Velocity modelling formula is an essential process in geophysical exploration that provides a clearer picture of the subsurface estimating the speed at which seismic waves travel through different geological layers. This information is vital for accurate seismic imaging, particularly in the oil and gas industry, geothermal energy, and other fields that require detailed knowledge of the Earth’s structure. By creating a precise model of seismic velocities, geophysicists can more accurately interpret seismic data, make better drilling and development decisions, and reduce risks associated with subsurface exploration.
In this article, we’ll discuss the fundamentals of velocity modelling, explore its applications, and look at the latest advancements that are transforming this field.
What is Velocity Modelling?
Velocity modelling formula is the process of creating a map or model that represents the seismic wave speeds across various subsurface layers. In geophysics, seismic waves are generated and travel through the earth’s layers, each of which affects the speed and direction of the waves based on the density and composition of the materials they encounter.
The data collected from seismic surveys—reflected seismic waves—must then be translated into images of the Earth’s subsurface. However, the accuracy of these images depends on knowing the exact speed of seismic waves in each layer. This is where velocity modelling becomes critical. By estimating the velocities of these waves, geoscientists can produce clearer, more accurate representations of geological formations, helping identify potential reservoirs and hazards in the subsurface.
Types of Velocity Models
Different velocity models are used depending on the objectives of the seismic survey and the geological complexity of the area being studied. Here are the primary types:
Layer Cake Velocity Models: These are simpler models that assume uniform, flat layers in the subsurface. They’re useful in settings with horizontal stratigraphy but often lack the detail needed for more complex geological environments.
Gridded Velocity Models: These models use a 3D grid to represent variations in wave velocities at different points in the subsurface. They allow for greater detail and are ideal for areas with complex geological structures, such as fault zones or undulating layers.
Interval and Root-Mean-Square (RMS) Velocity Models: Interval velocities represent the average velocity of seismic waves within specific layers, while RMS velocities represent the effective velocity across multiple layers. These models are used to calculate the depths of different layers accurately.
Anisotropic Models: In complex areas where seismic wave velocity varies with direction, anisotropic models are necessary. These models account for variations in velocity based on the orientation of rock formations, enabling more precise imaging in areas with complex structures like fractured or tilted rocks.
The Process of Building a Velocity Model
The creation of a velocity model is a multi-step process that involves collecting and interpreting seismic data. Here’s a brief look at the key steps:
Data Acquisition: The first step is gathering seismic data through surveys using sources (such as dynamite or vibroseis trucks) and sensors. This data provides initial readings of seismic wave travel times and paths.
Picking and Interpreting Seismic Horizons: Geophysicists interpret seismic reflections from different layers, known as horizons, and map them across the survey area. The time it takes for waves to travel to these horizons and back is measured and used to estimate velocity.
Initial Model Construction: Based on geological knowledge and seismic reflection data, an initial velocity model is created. This model assumes approximate velocities for each layer and uses average values based on known rock properties.
Velocity Analysis and Refinement: With advanced algorithms, the initial model undergoes refinement through processes like tomographic inversion and depth migration. These techniques iteratively adjust velocity values and improve the model’s accuracy, especially in areas with varying rock types or complex structures.
Model Validation: The final model is validated against well log data, drilling data, or other geological information, ensuring that it reflects the true subsurface characteristics.
Applications of Velocity Modelling
Velocity modelling plays a crucial role across a variety of applications:
Oil and Gas Exploration: In the oil and gas industry, velocity modelling is essential for identifying and accurately mapping potential hydrocarbon reservoirs. By creating precise models of subsurface structures, geophysicists can reduce drilling risks and costs, enhancing the likelihood of successful extraction.
Seismic Imaging and Migration: For accurate seismic imaging, velocity models are necessary to correct distortions caused changes in wave speed through different layers. Migration techniques, such as depth migration, rely on these models to reposition seismic reflections at their true subsurface locations, creating a clearer image of geological structures.
Geothermal Energy Exploration: In geothermal exploration, velocity models help identify rock types and structures that can hold heat and serve as sources of geothermal energy. They also enable accurate mapping of faults and fractures, crucial for harnessing geothermal resources safely.
Carbon Capture and Storage: Carbon storage requires precise knowledge of subsurface rock formations and potential containment areas. Velocity models assist in identifying stable zones and monitor injected carbon dioxide in underground reservoirs, ensuring safe storage practices.
Earthquake Studies and Hazard Assessment: Velocity models also have applications in seismology for assessing the geological risks of earthquakes. They help scientists understand how seismic waves travel through fault zones and other active areas, providing valuable information for seismic hazard assessment and mitigation.
Advances in Velocity Modelling Technology
As velocity modelling has grown in importance, the technology and methodologies supporting it have advanced significantly. Some of the most transformative developments include:
Tomographic Inversion Techniques: Seismic tomography is one of the most powerful tools for refining velocity models. By analysing seismic waves’ paths and travel times across a grid, tomographic inversion produces highly accurate models, even in complex geological environments. It’s especially useful for areas with high structural complexity, like fold belts and faulted regions.
Machine Learning and Artificial Intelligence: AI and machine learning are beginning to play a transformative role in velocity modelling automating parts of the modelling process. Algorithms can analyse large amounts of seismic data, learn patterns, and help predict velocity variations, making the modelling process faster and more accurate.
Full-Waveform Inversion (FWI): FWI is a highly detailed inversion technique that uses the entire seismic wavefield, rather than just certain phases or amplitudes, to refine velocity models. This technique can reveal smaller-scale features, leading to clearer, more accurate images and better model resolution.
Integration with Well Log and Borehole Data: Incorporating borehole and well log data into velocity models allows for more precise calibrations. This integration combines surface seismic data with measurements taken directly from the subsurface, helping to ground models in real-world data and improve their accuracy.
Cloud Computing and Parallel Processing: Velocity modelling involves intensive computations, and recent advances in cloud computing and parallel processing allow for faster data processing and model generation. Cloud-based velocity modelling tools also enable collaboration and accessibility for geophysicists working remotely or in different locations.
The Future of Velocity Modelling
Looking ahead, velocity modelling will likely continue to benefit from advancements in AI, full-waveform inversion, and data integration techniques. Real-time velocity modelling, for example, may soon be possible, allowing seismic data to be processed as it is collected. This capability would provide immediate feedback and improve decision-making during drilling and exploration activities.
In addition, enhanced 4D velocity modelling, which incorporates changes over time, could become crucial for monitoring reservoir depletion, carbon sequestration, or geothermal field sustainability. As our understanding of complex subsurface systems grows, velocity modelling will remain an invaluable tool, supporting everything from resource extraction to sustainable energy and environmental protection.
Velocity modelling has proven indispensable in advancing our understanding of the Earth’s subsurface. From oil and gas exploration to carbon capture and storage, this technology provides a clearer, more accurate view of the complex geological structures below. With ongoing advancements in computing power, machine learning, and inversion techniques, velocity modelling is set to become faster, more accurate, and more versatile. These developments not only enhance subsurface exploration but also improve safety and efficiency across a variety of industries.
