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.
