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

Behounek, Michael; Ashok, Pradeepkumar

doi:10.2118/215132-ms

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…The case of Georgia serves as a compelling example of how AI can be strategically applied to overcome longstanding financial management challenges. Behounek and Ashok (2023) detail the successful deployment of an AI-based drilling advisory system across an operator's fleet, emphasizing the critical factors that contributed to its success. The case study outlines a strategic approach focusing on realistic long-term vision, careful tool and technique selection, targeted change management, and sustained management support.…”

Section: Case Studies: Successful Implementation Of Ai In Financementioning

confidence: 99%

Transforming Financial Planning With Ai-Driven Analysis: A Review and Application Insights

Lawrence Damilare Oyeniyi,

Chinonye Esther Ugochukwu,

Noluthando Zamanjomane Mhlongo

2024

Financ. account. res. j.

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In the ever-evolving tapestry of financial planning, the integration of Artificial Intelligence (AI) emerges as a pivotal force, redefining the contours of strategic decision-making and operational efficiency. This paper delves into the historical progression, current implementations, and the multifaceted impact of AI within the financial planning sphere, aiming to unravel the complexities and transformative potential of AI technologies. Through a rigorous examination of peer-reviewed literature and empirical studies, the research meticulously maps the trajectory of AI's integration in finance, from its nascent stages to its current stature as a cornerstone of financial innovation. The study's methodology, rooted in qualitative analysis, systematically explores the enhancements AI brings to financial decision-making, the challenges it poses, including ethical considerations and regulatory compliance, and the qualitative shifts in financial strategies engendered by AI adoption. The findings illuminate AI's dual role as both a catalyst for unprecedented efficiency and a harbinger of new challenges, underscoring the need for a balanced approach to its integration. Conclusively, the paper advocates for a harmonious blend of innovation and ethical stewardship, recommending that financial institutions embrace AI's potential while rigorously addressing its challenges through continuous learning, adaptability, and ethical vigilance. The recommendations aim to guide stakeholders through the labyrinth of AI integration, ensuring that financial planning becomes more efficient and strategic and remains equitable and transparent. This study serves as a beacon for future exploration, offering insights into navigating the complexities of AI-driven financial planning. Keywords: Artificial Intelligence, Financial Planning, Strategic Decision-Making, Ethical Considerations, Regulatory Compliance, Technological Innovation.

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Section: Case Studies: Successful Implementation Of Ai In Financementioning

confidence: 99%

Transforming Financial Planning With Ai-Driven Analysis: A Review and Application Insights

Lawrence Damilare Oyeniyi,

Chinonye Esther Ugochukwu,

Noluthando Zamanjomane Mhlongo

2024

Financ. account. res. j.

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Applying Artificial Intelligence to Optimize Engine Power Demand

Trueheart,

Koritala,

Stopkoski

2024

IADC/SPE International Drilling Conference and Exhibition

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The oil and gas industry faces pressure to reduce emissions and minimize fuel consumption to decrease the carbon footprint of drilling operations. Like other players in the sector, drilling contractors have made a commitment to achieving measurable improvements that reduce greenhouse gas (GHG) emissions through the application of new technologies. The challenge is in identifying areas of operation with the greatest potential to deliver efficiency gains and finding ways to realize the reductions. In evaluating rig operations, it became clear that optimizing engine usage has the potential to deliver considerable cost savings and at the same time improve drilling performance. Ideally, this would mean automating the process, but automating drilling processes is not straightforward. It requires in-depth knowledge of rig operations and an understanding of the interplay between the driller, who controls the drilling process, and the generators that power the rig. One solution is to use artificial intelligence (AI) to analyze operational data to capture drilling insights that reliably predict power usage, ensuring operations can be executed as planned and that power demand never exceeds power availability. With the introduction of AI on AC drilling rigs, data monitored by the electronic drilling recorder (EDR) can be analyzed to correlate rig activities with power usage, enabling improvements in drilling efficiency and power management. Using AI changes the paradigm, making it possible to proactively forecast and deliver the correct amount of power to the rig as demand changes throughout the drilling process. This paper delves into the application of AI and automation of the power generation system to demonstrate its impact in revolutionizing drilling operations by forecasting and delivering power needs to deliver efficient drilling programs that use less fuel, produce lower emissions, extend equipment service life, and make fewer demands on the driller and rig crew.

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Review of Stuck Pipe Prediction Methods and Future Directions

Montes,

Ashok,

van Oort

2024

SPE Annual Technical Conference and Exhibition

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Stuck pipe events continue to be a major cause of non-productive time (NPT) in well construction operations. Considerable efforts have been made in the past to construct prediction models and early warning systems to prevent them—a trend that has intensified in recent years with the increased accessibility of artificial intelligence tools. This paper presents a comprehensive review of existing models and early-warning systems and proposes guidelines for future improvements. In this paper, we review existing prediction approaches for their merits and shortcomings, investigating five key aspects: (1) the frequency and spatial bias of the data with which the models are constructed, (2) the selection of the variable space, (3) the modeling approach, (4) the assessment of the model's performance, and (5) the model's facility to provide intuitive and interpretable outputs. The analysis of these aspects is combined with advancements in anomaly detection across other relevant domains, such as the internet of things, to construct guidelines for improvement of real-time stuck pipe prediction. Existing solutions for stuck pipe prediction face numerous challenges, allowing this problem area to remain a missing component in the broad scope of progressive drilling automation. In our analysis, we looked at notable approaches, including decentralized sticking prediction, sophisticated data-driven models coupled with explanation tools, and data-driven models coupled with physics-based simulations (hybrid sticking predictors). However, even these sophisticated approaches face challenges associated with: general, non-specific applicability; robustness; and interpretability. While the best approaches tackle some of these challenges, they often fail to address all of them simultaneously. Furthermore, we found that there is no standardized method for assessing these models’ performance or for conducting comparative studies. This lack of standardization leads to an unclear ranking of (the merits and shortcomings of) existing prediction models. Lastly, we encountered cases where unavailable information, i.e., information that would not be available when the model is deployed in the field for actual stuck pipe prediction, was employed in the models’ construction phase (we will refer to this as "data leakage"). These findings, along with good practices in anomaly detection, are compiled in terms of guidelines for the construction of improved stuck pipe prediction approaches. This paper is the first to comprehensively analyze existing methods for stuck pipe prediction and provide guidelines for future improvement to arrive at more universally applicable, real-time, robust and interpretable stuck pipe prediction. Moreover, the application of these guidelines is not limited to stuck pipe, and can be used for predictive modeling of other types of drilling abnormalities, such as lost circulation, drilling dysfunctions, etc. Additionally, these guidelines can be leveraged in any drilling application, whether it is for oil and gas recovery, geothermal energy, carbon storage, etc.

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

Cited by 4 publications

References 16 publications

Transforming Financial Planning With Ai-Driven Analysis: A Review and Application Insights

Transforming Financial Planning With Ai-Driven Analysis: A Review and Application Insights

Applying Artificial Intelligence to Optimize Engine Power Demand

Review of Stuck Pipe Prediction Methods and Future Directions

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