Strategic Waterflood Optimization with Innovative Active Injection Control Devices in Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract This paper presents the successful implementation of an innovative approach for improving oil recovery by water injection optimization with Injection Control Devices (ICD's) in unconventional reservoirs. Over the past decade, operators in Southeast Saskatchewan have been continually innovating and finding efficiencies to improving water injectivity across horizontal wellbores. In late 2016, the Crescent Point Energy started a trial campaign applying ICD's in relatively low flow rate environments to offset production decline and improve recovery in the Bakken, Shaunavon and Midale formations. Early term results show greater than 25% improvement is possible in oil recovery over typical waterflood configurations. The operator had applied waterflood as a secondary recovery method and found field trials to stabilize production decline and improve ultimate recovery. The effectiveness of waterflood in unconventional, fractured reservoirs has been debated. In some cases, short circuiting of injection water to production wells through fracture channels has occurred. This has been found to reduce sweep efficiency. The issue was evaluated and it was determined there was a need to equalize the injection profile across the horizontal wellbore. Understanding the flow profiles of injection wells was instrumental in developing diversion strategies. Applying Distributed Temperature Surveys (DTS) has been found to be an effective method of estimating flow profiles at relatively low flow rates. Using processed DTS data, a reservoir simulation model has been used to match unique injection profiles along horizontal wellbores. The results from these models supported the need to pursue injection diversion. Several means to optimize injection profiles have been trialed, the results of which support the theory of sweep optimization. However, the limited number of isolated injection points achievable with given wellbore diameters has impeded potential for this development. The introduction of ICD injection strings allows for an optimum number of injection points, improving sweep efficiency and accelerating voidage replacement. This paper reviews the design, execution and evaluation process used in more than 50 successful ICD installations in various fields across Saskatchewan. The performance of these ICD strings was then monitored and evaluated in collaboration with the operator and service providers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".