S-Cube is on a mission with one clearly defined focus to create breakthrough Full Waveform Inversion (FWI) technology which runs on a uniquely-engineered cloud-native scalable architecture on AWS.
With the scalability and flexibility to advance its rapidly-evolving capability, it has developed a next-generation FWI optimisation framework known as X-Waveform Inversion or XWITTM.
XWITM has put S-Cube on an accelerated growth trajectory since 2019 towards its ultimate objective of accelerated and automated seismic imaging in a single unified feedback loop with zero or minimal human intervention.
- AWI (concept) (2014)
- Lift and Shift to AWS (2018)
- RWI (concept) (2018)
- AWI++ (2019)
- EC2 Spot XWITM on AWS (2019)
- Cloud-Native XWITM on AWS (2020)
- AWI + RWI v2.0 (2021)
The S-Cube Story
A journey to create the most advanced earth soundwave property extractor for imaging the subsurface
S-Cube was spun out from the Earth Science & Engineering Department of Imperial College London to advance data-driven automated earth parameter learning from raw exploration seismic data.
It was formed on the back of breakthroughs by Imperial College researchers made in the algorithm of Full Waveform Inversion (FWI). This is a form of extreme machine learning for seeing into the earth using seismic field recordings and involving compute intensive wavefield simulations.
S-Cube through XWITM has taken the powerful technique to the next level through algorithmic developments and a cloud-native architecture which can consume data directly from the seismic survey and turn them into actionable subsurface volumes.
The first major application of time-domain waveform inversion incorporating anisotropic wave propagation was conducted by founding members of S-Cube in this award-winning study. This was at a site in the North Sea with a known shallow gas accumulation in the clastic section situated above a chalk reservoir. The waveform inversion successfully defines the structure using a data feed from a ocean-bottom seismic survey as shown below. The publication won best Geophysics Paper for 2013 from SEG.
Ocean-bottom survey receiver cables are positioned on the seafloor with 1440 individual hydrophone locations marked as dots shown on the horizontal slices below. The left column is the input model grid with a manually inserted low blob to approximate the gas accumulation whilst the right column shows the iterated model grid converting the low velocity feature into a defined structure.
Also displayed are the simulated and recorded traces from a shot firing into a cable of receivers over the anomaly. The details of the gas charged interbedded layers and faults get captured as low velocity features in the model grid as the data fitting feedback loop allow the simulations to increase their fit with the recordings.
What happens if we eliminate the low velocity zone from the starting model completely? The gas cloud is detected but the model iterates to a local minimum trap from which it cannot recover. This is due to a phenomenon known as cycle-skipping where the predicted data shifts in a direction which causes it to misalign with the observed data.
As a result of cycle skipped mismatch, the wider applicability of the generic technique is limited given reliance on a-priori knowledge of the target, i.e. an accurate starting model. S-Cube was able to specifically tackle this through restructuring of the data-fitting feedback loop optimisation cost function, making it sufficiently convex to be able to start further from the true answer without misconvergence.
Adaptive Waveform Inversion cost function
The breakthrough methodology invented and patented by S-Cube
S-Cube formulates Adaptive Waveform Inversion (AWI) using convolutional filters between the predicted and recorded data and minimising the misfit to send the non zero lag filter coefficients to zero. This solves a long-standing problem in waveform inversion theory - succumbing to false local minima due to oscillatory seismic data.
Repeating the previous example with the adaptive cost function and changing nothing else gives the result below. It enables successful recovery of the gas cloud from a cycle-skipped initial position at which point conventional waveform inversion with a point-to-point difference norm cost function can be applied to return to the previously obtained best fit result.
Enables the global minimum solution to be reached using iterative local optimisation
AWI was patented and became the recipient of the Bonarelli Award for 2015 by the EAGE, the leading European professional organisation in petroleum geoscience for the long-standing problem in localised optimisation it was able to provide a solution for.
Following intensive algorithmic development, AWI and its family of implementations improved the steepest-descent search directions and ultimately the inversion convergence in numerous examples where conventional FWI was an inadequate technique. The technology is now licensed and in mainstream use within Equinor, Woodside Energy and most recently in 2019 within Chevron.
For example, the application of AWI to a narrow-azimuth towed-steamer dataset offshore Myanmar led to a pre-drill prediction of a gas bearing formation. The low velocity interval prediction is confirmed by a subsequent well penetration of the identified zone.
Cloud-native S-Cube XWITM launches on Amazon Web Services
We ran our first major cloud project here at the end of 2018. Following that new users can now access the industry’s leading FWI algorithm on demand in a managed VPC environment hosted on the cloud with the benefit of multiple cases running concurrently for tuning purposes to enhance convergence to the basin of the misfit global minimum. S-Cube Cloud consists of the following components:
- underlying XWITM optimisation toolbox and FWI engine
- cloud-native MPI-free parallelisation layer
- real-time monitoring web-based dashboard
The technology featured at numerous events in 2019 on S-Cube and AWS platforms.
EAGE 2019 - London
S-Cube exhibition booth
ISC 2019 - Frankfurt
AWS exhibition booth
Run Full Waveform Inversion on the Cloud
Use XWITM on AWS to discover a step change in accuracy and resolution of your velocity model.