Data-Driven Decision-Making Strategy for Thermal Well Completion
Bibliographic record
Abstract
Abstract Various wellbore completion strategies have been developed for thermal wells in Western Canada. The idea in this paper is estimating the improvement of oil production and steam injection if flow control devices (FCDs) will be installed for the next wells to be drilled, or if FCDs were installed at a particular well-pad that has not yet been completed with any FCDs. The approach is based on labeled real data for 68 well-pads from seven major thermal projects in Western Canada. Three phases make up the paper's methodology. The first phase compares wells with and without FCDs to evaluate the performance of the FCDs based on normalized oil production and cumulative steam oil ratio (cSOR). The second phase involves clustering well-pads using an unsupervised incremental-dynamic algorithm. An estimation of FCD contribution to enhancing oil production and cSOR is also performed for test well-pads based on their most similar cluster. In the third phase, cross-validation is employed to ensure that the estimation is trustworthy, and that the procedure is generalizable. To evaluate the performance of FCDs, a reliable comparison was made using normalized oil production and cSOR. Based on our analysis from October 2002 to March 2022, successful FCD deployment resulted 42% more normalized oil and a 37% reduction in cSOR. Among these, liner deployed (LD) FCDs increased oil production by 44% while decreasing cSOR by 58%. Although tubing deployed (TD) FCDs are installed in problematic wells, they produced 40% more oil while decreasing cSOR by 21% in successful cases. Successful inflow control devices (ICDs) increased oil production by 40% while lowering cSOR by 45%. Successful outflow control devices (OCDs) increased oil production by 82% while reducing cSOR by 22%. The clustering algorithm separates the database into four clusters that will be utilized in the estimating phase. In the estimation phase, ten well-pads (15% of the database) are presumed to be new well-pads to be drilled (test data). Based on the estimation results, the root mean square errors (RMSEs) for FCDs contribution to enhancing oil production and cSOR for the test well-pads are 12%. Cross-validation was also performed to assess the approach's predictability for new data, to verify that our technique is generalizable. The findings indicate that FCDs might result in lower capital expenditures (CapEx) and greenhouse gas (GHG) emissions intensity for SAGD well-pad developments, allowing them to reduce emissions. The conclusions of this research will aid production engineers in their knowledge of relative production performance. The findings may be used to examine paradigm shifts in the development of heavy oil deposits as technology advances while keeping economic constraints in mind.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".