We are active researchers and publish our work in top-tier conferences and journals
Optimizing Shale Infill Development with AI: Child Well Meta Models
The optimization of infill well placement within drilling spacing units (DSUs) containing parent wells is hindered by the large degree of uncertainty around child well performance. Infill child wells have been observed with up to a 40% degradation from their unbounded parents in the Midland Basin. We present an AI enabled workflow to predict infill well performance and determine optimal child placement and design.
New Methodology Merging Seismic, Geologic, and Engineering Data to Predict Completion Performance
Previous work in hydraulic fracture performance identifies rock quality and completion quality as key drivers of good production; however, quantifying rock quality in a systematic method along the wellbore is a difficult task. Novel methodologies are developed for calculating rock-quality measurements along the wellbore using seismic data calibrated to geologic information encompassing petrophysical and geomechanical parameters and merging these metrics with stage-level engineering observations. Far-field wellbore seismic attributes correlate with stage completion performance and are promising predictors for improved well design. Further, these integrated attributes may contribute to the fundamental understanding of the hydraulic fracturing process and to the development of more robust and powerful computation models of overall well performance.
Predicting Subsurface Stress using Machine Learning: A Midland Basin Case Study
The state of stress in the subsurface controls how hydraulic fractures will propagate both vertically and horizontally. Accurate models of subsurface stress are needed for optimized well spacing in stacked pay, where lithology can cause large changes in stress between zones. Conventional methods to determine stress require mechanical parameters derived from sonic logs. This approach doesn’t account for stress relaxation that can occur in clay-rich lithologies. Sonic logs may also not be available at the location of interest. The visco-plastic stress relaxation (VSR) theory recently proposed by Singh and Zoback (2022) can produce profiles of Shmin by integrating log and calibration data from the nearby area. This method is applied to a development in the Southern Midland basin. The resulting stress profile was used to optimize spacing for a new pad development.
Variations of the Least Principal Stress with Depth and Resultant Frac Fingerprints
In a recent paper, Petro.ai presented observational data and modeling results which support the hypothesis that the degree of vertical to horizontal hydraulic fracture propagation during multi-stage hydraulic fracturing is largely controlled by variations of the least principal stress with depth. Using two different types of analysis approaches, we investigate complex patterns of vertical and horizontal hydraulic fracture growth from the Midland Basin. In each case, we show that pattern of hydraulic fracture propagation (and resultant drainage volumes) are largely governed by the detailed variation of the magnitude of the least horizontal stress with depth and exact position of a given stage. In gun barrel view, this complex pattern we refer to as a frac fingerprint for convenience. The frac fingerprint depends on the exact vertical position of a frac stage with respect to the variations of the least principal stress in the layers both above and below the stage depth. We briefly discuss the implication of these concepts for choosing optimal well spacings and landing depths and the relationships between hydraulic fracture geometry and drainage volumes.
