Smart Well Testing Equipment: Product Innovations in Ai-Driven and Automated Testing Tools
Keywords:
Automated well testing, smart well testing, wellhead sensors, reservoir performance optimizationAbstract
The oil and gas industry are incorporating AI and automation in healthy, well-testing equipment since it assures increased efficiency, safety, and accurate measurements. These developments have culminated in the enactment of absolutely computerized systems whereby human interface with high-risk conditions is minimized. These systems encompass real-time data analysis and machine learning algorithms to fine-tune testing parameters for accurate reservoir descriptions. The system also enhances decision-making processes. Others include automatic well control modules and enhanced separators that control fluids’ motion for improved measurement and minimized emissions. AI is not just limited to automation but also offers characteristics such as the ability to predict the health of the equipment and the possible failures in its performance, thereby significantly reducing downtime. The combination of AI with sophisticated sensors provides substantial data acquisition that assists operators in decision-making as regards well location and manufacturing tactics. These innovations help optimize economic gains, but they also achieve the environmental purpose of eliminating the carbon emissions that the usual testing methods create.
References
Ahmed, T. (2019). Reservoir engineering handbook. Gulf Professional Publishing.
Anderson, R. (2020). Smart well testing equipment: A new era in reservoir management. Hydrocarbon Engineering, 27(5), 17-24.
Brown, D. (2021). Optimizing reservoir management with smart technologies. Petroleum Technology Journal, 39(1), 22-31.
Brown, P., & Liu, X. (2020). Monitoring well performance: Best practices and equipment. Oilfield Journal, 34(2), 123-135.
Boyun, G., Willhite, G. P., & Chenevert, M. E. (2013). Petroleum production systems. Prentice Hall.
Cohen, A., Karasaki, K., Benson, S., Bodvarsson, G., Freifeld, B., Benito, P., Cook, P., Clyde, J., Grossenbacher, K., Peterson, J., Solbau, R., Thapa, B., Vasco, D., & Zawislanski, P. Hydrogeologic characterization of fractured rock formations: A guide for groundwater remediators.
Economides, M. J., Oligney, R. E., & Valko, P. P. (2012). Unified fracture design. Gulf Publishing Company.
EnergyTech Insights. (2019). The impact of smart well testing on cost reduction and productivity improvement in the oil and gas sector. Retrieved from https://www.energytechinsights.com/smart-well-testing
Global Energy Systems. (2020). Advanced data acquisition systems in oil and gas production. International Journal of Petroleum Engineering, 28(1), 75-90.
Johnson, R., & Lee, H. (2019). Challenges in reservoir management and decision-making. Journal of Oil and Gas Technology, 12(4), 215-228.
Jing, J., Shan, H., Zhu, X., Huangpu, Y., & Tian, Y. (2022). Wellbore temperature and pressure calculation of offshore gas well based on gas–liquid separated flow model. Processes, 10(10), 2043. https://doi.org/10.3390/pr10102043
Jones, L. (2020). Digital innovations in oil well testing. Energy Insights, 10(3), 45-50.
Jones, M., & Smith, J. (2018). Automation and its impact on oilfield testing accuracy. Petroleum Engineering Journal, 22(3), 145-159.
Lee, H. (2020). The role of predictive analytics in well testing. Journal of Petroleum Data Science, 25(3), 55-60.
Nind, T. (2017). Well testing in oil and gas industry. Springer.
Oil & Gas Review. (2022). Smart well testing in multi-zone reservoirs: Enhancing efficiency without full well closure. Oil & Gas Review, 45(1), 92-108.
PetroTech. (2021). Understanding well performance through testing: Techniques and tools. PetroTech Research Journal, 39(6), 309-320.
Roberts, D., Evans, M., & Foster, G. (2020). Innovative testing methods in oil and gas production. Journal of Petroleum Science, 33(5), 178-190.
Rodriguez, A., & Nelson, T. (2019). Wireless telemetry and cloud analytics in oilfield testing. Journal of Digital Petroleum, 27(2), 211-225.
Sabet, M. A. (2021). Oil well testing handbook. Gulf Professional Publishing.
Singh, K., Sharma, P., & Verma, A. (2021). Improving recovery ratios through advanced well testing equipment. Energy Science and Technology, 18(3), 89-102.
TechOil Journal. (2021). Flow rate monitoring and fluid composition analysis in smart wells. TechOil Journal, 47(8), 54-67.
Tarek, A. (2020). Advanced well completion engineering. Gulf Professional Publishing.
STEM fields. International Journal of All Research Education and Scientific Methods, 11(08), 2090-2100.
Rahman, M. A. (2024). Optimization of Design Parameters for Improved Buoy Reliability in Wave Energy Converter Systems. Journal of Engineering Research and Reports, 26(7), 334-346.
Rahman, M. A., Uddin, M. M., & Kabir, L. (2024). Experimental Investigation of Void Coalescence in XTral-728 Plate Containing Three-Void Cluster. European Journal of Engineering and Technology Research, 9(1), 60-65.
Rahman, M. A. (2024). Enhancing Reliability in Shell and Tube Heat Exchangers: Establishing Plugging Criteria for Tube Wall Loss and Estimating Remaining Useful Life. Journal of Failure Analysis and Prevention, 1-13.
Murali, S. L. ADVANCED RRAM AND FUTURE OF MEMORY.
Chen, Y., & Li, C. (2017, November). Gm-net: Learning features with more efficiency. In 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) (pp. 382-387). IEEE.
Elemam, S. M., & Saide, A. (2023). A Critical Perspective on Education Across Cultural Differences. Research in Education and Rehabilitation, 6(2), 166-174.
Rout, L., Chen, Y., Kumar, A., Caramanis, C., Shakkottai, S., & Chu, W. S. (2024). Beyond first-order tweedie: Solving inverse problems using latent diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9472-9481).
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