Upscaling of outcrop information for improved reservoir modelling – exemplified by a case study on chalk

2021 ◽  
pp. petgeo2020-126
Author(s):  
Dongfang Qu ◽  
Peter Frykman ◽  
Lars Stemmerik ◽  
Klaus Mosegaard ◽  
Lars Nielsen
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Petrophysical Properties ◽  
Fine Scale ◽  
Reservoir Modelling ◽  
Reservoir Models ◽  
Scale Models ◽  
Correlation Information ◽  
Scale Difference ◽  
Subsurface Reservoir

Outcrops are valuable for analogous subsurface reservoirs in supplying knowledge of fine-scale spatial heterogeneity pattern and stratification types, which are difficult to obtain from subsurface reservoir cores, well logs or seismic data. For petrophysical properties in a domain where the variations are relatively continuous and not dominated by abrupt contrasts, the spatial heterogeneity pattern can be characterized by a semivariogram model. The outcrop information therefore has the potential to constrain the semivariogram for subsurface reservoir modelling, even though it represents different locations and depths, and the petrophysical properties may differ in magnitude or variance. However, the use of outcrop derived spatial correlation information for petrophysical property modelling in practice has been challenged by the scale difference between the small support volume of the property measurements from outcrops and the typically much larger grid cells used in reservoir models. With an example of modelling the porosity of an outcrop chalk unit in eastern Denmark, this paper illustrates how the fine-scale spatial correlation information obtained from sampling of outcrops can be transferred to coarser scale models of analogue rocks. The workflow can be applied to subsurface reservoirs and ultimately improves the representation of geological patterns in reservoir models.

Anisotropic Dispersion and Upscaling for Miscible Displacement

SPE Journal ◽  
2015 ◽  
Vol 20 (03) ◽  
pp. 421-432 ◽  
Author(s):  
Olaoluwa O. Adepoju ◽  
Larry W. Lake ◽  
Russell T. Johns
Keyword(s):  
Finite Thickness ◽  
Miscible Displacement ◽  
Cell Permeability ◽  
Numerical Dispersion ◽  
Fine Scale ◽  
Direction Parallel ◽  
Miscible Displacements ◽  
Reservoir Models ◽  
Transverse Dispersion ◽  
Scale Models

Summary Dispersion is the irreversible mixing that occurs during miscible displacements. Dispersion can reduce local displacement efficiency by lessening solvent peak concentration or increase volumetric sweep efficiency by spreading of the injected solvent to more of the reservoir. Dispersion is therefore an important parameter in predicting and simulating miscible displacements. The difficulty of simulating miscible displacement and understanding the effect of dispersion is also compounded by numerical dispersion, which increases the apparent dispersion in finite-difference simulation models. This paper presents an approach to estimate the total longitudinal and transverse dispersion in large-scale media by use of continuous solvent injection in a medium of finite thickness. The simulations are based on the experimental arrangement of Blackwell (1962) to estimate transverse dispersion, with experiments consisting of coinjecting two miscible fluids into different sections of the medium at similar rates. This model arrangement, coupled with analytical solutions for the 2D convection/dispersion equation for a continuously injected solvent, allows us to determine longitudinal and transverse dispersivity simultaneously for the flow medium. In this manner, we investigate the effects of stochastic permeability distributions and other scaling groups affecting first-contact-miscible simulations on dispersion. Sensitivity analysis of dispersion in stochastic permeability fields confirms that both longitudinal and transverse dispersion are scale dependent. Results also show that the effect of increasing autocorrelation of cell permeability in the longitudinal direction (parallel to flow) is to increase longitudinal dispersion, as solvent travels through more continuous layers, while reducing transverse dispersion. Increasing autocorrelation in the transverse direction reduces dispersion in both directions. This reduction is caused by equilibration of solvent concentrations in continuous sections of the reservoir, resulting in reduced solute fingering and channeling. Finally, we developed a simple procedure to use the estimated dispersivities to determine a priori the maximum gridblock size that will maintain an equivalent level of dispersion between fine-scale models and upscaled coarse models. Large gridblock sizes can be used for highly heterogeneous and layered reservoir models. Nonuniform coarsening (upscaling) methods were also recommended and validated for reservoir models with sets of sequential but different permeability distributions. The procedure was extended to multicontact miscible simulations. The sweep and recovery from upscaled multicontact miscible simulations were comparable with those of fine-scale models.

scholarly journals Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1056
Author(s):  
Yu Chen ◽  
Mengke Zhu ◽  
Qian Zhou ◽  
Yurong Qiao
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spatial Data ◽  
Central Area ◽  
Urban System ◽  
Entropy Method ◽  
Driving Factors ◽  
Spatial Data Analysis ◽  
Eastern Region ◽  
Urban Resilience

Urban resilience in the context of COVID-19 epidemic refers to the ability of an urban system to resist, absorb, adapt and recover from danger in time to hedge its impact when confronted with external shocks such as epidemic, which is also a capability that must be strengthened for urban development in the context of normal epidemic. Based on the multi-dimensional perspective, entropy method and exploratory spatial data analysis (ESDA) are used to analyze the spatiotemporal evolution characteristics of urban resilience of 281 cities of China from 2011 to 2018, and MGWR model is used to discuss the driving factors affecting the development of urban resilience. It is found that: (1) The urban resilience and sub-resilience show a continuous decline in time, with no obvious sign of convergence, while the spatial agglomeration effect shows an increasing trend year by year. (2) The spatial heterogeneity of urban resilience is significant, with obvious distribution characteristics of “high in east and low in west”. Urban resilience in the east, the central and the west are quite different in terms of development structure and spatial correlation. The eastern region is dominated by the “three-core driving mode”, and the urban resilience shows a significant positive spatial correlation; the central area is a “rectangular structure”, which is also spatially positively correlated; The western region is a “pyramid structure” with significant negative spatial correlation. (3) The spatial heterogeneity of the driving factors is significant, and they have different impact scales on the urban resilience development. The market capacity is the largest impact intensity, while the infrastructure investment is the least impact intensity. On this basis, this paper explores the ways to improve urban resilience in China from different aspects, such as market, technology, finance and government.

Fine-Scale Fire Spread in Pine Straw

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 69
Author(s):  
Daryn Sagel ◽  
Kevin Speer ◽  
Scott Pokswinski ◽  
Bryan Quaife
Keyword(s):  
Fire Spread ◽  
Small Scale ◽  
Natural Setting ◽  
Fine Scale ◽  
Prescribed Burn ◽  
Fire Dynamics ◽  
Rate Of Spread ◽  
Scale Models ◽  
Fire Front ◽  
Visible Images

Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale.

Survival analysis at multiple scales for the modeling of track geometry deterioration

2017 ◽  
Vol 232 (3) ◽  
pp. 842-850 ◽  
Author(s):  
Negin Alemazkoor ◽  
Conrad J Ruppert ◽  
Hadi Meidani
Keyword(s):  
Survival Analysis ◽  
Multiple Scales ◽  
Fine Scale ◽  
Coarse Scale ◽  
Track Geometry ◽  
Time Period ◽  
Scale Models ◽  
Optimal Maintenance ◽  
Maintenance Decisions

Defects in track geometry have a notable impact on the safety of rail transportation. In order to make the optimal maintenance decisions to ensure the safety and efficiency of railroads, it is necessary to analyze the track geometry defects and develop reliable defect deterioration models. In general, standard deterioration models are typically developed for a segment of track. As a result, these coarse-scale deterioration models may fail to predict whether the isolated defects in a segment will exceed the safety limits after a given time period or not. In this paper, survival analysis is used to model the probability of exceeding the safety limits of the isolated defects. These fine-scale models are then used to calculate the probability of whether each segment of the track will require maintenance after a given time period. The model validation results show that the prediction quality of the coarse-scale segment-based models can be improved by exploiting information from the fine-scale defect-based deterioration models.

scholarly journals Spatiotemporal Variations in Mercury Bioaccumulation at Fine and Broad Scales for Two Freshwater Sport Fishes

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1625 ◽  
Author(s):  
Shyam Thomas ◽  
Stephanie Melles ◽  
Satyendra Bhavsar
Keyword(s):  
Climate Change ◽  
Environmental Factors ◽  
Northern Pike ◽  
Space Time ◽  
Fine Scale ◽  
Spatiotemporal Variations ◽  
Scale Models ◽  
Change Context ◽  
Mercury Bioaccumulation ◽  
Broad Scale

Bioaccumulation of mercury in sport fish is a complex process that varies in space and time. Both large-scale climatic as well as fine-scale environmental factors are drivers of these space-time variations. In this study, we avail a long-running monitoring program from Ontario, Canada to better understand spatiotemporal variations in fish mercury bioaccumulation at two distinct scales. Focusing on two common large-bodied sport fishes (Walleye and Northern Pike), the data were analyzed at fine- and broad-scales, where fine-scale implies variations in bioaccumulation at waterbody- and year-level and broad-scale captures variations across 3 latitudinal zones (~5° each) and eight time periods (~5-year each). A series of linear mixed-effects models (LMEMs) were employed to capture the spatial, temporal and spatiotemporal variations in mercury bioaccumulation. Fine-scale models were overall better fit than broad-scale models suggesting environmental factors operating at the waterbody-level and annual climatic conditions matter most. Moreover, for both scales, the space time interaction explained most of the variation. The random slopes from the best-fitting broad-scale model were used to define a bioaccumulation index that captures trends within a climate change context. The broad-scale trends suggests of multiple and potentially conflicting climate-driven mechanisms. Interestingly, broad-scale temporal trends showed contrasting bioaccumulation patterns—increasing in Northern Pike and decreasing in Walleye, thus suggesting species-specific ecological differences also matter. Overall, by taking a scale-specific approach, the study highlights the overwhelming influence of fine-scale variations and their interactions on mercury bioaccumulation; while at broad-scale the mercury bioaccumulation trends are summarized within a climate change context.

APPLICATIONS OF MODERN AND ANCIENT GEOLOGICAL ANALOGUES IN CHARACTERISATION OF FLUVIAL AND FLUVIAL-LACUSTRINE DELTAIC RESERVOIRS IN THE COOPER BASIN

2000 ◽  
Vol 40 (1) ◽  
pp. 393 ◽  
Author(s):  
S.C. Lang ◽  
J. Kassan ◽  
J.M. Benson ◽  
C.A. Grasso ◽  
L.C. Avenell
Keyword(s):  
Western Siberia ◽  
Reservoir Architecture ◽  
Reservoir Models ◽  
3D Geometry ◽  
Mouth Bar ◽  
Lacustrine Delta ◽  
Crevasse Splay ◽  
Subsurface Reservoir ◽  
Central Thailand ◽  
Cooper Basin

Reservoir characterisation in fluvial and fluvial- lacustrine delta successions is enhanced by the use of appropriate modern and ancient analogues to understand subsurface reservoir architecture and to help build appropriately scaled reservoir models. Two case studies of reservoir characterisation in the Cooper Basin are used to illustrate the value of analogues. Firstly the Late Permian Toolachee Formation crevasse splay reservoirs of the Cooper Basin, southwest Queensland are outlined, and analogues from the Ob River in Western Siberia illustrate the relative scale of crevasse splay deposits within avulsion belts in a cool-temperate peat-forming environment. The South Blackwater coal mine in the Permian Bowen Basin is used as an analogue to quantify the 3D geometry and reservoir architecture of crevasse splays and to highlight subsurface reservoir heterogeneity.Secondly, the Early Permian Epsilon Formation shallow water lacustrine delta reservoirs are outlined, and analogues from the extant geometry of the distributary channels and relict mouth bar deposits from the fluvial dominated Neales Delta in Lake Eyre are used to interpret flow rate decline trends and probable reservoir architecture. The subsurface Tertiary lacustrine deltaic complex of the Sirikit Field from the Phitsanulok Basin, central Thailand, is selected as an ancient analogue for the multistorey reservoirs developed within amalgamated mouth bar complexes intersected in the lower Epsilon Formation.

Testing plausible upper-mantle compositions using fine-scale models of the 410-km discontinuity

1999 ◽  
Vol 26 (11) ◽  
pp. 1641-1644 ◽  
Author(s):  
James B. Gaherty ◽  
Yanbin Wang ◽  
Thomas H. Jordan ◽  
Donald J. Weidner
Keyword(s):  
Upper Mantle ◽  
Fine Scale ◽  
Scale Models

Fine-scale spatial heterogeneity and incoming seed diversity additively determine plant establishment

2012 ◽  
Vol 100 (4) ◽  
pp. 939-949 ◽  
Author(s):  
Paul J. Richardson ◽  
Andrew S. MacDougall ◽  
Douglas W. Larson
Keyword(s):  
Spatial Heterogeneity ◽  
Fine Scale ◽  
Plant Establishment

Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation

2006 ◽  
Vol 321-323 ◽  
pp. 1266-1269 ◽  
Author(s):  
J. Kim ◽  
P. Ramuhalli ◽  
L. Udpa ◽  
S. Udpa
Keyword(s):  
Neural Network ◽  
Spatial Correlation ◽  
Principal Component ◽  
Automated Analysis ◽  
Classification Systems ◽  
Inspection Systems ◽  
Weld Inspection ◽  
Correlation Information ◽  
Trained Neural Network ◽  
Analysis System

A key requirement in most ultrasonic weld inspection systems is the ability for rapid automated analysis to identify the type of flaw. Incorporation of spatial correlation information from adjacent A-scans can improve performance of the analysis system. This paper describes two neural network based classification techniques that use correlation of adjacent A-scans. The first method relies on differences in individual A-scans to classify signals using a trained neural network, with a post-processing mechanism to incorporate spatial correlation information. The second technique transforms a group of spatially localized signals using a 2-dimensional transform, and principal component analysis is applied to the transform coefficients to generate a reduced dimensional feature vectors for classification. Results of applying the proposed techniques to data obtained from weld inspection are presented, and the performances of the two approaches are compared.

scholarly journals Research on Spillover Effect of Urbanization on Rural Land Transfer Based on the SDM Model of Intelligent Computing

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Fucheng Yang ◽  
Guoyong Liu
Keyword(s):  
Panel Data ◽  
Spatial Heterogeneity ◽  
Spatial Correlation ◽  
Spillover Effect ◽  
Rural Land ◽  
Spatial Agglomeration ◽  
Intelligent Computing ◽  
Land Transfer ◽  
Southern Xinjiang

In order to explore the spillover effect of urbanization on rural land transfer, this paper uses the panel data of various regions and cities in Xinjiang from 2008 to 2018. Moran's I method is used to test and analyze the spatial correlation between urbanization and farmland transfer. Intelligent computing SDM is used to analyze the spillover effect of urbanization on farmland transfer. The results show that there is spatial correlation between farmland transfers in Xinjiang. There is spatial heterogeneity in the spatial agglomeration of urbanization and farmland transfer in northern and southern Xinjiang. The content of this paper can provide some reference and ideas for follow-up research.

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