Hydraulic Fracturing Techbook: Diagnostic Methods for Reservoir Stimulation

Production analysis has been regarded as the gold standard of comparison. While production is where revenue is accrued, many other diagnostics have recently become economically viable to help explain differences in stimulation effectiveness. Diagnostics must be cost-effective to render the return on invest­ ment (ROI) that operators seek in the drive to create more value from oil well completions.

Pressure monitoring has emerged as the most cost-effective diagnostic application because of the low cost of devices, minimal operational impacts and vast ability to analyze the data created. For the purposes of understanding more about this technol­ogy, Reveal Energy Services combined pressure-based fracture maps (PBFMs) and post-stimulation leak-off analysis to identify reservoir stimulation effective­ness for a full-field pressure monitoring application. Additionally, this method evaluated stimulation at each stage rather than having coarse aggregations at the well level, such as production analysis.

The two diagnostic methods were implemented on multiple pads across the Delaware Basin. These independent diagnostic applications monitored different areas of the reservoir and, when combined, provide a full-field evaluation of each monitored treatment stage. This article reviews the combina­tion of the two methods with the treatment design variables to understand the completion trends that have resulted in optimal rock stimulation.

Pressure monitoring study

The two independent pressure monitoring tech­niques enabled the full-field stimulation evaluation. This study leverages datasets from multiple opera­tors in a cooperative way. The operators agreed to aggregate and anonymize the data so there is an appropriate distribution to ensure valid conclusions.

The study was based in the Wolfcamp A Forma­tion in the Delaware Basin. Stage-level data that typically include many more datapoints than well level data were considered. Several environmental factors must be similar for comparisons. Using data from similar geographical and geological perspec­tives helped to keep these environmental factors as close as possible.

The study proceeded as follows:

• First, an independent analysis was conducted for each pressure monitoring method.
• Second, a relative score was given for each method as low (1), mid (2) and high (3).
• Third, a proxy stimulated rock volume (SRV) was calculated by multiplying the scores from both methods.
• Finally, the average completion parameters were calculated (per foot of stage length) and com­pared to identify relevant distinctive trends.

Method 1 applied PBFMs, a Reveal Energy Ser­vices application in which a digital twin model ren­ders the hydraulic extent for a dominant fracture in a given stage to determine fracture height, half-length and azimuth (Figure 1a). The pressure is monitored from an isolated stage in a nearby child, or infill, well. The location of the monitoring stage and treatment stages are inputs into the digital twin model, and the iterative solver updates the geometries so the modeled pressure response matches the observed pressure response. This is a far-field monitoring application because the stress extends through the reservoir to the monitor.

Method 2 applied post-stimulation leak-off analysis, which utilizes the pressure gauge on the treatment well to monitor the pressure decline after the pumps have been stopped for a period up to 1 hour. The rate of pressure decline is proportional to the amount of reservoir contact achieved during stimulation and, therefore, can be used in a stage to-stage comparison (Figure 1b). This monitoring is a near-field measurement because the pressure decline is directly measured from the treatment stage. The combination of the far-field and near-field monitoring provides a full-field evaluation for each completion stage. The full-field method improves the estimation of the SRV for a given stage.

Area is from the PBFM of the dominant fracture, the Length is the stage length and the efficiency factor is derived from the leak-off analysis. The SRV for a well is the summation of each stage’s individ­ual SRV contribution. A proxy to the SRV is used by multiplying the score from the post-stimulation leak-off analysis and the PBFM relative size with Area, and each stage is evaluated individually.

Observations and insights

This study arrived at three main insights. The stages with better SRV scores had lower proppant loading (pounds per foot) for a given fracture area. For example, of all the stages with a mid (2) rating for fracture area in the PBFM calculation, the high­est SRV group had the lowest proppant loading, and the lowest SRV group had the highest prop­pant loading (Figure 2).