Automatic Seismic Salt Interpretation with Deep Convolutional Neural Networks

One of the most crucial tasks in seismic reflection imaging is to identify the salt bodies with high precision. Traditionally, this is accomplished by visually picking the salt/sediment boundaries, which requires a great amount of manual work and may introduce systematic bias. With recent progress of deep learning algorithm and growing computational power, a great deal of efforts have been made to replace human effort with machine power in salt body interpretation. Currently, the method of Convolutional neural networks (CNN) is revolutionizing the computer vision field and has been a hot topic in the image analysis. In this paper, the benefits of CNN-based classification are demonstrated by using a state-of-art network structure U-Net, along with the residual learning framework ResNet, to delineate salt body with high precision. Network adjustments, including the Exponential Linear Units (ELU) activation function, the Lov\'{a}sz-Softmax loss function, and stratified $K$-fold cross-validation, have been deployed to further improve the prediction accuracy. The preliminary result using SEG Advanced Modeling (SEAM) data shows good agreement between the predicted salt body and manually interpreted salt body, especially in areas with weak reflections. This indicates the great potential of applying CNN for salt-related interpretations.Comment: 11 pages, 7 figure

Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration

Public education and outreach leads to a better informed public on Puget Sound and watershed issues. Using beach life and spawning salmon as a way to share knowledge and start the conservation conversation, the Beach Naturalist and Cedar River Salmon Journey programs have been educating Puget Sound residents for over 15 years. These programs benefit two audiences: the volunteers who serve in the program and the public who participate. Volunteers are provided in-depth information about Puget Sound life, watersheds, salmon and conservation strategies. These passionate volunteers translate this information and share it with the public they engage in the environments we hope to protect: at local beaches in the nearshore, the Chittenden Locks along salmonid migratory routes and at salmon spawning locations along the Cedar River. By providing opportunities for the public to learn more and create personal connections with the animals and habitat we share, we suggest choices people make in their daily lives that can help protect the watershed

Evaluation of CO2 storage potential in offshore strata, mid-south Atlantic: Southeast Offshore Storage Resource Assessment (SOSRA)

Subsurface geological storage of CO2 has the potential to significantly offset greenhouse gas emissions for safe, economic, and acceptable public use of fossil fuels. Due to legal advantages and vast resource capacity, offshore CO2 storage provides an attractive alternative to onshore options. Although offshore Lower Cretaceous and Upper Jurassic reservoirs have a vast expected storage capacity, quantitative assessment of the offshore storage resource in the southeastern United States is limited. This work is a part of the Southeast Offshore Storage Resource Assessment (SOSRA) project, which presents quantitative evaluation of a high-quality potential geological repository for CO2 in the Mid- and South Atlantic Planning Areas. This is the first comprehensive investigation and quantitative assessment of CO2 storage potential for the outer continental shelf within the Lower Cretaceous and Upper Jurassic rocks, including the Southeast Georgia Embayment and most of the Blake Plateau. An interpretation of 200,000 km of legacy industrial 2D seismic reflection profiles and geophysical well logs (TRANSCO 1005-1, COST GE-1, and EXXON 564-1) are utilized to create structure and thickness maps for the potential reservoirs and seals. Three target reservoirs isolated by seals based on their effective porosity values are identified and assessed. A quantitative evaluation of CO2 Storage Potential in the Offshore Atlantic Lower Cretaceous and Upper Jurassic Strata is calculated using the DOE-NETL equation for saline formations. The prospective storage resources evaluation ranges between 450 and 4700 Mt of CO2 within the Lower Cretaceous and between 500 and 5710 Mt within the Upper Jurassic sandstone rocks at P10 to P90. The efficiency factor of the dolomite ranges from 0.64 to 5.36 percent at P10 to P90 for the formation scale. Facies classification of five offshore wells in the Southeast Georgia Embayment was applied to the Machine Learning approach using Support Vector Classifier (SVC) and Random Forest Classifier (RFC). As a result, the SVC and RFC algorithms were compared to evaluate facies classification accuracy; the RFC had the most accurate and effectively used outcomes to classify lithofacies. The Machine Learning approach resulted in reliable and accurate values of predicted facies classification to improve CO2 storage estimation