In this study, a series of experimental tests were performed to evaluate proppant and fluid placement in a lab setting. A Computational Fluid Dynamics (CFD) model was built and calibrated using experimental data from the above lab tests. The model was also validated by field-scale testing data from literature. The field-scale testing was performed on surface at a pumping rate of 90 bpm with a 5.5-in casing assembly. With the model validated, simulations were performed to evaluate the impact of key parameters on fluid and proppant placement in individual perforations and clusters. Some key parameters included:

1. casing size
2. cluster spacing
3. cluster count per stage
4. pumping rate
5. fluid properties
6. proppant properties
7. perforation parameters (size, orientation, number)
8. stress shadowing effects

The results from this study show that the best practice to improve fluid placement is limited entry design and stage length selection, and the best practice to improve proppant placement is perforation count and orientation optimization and selection of pumping rate and proppant properties.

A machine learning model was developed to efficiently predict proppant placement along a multi-cluster stage based on extensive CFD modeling results. Its high computational efficiency permits quick sensitivity analyses to optimize perforation and fracturing designs for near-uniform fluid and proppant placements across all clusters in each stage.

Event Format:

Lunch-and-Learn  at NexTiers Headquarters West-side of Houston. Several food options will be provided for those with various dietary needs. Parking is free. Doors open at 11:30 AM for networking. 

Speakers:

     Xinghui “Lou” LIU

• MS from Montana Tech and PhD from University of Oklahoma, both in petroleum engineering.
• 30+ year’s industrial career with Chevron, Halliburton, Pinnacle, RES and PetroChina on well stimulation; formation damage, acidizing, fracture and proppant transport modeling; fracture diagnostics; unconventional development, well testing, geochemical modeling, and R&D, etc.
• Joined Chevron in 2014, currently working as Sr. Stimulation Advisor at Chevron Technical Center, providing technical supports and conducting R&D research on well stimulation and completion.

    Jiehao Wang

• Ph.D. in Energy and Mineral Engineering (Geomechanics) from Penn State ​
• Research interests in geomechanics and well stimulation​
• Working as a Geomechanics Specialist for unconventional development in Chevron Technical Center since Feb. 2020.