Millions of barrels of oil are produced every day from U.S. shale reservoirs. Yet this amount is small compared to the precise quantity of oil locked away in these subsurface rocks. The oil and fuel business has put fiber-optic sensing cables downhole to higher perceive hydraulic fracture results and why stimulation and manufacturing processes do not free the trapped oil at anticipated charges.
Unfortunately, the streams of data acquired from these sensors are large and onerous to kind via. A multidisciplinary group, together with researchers at Texas A&M University and a school member from the Colorado School of Mines, has created an algorithm to wash up the subsurface data from fracturing efforts and provide a transparent view of how and the place these processes succeeded and failed in shale reservoir rocks.
“Our quantitative characterization retrieves more information about fracture geometries within a reservoir than a simple qualitative analysis would,” mentioned Kan Wu, affiliate professor and Chevron Corporation Faculty Fellow within the Harold Vance Department of Petroleum Engineering. “We’ve tested our algorithm and already applied it in the field.”
The results had been revealed Nov. 11 within the Society of Petroleum Engineers’ SPE Production & Operations journal.
Traditional data interpretation strategies, although useful to engineers, are based mostly strictly on qualitative data or possibilities based mostly on patterns of knowledge. In distinction, the algorithm was developed to collect quantitative data that is countable, like temperature, strain or rock deformation adjustments inside a reservoir. It acknowledges the outcomes that occurred to create the adjustments and precisely fashions how far and quick the fractures traveled, what instructions they went and the way large they grew to become.
Low-frequency distributed acoustic sensing (DAS) data gathering has solely been round for 5 years, so not all data acquired from the wells with fiber optics has been absolutely deciphered. Also, every nicely has its personal vary of traits as a result of monumental variations of subsurface buildings. This complexity is why Wu and her colleagues, fellow school member George Moridis, professor and Robert L. Whiting Chair, and Ge Jin, assistant professor of geophysics at Mines, wanted a substantial period of time to meticulously develop their algorithm.
First, the researchers examined the algorithm’s capacity to wash the data and interpret easy streams from recognized fracture processes. That approach they may backtrack or reverse the data to seek out the place to begin of a fracture’s development. As the algorithm was expanded to know extra complicated data, they improved its capacity to assume in a ahead method and predict how new and complicated fractures provoke and develop.
Wu is an skilled in rock mechanics, Jin an skilled in geophysics and DAS expertise, and Moridis is an skilled in superior numerical strategies and high-performance computing of coupled processes. Because of the multidisciplinary backgrounds of the undertaking group, the algorithm possesses unbelievable flexibility to develop and adapt to the kind of data it receives. For occasion, Yongzan Liu, the graduate pupil on the undertaking for over two years, is now a postdoc researcher utilizing related strategies and modeling on fiber-optic data from hydrate-bearing sediments to observe pure fuel manufacturing for the Lawrence Berkeley National Laboratory.
Liu, Wu, Moridis and Jin are the primary to develop this kind of algorithm and publish results. The objective of their analysis is to finally automate the algorithm in order that suggestions from fracturing occasions occurs in close to actual time on a drill web site. This approach, engineers can shortly tailor fracture design efforts to every nicely’s specific composition.
“The industry needs this type of tool to understand fracture geometry and to monitor fracture propagation,” mentioned Wu. “The more efficient it becomes, the better it will help optimize hydraulic fracture and completion designs and maximize well production.”
Yongzan Liu et al, Quantitative Hydraulic-Fracture-Geometry Characterization with Low-Frequency Distributed-Acoustic-Sensing Strain Data: Fracture-Height Sensitivity and Field Applications, SPE Production & Operations (2021). DOI: 10.2118/204158-PA
Texas A&M University
New algorithm efficiently diagnoses shale fracture results from fiber-optic data (2021, November 24)
retrieved 24 November 2021
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