Resilience grant strand 2 for IUK project SHAIR - automated seismic interpretation using neural networks

IUK 项目 SHAIR 的抗灾补助金第 2 部分 - 使用神经网络进行自动地震解释

• 批准号: 10023393
• 金额: $ 12.74万
• 依托单位: SUB SALT SOLUTIONS LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10023393
• 关键词: Resilience; grant; strand; IUK; project

SHAIR will create a new software service that will deliver highly accurate interpretation of seismic data to determine the likely presence of oil or gas.The main focus of this project is to develop new technology for increasing the accuracy and automation for imaging the subsurface using AI/ML within an industry-leading algorithm known as XWI. The computational power needed to run these algorithms is massive -- exceptionally 1 million virtual CPUs have been used.Technology from S-Cube's partner in SHAIR, RagnaRock Geo from Norway, currently contains an AI driven horizon interpretation with minimal human interpretation. It takes in subsurface volumes and uses their improved Geophysics-guided Neural Networks (GNNs) innovation of Convolutional Neural Networks (CNNs) to detect discontinuities in the rock structure, and interprets these as 'horizons'.S-Cube's technology, XWI, spun out from a world leading research group from Imperial College London is the result of 20 years of development, covered by multiple patents. It is an advancement of traditional Full-Waveform Inversion (FWI) producing high quality 3D subsurface models useful in assessing subsurface features ahead of drilling.The main result will be a novel suite of tools available to energy companies as a SaaS product via which users will pay to more accurately analyse their data. The new technology will enhance existing XWI algorithms generating superior velocity models constrained using automatically-generated seismic horizons.This will be deployed as a new service that makes it more cost effective for oil companies to grow reserves and minimise emissions with more accuracy than the traditional method of analysis used to discover oil.Energy companies will become more profitable via better analysis and a higher rate of success and be able to diversify from petroleum to new sources of lower emissions subsurface energy. This improved interpretation of data should reduce the likelihood of drilling for hydrocarbons which results in none being found and therefore can help the environment with fewer wells drilled.

SHAIR将创建一个新的软件服务，将提供地震数据的高精度解释，以确定可能存在的石油或天然气。该项目的主要重点是开发新技术，以提高精度和自动化，在行业领先的算法XWI中使用AI/ML进行地下成像。运行这些算法所需的计算能力是巨大的--特别是使用了100万个虚拟CPU。S-Cube在SHAIR的合作伙伴、挪威的RagnaRock Geo的技术目前包含人工智能驱动的水平解释，最大限度地减少了人工解释。它采用地下体积，并使用其改进的地球物理学引导神经网络（GNNs）卷积神经网络（CNN）创新来检测岩石结构中的不连续性，并将其解释为“地平线”。S-Cube的技术XWI是从伦敦帝国理工学院的世界领先研究小组中分离出来的，是20年发展的结果，拥有多项专利。这是传统的全波形反演（FWI）的一个进步，它可以生成高质量的3D地下模型，用于在钻井前评估地下特征。主要结果将是能源公司可以作为SaaS产品使用的一套新工具，用户可以通过该工具更准确地分析他们的数据。这项新技术将增强现有的XWI算法，生成上级速度模型，生成的地震层位。这将作为一项新的服务部署，使石油公司能够以更高的成本效益增加储量，并以比用于发现石油的传统分析方法更高的精度最大限度地减少排放。能源公司将通过更好的分析和更高的成功率获得更高的利润，并能够从石油转向低排放地下能源的新来源。这种改进的数据解释应该减少钻探碳氢化合物的可能性，从而减少钻探威尔斯的可能性。