Lessons Learned from Drilling Horizontal Wells on Didon Field; Offshore Tunisia

Jardine, Paul; Chaaouri, Mohsen; Guyard, Alain; DiPasquale, Maurizio

doi:10.2118/127707-ms

Cited by 2 publications

References 11 publications

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Drilling Optimisation of Extended Reach Multilateral Wells to Maximise Reservoir Contact in Carbonate

Verma

Rodríguez

Qasin

et al. 2017

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Energy consumption and demand are steadily increasing. Hydrocarbons have been an important energy provider for several decades, but production from mature oil and gas producers is declining. Great effort is put into improving oil and gas reservoir recovery to meet this rise in energy demand. In the subject reservoir where the pay zone is a thick, multi-layered limestone, characterized by low-permeability; conventional techniques yield lower than expected production results. To improve production and the ultimate recovery of the field, extended-reach drilling (ERD) wells with long horizontal multilaterals (Quad and Penta-laterals well types) were drilled to attain maximum reservoir contact (MRC), ranging from 10 to 14 Km. These wells equipped with intelligent completions, enable uniform contribution along the extended horizontal intervals. This contribution is achieved through better flow management of the different sections of reservoir contact, reducing operational drawdown pressures, and delaying gas and water breakthrough. The result is high well potentials, improved long-term performance of sweep and recovery, and increasing net worth of the drilling investment. This paper presents the lessons learned from hundreds of ERD multilateral wells drilled in the field, including integrated operations, progressive approaches and innovative applications, improved drilling practices on a continuous basis, and the tools and techniques used to drill and complete the wells safely and efficiently. These efforts achieved a milestone record rate of penetration (ROP) in the Middle East of 5,000 feet per day, and as a direct result contributed to minimizing well delivery time by 35 % and average 25% reduction in well cost in an always challenging drilling environment. The design approach, job execution and evaluation of drilling performance are presented in this paper; as well as key technical challenges and risks encountered during planning and execution stages and how these were mitigated and overcome for MRC improvement and optimization. Well construction was challenged to meet the complex multi-lateral with long cantilever sections. The MRC optimization schemes applied in the field resulted in dramatically reduced days of drilling operations that led to millions of dollars in project savings and the achievement of world class drilling records.

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Drilling Optimisation of Extended Reach Multilateral Wells to Maximise Reservoir Contact in Carbonate

Verma

Rodríguez

Qasin

et al. 2017

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Virtual Meter with Flow Pattern Recognition Using Deep Learning Neural Networks: Experiments and Analyses

Mercante,

Netto

2024

SPE Journal

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Summary Operators often require real-time measurement of fluid flow rates in each well of their fields, which allows better control of production. However, petroleum is a complex multiphase mixture composed of water, gas, oil, and other sediments, which makes its flow challenging to measure and monitor. A critical issue is how the liquid component interacts with the gaseous phase, also known as the flow pattern. For example, sometimes liquids can accumulate in the lower part of the pipeline and block the flow completely, causing a gas pressure buildup that can lead to unstable flow regimes or even accidents (blowouts). On the other hand, some flow patterns can also facilitate sediment deposition, leading to obstructions and reduced production. Thus, this work aims to show that deep neural networks can act as a virtual flowmeter (VFM) using only a history of production, pressure, and temperature telemetry, accurately estimating the flow of all fluids in real time. In addition, these networks can also use the same input data to detect and recognize flow patterns that can harm the regular operation of the wells, allowing greater control without requiring additional costs or the installation of any new equipment. To demonstrate the feasibility of this approach and provide data to train the neural networks, a water-air loop was constructed to resemble an oil well. This setup featured inclined and vertical transparent pipes to generate and observe different flow patterns and sensors to record temperature, pressure, and volumetric flow rates. The results show that deep neural networks achieved up to 98% accuracy in flow pattern prediction and 1% mean absolute prediction error (MAPE) in flow rates, highlighting the capability of this technique to provide crucial insights into the behavior of multiphase flow in risers and pipelines.

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Lessons Learned from Drilling Horizontal Wells on Didon Field; Offshore Tunisia

Cited by 2 publications

References 11 publications

Drilling Optimisation of Extended Reach Multilateral Wells to Maximise Reservoir Contact in Carbonate

Drilling Optimisation of Extended Reach Multilateral Wells to Maximise Reservoir Contact in Carbonate

Virtual Meter with Flow Pattern Recognition Using Deep Learning Neural Networks: Experiments and Analyses

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