis of geodetic data. An investigative approach, which can be used for the space-time analysis of the displacements (and even deformations) of any small-and large-area facility being under geodetic monitoring, is proposed. Changes in the displacements of check points on a hydrotechnical facility are analysed in both the area and time domain in order to assess the structure's behaviour and safety condition throughout its entire service life.

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Section: Methodsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Analysis and Short-Term Forecasting of Hydrotechnical Facility Displacements

Namysłowska-Wilczyńska¹,

Janusz²

2020

Int J Earth Sci Geophys

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“…In contrast to estimating a causal effect, the domain of prediction serves the purpose of using available information at a given time to predict an outcome of interest without making inferences about causality, and thus does not require knowledge about the causal structure of the data. Machine learning algorithms are increasingly used to optimize prediction (Ma 2019). The application of machine learning methods to address a causal question can result in biased estimates when the variables used for prediction are the consequences of at least two causes, also called collider bias (Munafo et al 2018).…”

Section: Use Of Other Methodsmentioning

confidence: 99%

“…The relationship among productivity, covariates, and treatment may indeed not be linear, and interactions between variables, if existing, are not accounted for. Collinearity between variables may also bias causal estimation (Ma 2019). The last section of this paper briefly discusses the limitations of causal inference and the differences to more advanced regression techniques such as machine learning algorithms.…”

Section: Presentation Of the Marcellus Playmentioning

confidence: 99%

Using Causal Inference in Field Development Optimization: Application to Unconventional Plays

et al. 2019

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In the current era of big data and machine learning, a strong focus exists on prediction and classification. In industrial applications, however, many important questions are not about prediction or classification; rather, they are causal: if I change A, what will happen to B? Traditional regression techniques such as machine learning optimize predictions based on correlations seen in the data and are not robust tools for epidemiologists and biostatisticians when evaluating the efficacy of new treatments or medications using observational data. Therefore, a set of statistical tools have been developed to go beyond correlations and aim to make inferences about causal relationships between variables. The goal of the present work is to apply one of these statistical tools, propensity score matching, in the oil and gas context, which is a novel application of the method. Two case studies are presented, one on proppant type and the other on lateral length, to determine their respective impacts on productivity.

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“…The denominator n e is sometimes exchanged with n e − 1, what is called Bessel's correction [14] [15] and is useful to remove part of standard deviation estimator bias when ne is small. We are not going to use Bessel's correction in this article, as it is not relevant for image segmentation.…”

Section: Didactic Algorithmmentioning

confidence: 99%

Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes

Conceição¹,

Mendonça²,

Lentini³

2020

CWEEE

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Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former's complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a naïve MWSD, by making the complexity of the computational process fall from O(N 2 n 2) to O(N 2 n), where N is a square input array side, and n is the moving window's side length. The Numba python compiler was used to make python a competitive high-performance computing language in our optimizations. Our results show efficiency benchmarks.

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Quantitative Geosciences: Data Analytics, Geostatistics, Reservoir Characterization and Modeling

Cited by 42 publications

References 0 publications

Spatial-Temporal Analysis and Short-Term Forecasting of Hydrotechnical Facility Displacements

Spatial-Temporal Analysis and Short-Term Forecasting of Hydrotechnical Facility Displacements

Using Causal Inference in Field Development Optimization: Application to Unconventional Plays

Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes

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