Sparse Seabed Sensor Arrays for 4D Imaging
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Solution Overview
Problem
Deploying and maintaining large numbers of seabed sensors for geophysical surveys is difficult and expensive, limiting the effectiveness of 4D surveying and 3D/4D imaging of geological structures and hydrocarbon reservoirs, especially in marine environments where traditional dense sensor arrays are costly and impractical.
Innovation Solution
The implementation of highly-sparse seabed acquisition geometry designs that utilize sparse receiver grids with sensor spacings of 500 meters or more, optimized for 3D and 4D imaging, allowing for extended illumination using higher-order sea surface reflections and enabling the use of sparse arrays of seabed sensors without compromising image quality, by processing primary and higher-order wavefields and applying techniques like reciprocity and wavefield separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large numbers of seabed sensors are deployed for geophysical surveys, then imaging quality and survey coverage are improved, but deployment cost and operational difficulty increase
Solution Approach 1:
The patent divides the dense sensor array into multiple sparse sub-arrays or streams, each independently acquired and processed. This segmentation allows the system to achieve comprehensive imaging coverage through computational methods while physically deploying fewer sensors, thereby reducing deployment complexity while maintaining imaging quality.
Solution Approach 2:
The patent transitions from a purely spatial dimension of sensor deployment to include the temporal and computational dimensions. By using sparse acquisition combined with advanced processing techniques (such as wavefield separation, migration, and interpolation), the system achieves dense imaging results from sparse physical deployments across multiple time streams.
2Measurement precision
If dense sensor arrays are used for 3D/4D imaging, then image quality is improved, but deployment cost and maintenance difficulty increase
Solution Approach 1:
The patent employs partial action by deploying only a subset (sparse array) of the sensors that would be needed in a traditional dense array, yet through computational processing (acquiring and processing multiple streams), it achieves equivalent or superior imaging results. This reduces the physical deployment burden while maintaining image quality.
Solution Approach 2:
The patent changes the acquisition parameters by using sparse sensor spacing (e.g., 500m or more between sensors) combined with multiple stream acquisition and advanced processing parameters (wavefield separation, migration velocity models). This parameter transformation allows the system to achieve dense imaging coverage from sparse physical deployments.
3Device complexity
If sparse sensor arrays are used, then deployment cost is reduced, but survey repeatability and imaging capability deteriorate
Solution Approach 1:
The patent ensures continuity of useful action by acquiring data from multiple streams or sources over time, processing them together to create a complete imaging dataset. This continuous multi-stream acquisition compensates for the sparsity of individual sensor locations, maintaining survey repeatability and imaging capability while using fewer sensors per deployment.
Solution Approach 2:
The patent incorporates feedback mechanisms in the processing stage where acquisition data from sparse arrays is processed through iterative algorithms (wavefield separation, migration, imaging conditions) that use the recorded data to refine and enhance the final image quality, ensuring repeatability despite sparse physical deployment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces deployment costs, increases survey repeatability, and enhances imaging capabilities by maintaining image quality with fewer sensors, allowing for flexible sensor geometry and improved illumination of geological structures, especially in challenging marine environments.
Implementation Method 1
Acoustic waves generated by the survey source may be transmitted to the earth's crust
Implementation Method 2
reflected back and captured at the towed and/or seabed geophysical sensors
Implementation Method 3
optimized for 3D and 4D imaging, allowing for extended illumination using higher-order sea surface reflections
Data Source
AI summary
Disclosed are advantageous designs for highly-sparse seabed acquisition for imaging geological structure and/or monitoring reservoir production using sea surface reflections. The highly-sparse geometry designs may be adapted for imaging techniques using the primary and higher orders of sea surface reflection and may advantageously allow for the use of significantly fewer sensors than conventional seabed acquisition. The highly-sparse geometry designs may be relevant to 3D imaging, as well as 4D (“time-lapse”) imaging (where the fourth dimension is time). In accordance with embodiments of the invention, geophysical sensors may be arranged on a seabed to form an array of cells. Each cell in the array may have an interior region that contains no geophysical sensors and may be sufficiently large in area such that a 500 meter diameter circle may be inscribed therein.