From Reservoir Characterization to Reservoir Monitoring: An Integrated Workflow to Optimize Field Development Using Geochemical Fingerprinting Technology
- Faye Liu (RevoChem LLC) | Jiang Wu (RevoChem LLC) | Muqing Jin (RevoChem LLC) | Douglas L. Hardman (Diamondback Energy) | Dave Cannon (Diamondback Energy)
- Document ID
- Unconventional Resources Technology Conference
- SPE/AAPG/SEG Unconventional Resources Technology Conference, 20-22 July, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Unconventional Resources Technology Conference
- 26 in the last 30 days
- 27 since 2007
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Traditional geochemical techniques such as pyrolysis and Soxhlet extraction have been used for decades to guide conventional exploration. However, geochemical data obtained in the traditional way falls short of the demands of unconventional reservoir development. We have recently developed innovative analytical and data processing technology that allows geochemical information in the produced oil and rock samples to be captured at a much higher resolution (up to an order of magnitude) and fidelity. These unprecedented geochemical data reveal 1) static reservoir characteristics such as reservoir quality, oil saturation, and 2) dynamic reservoir performance characteristics such as frac growth, drainage height, and inter-well communication. These reservoir characteristics can have impacts throughout the lifecycle of unconventional reservoir development from well stacking & spacing, completion design, reservoir management, and EOR/IOR decisions.
Based on high-resolution/hi-fidelity geochemical fingerprint data, we have developed an integrated workflow to provide new reservoir characterization and production monitoring information that lead directly to enhanced development opportunities for unconventional reservoirs. By data mining the fingerprint data from the rock baseline, a group of Reservoir Characterization Indices (RCI) were developed, including reservoir quality index (RQI) and oil-in-place index (OIPI). Different from other rock-based core analysis, the RCI provided an independent dataset directly from the oil residing in the rock samples. They correlate well with petrophysical data and compliment landing decisions for lateral wells. Produced oil samples were collected from legacy and infill wells. Fingerprints based on over 2000 compounds, resolved from each produced oil sample, were used to reveal well communication through time, as well as quantitative vertical drainage variation against the vertical profile previously established using the core/cutting samples. Integrating the dynamic reservoir monitoring data with the static RCI data, geochemical fingerprinting technology helps operators identify key factors controlling unconventional well performance, such as well spacing, and significantly improves the operator’s ability to predict performance of future development strategies.
Key conclusions from the study include: 1) RCI generated in the workflow conformed well with independent petrophysical analysis; 2) Indications of similar zonal contribution in wells that were landing in different intervals; 3) Drainage geometries in all four landing zones appear to have distinct differences; 4) Distinct overlapped drainage geometries are also evident; 5) Parent wells experience changes in drainage geometry profile post-stimulation of offset child wells, then returned to their established geometry in a relatively short period of time.
|File Size||2 MB||Number of Pages||20|