ASSESSING FAULT SLIP TENDENCY IN AREAS OF KANSAS AND OKLAHOMA UNDERGOING SIGNIFICANT DEEP FLUID INJECTION: APPLICATIONS OF A NEW TOOL, PYFAULTSLIP

A significant obstacle that operators, stakeholders, and regulators face with safe injection of fluids for enhanced oil recovery, wastewater disposal, or CO2 storage is quantifying the risk of induced slip / seismicity. While there are several existing software packages to assess ‘slip tendency’, they do not fully meet our needs for assessing slip tendency in specific use-cases, and are not open source, so there is no ability to extend those software packages as a member of the scientific community. We have developed pyFaultSlip, an open-source package written in python that can perform a 2D (e.g. lineaments or mapped fault traces) or 3D (mapped fault planes from 3D seismic) slip tendency analysis. Slip occurs during fluid injection when the effective normal stress on a fault is sufficiently reduced, causing the the ratio of shear stress to effective normal stress to exceed the coefficient of friction for that fault. We use a 3D stress representation imposed onto a planar fault geometry. Uncertainty in input / assumed parameters is propagated using a monte carlo approach and results are represented using the fluid pressure that correlates to a selected probabilistic cutoff.

To compare pyFaultSlip with other fault slip software, we performed an analysis on published and newly mapped 2D subsurface faults in Oklahoma and Kansas. Stress parameters were used from a combination of previously published data and new analysis of well data. These datasets also help ‘stress-test’ pyFaultSlip for analyzing large datasets containing 100s – 1000s of detailed faults. We will show these results and compare them with previously published results using different tools. We hope this tool will provide a starting framework for future open source and community-based contributions to aid in the safe implementation of deep fluid injection.