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Quantitative assessment of interpretational uncertainty in geological mapping with machine learning

利用机器学习对地质测绘解释不确定性进行定量评估

基本信息

批准号：
2136845
负责人：
金额：
--
依托单位：
Heriot-Watt University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2136845
关键词：
Quantitative assessment interpretational uncertainty geological

项目摘要

This project aims to tackle the fundamental problem of how to adequately capture and preserve geological uncertainty in reservoirs modelling workflows. Traditional reservoir characterization workflows integrate interpretations from data of different nature - seismic, wireline, core... These interpretations are done by relevant domain experts, who are often separated into siloes, focused only on their aspect of the data, and furthermore can be subject to the experiential bias. When data is finally combined together to create a reservoir model, it is typically done by a single person who must create a coherent representation of the reservoir and its associated uncertainties. Typically, the resulting models are often "best" fits to each bit of data and do not adequately quantify the uncertainties subject to multiple possible interpretations given the sparse nature of the data. Instead we should seek to generate models that combine data in different possible ways to capture the fullest representation of the uncertainty and preserve the knowledge of uncertainties associated with each input data type and its interpretation. The project will develop a way to elicit a wide range of geological concepts as models directly from the data by discovering a variety of data combinations (data types and how they are used/fused together) through using machine learning (ML), while preserving geological knowledge. This will enhance the estimation of uncertainty in our reservoir modelling workflows based on finding a range of unique data combinations/fusions that are coherent (geologically realistic) and unbiased. Achieving this will make a step change enhancement in subsurface uncertainty modelling practice, by identifying a wider possible spread of geological scenarios and providing a quantitative way in assessing their probability. This process will also be significantly faster than a manual approach to developing different ways to use the data to build models. A key element of the project will be to embed geoscience understanding into data driven workflows with machine learning (ML). The challenge is that recent advances in computer science need to be adapted for best implementation in geoscience to capture the context understanding and thinking as it is performed by domain experts. Rigorous ML approach will ensure there is no preferential bias in the way multiple interpretations are elicited from the data. Proper application of ML is able to quantify the impact of the tendencies hidden in the data and how their combinations define possible sedimentological settings. Geologically consistent features need to be derived from the relevant data with account for the associated uncertainty to enhance performance of ML prediction and retain geological realism in ML predictions. The latter requires introduction of solid geoscience understanding into ML prediction model design. Embedded geoscience knowledge will ensure geological consistency of the multiple elicited interpretation leads. The outcome of the PhD project will be a more robust handling of geological uncertainty through novel workflows that use modern machine learning techniques with embedded geoscience understanding. In particular, this will involve adapting Artificial Intelligence (AI) methods, such as deep learning and semi-supervised learning, feature selection, to collate interpretable patterns with the geological context to ensure depositional consistency and geological sense of the predictive models. The feasibility of the novel scientific approach will be justified with a real field study using a modern dataset from the Sea Lion Field, which features modern 3D seismic, as well as log and core data of a comprehensive nature. Once established, this method could be applied to any project where modelling of the subsurface is required.

该项目旨在解决如何在储层建模工作流程中充分捕获和保留地质不确定性的基本问题。传统的储层表征工作流程集成了来自不同性质的数据的解释——地震、电缆、岩心……这些解释是由相关领域的专家完成的，他们通常被分成各自的孤岛，只关注数据的各个方面，而且可能会受到经验偏差的影响。当数据最终组合在一起创建油藏模型时，通常由一个人完成，他必须创建油藏及其相关不确定性的连贯表示。通常，所得到的模型通常是对每一位数据的“最佳”拟合，并且由于数据的稀疏性质而不能充分量化受多种可能解释影响的不确定性。相反，我们应该寻求生成以不同可能方式组合数据的模型，以捕获不确定性的最完整表示，并保留与每种输入数据类型及其解释相关的不确定性知识。该项目将开发一种方法，通过使用机器学习 (ML) 发现各种数据组合（数据类型以及它们如何使用/融合在一起），直接从数据中引出广泛的地质概念作为模型，同时保留地质知识。这将基于寻找一系列连贯（地质现实）且无偏见的独特数据组合/融合，增强对油藏建模工作流程中不确定性的估计。实现这一目标将通过识别更广泛的可能分布的地质场景并提供评估其概率的定量方法，从而使地下不确定性建模实践发生重大变化。这个过程也将比手动方法更快地开发使用数据构建模型的不同方法。该项目的一个关键要素是通过机器学习 (ML) 将地球科学理解嵌入到数据驱动的工作流程中。挑战在于，计算机科学的最新进展需要适应地球科学的最佳实施，以捕获领域专家执行的上下文理解和思维。严格的机器学习方法将确保从数据中得出多种解释的方式不存在偏好偏差。机器学习的正确应用能够量化数据中隐藏趋势的影响以及它们的组合如何定义可能的沉积学设置。需要从相关数据中导出地质一致的特征，并考虑相关的不确定性，以提高机器学习预测的性能并保留机器学习预测的地质真实性。后者需要将扎实的地球科学理解引入机器学习预测模型设计中。嵌入的地球科学知识将确保多种引出的解释线索的地质一致性。该博士项目的成果将是通过使用现代机器学习技术和嵌入式地球科学理解的新颖工作流程，更有效地处理地质不确定性。特别是，这将涉及采用人工智能（AI）方法，例如深度学习和半监督学习、特征选择，将可解释的模式与地质背景进行比较，以确保预测模型的沉积一致性和地质意义。这种新颖的科学方法的可行性将通过使用海狮场的现代数据集进行的实际现场研究来证明，该数据集具有现代 3D 地震以及综合性质的测井和岩心数据。一旦建立，该方法可以应用于任何需要地下建模的项目。