Closed-Loop Data Analytics for Wells Construction Management in Real Time Centre

Developing artificial intelligence (AI)-based drilling advisory software is generally straightforward when good quality labeled data are available. However, deploying such systems in the field for use by a rig crew requires careful planning and execution and often fails to provide the value proposed. It is statistically estimated that most AI projects fail, and that most companies that trial AI solutions report minimal to no impact from AI. This paper details the successful deployment and ongoing success of an AI-based drilling advisory system on rigs across an Operator's fleet, as well as deployment decisions that helped make it a high-value, sustainable, and successful program. The Operator developing and deploying the AI system focused on five main aspects to anchor this project: setting a realistic long-term vision for automation, choosing the right tools and techniques, implementing a targeted change management plan, careful selection of team members, and planning for sustained management support. For the longer-term automation vision, decisions on where to deploy the AI models - at the rig or managed from the central office, what parts of the solution to develop in-house or out-source to achieve cost objectives, and how soon to scale AI to all rigs in the fleet were key. Finally, a thoughtful change management plan was implemented taking into consideration the company culture and industry best practices. The project launched in 2015, with the decision to deploy the AI models at the edge/rig site with an ability to push updates from a remote, central support group as needed. The AI model platform was outsourced; AI models were developed /validated one model at a time, and then deployed to all the rigs as soon as possible. The platform and models were modularized to enable rapid prototyping, field deployment, and iterative change. A key Program Sponsor along with other Stakeholders were identified for each rig, and carefully managed to ensure ongoing support, successful adoption, and regular feedback. Transparency on how the model performed calculations was shared readily to ensure acceptance of the results by the drilling engineers and the rig site crew. An agile development and deployment cycle was adopted to maintain rig crew interest to continuously use and improve the system. Over the past eight (8) years, more than ten (10) AI models have been added incrementally to the rig-based system, which has enabled a 10% improvement in drilling performance year over year. This paper details the decisions and processes that resulted in the successful deployment of an AI-based drilling advisory system for rigs in North America and Europe. The learning and insights from this multi-year (8 years and ongoing) deployment should provide valuable insights to those planning to deploy AI software at scale, at the edge.

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Enhancing Stuck Pipe Risk Detection in Exploration Wells Using Machine Learning Based Tools: A Gulf of Mexico Case Study

Gomes,

Jaritz,

Robinson

et al. 2024

IADC/SPE International Drilling Conference and Exhibition

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We present a case study on the utilization of a machine learning (ML)-based computational tool for detecting stuck pipe risks early in live operations. The system was used in two Gulf of Mexico (GoM) wildcat exploration wells. The risk detection approach is based on a novel technology using physics-informed machine learning models to analyze real-time data and detect potential stuck pipe incidents in live operations. The ML models were pre-trained on a variety of wells from different fields. The system was designed for out-of-the-box usage, which supports operational monitoring for exploration wells without pre-training on offset well data. The methodology and the process of integrating the computational tool into live operations, and the flow of data between the tool and the drilling operation is described. Additionally, the paper delves into drilling practices that helped to prevent stuck pipe and examine specific incidents that were unavoidable. The application ran stably throughout the operations, with high uptime and few false warnings in both wells; on average, fewer than one false alert was observed per day of operations. The pre-trained models proved effective, requiring no additional training; this generalizability is an important prerequisite for utility when applied to exploration wells, where offset data may be unavailable. However, due to lack of personnel to follow up the system's outputs in real-time, the benefits were limited. The first well was drilled without stuck pipe incidents. Some sticking risk symptoms were identified during the operation, especially in a fault zone. The post-well analysis indicates that good drilling practices were enough to mitigate the risks. The drilling practices responsible for the success of the operation will be discussed. In the second well, there were stuck pipe incidents. The application provided some indications of stuck symptoms but with some limitations for how far in advance the risk could be detected. The causes of the stuck incidents, the challenges in avoiding them, and updates to the risk detection system for identifying these, will be explored. Based on the experience described in the paper, the authors will offer recommendations for optimal technology utilization both from the application's and organizational perspectives.

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Closed-Loop Data Analytics for Wells Construction Management in Real Time Centre

Cited by 4 publications

References 3 publications

Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements

Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements

The Secrets to Successful Deployment of AI Drilling Advisory Systems at a Rig Site: A Case Study

Enhancing Stuck Pipe Risk Detection in Exploration Wells Using Machine Learning Based Tools: A Gulf of Mexico Case Study

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