The discovery of the largest pre-Mesozoic ultra-deep strike-slip fault-controlled oilfield in the northern Tarim Basin, China, has marked a cornerstone in oil exploration methodology, showcasing the recasting of traditional approaches through deep learning.
Researchers have innovated a deep transfer learning workflow utilizing Unet to accurately detect strike-slip faults overshadowed by complex geological formations, underlining the potential for significant advancements in fault characterization and oil production. The results demonstrate the discovery of multiple NW- and NE-striking faults, essential for optimizing well trajectories and development plans.
The Tarim Basin, renowned as the largest superimposed basin in China, is undergoing a shift in exploration strategies. This study, highlighting the Halahatang oilfield, excitedly notes the estimated reserves surpassing 1 billion tons of hydrocarbons buried at depths exceeding 6000 meters. Bearing in mind traditional limitations faced by seismic methods, this study introduces a robust methodology that effectively distinguishes fault structures despite common hindrances such as typical karstification.
As simulation and synthetic techniques are prevalent in the training of automated detection systems, this research takes a significant stride. The authors identified that conventional seismic attributes were often inadequate in accurately reflecting the fault’s geometry, necessitating the improvement facilitated by deep learning technologies.
The pioneers of this methodology highlighted the role and importance of constructing fault labels using seismic-well combinations. This process minimizes subjective interpretation biases, offering an objective and accurate training platform for deep learning models. The collaborative approach resolves issues stemming from a scarcity of fault labels in seismic data, allowing for the extraction of meaningful features of the faults.
As a pivotal area in hydrocarbon accumulation, the study highlights that four styles of strike-slip faults have been recognized based on their geometric properties and connectivity. Notably, the study asserts that fault continuity is crucial for determining hydrocarbon accumulation, noting that thorough and hard linkage types of faults enhance hydrocarbon reserves significantly, whereas isolated or soft-linkage faults tend to limit such accumulation.
By reprocessing 3D seismic data and employing advanced imaging techniques, the study's findings reveal a clear mapping of strike-slip fault geometries and structural styles critical for efficient resource extraction.
This kind of advanced deep learning implementation within geoscience is set to enrich exploration methodologies and enhance the understanding of underground reservoirs. The successful identification of strike-slip faults in carbonates, as illustrated in this study, marks a transformative moment in petroleum engineering, promising to steer future exploration in similar geological settings and pushing the boundaries of current methodologies.
In conclusion, the substantial implications of deep learning in fault characterization showcased in the Halahatang oilfield study brings forth hope in unlocking complex oil reserves, optimizing extraction techniques and paving the way for new discoveries in the oil and gas sector.
