Guest Editorial: Addressing Challenges of Shale Development
Authors: Kashikar Sudhendu 
Published: March 2020

Guest editorial

The performance of unconventional wells depends on variables within operator control, such as stage length and fluid volume, and outside of that control, such as rock properties, geology, and market economics. The completion design must account for all of these factors. The other challenge has been the lack of availability of low-cost data that can be collected on a large scale to properly distinguish and separate the impact of these factors on completion performance. Collection and analysis are now possible with pressure data from the treatment and offset wells. The pressure data are a low-cost option to enable measurements on every well and implement established best practices for continuous improvement.

Complex, Multifactor Dynamic Shale resource development is a complex, multifactor dynamic with optimization, commonly performed against one or more objectives: cost, resource recovery, and utilization. For the most part, the practice has been based on a few pilot projects in appraisal or early development and applying the lessons learned formulaically to capture economies of scale. This approach, although appearing straightforward, has fundamental flaws because of the variables within and outside operator control.

Although the data from these pilot projects are accurate, the data may not be fully representative of the conditions encountered during full-field development. These data become anecdotal instead of systematic by not fully and accurately representing the next pad or pads. If design A yields the same 90-day production at 10% lower cost in a series of wells than design B wells, is design A the better one? Without knowing the impact of rock properties and understanding whether A and B resulted in statistically separable outcomes, we do not know. Deciding to proceed with design A over B in the absence of such knowledge could lead to either understimulated or over-capitalized wells.

One popular approach to solve for this knowledge gap is to leverage public and private completion and production data comprising several hundred to several thousand wells within a given region. The data analytics can offer insight from correlations derived from measured completion parameters, production, and cost. A shortcoming of this approach, however, is extrapolating results into yet-to-be sampled completion design parameters.

For example, data were collected to adequately sample completion designs ranging from 5 to 12 clusters and were used to calibrate physics-based models. Should these models be used to predict the results for a new design with 16 clusters? In many cases, the model has not been calibrated and tested for 16 clusters to ascertain the accuracy and uncertainty in the model predictions, which could result in large uncertainties in future well performance. This commonly occurring situation of extrapolating results to the next wells with the inherent shortcomings can be resolved with pressure-based fracture measurements.

Accurate Design of Experiment Pressure-based fracture measurements evaluate the separability of variables between two completion designs. These measurements can be deployed at a fraction of the cost of legacy technology, do not require a field footprint, and can be deployed at scale without affecting project economics. Operators are understanding and optimizing key completion parameters within and outside of their control to ensure an accurate design of experiment (DOE) that yields systematic insights. This approach is common to manufacturing industries.