Multivariate Analytics (MVA) is a powerful tool to assess the individual impact of geologic, completion, and well design variables on horizontal well production. This paper focuses on the MVA predictive models of 6-month oil production and the workflows applied to normalize geological variation when evaluating various drilling and completion designs.
Horizontal well and completion designs implemented throughout the Midland Basin continue to evolve, as technical knowledge and insights improve. With the availability of a large well-ordered dataset it is possible to analyze the impact of discrete variables on production. Due to basin-wide variation in geology, burial history, thermal diagenesis, and horizontal targeting, it is critical to normalize these effects in order to understand the impact of discrete drilling and completion variables on production. Laredo Petroleum has developed a rigorous, repeatable process to do so utilizing MVA.
Well design variations such as horizontal inclination, dog-leg severity, wellbore azimuth, and build-rate in the curve can also impact the ultimate production of a well. MVA results indicate modest production uplift is possible through modification of well designs, which can also reduce the amount of directional drilling adjustments required. Completion design is another key contributor to well performance: Important variables including fluid type, water volume, sand volume, cluster spacing and pump rate are incorporated into the MVA workflow. Proprietary and publically available completion and microseismic data sets are used to assess the variation in stimulated fracture networks, which can be correlated to completion design and 6-month oil production. One of the results from this analysis is the positive correlation of increased proppant volumes with higher 6-month oil production.
Utilizing MVA workflows, Laredo has developed a repeatable process to normalize local and regional variability of geology drivers, such as vertical and lateral facies variability and diagenesis in order to leverage large data sets and assist in operational decisions. This MVA workflow has yielded critical insights into understanding drivers of well performance and designing future completion and well package tests.