Abstract

This paper outlines a data collection and diagnostics case study involving multiple horizontal shale wells. We look at well production profiles using rate transient analysis, differences in near wellbore complexity, geologic variations within the area of interest, as well as compositional differences in the rocks based on cores obtained from within the stimulated reservoir. The Hydraulic Fracturing Test Site is a multi-well experiment involving stimulation of unconventional shale wells in the southeastern Midland portion of the Permian Basin. The targeted formations include both the upper as well as the middle Wolfcamp formations, also referred alternatively as Wolfcamp A and Wolfcamp B. Data integration and analysis shared in this paper help us understand the various geologic controls impacting well productivity, particularly the wide variance observed between the Wolfcamp A and Wolfcamp B formations. Rate transient analysis indicates similar system permeabilities for stimulated wells. However, we observe higher effective fracture half-lengths for upper Wolfcamp wells. Using observations from 3D seismic interpretations (such as pad scale faults) as well as petrophysical and image log data, we highlight the substantial differences in stimulation as we move along the well laterals from the heel toward the toe sections. These differences are further reconciled with observations from zones with high data density at the core locations through stimulated rock, as well as independent data such as microseismic emissions. At the test site, Wolfcamp A was found to be relatively quartz rich with significant heterogeneity whereas Wolfcamp B is richer in clay and organic content. This impacts the geomechanical characteristics of the rock mass with much higher natural fracture density in the shallower interval. Thus, the fracture growth is more uniform in the deeper interval and more heterogeneous with branching likely in upper interval. Increased complexity also leads to consistently better productivity from the wells in the shallower interval as demonstrated from RTA results. This case study is unique because it provides valuable insights from actual sampling of the stimulated zones in hydraulically fractured wells and helps understand impact of various factors that contribute toward variability in well production. The findings from this study provides insights into need for optimization of completion designs in the various Wolfcamp landing zones, such as optimization of cluster or fracture spacing in various Wolfcamp intervals. In addition, it provides a useful template for data collection and research direction in future field test sites of similar nature in unconventional reservoirs.

Highlights

  • Test siteThe Hydraulic Fracturing Test Site (HFTS-1) is in the eastern part of Midland Basin, between Central Basin Platform and the Eastern Shelf

  • We have looked at multitude of diagnostic information as well as general observations from HFTS wells

  • Based on Rate transient analysis (RTA) analysis, we find that most of the wells at the test site have similar stimulated rock volume (SRV) permeability

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Summary

Introduction

The Hydraulic Fracturing Test Site (HFTS-1) is in the eastern part of Midland Basin, between Central Basin Platform and the Eastern Shelf. A fundamental element of this test program was the collection and analysis of SRV core across stimulated wells at the test site to better understand fracture and proppant transport behavior far-field as well as how various factors impact the productivity of the wells Such large-scale field studies involving sampling of SRV is relatively rare. The two most relevant motivations behind this study were to understand the likely drivers behind the observed variance in well productivity as well as the impact of geologic heterogeneity at the test location on performance of hydraulic stimulation program Various datasets, such as OMBI derived fracture distribution, pumping data, production data analysis, lithologic variability between intervals could all help us better understand the factors driving well performance.

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