The Application of DBSCAN Algorithm to Improve Variogram Estimation and Interpretation in Irregularly-Sampled Fields

The empirical variogram is a measure of spatial data correlation in geostatistical modeling and simulations. Typically, the empirical variogram is estimated for some defined lag intervals by applying method of moments on an underlying variogram cloud. Depending on the distribution of pair-wise lag values, the variogram cloud of an irregularly-sampled field may exhibit clusteredness. Issues of noisy, uninterpretable and inconsistent empirical variogram plots are commonly encountered in cases of irregularly-sampled fields with clustered variogram clouds. An insightful diagnosis of these problems and a practical solution are the subject of this paper. This research establishes the fact that these problems are caused by the neglect of variogram cloud cluster configurations when defining lag intervals. It is here shown that such neglect hinders the optimal use of spatial correlation information present in variogram clouds. Specifically, four sub-optimal effects are articulated in this paper as the consequence of the neglect. Consequently, this research presents an efficient cluster-analysis – driven technique for variogram estimation in cases of irregularly-sampled fields with clustered variogram clouds. The cluster analysis required for this technique is implemented using an unsupervised machine learning algorithm known as Density-based Spatial Clustering of Applications with Noise (DBSCAN). This technique has been applied to a real field to obtain a stable, interpretable and geologically consistent variogram plot. It has also been applied to a synthetic field and was found to give the lowest estimation error among other techniques. This technique would find usefulness in geo-modeling of natural resource deposits wherein irregular sampling is prevalent

Sampling Grid Shifting Algorithm: A Non-ergodic Spatial Bootstrap Technique for Regular and Irregular Sampling Patterns

Accounting for uncertainty in statistical model parameters is an essential part of geostatiscal reservoir characterization. While parameter uncertainty may be assessed in its ergodic form; the non-ergodic is a better characterization of the variability in the random field. Assessing non-ergodic parameter uncertainty requires re-sampling (bootstrapping) techniques. Existing techniques for such non-ergodic re-sampling are plagued with some limitations/complications. This paper therefore presents a spatial bootstrap algorithm that overcomes the limitations/complication. For a discretized field, the algorithm implements simultaneous displacements (shiftings) of all sampling points through the same distance vector. The shiftings are done across the dimensions of the field subject to the dimensionality of the sampling. In each dimension, the sampling points are shifted successively through a distance equivalent to the gridblock length in that dimension. At each shifting, a shifted sampling grid, of similar configuration as the original sampling grid, is generated. Using the shifted sampling grid, the algorithm resamples a full-grid simulated realization of the field. The assumption of second-order stationarity implies that a sample from a shifted sampling grid is considered a repeated sample of the original sample. The algorithm has been scripted in R statistical computing environment and applied to an irregularly-sampled 3-D field with satisfactory results

Descriptive statistics and probability distributions of volumetric parameters of a Nigerian heavy oil and bitumen deposit

The absence of geostatistical modeling of volumetric parameters of the long-discovered Nigerian heavy oil and bitumen deposits is responsible for the inconsistencies surrounding estimates of hydrocarbon-in-place contained therein. An exploratory data analysis (EDA) is a pre-cursor to such modeling. As part of EDA, this work presents the descriptive statistics and probability distributions of the volumetric parameters of a Nigerian heavy oil and bitumen deposit. Raw data from the existing works have been assembled into a database. Using basic principles, porosity have been computed, from the raw data, for several core samples retrieved from the two bituminous horizons in the deposit. The computed database has been partitioned into the two horizons, using depth-to-top and thickness data. Furthermore, this work has conducted detailed analyses and offers robust discussions on the descriptive statistics and probability distributions of the porosity, depth-to-top, and thickness databases. The statistics and distribution curves obtained are observed to exhibit good correlations with existing geologic, stratigraphic, and textural data. An hypothesis suggesting the two horizons belong to same geological population has been formulated and tested at field and well levels; with results affirming the hypothesis. The descriptive statistics and probability distributions obtained offer a significant understanding of the characteristics and features of the available data. In addition, the distributions now become prior information to which reservoir descriptions would be constrained, in the future conditional simulation stage of this work. The correlation of core data obtained here with the existing geologic, stratigraphic, and textural data would promote data integration in the characterization of this deposit

Evaluation of changes in sexual response and factors influencing sexuality during pregnancy among Nigerian women in Jos, Nigeria

Background: Pregnancy is characterized by physical, hormonal and psychological changes that could influence women’s sexuality. The study aimed at ascertaining changes in the women’s sexual domains as well as factors affecting their sexual responses.Methods: A total of 177 healthy heterosexual pregnant Nigerian women at term and in stable marital relationships were included in the study. Authors’ designed structured questionnaire featuring socio-demographic and obstetric characteristics as well as assessment of their sexual desire, arousal, orgasm, sexual satisfaction and pain compared to the pre-pregnancy period was used to collect the information. Data was analyzed using SPSS version 16 for windows.Results: Mean age of the women was 30.9 ± 4.7 years. Majority of them reported decline in sexual desire, arousal, frequency of orgasm and sexual satisfaction compared to the pre-pregnancy period. Reduce sexual desire was marked in the first trimester but sexual desire peaked in second trimester. Women aged ≥31 years were four times more likely to experience increase frequency of orgasm (OR 4.0, 95% CI 1.9 – 8.7, P = 0.02) while those with tertiary education (OR 2.2, 95% CI 1.1 – 4.2, P = 0.02) and unplanned pregnancy (OR 2.4, 95% CI 1.8 – 5.0, P = 0.04) were more likely to experience decreased sexual satisfaction compared to the pre-pregnancy period.Conclusions: Pregnancy is associated with decline in all domains of female sexual response cycle among the women. Older maternal age positively impacts on frequency of attainment of orgasm while tertiary educational level and unplanned pregnancy negatively affect their sexual satisfaction during pregnancy

Improving postpartum care delivery and uptake by implementing context-specific interventions in four countries in Africa: a realist evaluation of the Missed Opportunities in Maternal and Infant Health (MOMI) project.

Postpartum care (PPC) has remained relatively neglected in many interventions designed to improve maternal and neonatal health in sub-Saharan Africa. The Missed Opportunities in Maternal and Infant Health project developed and implemented a context-specific package of health system strengthening and demand generation in four African countries, aiming to improve access and quality of PPC. A realist evaluation was conducted to enable nuanced understanding of the influence of different contextual factors on both the implementation and impacts of the interventions. Mixed methods were used to collect data and test hypothesised context-mechanism-outcome configurations: 16 case studies (including interviews, observations, monitoring data on key healthcare processes and outcomes), monitoring data for all study health facilities and communities, document analysis and participatory evaluation workshops. After evaluation in individual countries, a cross-country analysis was conducted that led to the development of four middle-range theories. Community health workers (CHWs) were key assets in shifting demand for PPC by 'bridging' communities and facilities. Because they were chosen from the community they served, they gained trust from the community and an intrinsic sense of responsibility. Furthermore, if a critical mass of women seek postpartum healthcare as a result of the CHWs bridging function, a 'buzz' for change is created, leading eventually to the acceptability and perceived value of attending for PPC that outweighs the costs of attending the health facility. On the supply side, rigid vertical hierarchies and defined roles for health facility workers (HFWs) impede integration of maternal and infant health services. Additionally, HFWs fear being judged negatively which overrides the self-efficacy that could potentially be gained from PPC training. Instead the main driver of HFWs' motivation to provide comprehensive PPC is dependent on accountability systems for delivering PPC created by other programmes. The realist evaluation offers insights into some of the contextual factors that can be pivotal in enabling the community-level and service-level interventions to be effective

Reservoir Characterization, variogram estimate, machine learning, Upstream Oil & Gas, agbabu field porosity data, variogram cloud plot, porosity data, estimation, variogram model, variogram

Deposits of heavy oil and natural bitumen have been long-discovered in the Dahomey basin south-western Nigeria. However, inconsistency in estimates of volumes of hydrocarbon contained in these deposits has inhibited commercial interest in the deposits. The inconsistency is attributable to the little or no consideration for spatial variability in those studies. This work is therefore motivated by the need for spatially-coherent geomodels leading to reliable volumetric estimates. An existing database of porosity, depth-to-top and thickness attributes of a section of the deposits located at Agbabu is the subject of this work. This work conducted exploratory spatial data analysis (ESDA) as well as empirical variogram estimation, interpretation and modeling of the attributes. Here, the estimation and interpretation of empirical variogram faced a number of challenges with potentials to render the estimates uninterpretable, unstable and inconsistent with geologic information. These include presence of spatial outlier data, clusteredness of variogram clouds, data paucity, and irregular distribution of point-pairs on variogram clouds. Spatial outliers were either removed or correlated with existing geologic information. The clusteredness issues were resolved using a machine-learning – aided variogram estimation technique recently proposed. Variogram cloud binning approach was deployed to handle irregular distribution of point-pairs. In attempting to deploy an automatic fitting algorithm, cases of insufficient empirical points leading to lack of convergence were encountered. Such cases were resolved by adopting a combination of manual and automatic fitting approaches. Ultimately, this work presents a three-dimensional anisotropic (zonal) porosity variogram model and two-dimensional anisotropic (geometric) models for the depth-to-top and thickness variograms. These models are suitable inputs to spatial interpolation algorithms in generating maps of these volumetric attributes

Effect of Curing Age on the Prospect of Used Plastics to Enhance Engineering Properties of Road Pavements within a Development and Property Agency Estate in Benin City, Edo State, Nigeria

This study investigated the effect of curing age on the possibility of using plastic powder to enhance the engineering properties of subgrade for road pavements. The soil samples utilized for the study were collected from four distinct locations within the Edo Development and Property Agency estate in Benin City, Edo State, Nigeria using appropriate standard methods. They were stored in airtight polythene bags and taken to the University of Benin Geotechnical Laboratory for testing. Polyethylene terephthalate (PET) plastics sourced from recycled soft drink and bottled water containers were pulverized and added to the soil in various proportions of 2%, 4%, 6%, 8% and 10% by weight of the soil. The resulting mix was subjected to various tests such as Atterberg limits, compaction and California Bearing Ratio (CBR). The results showed that the addition of the PET plastic powder led to substantial transformation in the soil’s properties. There was a reduction in the liquid limit, plastic limit and plasticity index, as the proportion of the plastic powder increased. The maximum dry density (MDD) and the optimum moisture content (OMC) was also seen to increase and decrease correspondingly as the proportion of the plastic powder was increased in the soil. The results also showed that as proportion of the plastic powder in the soil was increased, the CBR of the soil also increased. This increase in the soil strength was also observed as the curing age of the CBR samples increased from 0 to 14 days. This shows that a combination of extended curing periods and a larger proportion of plastic powder can significantly improve the load-bearing capacity and saturation resistance of the soil. This study underscores the considerable potential of plastic powder stabilization in elevating the engineering properties of subgrade materials, thereby conferring notable benefits to the domain of road pavement construction

Reservoir Characterization, variogram estimate, machine learning, Upstream Oil & Gas, agbabu field porosity data, variogram cloud plot, porosity data, estimation, variogram model, variogram

Deposits of heavy oil and natural bitumen have been long-discovered in the Dahomey basin south-western Nigeria. However, inconsistency in estimates of volumes of hydrocarbon contained in these deposits has inhibited commercial interest in the deposits. The inconsistency is attributable to the little or no consideration for spatial variability in those studies. This work is therefore motivated by the need for spatially-coherent geomodels leading to reliable volumetric estimates. An existing database of porosity, depth-to-top and thickness attributes of a section of the deposits located at Agbabu is the subject of this work. This work conducted exploratory spatial data analysis (ESDA) as well as empirical variogram estimation, interpretation and modeling of the attributes. Here, the estimation and interpretation of empirical variogram faced a number of challenges with potentials to render the estimates uninterpretable, unstable and inconsistent with geologic information. These include presence of spatial outlier data, clusteredness of variogram clouds, data paucity, and irregular distribution of point-pairs on variogram clouds. Spatial outliers were either removed or correlated with existing geologic information. The clusteredness issues were resolved using a machine-learning – aided variogram estimation technique recently proposed. Variogram cloud binning approach was deployed to handle irregular distribution of point-pairs. In attempting to deploy an automatic fitting algorithm, cases of insufficient empirical points leading to lack of convergence were encountered. Such cases were resolved by adopting a combination of manual and automatic fitting approaches. Ultimately, this work presents a three-dimensional anisotropic (zonal) porosity variogram model and two-dimensional anisotropic (geometric) models for the depth-to-top and thickness variograms. These models are suitable inputs to spatial interpolation algorithms in generating maps of these volumetric attributes