Since the start of the "Shale Revolution" more than ten years ago, hydraulic fracturing stimulation providers have offered operators an increasing number of completion technologies and a large variety of stimulation designs. The evolution of completion practices across North America reveals a trend toward increasing stimulation intensity: more fracs, shorter stages and larger proppant volumes in all the unconventional plays. Although the result of this trend has generally been more productive wells, the optimization of completion and stimulation practices have been slow and in many cases resulted in significant over-capitalization. The question we need to answer for each of the unconventional resources is: Which combination of completion and stimulation options is the most cost effective and maximizes economic value? The traditional approach of industry towards stimulation de-risking has been through the implementation of "trials" comparing the well performance of several wells, completed with the new technology, against the production of a group of "reference" wells. While this empirical approach appears to be relatively straight forward, there are some obvious challenges associated with sample size and the cycle-time duration of these tests. Typically, this approach also requires a relatively large number of wells to compensate for uncertainties associated with the well-to-well subsurface variability. Experience has shown that this approach takes many years and some degree of over-capitalization to determine the optimum completion. This paper outlines an alternative approach that can be performed in a few wells and that allows accelerating the optimization process. It includes experimental design considerations, acquisition of stimulation distribution effectiveness information, as well as, production data obtained by continuously monitoring a well using Fiber Optics (FO). In the example, we used Distributed Acoustic Sensing (DAS) to derive discrete production profiling results over a period of more than two years. This has enabled us to track the gas production of two competing completion designs in a single well with minimum production deferral and Health Safety Environment (HSE) exposure. Continuously monitoring production of each stage, perforation cluster or sleeve-entry opens the possibility of testing different completion technologies or designs by comparing the performance of each type in different segments of the same well. This not only enables us to accelerate the completion optimization process and reduce over-capitalization risk but also the same FO cable can be interrogated to investigate other aspects of the stimulation that can impact stimulation quality and production results. This alternative approach towards completion de-risking using a few wells and continuous FO monitoring provides critical information that could make the evaluation of stimulation technology faster, with higher confidence, and more cost effective than traditional evaluation/optimization methods.
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SPE/AAPG/SEG Unconventional Resources Technology Conference
July 23–25, 2018
Houston, Texas, USA
Accelerated Stimulation Optimization via Permanent and Continuous Production Monitoring Using Fiber Optics
Gustavo A. Ugueto C.;
Gustavo A. Ugueto C.
Shell Exploration & Production Company
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Magdalena Wojtaszek;
Magdalena Wojtaszek
Shell International
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Paul T. Huckabee;
Paul T. Huckabee
Shell Exploration & Production Company
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Alan Reynolds;
Alan Reynolds
Shell Exploration & Production Company
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Jim Brewer;
Jim Brewer
Shell Exploration & Production Company
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Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, July 2018.
Paper Number:
URTEC-2901897-MS
Published:
July 23 2018
Citation
Ugueto C., Gustavo A., Wojtaszek, Magdalena, Huckabee, Paul T., Reynolds, Alan, Brewer, Jim, and Luis Acosta. "Accelerated Stimulation Optimization via Permanent and Continuous Production Monitoring Using Fiber Optics." Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, July 2018. doi: https://doi.org/10.15530/URTEC-2018-2901897
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