Generative Adversarial Network Applied to Ore Type Modeling in Complex Geological Environments

Mathematical Geosciences - In complex geological environments, the spatial domain characterization of different ore types has been one of the most important challenges in the assessment of mineral...     

Stochastic Seismic Waveform Inversion Using Generative Adversarial Networks as a Geological Prior

Mathematical Geosciences - We present an application of deep generative models in the context of partial differential equation constrained inverse problems. We combine a generative adversarial...     

GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)

Conditional facies modeling combines geological spatial patterns with different types of observed data, to build earth models for predictions of subsurface resources. Recently, researchers have used generative adversarial networks (GANs) for conditional facies modeling, where an unconditional GAN is first trained to learn the geological patterns using the original GAN’s loss function, then appropriate latent vectors are searched to generate facies models that are consistent with the observed conditioning data. A problem with this approach is that the time-consuming search process needs to be conducted for every new conditioning data. As an alternative, we improve GANs for conditional facies simulation (called GANSim) by introducing an extra condition-based loss function and adjusting the architecture of the generator to take the conditioning data as inputs, based on progressive growing of GANs. The condition-based loss function is defined as the inconsistency between the input conditioning value and the corresponding characteristics exhibited by the output facies model, and forces the generator to learn the ability of being consistent with the input conditioning data, together with the learning of geological patterns. Our input conditioning factors include global features (e.g., the mud facies proportion) alone, local features such as sparse well facies data alone, and joint combination of global features and well facies data. After training, we evaluate both the quality of generated facies models and the conditioning ability of the generators, by manual inspection and quantitative assessment. The trained generators are quite robust in generating high-quality facies models conditioned to various types of input conditioning information.

     

基于贝叶斯网络的地震相分类

为了解决传统多地震属性的地震相分类方法中"难以引入先验信息用以指导分类,难以给出地震相分类结果可靠程度的定量估计,且各分类参数的权值较难确定"这3个问题,提出了一种新的基于贝叶斯网络的地震相分类方法.该分类方法有效地融合了先验信息和训练样本的分布特征,对提取的多种地震属性进行智能分析,以概率推理的方式得到各地震相类别的概率值,并根据概率分布估计分类结果的可靠程度.详述了贝叶斯网络用于地震相分类的原理与方法,并结合理论地震数据,验证了该方法的可行性和正确性.      

Delineation of Integrated Anomaly with Generative Adversarial Networks and Deep Neural Networks in the Zhaojikou Pb-Zn Ore District,Southeast China

Geochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert’s knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb-Zn deposit, Southeast China. Three hundred fifty two samples were collected, and ea...     

Three-Dimensional Structural Geological Modeling Using Graph Neural Networks

Mathematical Geosciences - Three-dimensional structural geomodels are increasingly being used for a wide variety of scientific and societal purposes. Most advanced methods for generating these...     

Surpac软件在三维地质建模中的应用

当今矿业领域开始不断向着数字化、可视化的方向发展,数字矿山已成为矿山采矿行业未来发展的主要趋势。该文在基于某地区金矿的勘查数据,介绍了Surpac三维数学建模软件的主要功能,模块和在地学领域中的应用,并综合论证该软件未来的发展优势和前景。     

大地构造相在东昆仑造山带地质填图中的应用

不同的造山带是由不同的大地构造相单元组合而成, 大地构造相的划分揭示了造山带的基本框架和形成演化的规律.在对东昆仑造山带1∶2 5万冬给措纳湖幅地质填图中, 以时间演化和大地构造背景为主线, 根据不同演化阶段、不同部位出现的构造古地理单元、盆地类型和物质建造类型, 对填图区大地构造相进行了较精细深入划分, 共划分出七大相类、2 1种相, 如扩张洋脊相、分支(扩张) 海槽相、碳酸盐岩海山相、碳酸盐岩台地相、深海平原相、大陆碎块相、前陆盆地相和磨拉石盆地相等, 编制了1∶2 5万冬给措纳湖幅大地构造相图和造山作用过程与大地构造相演变图.大地构造相在地质填图中的应用进一步深化了造山带填图中的地层单元空间配置关系和盆地沉积充填序列的研究, 较全面细致地揭示了东昆仑造山带东段造山带形成、物质组成及演化过程      

一体化地质建模在新近系礁灰岩储层定量表征中的应用

以LH油田三维地质建模为实例,针对礁灰岩储层内部结构复杂的情况,提出了一种综合应用地震、测井及地质资料进行礁灰岩储层研究和地质建模的方法。LH油田主要含油层段为中新统珠江组礁灰岩,储层为台地边缘生物礁相,储层内部结构复杂。以井震资料为基础,以生物礁沉积过程和成岩作用为指导,对礁体内部结构进行了分析,预测了主力油层内部的差物性层空间展布。在地质模式、井点信息和地震属性约束下,采用随机模拟的手段,建立了地质模型,实现了多学科综合的储层定量表征,对无井区储层特征进行了预测,为开发方案的设计提供了地质依据。     

三维地质建模应用于以钻代槽的方法研究

随着生态文明建设的进一步加强,绿色勘查理念的进一步深入,通过浅钻代替槽探在基础地质调查、矿产调查的应用中得到了进一步推广。在以钻代槽方法研究中,为了更为直观的用钻孔信息模拟槽探揭示地质体（矿体）的三维结构,研究了基于钻孔数据的三维地质建模方法,认为浅钻揭露的垂向地质体信息更丰富,基于钻孔,结合专家干预,可快速构建钻孔控制区域的三维地质模型,形象、直观、易用。同时,通过模型构建可以用来指导浅钻布孔。     

虚拟钻孔在深层三维地质建模中的应用

在三维地质建模过程中,如何更好地利用多源数据帮助,建立拟合度高、符合地层分布规律的三维地质模型是目前业内的一个热点问题。在深层三维地质建模原始资料较为稀缺的情况下,通过提取物探地质剖面数据信息并加以利用,可以很好地丰富建模初始资料的来源。基于SKUA等GIS软件平台,本文作者提出了剖面数据→生成虚拟钻孔→提取marker数据→marker数据融合校正的方法,从而实现对建模过程的有效干预和控制,使生成的模型不断趋于精确。同时,在工程实例中检验了该方法的准确性和模拟效果。     

神经网络微地震相分析方法及应用

传统地震相分析满足不了目前油田勘探开发的需要.神经网络微地震相分析将神经网络应用于高质量地震三维数据体中,可对单一反射同相轴进行波形信号分析和训练,建立模型地震道,并以之对实际地震道进行分类,产生在平面和剖面上精度较高的微地震相分布.新场气田和塔河油田的实际应用表明,神经网络微地震相分析是可靠的和有效的.     

地质流体状态方程

几乎所有的地球化学过程都有地质流体参加, 定量地了解地质流体的物理化学性质是定量研究地球化学过程的基础.100多年以来, 广大化学和实验地球化学工作者做了大量的实验测定工作, 可是所有这些工作之和, 仅仅覆盖地球范围内一个不大的温压空间, 远远不能满足地球化学研究的需要.近年来, 我们试图通过分子水平上的研究, 结合热力学和统计力学方面的知识, 在重现前人实验结果的基础上, 研究实验工作者没有或不能研究的温压和成分空间, 得到了一系列能够精确预测地质流体在广阔的温压范围内的物理化学性质的状态方程.这些状态方程不仅能够重现实验数据, 而且具有良好的外延能力, 可以应用于地球化学领域诸多方面的研究.重点讨论了几个状态方程(包括纯流体状态方程含水溶液状态方程和含盐-水-气的状态方程) 在预测流体的溶解度、相平衡、化学位和PVT性质方面的应用.简要介绍了近年来笔者应用分子动力学和蒙特卡罗模拟在地质流体研究方面所取得的成果      

白云岩储层地震相分析技术研究与应用

地震相分析是确定沉积环境的重要手段之一。通过对研究区分析研究,确定白云岩储层相同岩性不同物性之间在地震波形、振幅、频率上均存在差异,以此特征为依据,建立地震响应与有效储层之间的对应关系,应用地震相分析技术划分地震相,进一步转化成沉积相,划分有利区,对下步大规模开发具有重要意义。     

Uncertainties in Facies Proportion Estimation II: Application to Geostatistical Simulation of Facies and Assessment of Volumetric Uncertainties

Geostatistical simulations of lithotypes or facies are commonly used to create a geological model and to describe the heterogeneities of petroleum reservoirs. However, it is difficult to handle such models in the framework of multiple realizations to assess the uncertainty of hydrocarbon in place. Indeed, the hydrocarbon in place is correlated with the facies proportions, which are themselves uncertain. The uncertainty model of facies proportions is not easy to describe because of closure relationships. A previous attempt was made with a nonparametric approach using the resampling technique. It has been successful in a stationary case but it is difficult to extend it to nonstationary cases. In this paper, we have applied the vectorial beta parametric model or Dirichlet model. It has provided much more realistic uncertainties on volumetrics in very different geological and geostatistical contexts.     

Modeling Conditional Distributions of Facies from Seismic Using Neural Nets

We present a general, flexible, and fast neural network approach to the modeling of a conditional distribution of a discrete random variable, given a continuous or discrete random vector. Although many more applications of the neural net technique could be envisioned, the aim is to apply the developed methodology to the integration of seismic data into reservoir models. Many geostatistical methods for integrating seismic data rely on a screening assumption of further away seismic events by the colocated seismic datum. Such assumption makes the task of modeling cross-covariances and local conditional distributions much easier. In many cases, however, the seismic data exhibit distinct and locally varying spatial patterns of continuity related to geological events such as channels, shale bodies, or fractures. The previous screening assumption prevents recognizing and hence utilizing these patterns of seismic data. In this paper we propose to relate seismic data to facies or petrophysical properties through a colocated window of seismic information instead of the single colocated seismic datum. The variation of seismic data from one window to another is accounted for. Several examples demonstrate that using such a window improves the predictive power of seismic data.     

基本层序和相分析是区调中组建立的基础

以赣西北地区中、上寒武统划分为例,阐述基本层序、相分析在岩石地层单位划分和对比中的作用、地位。划分岩石地层单位不能仅仅根据岩性特征,而必须进行基本层序和系统的相分析。在这样的基础上,划分的岩石地层单位才具有准确性、合理性和野外实用性。     

A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning

Mathematical Geosciences - Three-dimensional geological structure analysis is fundamental to geoscientific research. With the application of artificial intelligence in geological structure...     

三维地质建模技术及其在煤田构造中的应用

煤炭地质勘查的研究对象、勘探方法、数据资料在空间上都是三维的,适合于采用三维地质建模技术进行管理。根据三维地质建模与煤炭地质勘查是否同时,可将煤炭地质勘探三维地质建模分为两类,一类为勘探工作中,主要以钻孔数据为主,用于制作三维地质模型的原型,生成煤层底板等高线、剖面图草图,供地质工程师进一步加工,构建精细的三维地质模型;另一类为勘探工作后,即以已经做好的煤层底板等高线图、剖面图为主要数据源,构建精细的三维地质模型。精细的三维地质模型可用于煤矿设计、数字矿山建设。三维地质建模技术的应用有利于煤炭地质构造的研究,主要表现在:第一,可对地面、钻探、物探、化探多种手段获得的探测资料进行集成与同化处理,提高信息空间的一致性;第二,地质构造要素表达具有直观性与三维立体性,如断层的表达,其在三维环境中不仅可表达为一个面,并且能清晰反映断层与地层、以及断层与断层之间的相互切割关系,弥补了平、剖面图上仅表现为一条断层线的不足;第三,可平、剖面三维联动编辑,很好地保证平面图与剖面图的空间一致性;第四,可以在剖面上以画草图的方式确定剖面上煤层对比与断层切割方案。