An approximate method for joint sequential simulation of multiple spatial variables

Multivariate simulation is an important longstanding problem in geostatistics. Fitting a model of coregionalization to many variables is intractable and often not permitted; however, the matrix of collocated correlation coefficients is often well informed. Performing a matrix simulation with LU decomposition of the correlation matrix at each step of sequential simulation is implemented in some software. The target correlation matrix is not reproduced because of conditioning to local data and the particular variable ordering in the sequential/LU decomposition. A correction procedure is developed to calculate a modified correlation matrix that leads to reproduction of the target correlation matrix. The theoretical and practical aspects of this correction are developed.     

Inverse distance interpolation for facies modeling

Inverse distance weighted interpolation is a robust and widely used estimation technique. In practical applications, inverse distance interpolation is oftentimes favored over kriging-based techniques when there is a problem of making meaningful estimates of the field spatial structure. Nowadays application of inverse distance interpolation is limited to continuous random variable modeling. There is a need to extend the approach to categorical/discrete random variables. In this paper we propose such an extension using indicator formalism. The applicability of inverse distance interpolation for categorical modeling is then illustrated using Total’s Joslyn Lease facies data.     

A rock physics model to map gross lithology using compaction

Differential compaction has long been used by seismic interpreters to infer subsurface geology using knowledge of the relative compaction of different types of sediments. We outline a method to infer the gross fraction of shale in an interval between two seismic horizons using sandstone and shale compaction laws. A key component of the method involves reconstruction of a smooth depositional horizon by interpolating decompacted thicknesses from well control. We derive analytic formulae for decompaction calculations using known porosity–stress relations and do not employ discrete layer iterative methods; these formulae were found to depend not only upon the gross fraction of shale but also on the clay content of the shales and the thickness of the interval. The relative merits of several interpolation options were explored, and found to depend upon the structural setting. The method was successfully applied to an oil sands project in Alberta, Canada.     

A conceptual framework for multi-regional climate change assessments for international market systems with long-term investments

A conceptual framework for climate change assessments of international market systems that involve long-term investments is proposed. The framework is a hybrid of dynamic and static modeling. Dynamic modeling is used for those system components for which temporally continuous modeling is possible, while fixed time slices are used for other system components where it can be assumed that underlying assumptions are held constant within the time slices but allowed to vary between slices. An important component of the framework is the assessment of the “metauncertainty” arising from the structural uncertainties of a linked sequence of climate, production, trade and decision-making models. The impetus for proposing the framework is the paucity of industry-wide assessments for market systems with multiple production regions and long-term capital investments that are vulnerable to climate variations and change, especially climate extremes. The proposed framework is pragmatic, eschewing the ideal for the tractable. Even so, numerous implementation challenges are expected, which are illustrated using an example industry. The conceptual framework is offered as a starting point for further discussions of strategies and approaches for climate change impact, vulnerability and adaptation assessments for international market systems.     

Fluctuations of the Fennoscandian Ice Sheet recorded in the anisotropy of magnetic susceptibility of periglacial loess from Ukraine

The azimuth of imbrication of minimum magnetic susceptibility axes in the youngest loess from Ukraine defines prevailing wind directions during aeolian sedimentation. It changes along the studied sections. These changes can be directly correlated with the fluctuations of the Fennoscandian Ice Sheet. The northern and northeastern winds noted in the loess succession separated by a period when southwestern to southeastern winds were predominant may be correlated with two main phases of ice‐sheet advance during the Last Glacial Maximum. The ice‐sheet advances towards the areas of loess deposition generated katabatic winds that influenced aeolian sedimentation in the periglacial zone. A period of relatively stable wind directions during a younger phase of the Last Glacial Maximum was interrupted by periods with more chaotic wind regime most probably caused by fluctuations of the Fennoscandian Ice Sheet during its retreat from the peri‐Baltic part of Europe. These intervals occur where initial soils developed. The distribution of anisotropy of magnetic susceptibility axes defined along the periglacial loess sections from central and eastern Europe can serve to constrain fluctuations of the Fennoscandian Ice Sheet.     

Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series

ABSTRACT

Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.     

Regional-scale monitoring of cropland intensity and productivity with multi-source satellite image time series

In the context of growing populations and limited resources, the sustainable intensification of agricultural production is of great importance to achieve food security. As the need to support management at a range of spatial scales grows, decision-support tools appear increasingly important to enable the timely and regular assessment of agricultural production over large areas and identify priorities for improving crop production in low-productivity regions. Understanding productivity patterns requires the timely provision of gapless, spatial information about agricultural productivity. In this study, dense 30-m time series covering the 2004–2014 period were generated from Landsat and MODerate-resolution Imaging Spectroradiometer (MODIS) satellite images over the irrigated cropped area of the Fergana Valley, Central Asia. A light-use efficiency model was combined with machine learning classifiers to assess the crop yield at the field level. The classification accuracy of land cover maps reached 91% on average. Crop yield and acreage estimates were in good agreement (R² = 0.812 and 0.871, respectively) with reported yields and acreages at the district level. Several indicators of cropland intensity and productivity were derived on a per-field basis and used to highlight homogeneous regions in terms of productivity by means of clustering. Results underlined that regions with lower water-use efficiency were not only located further away from irrigation canals and intake points, but also had limited access to markets and roads. The results underline that yield could be increased by roughly 1.0 and 1.4 t/ha for cotton and wheat, respectively, if the access to water would be optimized in some of the regions. The minimum calibration requirement of the method and the fusion of multi-sensor data are keys to cope with the constraints of operational crop monitoring and guarantee a sustained and timely delivery of the agricultural indicators to the user community. The results of this study can form the baseline to support regional land- and water-resource management.     

Thermal Bioclimate and Climate Tourism Analysis for Odessa,Black Sea

Climate is a main component of nature that, in addition to being a valuable resource, determines the possibility of development of tourism. Apparently, climate and its characteristics determine touristic demand. Choosing a place for vacation or recreation, tourists primarily prefer an optimal climate and weather conditions. Human‐biometeorological resources characterize the connection of climate with a thermal state and health of humans, peculiarities of recreation and health evaluation of the environment. In order to distinguish thermal bioclimate, the physiologically equivalent temperature is used, based on the various meteorological parameters (air temperature, relative humidity, wind speed and radiation) that reflect the human conditions and the thermal comfort perception. In addition for the analysis of the climatic tourism potential, the Climate‐Transfer/Tourism‐Information Scheme was applied. The results can be useful for tourists in order to determine the best time to take a vacation based on their recreational activity such as sports, medical, health (thalassotherapy, heliotherapy, balneology etc.) according to bioclimatic conditions and their own preferences.     

Statistical approach to inverse distance interpolation

Inverse distance interpolation is a robust and widely used estimation technique. Variants of kriging are often proposed as statistical techniques with superior mathematical properties such as minimum error variance; however, the robustness and simplicity of inverse distance interpolation motivate its continued use. This paper presents an approach to integrate statistical controls such as minimum error variance into inverse distance interpolation. The optimal exponent and number of data may be calculated globally or locally. Measures of uncertainty and local smoothness may be derived from inverse distance estimates.     

Collocated Cokriging Based on Merged Secondary Attributes

There exist many secondary data that must be considered in in reservoir characterization for resource assessment and performance forecasting. These include multiple seismic attributes, geological trends and structural controls. It is essential that all secondary data be accounted for with the precision warranted by that data type. Cokriging is the standard technique in geostatistics to account for multiple data types. The most common variant of cokriging in petroleum geostatistics is collocated cokriging. Implementations of collocated cokriging are often limited to a single secondary variable. Practitioners often choose the most correlated or most relevant secondary variable. Improved models would be constructed if multiple variables were accounted for simultaneously. This paper presents a novel approach to (1) merge all secondary data into a single super secondary variable, then (2) implement collocated cokriging with the single variable. The preprocessing step is straightforward and no major changes are required in the standard implementation of collocated cokriging. The theoretical validity of this approach is proven, that is, the results are proven to be identical to a “full” approach using all multiple secondary variables simultaneously.