This field study, the first major test on the Department of Energy's (DOE) Austin Chalk / Eagle Ford Shale Laboratory (ACEFSL), explores the application of Diagnostic Fracture Injection Tests (DFITs) on fiber optics. Commonly, DFITs induce a reservoir fracture to measure initial stresses, pore pressures, and flow capacity to then integrate into models to optimize development. This field test captures material insights beyond typical DFITs that influence 3D fracture growth and well productivity understandings.

A novel technique was added on a DFIT conducted across multiple permanent fiber optics wells to detect strain and temperature changes throughout the process. A standard DFIT was initially injected across the high-resolution fiber optics (in-well), followed by the detailed recording of the behavior over several weeks. Subsequently, a much larger DFIT was pumped into the same location to induce a discernible response on an offset, equally instrumented fiber well. This methodology created a layered, comprehensive data set, expanding our understanding of DFIT and fracture mechanisms.

The trial yielded compelling findings—it was possible to identify volume balance characteristics of fracture growth, time of arrival, orientations, and pressure response mechanics. Moreover, a detailed picture of near and far-field fracture dynamics during stimulation and subsequent phases emerged, with the recorded data showing clear correlations and understandings related to the interconnected processes involved in fracture growth and reservoir fluid dynamics, which may now be included in all development analyses. In this paper, the results of this field test will also be simulated and compared to full stimulation stages on this well with additional field measurements to illustrate the implication of these diagnostics and interpretations, especially in areas with complex geology.

This paper provides unique insights that influence our understanding of well development by directly tying common field tests to subsurface fundamentals with more advanced fiber diagnostics to improve our predictive workflows. Many of the factors explored include near well failure mechanics, fracture growth & volume characteristics, and dynamic responses related to poroelasticity and stress. These learnings deliver actionable knowledge that can be used to drive resource development efficiency and optimization for many operators to continue advancing the petroleum industry.

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