Characteristics and Contributions of Noise Generated by Mechanical Cutting During Conductor Removal Operations; Volume 3: Appendices B to F

Publication Title: Characteristics and Contributions of Noise Generated by Mechanical Cutting During Conductor Removal Operations; Volume 3: Appendices B to F

Subject: Anthropogenic Properties
Marine Mammals
Point Arguello Unit Platforms; Platform Hermosa; Santa Barbara County; California; oil and gas platform; Pacific Outer Continental Shelf; passive acoustics; particle motion; marine mammal; field measurements; conductor removal

CSE Reference: Fowler K,Pellerin T,Zoidis A (University of New Hampshire, Durham, NH). 2022. Characteristics and contributions of noise generated by mechanical cutting during conductor removal operations; Volume 3: Appendices B to F. Camarillo (CA): Department of the Interior, Bureau of Ocean Energy Management. 252 p. Obligation No.: Characteristics and contributions of noise generated by abrasive cutting during conductor-removal operations. Report No.: OCS Study BOEM 2022-029. URL: https://espis.boem.gov/Final%20Reports/BOEM_2022-029-Vol3.pdf

Abstract: Oil and gas production platforms in federal waters offshore southern California have been identified for decommissioning and the removal of the platform conductors will be accomplished using mechanical cutting. In particular, this study focuses on mechanical cutting activities at Platform Hermosa, which is one of the Point Arguello Unit Platforms. This study was conducted to analyze the sound pressure and particle motion levels associated with those mechanical cutting activities as well as evaluate the presence and contribution of marine mammals to the soundscape. A field program was conducted in support of this study and results showed that mechanical cutting noise contributed to the underwater acoustic environment, but below applicable marine mammal acoustic threshold criteria. A semi-automated review of the data from one recorder was conducted to document periods of vocalizing marine mammals. Optimization of the detection of marine mammal calls was explored using soundscape metrics and a deep neural network was also investigated.