Identification and Remediation of High Water Cut Stages in Multi-Stage Hydraulically Fractured Wells: A Case Study in the Midale Formation
Bibliographic record
Abstract
Abstract A new stage-by-stage analytical framework to identify and control high water influx stages in horizontal wells was successfully used in a multi-layer reservoir in Southeast Saskatchewan, Canada. The wells of interest were drilled in the Midale formation and hydraulically fractured through coil with cemented liners using a borate cross-linked guar gellant. The objective of the project was to develop a robust method to aggregate and analyze multidisciplinary data to identify stages with prolific water inflow and employ multi-cycle closeable sleeves to isolate them. When the correct data is available this framework can be applied at the time of completion to improve well economics through reduced lifting and disposal costs. A multivariate approach is necessary to begin to understand flow per stage in the absence of production logging. This framework involves collecting all available data: geological, geophysical, raw data-van feeds, pressure signatures, frac fluid viscosity profiles, break times, production and tracer concentrations to build a model for targeting sleeve closures. Data collected through on-site laboratories during the hydraulic fracturing treatment allow early identification of stages for further analysis. Stages where full breaking of frac fluids under reservoir conditions is not observed are identified as candidates for possible intervention. High frac fluid viscosities have the potential to increase fracture height growth and connect the wellbore to layers that have mobile water, depending on fracture height growth limiting mechanisms (Fisher, M. K. et al 2012). Particulate water tracers allow for water inflow to be uniquely attributed to individual stages, where higher concentrations are associated with higher inflow. Combined with pressure signatures and initial swabbing data, this technique can allow the operator to better optimize the well from day one. Production analysis before and after closing sleeves serves as the ultimate score-card for this framework. Mitigation of unwanted water influx was achieved using this framework to shut in certain stages early in the well's life. On-site quality control, detailed data collection during hydraulic fracturing, tracer concentrations and initial swabbing provided the necessary signals. Closeable sleeves permitted the fractures to heal immediately after the completion, but also allowed for the optionality to mitigate high water inflow if it was identified. This paper details the results from a well, where the water production decreased by approximately 30% while the oil rate increased. This framework allows for real-time data driven decision making. This enables significant efficiency gains in completion and intervention costs, equipment sizing, facility infrastructure, and lifting costs. This is accomplished through combining multiple datasets, including detailed on-site and after-the-fact frac analysis and solid particulate water tracers to leverage closeable sleeve technology. Potential areas for further applications of this framework include infill drilling in legacy waterfloods or when drilling wells in close vertical proximity to aquifers. This framework is most applicable when attempting to optimize fracture designs when working in new areas and new reservoirs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".