Integration of Digital Outcrop and Multisource Subsurface Data for Reservoir Modeling and Sweet Spot Mapping in Unconventional Resource Plays

Abstract

In unconventional plays natural fracture network is considered to be an important attribute that can have a significant impact on production. Understanding, predicting, and modeling the fracture network can facilitate better well placement and reservoir stimulation planning. The quality and accuracy of reservoir models could substantially benefit from integrated approach aiming to enhance the traditional subsurface fracture data with outcrop analogs and allow for improved reservoir characterization, including fracture connectivity and fracture drivers characterization.

This research integrates digital outcrop fracture data (LIDAR, UAVs, and photogrammetry) with subsurface fracture data through geological modeling. The outcrop analysis revealed the complexity of fracture sets (four major fracture sets) and distributions (lithofacies and structure driven). Outcrop-to-subsurface calibration was done through fracture data integration from horizontal wells’ image logs, core analysis, and microseismic trends. At this stage the geological model provides the insight on the fracture drivers and allows for accurate comparison and calibration of surface and subsurface fracture data.

While microseismic analysis and image log interpretations provide valuable information on fracture orientations and distributions, both of these methods were found to have their limitations. Image logs are incapable of detecting fractures striking parallel or sub-parallel to the wellbore orientation. Microseismic data is a valuable data source when interpreting induced fracture distributions and mineralized natural fractures reactivation. However, zones of closely spaced open natural fractures are capable of absorbing and transmitting energy resulting in lower levels of microseismicity and are thus are unlikely to be detected by microseismic.

The discrete fracture network model was designed as a stochastic representation of the natural fracture network in an attempt to preserve the complex heterogeneous nature of the Niobrara reservoir. Subsurface fracture intensity models were enhanced with fracture drivers concept developed at the outcrop. The “sweet spots” capable of yielding higher production are located in highly fractured areas within the chalk benches around the fault zones. The predictive capability of DFN models identifying fracture swarms can aid in development planning including well spacing, re-fracking stage locations planning, and overall completion strategy design.

AAPG Datapages/Search and Discovery Article #90323 ©2018 AAPG Annual Convention and Exhibition, Salt Lake City, Utah, May 20-23, 2018