Integrating Geochemical Fingerprinting Technology with Reservoir Surveillance Data – A Montney Case Study to Optimize Pad Design in Multi-Bench Development

An integrated approach was taken to utilize geochemistry information extracted from cuttings and produced oil samples to monitor vertical drainage between stacked reservoirs as well as to optimize well density in multi-bench cube development in the Montney play. Oil extracts from cuttings were profiled using high-resolution multi-dimensional gas chromatography (GCXGC) identifying 2,000+ compounds. Key reservoir properties such as indicators of oil saturation and matrix permeability were calculated using oil signatures extracted from cuttings. Subsequently produced oil were profiled using the same GCXGC method. Drainage frac height (DFH) and quantitative vertical production allocation by zone was conducted by building a geochemistry-based model and correlating the produced oils back to their contributing intervals represented by the cuttings. In this Montney case study, two neighboring pads with different cube designs were investigated. Pad 1 is a 12-well pad with 4 target benches and a stacked wellbore placement pattern, whereas Pad 2 is a 6-well pad with 3 target benches and a staggered wellbore placement pattern. Cuttings and produced oil samples were collected from both pads at every development bench, and geochemical DFHs and production allocations were estimated. The geochemical index from cuttings were in-line with the porosity log and wetness balance log collected while drilling. Based on the geochemical production allocation, both Pad 1 and Pad 2 showed vertical fracture growth was not constrained within the target bench. As a result, more vertical overlaps were detected which leads to a more severe vertical communication problem observed on Pad 1 where wellbores were vertically stacked above or below each other. In this study, the geochemical fingerprinting result was used to calibrate microseismic events. The quantitative production allocation allowed us to filter out any non-effective events and realistically capture the productive stimulated rock volume for optimizing well spacing and landing depth. Last but not least, the geochemical production allocation was successfully integrated with other reservoir surveillance data such as interference well testing.