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

Shair将创建一项新的软件服务，该服务将提供对地震数据的高度准确的解释，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，以提高在行业领先的XWI算法中使用AI/ML进行地下成像的准确性和自动化。运行这些算法所需的计算能力是巨大的--异常地使用了100万个虚拟CPU。来自S-立方体在沙尔的合作伙伴挪威的Ragna Rock Geo的技术目前包含人工智能驱动的地平线解释，只需最少的人工解释。它吸收了地下体积，并使用其改进的地球物理制导神经网络(GNNS)的创新卷积神经网络(CNN)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世界领先的研究小组剥离出来的，是20年来开发的结果，拥有多项专利。这是对传统的全波形反演(FWI)技术的一种改进，它可以产生高质量的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使用的一套新工具，作为一种SaaS产品，用户将通过这些工具来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，生成使用自动生成的地震层位约束的卓越速度模型。这项新服务将作为一项新服务部署，使石油公司能够比用于发现石油的传统分析方法更准确地增加储量和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变得更有利可图，并能够从石油转向新的低排放地下能源来源。这种对数据的改进解释应该会减少钻探碳氢化合物的可能性，因为钻探碳氢化合物的结果是没有发现碳氢化合物，因此可以减少钻探井的数量，从而有助于环境保护。

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.