Conditioning of Multiple-Point Statistics Facies Simulations to Tomographic Images

Geophysical tomography captures the spatial distribution of the underlying geophysical property at a relatively high resolution, but the tomographic images tend to be blurred representations of reality and generally fail to reproduce sharp interfaces. Such models may cause significant bias when taken as a basis for predictive flow and transport modeling and are unsuitable for uncertainty assessment. We present a methodology in which tomograms are used to condition multiple-point statistics (MPS) simulations. A large set of geologically reasonable facies realizations and their corresponding synthetically calculated cross-hole radar tomograms are used as a training image. The training image is scanned with a direct sampling algorithm for patterns in the conditioning tomogram, while accounting for the spatially varying resolution of the tomograms. In a post-processing step, only those conditional simulations that predicted the radar traveltimes within the expected data error levels are accepted. The methodology is demonstrated on a two-facies example featuring channels and an aquifer analog of alluvial sedimentary structures with five facies. For both cases, MPS simulations exhibit the sharp interfaces and the geological patterns found in the training image. Compared to unconditioned MPS simulations, the uncertainty in transport predictions is markedly decreased for simulations conditioned to tomograms. As an improvement to other approaches relying on classical smoothness-constrained geophysical tomography, the proposed method allows for: (1) reproduction of sharp interfaces, (2) incorporation of realistic geological constraints and (3) generation of multiple realizations that enables uncertainty assessment.     

断层破碎带综合地球物理找水模式——以干田坝村探采结合井为例

乌蒙山贫困缺水区水资源严重缺乏,找水需求大、难度大。为确定了ZK12探采结合井位置,本文首先分析了工区找水环境因素的地层时代、含水介质类型、构造背景、水源条件和水动条件等5个条件;其次,对富水特征的含水岩组、蓄水构造和富水地貌进行研究;最后,重点分析地球物理特征找水因素,介绍了视电阻率联合剖面法、大地电磁测深法、瞬变电磁法、激电测深和地球物理测井等“多方法”应用效果,通过视电阻率、视极化率、衰减度、半衰时等“多参数”,确定断层裂隙水的地球物理特征,建立由浅入深的“多层次”地球物理模式。最终,创新性提出了乌蒙山连片贫困缺水区重点村镇的“多方法、多参数、多层次”地球物理找水模式,克服单一物探方法找水的局限性,精准定井位,对地下水勘查起到示范作用。      

超高压变质岩物理性质的相关性对建立结晶岩区地球物理解释标尺的意义

大陆科学钻探为认识深部地壳的结构、组成、力学性质和物理状态提供了重要的基础数据。岩石物理性质的测量对于原位测井资料的校正和地表地球物理测量的解释具有重要意义,岩石物理性质和岩石学研究相结合,还能为岩石的变质作用以及变质过程中化学成分的迁移提供必要的约束。本文主要对CCSD100-3100m的综合岩石物理资料进行了相关性调查和聚类分析,并得出如下结论:(1)岩石物理性质中的地震波速度、密度、热导率之间具有强相关性,他们都受岩石中主要矿物的组成和含量所控制;(2)岩石的电阻率和磁化率受金属氧化物含量的控制,与岩性有相关性;孔隙度、渗透率与岩性没有相关性;(3)利用岩石的物理性质可以反演不同的岩性,其中地震波速度、密度和热导率对榴辉岩和片麻岩大类具有很好的分类效果,而结合磁化率和电阻率则能更好的区分出超基性岩、正片麻岩和副片麻岩。上述结论对综合地球物理解释中物理参数的选取和结晶岩区地球物理解释标尺的建立具有重要的意义。     

3D ground model development for an active landslide in Lias mudrocks using geophysical,remote sensing and geotechnical methods

A ground model of an active and complex landslide system in instability prone Lias mudrocks of North Yorkshire, UK is developed through an integrated approach, utilising geophysical, geotechnical and remote sensing investigative methods. Surface geomorphology is mapped and interpreted using immersive 3D visualisation software to interpret airborne light detection and ranging data and aerial photographs. Subsurface structure is determined by core logging and 3D electrical resistivity tomography (ERT), which is deployed at two scales of resolution to provide a means of volumetrically characterising the subsurface expression of both site scale (tens of metres) geological structure, and finer (metre to sub-metre) scale earth-flow related structures. Petrophysical analysis of the borehole core samples is used to develop relationships between the electrical and physical formation properties, to aid calibration and interpretation of 3D ERT images. Results of the landslide investigation reveal that an integrated approach centred on volumetric geophysical imaging successfully achieves a detailed understanding of structure and lithology of a complex landslide system, which cannot be achieved through the use of remotely sensed data or discrete intrusive sampling alone.     

Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks

An artificial neural network (ANN) model is proposed for the simultaneous determination of transmissivity and storativity distributions of a heterogeneous aquifer system. ANNs may be useful tools for parameter identification problems due to their ability to solve complex nonlinear problems. As an extension of previous study—Karahan H, Ayvaz MT (2006) Forecasting aquifer parameters using artificial neural networks, J Porous Media 9(5):429–444—the performance of the proposed ANN model is tested on a two-dimensional hypothetical aquifer system for transient flow conditions. In the proposed ANN model, Cartesian coordinates of observation wells, associated piezometric heads and observation time are used as inputs while corresponding transmissivity and storativity values are used as outputs. The training, validation and testing processes of the ANN model are performed under two scenarios. In scenario 1, all the sampled data are used through the simulation time. However, in the scenario 2, there are data gaps due to irregular observations. By using the determined synaptic network weights, transmissivity and storativity distributions are predicted. In addition, the performance of the proposed ANN is tested for different noise data conditions. Results showed that the developed ANN model may be used in simultaneous aquifer parameter estimation problems.     

中国大陆科学钻探工程的科学目标及初步成果

中国大陆科学钻探工程的主孔位于苏鲁超高压变质带南部的东海县，其穿过的岩石曾位于板块会聚边界的地幔深处，是研究大陆深俯冲及地幔动力学的最佳地区。中国大陆科学钻探的科学目标旨在通过对钻孔中获取的全部连续岩心、液态和气态样品及原位测井数据进行的全方位测量与综合研究，建立5000m孔深的各类多学科精细剖面，再造北中国板块与扬子板块会聚边界深部三维物质的组成和分布及三维结构构造；阐明板块会聚边缘的深部流体作用、壳一幔相互作用及地幔中物质循环和流变学；寻找超深地幔条件下形成的特征矿物，揭示超高压变质成矿机理；建立结晶岩地区地球物理理论模型和解释标尺；揭示超高压变质岩石的形成与折返模型及板块会聚边界的深部动力学机制。通过5km深孔营造的特殊地下空问，研究现代地壳的物理、化学及生物作用，并将建立亚洲第一个大陆科学钻探深孔长期观察实验站。中国大陆科学钻探工程已取得初步成果。主孔2000m岩心的深度和方位准确归位；建立了岩性、地球化学、构造、矿化、岩石物性、地下流体及各类测井等精细剖面；揭示了发现除超镁铁质岩外的各类岩石中的柯石英；通过SHRIMP测年准确确定超高压及退变质事件的年龄；初步揭示了超高压及退变质过程中流体的变化及水一岩作用；划分了构造岩片单元，确定了其边界的韧性剪切带性质，并发现早期构造信息；建立的随岩性变化的弹性波速度和热导率连续剖面对地震反射和热结构提供了岩石物性的制约；VSP地震剖面揭示了孔区深部的构造岩片结构，以及强地震反射层和大型韧性剪切带有关；发现地下特殊气体一甲烷、二氧化碳及氦气的异常，以及气体异常越往深部越频繁出现的规律。经DNA分析，在超高压岩心中发现在极端条件下形成的微生物。     

Integration of Information Technology into Geotechnical Engineering Practice: Concept and Development

Information Technology (IT) has been extensively used to predict, visualize, and analyze physical parameters in order to expedite routine geotechnical design procedures. This paper presents an example of the combined technique of IT and numerical analysis for routine geotechnical design projects. The proposed approach involves the development of ANN(s) using a calibrated finite element model(s) for use as a prediction tool and implementation of the developed ANN(s) into a GIS platform for visualization and analysis of spatial distribution of predicted results. A novel feature of the proposed approach is an ability to expedite a routine geotechnical design process that otherwise requires significant time and effort in performing numerical analyses for different design scenarios. A knowledge-based underground excavation design system that utilizes artificial neural networks (ANNs) as prediction tools is also introduced. Practical implications of the use of IT in geotechnical design are discussed in great detail.     

Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data

Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models. The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers, namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas of seismic data.     

大陆科学钻探测井岩石物理参数的提取

岩石物性及其物理参数的研究与提取,对于测井资料的综合解释具有重要的现实意义。岩石物性及其物理参数提取得准确与否,直接关系到测井资料综合解释的可靠性、准确性和精确性。通过对测井资料的分析、归纳,提取了识别中国大陆科学钻探预先导孔Ⅱ所钻遇18种不同岩性的岩石物理参数,为后续的测井解释研究工作提供了基础。     

Estimation of effective stress parameter of unsaturated soils by using artificial neural networks

Great efforts are required for determination of the effective stress parameter χ, applying the unsaturated testing procedure, since unsaturated soils that have the three‐phase system exhibit complex mechanical behavior. Therefore, it seems more reasonable to use the empirical methods for estimation of χ. The objective of this study is to investigate the practicability of using artificial neural networks (ANNs) to model the complex relationship between basic soil parameters, matric suction and the parameter χ. Five ANN models with different input parameters were developed. Feed‐forward back propagation was applied in the analyses as a learning algorithm. The data collected from the available literature were used for training and testing the ANN models. Furthermore, unsaturated triaxial tests were carried out under drained condition on compacted specimens. ANN models were validated by a part of data sets collected from the literature and data obtained from the current study, which were not included in the training phase. The analyses showed that the results obtained from ANN models are in satisfactory agreement with the experimental results and ANNs can be used as reliable tool for prediction of χ. Copyright © 2007 John Wiley & Sons, Ltd.     

Quantitative Integration of High-Resolution Hydrogeophysical Data: A Novel Approach to Monte-Carlo-Type Conditional Stochastic Simulations and Implications for Hydrological Predictions

Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative Integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant advancements in the way that larger-scale structural information contained in the hydrogeophysical data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the Implications arising.     

Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt

Ras Fanar field is one of the largest oil-bearing carbonate reservoirs in the Gulf of Suez. The field produces from the Middle Miocene Nullipore carbonate reservoir, which consists mainly of algal-rich dolomite and dolomitic limestone rocks, and range in thickness between 400 and 980 ft. All porosity types within the Nullipore rocks have been modified by diagenetic processes such as dolomitization, leaching, and cementation; hence, the difficulty arise in the accurate determination of certain petrophysical parameters, such as porosity and permeability, using logging data only. In this study, artificial neural networks (ANN) are used to estimate and predict the most important petrophysical parameters of Nullipore reservoir based on well logging data and available core plug analyses. The different petrophysical parameters are first calculated from conventional logging and measured core analyses. It is found that pore spaces are uniform all over the reservoirs (17–23%), while hydrocarbon content constitutes more than 55% and represented mainly by oil with little saturations of secondary gasses. A regular regression analysis is carried out over the calculated and measured parameters, especially porosity and permeability. Fair to good correlation (R <65%) is recognized between both types of datasets. A predictive ANN module is applied using a simple forward backpropagation technique using the information gathered from the conventional and measured analyses. The predicted petrophysical parameters are found to be much more accurate if compared with the parameters calculated from conventional logging analyses. The statistics of the predicted parameters relative to the measured data, show lower sum error (<0.17%) and higher correlation coefficient (R >80%) indicating that good matching and correlation is achieved between the measured and predicted parameters. This well-learned artificial neural network can be further applied as a predictive module in other wells in Ras Fanar field where core data are unavailable.     

基于Elman型神经网络的金川二矿地表岩移时序预测 模型

建立于煤矿开采基础之上的矿山开采沉陷理论和预测方法并不适用于象金川这样厚大、陡倾的金属矿床开采的岩移问题,因此,本文探讨利用神经网络来对地表岩移进行预测。根据Elman神经网络能够逼近任意非线性函数的特点和具有反映系统动态特性的能力,提出了利用Elman神经网络建立地表岩移时序预报模型的方法。利用金川二矿区GPS监测所得到的时间序列数据,通过对Elman神经网络模型预测值与GPS实测值之间的比较,结果表明模型预测显示了良好的准确性,特别是在时间步长较短情况下,应用于实际预测一定程度上可以弥补金属矿山岩移预测方法不足的缺憾。     

The SISMA prototype system: integrating Geophysical Modeling and Earth Observation for time-dependent seismic hazard assessment

An innovative approach to seismic hazard assessment is illustrated that, based on the available knowledge of the physical properties of the Earth structure and of seismic sources, on geodetic observations, as well as on the geophysical forward modeling, allows for a time-dependent definition of the seismic input. According to the proposed approach, a fully formalized system integrating Earth Observation data and new advanced methods in seismological and geophysical data analysis is currently under development in the framework of the Pilot Project SISMA, funded by the Italian Space Agency. The synergic use of geodetic Earth Observation data (EO) and Geophysical Forward Modeling deformation maps at the national scale complements the space- and time-dependent information provided by real-time monitoring of seismic flow (performed by means of the earthquake prediction algorithms CN and M8S) and permits the identification and routine updating of alerted areas. At the local spatial scale (tens of km) of the seismogenic nodes identified by pattern-recognition analysis, both GNSS (Global Navigation Satellite System) and SAR (Synthetic Aperture Radar) techniques, coupled with expressly developed models for interseismic phase, allow us to retrieve the deformation style and stress evolution within the seismogenic areas. The displacement fields obtained from EO data provide the input for the geophysical modeling, which eventually permits to indicate whether a specific fault is in a “critical state.” The scenarios of expected ground motion (shakemaps) associated with the alerted areas are then defined by means of full waveforms modeling, based on the possibility to compute synthetic seismograms by the modal summation technique (neo-deterministic hazard assessment). In this way, a set of deterministic scenarios of ground motion, which refer to the time interval when a strong event is likely to occur within the alerted area, can be defined both at national and at local scale. The considered integrated approach opens new routes in understanding the dynamics of fault zones as well as in modeling the expected ground motion. The SISMA system, in fact, provides tools for establishing warning criteria based on deterministic and rigorous forward geophysical models and hence allows for a well-controlled real-time prospective testing and validation of the proposed methodology over the Italian territory. The proposed approach complements the traditional probabilistic approach for seismic hazard estimates, since it supplies routinely updated information useful in assigning priorities for timely mitigation actions and hence it is particularly relevant to Civil Defense purposes.     

Prediction of pile settlement using artificial neural networks based on standard penetration test data

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.     

发展测井和井中物探技术提高地质工作效果和经济效益

地矿系统的测井和井中物探工作始于1954年, 三十多年来的主要成果是:(一)圆满地完成了历年能源矿产的测井任务, 取得了显著效果。(二)水文测井得到稳步发展, 技术水平逐步提高。(三)开拓了井中物探领域, 为寻找井底、井旁隐伏矿奠定了基础。(四)扩大了测井在原位测定矿层品位和区域物探领域中的应用。(五)取得了较显著的技术进步和科技成果。存在的主要问题有三个即:(一)对地下物探的作用估计不足, 像有形成正式而完整的技术规定。(二)一些方法技术和仪器设备没有跟上地质任务变化的需要。(三)技术力量过于分散, 形不成有力的拳头。在分析当前形势的基础上, 指出地下物探面临着机遇和困难并存的局面, 今后的奋斗目标是:推广数字测井技术, 发展微机控制、实时处理的井中物探仪器系列;地下物探要发展全方位服务, 力争更直接地参与国家经济建设, 并提出了四个方面的钻孔要进行测井, 三个方面的钻孔要进行井中物探。     

激发极化法找水的一些新进展

本文介绍了我国近年来激发极化法找水在基础理论研究、数据处理和解释软件以及仪器等方面的一些最新发展状况。尽管激电法是一种间接的找水方法,但多年的工作结果表明,与其它学科(如水文地质学)相配合,可显著提高寻找地下水的地质效果。     

测井在永城煤田地质勘探中的应用效果

永城煤田城郊矿区，从普查到精查勘探均开展了测井工作。测井解释的煤层深度、厚度、结构，地质采用率在94．4％以上，测井对划分钻孔岩性，确定物性标志层、断层(点)，提供了可靠的基础资料。     

滑坡监测的指标体系与技术方法

滑坡监测目的:了解和掌握滑坡体的演变过程,及时捕捉崩滑灾害的特征信息,为崩塌滑坡的正确分析、评价、预测、预报及治理工程等提供可靠资料和科学依据。滑坡监测指标包括地质宏观形迹监测、地面位移监测、深部位移监测、诱发因素监测、水压力监测和滑坡地球物理、地球化学场监测等。滑坡监测技术方法通常有地面宏观形迹的简易观测、地面仪器监测、空间遥测和遥感监测、综合的实时监测预报系统等。论文还介绍了宝塔滑坡监测系统实例。      

松辽盆地科学钻探工程松科二井东孔上白垩统地球物理测井科学成果

松辽盆地科学钻探工程是围绕地球深部资源、古环境与古气候等一系列重要地球科学问题而实施的重大科学工程。作为该工程的主体钻孔, 松科二井东孔设计为松辽盆地最深钻孔, 设计深度6 400.0 m, 计划穿透白垩系地层、钻至盆地基底。由于上白垩统井段取心量非常少, 因此地球物理测井的作用尤为重要。该孔正式开钻后, 采用先进的成像测井设备对一开和二开钻井井段(上白垩统井段)先后实施4次裸眼井综合测井和套管井测井作业, 并系统地开展测井综合评价, 取得以下科学成果: (1)原位获取了常规测井、特殊测井和固井质量检查测井资料共20种, 资料丰富、质量优良, 建立了上白垩统连续的岩石物理参数标准剖面; (2)完成了上白垩统地层划分、岩性识别、沉积和构造精细解释; (3)识别出泉头组油气储层、嫩江组和青山口组烃源岩层、嫩江组高放射性异常层, 以及上白垩统地热开发潜力层; (4)探索了青山口组古气候分析的有利测井指标; (5)及时提供准确的井径、钻孔轨迹和固井质量等关键钻探工程信息。研究成果对于松辽盆地地球物理勘探科学“标尺”建立、古环境与古气候研究、钻探工程施工起到至关重要的作用, 为整个钻探工程科学目标的实现奠定良好基础。