The application of expert systems to the identification and use of microfossils in the petroleum industry

Development of an expert system for microfossil identification and their use in dating rocks demonstrates that such systems can be used in the evaluation and interpretation of basic geological data. Palaeontological data lends itself to this kind of approach because it is controlled to a greater extent than other geological data by internationally accepted rules and practice. Two fossil groups, planktonic foraminifera and conodonts, have been chosen for initial development, both groups having a long history of study.     

The topology of geology 2: Topological uncertainty

Uncertainty is ubiquitous in geology, and efforts to characterise and communicate it are becoming increasingly important. Recent studies have quantified differences between perturbed geological models to gain insight into uncertainty. We build on this approach by quantifying differences in topology, a property that describes geological relationships in a model, introducing the concept of topological uncertainty. Data defining implicit geological models were perturbed to simulate data uncertainties, and the amount of topological variation in the resulting model suite measured to provide probabilistic assessments of specific topological hypotheses, sources of topological uncertainty and the classification of possible model realisations based on their topology. Overall, topology was found to be highly sensitive to small variations in model construction parameters in realistic models, with almost all of the several thousand realisations defining distinct topologies. In particular, uncertainty related to faults and unconformities was found to have profound topological implications. Finally, possible uses of topology as a geodiversity metric and validation filter are discussed, and methods of incorporating topological uncertainty into physical models are suggested.     

降低矿产资源定量预测不确定性的双向预测方法

成矿过程是一个复杂的物理化学过程,由于地质自身的不确定性、原始数据采集和处理的不当、预测方法中经验参数的不确定性等多重因素的叠加,造成矿产资源定量预测结果中潜在大量不确定性.在科学认识这些不确定性的基础上,如何降低不确定性是预测评价研究的一个重要方向.以地质异常理论和成矿动力学为指导,双向预测评价方法是降低地质异常分析...     

基于Monte Carlo模拟的三维剖面地质界线不确定性分析

三维剖面地质界线是构建三维地质结构模型的重要基础数据,其不确定性会影响三维模型的几何形态和属性分布。以单一分布为假设前提的统计学不确定性分析方法掩盖了其他概率分布特征对模型的影响。突破单一误差分布条件的假设前提,本文使用Monte Carlo方法模拟了不同概率分布情况下地质剖面数据中地质界线的抽样采集,以及地质界线空间分布的不确定性;依托地质界线空间位置与地质属性的耦合关系,提出了用地质属性概率分布实现地质界线空间不确定性的定量可视化,并结合实际地质剖面探讨了多种概率分布条件下地质界线的空间不确定性。实例研究表明,基于Monte Carlo模拟的不确定性分析方法可以突破单一误差分布假设条件,结合地质属性概率可充分揭示出建模数据的内在不确定性与模型外在要素形态之间的耦合关系。     

Risk quantification with combined use of lithological and grade simulations: Application to a porphyry copper deposit

The uncertainty in the recoverable tonnages and grades in a mineral deposit is a key factor in the decision-making process of a mining project. Currently, the most prevalent approach to model the uncertainty in the spatial distribution of mineral grades is to divide the deposit into domains based on geological interpretation and to predict the grades within each domain separately. This approach defines just one interpretation of the geological domain layout and does not offer any measure of the uncertainty in the position of the domain boundaries and in the mineral grades. This uncertainty can be evaluated by use of geostatistical simulation methods. The aim of this study is to evaluate how the simulation of rock type domains and grades affects the resources model of Sungun porphyry copper deposit, northwestern Iran. Specifically, three main rock type domains (porphyry, skarn and late-injected dykes) that control the copper grade distribution are simulated over the region of interest using the plurigaussian model. The copper grades are then simulated in cascade, generating one grade realization for each rock type realization. The simulated grades are finally compared to those obtained using traditional approaches against production data.     

油田开发阶段储层参数精细评价

以胜索油田为例,详尽论述了油田开发阶段储层参数精细评价的思路和方向,包括测井资料预处理与标准化、不同开发时期储层四性参数变化分析、测井解释模型建立及参数求取、储层参数模糊评价。开发阶段应通过储层四性分析,利用生产测试资料和神经网络方法建立测井解释模型及处理,能有效提高解释度。精细处理结果及模糊数学方法的应用,准确地定量表征了储层的优劣,在油田剩余油挖潜中产生了较好的应用效果。     

地质标准物质不确定度评估方法初探

在分析地质标准物质标准值不确定度来源的基础上,提出了在多个实验室协作研制地质标准物质时,协作单位除提供重现性检测数据外,还应分别提供各项目检测数据的合成不确定度。分析方法或实验室之间的平均值的合成不确定度按不等精度方法处理。标准物质标准值的不确定度由分析方法、检测实验室、样品均匀性和样品稳定性的不确定度合成后乘以扩展不确定度置信水平下的包含因子而得。     

Using Sequential Indicator Simulation as a Tool in Reservoir Description: Issues and Uncertainties

Flow simulation studies require an accurate model of the reservoir in terms of its sedimentological architecture. Pixel-based reservoir modeling techniques are often used to model this architecture. There are, however, two problem areas with such techniques. First, several statistical parameters have to be provided whose influence on the resulting model is not readily inferable. Second, conditioning the models to relevant geological data that carry great uncertainty on their own adds to the difficulty of obtaining reliable models and assessing model reliability. The Sequential Indicator Simulation (SIS) method has been used to examine the impact of such uncertainties on the final reservoir model. The effects of varying variogram types, frequencies of lithology occurrence, and the gridblock model orientation with respect to the sedimentological trends are illustrated using different reservoir modeling studies. Results indicate, for example, that the choice of variogram type can have a significant impact on the facies model. Also, reproduction of sedimentological trends and large geometries requires careful parameter selection. By choosing the appropriate modeling strategy, sedimentological principles can be translated into the numerical model. Solutions for dealing with such issues and the geological uncertainties are presented. In conclusion, each reservoir modeling study should begin by developing a thorough quantitative sedimentological understanding of the reservoir under study, followed by detailed sensitivity analyses of relevant statistical and geological parameters.     

Construction of a fluvial facies knowledge graph and its application in sedimentary facies identification

Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies. Traditional sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized terminology. Therefore, it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies analysis. These hurdles could be partly alleviated by having a standardized, structured, and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification system. To this end, this study constructed a knowledge system for fluvial facies and carried out knowledge representation. Components include a domain knowledge graph for types of fluvial facies (meandering, braided and other fluvial depositional environments) and their characteristic features (bedforms, grain size distribution, etc.) with visualization, a method for query and retrieval on a graph database platform, a hierarchical knowledge tree-structure, a data-mining clustering algorithm for machine-analysis of publication texts, and an algorithm model for this area of sedimentary facies reasoning. The underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of publications. For testing, 17 sets literature publications data that included details of sedimentary facies data (bedforms, grain sizes, etc.) were submitted to the artificial intelligence model, then compared and validated. This testing set of automated reasoning results yielded an interpretation accuracy of about 90% relative to the published interpretations in those papers. Therefore, the model and algorithm provide an efficient and automated reasoning technology, which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field.     

油气田开发中油气藏地质成因分析

本文从油气藏地质成因分析的基础资料入手,总结该项研究的主要内容包括:构造、储层和油气水分布特征等3方面.结合自身科研实践,认为油气藏地质成因分析方法主要包括:野外露头和现代沉积考察、岩心观察描述、显微镜下薄片观察鉴定、地球物理解释预测、地质统计学分析、分析测试、各种物理模拟和数值模拟、油气藏动态监测和生产动态等,并阐述了不同研究方法的优缺点.目前油气藏地质成因分析存在的主要问题包括:对油气藏地质成因分析重视程度不够、研究方法偏定性、地质成因分析和油气藏表征结合不紧密、油气藏表征精度制约了地质成因分析的准确度、油气藏地质成因分析综合性不强、特殊类型油气藏地质成因分析还存在诸多难题等.本文指出了该研究未来发展方向.主要包括:依靠油气藏地质成因分析解决油气田开发中的难题、通过各种模拟方法提高油气藏地质成因分析定量化水平、加强地质成因分析以提高油气藏表征水平、利用油气藏表征促进地质成因分析进步、拓展油气藏地质成因分析在油气田开发中应用的领域、特殊类型油气藏地质成因分析等.     

西北黄土高原区滑坡遥感解译研究--以陕西延安、宁夏彭阳等地为例

西北黄土高原区面积广,滑坡数量众多,利用遥感技术加快减灾、防灾,开展编目、区划研究,具有重要的研究意义。目前,利用人机交互解译、计算机分类,滑坡灾害解译和识别已取得许多成功的经验。受数据源、数据处理和解译等交叉学科发展的影响,应用多侧重影像预处理、解译标志建立和技术方法研究,滑坡灾害遥感解译不确定性等关键问题研究较少,解译标志也过分关注色和形描述,与滑坡发育规律密切的地形地貌等重要解译标志尚需完善。本文以西北黄土高原陕西延安、宁夏彭阳等地为研究区,从尺度效应、波段组合、数据融合等方面分析滑坡灾害遥感解译的不确定性,结果表明地貌、滑坡和承灾体、岩土体变化呈等级结构,与之对应遥感数据的时空尺度不同,利用ETM+, Spot 5和QuickBird等不同分辨率数据进行识别分类,能减少解译的不确定性;波段组合能有效反映地层岩性时,更适合滑坡识别和分类;高空间分辨率数据的融合算法以主成分或HIS较佳。基于分析结果,采用Spot 5经自然彩色变换主成分融合,依据研究区黄土滑坡的典型特征及影像表现,利用人机交互解译技术建立滑坡解译标志,为编目和区划、滑坡灾害计算机解译等提供依据。     

Importance of establishing sources of uncertainty for the derivation of reliable SHRIMP ages

The ion microprobe, as exemplified by SHRIMP, has long been an invaluable resource for the derivation of geological ages. The derivation of those ages is critically dependent on the identification and individual quantification of all sources of contributing uncertainty. In recent years, it has been proposed that the only component of uncertainty arising from the instrument itself is predictable from counting statistics. The adoption of that approach has led to several conclusions including: (i) that zircon U–Pb ages are relatively easily reset, which necessitates the enhanced editing of individual analyses before a grouped age can be obtained; and (ii) that other studies have overestimated analytical uncertainties and, as a consequence, have reported incorrect and/or overly imprecise ages. We present evidence for the presence of additional sources of instrument‐related uncertainty that necessitates a different (but not new) approach for the processing of SHRIMP data. Fortunately, this complication does not represent a serious problem, provided that a high‐quality zircon‐calibration standard has been used for Pb/U calibration. SHRIMP ages obtained some time ago from the Crudine Group of the Hill End Trough (New South Wales) have recently been placed at the centre of this controversy. A significant part of the problem is that most of those ages were based on a standard (SL 13) that is now known to be heterogeneous. The more reliable parts of the original data have been re‐reprocessed on the basis of the new evidence. They fail to detect a significant age difference between the bottom and the top of the Merrions Formation, a conclusion that is contrary to earlier expressed opinions.     

Uncertainty in dam safety risk analysis

The character and importance of uncertainty in dam safety risk analysis drives how risk assessments are used in practice. The current interpretation of uncertainty is that, in addition to the aleatory risk which arises from presumed uncertainty in the world, it comprises the epistemic aspects of irresolution in a model or forecast, specifically model and parameter uncertainty. This is true in part but it is not all there is to uncertainty in risk analysis. The physics of hazards and of failure may be poorly understood, which goes beyond uncertainty in its conventional sense. There may be alternative scenarios of future conditions, for example non-stationarity in the environment, which cannot easily be forecast. There may also be deep uncertainties of the type associated with climate change. These are situations in which analysts do not know or do not agree on the system characterisation relating actions to consequences or on the probability distributions for key parameters. All of these facets are part of the uncertainty in risk analysis with which we must deal.     

A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping

Quantification of a mineral prospectivity mapping (MPM) heavily relies on geological, geophysical and geochemical analysis, which combines various evidence layers into a single map. However, MPM is subject to considerable uncertainty due to lack of understanding of the metallogenesis and limited spatial data samples. In this paper, we provide a framework that addresses how uncertainty in the evidence layers can be quantified and how such uncertainty is propagated to the prediction of mineral potential. More specifically, we use Monte Carlo simulation to jointly quantify uncertainties on all uncertain evidence variables, categorized into geological, geochemical and geophysical. On stochastically simulated sets of the multiple input layers, logistic regression is employed to produce different quantifications of the mineral potential in terms of probability. Uncertainties we address lie in the downscaling of magnetic data to a scale that makes such data comparable with known mineral deposits. Additionally, we deal with the limited spatial sampling of geochemistry that leads to spatial uncertainty. Next, we deal with the conceptual geological uncertainty related to how the spatial extent of the influence of evidential geological features such as faults, granite intrusions and sedimentary formations. Finally, we provide a novel way to interpret the established uncertainty in a risk-return analysis to decide areas with high potential but at the same time low uncertainty on that potential. Our methods are illustrated and compared with traditional deterministic MPM on a real case study of prospecting skarn Fe deposition in southwestern Fujian, China.     

A Bayesian approach to calibrating apatite fission track annealing models for laboratory and geological timescales

We present a new approach for modelling annealing of fission tracks in apatite, aiming to address various problems with existing models. We cast the model in a fully Bayesian context, which allows us explicitly to deal with data and parameter uncertainties and correlations, and also to deal with the predictive uncertainties. We focus on a well-known annealing algorithm [Laslett, G.M., Green, P.F., Duddy, I.R., Gleadow. A.J.W., 1987. Thermal annealing of fission tracks in apatite. 2. A quantitative-analysis. Chem. Geol., 65 (1), 1-13], and build a hierachical Bayesian model to incorporate both laboratory and geological timescale data as direct constraints. Relative to the original model calibration, we find a better (in terms of likelihood) model conditioned just on the reported laboratory data. We then include the uncertainty on the temperatures recorded during the laboratory annealing experiments. We again find a better model, but the predictive uncertainty when extrapolated to geological timescales is increased due to the uncertainty on the laboratory temperatures. Finally, we explictly include a data set [Vrolijk, P., Donelick, R.A., Quenq, J., Cloos. M., 1992. Testing models of fission track annealing in apatite in a simple thermal setting: site 800, leg 129. In: Larson, R., Lancelet, Y. (Eds.), Proceedings of the Ocean Drilling Program, Scientific Results, vol. 129, pp. 169-176] which provides low-temperature geological timescale constraints for the model calibration. When combined with the laboratory data, we find a model which satisfies both the low-temperature and high-temperature geological timescale benchmarks, although the fit to the original laboratory data is degraded. However, when extrapolated to geological timescales, this combined model significantly reduces the well-known rapid recent cooling artifact found in many published thermal models for geological samples.     

基于位移的滑坡时间概率预测方法

滑坡位移是滑坡稳定性状态的重要表观特征。对数拟合法是一种基于位移观测数据对滑坡时间进行预测的常用方法。在实际应用中,受测量误差、环境噪声和模型假设等因素的影响,滑坡时间很难准确预测。本文将滑坡时间预测中的不确定性分为观测不确定性和模型不确定性两类。基于残差Bootstrap方法,提出了观测不确定性的标定方法;搜集了降雨型滑坡案例,标定了对数拟合法预测降雨型滑坡时间的模型不确定性。在此基础上,提出了可同时考虑以上两种不确定性的滑坡时间概率预测方法。算例表明,概率方法既能提供滑坡最可能发生的时间,也能提供滑坡在不同时间点之前发生的概率水平,其预测结果与实际观测滑坡时间更为符合,可为滑坡灾害的灾害预警提供有用的信息。     

Quasi-Stochastic Analysis of Uncertainty for Modelling Structurally Controlled Failures

In one approach to predicting the behaviour of rock masses, effort is being devoted to the use of probabilistic methods to model structures interior to a rock mass (sometimes referred to as ‘inferred’ or ‘stochastic’ structures). The physical properties of these structures (e.g. position, orientation, size) are modelled as random parameters, the statistical properties of which are derived from the measurements of a sample of the population (sometimes referred to as ‘deterministic’ structures). Relatively little attention has been devoted to the uncertainty associated with the deterministic structures. Typical geotechnical analyses rely on either an entirely stochastic analysis, or deterministic analyses representing the structures with a fixed shape (i.e. disc), position, size, and orientation. The simplifications assumed for this model introduce both epistemic and stochastic uncertainties. In this paper, it is shown that these uncertainties should be quantified and propagated to the predictions of behaviour derived from subsequent analyses. We demonstrate a methodology which we have termed quasi-stochastic analysis to perform this propagation. It is shown that relatively small levels of uncertainty can have large influence on the uncertainties associated with geotechnical analyses, such as predictions of block size and block stability, and therefore this methodology can provide the practitioner with a method for better interpretation of these results.     

构造解析、地质学研究范式与理论创新——藏东南侧向碰撞带构造演化研究实践

运用板块构造理论解释造山带地质演化是当前地质学研究的难点,也是地质学基础理论创新的可能方向。包括印度 欧亚大陆侧向碰撞带在内的国内、外造山带构造演化尚未取得共识,大多表现为“大量高质量数据与诸多充满争议的演化模型共存”。产生这些争议的主要原因包括“高质量数据”的时、空分布样式未受足够重视、以及部分关键地质体物理属性鉴别存在争议。这些问题为地质学发展与理论创新留下了巨大的空间。持续10多年西南三江造山带区域地质填图、构造解析揭示了侧向碰撞带构造格架、地壳变形历史,提出了印度 欧亚大陆碰撞新的三阶段模型。这一研究实践表明,严格按照 “构造解析方法”体现的“三步骤”研究范式开展区域地质填图是造山带理论创新的基础与保障。区域地质填图是造山带研究中难度最大的工作之一,要求填图人员必须具备广泛、坚实的地质学理论基础,以及运用基础理论解决实际问题的能力。     

An Approach for the Reliable Evaluation of the Uncertainties Associated to Petrophysical Properties

The new frontiers of the oil industry are deep offshore reservoirs, fields located in harsh environments with unconventional hydrocarbon accumulations. Extraction of oil or gas from these environments share technical challenges and high development costs. Consequently, an evaluation of the risks associated with exploiting these resources is needed before any investment decisions are made. Potential risks can be properly assessed only by considering all the possible sources of uncertainty affecting the reservoir characterization. Porosity, fluid saturations and net to gross are strategic information used to both calculate hydrocarbon originally in place and define proper field development plans. These quantities are obtained through the log interpretation process, which is an inverse problem where the main petrophysical characteristics are calculated as the acceptable minimum of a cost function describing the discrepancy between measured and simulated logs, the latter being reproduced on the basis of an assumed formation model. The results of the calculation process can be affected by several uncertainties related to the physics and calibration of the measuring tools, the identification of the proper formation model, and the quantification of the model coefficients. An effective methodology able to provide a reliable evaluation of the hydrocarbon volume and the assessment of the associated uncertainties is presented and discussed in this paper. The log interpretation process was approached as a linearly constrained optimization problem, solved by coupling a Lagrange-Newton method with a primal active set algorithm. The evaluation of the uncertainties was obtained by coupling the optimization algorithm with the Monte Carlo approach. The results obtained by the application of the methodology to a real case are shown.     

Structural Interpretation of Sparse Fault Data Using Graph Theory and Geological Rules

Structural uncertainty exists when associating sparse fault interpretations made from two-dimensional seismic lines or limited outcrop observations. Here, a graph formalism is proposed that describes the problem of associating spatial fault evidence. A combinatorial analysis, relying on this formalism, shows that the number of association scenarios is given by the Bell number, and increases exponentially with the number of pieces of evidence. As a result, the complete exploration of uncertainties is computationally highly challenging. The available prior geological knowledge is expressed by numerical rules to reduce the number of scenarios, and the graph formalism makes structural interpretation easier to reproduce than manual interpretation. The Bron–Kerbosch algorithm, which finds maximal cliques in undirected graphs, is used to detect major possible structures. This framework opens the way to a numerically assisted exploration of uncertainties during structural interpretation.