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Abstract

Hydrocarbon wells are the most critical and challenging asset in any oil and gas field development and operation plan. The quality of the history matched reservoir simulation model and reliability of future field performance forecasts depend heavily on the accuracy of the well model. In typical reservoir simulation studies, a tremendous amount of time is devoted to gather and validate data required to construct the simulation model, particularly well related data, including well trajectories, completions, production and injection rates, well logs and downhole flow control devices. This issue can become more challenging when thousands of wells are involved with multiple configurations and complex completions. Therefore, it is critical to ensure the quality and accuracy of well data to have consistent, comprehensive and reliable reservoir models that can be used to forecast reservoir performance. In this paper, an advanced system for extracting, validating and pre-processing complex well information from the corporate database to perform well modeling and simulation is presented. The paper demonstrates how this system ensures the validity and accuracy of well models by applying advanced quality control measures with strong capabilities for detecting data inconsistencies. The paper starts with a description of the system and how the pre-processed wells contribute in building an integrated environment to serve complex well modeling. The paper demonstrates that the quality control process leads to an automated, efficient and easy well data processing procedure with a significant degree of reliability. In addition, real cases and lessons learned from this experience are discussed. The system implements new design and algorithms while dealing with a massive amount of data gathered from giant onshore and offshore oil and gas fields. This paper shows how Saudi Aramco applies creative solutions for the best utilization of the Upstream corporate data to support decision making, increase productivity and save costs.

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/content/papers/10.3997/2214-4609-pdb.395.IPTC-17541-MS
2014-01-19
2021-10-24
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.395.IPTC-17541-MS
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