Description
Abstract
A key challenge faced in unconventional reservoirs development is well densities coupled with modern completion designs to economically optimize resource development. Most data analytic applications use single well production and completion data to explore optimized strategies. However, the combination of different well densities and completion strategies complicate single well analytics and thus could be improved upon by using multi-well, normalized section-based data analytics. This approach can help identify what changes can be made to the combination of wells per section and completion metrics to optimize asset recovery or help confirm that current strategies are working.
In most basins, the State Public Land Survey System (PLSS) sections are the main grid used to define each section. In the Permian, the Texas Block and Survey sections are used, whereas in the Eagle Ford, a custom grid with 1-mile by 1-mile sections are used. Total lateral length within the section, pumped proppant and fluid, and production volumes are rolled up within each grid section and normalized by section area on a per-acre basis. Examples in the paper mainly use three-year cumulative oil production and simple three-year payout economics as performance metric. Processed section level data is then visualized in both map views and cartesian plots, to draw insights on section-level performance in specific areas and then determine the best strategies to optimize asset recovery.
The key application of this approach is to draw insights relating to differing development strategies in each area. Trends of fluid treatment job sizes as a function of wells per section (WPS) can be inferred. Together, with trends of section production as well as normalized well productivity on a per-foot basis as a function of WPS, it is possible to assess which development strategy might work best. The trends of section production versus WPS and completion metrics are generally much better than for single-well analytics. The examples show how optimum performance and development strategy may change for different unconventional basins.
Section-based data analytics has proven to be an excellent complement to well-based data analytics and physical models as it provides better data correlations for the whole “development system” compared to single-well analytics. This approach is largely automated and can be used in any basin to gain insight into the section-level performance of a given well development and completion strategy.