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The future of the upstream industry lies in the adoption of innovative geoscience digitalization solutions to optimize operations. Machine learning applications for geoscience data have been in use for more than 25 years but have recently become critical due to massive growth in the amount of petrotechnical data being acquired. As machine learning evolves, it will play an increasingly visible role in analyzing surface and subsurface data.

Global energy companies trust the AspenTech® Subsurface Science & Engineering portfolio to solve their most complex exploration and production challenges while reducing geological risks and minimizing impact on the environment. Get a quick look at our products, with QR codes linking to more details. Download now.

Full azimuth angle domain imaging provides an alternate way to map events in structurally complex areas. Information about continuous surfaces can be derived from specular gathers, while diffraction gathers are used to derive information about discontinuity i.e., faults and small-scale fractures.

The latest advancements in seismic processing and imaging, such as geostatistical interval velocity derivation and full-azimuth imaging, have improved many geologically complex and structurally deeper sediments.

High-quality subsurface images are critical for uncovering geological and geophysical aspects of the subsurface. Much vintage data was acquired in areas where no new surveys are available for a variety of reasons, so that reprocessing existing data may be the only option. The reprocessing results discussed in this paper have tremendously improved image quality and increased the interpreter’s confidence by resolving depth issues, fault positioning and reservoir connectivity.

Natural and induced fractures play a vital role in determining hydrocarbon producibility in basement exploration. An accurate understanding of fracture networks is critical for optimizing development in naturally fractured reservoirs, since the intensity and orientation of fractures significantly affect fluid flow in the reservoir rocks. Assorted techniques are available to characterize these fractures.

Sub-basalt imaging is difficult due to loss of seismic energy, attenuation, absorption, scattering and mode conversion.

In the presence of complex geology and lateral velocity variations, unresolved velocity anomalies in the overburden degrade deeper imaging.

In depth imaging, depth calibration using well-tie updated velocity is generally applied to poststack data. This is the most correct type of depth calibration as it positions structures in their proper places.