Development of a Digital Twin for Well Integrity Management in Underground Gas Storage Fields

Busollo, Carlo; Mauro, Stefano; Nesci, Andrea; Scimmi, Leonardo Sabatino; Baronio, Emanuele

doi:10.2118/206252-ms

Cited by 4 publications

References 21 publications

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Digital Twin and Big Data Technologies Benefit Oilfield Management

Lai¹,

Zhang²,

Jiang³

et al. 2022

Day 3 Wed, November 02, 2022

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The oil & gas industry has been value added from our digital assets since this new century, which helped our industry dig out more advanced algorithm, more robust logic to address the challenge from HPHT wells and deep-water wells. Nowadays the operators are facing much more challenges in oilfield management especially how to improve their decision efficiency and situation awareness. Thanks to the different sensors we deployed on oilfield from drilling to completion and production, tremendous data contributed to the digital asset we are having now. The digital twin makes oilfield management much easier than ever before, hundreds of wells’ performance could be displayed in front of the decision maker or key management level of oil companies, and big data technique helps them get easy understanding of real time behavior on well construction progress, cost management, pain spot of each project. Combining these two methods, it is possible to have an up-to-date awareness of oilfield development status and perceptual intuition to very detail situations. There is a major operator manages over 200 wells per year and some of these wells are challenging exploration well with measured depth over 20000ft which requires experienced team to get the well to total depth, also a lot of shale gas wells with lateral intervals over 8000ft which demands intensive control of cost. All above operations or targets need be done under a safe and efficient way, then the management team taking digital twins to monitor the real time well status which help them get up to date information about whole oilfield status like drilling, completion, production and more. Big data analysis is also used to help enhance the decision- making efficiency and overcome puzzles that traditional method could not solved, like recommending the best practice way on well construction engineering parameters, or ROI (return on investment) assess. The oil company could achieve a better management level with less human resources and much more workload. By the advantages of digital twins and big data analysis, the oil company now managing more than 200 drilling rigs and 300 completion wells in the high efficiency way, and now involving the production wells into next phase digital construction target. Furthermore, considering develop an integrative digital twin of geology and engineering map which get whole formation and well construction more intuitive. Besides, it is proven that digital method like digital twins and big data technique could improve the skill of oilfield management significantly, which optimized the resource and expenditures investigated in modern oil and gas industry.

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Digital Twin and Big Data Technologies Benefit Oilfield Management

Lai¹,

Zhang²,

Jiang³

et al. 2022

Day 3 Wed, November 02, 2022

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Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells

Baronio

Abdo

Mauro

et al. 2023

SPE EuropEC - Europe Energy Conference Featured at the 84th EAGE Annual Conference &Amp; Exhibition

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Underground gas storage (UGS) are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. Today, with the advancement of continuous remote monitoring and digitalization, evaluating the integrity of UGS wells has become quicker and more efficient. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. UGS wells present additional criticalities with respect to normal production wells due to its longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for a safe management of the well barriers. The proposed digital twin has been developed using material and energy balances and considering each annulus as a separate control volume. Each control volume can exchange heat and mass through predesigned barriers. Simulating evolution in time of pressure and temperature in the control volumes., and comparing results with data from field, allows the identification of position and size of leaks. A genetic algorithm is applied to optimize placement of leaks on their specific barriers. The system aims to identify the position and dimension of possible leaks by matching historical pressure, temperature, and flow data. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The system has been in use for little over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcased a clear readiness for predictive capabilities aimed to select, plan and design fit for purpose mitigating actions. This paper highlights the power that a digital twin can present leveraging field data with advanced algorithms. The paper also showcases workflows that allow convenient, efficient, and timely evaluation of well integrity, which leads to safer operating conditions and lower operational costs.

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Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells

et al. 2023

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Summary Underground gas storage (UGS) wells are essential components in energy security. However, UGS wells present a complicated and delicate combination of elements where ensuring safe and secure functionality over long periods is paramount. This paper showcases how a digital twin is used to evaluate and forecast the link between leaks and temperature and pressure trends in a UGS well, allowing the identification and quantification of defects and, subsequently, well barrier integrity. The digital twin used for this application presents advantages compared with other solutions present on the market with regard to the simplified configuration; that is, with minimal input data, the system can produce an accurate and useful output, which is then used in the well integrity decision-making process. UGS wells present additional criticalities with respect to normal production wells due to their longer life span and the repetitive production and injection cycles. This makes early and accurate leak detection essential for the safe management of the well barriers. The proposed digital twin simulates the trends of pressure and temperature within each annulus and compares results with data from the field, allowing the identification of the position and size of leaks. A genetic algorithm is applied to optimize the placement of leaks on their specific barriers. Once a leak is identified, a risk assessment is conducted to evaluate the overall integrity of the well. If the status of the well is found to be critical enough, an intervention may be planned. The studies presented show how the digital twin has been used on two wells with similar problems. At first, it has confirmed the necessity to put the well out of service as opposed to planning maintenance, thereby saving both time and cost. In the second case, it allowed the validation of a solution that led to a 60% reduction in failure consequence, allowing the well to continue operating without major costs or risks. The errors of the resulting simulations were always confined within the 0.5 bar limit highlighting its accuracy. The system has been in use for over a year and has shown great potential in accurate and efficient identification of leaks. This has accelerated the process of well integrity evaluation and allowed timely interventions on wells that required it. On the other hand, the process has highlighted cases where previous assumptions about leak location and size were corrected using the digital twin, therefore reducing the costs of interventions. Finally, the model showcases a clear readiness for predictive capabilities aimed to select, plan, and design fit for purpose mitigating actions.

show abstract

Development of a Digital Twin for Well Integrity Management in Underground Gas Storage Fields

Cited by 4 publications

References 21 publications

Digital Twin and Big Data Technologies Benefit Oilfield Management

Digital Twin and Big Data Technologies Benefit Oilfield Management

Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells

Case Study of the Use of a Digital Twin for Leak Detection and Quantification in Underground Gas Storage Wells

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