Geostatistical significance of differences for spatial subsurface phenomenon

Salazar, J; Pyrcz, Michael J.

doi:10.1016/j.petrol.2021.108694

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Considering the spatial correlation between variables, this article chooses the most general SDM (Spatial Durbin Model) [43,44]. The model is as follows: In the above formula, ce represents carbon emissions, and ser represents the agglomeration index of producer service industry, which is calculated by location entropy.…”

Section: Spatial Correlation Analysismentioning

confidence: 99%

Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China

Zhuang

2022

Sustainability

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Although China has a high rate of economic development, it still faces the problems of unstable industrial structure, low industrial level, and large carbon emissions, which pose huge challenges to China’s sustainable development. China is working hard to develop producer services to achieve industrial transformation and reduce carbon emissions. In this context, there is an extremely urgent need to conduct academic research on changes in producer service agglomeration and carbon emissions. Whether the producer service industry has agglomeration characteristics, and whether the producer service industry affects carbon emissions through multiple paths, are issues worthy of attention. Therefore, this paper takes China as the research area, selects the provincial administrative unit as the research sample, and conducts research on these issues by using exploratory spatial data analysis methods and various spatial econometric models under the guidance of the relevant theories of new economic geography. Our research finds that producer services have significant spatial agglomeration characteristics, and they also have significant spatial differentiation patterns. In addition, the agglomeration of producer services can significantly promote the reduction of carbon emissions and can show obvious spillover effects. Finally, in response to the research conclusions of this paper, we also put forward countermeasures and suggestions from the perspective of the common development of producer service industry and manufacturing industry, hoping to promote China’s transformation from industrial economy to service economy, to maximize the use of producer service industry accumulated dividends.

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Section: Spatial Correlation Analysismentioning

confidence: 99%

Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China

Zhuang

2022

Sustainability

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A Geostatistical Heterogeneity Metric for Spatial Feature Engineering

Liu¹,

Garland

Carrasco-Ochoa³

et al. 2023

Springer Proceedings in Earth and Environmental Sciences

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Heterogeneity is a vital spatial feature for subsurface resource recovery predictions, such as mining grade tonnage functions, hydrocarbon recovery factor, and water aquifer draw-down predictions. Feature engineering presents the opportunity to integrate heterogeneity information, but traditional heterogeneity engineered features like Dykstra-Parsons and Lorenz coefficients ignore the spatial context; therefore, are not sufficient to quantify the heterogeneity over multiple scales of spatial intervals to inform predictive machine learning models. We propose a novel use of dispersion variance as a spatial-engineered feature that accounts for heterogeneity within the spatial context, including spatial continuity and sample data and model volume support size to improve predictive machine-learning-based models, e.g., for pre-drill prediction and uncertainty quantification. Dispersion variance is a generalized form of variance that accounts for volume support size and can be calculated from the semivariogram-based spatial continuity model. We demonstrate dispersion variance as a useful predictor feature for the case of hydrocarbon recovery prediction, with the ability to quantify the spatial variation over the support size of the production well drainage radius, given the spatial continuity from the variogram and trajectory of the well. We include a synthetic example based on geostatistical models and flow simulation to show the sensitivity of dispersion variance to production. Then we demonstrate the dispersion variance as an informative predictor feature for production forecasting with a field case study in the Duvernay formation.

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Rock typing and uncertainty assessment in geological and petrophysical properties by integrating electrofacies, hydraulic flow units, and geostatistical techniques in the Kangan gas field, Zagros basin

Bakhtiyari,

Qajar,

Karimian Torghabeh

et al. 2023

Acta Geophys.

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The structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios.

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Geostatistical significance of differences for spatial subsurface phenomenon

Cited by 4 publications

References 28 publications

Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China

Producer Services Agglomeration and Carbon Emission Reduction—An Empirical Test Based on Panel Data from China

A Geostatistical Heterogeneity Metric for Spatial Feature Engineering

Rock typing and uncertainty assessment in geological and petrophysical properties by integrating electrofacies, hydraulic flow units, and geostatistical techniques in the Kangan gas field, Zagros basin

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