Modeling rate of penetration in a south Texas oil field with aggregated well data in a supercomputing framework

Abstract

The objective of this work is to develop an accurate and practical tool for drilling engineers supporting operations to predict rate of penetration (ROP) in the tangent section of the wellbore using easily obtainable data. Historically, tacit knowledge has been used to predict both ROP and the ideal parameters. Such a tool is valuable for planners seeking to adjust rig schedules. Further, this tool could easily be modified to also optimize parameters. This work was comprised of two major efforts: data acquisition and wrangling, and modeling. Data was obtained from three distinct sources: well files, drilling logs, and survey logs. Data on bit geometry or formation was not available. This data was manually downloaded and imported into Python. Due to the size of the data, a university-owned high-performance computer (HPC) was required to process the data. Special care was given to optimizing for memory efficiencies that allowed the HPC to perform these operations. A test data set of 5 wells was used to pilot the data wrangling process and initial linear regression models. Four different model types were produced and evaluated: linear regression, polynomial regression, nonlinear regression, and neural networks. Neural networks provided the best prediction with a R2 of 0.85. The most important variables affecting ROP in the tangent section in descending order are: total pump output, rotary speed, hook load, differential pressure, and bit type. To the best of our knowledge, this is the largest dataset of ROP data found in literature; containing over 350 wells and 30 million rows of data. This workflow can be adopted to create other field-specific models or adapted to evaluate other sections of the wellbore. More immediately, this work creates a large database ready to be utilized for developing other models undergirded by different computational methodologies.