Geostatistical Integration Research Articles

Integration of rock physics, geostatistics, and Bayesian algorithm to estimate porosity in tight oil reservoirs

The conglomerate reservoirs in the Triassic Baikouquan Formation have been identified as the major target for hydrocarbon exploitation in the Mahu Sag, Junggar Basin, Western China. Nevertheless, conglomerate reservoirs typically have lower porosity than sandstone reservoirs. Therefore, determining sweet spots is critical in reservoir modeling and further hydrocarbon exploitation. To investigate favorable areas and better predict the porosity of the Baikouquan tight oil reservoir, integrated methods were employed, including casting thin sections, scanning electron microscopy (SEM), wireline logs, rock physics template (RPT) modeling, geostatistical simulation, and Bayesian updating. The results showed that pore types consist of microfractures, intercrystal micropores, residual intergranular pores, and dissolution pores. Four rock types (RT) were identified via the built RPT, wherein velocity and porosity had a nonlinear relationship. Afterward, porosity simulation was performed and compared using sequential gaussian simulation (SGS), SGS with collocated cokriging (SGSC), and SGS with Bayesian updating (SGSB). Prior to the simulation, all the data were preprocessed to follow a Gaussian (normal) distribution. The range and azimuth of different RT in variogram modeling were then determined using data from horizontal wells. Consequently, it was revealed that the probability distribution frequency (PDF) of SGS with Bayesian updating was very similar to the hard data, whereas the PDF associated with SGS and SGSC may be biased. Particularly the PDF was too narrow for high values. The cumulative distribution function (CDF) of SGSB also showed a similar trend to the CDF of the input data. The ensemble-averaged (E-type) maps and quantiles maps revealed that SGSB had an advantage over collocated cokriging because it could capture the non-linear relationships between impedance and porosity. This study indicates that Bayesian updating is a reliable technique for incorporating secondary data in the calculation of reservoir properties.

Modeling and mapping the spatial variability of soil micronutrients in the Tigris basin

BackgroundCrop production is negatively impacted by excess and lack of soil micronutrients. Due to anthropogenic and natural factors, soil micronutrients vary greatly in space, necessitating time- and money-consuming large-scale sampling. Therefore, modeling their spatial distributions and forecasting in non-sampled areas are essential for high crop production. MethodsIn this study, regional variations in soil micronutrient content of the Upper Tigris Basin were modeled to produce local change maps for the development of site-specific nutrient management systems. The concentrations of extractable zinc (Zn), copper (Cu), manganese (Mn), and iron (Fe) in soil samples taken at 388 different sites between 0 and 20 cm deep were determined. Using variogram and kriging analyses, the spatial distribution of the micro element concentrations was modeled and mapped in a GIS environment. ResultsThe micronutrients demonstrated significant variability with a high coefficient of variation (CV > 35%). It was found that the spatial dependence of the samples ranged from low for Fe and Cu to high for Zn and Mn. The spatial distribution of soil micronutrients was influenced by soil texture in addition to distance. Overall, the results demonstrated that the management of site-specific micronutrients may be aided by the integration of geostatistics and GIS, which is particularly beneficial in terms of effective management of the lands and the optimal use of inputs. ConclusionOverall, the findings showed that the integration of geostatistics and GIS may be helpful in the management of site-specific micronutrients, which is especially advantageous in terms of efficient management of the lands and the best use of inputs.

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.

Geostatistical Classification of Intertidal Surface Sediments Using Log-ratio Transformation and High-resolution Remote Sensing Imagery

Park, N.-W. and Jang, D.-H., 2020. Geostatistical classification of intertidal surface sediments using log-ratio transformation and high-resolution remote sensing imagery. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 157-165. Coconut Creek (Florida), ISSN 0749-0208.This paper presents a multivariate geostatistical approach to classify intertidal surface sediments by combining compositional data analysis and high-resolution remote sensing imagery. An isometric log-ratio (ilr) transformation is first applied to the sediment composition data prior to employing geostatistical analysis to consider the compositional properties of the sediment compositions. To complement the information deficiency of sparse field measurements, high-resolution remote sensing imagery is considered as exhaustive soft information and integrated with the ilr transformed balances via simple kriging with varying local means (SKLM). An inverse ilr transformation is then applied to the SKLM results to obtain sediment compositions over the study area. Finally, Shepard's classification scheme is applied to the sediment compositions to classify the intertidal surface sediments. A case study was conducted on the Baramarae tidal flats in Korea with high-resolution KOMPSAT-2 imagery to demonstrate the effectiveness of the proposed geostatistical approach. The classification results produced by the integration of high-resolution remote sensing imagery via ilr transformation and SKLM outperformed those based on cokriging of sediment compositions, with an improvement of approximately 11.7 %p in overall accuracy. This improvement was attributed to the superior prediction capability of SKLM for sediment compositions. Further, detailed variations in the sediment distributions in the study area, which could not be observed when using only a limited number of sediment samples, could be revealed by integrating the high-resolution remote sensing imagery. Therefore, the geostatistical integration approach that properly accounts for both the property of sediment compositions and the exhaustive soft information from remote sensing imagery could be effectively applied for the classification of intertidal surface sediments.

Geostatistical Integration of Field Measurements and Multi-Sensor Remote Sensing Images for Spatial Prediction of Grain Size of Intertidal Surface Sediments

Park, N.-W., 2019. Geostatistical integration of field measurements and multi-sensor remote sensing images for spatial prediction of grain size of intertidal surface sediments. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 190-196. Coconut Creek (Florida), ISSN 0749-0208.The objective of this paper is to demonstrate the potential benefit of using high-resolution optical and SAR images for the geostatistical mapping of grain size of intertidal surface sediments. The grain size values from field measurements are integrated with reflectance and backscattering coefficients from multi-sensor images via regression kriging. The trends of grain size variations are estimated using support vector regression (SVR) modeling to account for a nonlinear relationship, and rank transformation is applied to original input variables to highlight the relative differences in input values from multi-sensor images. Unlike the conventional regression-based mapping approach, the residual component that cannot be explained by the multi-sensor remote sensing images is considered and predicted via kriging. The final grain size values are then obtained by adding these two components. From a case study on the Baramarae tidal flats in Korea with KOMPSAT-2 and COSMO-SkyMed images, the integration of multi-sensor images with field measurements via SVR and rank transformation could explain 58 % of grain size variance, leading to a significant improvement in predictive performance (approximately 29 %) over ordinary kriging based on field measurements only. Furthermore, using reflectance and scattering information from multi-sensor images generated the grain size distribution with more detailed variations in the study area. Therefore, the synergistic use of multi-sensor images within an advanced geostatistical integration framework is expected to be very effective for the reliable mapping of the grain size of intertidal surface sediments when only a limited number of field measurements are available.

Quantifying spatial uncertainty in rock through geostatistical integration of borehole data and a geologist's cross-section

In order to increase understanding of spatial uncertainty in subsurface conditions for problems in geological and geotechnical engineering, this study develops a geostatistical approach for incorporating a geologist's interpreted cross-section to quantify spatial uncertainty of lithology. In this work, the Sequential Indicator Cosimulation (COSISM) algorithm and the information theory concept of entropy are utilized to generate uncertainty models. A sensitivity analysis is used to study how both borehole data and the cross-section are incorporated into the algorithm, which is quantified using entropy as a metric for uncertainty in geologic unit boundary locations. A verification methodology is then developed to identify the most appropriate combination of input parameters for combining these two data types; recommendations from the verification methodology are made by comparing the calculated entropy to accuracy at the global scale on a point-by-point basis. This methodology is then used to investigate how calibrated input parameters vary due to differences in the input cross-section based on different levels of understanding of the subsurface geology by iteratively providing a geologist with additional borehole data after a cross-section is developed. Results show that two of the parameters, variogram range and the number of conditioning data, can be used to most directly reflect geologist understanding in the simulations. Finally, this study provides guidelines for incorporating a geologist's understanding of a geological environment into this modeling framework, as results show cross-sections that are locally accurate are better represented by different parameters than cross-sections that are not locally accurate but are globally accurate. These guidelines are intended to aid geologists and engineers in understanding how to incorporate a geologist's interpretation of the subsurface lithology or categorical geotechnical parameters at a particular worksite into a structured geostatistical framework.

Geospatial modeling of surface soil texture of agricultural land using fuzzy logic, geostatistics and GIS techniques

ABSTRACTSoil texture is a key controlling factor of soil properties and its functions include water and nutrient holding capacity, retention of pollutants, root development, soil biodiversity, and biogeochemical cycling. From the geotechnical standpoint, it is interesting to analyze the soil texture in regions due to its relation with the infiltration and runoff processes and, consequently, the effect on erosion processes. The purpose of this study is to present a methodology that provides the soil texture spatial variation by using Fuzzy logic theory and geostatistical technique in Geographic Information System (GIS) platform. A total of 140 soil samples were taken from topsoil (0–30 cm) in the study area located in the north of Guilan Province, the southern coast of Caspian Sea, Northern Iran. The soil textural classes were converted to numerical values (fuzzy values) using the fuzzy logic concept. The fuzzy values were spatially interpolated by ordinary kriging method such that the fitted model on experimental semi-variogram was exponential with moderate structure. The results showed the accuracy of soil texture predictive map was acceptable according to the values of normalized root-mean-square error for train data set (0.182) and test data set (0.179). The knowledge of the spatial variability of soil properties such as the soil texture can be an important tool for land-use planning in order to reduce the potential soil losses during rainy seasons. The results indicated that the integration of fuzzy logic, geostatistics, and GIS can improve the interpolation process.

Revitalizing seismic data with new imaging solutions to positively impact field development

Evaluating, planning, and forecasting are integral parts of asset development and continue throughout the life cycle of a producing field. The right decisions are required to lower risk and maximize economic recovery in challenging environments. The Claymore Complex is located in the North Sea and was discovered in 1977. A number of geologic challenges affect the imaging and hence field development including a system of shallow interweaving Quaternary channels, numerous high-contrast layers of varying composition, overburden structural complexity, and a sequence of tilted fault blocks containing the main reservoir systems. Historically, seismic processing over the area has not fully solved these challenges, resulting in significant imaging uncertainty. The Claymore Complex has an abundance of data including a large population of well information and interpretation. As part of a data revitalization process, geostatistical integration of these auxiliary data into a velocity model building sequence using full-waveform inversion and wavelet shift tomography enabled the generation of an accurate high-resolution velocity model. Access to a recent 3D survey acquired obliquely to existing data improved subsurface illumination for both the model building and imaging phases. Near-surface imaging effects and their impact on reservoir positioning and clarity were improved using the upgraded velocity model and dual-azimuth data. Shallow imaging challenges were mitigated by utilizing the additional illumination and angular diversity contained within the multiple reverberations. The revitalization of the Claymore area seismic data has challenged the current understanding of the geologic framework. Confidence has been improved by solving depth conversion problems and increasing the understanding of fault positioning and reservoir connectivity, which are invaluable for future field development.

Facies and porosity 3D models constrained by stochastic seismic inversion to delineate Paleocene fluvial/lacustrine reservoirs in Melut Rift Basin, Sudan

Abstract The Paleocene sandstones of the Yabus and Samma formations are the main oil reservoirs in the Melut Rift Basin of interior Sudan. In our study area, we implemented a multi-modeling approach to reveal heterogeneity and reservoir characteristics of this sandstone. It estimates acoustic impedance from seismic data using deterministic and stochastic inversion algorithms. We integrated petrophysical and geostatistical analyses of the well logs to produce facies and porosity 3D models. The study populated and evaluated three different porosity models; one from well logs only, a second from well logs constrained by inversion results, and a third from well logs constrained by inversion results and facies. Stochastic inversion reveals a significant vertical facies heterogeneity as less as two meters in Yabus Sandstone. This heterogeneity is a lithology dependent and facies distribution is structurally controlled by major faults that developed during the third rift phase. The geostatistical integration of seismic inversion, sedimentologic and stratigraphic analyses reveal lateral facies distribution in the study area. The significant correlation between facies distribution and porosity allows reliable mapping of thin sandbodies. The facies and porosity 3D models indicate vertically-stacked channels with isolated sandbodies in Yabus Sandstone. This strongly suggests a meandering origin for this sandstone. However, the thick sandbodies observed in Samma Formation might indicate a braided channel setting. The dominant claystone and mudstone facies deposited in Melut Formation indicate a shallow lacustrine setting. The reservoir quality decreases with depth from the Paleocene Yabus and Samma formations at the top towards the Cretaceous Melut Formation at the bottom. The porosity estimates support this conclusion as detected low porosity (as less as 5%) in Melut Formation with respect to higher porosity (as high as 25%) in Yabus and Samma formations. The facies and porosity 3D models enhanced our understanding in predicting the reservoir heterogeneity, quality and architecture of this sandstone.

Aquifer structure identification through geostatistical integration of geological parameters: case of the Triassic sandstone aquifer system (SE Tunisia)

This paper presents an integrated modeling approach for constructing a geometric model of an Early to Middle Triassic sandstone aquifer system, commonly referred to as the Sahel El Ababsa aquifer, in southeastern Tunisia. The approach combines thorough interpretations derived from geological data along with various geostatistical methods to create accurate representations of the faulted bounding surfaces. The resulting architectural model was used to define the main aquifer system compartments and explain the nature of the hydraulic relations and communications within and beyond the aquifer system. A database was constructed based on multiple and various data sources, including water boreholes, exploration-petroleum wells, and seismic sections. These features were georeferenced and linked to the database used to build a stratigraphic framework of the area. The geological correlations based on wells information allowed a valuable observation on the structural trends and fault network as well as the lateral facies variation of the aquifer system. The derived interpreted faults were displayed on a georeferenced map which was later converted into a vertex database needed for the next modeling steps. A proper architectural modeling approach of the Early to Middle Triassic sandstone aquifer system was then performed using geostatistical methods that provide a variety of calculation techniques to model the reservoir bounding surfaces, through kriging methods including ordinary kriging and kriging with inequalities. The latter method was used to deal with the lack of data as it can retrieve information from the end of the boreholes that do not reach the surface to be calculated and integrate them in the modeling procedures. This enabled us to build the first architectural model with an accurate description of the bounding surfaces of the Early to Middle Triassic rocks. In fact, although it has been the center of geological and hydrogeological studies, the Upper Permian surface has not previously been the focus of an integrated geomodeling study. The generated models were analyzed, verified through kriging variance maps, then compared with the outcomes of the geological study. The results depict a new structural scheme of the Sahel El Ababsa area and provide an in-depth characterization of the complex compartmentalization affecting the aquifer system that allowed the definition and grouping of the hydrostratigraphic units. Therefore, this work is the first attempt through which an integrated architectural modeling approach is used to characterize and identify the Sahel El Ababsa aquifer system.

Three-dimensional geostatistical integration of borehole and geophysical datasets in developing geological unit boundaries for geotechnical investigations

Borehole data are more commonly used than other geotechnical methods for determining the 3D geospatial information of a given site. However, the inadequate acquisition of continuous geotechnical information over an entire site limits the usefulness of this technique. In contrast, 2D continuous geophysical tomography over a large area reveals geophysical characteristics that can be transformed into geotechnical information. It is therefore possible to obtain more reliable stratal information by combining borehole datasets and geophysical measurements using geostatistical methods. In this study, a 3D geostatistical integration method incorporating site-specific geotechnical variability was proposed to construct a 3D geospatial database for developing geological unit boundaries for engineering projects. First, borehole datasets, reviewed by a geologist, were modified to fit a geostatistical trend of geological strata using a cross-validation-based outlier detection method. Then, site-specific geological layer criteria were determined based on proper statistical comparisons to convert geophysical values into boundary criteria for each geological layer. Finally, 3D geospatial information incorporating site-specific geomaterial characteristics was determined using indicator kriging. The proposed framework was established as functional modules in a geographic information system (GIS) platform and applied to three testbeds in Korea, incorporating a variety of site conditions such as spatial variability of coastal and mountainous areas, to validate its applicability depending on various geophysical characteristics. Application of the proposed framework indicates increases in estimation accuracy of 3D subsurfaces using geophysical and borehole information. However, the geostatistical integration of the two datasets has limited application in areas of geological complexity, such as areas with faults or anisotropic geological strata.

Geostatistical Integration of Coarse Resolution Satellite Precipitation Products and Rain Gauge Data to Map Precipitation at Fine Spatial Resolutions

This paper investigates the benefits of integrating coarse resolution satellite-derived precipitation estimates with quasi-point rain gauge data for generating a fine spatial resolution precipitation map product. To integrate the two precipitation data sources, a geostatistical downscaling and integration approach is presented that can account for the differences in spatial resolution between data from different supports and adjusts inherent errors in the coarse resolution precipitation estimates. First, coarse resolution precipitation estimates are downscaled at a fine spatial resolution via area-to-point kriging to allow direct comparison with rain gauge data. Second, the downscaled precipitation estimates are integrated with the rain gauge data by multivariate kriging. In particular, errors in the coarse resolution precipitation estimates are adjusted against rain gauge data during this second stage. In this study, simple kriging with local means (SKLM) and kriging with an external drift (KED) are used as multivariate kriging algorithms. For comparative purposes, conditional merging (CM), a frequently-applied method for integrating rain gauge data and radar precipitation, is also employed. From a case study with Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation products acquired in South Korea from May–October in 2013, we found that the incorporation of TRMM data with rain gauge data did not improve prediction performance when the number of rain gauge data was relatively large. However, the benefit of integrating TRMM and rain gauge data was most striking, regardless of multivariate kriging algorithms, when a small number of rain gauge data was used. These results indicate that the coarse resolution satellite-derived precipitation product would be a useful source for mapping precipitation at a fine spatial resolution if the geostatistical integration approach is applied to areas with sparse rain gauges.

Geostatistical Integration and Uncertainty in Pollutant Concentration Surface under Preferential Sampling

Introduction: This work focused on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inference. Methods: We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. Results: We found greater predicted standard deviation differences in areas not properly covered by the air quality network. Conclusion: In this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

Geostatistical integration and uncertainty in pollutant concentration surface under preferential sampling.

In this paper the focus is on environmental statistics, with the aim of estimating the concentration surface and related uncertainty of an air pollutant. We used air quality data recorded by a network of monitoring stations within a Bayesian framework to overcome difficulties in accounting for prediction uncertainty and to integrate information provided by deterministic models based on emissions meteorology and chemico-physical characteristics of the atmosphere. Several authors have proposed such integration, but all the proposed approaches rely on representativeness and completeness of existing air pollution monitoring networks. We considered the situation in which the spatial process of interest and the sampling locations are not independent. This is known in the literature as the preferential sampling problem, which if ignored in the analysis, can bias geostatistical inferences. We developed a Bayesian geostatistical model to account for preferential sampling with the main interest in statistical integration and uncertainty. We used PM10 data arising from the air quality network of the Environmental Protection Agency of Lombardy Region (Italy) and numerical outputs from the deterministic model. We specified an inhomogeneous Poisson process for the sampling locations intensities and a shared spatial random component model for the dependence between the spatial location of monitors and the pollution surface. We found greater predicted standard deviation differences in areas not properly covered by the air quality network. In conclusion, in this context inferences on prediction uncertainty may be misleading when geostatistical modelling does not take into account preferential sampling.

Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index

Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusion was significant. It is expected that the indicator approach presented in this paper could be a useful methodology for assessing the spatial quality of classification results in a probabilistic way.

Integration of sequence stratigraphy and geostatistics in 3-D reservoir modeling: a case study of Otumara field, onshore Niger Delta

Sequence stratigraphic concepts for continental settings were assessed to analyze depositional systems of the formations penetrated by wells in the Otumara field. Identification and delineation of five sequences and their bounding surfaces was carried out using well logs. Reservoir sands A to D were mapped using conventional 3-D seismic interpretation techniques. Geostatistical simulation was carried out to provide equiprobable representations of the reservoirs, and the distribution of reservoir parameters and system tracts delineated from the stratigraphic framework. The modeled reservoir properties resulted in an improved description of reservoir distribution and connectivity. Reservoir sands A and B have the highest distribution of both highstand systems tract (HST) and lowstand systems tract (LST) deposits, while reservoir sands C and D have the lowest. Since reservoir sands C and D are from deeper depth, the results indicate that HST and LST decrease with depth while transgressive systems tract (TST) increases with depth. Correlating the 3-D geostatistical model with structures shows prospects with low and high hydrocarbon saturation. Crossplot of porosity and permeability for all reservoirs yielded good correlation. The crossplot of systems tract and hydrocarbon saturation with lithofacies as Z value shows a strong correlation of 0.89. The result also indicates that high hydrocarbon saturation is related to sandy facies of lowstand systems tract. Thus, the LST has the highest hydrocarbon potential. The models resulting from this study can be used to improve reservoir management and well placement, and to predict reservoir performance in Otumara field.

Research on the relationship between vegetation and soil resource patterns on lands abandoned at different times

Many studies on the effect of vegetation on soil resources have compared the patterns of soil resources in different land environments. Few studies have compared the spatial pattern of soil resources in different secondary succession stages. Although various studies have evaluated the heterogeneous soil resources patterns caused by vegetation, few have provided high-resolution analyses of heterogeneous soil resource patterns on a fine scale, with the exception of trees and shrubs. This study has been performed on four abandoned farms (abandoned for 4, 12, 22, and 50years) to evaluate the dynamic effect of vegetation on soil resource patterns at the scale of herbaceous plants. It was found that dominant species in a vegetation family at a given succession stage will play a primary role in the formation of resource islands. Resource islands developed during vegetative succession and subsequently change the patterns of soil resources. There are different soil resource patch sizes and soil parameter patterns on farms abandoned for different periods. A method was developed for this study called the Integration of Geostatistics, Point pattern analysis and Spatial comparison to provide an effective method to research the effect of vegetation on soil resource patterns at a fine scale.

3D geology and reservoir modeling in oil industry: Geologic model construction by integration of sedimentology, sequence stratigraphy, seismic geomorphology, exploration geophysics and geostatistics

3D geology and reservoir modeling in oil industry: Geologic model construction by integration of sedimentology, sequence stratigraphy, seismic geomorphology, exploration geophysics and geostatistics

고해상도 주제 정보 생성을 위한 저해상도 원격탐사 자료의 지구통계학기반 상세화 및 정밀 관측 자료와의 통합

이 논문에서는 저해상도 원격탐사 자료 기반 주제도의 상세화를 목적으로 2단계로 구성된 지구통계학적 통합 기법을 제안하였다. 우선 영역-점 변환 크리깅을 이용하여 저해상도 부가 자료의 상세화를 수행하고, 이 정보는 이후 통합 과정에서 경향 성분으로 이용된다. 그리고 상세화된 부가 자료와 소수의 정밀 관측 자료와의 통합에 가변적 지역 평균 기반 단순 크리깅을 적용한다. 제안 기법은 저해상도 부가 자료의 상세화를 통해 해상도 차이에 따른 정밀 관측 자료와 부가 자료와의 통계적 연관성을 반영할 수 있으며, 정밀 조사 자료와의 통합을 통해 부가 자료의 오류를 보정할 수 있는 장점이 있다. 제안 기법의 적용성 평가를 위해, 지상 관측 강수 자료와 TRMM 자료와의 통합을 이용한 고해상도 강수 주제도 제작 연구를 수행하였다. 실험 결과, 영역-점 변환 크리깅을 통해 원 자료 스케일의 TRMM 강수값을 재생산할 수 있는 다양한 목표 고해상도에서의 상세 정보 추출이 가능하였다. 그리고 이 자료를 정밀 관측 자료와 통합함으로써 정밀 관측 자료만을 이용하는 단변량 공간 예측 기법에 비해 향상된 예측 정확도를 보였다. 따라서 제안 기법은 서로 다른 해상도를 가지는 자료를 대상으로 저해상도 부가 자료의 상세화에 효율적으로 이용될 수 있을 것으로 기대된다. This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.

Technology Focus: Reserves/Asset Management (December 2012)

Technology Focus While preparing for this month’s feature on reserves and asset management, I had the privilege of reviewing more than 100 abstracts from papers presented on the topic over the past year at SPE workshops and conferences. Interestingly, more than 10% of those abstracts addressed risk in some fashion. Integration was another common topic. Finally, I was a bit surprised by the number of abstracts that contained the word “practical” in their title. It struck me as a challenge we all face as part of our daily activities—integrating risk in a practical way. The subject matter of the reserves papers I reviewed was widely varied but extremely useful. Papers addressed practical application of engineering processes to comply with the Securities and Exchange Commission’s 2009 modernized rules, innovative reserves forecasting approaches for unconventional reservoirs, the integration of geostatistics and reservoir modeling in reserves estimation, case studies where reserves were both under- and overestimated, and a variety of other topics. As a topic, asset management also covers a wide range of issues. Papers continued to offer insight into field development optimization, techniques to manage risk, application of project management systems, innovative integrated asset models, and related topics. All of these papers provide in-depth, innovative analysis of issues resulting from the constant growth and development of our industry. Choosing only four papers to be highlighted was a challenge. There were many, many quality papers covering current topics of interest. I tried to select papers that span the interests of the global industry and hit a balance between practical and theoretical information. The papers I selected for additional reading represent interesting case studies of somewhat innovative reserves estimation approaches. I hope you find both the highlighted papers and those for additional reading as interesting and thought provoking as I did. Finally, anyone interested in more in-depth information on either reserves or asset management will find a wealth of additional information at www.jptonline.org, where the full-length papers of those synopsized here are available free to SPE members for 2 months. All of the papers contribute to the advancement of knowledge in our industry, and the authors who contributed to that cause by writing a paper should be congratulated. Recommended additional reading at OnePetro: www.onepetro.org. SPE 151045 Interference Study in Shale Plays by Ayantayo Ajani, The University of Tulsa SPE 143843 Reserves Follow-Up Using an Integrated Deterministic/Probabilistic Approach by Hugo Araujo, Repsol-YPF, et al. SPE 142936 Integrated Asset Model in Camisea, Peru by A. Downie, Baker Hughes, et al. SPE 146788 Field Development Optimization Under Uncertainty: Screening Models for Decision Making by B.A. Ogunyomi, The University of Texas at Austin, et al.