Horizontal wells in unconventional reservoirs exhibit steep decline rates yielding low recovery factors. Enhanced Oil Recovery (EOR) in tight oil reservoirs has received industry-wide attention to improve recovery factor per lease unit. Historically, a few operators in Eagle Ford and Bakken have implemented EOR pilots primarily utilizing gas Huff-n-Puff (HnP) method.
The key objectives of this study were 1) to improve reservoir characterization through history matching of a fully integrated high-resolution reservoir model, while incorporating all the parent/infill and the refractured wells, and 2) to analyze and design HnP strategies that yield the best EOR performance.
We employed a high-resolution, multi-phase, 3D reservoir model covering the entire lease unit volume which integrates multi-disciplinary information. The model contains 13 horizontal wells that were drilled and produced from 2011 through 2022, with two (2) of them being recently refractured in 2022. The calibration effort consisted of matching the daily rates (oil, gas, and water) and bottomhole pressure values of all the wells, during the hydraulic fracturing, flow back, and depletion periods within the unified model.
The model area includes a horizontal science well which was sponsored by the Department of Energy’s (DOE) Hydraulic Fracturing Test Site (HFTS-1, Phase 3) project. This well has multiple bottom-hole gauges installed along the lateral which were used to improve accuracy during model calibration.
We present our key learnings and parameters for each discipline that impact the performances of the wells. These include the impacts of well placement, completions, prior depletion, and well-to-well interference on predicted HnP performance. Multiple calibration scenarios were considered to account for uncertainties
associated with our geophysical understanding, while assessing risks related to different HnP design strategies, specifically injected gas conformance and control. We introduce a framework that emphasizes the significance of three key factors, which we refer to as the "3C's of HnP". These three factors are defined as Connectivity, Contact, and Containment. By considering these key factors in the HnP design, operators may significantly increase their chances of achieving successful EOR implementations.
The high degree of uncertainty and complexity associated with the subsurface characterization presents a unique challenge in predicting EOR performance. To our knowledge, this paper presents the first ever use of a full-lease unit, high resolution integrated 3D unconventional reservoir model that contains all the parent, infills, and refractured wells to understand the impacts of prior depletion and well-to-well interference prior to the start of EOR. The model calibration was performed by improving the geomechanical understanding of the reservoir, while maintaining the original heterogeneity of the characterization. Neither regional modifiers nor well-specific alterations were utilized. The dynamic nature of the connectivity was modeled through hysteretic (elasto-plastic) rock mechanics and placement of proppants.
This study presents a comprehensive modeling approach to design a HnP pilot by integrating all multi-disciplinary information from geology, geophysics, petrophysics, geomechanics, and engineering, and it attempts to identify and quantify uncertainties related to each discipline. We believe that asset teams will benefit from learnings derived from this modeling effort and can integrate the design strategies introduced in this study to their HnP plans.
