Workflow Optimization Using Python Programming, a Tool Kit for Every Geoscientist

Abstract

The Energy & Environmental Research Center (EERC), in partnership with the U.S. Department of Energy National Energy Technology Laboratory and the IEA Greenhouse Gas R&D Programme, has been investigating the viability of storing large quantities of CO₂ in geologic formations. As part of this investigation, the EERC has developed an approach that integrates site characterization, modeling and simulation, risk management, and monitoring throughout a project to develop meaningful results. Each of these aspects involves manipulation of data, whether in a relational database, model, or map-based product, which may involve one or more software packages. Several time-saving and data optimization schemes have been developed using the Python programming language. This object-oriented approach has proven successful in building semiautomated workflows to optimize geographic information system (GIS), petrophysical modeling, and 3-D modeling applications. The following three examples of Python workflows have significantly enhanced project efficiency, allowing for more time to be spent interpreting results and meeting project deadlines: 1) a GIS workflow to build maps to track CO₂ movements from output files of numerical simulation, increasing efficiency by nearly ten times; 2) a workflow to optimize petrophysical analysis by clipping, shifting, and combining geophysical well logs; and 3) a workflow to improve data transfer from a 3-D geocellular model to dynamic simulation by calculating injectivity to optimize well placement and perforations and to calculate bottomhole pressure constraints. While other programming languages can perform similar tasks, Python has an easy-to-learn syntax and module database suitable for any scientist.

AAPG Datapages/Search and Discovery Article #90193 © 2014 Rocky Mountain Section AAPG Annual Meeting, Denver, Colorado, July 20-22, 2014