Application of artificial intelligence and machine learning in drilling optimization (Record no. 197799)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03057nam a2200217 4500 |
| 008 - CÓDIGOS DE INFORMACIÓN DE LONGITUD FIJA - INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 260224s2025 xxu esp |
| 041 ## - IDIOMA | |
| Idioma | Español |
| 245 00 - TITULO | |
| Título | Application of artificial intelligence and machine learning in drilling optimization |
| 260 ## - PUBLICACION, DISTRIBUCION, ETC | |
| Lugar de publicación, distribución, etc. | |
| Nombre de publicador, distribuidor, etc. | |
| Fecha de publicación, distribución, etc. | 2025 |
| 270 ## - FECHA DE CARGA | |
| Fecha de carga | 15/10/2025 ; 15/10/2025 |
| 300 ## - DESCRIPCION FISICA | |
| Otra extensión | 11 p. |
| 520 ## - RESUMEN, ETC | |
| Resumen | The oil and gas drilling industry continues to evolve rapidly, driven by the integration of artificial intelligence (AI) and machine learning (ML) to optimize performance, enhance safety, and reduce costs. This paper presents a detailed examination of AI/ ML applications in four pivotal domains of drilling engineering: real-time parameter optimization, predictive equipment maintenance, autonomous geosteering, and proactive safety management. In drilling parameter optimization, advanced AI models analyze high-frequency data streams weight on bit (WOB), rotary speed (RPM), mud properties, and formation characteristics to dynamically adjust operations, achieving ROP improvements of up to 30% and torque reductions exceeding 25% in recent field deployments, surpassing traditional empirical methods. Predictive maintenance has matured with AI-driven systems processing vibration, temperature, and pressure data from critical rig components like top drives and pumps, cutting unplanned downtime by 40% in offshore operations, as evidenced by 2024 trials in the North Sea. For geosteering, cutting-edge ML algorithms, including deep reinforcement learning and real-time Bayesian updating, autonomously steer wells with precision, improving reservoir exposure by 15–20% over manual methods, as demonstrated in Permian Basin case studies from late 2024. Safety advancements are equally compelling, with AI systems now detecting subtle precursors to hazards such as kicks, lost circulation, and stuck pipe up to 60 minutes earlier than conventional monitoring, reducing incident rates by 35% in Gulf of Mexico operations last year. These applications rely on robust methodologies: integrating vast datasets from IoT-enabled rigs, training sophisticated models (e.g., neural networks and hybrid physics-ML approaches), and deploying them in real-time control loops. Field results from 2023–2025 underscore the impact operators report cost savings of 10–15% per well, alongside safer and faster drilling campaigns. This paper concludes that AI and ML are no longer supplementary but foundational to modern drilling, enabling a shift toward fully autonomous, data-driven operations that redefine industry benchmarks for efficiency and risk management. |
| 773 ## - CORRECCIÓN | |
| Partes relacionadas | |
| 942 ## - DESC. DE MATERIAL | |
| Tipo de item KOHA | Congresos (trabajos presentados) |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Beal, Vinicius |
| 9 (RLIN) | 63012 |
| 100 1# - RESPONSABLE PERSONAL | |
| Apellido, Nombre | Aghdam, Sohail Rashidi |
| 9 (RLIN) | 63013 |
| 111 2# - AUTORIDAD DE CONGRESO | |
| Nombre de congreso | Congreso Latinoamericano de Perforación, Terminación e Intervención de Pozos (5to. : 2025 sept. 8-11 : Buenos Aires) |
| 9 (RLIN) | 62910 |
| 856 ## - LINK | |
| Link | <a href="https://biblioteca.iapg.org.ar/ArchivosAdjuntos/5toCongresoLatPerfTerInter2025/AppArtificial.pdf">https://biblioteca.iapg.org.ar/ArchivosAdjuntos/5toCongresoLatPerfTerInter2025/AppArtificial.pdf</a> |
| Biblioteca propietaria | Biblioteca actual | Fecha de adquisición | Inventario | Total de préstamos | Inventario | Fecha de carga | Tipo de item KOHA |
|---|---|---|---|---|---|---|---|
| IAPG-Colección | IAPG-Colección | 06/03/2026 | 200070242 | 200070242 | 06/03/2026 | Congresos (trabajos presentados) |
