Markos Sourial是一位地球物理学家，专长于高级地震数据处理。他的专业领域涵盖全球不同盆地（包括墨西哥湾）的海洋、海底地震（OBS）、陆地和过渡带（TZ）勘探。Sourial运用现代成像和反演技术，例如海底节点（OBN）、多方位角（MAZ）、宽方位角（WAZ）、全波形反演（FWI）和4D地震，以降低地下不确定性并改进储层表征。

Sourial 在石油和天然气行业从业 25 年，他的足迹遍布从家乡开罗到加拿大再到休斯顿的各地。如今，他担任 SLB 的地质解决方案团队经理，负责监督北美地区的业务。

我与 Sourial 探讨了他在全球盆地利用地球物理技术和工具的经验，加速发展的技术如何影响地震数据处理以及石油和天然气行业面临的挑战，以及寻求从事地下地震成像事业的年轻专业人士的职业前景。

EW：马科斯，你获得了地球物理学的学士和硕士学位，并接受过地震学方面的培训。你是如何最终专攻 地震数据处理的呢？

MS：数据处理在环境研究和地震学中至关重要。我最初在埃及国家天文地球物理研究所担任助理研究员，由此踏入地球物理领域。这份工作让我有机会参与各种重要项目，并与政府部门和当地大学合作，从而在环境研究和地震学方面积累了丰富的实践经验。

我负责运用电阻率法和环境地震能量法等不同的地球物理方法采集现场数据，并进行数据处理，最终提供分析结果和解决方案。这使我有机会参与多地震工程项目，并解决诸如地质工程、垃圾填埋场调查和地质灾害识别等具有挑战性的环境问题。数据处理对我来说是一个非常有趣的领域，因为它就像解谜一样，每个数据集和每个项目都带来新的挑战。

EW：您处理过海洋、海底地震仪、陆地和过渡带的数据集。这些类型的地震数据在技术和操作方面有哪些主要区别？您是如何调整工作流程的？

MS：每种地震采集技术和环境都有其独特的限制，因此处理优先级也不同。

拖缆式海洋勘测通常具有稳定的震源-接收器几何结构和相对较低的环境噪声，因此处理重点在于去鬼波、多次波衰减、精确的振幅处理和高分辨率成像。在实际操作中，拖缆较长且对海况敏感，这会影响采集几何结构，进而影响我们对震源/接收器行为的建模和补偿方式。

海底地震仪（OBS）将接收器移动到海底，并记录多分量数据（例如压力和质点速度）。这种几何结构可实现全方位覆盖和极长的偏移距。这非常适合探测盐下和侧翼目标，但需要强大的工作流程来进行波场分离（压力-阻抗转换或分量旋转）、耦合校正以及处理方向和时间问题。

海底地震仪（OBS）数据可以揭示转换波信息，并为速度模型构建提供更丰富的信息，但同时也带来了诸如海底倾斜或生物污损等环境因素，这些因素必须加以考虑。过渡带（TZ）混合了海洋和陆地条件，例如潮汐窗口、混合的震源/接收器类型以及不规则的几何形状。它是操作上最复杂的区域，需要采用混合多次波解调策略和精细的几何处理。

由于人为噪声和近地表非均质性，陆上测绘通常噪声最大。静校正问题、不均匀的源接收器间距和地滚波是陆上数据处理的主要问题，因此我们高度依赖于稳健的地滚波衰减、地表一致的静校正和交互式质量控制。多年来，我逐渐摒弃了千篇一律的流程，转而采用模块化、自适应的工作流程，并根据采集类型和地质情况进行定制。我经常在项目早期阶段进行小规模的处理分支原型测试，以检验不同的去噪策略或反演约束对最终图像的影响。这种实验方法使我们能够量化各种方案的优缺点，并在进行全面处理之前选择最具成本效益的方案。

早期对预处理（包括噪声抑制、传感器驱动校正和精细几何校正）的投入，可以节省后续数周的时间。与采集团队保持密切沟通同样重要。了解现场实际情况有助于做出切合实际的处理决策，并减少意外情况的发生。

EW：您的技术工具包包括MAZ、WAZ、OBN、FWI和延时或4D处理等先进技术。这些方法如何促进墨西哥湾和其他复杂海域的成像工作？

MS：由于其复杂的盐下构造、崎岖的盐层几何形态和深水地层，美国南部近海是世界上最具挑战性的地震成像环境之一。墨西哥湾的深水盐下环境呈现出陡倾反射层和崎岖盐层几何形态，窄方位角拖缆勘探难以有效探测。MAZ 和 WAZ 采集扩大了角度覆盖范围和偏移距，从而改善了成像效果、断层连续性并减少了偏移伪影。

OBN采集进一步推动了这一发展。海底接收器能够记录具有全方位覆盖和极长偏移距的多分量波场，从而实现拖缆在某些环境下无法实现的成像和模型构建。FWI通过将丰富的波场信息转换为高分辨率速度模型，进一步完善了采集技术的进步。FWI并非仅仅将波场能量视为需要拾取的波峰，而是利用反射、折射和转换能量迭代构建速度模型，从而更好地解释数据。在实践中，将OBN与FWI相结合通常能够显著提升盐下深度成像的质量。这意味着更高的速度精度、更少的深度模糊性以及更高的解释人员信心。

我们还探索了全波形反演（FWI）衍生产品，例如伪反射率图像，这些图像可以增强高频细节，并可作为解释人员的补充输入。根据我在美国海上项目方面的经验，我亲眼目睹了这些技术和创新如何共同提高了成像可靠性，降低了钻井风险，并在世界上技术难度最高的海上盆地之一中实现了更精确的储层圈定和开发规划。

EW：您能否描述一个 4D 分析与 3D 分析相比改变了方案的项目？

MS：传统的3D地震勘探可以提供地下结构的详细静态图像，而时移4D地震勘探（本质上是随时间重复进行的3D勘探）则增加了时间维度，使我们能够监测油藏在生产或注入过程中的变化。4D地震勘探能够监测油藏动态和流体运动，揭示流体（例如石油、天然气、水）随时间在油藏中的流动情况，从而识别水突破、气锥或未排空隔室等现象。如果没有4D地震勘探，这些动态变化将无法观测，只能依赖稀疏的井数据。

在2022年的一个深水项目中，我们采用单一且一致的工作流程处理了基线调查和三次监测调查数据，以消除处理过程中产生的差异。4D分析结果突显了部分油藏的早期水突破，并绘制了静态3D分析无法解析的压力驱动饱和度变化图。凭借这些4D分析结果，作业者调整了注水井的位置，并优先考虑了针对未被波及区域的加密井。

直接的益处在于改进了采收率规划，避免了不必要的侧钻，这些决策直接影响了最终采收率的预估和项目经济效益。除了井位布置之外，4D结果还通过突出显示注水效率欠佳的区域，为生产策略提供了信息，从而促使我们调整注水计划。我们还应用4D全波形反演（4D-FWI）来更好地对齐不同年份的速度模型，这提高了4D信号的灵敏度，并减少了解释振幅变化时的不确定性。这些改进缩短了解释周期，并为油藏工程师提供了更可靠的模拟模型输入数据。

EW：目前地震数据处理面临的最大技术挑战是什么？运营商和服务公司正在采取哪些实际措施来应对这些挑战？

MS：地震勘探的不确定性始终是一个棘手的问题：照明不足、速度模糊以及环境噪声等因素共同限制了我们准确解析地下特征的能力。为了解决这一问题，业界正在将改进的采集技术（例如，海底噪声采集 (OBN)、多方位角/广角角采集 (MAZ/WAZ)）与先进的成像技术（例如，全波形反演 (FWI)、逆时偏移）以及严格的勘测设计相结合，以确保4D项目的可重复性。OBN的360°方位角覆盖和长偏移距，结合FWI​​基于物理原理的反演方法，是降低复杂盆地不确定性的两项最有效的技术进步。然而，成本、物流和数据量等实际问题依然存在，需要精心的项目设计和高效的处理流程。

数据量和处理成本是实际的限制因素——OBN调查会产生海量数据集——因此，高效、可扩展的处理基础设施和云端工作流程是解决方案的一部分。我们越来越依赖混合本地/云架构和容器化工作流程来并行处理任务并加快周转速度。

自动化质量控制仪表盘和可复现的工作流程有助于团队在大型项目和多个年份的数据中保持一致性。在人员方面，保持强大的数据解读和处理能力，并促进采集、处理和油藏团队之间的紧密协作，对于将数据转化为可执行的决策仍然至关重要。

EW：您曾在埃及、加拿大和美国工作过。您是否发现地震投资或战略方面存在显著的区域差异？

MS：完全正确。地震勘探能力的投资与钻井成本和地质复杂性密切相关。在墨西哥湾，由于钻井成本高昂且失败风险巨大，运营商将地震勘探视为一项战略资产。他们愿意投资昂贵的数据采集和高端处理，以降低不确定性。相比之下，一些新兴盆地的运营商可能更注重快速、低成本的勘探，这些勘探“足够好”用于早期勘探，然后随着勘探前景的成熟而不断迭代改进。

当地监管要求、高端处理供应商的供应情况以及合格人员的可用性也会影响这些选择。我在2024年和2025年由勘探地球物理学家协会（SEG）和美国石油地质学家协会（AAPG）组织的行业会议上展示了高级案例成果，这有助于说明高端地震工作流程的投资回报，并促使一些项目更广泛地采用这些工作流程。

EW：您对地震数据处理的看法是如何演变的？您认为未来十年将有哪些趋势？

MS：我的观点从将数据处理视为一个技术流程转变为将其视为一个战略决策支持工具。在我职业生涯早期，工作流程主要集中在噪声抑制、静校正和偏移等常规任务上。我见证的最大转变之一是人工智能和机器学习（AI/ML）作用的日益增强。这些技术通过自动化噪声衰减、速度模型构建和故障检测等任务，正在改变我们处理大型数据集的方式。

鉴于获取复杂深部目标需要海量数据，人工智能已成为高效处理和分析的必备工具。人工智能能够加快处理速度，并帮助我们识别传统方法可能遗漏的细微模式，这在处理复杂地质和深部目标时至关重要。

未来十年，我预计人工智能/机器学习将与基于物理的成像技术进行更深入的融合。人工智能将实现许多常规任务的自动化，例如初至拾取、噪声抑制和异常检测，而基于物理信息的反演方法（例如全波形反演）将确保地质精度。将自动化与地球物理约束相结合的混合方法将成为成功工作流程的基础。

EW：人工智能和机器学习现在是否准备好带来变革性的突破，还是我们正处于过渡阶段？

MS：我认为这正处于一个高级过渡阶段。人工智能/机器学习已经展现出实际价值——自动化耗时任务、加速质量控制并辅助解释。深度学习模型在断层检测、层位追踪和相分类方面展现出巨大潜力。但仍存在一些挑战，例如标记训练数据的获取、跨盆地泛化以及可解释性等。

为了缓解这些问题，人们正越来越多地探索迁移学习、半监督学习和物理信息神经网络等技术。最具影响力的进展将来自将物理约束嵌入机器学习工作流程的混合模型，这些模型能够创建可解释、可迁移的工具，从而增强而非取代专家判断。

EW：对于有兴趣从事地下成像的早期地球物理学家，您有什么职业建议吗？

硕士阶段：在依赖自动化之前，务必掌握基础知识，包括波传播、速度分析和静力学。花时间进行数据采集，了解现场限制及其对数据质量的影响。

学习编程和基础机器学习工具；数据科学素养日益重要。学习地震反演和振幅随偏移距变化的概念，以便将地震输出与储层属性联系起来。

保持好奇心，记录你的经验教训，并广泛合作。

出色的成像成果往往来自跨学科团队，他们需要结合采集技术、处理技能和解读洞察力。此外，还要积极寻找导师，参加学术会议，尽可能发表论文，并在掌握基础知识和运用新技术之间保持平衡。

不久的将来，人工智能很可能将更深入地融入现有的地震勘探工作流程中。它不会取代地球物理学家，但会提高他们的工作效率，增强结果的一致性，并为深入了解地下情况开辟新的可能性。

Markos Sourial is a geophysicist specializing in advanced seismic data processing. His expertise spans marine, ocean- bottom seismic (OBS), land, and transition zone (TZ) surveys across diverse basins worldwide, including the Gulf of Mexico (GOM). Sourial applies modern imaging and inversion technologies, such as ocean-bottom node (OBN), multiazimuth (MAZ), wide-azimuth (WAZ), full-waveform inversion (FWI), and 4D seismic to reduce subsurface uncertainty and improve reservoir characterization.

Sourial’s 25-year career in the oil and gas industry has taken him from his native Cairo to Canada and Houston, where he now serves as geosolutions team manager overseeing North American operations for SLB.

I spoke with Sourial about his experiences leveraging geophysical techniques and tools in global basins, how accelerating technology is affecting seismic data processing and challenges in the oil and gas industry, and the landscape for young professionals seeking a career in subsurface seismic imaging.

EW: Markos, you earned BS and MS degrees in geophysics, and you trained in seismology. How did you come to specialize in seismic data processing?

MS: Data processing is critical in environmental studies and earthquake seismology. I embarked on the geophysics field as an assistant researcher in the National Research Institute for Astronomy and Geophysics in Egypt. That role offered me the chance to gain extensive practical experiences in both environmental studies and seismology by participating in various high-profile projects and collaborating with civil authorities and local universities.

I was responsible for acquiring data from the field using different geophysical approaches, such as resistivity and ambient seismic energy, and processing it and delivering analysis outcomes and proposed solutions. This allowed me to be involved in multiseismic engineering projects and tackle challenging environmental problems such as geoengineering, landfill investigations, and geohazard identifications. Data processing is a very interesting field for me because it offers a puzzle-solving aspect in which every single data set and every single project presents a new challenge.

EW: You have processed marine, OBS, land, and transition-zone data sets. What are the key technical and operational differences across these types of seismic data, and how have you adapted your workflows?

MS: Each seismic acquisition technique and environment impose distinct constraints and therefore different processing priorities.

Towed-streamer marine surveys typically have consistent source-receiver geometry and relatively low ambient noise, so processing emphasis is on deghosting, multiple attenuation, careful amplitude handling, and high-resolution imaging. Operationally, streamers are long and sensitive to sea conditions, which affects acquisition geometry and thus how we model and compensate for source/receiver behavior.

OBS moves receivers to the seabed and records multicomponent data (i.e., pressure and particle velocity). That geometry gives full-azimuth coverage and very long offsets. This is excellent for illuminating subsalt and flank targets but requires robust workflows for wavefield separation (pressure-to-impedance processing or component rotation), coupling correction, and handling orientation and timing issues.

OBS data can reveal converted-wave information and provide richer information for velocity model building, but it also brings environmental complications like seabed tilt or biofouling, which must be accounted for. The TZ mixes marine and onshore conditions such as tidal windows, mixed source/receiver types, and irregular geometry. It’s the most operationally complex and demands hybrid demultiple strategies and careful geometry handling.

Onshore land surveys are typically the noisiest due to cultural noise and near-surface heterogeneity. Statics problems, uneven source-receiver spacing, and ground-roll dominate land processing, so we rely heavily on robust ground-roll attenuation, surface-consistent statics, and interactive quality control. Over the years, I moved away from one-size-fits-all flows to modular, adaptive workflows tailored to acquisition type and geology. I often prototype small-scale processing branches early in projects to test how different denoising strategies or inversion constraints affect final images. This experimental approach allows us to quantify trade-offs and select the most cost-effective path before committing to full-scale processing.

Early investment in preprocessing—noise suppression, sensor-driven corrections, and careful geometry—saves weeks downstream. Equally important is close communication with acquisition teams. Understanding the field realities guides realistic processing decisions and reduces surprises.

EW: Your technical toolkit includes advanced techniques such MAZ, WAZ, OBN, FWI, and time-lapse, or 4D, processing, among others. How have these methods advanced imaging in the GOM and other challenging basins?

MS: The southern offshore US is one of the most challenging seismic imaging environments in the world due to its complex subsalt structures, rugose salt geometries, and deepwater stratigraphy. The GOM’s deepwater subsalt environment presents steeply dipping reflectors and rugose salt geometries that are poorly illuminated by narrow-azimuth streamer surveys. MAZ and WAZ acquisitions expand angular coverage and offsets, improving illumination, fault continuity, and reducing migration artifacts.

OBN acquisition pushes that further. Seafloor receivers record multicomponent wavefields with full-azimuth coverage and very long offsets, enabling imaging and model-building that streamers cannot achieve in some settings. FWI complements acquisition advances by converting the rich wavefield into high-resolution velocity models. Instead of treating wavefield energy as just arrivals to pick, FWI uses reflected, refracted, and converted energy to iteratively build a velocity model that better explains the data. In practice, combining OBN with FWI often yields a step-change in depth imaging beneath salt. This means better velocity fidelity, fewer depth ambiguities, and improved interpreter confidence.

We’ve also explored FWI-derived products such as pseudo-reflectivity images that enhance high-frequency detail and can be used as complementary input to interpreters. In my experience with offshore US projects, I have seen how these technologies and innovations have collectively increased imaging confidence, reduced drilling risk, and enabled more accurate reservoir delineation and development planning in one of the world's most technically demanding offshore basins.

EW: Can you describe a project where 4D changed the plan compared with 3D analysis?

MS: While traditional 3D seismic provides a detailed static image of the subsurface, time-lapse 4D seismic (which is essentially repeated 3D surveys over time) adds a time dimension, allowing us to monitor how the reservoir changes during production or injection. 4D seismic monitors the reservoir dynamics and fluid movement as it reveals how fluids (i.e., oil, gas, water) move through the reservoir over time, identifying water breakthrough, gas coning, or undrained compartments. Without 4D, these dynamic changes are invisible and you would have to rely only on sparse well data.

In a 2022 deepwater project, we processed a baseline and three monitor surveys through a single, consistent workflow to eliminate processing-induced differences. The 4D differences highlighted early water breakthrough in parts of the reservoir and mapped pressure-driven saturation changes that static 3D could not resolve. Armed with that 4D insight, the operator revised injector placement and prioritized infill wells that targeted unswept compartments.

The immediate benefit was improved recovery planning and avoidance of unnecessary sidetracks, decisions that directly impacted estimated ultimate recovery and project economics. Beyond well placement, the 4D results informed production strategy by highlighting regions where water-injection efficiency was suboptimal, prompting adjustments to injection schedules. We also applied 4D-FWI to better align velocity models across vintages, which improved the sensitivity of the 4D signals and reduced ambiguity when interpreting amplitude changes. Those improvements shortened the interpretation cycle and gave reservoir engineers higher-confidence inputs for simulation models.

EW: What are the largest technical challenges facing seismic processing today, and what practical steps are operators and service companies taking to address them?

MS: The persistent issue is seismic uncertainty: limited illumination, velocity ambiguity, and environmental noise that collectively limit our ability to resolve subsurface features confidently. To tackle this, the industry is blending improved acquisition (e.g., OBN, MAZ/WAZ) with advanced imaging (e.g., FWI, reverse-time migration) and rigorous survey-design for repeatability in 4D projects. OBN’s 360° azimuthal coverage and long offsets, combined with FWI’s physics-based inversion, are two of the most effective advances for reducing uncertainty in complex basins. Still, practical issues—cost, logistics, and data volume—remain and require careful project design and efficient processing workflows.

Data volume and processing cost are practical constraints—OBN surveys generate massive data sets—so efficient, scalable processing infrastructures and cloud-enabled workflows are part of the solution. We are increasingly relying on hybrid on-premises/cloud architectures and containerized workflows to parallelize processing tasks and accelerate turnaround.

Automated quality-control dashboards and reproducible workflow pipelines help teams maintain consistency across large projects and multiple vintages. On the human side, maintaining strong interpreter and processing expertise, and promoting tight collaboration across acquisition, processing, and reservoir teams remains critical to turning data into actionable decisions.

EW: You’ve worked across Egypt, Canada, and the US. Have you seen meaningful regional differences in seismic investment or strategy?

MS: Absolutely. Investment in seismic capabilities closely follows drilling cost and geological complexity. In the GOM, operators treat seismic as a strategic asset because the cost of drilling and the risk of failure are high. They are willing to invest in expensive acquisition and high-end processing to reduce uncertainty. In contrast, operators in some emerging basins may emphasize rapid, lower-cost surveys that are “good enough” for early exploration, then iterate as prospects mature.

Local regulatory requirements, access to high-end processing vendors, and the availability of qualified personnel also shape these choices. My presentations of advanced-case results at industry conferences organized by the Society of Exploration Geophysicists (SEG) and the American Association of Petroleum Geologists (AAPG) in 2024 and 2025 helped illustrate the return on investment of high-end seismic workflows and influenced broader adoption in some projects.

EW: How has your view of seismic processing evolved, and what trends do you expect to define the next decade?

MS: My view shifted from seeing processing as a technical sequence to treating it as a strategic decision-support tool. Early in my career, workflows focused on routine tasks such as noise suppression, statics, and migration. One of the biggest shifts I’ve seen is the growing role of artificial intelligence and machine learning (AI/ML). These technologies are transforming how we handle large data sets by automating tasks like noise attenuation, velocity model building, and fault detection.

Given the vast amount of data required to achieve complex deep targets, AI has become an essential tool for efficient processing and analysis. AI speeds up processing and helps us identify subtle patterns that might be missed by traditional methods, which is critical when dealing with complex geology and deep targets.

Over the next decade, I expect deeper integration of AI/ML with physics-based imaging. AI will automate many routine tasks—first-break picking, noise reduction, anomaly detection—while physics-informed inversion such as FWI will ensure geological accuracy. Hybrid approaches combining automation with geophysical constraints will define successful workflows.

EW: Are AI and ML ready to deliver transformative breakthroughs now, or are we in a transitional phase?

MS: I believe this is an advanced transitional phase. AI/ML already delivers practical value—automating time-consuming tasks, accelerating quality control, and aiding interpretation. Deep-learning models show promise in fault detection, horizon tracking, and facies classification. But there are challenges that include labeled training data, generalization across basins, and explainability.

Techniques such as transfer learning, semi-supervised learning, and physics-informed neural networks are increasingly explored to mitigate these issues. The most impactful advances will come from hybrid models that embed physical constraints into ML workflows, creating interpretable, transferable tools that augment expert judgment rather than replace it.

EW: Do you have any closing career advice for early-career geophysicists interested in subsurface imaging?

MS: Master the fundamentals, including wave propagation, velocity analysis, and statics, before relying on automation. Spend time in acquisition to appreciate field constraints and their impact on data quality.

Learn programming and basic ML tools; data-science literacy is increasingly valuable. Learn seismic inversion and the concepts of amplitude variation with offset so you can bridge seismic outputs with reservoir properties.

Stay curious, document your lessons, and collaborate broadly.

Great imaging results often come from interdisciplinary teams that combine acquisition know-how, processing skills, and interpreter insight. Also, seek mentors, attend conferences, publish where possible, and maintain a balance between mastering fundamentals and adopting new tools.

The near future will likely focus on integrating AI more deeply into existing seismic workflows. It will not replace geophysicists, but it will enhance their productivity, improving consistency and opening new possibilities for subsurface insight.