Artificial lift system optimization using machine learning applications

Syed, Fahad I.; Alshamsi, Mohammed; Dahaghi, Amirmasoud Kalantari; Neghabhan, Shahin

doi:10.1016/j.petlm.2020.08.003

Cited by 48 publications

(16 citation statements)

References 5 publications

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“…In the area of production, ML models and its hybrids are mainly used for the monitoring, prediction, forecasting, selection and detection of various characteristics and components critical for optimal production. This includes, choke valve flow-rates, production at ultra-high water cut stages, precipitation of asphaltene, sand production, separator selection, faulty events, production forecasting and predictive maintenance [20][21][22][23][24][25][26][27][28][29].…”

Section: Ai In Oil and Gas Upstreammentioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

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The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PDF

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Section: Ai In Oil and Gas Upstreammentioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

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“…These selection criteria sometimes need an upgrade or replacement to keep up with the subsurface and surface condition resulting in loss of man-hour. Hence, modern technology such as IoT, AI, and ML can be used to improve operations abilities to make systematic decisions and forecast future occurrences based on past events and production trend [172]. Kandziora et al [173] used a unique AI-based application that allows the operator to prevent electrical submersible pump failure 12 days before the actual failure occurred and at the same time optimizing production.…”

Section: ) Artificial Lift Optimizationmentioning

confidence: 99%

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

et al. 2021

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The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012-2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored.INDEX TERMS Artificial intelligence (AI), cyber-physical systems, digital-twin, framework, oil and gas (O&G), industry revolution 4.0 (IR 4.0), industry 4.0 (I4.0), internet of things (IoT), simulation, upstream sector

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“…It is concluded that ESP is still a feasible method for deep distance of well even though gas lift is over ESP in the range but ESP is simpler and less space installation. [3] Figure 3. Comparison of Artificial Lift [5]…”

Section: Comparison Of Artificial Liftmentioning

confidence: 99%

“…In order to supply the high demand consumption of energy the well production must reach the oil target production by doing various feasible attempts. Various research has been done to enhance oil productivity [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Esp Performance Dn1800 to Increase Well Productivity

Anggara

2021

SJMEkinematika

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The use of the Electrical Submersible Pump (ESP) in the oil lifting method is very popular because it is easy to install, less required installation of tools in the field and a high efficiency. To achieve the Q target, ESP parameters such as the number of stages and RPM need to be analyzed to align with the IPR (Inflow Performance Flow) curve. The use of nodal analysis is used to determine the relationship between Pwf and head pump. Iteration needs to be done to determine the range of the number of stages so that it aligns with characteristics of well. It is found that the recommended range stage is 580-600 at a well depth of 7684 ft. Moreover, it is found that with 3600 RPM and 600 stages is able to reach the Q target. The relationship between the number of stages and RPM value with Pwf is inversely proportional.

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Artificial lift system optimization using machine learning applications

Cited by 48 publications

References 5 publications

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

Evaluation of Esp Performance Dn1800 to Increase Well Productivity

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