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Velocity model updates using machine learning

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Cameron, G.H., 2023, PSDM velocity model building using a Deep Convolutional Neural Network, CSEG Recorder.

Building PSDM velocity models in complex structure land environments is difficult. A machine-learning method using a convolutional neural network (CNN) incorporates both human and artificial intelligence to overcome these difficulties. The supervised learning process used a large representative dataset to train the CNN, learning the convolutional weights that best map the input seismic shot records to the target velocity model. With careful consideration of both training data and network architecture, the CNN can accurately predict velocity models on both synthetic and field data.

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AAPG ERC 2023: Complex-structure seismic processing on the Ioannina Block

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Rogers, R. K., Konstantopoulos, T.A., Gouliotis, L., Carbonara, S., and Vestrum, R.W, 2023, Complex Structure Seismic Processing with Model Moveout: Case study of the Ioannina Block, NW Greece, AAPG ERC 2023, Cyprus

Ioannina Block is a fold-and-thrust belt located onshore NW Greece that contains fault systems and complex deformation making seismic imaging extremely difficult. Time and depth processing of ten 2D seismic lines totaling 400 km was undertaken by a team of Geoscientists from Energean and Thrust Belt Imaging. The strategy for successful seismic imaging relied on a collaborative approach integrating a seismic processing workflow that tackled each of the key processing challenges with the structural knowledge and expertise of Energean Geoscientists.

This presentation focusses on three key steps, Time Processing, Anisotropic Depth Imaging and Anisotropic Depth Imaging with Model Moveout.

Time Processing corrects for near-surface weathering effects through statics corrections and is the starting point for depth velocity model building.

Depth Imaging allows for the seismic signal to be observed in true depth from surface. To achieve this, an accurate rock velocity model is required along with the parameterization to correct for anisotropy. Building the depth velocity model and collaboration with the Geoscience team is discussed.

Depth Processing with Model Moveout was used to further improve the final image.
In place of the time statics the weathering layers are dealt with by first superimposing the Near Surface Velocity Model onto the depth velocity model and recalculating the residual statics using data that has been flattened with Model Moveout, a more accurate method than the standard approach of NMO.

A collaborative approach between TBI and Energean Geoscientists combined a careful analytical data driven approach with a structural, geologic perspective resulting in improved seismic imaging as compared to previous efforts.

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AAPG ICE 2022: Weathering corrections for depth imaging using MMO

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MacArthur, T., Vestrum, R.W., and Cameron, G.H., 2022, Correcting for Near-Surface Velocity Variation in Seismic Depth Imaging Using Model Moveout (MMO), AAPG ICE 2022, Cartagena, Colombia

In complex-structure environments like the Andes mountains and foothills, outcropping rocks generate a significant velocity variation in the near surface. In time processing, we intend to correct for these nearsurface fluctuations with weathering statics corrections, however, this solution assumes vertical raypaths through the weathering layer, and this assumption is violated when we have high-velocity rocks outcropping at the surface. With depth imaging, we have an opportunity to include the weathering velocities in the PSDM velocity model and raytrace through the weathering layers to get a more accurate correction for nearsurface velocity variation.

But then, what do we do with the weathering correction we calculated in the time processing? The weathering velocities from the refraction tomography are now in the PSDM velocity model, but the reflection statics calculated in the time processing are coupled to the refraction statics and time-processing velocities. These statics are therefore decoupled from the PSDM velocity model. Calculating new reflection statics using the PSDM velocity model resolves this issue. We propose a method where we discard all weathering statics from the time processing and perform all nearsurface velocity corrections in the depth domain—including reflection statics.

We applied this workflow to a seismic dataset from the foothills of the Colombian Andes, which shows significant imaging improvements below the mountainous areas of the seismic survey.

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SEG/AAPG IMAGE ’23 case history from Camisea Perú

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Vestrum, R.W., Soldo, J., Zunino, E., Muzzio, M.E., Chung, J.F., 2023, Geologically constrained model building for seismic depth imaging in Camisea, Perú, The International Meeting for Applied Geoscience & Energy, Houston, USA.

  • Integrated seismic case study from the Peruvian Andes
  • Geologic constraints are essential to seismic imaging in complex-structure land areas
  • Seismic reflectors on the final depth image matched well depths across the block without vertical scaling after migration
  • Revised volume redefined the structural model and revealed an additional subthrust target

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Structural Geology is a Key to Seismic-Imaging Success

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Vestrum, R.W. and MacArthur, T.R., 2023, Structural Geology is a Key to Seismic-Imaging Success, AAPG Explorer.

  • Seismic data in structured land areas are characterised by low data density, low signal-to-noise ratios, and high structural complexity
  • Automated methods for velocity model building break down under these conditions
  • Structural-geology constraints are key to seismic imaging success in these areas, as illustrated graphically with this fault-geometry scenario test

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APGCE 2022: Convolutional neural networks to augment PSDM velocity model building

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Cameron, G.H. and Vestrum, R.W., 2022, Convolutional neural networks to augment PSDM velocity model building, APGCE Conference, Kuala Lumpur, Malaysia

  • Built convolutional neural network (CNN) to estimate TTI PSDM velocity models from shot gathers
  • Traditional automated methods for PSDM velocity estimation in complex-structure land areas are unstable, so we rely on the human understanding of the geology to build geologic models
  • The goal is to use machine learning to supplement human learning
  • Field-data example shows imaging improvements on certain shallow reflectors, which shows the potential of the method

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Greg presents poster in Kuala Lumpur

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AAPG ICE 2022: Structural Styles: Challenges in Seismic Imaging

Vestrum, R.W., and Cameron, G.H., 2022, Structural Styles: Challenges in Seismic Imaging, AAPG ICE 2022, Cartagena, Colombia

This presentation develops the themes presented in Chapter 2 of the AAPG publication, Andean Structural Styles: A Seismic Atlas. Seismic data in areas like the Andes have unique challenges that break traditional seismic-imaging methods designed for offshore exploration. Reducing exploration risk in these basins requires a workflow tailored to the geologic setting. The under-constrained nature of the seismic data requires tight integration with the structural geologist.

Seismic imaging is a vital tool for mapping the complex geologic structures of the Andes. The method of imaging the Earth’s subsurface with seismic waves is powerful, and it has certain limitations—especially when deployed in complex-structure land areas like the mountain ranges and high plains of the Andes. Understanding the technologies involved and how they are applied to this specific geologic setting will improve our understanding of the risks and uncertainties involved in the interpretation of structures on seismic images.

Seismic data in thrust-belt environments are typically low data density and have low signal-to-noise ratios, all while attempting to image complex geologic structures. The data are acquired over rough topography with laterally varying velocities from the surface down. If the near surface is the lens through which we image the subsurface, our lens is bumpy and distorted. These are the challenges of seismic processing in fold thrust belts, and decades of technology development has gone into facing those challenges, from weathering corrections for the near-surface, to advance migration algorithms that can image below major thrust faults.

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AAPG ICE 2019: Geologically constrained seismic imaging in Andean thrust belts

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Vestrum, R.W., 2019, Geologically Constrained Anisotropic Depth Imaging in Andean thrust belts, AAPG ICE, Buenos Aires.

  • Seismic data in structured land areas have severe limitations
  • Geologic interpretation and human collaboration can overcome these limitations
  • Examples from Colombia and Peru show how we resolve these issues through geoscience collaboration
  • Increased accuracy of an imaging algorithm also means increased sensitivity: PSTM → PSDM → RTM

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CSEG Recorder 2017: Myanmar PSDM case history

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Vestrum, R.W., Gonguet, G., and MacArthur, T., 2017, Geologically Constrained Anisotropic Depth Imaging in the Central Burma Basin, Onshore Myanmar, CSEG Recorder.

  • 2D seismic imaging along the foothills of the Indo-Burma range
  • Interpretive geologic constraints required for model building in noisy seismic data
  • Resulting seismic images showed structural details that time processing could not fully image.

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AAPG ICE Barcelona 2016: Impact of a tightly folded anisotropic layer on imaging in Papua New Guinea – a modelling study

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Cameron, G.H., Gillam, D., and Vestrum, R.W., 2016, Impact of a tightly folded anisotropic layer on imaging in Papua New Guinea – a modelling study. AAPG ICE, Barcelona, Spain.

  • FD acoustic anisotropic modelling quantifies the imaging problem resulting from tight folds in the near surface
  • TTI PSDM is sensitive to the model dip
  • Surface-geology measurements helped constrain model dip

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