Geostatistical Models Research Articles

Bayesian geostatistical modeling for discrete‐valued processes

AbstractWe introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on nearest‐neighbor mixture processes (NNMP), referred to as discrete NNMP. To define the joint probability mass function (pmf) over a set of spatial locations, we build from local mixtures of conditional pmfs using a directed graphical model, with a directed acyclic graph that summarizes the nearest neighbor structure. The approach supports direct, flexible modeling for multivariate dependence through specification of general bivariate discrete distributions that define the conditional pmfs. In particular, we develop a modeling and inferential framework for copula‐based NNMPs that can attain flexible dependence structures, motivating the use of bivariate copula families for spatial processes. Moreover, the framework allows for construction of models given a pre‐specified family of marginal distributions that can vary in space, facilitating covariate inclusion. Compared to the traditional class of spatial generalized linear mixed models, where spatial dependence is introduced through a transformation of response means, our process‐based modeling approach provides both computational and inferential advantages. We illustrate the methodology with synthetic data examples and an analysis of North American Breeding Bird Survey data.

Evaluating drivers of spatiotemporal variability in individual condition of a bottom-associated marine fish, Atlantic cod (Gadus morhua)

Abstract An organism’s body condition describes its mass given its length and is often positively associated with fitness. The condition of Atlantic cod (Gadus morhua) in the Baltic Sea has declined dramatically since the early 1990s, possibly due to increased competition for food and hypoxia. However, the effects of biotic and abiotic variables on body condition have not been evaluated at local scales, which is important given spatial heterogeneity. We evaluate changes in distribution, experienced environmental conditions, and individual-level condition of cod in relation to covariates at different spatial scales using geostatistical models with spatial and spatiotemporal random effects. Sprat, Saduria entomon, temperature and oxygen were positively associated with condition, and depth was negatively associated. However, the effects of explanatory variables were small—spatial and spatiotemporal latent variables explained 5.7 times more variation than all covariates together (year excluded). Weighting environmental oxygen with local biomass densities revealed steeper declining trends compared to the unweighted oxygen in the environment, while the effect of weighting was less clear for condition. Understanding the drivers of spatiotemporal variation in body condition is critical for predicting responses to environmental change and to effective fishery management; yet low explanatory power of covariates on individual condition constitutes a major challenge.

Effects of aquitard windows on groundwater fluctuations within a coastal leaky aquifer system: An analytical and experimental study

Low-permeability aquitards are important sedimentary units that protect the underlying confined aquifers from contamination by seawater and anthropogenic sources in coastal leaky aquifer systems. Therefore, the integrity of the aquitard is of crucial importance. However, the aquitard integrity may be vulnerable to horizontal heterogeneities in the form of aquitard windows. In this study, an innovative analytical model was developed to describe groundwater flow in a coastal leaky aquifer system with horizontal heterogeneities in hydraulic properties of either the aquitard or the confined aquifer. Consequently, the effects of aquitard windows, which manifest as horizontally local heterogeneities in hydraulic conductivity and/or specific storage, on the tidal wave propagation can be considered in the analysis. Analytical solutions were derived for tidal-head or constant-flux inland boundary conditions. The derivation process avoided previous methodologies that involved the assembly of large-size matrices. The newly developed analytical model can cover multiple existing analytical models that are associated with the assumptions of homogeneity in hydraulic properties of the aquifer and the aquitard, infinite horizontal extent of the coastal leaky aquifer system, impermeability of the aquitard, or ignorance of aquitard storativity. Analytical investigations revealed that high-permeability aquitard windows acting as preferential-flow conduits tended to dampen the tide-induced head propagation within the underlying confined aquifer. Moreover, an aquitard with a higher storativity (but the same permeability) and of considerable size can result in an apparent propagation bias. The newly developed analytical model in this study was further corroborated through sandbox experiments. Both piezometer measurements and parameter estimates indicated the non-negligible effects of high-permeability aquitard windows on the tidal wave propagation. The sandbox investigation also highlighted the importance of geostatistical inverse modeling in characterizing local heterogeneities within the confined aquifer and in detecting the location and size of aquitard windows, when measurement data of tide-induced aquifer head fluctuations were provided.

A Pattern Classification Distribution Method for Geostatistical Modeling Evaluation and Uncertainty Quantification

Geological models are essential components in various applications. To generate reliable realizations, the geostatistical method focuses on reproducing spatial structures from training images (TIs). Moreover, uncertainty plays an important role in Earth systems. It is beneficial for creating an ensemble of stochastic realizations with high diversity. In this work, we applied a pattern classification distribution (PCD) method to quantitatively evaluate geostatistical modeling. First, we proposed a correlation-driven template method to capture geological patterns. According to the spatial dependency of the TI, region growing and elbow-point detection were launched to create an adaptive template. Second, a combination of clustering and classification was suggested to characterize geological realizations. Aiming at simplifying parameter specification, the program employed hierarchical clustering and decision tree to categorize geological structures. Third, we designed a stacking framework to develop the multi-grid analysis. The contribution of each grid was calculated based on the morphological characteristics of TI. Our program was extensively examined by a channel model, a 2D nonstationary flume system, 2D subglacial bed topographic models in Antarctica, and 3D sandstone models. We activated various geostatistical programs to produce realizations. The experimental results indicated that PCD is capable of addressing multiple geological categories, continuous variables, and high-dimensional structures.

Overlapped Bayesian spatio-temporal models to detect crime spots and their possible risk factors based on the Opole Province, Poland, in the years 2015–2019

Geostatistical methods currently used in modern epidemiology were adopted in crime science using the example of the Opole province, Poland, in the years 2015–2019. In our research, we applied the Bayesian spatio-temporal random effects models to detect ‘cold-spots’ and ‘hot-spots’ of the recorded crime numbers (all categories), and to ascertain possible risk factors based on the available statistical population (demographic), socio-economic and infrastructure area characteristics. Overlapping two popular geostatistical models in the analysis, ‘cold-spot’ and ‘hot-spot’ administrative units were detected which displayed extreme differences in crime and growth rates over time. Additionally, using Bayesian modeling four categories of possible risk factors were identified in Opole. The established risk factors were the presence of doctors/medical personnel, road infrastructure, numbers of vehicles, and local migration. The analysis is directed toward both academic and police personnel as a proposal for an additional geostatistical control instrument supporting the management and deployment of local police based on easily available police crime records and public statistics.

Adaptive LASSO estimation for functional hidden dynamic geostatistical models

We propose a novel model selection algorithm based on a penalized maximum likelihood estimator (PMLE) for functional hidden dynamic geostatistical models (f-HDGM). These models employ a classic mixed-effect regression structure with embedded spatiotemporal dynamics to model georeferenced data observed in a functional domain. Thus, the regression coefficients are functions. The algorithm simultaneously selects the relevant spline basis functions and regressors that are used to model the fixed effects. In this way, it automatically shrinks to zero irrelevant parts of the functional coefficients or the entire function for an irrelevant regressor. The algorithm is based on an adaptive LASSO penalty function, with weights obtained by the unpenalised f-HDGM maximum likelihood estimators. The computational burden of maximisation is drastically reduced by a local quadratic approximation of the log-likelihood. A Monte Carlo simulation study provides insight in prediction ability and parameter estimate precision, considering increasing spatiotemporal dependence and cross-correlations among predictors. Further, the algorithm behaviour is investigated when modelling air quality functional data with several weather and land cover covariates. Within this application, we also explore some scalability properties of our algorithm. Both simulations and empirical results show that the prediction ability of the penalised estimates are equivalent to those provided by the maximum likelihood estimates. However, adopting the so-called one-standard-error rule, we obtain estimates closer to the real ones, as well as simpler and more interpretable models.

Regional characterization of vs30 with hybrid geotechnical and geological data

Regional characterization of soil properties requires not only geostatistical models considering the spatial variability of soil properties but also methods that account for various sources of heterogeneous information. This paper presents a multi-conditional random field approach to characterize the regional characteristics of the average shear-wave velocity in the top 30 meters of subsoil (vs30) based on hybrid geotechnical and geological data. The workflow for integrating multiple sources of soil properties information in a random field model for regional vs30 mapping is developed. A detailed two-dimensional (2D) synthetic digital soil field is generated to assess and verify this workflow. With the generated synthetic field, parametric studies on the investigation plans, the value of Markov Bayes coefficient B, element size, and a predefined grid of secondary data are performed. Practice on whether to incorporate secondary data for vs30 mapping and the determination of coefficient B are provided.

Transforming microseismic clouds into near real-time visualization of the growing hydraulic fracture

SUMMARY Microseismic observations during unconventional reservoir stimulation are typically seen as a proxy for clusters of hydraulic fractures and the extent of the stimulated reservoir. Such straightforward interpretation is often misleading and fails to provide a physically reasonable image of the fracturing process. This paper demonstrates the application of a physics-based machine learning algorithm which enables a rapid and accurate fracture mapping from the microseismic data. Our training and validation data set relies on a history-matched geomechanical modelling workflow implemented in GEOS software for the Hydraulic Fracturing Test Site 1 (HFTS-1) project. For this study we augmented the simulated fracture growth through geostatistical modelling of induced seismicity, so that the synthetic microseismic catalogue matches the main statistical properties of the field observations. We formulated the problem of mapping the actual fracture in the clutter of events to parallel common video segmentation workflows: several past video frames (microseismic density snapshots) are passed through a deep convolutional network to classify whether a given voxel is associated with a fracture or intact rock. We found that for accurate fracture mapping, the network’s input and architecture must be augmented to incorporate the fluid injection parameters (pressure, rate, concentration of proppant, and location of the perforation within the cluster). The error rate for the network reached as little as 10 per cent of the fracture area, while a conventional microseismic interpretation approach yielded ∼300 per cent. Our approach also yields must faster predictions than conventional methods (minutes instead of weeks), and could enable engineers to make rapid decisions regarding engineering parameters (pumping rate, viscosity) in real time during stimulation.

A new hazard assessment workflow to assess soil contamination from large and artisanal scale gold mining

Gold mining activities are undertaken both at large and artisanal scale, often resulting in serious ‘collateral’ environmental issues, including environmental pollution and hazard to human and ecosystem health. Furthermore, some of these activities are poorly regulated, which can produce long-lasting damage to the environment and local livelihoods. The aim of this study was to identify a new workflow model to discriminate anthropogenic versus geogenic enrichment in soils of gold mining regions. The Kedougou region (Senegal, West Africa) was used as a case study. Ninety-four soil samples (76 topsoils and 18 bottom soils) were collected over an area of 6,742 km2 and analysed for 53 chemical elements. Robust spatial mapping, compositional and geostatistical models were employed to evaluate sources and elemental footprint associated with geology and mining activities. Multivariate approaches highlighted anomalies in arsenic (As) and mercury (Hg) distribution in several areas. However, further interpretation with enrichment factor (EFs) and index of geoaccumulation (IGeo) emphasised high contamination levels in areas approximately coinciding with the ones where artisanal and small scale mining (ASGM) activities occur, and robust compositional contamination index (RCCI) isolated potentially harmful elements (PHE) contamination levels in very specific areas of the Kedougou mining region. The study underlined the importance of complementary approaches to identify anomalies and, more significantly, contamination by hazardous material. In particular, the analyses helped to identify discrete areas that would require to be surveyed in more detail to allow a comprehensive and thorough risk assessment, to investigate potential impacts to both human and ecosystem health.

Incorporating outcrop observations and laboratory measurements in 3D reservoir models: An integrated study to enhance the workflow of modeling bioturbated carbonate reservoirs

Although the common characteristics of bioturbated reservoirs are well-documented, the numerical modeling of such reservoirs remains poorly understood. This study aims to address this gap by introducing a workflow for modeling bioturbated strata in the Hanifa Formation, central Saudi Arabia. The workflow involves integrating sedimentological, stratigraphic, and ichnological observations, outcrop photogrammetry, petrophysical analysis, geostatistical modeling, and fluid flow simulation to numerically model burrow abundance (in burrow percentage [BP]), porosity, and permeability of bioturbated strata. A trend volume that mimics the distribution of the BP was generated to model the distribution of the porosity and permeability in 3D models of the outcrop. Three porosity models reflecting the porosity of burrow infills, porosity of the host rock matrix, and their summation were generated. Four scenarios of permeability modeling resulted in eight 3D permeability models, which were then used to generate eight cases of fluid flow simulation for a hypothetical reservoir similar to the bioturbated strata of the outcrop. The results indicated that the flow capacity of the 3D model strongly depends on the BP, whereas the storage capacity does not, due to the presence of microporosity in the host rock matrix, which is substantially higher than the infills porosity but reflects pores that are extremely smaller in size. The results of the fluid flow simulation indicated that production data is very sensitive to permeability models, emphasizing the importance of selecting the correct permeability modeling scenario to accurately simulate bioturbated reservoirs. The insights derived from these permeability modeling scenarios can aid in identifying crucial parameters and variables that affect the permeability and productivity of bioturbated hydrocarbon reservoirs and water aquifers. Refining and validating these scenarios with real-world data from subsurface reservoirs can lead to the development of more accurate models of these systems. The findings from these models can guide future investigations and support the creation of more effective strategies for managing hydrocarbon reservoirs and water aquifers.

Uncertainty of spatial averages and totals of natural resource maps

AbstractGlobal, continental and regional maps of concentrations, stocks and fluxes of natural resources provide baseline data to assess how ecosystems respond to human disturbance and global warming. They are also used as input to numerous modelling efforts. But these maps suffer from multiple error sources and, hence, it is good practice to report estimates of the associated map uncertainty so that users can evaluate their fitness for use.We explain why quantification of uncertainty of spatial aggregates is more complex than uncertainty quantification at point support because it must account for spatial autocorrelation of the map errors. Unfortunately, this is not done in a number of recent high‐profile studies. We describe how spatial autocorrelation of map errors can be accounted for with block kriging, a method that requires geostatistical expertise. Next, we propose a new, model‐based approach that avoids the numerical complexity of block kriging and is feasible for large‐scale studies where maps are typically made using machine learning. Our approach relies on Monte Carlo integration to derive the uncertainty of the spatial average or total from point support prediction errors. We account for spatial autocorrelation of the map error by geostatistical modelling of the standardized map error.We show that the uncertainty strongly depends on the spatial autocorrelation of the map errors. In a first case study, we used block kriging to show that the uncertainty of the predicted topsoil organic carbon in France decreases when the support increases. In a second case study, we estimated the uncertainty of spatial aggregates of a machine learning map of the above‐ground biomass in Western Africa using Monte Carlo integration. We found that this uncertainty was small because of the weak spatial autocorrelation of the standardized map errors.We present a tool to get realistic estimates of the uncertainty of spatial averages and totals of natural resource maps. The method presented in this paper is essential for parties that need to evaluate whether differences in aggregated environmental variables or natural resources between regions or over time are statistically significant.

Process engineering solutions for reclamation of former Vostsibelement battery plant industrial site

The purpose of the present research is the inventory of the sources of accumulated contaminants at the former Vostsibelement industrial site located within the municipality of the town of Svirsk of the Irkutsk region (Russia) as well as the analysis of their impact on the environmental conditions. The conducted study included the calculation of the numerical assessment criterion of the overall impact of the accumulated environmental damage site on the state of environmental safety. On the basis of the results obtained, it was concluded that the industrial site of the former Vostsiblement factory is a source of accumulated harm, as well as an extremely environmentally hazardous site posing a potential threat to the entire upper territory of the Bratsk reservoir due to its location within the boundaries of the municipality of the town of Svirsk and the water protection zone of the Angara river. Based on the monitoring data, a geostatistical dispersion model of mobile lead in soil was constructed and anomalous contamination zones were identified. Three areas with an anomalously high content of mobile forms of lead (more than 6000 mg/kg) were identified in the soil on the territory of the industrial site of the former Vostsibelement battery factory. The content of mobile lead in the surface layer (0–20 cm) throughout the industrial site is not less than 100 mg/kg, which is 17 times higher than the maximum allowable concentration. The established excess of lead content in silverweed cinquefoil (Potentilla anserina) amounted to 10 background readings. A single point sample of bottom sediments of the Angara river at the entry of the industrial site, four meters from the water edge demonstrates an excess of the background content for lead, copper, zinc by 59, 12 and 4.7 times, respectively. The conducted research, analysis of the data obtained and review of the best available technologies allowed to introduce a concept for an environmentally friendly recuperative technology for the reclamation of the former Vostsibelement factory industrial site, which is very effective both from the technical and economic, and from the legal and social-ecological point of view. It also can be considered as an option to eliminate the accumulated harm of an extremely hazardous site.

Geostatistical Modeling and Prediction of Rift Valley Fever Seroprevalence among Livestock in Uganda.

Uganda reported cases of Rift Valley fever virus (RVFV) for the first time in almost 50 years in 2016, following an outbreak of Rift Valley fever (RVF) that caused four human infections, two of which resulted in death. Subsequent outbreak investigation serosurveys found high seroprevalence of IgG antibodies without evidence of acute infection or IgM antibodies, suggesting the possibility of undetected RVFV circulation prior to the outbreak. After the 2016 outbreak investigation, a serosurvey was conducted in 2017 among domesticated livestock herds across Uganda. Sampling data were incorporated into a geostatistical model to estimate RVF seroprevalence among cattle, sheep, and goats. Variables resulting in the best fit to RVF seroprevalence sampling data included annual variability in monthly precipitation and enhanced vegetation index, topographic wetness index, log human population density percent increase, and livestock species. Individual species RVF seroprevalence prediction maps were created for cattle, sheep, and goats, and a composite livestock prediction was created based on the estimated density of each species across the country. Seroprevalence was greater in cattle compared with sheep and goats. Predicted seroprevalence was greatest in the central and northwestern quadrant of the country, surrounding Lake Victoria, and along the Southern Cattle Corridor. We identified areas that experienced conditions conducive to potential increased RVFV circulation in 2021 in central Uganda. An improved understanding of the determinants of RVFV circulation and locations with high probability of elevated RVF seroprevalence can guide prioritization of disease surveillance and risk mitigation efforts.

Hybrid Modeling of Persistent Shoreline Oil Residues on Abu Ali Island, Saudi Arabia: Extent, Degree, and Remediation Implications

Extensive intertidal asphalt pavements and oiled sediment accumulations extend more than 20 km along the northern shoreline of Abu Ali Island, located north of Jubail on the Arabian (Persian) Gulf coast of Saudi Arabia. This shoreline oiling likely originated from two platforms in the Nowruz oil field, which spilled oil from 1983 to 1985; this was one of the largest marine spills in history, with shoreline impacts that were little known. In this study, we used a novel methodology that combined remote sensing analyses with hybrid machine learning–geostatistical modeling of field-collected data to quantify the distribution, extent, and volume of these contaminated sediments to investigate the mechanisms for their persistence and to support the development of remediation plans. After nearly 40 years, approximately 25,000 m3 of contaminated sediments remain, with nearly 50% of these buried underneath clean sediments. The presence of exposed or subsurface carbonate beach rock platforms or ramps clearly influences the ongoing persistence of these asphalt pavements by protecting them from physical energy and sediment mobilization. These rock platforms complicate potential remediation options, with more than 66% of the modeled volume of asphalt pavement estimated to be directly on top of and in contact with carbonate beach rock. The asphalt pavements present persistent ongoing PAH toxicity and continually shed smaller fragments when exposed to wave energy along with localized sheens and liquid oil, presenting a pathway for ongoing chronic exposure of biota.

Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density

The generalized linear geostatistical model (GLGM) is a formal approach of regression kriging that would be advantageous over commonly used modelling approaches for digital soil mapping (DSM). However, it has not been well explored in the literature, due to heavy computation. This study evaluates such formal approach for mapping soil organic matter at a regional scale (179,700 km2). We hypothesized that GLGM would be a better approach than other approaches as it can model nonlinear relationships and spatially consider the residuals. However, the accuracy would depend on sampling density. We compared GLGM with multiple linear regression (MLR), ordinary kriging (OK), regression kriging (RK), random forest (RF), generalized linear mixture model (GLMM), and generalized additive model (GAM). The effect of sampling density on the performance was also investigated by fitting the models based on resampling the samples with a series of sizes ranging from 100 to 1200. Results showed that GLGM generally improved the accuracy of DSM, compared with MLR, OK, RF, GLMM, and GAM, especially for large sample sizes, although the improvement was not significant. In a few cases, GLGM outperformed RK. The GLGM modelling and its prediction were largely influenced by sampling densities. Given small sample sizes, GLGM was unstable and the parameters were highly variable depending on the modelling realizations. Other influencing factors include linear and smooth correlations between soil and environmental covariates, spatial autocorrelation of the residuals, compatibility of spatial scales of soil samples and environmental covariates, and the scale of soil variation. For these factors, researchers with a more dense soil sampling are needed in future to explore the benefits of GLGM as a hybrid model for spatial prediction of soil.

Spatial variability of petrofacies using supervised machine learning and geostatistical modeling: Sycamore Formation, Sho-Vel-Tum Field, Oklahoma, USA

The Sycamore Formation at the Sho-Vel-Tum Field primarily consists of clay-rich mudstones (Mdst) and quartz-rich siltstones. The clay-rich Mdst are mainly composed of clays, quartz grains, some allochems, and detrital organic matter. The siltstones are structureless and are divided into two petrofacies: high porosity and permeability massive calcareous siltstones and low porosity and permeability massive calcite-cemented siltstones. Core and well-log data provide mineralogical, lithologic, and porosity information that is useful to define petrophysical facies (petrofacies) and to create facies logs within the Sycamore Formation. We used the data to establish the Sycamore Formation stratigraphic architecture and to map its spatial variability and reservoir properties. To classify the Sycamore Formation petrofacies in noncored wells, we developed a machine learning-based workflow that compares more than 1800 classification models and selects the best combination of well logs, algorithms, and hyperparameters to predict defined petrofacies. The process includes combinations of well logs that were optimized in four classification algorithms: artificial neural network, K-nearest neighbor, support vector machine, and random forest. To adjust each classifier, we used a grid search and a fivefold cross validation to find the best combination of three hyperparameters to improve results of each algorithm. This workflow allows for the efficient extraction of information from cores at a low cost. After we generated the petrofacies logs in noncored wells, we combined them with multiple constraints to create a 3D petrofacies model for the Sycamore Formation at the Sho-Vel-Tum Field and analyze the stratigraphic and diagenetic controls on petrofacies and its impact in reservoir quality.

Reservoir heterogeneity controls of CO2-EOR and storage potentials in residual oil zones: Insights from numerical simulations

Residual oil zones (ROZs) have large potential for CO2 enhanced oil recovery (EOR) and geologic storage. During CO2 injection, the migration of CO2 in ROZs controls the performance of both EOR and storage. However, it has not been clearly visualized and understood that how geological heterogeneity factors control the transport of CO2 in ROZs. In this study, the oil recovery performance and geologic storage potential during continuous CO2 injection in a representative ROZ are studied based on geostatistical modelling and high-fidelity three-phase flow simulation. We examined the influence of autocorrelation length of permeability, global heterogeneity (Dykstra–Parsons coefficient), and permeability anisotropy on cumulative oil recovery and CO2 retention fraction. Simulation results indicate that, as the permeability autocorrelation length increases, the cumulative oil recovery and CO2 storage efficiency decrease. This results from the accelerated migration of CO2 along high permeability zones (i.e., gas channeling). The increase in global heterogeneity and permeability anisotropies can lead to low oil recovery and poor CO2 sequestration performance, depending on the degree of CO2 channeling. The net utilization ratio of CO2 (CO2 retained/oil produced) unfavorably increases with both autocorrelation length and Dykstra–Parsons coefficient, but decreases with the increase in kv/kh. Such a decrease is attributed to enlarged swept volume induced by gravity override. The study provides important implications for field-scale CO2 EOR and storage applications in ROZs.

Spatiotemporal distributions of under-five mortality in Ethiopia between 2000 and 2019.

Under-five mortality declined in the last two decades in Ethiopia, but sub-national and local progress remains unclear. This study aimed to investigate the spatiotemporal distributions and ecological level factors of under-five mortality in Ethiopia. Data on under-five mortality were obtained from five different Ethiopian Demographic and Health Surveys (EDHS), conducted in 2000, 2005, 2011, 2016, and 2019. Environmental and healthcare access data were obtained from different publicly available sources. Bayesian geostatistical models were used to predict and visualize spatial risks for under-five mortality. The national under-five mortality rate in Ethiopia declined from 121 per 1000 live births in 2000 to 59 per 1000 live births in 2019. Spatial variation in under-five mortality was observed at regional and local levels with the highest rates reported in the Western, Eastern, and Central parts of Ethiopia. Spatial clustering of under-five mortality was significantly associated with population density, access to a water body, and climatic factors such as temperature. Under-five mortality rate declined over the past two decades and varied substantially at sub-national and local levels in Ethiopia. Increasing access to water and health care may help to reduce under-five mortality in high-risk areas. Therefore, interventions targeted to reduce under-five mortality should be strengthened in the areas that had a clustering of under-five mortality in Ethiopia by increasing access to quality health care access.

Bioclimatic Zoning for Sheep Farming through Geostatistical Modeling in the State of Pernambuco, Brazil.

The Intergovernmental Panel on Climate Change (IPCC) has pointed out the high vulnerability of developing countries to climate change, which is expected to impact food and income security. Sheep farming is one of the main animal productions among the families located in the most vulnerable regions of the semiarid region of Pernambuco state, a Brazilian territory known for its high temperatures, low relative humidity, and high net solar radiation. Therefore, the objective of this study was to identify different regions of Pernambuco that may be more suitable for different breeds of sheep, based on non-parametric statistics and kriging maps of the temperature and humidity index (THI). THI values were determined based on mean annual temperature and wind speed extracted from the TerraClimate remote sensing database. Pernambuco state presented THI values ranging from 66 to 79, with the hair breeds having a high potential for exploitation in almost all territories, including the main meat-producing breeds. The East Friesian breed, a high milk producer, would be well suited to the Agreste mesoregion, a territory that, like the Pajeú and Moxotó microregions, also proved favorable for the introduction of three wool breeds (Suffolk, Poll Dorset, and Texel) known as major meat producers. The kriging maps of the THI values successfully allowed the identification of strategic development regions of Pernambuco state with high potential for sheep breeding.

Determining the drilling mud window by integration of geostatistics, intelligent, and conditional programming models in an oilfield of SW Iran

Accurate knowledge of pore and fracture pressures is essential for drilling wells safely with the desired mud weight (MW). Overpressure occurs when the pore pressure is higher than the normal hydrostatic pressure. There is a challenge regarding the pressure studies domain in an oilfield in SW Iran, where lack of geo-mechanical data limits exact mud window calculation. Also, the reservoir generally consists of carbonate rocks and contains no shale interbeds, so mechanical stratigraphy based on Gamma ray could not be applied. This study is to provide safe drilling considering MW to prevent the flow or loss in the vicinity of the new wells in the studied field. In this research, the formation pressures and mud window models are determined by combining geostatistical, intelligent, and conditional programming models and compared with real data. The conditional programming was also used to correct small out-of-range data. The highest correlation between the final effective pressure and velocity cube was observed in lower Fahliyan Formation with 0.86 and Ilam with 0.71.The modeled MW difference ranged between 2.5 and 30 PCF. Also, the maximum modeled MW is 150 PCF in the upper Fahliyan Formation. Heavy mud of more than 130 PCF is suggested for drilling the Khalij member and continues to the end of stratigraphy column. Best observed correlation comparing the drilled and modeled MW, especially achieved in the Fahliyan reservoir Formation with more than 100 PCF and the Ilam Formation with 80–100 PCF. Finally, 3D formation pressures are presented and recommended for further safe drillings.