PETRONAS Indonesia Case Study at IPA Convex 2024
At the 48th Annual IPA Convention & Exhibition, Fierzan Muhammad, Manager of Joint Ventures for PETRONAS Indonesia, showcased a compelling case study titled ‘Resolving Thin-Bedded Oil-Bearing Carbonate Reservoir Below Seismic Resolution Through Seismic Machine Learning: A Case Study in CD Carbonate Reservoir, East Java Basin, Indonesia.’ Click the button below to learn more about this fascinating application of the Paradise® AI workbench.
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Machine Learning is changing the way interpretation is done. Find out how geoscientists are using machine learning to reveal unprecedented levels of detail in seismic data.
This paper presents a case study and an answer for 3D seismic interpretation dilemma, where the application of machine-learning technology for automated fault detection could offer various advantages over manual interpretation.
Seismic ML is a robust method and fast to generate new insight of seismic resolution for interpretation, maximizing and extracting tens of seismic attributes to be used simultaneously in predicting and determining porous zones which are associated with karst.
An Enhanced Fault Detection Workflow Combining Machine Learning and Seismic Attributes Yields an Improved Fault Model for Caspian Sea Asset
Spend less time looking for the answers and more time putting those answers to use.
The Paradise AI Workbench allows you to:
- Extract more geological details through machine learning vs traditional methods
- Produce higher resolution interpretations of reservoirs and their geologic features
- Detect faults and classify seismic facies automatically while eliminating uncertainty
- Integrate faults with stratigraphic analysis for a more complete structural understanding