Error propagation in a fuzzy logic multi-criteria evaluation for petroleum exploration

This article applies error propagation in a Monte Carlo simulation for a spatial-based fuzzy logic multi-criteria evaluation (MCE) in order to investigate the output uncertainty created by the input data sets and model structure. Six scenarios for quantifying uncertainty are reviewed. Three scenarios are progressively more complex in defining observational data (attribute uncertainty); while three other scenarios include uncertainty in observational data (position of boundaries between map units), weighting of evidence (fuzzy membership assignment), and evaluating changes in the MCE model (fuzzy logic operators). A case study of petroleum exploration in northern South America is used. Despite the resources and time required, the best estimate of input uncertainty is that based on expert-defined values. Uncertainties for fuzzy membership assignment and boundary transition zones do not affect the results as much as the attribute assignment uncertainty. The MCE fuzzy logic operator uncertainty affects the results the most. Confidence levels of 95% and 60% are evaluated with threshold values of 0.7 and 0.5 and show that accepting more uncertainty in the results increases the total area available for decision-making. Threshold values and confidence levels should be predetermined, although a series of combinations may yield the best decision-making support.     

元胞自动机在城市模拟中的误差传递与不确定性的特征分析

元胞自动机(Cellular Automata,简称CA)已越来越多地用于地理现象的模拟中,如城市系统的演化等。城市模拟经常要使用GIS数据库中的空间信息,数据源中的误差将会通过CA模拟过程发生传递。此外,CA 模型只是对现实世界的近似模拟,这就使得其本身也具有不确定性。这些不确定因素将对城市模拟的结果产生较大的影响,有必要探讨CA在模拟过程中的误差传递与不确定性问题。本文采用蒙特卡罗方法模拟了CA误差的传递特征,并从转换规则、邻域结构、模拟时间以及随机变量等几个方面分析了CA不确定性产生的根源。发现与传统的GIS模型相比,城市CA模型中的误差和不确定性的很多性质是非常独特的。例如,在模拟过程中由于邻域函数平均化的影响,数据源误差将减小;随着可用的土地越来越少,该限制也使城市模拟的误差随时间而减小;模拟结果的不确定性主要体现在城市的边缘。这些分析结果有助于城市建模和规划者更好地理解CA建模的特点。     

Developing an exploration decision support system (EDSS): A strategy for combining information and analytics

Petroleum exploration companies enter the twenty first century facing an increasingly competitive and risky environment. Under those circumstances, there is a growing need for better systematic decision-making that explicitly embodies the firm's desired goals and resource constraints. Computer-aided decision making, or decision support systems (DSS), provide an aid for those exploration management problems that are large, complex, unstructured, and involve management mudgment. Almost every present day DSS falls into one of two general classes. Vehicle DSSs such as linear/nonlinear programming models and other optimization routines, propose and impose specific methodologies to the decision-maker. On the other hand, toolbox DSSs, such as simulation programs, statistical functions, and graphical packages, are generally flexible in enabling their users to employ a variety of approaches and tools for their decision tasks but provide little guidance on both problem representation and investigation. This paper describes the development of a hybrid DSS model that combines the advantages of both the vehicle and toolbox systems components to provide a comprehensive approach to exploration planning from geological development through the capital allocation process. The Exploration Decision Support System (EDSS) preserves the flexibility of the toolbox system while enriching the problem-solving strategies available to the firm. The central objectives for developing an EDSS framework are: (1) better decisions about resource allocations; (2) more systematic understanding of the factors affecting exploration decisions; (3) improved communication about E&P performance objectives and constraints at all levels of decision-making; and (4) an explicit vehicle for continuous improvement of the petroleum exploration firm's decision-making process. The EDSS model can guide geological and exploration managers toward a more formal evaluation of projects, provide insight into the impact of competing choice alternatives, and significantly improve the quality of exploration decisions.     

A Petroleum System for the Salina Basin in Kansas Based on Organic Geochemistry and Geologic Analog

The Salina Basin historically has been an exploration desert—a home of dryholes. Although this basin, which underlies much of north-central Kansas, may never be a prolific source of hydrocarbons, recent research into the maturation and geochemistry of organic matter and oils in Kansas can provide guidelines for a new exploration strategy. The Salina Basin is similar to the oil-productive Forest City Basin in northeastern Kansas in many ways. Both basins originated as a single large basin (i.e., the North Kansas Basin) prior to the rise of the Nemaha Uplift in Late Mississippian-Early Pennsylvanian time. Their Paleozoic stratigraphy thus is similar and the axes of both basins are presently at approximately the same depth. Thermal maturation modeling and available organic-matter maturation data indicate that the lower Paleozoic rocks in the axes of both basins are in the early stages of oil generation. In the Forest City Basin the Ordovician Simpson Group is the deepest known hydrocarbon source-rock—oil-reservoir interval, and by analogy, exploration tests in the Salina Basin, at a minimum, should penetrate through this stratigraphic interval. Ordovician Simpson Group shales in the Forest City Basin are the source rocks for a geochemically distinct oil, which also occurs in Ordovician reservoirs in the extreme southern end of the Salina Basin. To increase the odds of success in an exploration program in the Salina Basin, wildcat wells should be drilled where thermal maturation is greatest. The broad NW–SE-trending basin axis is the most logical area. Exploration tests along this axis in the northern end of the basin may have an extra advantage as organic matter in the Simpson Group may be more thermally mature because of greater burial depth during the Cretaceous. Along the eastern margin of the nearby Central Kansas Uplift and Pratt Anticline, several Paleozoic geologic structures, some of which contain major oil fields, are attributable to tectonic reactivation along the western margin of the Precambrian Central North American Rift System (CNARS). Prospective structural trends in the Paleozoic section of the Salina Basin are anticipated to be associated with this underlying tectonic boundary. The western margin of the CNARS trends NNE–SSW where it passes under the axis of the Salina Basin in northeastern Lincoln and southeastern Mitchell counties. This area is sparsely drilled, with less than two tests per township. If an exploration program can define lower Paleozoic structural closures in this region, these structures may represent the best chance for future petroleum discoveries.     

A framework for uncertainty assessment in simulation models

In this article, we introduce a conceptual framework for systematic identification and assessment of sources of uncertainty in simulation models. This concept builds on a novel typology of uncertainty in model validation and extends the GIScience research focus on uncertainty in spatial data to uncertainty in simulation modelling. Such a concept helps a modeller to interpret and handle uncertainty in order to efficiently optimise a model and better understand simulation results.

To illustrate our approach, we apply the proposed framework for uncertainty assessment to the TREE LIne Model (TREELIM), an individual-based model that simulates forest succession at the alpine tree line. Using this example, uncertainty is identified in the modelling workflow during conceptualisation, formalisation, parameterisation, analysis and validation. With help of a set of indicators we quantify the emerging uncertainties and assess the overall model uncertainty as a function of all occurring sources of uncertainty.

An understanding of the sources of uncertainty in an ecological model proves beneficial for: (1) developing a structurally valid model in a systematic way; (2) deciding if further refinement of the conceptual model is beneficial for the modelling purpose; and (3) interpreting the overall model uncertainty by understanding its sources. Our approach results in a guideline for assessing uncertainty in the validation of simulation models in a feasible and defensible way, and thus functions as a toolbox for modellers. We consider this work as a contribution towards a general concept of uncertainty in spatially explicit simulation models.     

Accounting for Parameter Uncertainty in Reservoir Uncertainty Assessment: The Conditional Finite-Domain Approach

An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent, and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also discussed.     

The comap as a diagnostic tool for non-stationary kriging models

In this study, we demonstrate a novel use of comaps to explore spatially the performance, specification and parameterisation of a non-stationary geostatistical predictor. The comap allows the spatial investigation of the relationship between two geographically referenced variables via conditional distributions. Rather than investigating bivariate relationships in the study data, we use comaps to investigate bivariate relationships in the key outputs of a spatial predictor. In particular, we calibrate moving window kriging (MWK) models, where a local variogram is found at every target location. This predictor has often proved worthy for processes that are heterogeneous, and most standard (global variogram) kriging algorithms can be adapted in this manner. We show that the use of comaps enables a better understanding of our chosen MWK models, which in turn allows a more informed choice when selecting one MWK specification over another. As case studies, we apply four variants of MWK to two heterogeneous example data sets: (i) freshwater acidification critical load data for Great Britain and (ii) London house price data. As both of these data sets are strewn with local anomalies, three of our chosen models are robust (and novel) extensions of MWK, where at least one of which is shown to perform better than a non-robust counterpart.     

Cellular modelling as a tool for interpreting historic braided river evolution

Results are presented from a new cellular model of braided river dynamics that simulates flow, sediment transport, morphological change and the effects of braidplain vegetation. This model is used to investigate the effect of changes in upstream sediment supply on braided river systems over simulation periods of 200 years. Modelled changes in channel morphology, associated with both aggradation and degradation, were seen to be consistent with those reported in the literature. In addition, simulation results allowed the identification of diagnostic characteristics of aggrading and degrading reaches, in the form of relationships between the age, extent and relative elevation of fluvial surfaces. Interpretation of spatial patterns of valley floor surface characteristics in the Avoca River, New Zealand, on the basis of these relationships, allowed the identification of channel reaches that appear to be experiencing either aggradation or degradation. These inferences are shown to be consistent with independent evidence of spatial patterns of sediment supply to the main valley floor, derived from aerial photographs and an existing sediment source inventory. These results illustrate the potential for using cellular models to develop an improved understanding of natural river behaviour.     

Laboratory experiments and thermal calculations for the development of a next-generation glacier-ice exploration system: Development of an electro-thermal drilling device

A next-generation drilling system, equipped with a thermal drilling device, is proposed for glacier ice. The system is designed to penetrate glacier ice via melting of the ice and continuously analyze melt-water in a contamination-free sonde. This new type of drilling system is expected to provide analysis data in less time and at less cost than existing systems. Because of the limited number of parameters that can be measured, the proposed system will not take the place of conventional drilling systems that are used to obtain ice cores; however, it will provide a useful method for quickly and simply investigating glacier ice.An electro-thermal drilling device is one of the most important elements needed to develop the proposed system. To estimate the thermal supply required to reach a target depth in a reasonable time, laboratory experiments were conducted using ice blocks and a small sonde equipped solely with heaters. Thermal calculations were then performed under a limited range of conditions. The experiments were undertaken to investigate the effects of the shape and material of the drill head and heater temperature on the rate of penetration into the ice. Additional thermal calculations were then performed based on the experimental results.According to the simple thermal calculations, if the thermal loss that occurs while heat is transferred from the heater to ice (in melting the ice) is assumed to be 50%, the total thermal supply required for heaters in the sonde and cable is as follows: (i) 4.8 kW (sonde) plus 0 W (cable) to penetrate to 300 m depth over 10 days into temperate glacier ice for which the temperature is 0 °C at all depths and to maintain a water layer along 300 m of cable; (ii) 10 kW (sonde) plus 19–32 kW (cable) to penetrate to 1000 m depth over 1 month into cold glacier ice for which the temperature is −25 °C at the surface and 0 °C at 1000 m depth and to maintain a water layer along 1000 m of cable; and (iii) 19 kW (sonde) plus 140–235 kW (cable) to penetrate to 3000 m depth over 2 months into an ice sheet for which the temperature is −55 °C at the surface and 0 °C at 3000 m depth and to maintain a water layer along 3000 m of cable. The thermal supply required for the cable is strongly affected by the thickness of the water layer, cable diameter, and the horizontal distance from the ice wall at which the ice temperature was maintained at its initial temperature. A large thermal supply is required to heat 3000 m of cable in an ice sheet (scenario (iii) above), but penetration into glacier ice (scenarios (i) and (ii) above) could be realistic with the use of a currently employed generator.     

Knowledge transfer and adaptation for land-use simulation with a logistic cellular automaton

Few studies have been conducted into the use of knowledge transfer for tackling geo-simulation problems. Cellular automata (CA) have proven to be an effective and convenient means of simulating urban dynamics and land-use changes. Gathering the knowledge required to build the CA may be difficult when these models are applied to large areas or long periods. In this paper, we will explore the possibility that the knowledge from previously collected data can be transferred spatially (a different region) and/or temporally (a different period) for implementing urban CA. The domain adaptation of CA is demonstrated by integrating logistic-CA with a knowledge-transfer technique, the TrAdaBoost algorithm. A modification has been made to the TrAdaBoost algorithm by incorporating a dynamicweight-trimming technique. This proposed model, CA_trans, is tested by choosing different periods and study areas in the Pearl River Delta. The ‘Figure of Merit’ measurements in the experiments indicate that CA_trans can yield better simulation results. The variance of traditional logistic-CA is about 2–5 times the variance of CA_trans until the number of new data reaches 30. The experiments have demonstrated that the proposed method can alleviate the sparse data problem using knowledge transfer.     

流域降雨径流路径的数字模拟技术

流域内降雨径流流动过程的水文分析具有重要意义。此文基于地理信息系统的栅格系统,利用数字高程模型(DEM)提供地形特征的能力,通过一系列相连的数据集,用计算机对流域降雨径流路径进行了数字模拟,其可以实现以正确的水文顺序跟踪地表径流在流域空间范围内的流动路径,并确定地表径流在流域空间内流动路径的水文计算等级,为基于流域降雨径流关系的建模研究提供了空间分析基础。最后把该项研究成果应用于黄家二岔小流域,证实该模拟技术具有一定的可靠性和实用性。     

中国降水未来情景的降尺度模拟

基于长时间序列(1964～2007年)的全国降水观测数据, 结合经纬度数据以及DEM、 坡向、坡度等系列地形特征数据, 利用空间统计方法, 在构建年平均降水降尺度模型的基础 上, 运用高精度曲面建模(HASM)方法对经过降尺度分析的HadCM3的A1Fi、A2a和B2a 三种情景T1～T4时段的全国未来平均降水进行高精度曲面模拟。模拟结果显示, 在T1～T4 时段内, A1Fi、A2a和B2a三种情景的全国平均降水均呈持续增加趋势。其中, 平均降水在 A1Fi情景中增加速度最快, B2a情景中增加速度最慢;A1Fi和A2a两种情景的平均降水均呈 加速增加趋势, 而B2a情景的平均降水则呈减速增加趋势。模拟结果表明, 本文构建的降尺 度模拟方法可以有效地实现IPCC GCM 的低分辨率的降水情景数据降尺度转换成高分辨率的 降水数据。     

Numerical simulation of artificial ground freezing in a fluid-saturated rock mass with account for filtration and mechanical processes

This study is devoted to the numerical simulation of the artificial ground freezing process in a fluid-saturated rock mass of the potassium salt deposit. A coupled model of nonstationary thermal conductivity, filtration and thermo-poroelasticity, which takes into account dependence of the physical properties on temperature and pressure, is proposed on the basis of the accepted hypotheses. The considered area is a cylinder with a depth of 256 meters and diameter of 26.5 meters and includes 13 layers with different thermophysical and filtration properties. Numerical simulation was carried out by the finite-element method. It has been shown that substantial ice wall formation occurs non-uniformly along the layers. This can be connected with geometry of the freezing wells and with difference in physical properties. The average width of the ice wall in each layer was calculated. It was demonstrated that two toroidal convective cells induced by thermogravitational convection were created from the very beginning of the freezing process. The effect of the constant seepage flow on the ice wall formation was investigated. It was shown that the presence of the slow flow lead to the delay in ice wall closure. In case of the flow with a velocity of more than 30 mm per day, closure of the ice wall was not observed at all in the foreseeable time.     

Challenges in using siliceous subfossils as a tool for inferring past water level and hydroperiod in Everglades marshes

Successfully rehabilitating drained wetlands through hydrologic restoration is dependent on defining restoration targets, a process that is informed by pre-drainage conditions, as well as understanding linkages between hydrology and ecosystem structure. Paleoecological records can inform restoration goals by revealing long-term patterns of change, but are dependent on preservation of biomarkers that provide meaningful interpretations of environmental change. In the Florida Everglades, paleohydrological hind-casting could improve restoration forecasting, but frequent drying of marsh soils leads to poor preservation of many biomarkers. To determine the effectiveness of employing siliceous subfossils in paleohydrological reconstructions, we examined diatoms, plant and sponge silico-sclerids from three soil cores in the central Everglades marshes. Subfossil quality varied among cores, but the abundance of recognizable specimens was sufficient to infer 1,000–3,000 years of hydrologic change at decadal to centennial resolution. Phytolith morphotypes were linked to key marsh plant species to indirectly measure fluctuations in water depth. A modern dataset was used to derive diatom-based inferences of water depth and hydroperiod (R² = 0.63, 0.47; RMSE = 14 cm, 120 days, respectively). Changes in subfossil quality and abundances at centennial time-scales were associated with mid-Holocene climate events including the Little Ice Age and Medieval Warm Period, while decadal-scale fluctuations in assemblage structure during the twentieth century suggested co-regulation of hydrology by cyclical climate drivers (particularly the Atlantic Multidecadal Oscillation) and water management changes. The successful reconstructions based on siliceous subfossils shown here at a coarse temporal scale (i.e., decadal to centennial) advocate for their application in more highly resolved (i.e., subdecadal) records, which should improve the ability of water managers to target the quantity and variability of water flows appropriate for hydrologic restoration.     

A context-sensitive correlated random walk: a new simulation model for movement

Computational Movement Analysis focuses on the characterization of the trajectory of individuals across space and time. Various analytic techniques, including but not limited to random walks, Brownian motion models, and step selection functions have been used for modeling movement. These fall under the rubric of signal models which are divided into deterministic and stochastic models. The difficulty of applying these models to the movement of dynamic objects (e.g. animals, humans, vehicles) is that the spatiotemporal signal produced by their trajectories a complex composite that is influenced by the Geography through which they move (i.e. the network or the physiography of the terrain), their behavioral state (i.e. hungry, going to work, shopping, tourism, etc.), and their interactions with other individuals. This signal reflects multiple scales of behavior from the local choices to the global objectives that drive movement. In this research, we propose a stochastic simulation model that incorporates contextual factors (i.e. environmental conditions) that affect local choices along its movement trajectory. We show how actual global positioning systems observations can be used to parameterize movement and validate movement models and argue that incorporating context is essential in modeling movement.     

A Time Monte Carlo method for addressing uncertainty in land-use change models

One of the main objectives of land-use change models is to explore future land-use patterns. Therefore, the issue of addressing uncertainty in land-use forecasting has received an increasing attention in recent years. Many current models consider uncertainty by including a randomness component in their structure. In this paper, we present a novel approach for tuning uncertainty over time, which we refer to as the Time Monte Carlo (TMC) method. The TMC uses a specific range of randomness to allocate new land uses. This range is associated with the transition probabilities from one land use to another. The range of randomness is increased over time so that the degree of uncertainty increases over time. We compare the TMC to the randomness components used in previous models, through a coupled logistic regression-cellular automata model applied for Wallonia (Belgium) as a case study. Our analysis reveals that the TMC produces results comparable with existing methods over the short-term validation period (2000–2010). Furthermore, the TMC can tune uncertainty on longer-term time horizons, which is an essential feature of our method to account for greater uncertainty in the distant future.