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snippet: Merged production and non-production for the U.S. Gulf Region, only using fields from non-production wells. The Advanced Infrastructure Integrity Model (AIIM) is a foundational, pre-trained artificial intelligence (AI) developed to help evaluate the integrity of offshore energy infrastructure including pipelines, platforms, wells in the U.S Gulf. Using multiple, multivariate machine learning models, AIIM analyzes complex data systems to deliver predictive insights for hazard prevention, life extension, and infrastructure reuse. Because it is built as a foundational model, users can leverage its AI-ready data and pre-trained framework as a starting point, integrating their own proprietary or localized datasets to further train and customize the model for their specific operational needs.
summary: Merged production and non-production for the U.S. Gulf Region, only using fields from non-production wells. The Advanced Infrastructure Integrity Model (AIIM) is a foundational, pre-trained artificial intelligence (AI) developed to help evaluate the integrity of offshore energy infrastructure including pipelines, platforms, wells in the U.S Gulf. Using multiple, multivariate machine learning models, AIIM analyzes complex data systems to deliver predictive insights for hazard prevention, life extension, and infrastructure reuse. Because it is built as a foundational model, users can leverage its AI-ready data and pre-trained framework as a starting point, integrating their own proprietary or localized datasets to further train and customize the model for their specific operational needs.
extent: [[-97.7339994116951,24.421810984276],[-80.3964636464254,30.4742069846854]]
accessInformation: Schooley, C., Romeo, L., Zaengle, D., Pfander, I., Duran, R., Sharma, M., Bauer, J., Rose, K., (2026) Advanced Infrastructure Integrity Modeling Dashboard, https://edx.netl.doe.gov/dataset/offshore-aiim-dashboard
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typeKeywords: ["ArcGIS","ArcGIS Server","Data","Feature Access","Feature Service","Hosted Service","Metadata","providerSDS","Service"]
description: <p><font color='#2f4f4f' size='3'><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'><span style='margin:0px; padding:0px;'>Series of well or wellbore records by surface hole (location of well or wellbore at seafloor) or bottomhole (location of the deepest point of the wellbore in the subsurface) for production wells and other wells. Production wells are defined as wells or wellbores the record of which has a type labeled as </span><span style='margin:0px; padding:0px;'>oil, gas, production, or hydrocarbon, or a production record exists that is associated with the well or wellbore record. </span><span style='margin:0px; padding:0px;'>Other wells are defined as wells or wellbores the record of which has a type labeled does not include oil, gas, production, or hydrocarbon. Additionally, other wells do not have an associated production record. </span></span><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif;'> </span></font></p><div style='margin:0px; padding:0px; overflow:visible; clear:both; font-family:&quot;Segoe UI&quot;, &quot;Segoe UI Web&quot;, Arial, Verdana, sans-serif;'><p style='margin:16px 0px 0px; padding:0px; font-kerning:none; background-color:transparent;'><font color='#2f4f4f' size='3'><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'>Dataset Citations:</span></font></p><p style='margin:16px 0px 0px; padding:0px; font-kerning:none; background-color:transparent;'><font color='#2f4f4f' size='3'><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'>Romeo, L., Schooley, C., Sharma, M., and J. Bauer. </span><span style='margin:0px; padding:0px; font-style:italic; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'>In Review.</span><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'><span style='margin:0px; padding:0px;'> </span><span style='margin:0px; padding:0px;'>U.S. Gulf Wellbore Dataset. </span></span><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif;'> </span></font></p></div><div style='margin:0px; padding:0px; overflow:visible; clear:both; font-family:&quot;Segoe UI&quot;, &quot;Segoe UI Web&quot;, Arial, Verdana, sans-serif;'><p style='margin:16px 0px 0px; padding:0px; font-kerning:none; background-color:transparent;'><font color='#2f4f4f' size='3'><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'><span style='margin:0px; padding:0px;'>Pfander</span><span style='margin:0px; padding:0px;'>, P.</span><span style='margin:0px; padding:0px;'>, Romeo</span><span style='margin:0px; padding:0px;'>, L., </span><span style='margin:0px; padding:0px;'>Sabbatino</span><span style='margin:0px; padding:0px;'>, M., </span><span style='margin:0px; padding:0px;'>Tetteh</span><span style='margin:0px; padding:0px;'>, D.A.</span><span style='margin:0px; padding:0px;'>, Cleaveland</span><span style='margin:0px; padding:0px;'>, C., </span><span style='margin:0px; padding:0px;'>Rich</span><span style='margin:0px; padding:0px;'>, M., </span><span style='margin:0px; padding:0px;'>Nelson</span><span style='margin:0px; padding:0px;'>, C., </span><span style='margin:0px; padding:0px;'>Sharma</span><span style='margin:0px; padding:0px;'>, M., </span><span style='margin:0px; padding:0px;'>Amrine</span><span style='margin:0px; padding:0px;'>, D.C., </span><span style='margin:0px; padding:0px;'>Bauer</span><span style='margin:0px; padding:0px;'>, J., and K. Rose. (2026).</span><span style='margin:0px; padding:0px;'> WELLS Database, </span></span><a href='https://edx.netl.doe.gov/dataset/wells_database' style='margin:0px; padding:0px; text-decoration:none;' target='_blank'><span style='margin:0px; padding:0px; text-decoration:underline; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'><span style='margin:0px; padding:0px;'>https://edx.netl.doe.gov/dataset/wells_database</span></span></a><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif; font-variant-ligatures:none !important;'><span style='margin:0px; padding:0px;'>,</span><span style='margin:0px; padding:0px;'> </span><span style='margin:0px; padding:0px;'>DOI:10.18141/1964068</span></span><span style='margin:0px; padding:0px; line-height:14px; font-family:Calibri, Calibri_EmbeddedFont, Calibri_MSFontService, sans-serif;'> </span></font></p></div><p>The AIIM Dashboard provides a user-friendly interface to view, access, and query predictive data generated by the Advanced Infrastructure Integrity Model (AIIM). AIIM is a foundational, pre-trained AI model developed to evaluate the physical integrity of offshore energy infrastructure including pipelines, platforms, and wells in the U.S. Gulf.</p><p>As many offshore structures approach or exceed their original design life, understanding their structural health is critical. This application allows users – including researchers, regulators, and industry - to interact directly with AIIM’s data and predicted asset lifespan, helping them identify opportunities for infrastructure reuse, plan hazard prevention strategies, and target areas for remediation.</p><p>To forecast remaining operational lifespan of offshore oil and gas infrastructure assets, the underlying AIIM model conducts a comprehensive, multivariate machine learning assessment. The application allows users to query and visualize how various factors impact structural integrity, including:</p><p>• Operational Wear-and-Tear: Daily operational stressors.</p><p>• Environmental Loadings: Short- and long-term effects from waves, storms, and ocean currents.</p><p>• Geohazards: Specific environmental risks, such as submarine landslide likelihood.</p><p>• Asset Characteristics: Structural age, materials, and platform or well types.</p><p>• Incident Reports: Historical incident event data.</p><p>A recipient of the 2022 TechConnect National Innovation Award recipient, NETL’s AIIM framework bridges the gap between advanced machine learning and practical, strategic decision-making. More information on the method and applications of AIIM can be found at https://edx.netl.doe.gov/offshore/portfolio-items/assessing-current-and-future-infrastructure-hazards. </p>
licenseInfo: <div><b>Disclaimer: </b></div><div>This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. </div><div><b><br /></b></div><div><b>Acknowledgement:  </b></div><div>This work was performed in support of the U.S. Department of Energy's Hydrocarbon and Geothermal Energy Office Energy Production Innovations field work proposal, FE-1025020, and executed through the National Energy Technology Laboratory (NETL) Research &amp; Innovation Center.</div><div><b><br /></b></div><div><b>Licensing:</b> </div><div>Creative Commons Attribution (For more information see: https://opendefinition.org/licenses/cc-by/) </div><div><b><br /></b></div><div><b>Citation:</b> </div><div>Schooley, C., Romeo, L., Zaengle, D., Pfander, I., Duran, R., Sharma, M., Bauer, J., Rose, K., (2026) Advanced Infrastructure Integrity Modeling Dashboard, https://edx.netl.doe.gov/dataset/offshore-aiim-dashboard</div><div><b><br /></b></div><div><b>Support: </b></div><div>Contact edxspatial@netl.doe.gov for support.  </div><div><br /></div>
catalogPath:
title: All_Wells_Simplified_0426_forApp
type: Feature Service
url:
tags: ["AIIM","Offshore","Wells","Production","Infrastructure"]
culture: en-US
portalUrl:
name: All_Wells_Simplified_0426_forApp
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spatialReference: WGS_1984_Web_Mercator_Auxiliary_Sphere