Successful Deployment of Multiple Inversion Workflows in Low Resistivity Contrast and High Noise Environment

Abstract Reservoir navigation objectives of any lateral is to geosteer in the desired target layer maximizing the productivity and/or injectivity. Target layers with low resistivity bounded by slightly higher resistivity shoulder beds create low resistivity contrast and high noise environment attenuating the boundary detection capability of Deep Azimuthal Resistivity (DAR) tools. This paper presents successful case studies where multiple DAR hybrid inversion workflows were implemented to counter this challenge and optimize production within a field. Carbonates target layers with inverse resistivity signature and heterogeneity from the offset wells were analyzed and incorporated into a detailed feasibility pre-drill reservoir navigation model which highlighted the challenges due to low resistivity contrast and high noise environments. Workflow parameters were optimized to ensure an effective depth of detection. While drilling, the DAR tool was deployed along with GR, Density, Density image and Neutron Porosity. Multiple hybrid inversion workflows with their optimized parameters reduced the impact of noise in low signal environment and increased the reliability of resistive boundary approach. This successful method was utilized for proactive geosteering in similar wells. The holistic approach of data incorporation to find targeted solutions in pre-drill phase highlighted the range of parameters to ensure reliable detection capability in the real time. During the drilling phase, optimization of the multiple inversion workflows enhanced resistive layer approach understanding which in turn helped in navigating along the optimum target layer. Heterogeneity in the target zone affected the resistive layer approach capability. The combined data from other sensors and the inversion results integrated into the geosteering workflow helped in taking informed well placement decisions in the complex heterogeneous environment heterogeneity. Upon successful utilization of the DAR technology along with proactive geosteering in the initial wells, this application was taken forward to all upcoming wells with similar target profiles. This Increased the net to gross of the wells compared to the previous wells which were navigated with a reactive geosteering approach. The limited boundary detection capability of DAR tool due to inverse signature, low resistivity contrast and high noise environment was successfully overcome through the application of optimized hybrid inversion workflows.