Oil & Gas
DIGITAL TECHNOLOGIES USED
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In this video, Ben Lasscock, Energy Solutions Group Technical Lead at Enthought, discusses a deep learning tool created by Enthought to automate the interpretation of sequence stratigraphy.
Seismic is the first field measurement in the oil exploration process. Offshore, hundreds of square miles can be covered in single survey, generating terabytes of data. Petroleum geophysicists must analyze this volume of data to identify potential hydrocarbon reservoirs.
In seismic surveys, an energy source at surface creates elastic waves that go into the Earth, where each different layer reflects a portion of the wave energy back to surface, where it is measured. The rest goes through to interact with deeper layers and reflect in the same way.
The challenge is to increase efficiency in handling, integrating, and analyzing data at every opportunity, freeing experts for analysis. Today this includes applying the latest machine learning techniques, which can both increase efficiency and increase chances of exploration success.
In partnership with this major independent, Enthought conducted R&D into the efficacy of using machine learning techniques and custom processing workflows to automatically delineate measured patterns throughout a seismic volume.
Basic stratigraphic units within seismic volumes which can contain hydrocarbons are defined by careful analysis by a skilled sequence stratigrapher. These patterns may be defined objectively and are considered to be predictable.1
Machine learning and AI can alleviate the drudgery of interpreting large seismic volumes and allow more time for experts to focus on quality and value. Our approach was designed to operate with minimal input from the interpreter. Where possible, we limited the complexity of machine learning systems, and applied standard seismic processing methods familiar to the interpreter, while taking significantly less time.
In a typical workflow, the sequence stratigrapher would interpret a seismic volume by analyzing a sequence of 2D cross-sections sampled regularly throughout. This typically involves interpreting 1 in every 10 or more of the possible cross sections to produce a consistent volume, an arduous and labor intensive process. In this proof of concept project using AI, the interpreter had only to interpret less than 1 in every 1000 possible cross-sections from the Poseidon data, training a machine, which then interpreted the remaining volume.
This approach is shown in two examples in these figures:
The schematic on the left shows an interpretation applied to an inline within the Poseidon seismic volume. The colored image demonstrates corresponding interpretation made by the automated system some distance from this initial interpretation. There is a high degree of consistency in what can be seen to be similar features.
In the second example, the LANDMASS-1 dataset was used. Example seismic facies were used to train the machine learning algorithm, seen in the tiles on the right, top to bottom; salt, chaotic, fault and horizon. The colored image is the automated interpretation of the F3 seismic data volume. The ability to deliver decision-ready quality image data with minimal expert time provides confidence in the potential of this new technique.
1Seismic Stratigraphy and Global Changes of Sea Level, Part 6: Stratigraphic Interpretation of Seismic Reflection Patterns in Depositional Sequences, R. M. Mitchum et al, AAPG Special Volumes, 1977