Abstract
Multiple transverse fractures initiated in a horizontal wellbore are the most effective method to maximize formation contact and access reserves in unconventional reservoirs. Most horizontal well operations in unconventional reservoirs utilize multi-stage/multi-cluster plug and perforation completions. Optimization of unconventional resources requires improvements in the number of effective clusters within the designed stage length and sizing fracture treatments that are appropriate for the lateral and vertical spacing between wellbores. However, it can be challenging for operators to routinely and inexpensively 1) evaluate cluster performance within each stage, 2) analyze fracture propagation and complexity pressures and 3) determine fracture geometry resulting from the treatment design. Fracture treatment stage data is under-utilized and provides an opportunity for completion and stimulation optimization.
This work focuses on a novel methodology to assess tubular and near well perforation pressure loss and limited entry success, determine cluster effectiveness, evaluate diverter performance, analyze fracturing pressures, and provide as-placed fracture geometry using stage treatment data. No changes to routine fracturing operations are required and stepdown tests are not necessary. After evaluating these key performance indicators, future completions can be optimized to achieve specific objectives including 1) improved cluster efficiency, 2) efficient fluid and mass placement per cluster, 3) stress and fracture shadowing component pressure management and 4) achieving fit-for-purpose created and propped fracture half-lengths. This work has been applied to multiple unconventional basins in North America.
The methodology honors first-order principles and has been calibrated with multiple diagnostic technologies including optic-fiber, stepdown testing, RA and CFT tracing, fracture modeling and production data analysis methods. Results will illustrate varying stimulation and completion design variables, including the effects of perforation size and number, cluster spacing, application of diverters and mechanical stress shadowing. The effects of cluster performance on fracture length will be shown. The methodology is useful for stand alone, single well applications, however, increased value is gained with multiple well and multiple pad assessments.
Using readily available hydraulic fracturing treatment data, completion and reservoir engineers and geoscience teams obtain meaningful results accelerating the learning curve and are provided a large parametric population for multivariant analysis and machine learning applications. The methodology presented provides a low-cost, scalable diagnostic solution for each completion stage within the horizontal well. Application of this methodology provides the fracture design engineer with new tools to optimize well performance using existing data.
