Artificial Intelligence Driving Automation to Enhance Drilling Operations—First Deployment Offshore Africa Case Study

Abstract Artificial-intelligence (AI) driven systems are becoming the new standard in the drilling domain. This is the first field application performed offshore in Africa with the main objective of enhancing drilling operational efficiency and bottomhole assembly (BHA) integrity by advising the optimal drilling parameters, operational procedures, and drilling dysfunction mitigations. This smart system, driven by AI, seamlessly integrates the drilling operations planned engineering and procedures with the real-time operational data to monitor and control the operations execution using a data-driven approach. By design, this system enables the coordination and integration of the data and teams through a digitally orchestrated process, while the AI is primarily in control of the operations. The system's current and next steps can be easily visualized and overridden by the driller if and when needed. For this first application in Africa, close coordination and collaboration between the operator, the technology and service provider, and the rig contractor was a key element from the early stages of the project. A rig survey determined the most appropriate locations for the required equipment on the rig. Once the system was deployed and commissioned, the calibration was performed on the top-hole drilled sections, while the driller received hands-on training with the utilization of the system. The system seamlessly loaded the digital drilling program and recalibrated the operational parameters, which included the rate of penetration (ROP), revolutions per minute, weight on bit (WOB), and flow rates, using the real-time data. At the time of writing, a total of three wells, each with three sections were drilled using this application, resulting in continuous ROP improvement up to 2.5X factor on the last well. The combination of science-based models with real-time data running on the edge device at the rig enabled a step-change in the way AI-driven systems can drive automation, thereby enhancing the drilling operations. In this paper we will briefly describe the reasoning from the operator the decision to continue with this digital transformation of the operations process, and the technical requirements and process for the first successful deployment in Africa, with an overview of the results achieved. Moreover, we will also describe a novel workflow for a fully autonomous operation that combines several smart applications enabling a seamless digital collaboration between the office and the rig.

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Publication Details

Published
2024-11-04
DOI
https://doi.org/10.2118/222475-ms
Citations
2
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
0.67
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article

Artificial Intelligence Driving Automation to Enhance Drilling Operations—First Deployment Offshore Africa Case Study

Elpidio Gravante, Nadjib Mouzali, Alessio Medea, Simone Borra et al.
2 citations
Reservoir Engineering and Simulation Methods
0.67
article

Artificial Intelligence Driving Automation to Enhance Drilling Operations—First Deployment Offshore Africa Case Study

Elpidio Gravante, Nadjib Mouzali, Alessio Medea, Simone Borra, Waldemar Szemat-Vielma, Espen Botnan, L. P. Bianchini, A. F. Zarra, M. Banjo
article en
2 citations

Abstract

Abstract Artificial-intelligence (AI) driven systems are becoming the new standard in the drilling domain. This is the first field application performed offshore in Africa with the main objective of enhancing drilling operational efficiency and bottomhole assembly (BHA) integrity by advising the optimal drilling parameters, operational procedures, and drilling dysfunction mitigations. This smart system, driven by AI, seamlessly integrates the drilling operations planned engineering and procedures with the real-time operational data to monitor and control the operations execution using a data-driven approach. By design, this system enables the coordination and integration of the data and teams through a digitally orchestrated process, while the AI is primarily in control of the operations. The system's current and next steps can be easily visualized and overridden by the driller if and when needed. For this first application in Africa, close coordination and collaboration between the operator, the technology and service provider, and the rig contractor was a key element from the early stages of the project. A rig survey determined the most appropriate locations for the required equipment on the rig. Once the system was deployed and commissioned, the calibration was performed on the top-hole drilled sections, while the driller received hands-on training with the utilization of the system. The system seamlessly loaded the digital drilling program and recalibrated the operational parameters, which included the rate of penetration (ROP), revolutions per minute, weight on bit (WOB), and flow rates, using the real-time data. At the time of writing, a total of three wells, each with three sections were drilled using this application, resulting in continuous ROP improvement up to 2.5X factor on the last well. The combination of science-based models with real-time data running on the edge device at the rig enabled a step-change in the way AI-driven systems can drive automation, thereby enhancing the drilling operations. In this paper we will briefly describe the reasoning from the operator the decision to continue with this digital transformation of the operations process, and the technical requirements and process for the first successful deployment in Africa, with an overview of the results achieved. Moreover, we will also describe a novel workflow for a fully autonomous operation that combines several smart applications enabling a seamless digital collaboration between the office and the rig.

Eni (Italy) (IT), Clínica Sagrada Esperança (AO)
Openalex Percentile: Top 30%
Reservoir Engineering and Simulation Methods
0.67
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