Drilling Simulation vs. Actual Performance in Western Canada
Bibliographic record
Abstract
Abstract During the past two years a drilling performance simulator has been applied in a pre-drill mode to more then 50 wells with an average depth of 3000 meters in Western Canada. The main goal of applying the simulator was to optimize the drilling performance by increasing rate of penetration (ROP) and decreasing drilling time and costs. To maximize performance the correct bits, hydraulics, operating parameters and pull depths were selected in advance. The drilling simulator uses offset well drilling data and records to generate the apparent rock strength which is then correlated to the formations depths for the new well to be drilled. Multiple combinations of drill bits and operating parameters are simulated to obtain the optimum drilling solution for the next well. The results indicate very good match between predicted and actual field results for ROPs as well as total drilling times and well costs. The simulation approach initially easily matched pacesetter performance of previously drilled wells in the mature field and then by further applying the simulator new pacesetters where set improving performance overall with more then 15 percent. The presimulated optimized results were sent to the field and during the drilling of the well the field data was overlaid with the presimulated field data to compare progress. During the drilling follow-up the bit wear and apparent rock strength calculated from the field data were compared to the presimulated to evaluate the current status of the bit and drilling progress. The follow-up process gave room to take immediate actions and a baseline for communication with the field and office during daily meetings. This paper presents the commercially available simulator and multiple cases which compares the pre-simulated to the actual performance. The results clearly indicate the large potential for the use of such a simulator in drilling operations.
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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) | 0.000 | 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 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".