Peptides are an important class of therapeutics. A high precision near seabed velocity model is built based on the auto-picked first break with tomography inversion, which provides a good solution for static problem of the survey. In this paper, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been combined for first-break picking in a large 3D OBN project of Caspian Sea. Deep learning includes Deep Belief Network (DBN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and so on. ![]() ![]() ![]() Generally, deep learning is a new neural network which has multiple hidden layers, mostly over 3 layers, compared with traditional neural network. In seismic exploration, artificial intelligence, like deep learning, has played a more and more important role now, from fault prediction, attribute identification to velocity and first break picking. In recent years, with the development of computer capacity and algorithm, artificial intelligence has changed our lives in many ways. Traditional first-break picking methods can't meet the production. Due to the rapid development of high-efficiency acquisition technique, such as WBH (wide-azimuth, broadband and high-density) acquisition technique and blended source acquisition technique, the quantity of seismic data, especially 3D seismic exploration, has leapt from GB to TB(some to PB), which sets a big challenge for first-break picking. Geoscientists from around the world continues trying their best to address the near-surface challenges. Small errors in first-break picking can greatly impact the seismic velocity model building, so it is necessary to pick high-quality travel times. The preliminary results of the 3D seismic data processing and interpretation are very encouraging and showing a clear improvement compared to the legacy 3D seismic data set.Īs well known, the modeling of the near-surface from first-break plays a significant role on the sub-surface imaging, reservoir characterization, and monitoring. Strong commitment to HSSE Standards and working as an integrated One-Team with full collaboration and a continuous and close communication between all the Team members are among the main Success Factors.Ī fast track 3D data cube is being produced at the time of writing this manuscript. The implementation of the seismic acquisition is successful by delivering high quality data on schedule and within the predetermined budget at the full satisfaction of all involved parties and stakeholders. ![]() The main objectives for the 3D OBN seismic survey are: proper imaging of structures, faults and fractures characterization, stratigraphy, petrophysical properties, Gas columns and clouds mitigation and fluid contacts in clastic reservoirs cited between1500-4700m depth. In 2022, an OBN seismic campaign was carried out aiming to resolve the imaging problems that exist in a legacy dataset. It poses great challenges on seismic processing and imaging. Shallow gas has been identified vastly distributed in this region via numerous site surveys, that creating slow-velocity and high-absorption dimming zone on the seismic imaging. Numerous hydrocarbon accumulations were discovered in highly faulted Pliocene-Pleistocene flower structures of different types. The Greater Cheleken Area (GCA) developed by Dragon Oil, situates on a complex NW-SE dextral transcurrent zone which separates the Northern from the Southern Caspian Basin (SCB).
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