Quantitative assessment of interpretational uncertainty in geological mapping with machine learning

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数据的解释-地震、电缆、岩心.这些解释是由相关的领域专家完成的，他们通常被分成孤岛，只关注数据的他们的方面，而且可能会受到经验偏见的影响。当数据最终组合在一起以创建储层模型时，通常由一个人完成，该人必须创建储层及其相关不确定性的连贯表示。通常情况下，所得到的模型往往是“最佳”拟合的每一位数据，并没有充分量化的不确定性受到多种可能的解释给定的稀疏性质的数据。相反，我们应该寻求生成以不同的可能方式组合联合收割机数据的模型，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（数据类型及其如何使用/融合在一起），同时保留地质知识，直接从数据中提取广泛的地质概念作为模型。这将增强我们的储层建模工作流程中的不确定性估计，其基础是找到一系列连贯（地质现实）和无偏的独特数据组合/融合。实现这一目标将通过确定更广泛的地质情景并提供评估其概率的定量方法，使地下不确定性建模实践得到逐步改善。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将地球科学理解嵌入到机器学习（ML）的数据驱动工作流程中。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ML方法将确保从数据中得出多种解释的方式没有偏好偏差。ML的正确应用能够量化隐藏在数据中的趋势的影响，以及它们的组合如何定义可能的沉积学背景。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ML预测的性能并在ML预测中保持地质真实性。后者需要将固体地球科学的理解引入ML预测模型设计中。嵌入的地球科学知识将确保多个引出的解释线索的地质一致性。博士项目的成果将是通过使用嵌入式地球科学理解的现代机器学习技术的新工作流程来更强大地处理地质不确定性。特别是，这将涉及调整人工智能（AI）方法，如深度学习和半监督学习，特征选择，以根据地质背景整理可解释的模式，以确保预测模型的沉积一致性和地质意义。新的科学方法的可行性将证明真实的现场研究，使用现代数据集从海狮油田，其特点是现代三维地震，以及日志和核心数据的综合性质。一旦建立，这种方法可以应用于任何需要地下建模的项目。

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.